A Review of Virtual power plant Definitions, … Review of Virtual power plant Definitions, Components, Framework and Optimization Abstract- This paper presents a comprehensive survey

International Electrical Engineering Journal (IEEJ)

Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365

http://www.ieejournal.com/

2010 Othman et. al., A Review of Virtual power plant Definitions, Components, Framework and Optimization

Abstract- This paper presents a comprehensive survey on the new

and interesting concept of virtual power plant (VPP). The survey

covers the virtual power plant definitions, components, and

framework and highlights the different techniques that can be

used for VPP operation optimization. Finally, a general

framework for the operation and the optimization of the virtual

power plant is proposed and discussed.

Keywords: Distributed energy resources, Virtual power plant, VPP

framework, optimization.

I. INTRODUCTION

The concept of integrating small generating units in the

power system has attracted great attention in the last few years.

Moreover, distributed generation (DG) plays an important

role in reinforcing the main generating power plants to satisfy

the growing power demand. DG can also be connected or

disconnected easily from the network unlike the main power

plants, thus providing higher flexibility. Properly planned and

operated DG installations have many benefits such as

economic savings due to the decrement of power losses,

higher reliability, and improved power quality.

However, the increased penetration of DG without

harmony between the generating units may lead to increment

of the grid power losses, undesirable voltage profiles,

unreliable operation of the protection devices, and unbalance

between the real consumption and the production. Therefore,

to achieve optimal economical operation of the main network,

DER units should be visible to the system operator.

The negative aspects of increased uncoordinated DG

penetration are the basic motivation for the introduction of

VPP concept. VPP is the aggregation of DG units,

controllable loads and storage devices connected to a certain

cluster in a single imaginative entity responsible for managing

the electrical energy flow within the cluster and in exchange

with the main network. The VPP concept was proposed early

in [1] with its framework. Earlier, DER was installed with a

“fit and forget” approach and they were not visible to the

system operators. VPP aggregated all DERs into a single

entity through which distributed energy resources (DERs)

would have system visibility and controllability and market

impacts as transmission-connected generators [2].

Different studies analyzed the VPP concept in three major

directions:

First direction concerned with classifying DGs inside

the VPP structure according to their capacity and

ownership. Two categories were reported; Domestic

DG (DDG) and Public DG (PDG). Another DG

classification was presented according to their

operational nature; either stochastic or dispatchable.

Second direction focused on the VPP structure both

technically and commercially; Technical VPP (TVPP)

and Commercial VPP (CVPP), and their

functionalities.

Third direction slanted towards the optimization of

the VPP operation. Some of these studies focused on

VPP structure optimization by selecting the optimal

size and location of the VPP components. On the other

hand, other studies highlighted the profit

maximization of the VPP.

This paper presents a literature review of VPP definitions,

components, and framework. Furthermore, it simplifies the

relations/correlations between VPP structure entities and their

responsibilities. Finally, a survey is presented of the different

techniques that can be proposed to optimize the operation of

the VPP.

II. VPP DEFINITIONS

The most recent VPP concept has various definitions which

all agree upon the fact that VPP is an aggregation of DG units

of different technologies in order to operate as a single power

plant that has the ability to control the aggregated units and to

manage the electrical energy flow between these units in order

to obtain better operation of the system [2-6]. In [2], VPP is

defined as “A flexible representation of a portfolio of

distributed energy resources (DER) that can be used to make

contracts in the wholesale market and to offer services to the

system operator”. In [3], VPP is defined as “An information

A Review of Virtual power plant Definitions,

Components, Framework and Optimization

Mahmoud M. Othman* Y. G. Hegazy** Almoataz Y. Abdelaziz*

*Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo,

Egypt

** Dean of information engineering and technology faculty, German university in Cairo



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



and communication system with centralized control over an

aggregation of DGs, controllable Loads and storage devices”.

In [4], VPP is defined as “An aggregation of DER including

different DER technologies, responsive loads and storage

devices which, when integrated have a flexibility and

controllability similar to large conventional power plants”. In

[5], VPP is defined as “A cluster of dispersed generator units,

controllable loads and storages systems, aggregated in order

to operate as a unique power plant. The generators can use

both fossil and Renewable Energy Sources (RES). The heart

of a VPP is an Energy Management System (EMS) which

coordinates the power flows coming from the generators,

controllable loads and storages”. In [6], VPP is defined as

“An aggregation of different types of distributed resources

which may be dispersed in different points of medium voltage

distribution networks”.

From the presented definitions a comprehensive definition

is proposed. VPP can be defined as “A concourse of

dispatchable and non dispatchable DGs, energy storage

elements and controllable loads accompanied by information

and communication technologies to form a single imaginary

power plant that plans, monitors the operation, and

coordinates the power flows between its component to

minimize the generation costs, minimize the production of

green house gases, maximize the profits, and enhance the

trade inside the electricity market”.

III. VPP COMPONENTS AND MODEL

VPP consists of three main components, distributed energy

resources, energy storage systems and information and

communication technologies as shown in Fig. (1).

Fig. 1 VPP simplified model

A. Distributed energy resources (DER)

DER can be either distributed generators or controllable

loads connected to the network. From the authors’ point of

view, DGs within the VPP premises can be classified

according to:

1) Type of the primary energy source:

According to the primary energy source type, DGs can be

classified into two categories;

Generators utilizing RES (such as wind-based generators,

photovoltaic arrays, solar-thermal systems, and small

hydro-plants).

Generators utilizing non-RES (such as Combined Heat and

Power (CHP), biomass, biogas, diesel generators, gas

turbines, and fuel cells (FC)).

2) Capacity of DG units:

According to DG units’ capacities, DGs can be classified

into two categories;

Small-scale capacity DGs that must be connected to the

VPP in order to gain access to the electricity market; or

they could be connected together with controllable loads to

form micro grids that may or may not participate in the VPP

based on their capacities.

Medium- and large-scale capacity DGs that can

individually participate in the electricity market but they

may choose to be connected to VPP to gain optimal steady

revenue.

3) Ownership of DG units:

DGs within the VPP premises may be;

Residential-, Commercial-, and Industrial-owned DGs used

to supply part/all of its load in its own premises. They can

be referred to as Domestic DGs (DDG) [3].

Utility-owned DGs that are used to support the main grid

supply shortage. They may be called Public DGs (PDG).

Commercial company-owned DGs that aim to gain profits

from selling power production to the grid. