Post-graduate course System Level Aspects of …...11.4.2016 Aki Kuusisto 1 1 INTRODUCTION Traditional energy grids rely on bulk power systems, which are traditionally localised and

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Post-graduate course

System Level Aspects of ElectricVehicles (5 cr.)Summary of the course book “S. Rajakaruna, F. Shahnia, A. Ghosh(Ed.): Plug In Electric Vehicles in Smart Grids, Energy management”

April 2016

Chapter 1Overview of Plug-in Electric Vehicles Technologies

Aki Kuusisto

Plug In Electric Vehicles In Smart

Grids: Energy Management

Summary of Chapter 1

11.4.2016 Aki Kuusisto

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1 INTRODUCTION

Traditional energy grids rely on bulk power systems, which are traditionally localised and

interconnected. Grids are designed to minimize and localise disruptions. Introduction of

renewable sources brings challenges, and development of smart grids may bring solutions

in the form of a self-healing grid.

Micro grids may work both as a part of the main grid, and as isolated grids. Power

may flow in both directions, and energy is generated by conventional and renewable

means. Wide generation portfolio requires inverters, thus more complexity.

Plug-in electric vehicles, or PEV’s, increase management demands, and their number

is increasing despite the higher price compared to conventional vehicles. Recharging of

electric vehicles requires new policies from power companies, but they can also be used

as emergency power reserve. This brings along the term “prosumer”, i.e. customer who

both uses and generates energy.

Future power systems need to consider both prosumers and bulk producers. Bulk

production is always necessary to balance the fluctuations of renewable generation and

prosumers’ intermittent load and generation. Future can be evaluated from two

viewpoints, economic progress or sustainability. Latter viewpoint is used in the text for

PEV evaluation, but the main focus is on the technology instead of philosophy.

2 TECHNOLOGY

Electric vehicles were first built in the 19th century, after lead acid batteries were

developed. Nickel-iron and nickel-zinc batteries, recharging systems, regenerative

braking and hybrid vehicles made these popular and efficient. In the struggle for dominant

vehicle type, Ford’s Model T was the breakthrough for gasoline powered cars, due to low

cost, high reliability, long range, availability of fuel and ease of use. In the 1970s the oil

crisis and natural awareness made electric vehicles attractive again, and the modern PEV

may be thought of as the end product of the atmosphere of that era.

Plug-in electric vehicles appear as ordinary vehicles, but their components are

different. They consist of an energy storage system and an electric motor. Storage system

consists of batteries, fuel cells, or other means of storing electricity, and are charged from

conventional power outlets or fast charging stations. Electricity moves through a

converter to the electric motor, from which the power moves through the transmission

and turns to traction. Requirements for vehicular use for these components include low

weight, low price, wide operating range, long life and robust design.

2.1 ENERGY STORAGE SYSTEMS

Energy Storage Systems (ESSs) store energy in various ways through various means.

Requirements vary for different applications, for PEVs important features are:

Specific power.

Storage capacity.

Specific energy.

Response time.

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Efficiency.

Self-discharge rate/charging cycles.

Sensitivity to heat.

Charge-discharge lifetime.

Environmental effects.

Capital/operating costs and maintenance.

Due to fundamental differences in the operation of batteries, fuel cells and capacitors,

it is likely that they remain suitable to different application. Upfront cost and energy

density are the biggest challenges for electrochemical storage devices. Specific energy

and energy density of batteries and capacitors cannot compete with hydrocarbons, but the

combined weight of hydrocarbon-using powertrain offsets this difference. High amount

of hydrocarbons’ energy can however be converted to useful form. Due to the reliability

of modern cars, electric powertrain components should also last at least 10 years or 240

000 km.

2.1.1 Batteries

Battery is an electrochemical device that transforms chemical energy into electricity.

Main types used in PEVs are nickel metal-hydride (NiMH) and lithium-ion (Li-ion)

batteries. NiMH are usually used in hybrid PEVs. Their components are harmless to the

environment and they offer high cycle-life and energy density. They are also safer than

other types at high voltages. Other good points include design flexibility, low

maintenance, high power density, cost, volumetric power and energy store, wide

operating temperature range and resistance to overcharge and discharge. Negative points

are limitations at extreme temperatures, memory effect, and drastic reduction in life if

repeatedly discharged at high load current.

Li-ion batteries are light and compact. They store more energy and have less of a

memory effect compared to NiMH, but cost may be higher. Compared to NiMH the

operating temperature range is narrower, environmental impact is greater and material

availability is lower. Li-ion is expected to be the cheapest rechargeable battery in 10

years, and the low availability of materials may not be an issue, since only 1 % of the

battery is lithium, and recycling is a possibility in the future.

Lead-acid batteries are a mature technology, and widely available. They have a good

high-rate performance, moderately wide range of operating temperatures, high cell

voltage, easy state of charge indication, and good performance for intermittent charge

operation. Deep discharges shorten the lifetime drastically, and energy density is low.

Valve-regulated batteries are promising, and improved flooded batteries will likely

remain primary ESS in vehicular applications where batteries aren’t drained deeply.

Flow batteries, such as zinc-bromine are also under development. Materials are widely

available, they are cheap and environmentally friendly, they operate in ambient

temperature and can withstand deep discharges and fast recharging. Problems are

charging safety and cooling requirement, and self-discharging when shut down. Nickel-

zinc is unsuitable for vehicles, as it grows tendrils quickly, leading to self-discharge.

Nickel-cadmium batteries have lower specific energy than NiMH or Li-ion, but they are

considered for heavier applications such as buses. They are expensive, but have good

power density, maintenance-free operation, wide temperature range, long cycle life,

acceptable self-discharge rate and they can be fully recharged and fast-charged.

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2.1.2 Fuel cells

Fuel cells convert chemical energy into electricity. They need a constant supply of fuel

and oxidant, which are converted into water, heat and electricity in the presence of a

catalyst. They can be direct, which are fed pure hydrogen, or indirect, which are fed

hydrogen-rich gas converted from natural gas or fossil fuel. Fuel cells are considered to

be efficient and non-polluting, and to offer much higher energy densities and efficiencies

than other forms of energy storage. Fuel cells come in many forms, current six are proton

exchange membrane (PEMFC), alkaline (AFC), phosphoric acid (PAFC), molten

carbonate (MCFC), solid oxide (SOFC) and microbial (MFC). PEMFC is on the verge of

commercialisation.

Hydrogen has a high energy content by weight. Low energy content per volume makes

storage a problem. Solutions include storing under high pressure, as a liquid in low

temperature, in a metal-hydride system, or carbon nanotubes and metal organic

frameworks. Other challenges are efficient, low-emission production and efficient

conversion from fossil fuels. Objective today is to develop low-cost, high-performance

and durable materials for fuel cells. For automotive applications operation above 100 ºC

with low humidification is desired. Currently fuel cells are too expensive and not durable

enough for vehicular use. Platinum is the key issue, being rare, expensive and easily

degraded in use. Nanomaterial development hasn’t yet proved a solution.

2.1.3 Capacitors

Electrochemical capacitors have high power density and low energy density. Energy

density is speculated to rise close to Li-ion battery level in the next generation. Capacitor

lifetimes are measured in decades and charge/discharge cycles in millions. Current

generation of capacitors are proposed for use as load balancers and for pulse power.

2.2 MOTORS

Electric motor is the core of an electric vehicle. Four types of motors used are the

Permanent Magnet motor, Induction motor, DC motor and Switched-Reluctance motor.

Electric motors operate in two modes. From rest to rated speed is an area of constant

torque, and from there to maximum speed is the area of constant power. Different types

and different governing systems have different speed ranges and areas. Low-speed motors

have a maximum speed of around 6000 rpm, medium speed motors up to 10 000 rpm,

and high speed motors above that. Higher speed means bigger gears for higher gear ratio,

and lower speed means bigger conductors, as the rated shaft torque increases. Engine

choice is thus a compromise between motor speed and gear size. Main factors when

choosing the motor are weight, efficiency and cost.

Permanent magnet (PM) motors include the DC motor, the synchronous motor and

the brushless DC motor. They have high power density, high efficiency, high power factor

and high stability. They are generally light and come in various speed ranges. Brushless

DC high speed motor doesn’t need a transmission, is efficient, small and light for its

power, and dissipates heat efficiently. Constant power running however requires

commutation angle adjustment. Nearly all of the light-duty PEVs use PM motors.

Induction motors are reliable, maintenance-free, cheap and rugged. They have been

used in medium to heavy-duty applications. Field control and torque control can be

separated with pulse width modulation control, giving extended constant power range and

high degree of control. Drawbacks are high losses, low efficiency and low power factor,

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which are more serious for high-speed high-power motors. They are also heavy, their

torque characteristics change with operating frequency, and efficiency is lower than PM

motors.

DC motors have extended constant power range, but due to mechanical commutators

they are limited in speed. Low speed and high torque means more iron, and the engine

becomes heavy. Because they have brushes and commutators, they also require

maintenance, unlike other studied motor types.

Switched Reluctance Motor Drives is a potential candidate. Absence of brushes,

magnets and rotor windings makes this a simple, reliable motor. It has a high speed range,

wide constant power range, high starting torque and high torque-inertia ratio. Typically

they have low cost due to simple construction. Power factor however is low, they generate

torque ripple, bus ripple, acoustic noise and electromagnetic interference noise.

Hysteresis motor fits applications where constant torque, constant speed and low noise

are required. They have high starting torque, but high magnetizing current and high

parasitic losses. Hybrid permanent magnet hysteresis synchronous motor combines

advantages of PM and HM motors, as it has both permanent magnets and hysteresis rings.

Unlike PM motors, they have a high start-up torque, and in synchronous speed the

combined torques of hysteresis and permanent magnets.

2.3 POWER ELECTRONIC CONVERTERS

Control systems comprise of semiconductor devices, converters/inverters, strategies,

packaging and integration. Development is required to make these systems efficient and

reliable at high currents above 400 A, high temperatures and under thermal cycling and

extreme vibrations. Sensorless operation is desired, and the systems need to be small

enough to fit passenger vehicles.

3 NEW POWER SYSTEM OPERATION PHILOSOPHIES

Renewable energy is a challenge to society and electricity grid reliability and

development. New sources require new innovations in grids and associated technologies,

as well as legislation and public opinion. Unpredictability of renewable generation means

that bulk power remains important. Current need is to develop reliable renewable energy

sources, smart grids and models of understanding the non-technological aspects, such as

social and ethical questions.

Developers must consider things from different viewpoints. One of them is the aspects

of the technology, ranging from physical basis to emotional reactions associated with the

technology. Another are the various stakeholders, who often have different values and

expectations, such as profit versus reliability. Third are the values, ideals, and belief

systems of the culture in which the development takes place. New applications need to be

attractive instead of threatening.

All components of this new technology need to be evaluated from all of these aspects.

Failure to develop a grid that is both economical, ethically acceptable and sustainable

would be catastrophically expensive. Existing technology must also be understood, as it

will need to be integrated with the new.

Chapter 2Smart Coordination Approach for PowerManagement and Loss Minimization in DistributionNetworks with PEV Penetration Based on Real TimePricing

Nida Riaz

SUMMARY

(Chapter 2)

Need of Electric Vehicles:

The increasing price of fuel has been increasing day by day and the natural reserves of fuel (Oil, Natural

Gas, Gasoline etc.) are diminishing. Therefore, the latest trend of the people is moving towards

purchasing PEVs (Plug in Electric Vehicles). Moreover the growing environmental concerns of carbon

footprint and pollution being caused by the fuel based cars are also promoting the use of PEVs (Plug in

electric vehicles). The common perception about carbon emission is that the emission is most significant

in operation of the car rather than production. An Electric vehicle utilizing electricity from the grid being

operated on renewable energy has almost no carbon emission.

Electric vehicles are also more efficient as compared to the conventional ICE cars. The operating and

maintenance cost is also low as compared to the conventional cars. PEVs can be used for energy storage

frequency control as they can provide energy during emergency condition and improves system security

and reliability.

Challenges Related to PEVs:

The merging trend of using PEVs can result in overloading on an existing distribution system

(Transformer, cables etc.) which can lead to drastic detrimental effects on the distribution system

including;

Higher losses

Branch congestion

System peak demand can become unpredictable

Unacceptable voltage deviation.

Significant increase in losses

Poor power quality

How to overcome these problems:

To overcome these problems proper PEV charging coordination is required.

The charging and discharging of PEVs can be controlled and electricity can be transferred from

Vehicle to Grid (V2G) or Grid to vehicle (G2V).

Smart metering is also another good option to control the charging behavior of the PEV owner

and improve the system load profile or demand. It can also help in reducing the peak load and

helps in demand side management.

A smart coordination method using time horizon method can help in coordination multiple charging and

discharging to reduce stress on the distribution system that can hurt system reliability, security and

performance.

Charging Algorithm:

The PEV charging algorithm defined in the book consist of;

Forecasting Module: This tells the number of cars at the same time in the parking garage at present and

in future, the driving schedules.

Optimizing Module: This module maximizes the energy delivered to the batteries and minimizes the

losses by taking into account the charging time zone priorities given by PEV owners.

In the chapter it is assumed that almost half of the customers in the distribution system own a PV and if

all of them charge their vehicles at the same time in the evening after coming back from work then it can

overload the distribution transformer which is already operating at 75% of loading. Thus to minimize this

problem the charging behavior of the customers have to be predicted and controlled. Otherwise in case of

charging at the peak load hours can lead to unpredictable peak load. DSM based charge control method

where dynamically configurable dispersed energy storage during peak and outage condition is proposed.

A PEV charging module based on TOU time of use tariff is also mentioned.

Time of use Tariffs is introduced to charge the vehicles during night under off peak hours. Often charging

is done at higher voltage in order to enable faster charging. In TOU tariffs incentives are provided to the

customers for off peak hours charging which compliments new intermittent renewable energy resources.

The peak to off peak price ratio of 8:1 has helped in 15% reduction of the peak load. Price elasticity of -

0.04% has reduced the peak load from 60% to 55% and consequently reduced the transformer overload

and other adverse effects on the distribution system.

One major issue is in controlling the customers charging behavior which is not only dependent on the

price or tariff but also on some convenience factors;

Work Schedules

Availability of charging stations outside home

Tolerance to less than fully charged battery

Regularity of driving.

Another solution used was the charging coordination strategies which have either;

Single objective optimization: to optimize cost or losses.

Multi Objective optimization: to optimize operating cost with losses

There are two different coordination approaches including;

Centralized Coordinated PEV Charging: is the one in which the central controller sends the message to

each of the PEV to set charging time and rate based on multiple factors. It is the optimal coordination

based on customer info and can be unscalable. It is stable and controlled.

Decentralized Coordinated PEV Charging: is the one in which each PEV can set its charging schedule and

its rate. It can be optimal or not and is flexible and easy to implement.

Power Management:

Since the power demand is increasing day by day and there is a huge variation in the electricity usage

which results in unbearable peak load. In order to avoid stress of this peak load on the grid and

transmission system, power management schemes are being used. PEM can be used at the;

Supply Side: when supply doesn’t meet the demand.

Demand Side: to reduce the cost of electricity production.

Power and Energy management includes load shedding and restoration, energy efficient processes and

equipment, rotating power wheeling, Use of electricity storage devices, Cogeneration, Reactive power

control. Load management is one of the techniques which have been proposed which further includes

TOU tariffs, Interruptible load tariffs, critical peak pricing, and real time pricing and distribution system

loss reduction.

The various load control options being used are:

Direct Load Control (DLC): in this the utility can switch off the load directly saving the system

from damage and losses

Interruptible Load Control (ILC): the utility notifies the customer beforehand about power

outage. It is also called planned load shedding.

TOU tariffs: Utility designs different rates to flatten the load profile and shifts the load to the off

peak hours.

More the DLC programs are controlled by duty cycle model and is given by:

t= Avg. load/connected load

The total cost of generating and delivering of electricity is to consumers has four categories including;

Customer services

Distribution services

Transmission services

Generation services

The consumers are billed for these services based on time based tariffs including; flat rate tariffs, time of

use tariffs, spot price etc.

The central idea of the chapter resonates around certain goals of charging coordination designing that are

minimizing the grid losses, each PV charger should operate independently and considers the information

efficiently provided by its smart meter, keeping the load on the distribution transformer, cables and other

equipment under rated values to avoid any damage to the system and the last but not the least designing

such a coordination so that the overall system losses reduce and the electricity provided to the EVs should

ne maximized.

Chapter 3Plug-in Electric Vehicles Management in SmartDistribution Systems

F. Rahman

System level aspects of electric vehicles | 1

Plug-in Electric Vehicles Management in Smart Distribution Systems

F. M. Mahafugur Rahman

Aalto University School of Electrical Engineering

1. Introduction

The advent of smart grids brings a set of new concepts in current power systems. The plug-in electric

vehicles (PEVs) fit the concept of the smart grids, since PEVs reflect the worry about the environment and

the future. PEVs are low carbon emission device which have energy storage systems (ESSs) to supply power

to an electric motor and mechanical shaft. Moreover, the PEVs may become a source of energy during

emergency conditions.

The PEVs will bring some problems to the power distributions systems. Charging the PEVs in residential

outlets may deteriorate the network operating conditions, such as load uncertainty, peak load, power losses,

harmonic distortion, voltage deviations, transformers and conductors overload, and early aging. The peak

load during charging PEVs will require reinforcements over the grid and new assets, though this new

capacity will remain idle rest of the time. Furthermore, the development of a new recharging infrastructure

on the streets and public buildings is required for the PEVs. In addition, since the load may become a

supplier in emergency conditions, it may change the ideology of power systems operation.

Since the PEVs represent an environmental-friendly green option with zero greenhouse gases emissions, free

or low fossil fuel usage and low noise, they are becoming popular. The PEVs integration represents a great

challenge for the electrical power systems, which has to be faced by electric power industry. In this sense, a

recharging policy must be addressed where the system losses and voltage profile are adequately managed.

2. PEV Management in Smart Distribution Systems

The integration of PEVs in distribution power systems will create a new load to be connected to the grid

mainly in the evening. This new load causes low voltage levels on further nodes of feeders, high currents

which can cause transformers and conductors overloading and exceed thermal limits, power losses and

harmonics distortion also increase. Moreover, this new connected load claims for investments on distribution

grid expansion and assets replacement, such as substation transformers, lines transformers, conductors, fuses

and circuit breakers. Since the new load is highly dependent on the time of the day, remaining idle during

almost the day, the entire system will operate on a lower efficiency point. Thus, the expensive investment on

grid expansion seems not to be the better option.

The smart grids present several solutions to manage the integration of PEVs to electrical power systems. The

real-time communication, advanced smart metering, demand management systems (DMS), demand response

and intelligent control systems provide real-time information on system variables, such as voltage levels,

currents, active and reactive power flows, and power losses. The aforementioned features control electrical

power grid, operation and management in all stages. The integration process maintains communication

between the distribution system operator (DSO) and the vehicles and between PEVs and aggregating agents,

and finally the vehicle-to-grid. Finally, the adoption of a recharge policy based on DMS keep the voltage on

acceptable levels, avoid peak loads, decreasing losses by using on-load tap changing transformers (LTCs)

and shunt capacitors (SC).

The intelligent DMS is responsible for managing the controllable loads, i.e. the PEVs, by shifting the PEVs

recharging process only in low demand time. For example, during the late night, the system have low load

and it is possible to supply PEVs with sufficient power to recharge up to suitable level for transportation

purposes and control the system operational variables. This time dependent charging approach also avoids


large investments for system expansion, making possible to supply a PEV fleet with the current network

infrastructure.

By the communication infrastructure, the charging control and management algorithm receive all the

information from the system and assesses the charging schedule for each PEV and the network operational

state. The task of the DMS may be performed in a centralized or distributed way.

2.1. Centralized Demand Management System

Typically, the vehicle usage has an ordinary schedule represented by the daily commuting, i.e., a round-trip

travel from home to work, and some deviations represented by the unscheduled travels and weekends.

Considering that all PEV owners have an individual schedule, this stochastic behavior can be represented by

a probabilistic distribution. Although these are random variables, they have a predictable behavior if enough

data is stored in order to assess them.

The consumer may choose either a standard charge process, if the vehicle will only be used for daily

commuting or a fast charge process if he wishes to use the vehicle soon. The charging process and charging

schedule are sent to the DSO through the real-time communication network. The PEVs connection

probability during the day for each feeder or geographical region helps the DSO to assess some important

variables like load forecasting. Several information such as nodal voltage, current, and power measurements,

LTCs, line step-voltage regulators (SVR) and SC status are sent to DSO operational center through

supervisory control and data acquisition systems (SCADA). The DSO runs the optimization and operational

algorithms in order to determine the individual connected PEVs charging schedules to reach their optimal

state of charge (SoC) within expected time. The DSO also assesses the tap positions for voltage control

devices such as LTCs, SVRs and SCs. The optimized data is sent back through real-time communication

system and the voltage control devices are controlled remotely from the substation.

2.2. Distributed Demand Management System

The distributed approach is based on the existence of local controllers spread over the entire system. The

local controllers with the help of computational agents are able to receive data from different devices and

assess the optimization and control algorithms in order to send back setup configurations to the controlled

devices.

A set of computational agents working on a system with specific tasks creates a multi-agent system. These

agents have interesting features, such as intelligence, learning capacity, sociability, communication capacity,

and hierarchy. The residential agents are responsible to control and assess the PEVs schedules according to

the information received based on the fast or standard recharging mode. The residential agents send request

to the feeder agents for a fixed amount of power. The feeder agents assess the simple calculations regarding

to the available feeder’s power and current capacity and send a requesting message to the substation agents.

Substation agents communicate with DSO Agent, which is the highest level. The DSO agent is able to

control and to communicate with all agents in different substations. The different agents send the solutions

to other agents in hierarchy, which update their controls and PEVs schedules according to the solution

presented.

2.3. Technical and Other Issues

Although the major parts of the necessary technologies already exist, the regulatory aspects and the

definition of standard practices are not answered yet in PEVs management in smart distribution grids. As

discussed before, the real-time communication system has a big role for bringing data from all over the

system and for taking them back to every smart device in the network. Thus the communication systems

need some mandatory requirements, such as reliability, robustness, data speed, data capacity, and signal

range. A merge of existing technologies, such as power line carrier (PLC), the Internet (TCP/IP), Zig-Bee,


WiMax, radio waves, mobile communication lines, and optical cables provide robust, fast and reliable

communications.

The geographical area is another important aspect on communication requirements. The amount of data

increases for the bigger geographical area or the demographical density. In this case, the faster and more

robust communications are required than on low density regions.

Using the data available for control purposes, e.g. voltage levels, currents, power flows, PEVs schedules,

real-time load measurements, the adaptation of smart tariff program is another issue. This program adopts a

variable electricity tariff for different hours of day, e.g., the electricity price may be higher in the evening to

discourage the electricity usage, and the electricity price may be lower at late night to stimulate the PEVs

recharging process at this time. In addition, a higher tariff can be charged for fast recharge in the PEVs.

Using this smart tariff, the DSO can encourage the choice for standard recharge. Thus, the fast recharging

will be used only when it is really necessary.

In addition to the technical considerations, one of the concerning points is related to the unavailability of the

vehicle due to low charging state, even after connected to the outlet. The customer oriented approaches for

DMS control can be implemented to cope with these situations. The customer-oriented programs are based

on customers programs with different features and with different electricity tariffs that persuades the

customer settled schedule and the customer convenience is assured.

The creation of a public infrastructure to provide a new set of services for the customers is another vital

issue. The infrastructure development is needed to help the transportation electrification, not only private

PEVs but also electric buses, trucks and other heavy duty vehicles. A set of services, such as charging points

on the streets and on parking lots, fast recharge points, and cheaper components can help on integration

process.

3. Case Study

A centralized DMS approach is considered as a case study to investigate some practical aspects of the PEVs

integration to the grid. The IEEE 34 node network is chosen for this work because of its wide variety of

components, such as two SVR and two SC, single-phase laterals and unbalanced load.

A heterogeneous geographic location of PEVs with low density areas and higher PEV density areas is

investigated in order to create a more realistic example. The same PEV percentages distribution for each

hundred vehicles are kept for all simulations, independently from the quantity of PEVs. Some different real

PEV models are also considered with different ESS capacity and features given in Table 1. The depth of

discharge (DoD) is the maximum allowable discharge for the ESS.

Table 1: PEV models.

PEV Models ESS capacity (kWh) Maximum DoD (%) Consumption (km/kWh)

Model 1 42 80 4.9

Model 2 24 65 6.15

Model 3 16 65 5.86

Since all PEVs do not run the same distance and one PEV also does not run the same distance every day, the

PEV’s commuting distance is represented by a normal distribution with a mean and a standard deviation.

Additionally, the connection hour is not the same too. Thus, a PEV connection probability is considered

during the simulations. While managing the PEV recharge, the DMS improves the system operational

conditions, such as power losses, load unbalance, and cost of recharging process. The voltage level,


ampacity, and power transformers loading are considered as restrictions. The objective function for this

problem is the power losses minimization through capacitor placement which is expressed as

where r is the branch number, R denotes the total number of branches, Zr is the impedance, n defines node,

Vmin = 0.90 p.u, Vmax = 1.05 p.u, Ir is the branch current, and Ir(max) is the maximum conductor ampacity.

3.1. Simulations and Results

The artificial immune system (AIS) is applied as optimization tool during the investigation. The 20 % of

model 1, 45 % of model 2, and 35 % of model 3 are chosen from Table 1. To simplify the case study, all

PEVs have 100 % SoC. A simulation is considered from 4 p.m. to 7 a.m. of the next day. As can be seen

from Fig. 1(a), the low voltage level in all three phases during some hours is evident as the need for DMS

control. The scenario is worse than this one when more PEVs are considered.

Fig. 1: Minimum voltage levels: (a) without a DMS control; (b) with a DMS control.

Repeating the simulation using the same set of vehicles and the same initial conditions, Fig. 1(b) shows the

voltage levels while applying the DMS control for the recharging process. From the simulation results, the

application of a DMS control decreases the power losses from 0.0473 to 0.0312 p.u. Each PEV recharge

power is assessed in such a way that the minimum voltage level (0.90 p.u) is matched in every hour.

The AIS optimization also gives the potential nodes for capacitor placement on the planning stage. The AIS

provides the solution for the switching during operating stage aiming for power losses minimization (1) with

the restrictions (1.1)–(1.2).

4. Discussions

Since the PEVs are real option as the prices decrease, the integration of PEVs to the distribution power

systems must be addressed. The DMS is responsible for managing the PEVs according to predefined rules in

order to assure operational restrictions for quality and safety purposes. The assessment is based on the

system’s load margin. The PEVs’ schedules depend on this load margin and some other combinatorial

situations which are represented by probabilities, such as the daily commuting distance, the hour of

connections, and the predefined recharge process chosen by the customer. The centralized and decentralized

DMS are described in order to integrate the PEVs to distribution power systems. The choice for perfect

model depends on the variables such as costs, current infrastructure, technology availability, and revenue

expectations.

Chapter 4An Optimal and Distributed Control Strategy forCharging Plug-in Electrical Vehicles in the FutureSmart Grid

Evgenia Vanadzina

Chapter 4. An Optimal and Distributed Control Strategy for Charging Plug-in Electrical Vehicles in

the Future Smart Grid. Zhao Tan, Peng Yang and Arye Nehorai.

Plug In Vehicles in Smart Grids: Energy Management. Sumedha Rajakaruna, Farhad Shahnia and

Arindam Ghosh, Springer Science+Business (2015), ISBN 978-981-287-302-6

Chapter Summary

by Evgenia Vanadzina

The chapter 4 “An Optimal and Distributed Control Strategy for Charging Plug-in Electrical Vehicles in

the Future Smart Grid” by Zhao Tan, Peng Yang and Arye Nehorai proposes an optimal scheduling

algorithm for plug-in electric vehicles’ (PEVs) charging and discharging based on alternating direction

method of multipliers (ADMM) and using advanced metering infrastructure (AMI) and energy-

management controller (EMC). Authors solve the problem of optimal PEV dispatch with the objective to

minimize consumer’s bill. Owners of the EVs can participate in the electricity market as a demand

response through the utility company. Therefore, they can sell electricity when the price in the market is

higher and consume electricity to charge the EV when electricity price is lower. In such a case, PEV is

considered as a storage and the PEV owners and the utility company can communicate both-ways. The

novelty of the proposed algorithm is that the authors test the possibility of data loss from AMI

malfunction and propose asynchronous ADMM approach.

The main idea behind the methodology is that consumer and the utility company have the same objective

– minimize load fluctuation costs. Meaning that electricity consumers are interested in minimizing their

electricity bills. While, the utility company is also interested in minimizing consumers bill as it would

result in flattened load curve and lower the fluctuation cost. The authors decentralized the problem

formulating parallel optimization problem, introducing auxiliary variables (𝑧𝑘). So the total electricity bill

for the consumers would be (the main objective function):

Where 𝑝𝐵 is the price of base load (equal to marginal generation cost to supply the base load), 𝑙𝑘 is the

total load of k users and 𝐶2 is the fluctuation price. 𝐹𝑘 denotes the feasible set for 𝑙𝑘 that satisfies the

technical constraints of the load consisting of base load, schedulable load, PEV load and some distributed

generation. The problem can be solved using Gauss-Seidel algorithm for ADMM cycles until

convergence is reached. Therefore, the process (iteration) is run as a negotiation between the utility

companies and each consumer trying to minimize the fluctuation.

Asynchronous ADMM is introduced to simulate such cases when AMI messages get lost due to random

noise in the transmitting lines. The principle of the method is to use the previous transmission to

substitute the missing information from AMI. The loss of information is simulated randomly.

The EV owners submit their charging schedules only to the utility company, therefore, protecting the

privacy of each owner. The authors provide numerical results for the case where there is only one

electricity supplier, 60 households with PEVs and distributed generation and 60 households only with

distributed generation. The battery sizes of the PEVs are chosen as either 10kWh or 20KWh. Three

different scheduling algorithms are simulated and compared in the paper: no optimization (EMC and AMI

devices are not applied); a greedy algorithm (everyone tries to lower its own electricity bill based on pre-

determined base price) and ADMM scheduling algorithm (proposed by the authors). The results indicate

that the most savings on bills comes from ADMM approach. Daily bill in proposed case would amount

455$, against 725$ in an non-optimized approach and 550$ in a greedy scenario. ADMM algorithm also

contributes significantly to the load flattening, see fig.1.

Fig.1. Load curves results of three different algorithms.

The main advantage of the proposed model is that even in the case of data loss, the ADMM approach

gives still better results than any other considered. Another advantage of the chapter is that authors test

the effect of battery discharging, the impact of pricing parameters, impact of different shares of

schedulable load and distributed generation on the results of the model. These sensitivity analyses provide

more credence to the model, as the results still indicate that ADMM is better solution.

Disadvantage of the proposed method is that its implementation requires all the participant of the demand

response to act perfectly competitive and rational. However, such arrangement would be difficult to

achieve in reality. Utility companies would tend to maximize profit rather than minimize cost of

fluctuation, therefore they would increase 𝐶2. As for PEV owners, they presumably would act as in the

“greedy” scenario, which is also considered as an alternative simulation by the authors. In addition, flatter

load curves are beneficial from the technical point of view, however, existence of the high prices in the

electricity market during peak hours makes sense from economical point of view. In the energy only

markets, scarcity electricity prices are the indicators for the investments in new generating technologies.

If the market does not provide incentives for investments, the regulator would consider introducing

capacity remuneration mechanisms (CRMs) which at the end would increase final electricity bill for the

consumers.

Finally, numerical results of the proposed model suggests that consumers’ bill can be decreased

substantially comparing to the alternative scenarios. Therefore, EV owners should be incentivized to

participate in the demand response program. Overall savings for the consumers can be up to 37%

comparing to the non-optimized simulation. Authors also note that the model can be extended to the

consideration of generation companies and retailers as separate entities with different aims, which would

make it more adapted to the real market situation.

Chapter 5Risk Averse Energy Hub Management ConsideringPlug-in Electric Vehicles Using Information GapDecision Theory

Olli Vilppo

Summary chapter 5 Olli Vilppo

Risk averse energy hub management considering plug-in electricvehicles using information gap decision theoryEnergy Hub is a combination of energy conversion units which satisfy different types of customer energydemands. The hub purchases energy from different resources and converts them to different outputforms. The general concept is depicted in the figure 1.

In the management of the energy hub there are uncertain parameters that are difficult to estimate inthe output and input. These include renewable power generation, customer demand behavior and theelectricity price.

To cope with the increasing volatility with the renewable generation it is possible to use energystorages. One of the possible energy storage that increases the hub flexibility is the use of PEV (plug inelectric vehicles) that are connected to the electricity grid. In this paper risk averse Information GapDecision Theory (IGDT) is proposed for optimal energy management of an energy hub. IDGT provides acomputationally efficient tool for decision makers in case they don’t have enough information of theuncertain parameters like their probability density function. In the figure 2 the energy Hub used in theexample calculations in described. All the nomenclatures are explained in the Appendix 1.

Fig.1 Energy Hub

Fig.2 The energy hub of this study

The objective function of the Hub is defined as follows:

If it is negative, the hub is making profit in the electricity markets.

The predicted hourly wind power generation, electricity price and electric and heat demands aredepicted in figure 3. The percentages in figure reflect following values: The wind capacity Capw is 15kW.The peak electric and heat load is 5 kW and 4,5 kW respectively. The peak value of electric price is 47$/kWh. The heat has a constant price 30 $/kWh. The table 1 shows the other characteristics of the hub.The hourly travel patterns of PEVs were also defined for simulation in order to know when they can beutilized.

Table 1

Fig. 3 The predicted value

ResultsFour scenarios were tested in the paper to demonstrate the applicability and strength of the proposedapproach of IGDT based modelling.

Base case: No uncertain parameters exist in the model.

The objective function to be minimized is the energy procurement cost. The decision variables (DVb)that the method selects for every hour are:

Result: Minimum OFb=-0,480165 $, which means that the energy hub is making this much profit in themarket when the decision variables are selected most economic way.

Wind uncertainty:

The objective function in this case is radius of wind power uncertainty αw.

The following optimization is solved:

The decision maker tries to maximize OF=αw for a given β-value. If the forecasted wind value doesn’tcome true, the total payments do not increase more than β of the base case profit OFb. Figure 4 showsthe simulation results for DVw when the β is increased from 0 to 1. In order to increase the immunity ofthe OF to wind uncertainty Pes(t), Pgf(t) are decreased and Pgchp(t) is increased. Both charging anddischarging are increased. For examble if the risk β=0,3 is selected Pes(kW)=146,4 ; Pgf(t)=36,6 and soforth.

Increasing β reduces the profits when compared to base case with no uncertainties but they make thehub less vulnerable to wind power generation uncertainty and more wind power generation uncertaintycan be tolerated. With β=0,3 maximum wind uncertainty that can be tolerated is 1,748692 % and thenOF= -0,3361.

Similar calculations were made to uncertain electric load (fig. 5) and heat load (fig. 6) separately.

With β=0,3 maximum electric load uncertainty that can be tolerated is αLh=3,963492 % and OF=-0,336115 $

With β=0,3 maximum heat uncertainty that can be tolerated is αLh=4,0403765 % and OF=-0,336115 $

Fig. 4 simulation results different variable versus uncertain wind.

Fig. 5 simulation results of different variable versus electricity load.

Fig. 6 simulation results of different variable versus heat load.

ConclusionThe obtained results from the simulated risk averse strategy assure the decision maker that even thoughpredicted values of uncertain parameters are not exact, he is secured (to some controlled extent) ofmaking some profit or at least not losing money.

Appendix: Nomenclature

Chapter 6Integration of Distribution Grid Constraints in anEvent-Driven Control Strategy for Plug-in ElectricVehicles in a Multi-Aggregator Setting

Aapo Aapro

System level aspects of electric vehicles - Summary

Aapo AaproTampere University of Technology

Chapter VI - Integration of Distribution Grid Constraints in an Event-Driven Control Strategy for Plug-in Electric Vehicles in a Multi-AggregatorSetting

The number of Plug-in Electric Vehicles (PEVs) is steadily increasing thorough the world. Major manu-facturers have been entering the markets with low-cost EVs, e.g. Elon Musk’s Tesla 3-series, which haveincreased the attraction towards the electric vehicles. However, on the grid-side point-of-view, rapidlyincreasing amount of PEVs may cause grid stability issues, such as overloading of the utility grids. Fur-thermore, electric markets are evidently affected by the charging of PEVs in a larger scale.

In order to avoid overloading the distribution grids, preventive actions are required from the DistributionSystem Operators (DSOs). The DSOs can enforce constraints for e.g. charging of PEVs in order tomaintain grid stability. Accordingly, two levels of different operative measures can be identified. First,the market operation level consists of actions such as trade volume processing, which addresses the is-sues related to PEV charging for relatively long time scale. The other one is the real-time operationlevel, which addresses the instantaneous demand for power and grid requirements. Therefore, real-timeoperation level is more dynamic and should response fast to demands.

It has been studied that the issues between the market operation level and the real-time operation levelare often partly neglected. Accordingly, the real-time operation level may not fulfill the requirementsobtained from the market level, e.g. when the distribution grid is already overloaded or otherwise con-strained to supply the required power. Therefore, the real-time operation level interferes the presumptionsfrom the aggregators, which complicates system operation.

The situation where the demand for active power exceeds the transfer capability of the distribution gridis called the grid congestion. The congestion can be mitigated by using traditional congestion mitiga-tion methods such as reactive power and voltage control, power flow coordination and increasing thepower capability of the grid. These are highly related to real-time level of the power system operation.In the distribution grids, the power and voltage control is usually performed by using tap changers andlarge capacitors, which are connected to the grid according to predetermined switching plan. The powerflow coordination is done by implementing different demand response (DR) schemes or by using voltagedroop control, and this is desirable due to sufficient flexibility and performance. The power transfer ca-pacity of the grid can be also improved, however, the increased overall system cost limits the utilizationof this possibility. As expected, transfer capacity cannot be improved instantly but rather in a long-term.

The grid congestion can also be managed by other methods which are slightly related to market levelcontrol. Generally, these are applied by enforcing penalties and additional fees for the network use ifnecessary.

Market-based control (MBC) is purely a market-level operation, which uses different market mecha-nisms to control the energy usage of PEVs. In a common MBC system hierarchy, an auctioneer agent

is the executive head in the hierarchical chain. The auctioneer agent is responsible for the energy pricesin the corresponding market area. The concentrator agent is below the auctioneer in the hierarchicaltree and gathers information, i.e. demand functions from various device agents. The device agents (e.g.PEVs) gathers demand functions, which indicates the requirement for energy and the energy costs. Theauctioneer assembles all the aforementioned information from lower level branches of the hierarchicaltree and forms an equilibrium price, which is transferred to the lower agents.

The market-based control is highly scalable due to practically unlimited amount of aggregators withindifferent aggregation levels. This provides faster communication and data-handling compared to a cen-tralized market system. Furthermore, the hierarchical chain induces low complexity to the system mak-ing the data-exchange process straightforward. Also, any kind of device with a demand function can beadded to the system, and the only information transferred upwards in the hierarchical tree is the demandfunction. Therefore, increased overall privacy is obtained.

As discussed earlier, efficient and reliable communications and data-handling are essential when formingthe demand functions for the aggregators. Furthermore, the control signal has to be sent back for energyconsumers (e.g. PEVs) in order to maintain energy targets from the markets. The process itself is verydynamic due to somewhat arbitrary charging behavior of customers’ PEVs, which introduces a require-ment for continuous coordination of the charging process. Accordingly, both market level and real-timesystem level functions have to be executed, which is in fact a event-driven approach to the problem. Theinteraction between the consumer (e.g., the PEV) and the aggregator allows rapid responses to changesin the grid conditions such as overloading situations. Therefore, the aggregator can adapt to ongoingsituation for example by controlling the power flow to other vehicles which might be connected to a gridpart with nominal operating condition. However, the message exchange between parties can be distortedby packet loss/delay, link reliability and the link capacity, which has to be taken into account if fast dy-namic response to different energy demands is desired.

The aggregator has a limited amount of methods to respond to PEV charging situations, which are re-ferred as coordinated charing. One method is to optimize the charging cost of the vehicle fleet for a24-hour time frame. This eventually leads to situation where multiple PEVs are charged at the same timewhen the electricity price is at the lowest. This may lead to severe power quality problems due to majorpower peaks and the results may be inferior compared to uncoordinated charging (i.e., dump charging)situations. However, the voltage droop control prevents the large scale power quality issues, yet it maycause insufficient charging for part of the PEVs connected to a specific aggregator.

Another way for aggregator to control the charging of the PEV fleet is related to the use of sources, suchas wind power, as an energy source. Here, the aggregator would use the capability of the PEVs to bal-ance the energy mismatch between the wind power predictions and the actual energy production. Thiseffectively reduces the imbalance between prediction and production and, therefore, is beneficial for theaggregator. Moreover, aforementioned method is beneficial for the distribution grid since the energy flowback and forth to PEVs is controlled flexibly and the multiple PEVs are not charged simultaneously.

Aforementioned discussion applied to cases with a single aggregator and, therefore, a direct comparisonwith multi-aggregator cases may not be directly applicable. However, research on the topic suggeststhat multi-aggregator setup does not introduce any severe issues especially in the energy balancing. Thedifference between the voltage deviations for single and multi-aggregator setups in the PEV charging isnegligible. Both aggregators may use voltage droop controllers effectively in order to maintain the gridstability and, therefore, other grid congestions methods may be unnecessary.

Chapter 7Distributed Load Management Using AdditiveIncrease Multiplicative Decrease Based Techniques

Tapio Aronen

Distributed Load Management Using Additive Increase Multiplicative Decrease Based Techniques (Ch. 7)

The future large amount of PEVs (Plug-in Electric Vehicle) can place a great load on the distribution powergrid. In many cases, PEVs can be charged at a time that is most suitable for the grid; the charging time isnot of great interest for the PEV users themselves. Thus, the grid could be allowed to mandate themoment at which the PEV charging occurs. As PEVs can also be considered to be energy storages, there isthe possibility that PEVs would be allowed to inject power into the grid (V2G – Vehicle-to-Grid), in orderto reduce peak loading in some parts of the grid and/or to balance out the intermittent nature ofrenewable energy production (e.g. photovoltaic). Additionally, PEV active rectifiers could be utilized tobalance reactive power in a local area. Thus reducing the need for long distance reactive power transfer,and, thus reducing grid transmission losses and offering local voltage support. Ideally, the PEVs can beused to balance all the reactive power consumed in the area of interest, for instance by the uncontrollable(the grid cannot control the power of these) loads.

If a large amount of loads start consuming power simultaneously, there can be a great load on thenetwork. Also there might not be renewable production available in the current moment. The solution ispeak shaving which means time-shifting the power demand by altering the moment of powerconsumption of controllable loads. Peak shaving thus also helps in regulating the grid frequency. On theother hand, this could mean that the SOC (state of charge) of the PEV battery does not suffice when theuser needs the car on a long drive, if V2G operation has been draining the battery. In such a catastrophicsituation, there has to be proper economical compensation offered to the customer.

An AIMD (Additive Increase Multiplicative Decrease) algorithm is applied in order to balance the powertransfer. This algorithm will control the flow of active and reactive power from and to the grid. The maintask is to share a limited amount of power among several loads.

At any moment of discrete time (denoted by integer ), the sum of power drawn or injected from the gridby the controllable loads ∑{ ( )} combined with the sum of power drawn from the grid by theuncontrollable loads { ( )} can be at maximum the amount of power available in the grid { ( )}. This isshown in equation (1) below,

( ) + ( ) ≤ ( )∀ .(1)

In such a case that the power requirement of the uncontrollable loads exceeds the combined outputpower available from the grid ( ) and the PEVs injecting power in V2G mode, the target scenario will bea “best-effort” solution, where the PEVs will be required to provide as much power as possible to mitigatethe effects of the power mismatch.

The number one goal is to maximize the amount of active power transferred to the PEVs without violatingthe constraints set by eq. (1) and also keeping in mind that the amount of apparent power, i.e., =

∗= ( ) + ( ) , which the active PEV converters can withstand sets limitations for the

maximum active and reactive power throughput for the PEV chargers. By participating in the energyexchange program with the grid, the customer allows the grid to mandate the SOC of their PEV battery.On the other hand, the SOC of the PEV batteries should always be kept at a level which will fulfil theoncoming travel needs of the customer. In case the customer will be required to travel a great distanceduring their next trip, such a distance that they will need the full capacity of the PEV battery, they will use

the option to temporarily opt out from the load balancing scheme and possibly incur a higher batterycharging tariff as a consequence.

An assumption is made that the PEV can modulate the active and reactive power exchanged with the gridto accommodate their own charging needs, the energy needs of the other PEVs connected, and also theneeds of the distribution grid itself. Hence, at each time step the PEVs are supposed to adjust their activeand reactive power consumption such that ( ) the maximum amount of the available active power istransferred to the PEVs, ( ) the PEV active converters are not overloaded and ( )the local area reactivepower requirement is satisfied by the minimum amount of long-distance transfer. Considering the pre-mentioned requirements, three charging scenarios are introduced:

1. Power Fairness (PF): Each PEV will receive from the grid or deliver (in V2G mode) to the grid anequal amount of power.

2. Energy Fairness (EF): The power received by each PEV depends on the expected needs of the user.Vice versa the power delivered (in V2G mode) is inversely proportional to the expected needs ofthe user. (The user travels less, thus they will require less charging power and on the other handthey can deliver a greater power to the grid (V2G) because they do not need the full capacity ofthe battery at any given moment).

3. Time Fairness (TF): PEVs that have been connected for a long time receive or deliver (in V2G mode)a smaller amount of power compared to recently connected PEVs.

Power Fairness (PF)The amount of power available from the grid for the purpose of PEV charging at any given moment is onaverage shared equally to each connected PEV. Mathematically this scenario can be modeled as aprioritized optimization problem.

· The first objective to solve is to find the maximum power available for (rapid) PEV charging in totalwhile still fulfilling the constraints set by eq. (1) and also making sure the complex power limits ofthe active converters are not exceeded. If the solution of the first objective allows for additionaldegrees of freedom, an objective with lower priority is solved.

· The second objective to solve is to fulfil the power fairness condition imposed in this scenario.This means that the difference in the amount of power transferred to/from each PEV is minimizedusing, e.g., AIMD (see latter sections). A question might arise now: how is it possible to providethe exact same power to each PEV when the respective SOC might differ and thus the PEVs areaccepting different amounts of charging power. This problem is overcome by using a runningaverage of the power consumption of each PEV and equalizing these averages. The average canbe derived either from the starting of the charging procedure or it can be, e.g., an average of thepast few minutes of charging.

· The third objective (with the lowest priority), viable in such a case that many of the PEV activeconverters are not already operating at their individual complex power limits, is to balance thereactive power.

Energy Fairness (EF)This charging strategy prioritizes the PEVs according to the time they are connected to the grid forcharging their batteries, and their energy requirements. Notice that this solution cannot be implemented

in a competitive scenario, where all PEV owners are only interested in their own needs. This could beimplemented at, e.g., a workplace where employees are not in competition with one another.

The amount of power required by each PEV is determined by the energy to fully charge their battery andby the time the PEV is expected to remain connected to the grid before it is used again. This is shown inequation (2) below,

( ) = min( )( ) , ,

(2)

where ( ) is the remaining time, ( ) is the amount of energy needed, is the apparent powerlimitation of the electrical power outlet and the relevant charger and ( ) is the power received by eachPEV, and is a discrete time step. The upper limit set by means that it is impossible to obtain anunrealistically large amount of energy in a small time. The goal is to provide every PEV the power theyrequire to have their batteries charged-up before it is time to commute. This solution thus prioritizesvehicles with a high desired charge rate ( ) and this means that those vehicles actually receive morepower than the ones with a lower desired charge rate.

Yet again, there are three objectives to solve like there were in the Power Fairness method earlier.

· The first objective is exactly the same as in Power Fairness method above.· The second objective is to minimize the amount of power mismatch between the desired charge

rate of each individual PEV and the actual power delivered to each individual PEV respectively, ona grid-level scale. A running average can again be utilized in the actual calculations, similarly as inthe Power Fairness method.

· The third objective is again to balance the reactive power. (See Power Fairness above)

Time Fairness (TF)This method is mainly intended for future where there is a great amount of charging socket outletsavailable in all daily locations one might travel to. For example, at a shopping mall it would be desirableto provide the customers an incentive to avoid reserving the parking spot for too long. By offering acharging power of greater magnitude during a predefined time period and then as time passes by thecharging power is reduced and this same repeats until the charging power diminishes remarkably. Thiswill reduce the attractiveness for the customer to lengthen their stay in that parking spot. The users arecategorized to different priority levels according to the time they have stayed in the parking.

The power usage is shared according to user priority level. Yet again, the same prioritized optimizationproblem is to be solved.

· The first objective is exactly the same as in Power Fairness method above.· The second objective is to minimize the amount of power mismatch between the assigned amount

of power based on the duration of stay in the parking spot for each individual PEV and the actualpower delivered to each individual PEV respectively. A running average can again be utilized inthe actual calculations, similarly as in the Power Fairness method.

· The third objective is again to balance the reactive power. (See Power Fairness above)

The Additive Increase Multiplicative Decrease Algorithm (AIMD)AIMD can be used to solve the minimization objectives in the three different scenarios (PF, EF, TF). AIMDis a distributed algorithm which means it is robust against possible failures and it places a lighter burdenon the communication infrastructure than centralized algorithms. AIMD relies on a central managementunit to broadcast a one-way binary capacity event (CE) signal to the PEVs. The PEVs react to this signal bychanging their power consumption or injection (injection, when operating in V2G mode). In case the AIMDalgorithm allows management for both the active and the reactive power it is called a double (prioritized)AIMD, a.k.a., DAIMD. With DAIMD, the active power is normally bound first, and reactive power second.

AIMD controls the active power delivered to the PEVs by switching between two phases. Let us considera situation where active power is flowing from the grid to the PEVs (G2V):

· The first phase is the additive increase (AI) phase, where the PEVs gently increase their chargingpower, according to the equation (3) below,

( + 1) = ( ) + ( ) . (3)The additive increase is scaled by a fixed integer , which is identical for all PEVs. The central control mayvary the value of if necessary, however, this requires additional communication equipment (costly).

The second phase is called the multiplicative decrease (MD) phase which occurs when eq. (1) is violated.I.e. when the sum of consumed power by all connected PEVs and the demand by uncontrollable loadsexceeds the maximum amount of power allowed by the power grid. When this capacity event (CE) occurs,the control logic in the grid notifies the PEVs to decrease their charge rate by one of the multiplicativefactors described in eq. (4) below,

( + 1) = ( ) ( ) ℎ ( ), ℎ( + 1) = ( ) ( ) ℎ 1− ( ).

ℎ , ℎ Ψ.

(4)

If the vehicles are operating in V2G mode, i.e., power is flowing from PEVs to the grid, the AIMD algorithmabove is inverted. This means that upon receiving a CE the PEVs increase their power injection additively,which corresponds to an actual decrease of the power consumption. Similarly, when no CE is received thePEVs decrease their power injection multiplicatively, which corresponds to an increase in powerconsumption.

The PEVs can automatically recognize the moment they need to change from receiving power from thegrid (G2V) to injecting power to the grid (V2G). The switch from G2V to V2G occurs after a CE if the actualpower consumption is very small. Let ( ) indicate whether the th PEV operates in G2V mode at timestep , i.e., ( ) = 1 means G2V and ( ) = 0 means V2G mode. Then, the indicator is updated aftera CE by

( + 1) = 1, ( + 1) > ( ) = 1,0, ( + 1) ≤ ( ) = 1,

(5)

Where is a positive integer parameter.

The return from V2G mode to G2V mode occurs when no CE is received, and the indicator changes as

( + 1) = 1, ( + 1) > − ( ) = 0,0, ( + 1) ≤ − ( ) = 0.

(6)

Chapter 8Towards a Business Case for Vehicle-to-Grid —Maximizing Profits in Ancillary Service Markets

Kari Lappalainen

System level aspects of electric vehicles Kari Lappalainen

Summary of Chapter 8: Towards a Business Case for Vehicle-to-Grid – Maximizing Profits in Ancillary Service Markets

A power system is a network of power lines connecting energy producing and consumingentities with each other. It can be divided into the transmission grid and the distribution grid.The primary task of transmission system operators (TSO) is to transfer energy from thegeneration units to regional or local distribution system operators (DSO) which then deliver itto the consumers. One key responsibility of a TSO is to ensure the stability of the power grid.Deviations between energy demand and supply lead to fluctuations of voltage and frequencywhich require an immediate action. This response is performed by ancillary services providedby fast responding generators capable of quickly ramping up or curbing down their poweroutput or by storage facilities which buffer energy excesses or shortages. However, most ofthese solutions are either costly, entail large space or exhibit low energy efficiencies. Onepossible solution is to utilise plug-in electric vehicles (PEV) of which batteries could beemployed as short term energy storages. However, economic concerns of vehicle-to-grid(V2G) concept act as a crucial barrier for its implementation into practice.

Depending on the response time and duration, ancillary services are divided intoregulation and primary, secondary and contingency reserve. Providers of ancillary servicesreceive an energy payment for the dispatched energy when up-regulation is required or forcurbing power generation when down-regulation is required and a capacity payment forholding regulation potential available.

The need for energy storages may be reduced in smart grids which support multi-directional energy flow. In smart girds, energy is not only generated at the high voltage levelsbut also in the distribution grid. The generators of the distribution grid could then also serveas ancillary service providers. In smart grids, PEVs can act as either consumers or producersby using their batteries as energy buffers. Employing PEVs as energy buffers in smart gridscould improve grid stability and facilitate the integration of renewable power generation.Providing energy to the grid by discharging a battery of a PEV during an energy shortage iscalled V2G while charging the battery during an energy excess is known as grid-to-vehicle(G2V). The V2G concept incorporates both the services. Thus, in this chapter, this term isused whenever no explicit distinction is necessary.

The energy and power levels of individual PEVs are too small to participate on mostelectricity markets. To meet the required power conditions, hundreds or thousands of PEVsare aggregated to a virtual power plant (VPP). This is achieved by an aggregator who servesas a mediator between the PEVs and the electricity market. The aggregator, considered avirtual entity rather than a physical one, trades energy at the market and ensures that the VPPis capable of providing the contracted power at all times. From the TSO’s point of view, thepower generation capacity of the VPP offered by the aggregator presents itself as a singleenergy source although it may originate from a variety of different PEVs.

In order to make the V2G concept applicable in practice, it is essential to prove itseconomic viability and to provide individual agents with a control strategy which maximizestheir profit. For this purpose, a cost-revenue model, a battery model and an optimizationmodel are introduced. Total annual profits can be calculated as the difference between


revenues and costs. The total revenue is the sum of the revenues attained from up-regulationand down-regulation services. In the case of up-regulation, the revenue consists of the regularenergy selling price and a compensation for providing up-regulation ancillary services. In thecase of down-regulation, the revenue is a compensation for providing down-regulationservices. In addition, in many electricity markets, capacity payments are provided for holdingregulation potential available. The total annual costs are calculated as the variable costsmultiplied by the total annual amount of energy cycled through the battery plus annual fixedcosts. The variable costs include the energy purchase costs and the battery pack depreciationcosts which are calculated as the purchase costs divided by the total possible energythroughput.

A crucial aspect for the profitability of V2G applications is battery degradation costs.To appropriately consider these costs, an understanding of battery aging processes isrequired. The performance fade of a cell can be separated into the capacity fade (measured inAh), energy fade (measured in Wh) and power fade (measured in W). The different agingmechanisms are triggered from the environment and the utilization mode. In general, highcurrents as well as extremely high and low state of charge (SOC) conditions accelerate theaging of cells.

In most electricity markets, electricity price estimates are available a certain period oftime in advance. This information may be used to decide when and at what power a battery ischarged or discharged to achieve the greatest possible profit. An agent may even acceptlosses in some periods to attain higher profits in the following ones. Maximization of theprofits can be formulated as a mathematical optimization problem with various constraints. Ina simple scenario, the power in a certain time interval is limited by the maximum charge rate(C-rate) defined by the battery specifications and by an SOC constraint. A more sophisticatedcontrol strategy should also account for time periods at which the PEV is expected to be inuse. Ensuring that the battery contains enough energy for the next trip leads to additionalconstraints.

The introduced models were applied in a case study building on the electricity marketdata of Singapore. In the case study, a battery pack capacity of 20 kWh was used. At thenational electricity market of Singapore electricity prices are adjusted on a half-hourly basis.Thus, the calculations build on time series with a 30-min resolution, dividing one day in48 periods. The prices are known 24 h in advance.

In the simplest possible scenario, a PEV is all the time grid-connected. This fairlyunrealistic assumption allows an upper bound estimate on the economic attractiveness of thedifferent ancillary service types. The outcome of the analysis is presented in Table 1. Resultsshow that annual profits from S$ 177 to S$ 912 can be gained (S$ 1 equals to 0.80 USD(November 6, 2014)). However, a large part of the profits results from just a dozenextraordinarily profitable periods which are most likely caused by a disruption in the powersystem. By leaving out those periods the maximum annual profits decrease to S$ 109.

The results of a more realistic scenario where typical commuting habits of thepopulation are assumed are presented in Table 2. The results for all the three reserve marketsshow an annual profit between S$ 60 and S$ 863 depending on the market and the appliedmobility pattern. In the regulation market annual profits are from S$ 262 to S$ 357. In thisscenario, extraordinarily profit periods are sometimes left out anyway and therefore do not


contribute as much to the resulting profits as in the simple scenario. In conclusion, profits inthis scenario might not be high enough to practically apply the V2G concept.

Table 1. Profits in different electricity markets regarding the simplest scenario.

Market Profits [s$/year] Profits without extraordinarilyprofitable periods [s$/year]

Reserve, primary 177 0Reserve, secondary 183 0Reserve, contingency 912 36Regulation 394 109

Table 2. Profits in different electricity markets regarding the mobility pattern-based scenario.

Market Profits [s$/year] Profits without extraordinarilyprofitable periods [s$/year]

Reserve, primary 60–178 0Reserve, secondary 128–171 0Reserve, contingency 597–863 21–25Regulation 262–357 71–90

The presented results indicate that under the given conditions V2G is yet unlikely tobe an attractive concept for PEV owners in Singapore. However, there are three main factorswhich could increase profits and thereby make V2G to be more attractive. The first factor isfurther development of batteries which may lead to reduced investment costs or to longercyclic lifetimes. The second scenario is to utilize more information on future prizes in theoptimization process. The third factor is to give a higher weight to capacity payments.Summing up all changes of realistically increasing profits may lead to annual profits of a fewhundred S$. The presented models have two main limitations: the inability to deal withuncertain price information and to make improved predictions based on knowledge from thepast and the difficulty of determining battery depreciation costs.

Chapter 9Integration of PEVs into Power Markets: A BiddingStrategy for a Fleet Aggregator

Topi Törhönen

Chapter 9 Summary | Topi Törhönen

Chapter 9 Summary

Integration of PEVs into Power Markets: A Bidding Strategy for a Fleet Aggregator

Chapter 9 deals with the problems that start with the large-scale introduction of plug-in electric vehicles in

the electricity networks and the power markets. Basically the problems start with the daily routines people

have and how or when they want to use their vehicles. People use their vehicles to go to work in the morning,

then park the car at their workplace’s parking lot, then leave from work to go perhaps shopping for groceries

and then head back home.

Now when thinking about PEVs the main concern becomes when the PEV should be charged. Without any

control and incentives regular people would just choose the easiest way and charge their car as soon as they

arrive back home or charge the car until the batteries are completely full or to a point where they need the

car again for another trip. Also, we don’t want all the PEVs to be charged at the same time because of the all

so important voltage and congestion problems it causes in the power grid. It’s also important to remember

that PEVs are usually parked and plugged in more time what is actually needed to fully charge the PEVs

batteries. In these cases it may be optimal and efficient to postpone and shift the charging hours to a better

time, for example to a time when the electricity price is lower or to a time when the power grid can avoid

voltage and congestion problems when the amount of PEVs requiring a charge is too big to be connected to

the grid.

In order to make optimal use of the network and the flexibility potential, the best results can only be achieved

with coordinated measures. This new situation requires and introduces a new entity, the PEV fleet

aggregator, to the network and the electricity market. This PEV fleet aggregator will do the bidding into the

day-ahead electricity market and tries to minimize charging costs while still satisfying the PEVs’ flexible

demand. When there’s a new entity involved in the electricity market bidding, the new aggregator is

expected to influence the electricity market prices in one way or another. The PEV owners are expected to

tell their driving requirements to the fleet aggregator. This way the aggregator takes the responsibility to

purchase the right amount of electricity to meet the expected demands, i.e. the aggregator decides the bid

price and the bid power level and submits it to the day-ahead electricity market. The bid price is the maximum

price the aggregator is prepared to pay for the given volume. The thing to note here is that most markets

allow the aggregator to submit prepared bidding curves to the market, resulting in a situation where the

aggregator can bid different volumes with different prices.

Now all the entities that do the bidding in the electricity market have and should have a clear bidding strategy

to guide them in the bidding process. With PEVs this situation becomes a bit tricky, because the problem the

aggregator is facing is formulated as a ”bi-level problem”, meaning the problem has two problem levels that

have to be taken into consideration. These problem levels are called the upper-level problem and the lower-

level problem. The upper-level problem represents the minimization of the charging costs while the lower-

level problem represents the market clearing.

As written before, the aggregator aims to minimize charging costs of the PEV fleet (upper-level) while still

satisfying the PEVs’ flexible demand. To succeed in this, the aggregator needs a strategic bidding algorithm.

The aim of this algorithm is to establish an optimal electricity bidding strategy, which is used to establish an

aggregated charging schedule for the PEV fleet. In order to produce a working algorithm the aggregator has

to take multiple things into consideration. First the aggregated representation of the PEV fleet is modelled

as a virtual battery with time varying energy and power constraints. These variations are derived from PEV

users’ (PEV fleet aggregator’s customers’) individual driving habits and patterns. The second important thing

to consider is the fact that the other bids and volumes in the electricity market are not known by the


aggregator. This is a really important problem to solve because the aggregator decides its bidding strategy

one day in advance, i.e. the day-ahead electricity market. It’s important because there is uncertainty in the

electricity market itself but also in the driving habits and requirements of the aggregator’s PEV fleet. All this

means that the uncertainties have to be modelled right beforehand, before deciding the bidding strategy for

the next day. Therefore, the aggregator has to rely on stochastic approach in the bidding strategy problem.

Basically this means that the PEV fleet aggregator has to make use of different scenarios based on historical

data to formulate some guidelines for uncertain market bids.

With these bi-level models the problem can be solved by transforming them into mathematical programs

with equilibrium constraints (MPECs). The MPECs can be solved for example with the use or mixed-integer

linear programming (MILP) reformulations and other methods.

As said before, the aggregation model requires a representation of the PEV fleet’s demand in order to apply

constraints to the model in aggregator’s cost minimization problem. The PEV fleet-s demand is flexible and

not fixed, so it’s not good to represent the demand with a single demand profile. The better way to represent

the demand is to have multiple demand profiles for the whole PEV fleet.

The PEV fleet is modelled as a virtual battery with its own energy state that has its own set of constraints on

the aggregation’s charging power. These make up constraints that link with each other between different

time-steps, in another words the bidding strategy becomes an intertemporal problem. To derive some

parameters from this virtual battery a bottom-up approach is used. This bottom-up approach starts with the

PEV fleet’s individual driving patterns. From the individual PEV driving patterns it’s possible to define lower

and upper energy bounds for the customer and later for the whole PEV fleet. The lower energy bound is

calculated by assuming that the charging is deferred (delayed) as much as possible, while the upper energy

bound is calculated by assuming that the PEV battery is charged as soon as the PEV is plugged in. The

minimum state of charge (SOC) is also taken into consideration to avoid battery degradation. Below you can

find an example of the energy bound computation for an individual vehicle.

It’s also possible to derive upper and lower bounds for the charging power of the PEV at any time step. Finally

these energy and power bounds are used to contribute to the final aggregated bounds when the vehicle is

connected to the grid. These modelling and energy/power bound things described above have some basic

mathematics behind them, so make sure you get the basic idea of the calculations on pages 239-240. The

thing to note here though is the actual aggregated energy and power bounds (below) found in this process.


By looking at the graph above you can clearly see two clear bound dips in the morning and in the evening.

This represents typical PEV fleet’s commuting patterns, as expected.

The bidding strategy itself is an optimization problem with multiple parts. Firstly the aggregator aims to

minimize charging costs (optimization) while satisfying the PEV fleet’s demand (driving requirements). In

addition to this, the clearing of the electricity market is an optimization problem. As described before, this

set of problems is formulated as a bi-level model that has interconnected levels. The bi-level structure of

the bidding problem is shown below.

Important thing here is to realize that the upper-level problem has an effect on the lower-level problem and

vice versa. The bidding problem is solved in the upper-level problems side and it affects the market clearing

through the aggregator’s bid prices. However, the market clearing problem in the lower-level problem side

is used to obtain a scheduled charging power and charging price for the aggregator. This of course affects the

costs, so it also has to affect the bidding strategy again in the upper-level. Therefore, these problems have to

be solved together, not as single problems. The model formulation and the solving process of the bi-level

model is shown on pages 243-246 and is not explained in this summary.

What matters here is the final solution of the formulation. The MPEC/MILP problem outputs multiple

solutions for each pre-defined market scenario. It outputs an optimal value for the bid price and a value for

the required/scheduled power volume for the PEV fleet. With each market scenario solved, the aggregator

can choose the right bidding strategy for each different scenario.

The conclusion of all this is neatly presented in four main points:

1. The flexibility of PEV demand allows the aggregator to significantly reduce charging costs.

2. Even low PEV market penetration has a significant impact on market prices due to the optimized

bidding strategies.

3. Uncertainties have to be modelled extremely carefully to avoid energy shortfalls.

4. When the model is properly formulated, the resulting MILP problem can be solved and managed

fairly easily using computers, although the number of scenarios have to be small enough.

Chapter 10Optimal Control of Plug-in Vehicles Fleets inMicrogrids

Jyri Kivimäki

Chapter 10: Optimal Control of Plug-in VehiclesFleets in Microgrids

Jyri KivimakiTampere University of Technology

[email protected]

Summary

In the recent years, the penetration of plug-in vehicles (PEVs) is greatly increasing. In addition to thetheir ability to reduce global fossil emissions, they can be used as part of the distribution network to offerload and supply resources for the grid. PEVs can be configured as a single units or fleets. In a individualvehicle approach, a PEV gives its contribution as a service provider whereas grid interacts with a groupof vehicles. A group of vehicles form a fleet, which is configured by the aggregators, who contract withthe grid operator through the day-ahead and hour-ahead markets to provide support services for the grid.In this way an more effective contribution can be obtained on the grid service provision.

In individual or fleet configuration, PEVs can provide several important ancillary services to the grid,such as regulation, spinning reserve and peak power, which are characterized by a high power valueand fast response. It has been evidenced that an optimal management of PEV fleets, as well as theirintegration with other distributed resources, can provide both economical and technical benefits. As themarket point of view, two most important services are spinning reserve and regulation, which differ inavailability and duration. However, they both require a fast response for the grid point of view. Spinningreserve is an additional power capacity that can be requested in the case of an outage. In that service,payment is based on available capacity i.e. for the amount of time the generator are available and ready.In contrast, regulation is required only for a short duration of generation. It consist of the use of control-lable generation units or aggregated loads to regulate frequency and voltage. The third service is peakpower service, which utilizes PEVs as generation units during peak load periods. However, the requiredsupply duration could be several hours and therefore, this service is impractical without series of vehiclesmanaged aggregators.

PEVs can provide services for the grid by utilizing one-way or two-way energy flow. In one-way energyflow charging PEVs is only considered whereas two-way energy flow utilizes also power to the grid whilea vehicle is parked. For example, in the charging mode, the PEV battery charging can be conductedeither without the application of any particular control strategy or with a local control strategy aimed atmaximizing the advantages of PEV owners in terms of costs.

In addition to direct connection to the distribution network, The PEV fleets can be also utilized in dif-ferent grid configurations to provide services. The PEV fleets can be included in a microgrid, wherethey further connected with other energy sources. In this way, PEV fleets can provide some services tothe same microgrid or the distribution network to which the microgrid is linked. This enables that thedistributed energy resources can be coordinated by a control system to provide the usual energy service,

as well as a different ancillary services. Moreover, microgrid can operate in both grid connected and is-landed modes, which can be very complex and therefore some optimal control of vehicle fleets is neededin order to operate grid efficiently.

The utilized optimization strategy must take into account all technical and operating limitations of thegrid resources to be integrated, as well as those regarding the grid itself. The optimization can be per-formed by using single-objective or multi-objective problem approach. The choice of the approach to beused depends on several aspects such as the perspective of the stakeholder who is performing the control.Single-objective approach is mainly used as the distribution system operator point-of-view, who can eas-ily and directly identify the best solution for system operation. On contrary, multi-objective approachestake into consider several objectives and therefore, they can be more effective.

In single objective approach, a unique objective is required to be fixed, while the other operation re-quirements are considered constraints. In the most general case, the problem can be formulated as thenon-linear constrained optimization problem based on the objective function fobj(X ,C) as a function sys-tem state vector X (e.g. the bus voltages) and control vector C (e.g., the PEV aggregators’ active andreactive power). The choice of the objective function depends on the strategy that has to be applied andthe services to be provided. In contrast, the constraints are typically related to the technical limitations(e.g., node voltages, which must fall into admissible ranges) of the grid and the distributed energy re-sources or services to be provided. Moreover, these are usually inputs of the problem of both the grid anddistributed energy resources. By taking into account the chosen constraints, optimized control actionsare performed in order to minimize the objective function fobj.

Occasionally, the strategy requires the satisfactory of many objectives that may be in conflict with eachother. Therefore, the optimization model can become relatively complex due to fact that the differentobjectives must be optimized concurrently while several constraints must be satisfied. Such a scenariorequires more advantage multi-objective optimization methods. This method allows the processing ofmany objectives, which are sometimes conflicting, and several constraints to determine a compromisedsolution that takes into consideration different perspectives. Mathematically, multi-objective problemneeds to minimize the multi-objective function F(X ,C), where F is the objective vector contain n amountof single objective function fobj as [ fobj1(X ,C), fobj2(X ,C), ..., fobjn(X ,C)]. X and C refers to state andcontrol vector as defined in the prior paragraph.

Compared to the single objective approach, multi objective approach has several solutions for specificoptimization problem. Therefore, some method is needed to find a solution which yield an optimumsolution of a set of points. The state-of-art predominant concept to define an optimal point is calledPareto optimality. A solution is Pareto optimal if there exists no feasible solution that can decreasesome objective functions without causing at least one objective function increase. Theoretically, thereare an infinite number of Pareto optimal solutions, so it is often necessarily to incorporate the decision-maker’s preferences to obtain a single, suitable solution, where the term preference is intended to meanthe relative importance of the different objective functions.

Chapter 11Energy Management in Microgrids with Plug-inElectric Vehicles, Distributed Energy Resources andSmart Home Appliances

Jukka Prusti

System level aspects of electric vehicles Jukka Prusti

1

Chapter 11: Energy Management in Microgrids with Plug-in

Electric Vehicles, Distributed Energy Resources and Smart Home

Appliances

Chapter 11 from the book Plug-in Electric Vehicles in Smart Grids: Energy Management deals with energy

management issues in Microgrids with high penetration of plug-in electric vehicles (PEVs), distributed

energy resources (DERs) and smart home appliances. Authors O. Arslan and O. E. Karasan state that smart

grids are transforming the way of producing and distributing energy today. According to them high

penetration of PEVs pose a risk to electricity distribution system by increasing the peak load. However

PEVs provide also an opportunity for the existing system by discharging electricity to grid with the help of

Vehicle-to-grid (V2G) technology. Authors in the book propose a mixed integer linear programming

(MILP) energy management optimization model to schedule the charging and discharging times of PEVs,

electricity storage units and running times on smart appliances.

In the beginning of the chapter 11 authors make an extensive literature review. There is a lot of literature

available concerning about energy management in microgrids. However in different literature works

authors have done different assumptions and completely similar kind of study have not been done in the

past.

The actual study starts with problem description. The authors consider a house owners who come together

to form a microgrid. Rather than buying all the energy solely from the grid, each household is generating a

certain amount of energy with DERs such as small wind turbines, photovoltaics or gas turbines. All the

houses have traditional time-inelastic (TIE) loads like refrigerators and televisions which need to be

satisfied when they are used. However each household also have smart appliances which are time-elastic

(TE) loads. These kind of loads such as water heaters need to be satisfied within a given time frame. In

addition to TE and TIE loads, the households also have PEVs. The difference between TE loads and PEVs

is that TE loads are non-preemptive. Unlike TE loads PEVs can be charged before the actual use. The PEVs

can also supply energy to the grid via V2G technology. Each household also have DERs for energy

production. Production of the DERs is intermittent and that is why the microgrid has electricity storage

units. If a certain house is underutilizing DERs, the extra energy can be used in another household in the

microgrid. The microgrid is also connected to national grid and there is a trading mechanism between the

grids. If DERs generate more energy than what are load demand and storage limits, the excess energy can

be sold to the national grid.

The authors introduce a methodology for energy management of microgrids. The energy management

model in based on mixed integer linear programming which is a mathematical optimization method. First

they define sets, parameters and variables that are used in the model. The parameters characterize the

behavior of the PEVs, DERs, smart appliances, storage units and the grid.

The objective of the model is to minimize the total costs of microgrid. The total cost in the model is a sum

of costs that are incurred due to energy generation, energy trade with the grid, maintenance of equipment,

battery degradations and transportation. The authors define the behavior and constraints of different

components such as PEVs with a large number of equations. The equations are mainly boundary conditions

for computer calculation. The energy management model contains also binary variables in order to model

that the results exclude simultaneous occurrence of energy transfer between microgrid and other entities:


2

PEVs, storage units and the grid. This is done because due to losses it is never optimal to for example charge

and discharge PEVs simultaneously.

The authors present a case study where a group of 100 houses forming a microgrid in California is

considered. Houses have certain amount of TIE loads, 0, 1 or 2 PEVs, 0, 1, 2 DERs and/or smart devices.

Thus the total number of PEVs, DERs and smart appliances in the microgrid are 90, 100 and 300. There is

also one storage unit in the microgrid. Authors use the energy management model they introduced earlier

for scheduling. They simulate 365 days to observe the impacts over one year time period. In this study four

types of PEVs are used with different all-electricity ranges. Model also utilizes data about average driving

patterns in order to give a realistic picture of transportation. Three types of DERs are considered in the case

study: photovoltaics, wind turbines and natural gas engine with combined heat and power. Electricity

generation is thus dependent on two natural factors: sun and wind. Daily and seasonal variations of sun

irradiance and wind are taken into account in the model. Model also consists a single storage unit with a

capacity of 85 kWh and charging efficiency of 93 % is considered.

In the case study 16 different scenarios are built in order to test the impacts of smart grid, PEVs, DERs,

gasoline and electricity pricing on costs and emissions that microgrid generates. The costs generated by the

grid, DERs, PEVs, smart appliances and TIE loads are sorted in order to explore the impacts more deeply.

Also NOx and CO2 emissions are separated. In the baseline scenario microgrid has PEVs and DERs. DER

capacities, gasoline and electricity pricing, driving patterns and the amount of smart appliances are at

normal level. In other scenarios all these variables are changed one by one while others are kept unaltered.

All the scenario settings are presented below in table 1.

Table 1 Scenario settings

Scenario PEVs DERs DER capacities

Gasoline pricing

Electricity pricing

Driving patterns

Smart appliances

1 + + Baseline Baseline Baseline Baseline Baseline

2 - + Baseline Baseline Baseline Baseline Baseline

3 + - Baseline Baseline Baseline Baseline Baseline

4 - - Baseline Baseline Baseline Baseline Baseline

5 + + *1,5 Baseline Baseline Baseline Baseline

6 + + *2,0 Baseline Baseline Baseline Baseline

7 + + Baseline $3,50-$3,60 Baseline Baseline Baseline




11 + + Baseline Baseline 9,60¢-9,70¢ Baseline Baseline

12 + + Baseline Baseline 19,20¢-19,40¢ Baseline Baseline

13 + + Baseline Baseline Baseline *2,0 Baseline

14 + + Baseline Baseline Baseline *4,0 Baseline

15 + + Baseline Baseline Baseline Baseline 0

16 + + Baseline Baseline Baseline Baseline *2,0

The models are solved by using a computer. All 16 scenarios are simulated and each of them are run for

365 days in order to study impacts on costs and emissions microgrid generates. Daily average energy

distribution among generation and consumption is listed in table 2.


3

Table 2 Daily average energy distribution of the microgrid in kWh

Scenario Grid DER PEV Appliances TIE loads

1 2 926,66 3 427,88 395,1 2 955,9 2 747,7

2 2 584,59 3 412,44 0,0 2 955,9 2 747,7

3 6 059,13 0,00 395,1 2 955,9 2 747,7

4 5 703,55 0,00 0,0 2 955,9 2 747,7

5 1 360,80 5 141,38 395,1 2 955,9 2 747,7

6 202,84 6 388,72 395,1 2 955,9 2 747,7

7 2 926,66 3 427,88 395,1 2 955,9 2 747,7

8 2 926,66 3 427,88 395,1 2 955,9 2 747,7

9 2 919,24 3 427,88 386,8 2 955,9 2 747,7

10 2 584,78 3 412,63 0,4 2 955,9 2 747,7

11 2 926,66 3 427,88 395,1 2 955,9 2 747,7

12 2 926,66 3 427,88 395,1 2 955,9 2 747,7

13 3 198,12 3 427,88 696,7 2 955,9 2 747,7

14 3 546,85 3 427,88 1 084,2 2 955,9 2 747,7

15 327,13 3 020,60 395,1 0,0 2 747,7

16 5 882,56 3 427,88 395,1 5 911,8 2 747,7

The total costs and emission values for each scenario are also calculated. Results are presented in the table

3.

Table 3 Daily average cost and emission impacts of microgrid

Scenario Cost (¢) NOx emission (kg) CO2 emission (kg)

1 30 325,48 15,82 3 107,68

2 36 450,71 15,03 2 927,99

3 38 370,51 13,75 3 304,50

4 44 472,55 12,97 3 124,25

5 26 303,93 16,86 3 009,28

6 22 989,24 17,57 2 927,80

7 30 146,85 15,82 3 107,68

8 29 968,21 15,82 3 107,68

9 29 789,45 15,81 3 104,04

10 27 963,66 15,04 2 932,15

11 44 440,09 15,82 3 107,68

12 72 669,29 15,82 3 107,68

13 42 651,78 16,46 3 265,40

14 70 645,14 17,30 3 484,59

15 16 691,44 8,84 1 513,53

16 44 581,47 22,53 4 716,61


4

An average electricity generation and consumption in the base scenario are 6 354,6 kWh and 6 098,7 kWh.

There difference between production and consumption is due to system losses. When PEVs are excluded

from the model less energy is purchased from the grid. However the total cost of the system increases. This

happens because now rather than driving with electricity gasoline is used for transportation. However the

emissions are decreasing when more gasoline is used. One has to notice that when only electricity is used

for driving the energy is generated solely by the grid. The emissions of grid are higher than that of gasoline

and that is way the emissions are decreasing. One significant result of this study is that when electricity is

utilized for transportation the source of energy is crucial from emission reduction point of view.

Another significant insight of this study is that even though the all electricity range of PEV increases the

charge depletion mode drive percentages almost do not change. In other words PEVs with 20 mile all

electricity range drive use almost as much electricity for transportation than PEVs with all electricity range

40 or 60 miles. The average daily travel distances are short enough that most of the benefits of PEVs are

attained with 20 mile all electricity range PEVs.

Third critical insight of this study was that decreasing cost of gasoline does not affect the PEV electricity

requirement. However there is a critical point in gasoline pricing beyond which drivers prefer gasoline over

electricity. Electricity pricing doesn’t either affect electricity purchasing from the grid. This is due to that

DERs are already utilized at a full capacity and microgrid still has to satisfy the loads. Therefore the

microgrid purchases the energy that it requires in excess of the generation capacity of the DERs regardless

from the electricity prices.

In conclusion smart management of loads is a way to handle the excess energy requirement of PEVs as well

as peak load increase due to PEVs. The authors introduce a mixed integer linear programming energy

management optimization model to schedule the charging and discharging times of PEVs, electricity

storage units, and running times of smart appliances. Major findings of the case study are that simultaneous

charging and discharging of batteries do not occur in the model due to system losses and the source of

electricity generation for the PEVs is crucial from the emissions point of view. Also an important result is

that after a basic all-electric range, cost and emission benefits are almost similar for all longer all-electric

range vehicles.

Source:

O. Arslan, O. E. Karaşan, Energy Management in Microgrids with Plug-in Electric Vehicles, Distributed

Energy Resources and Smart Home Appliances, Plug In Electric Vehicles in Smart Grids: Energy

Management, Springer Singapore, 2014, pp. 291-314.

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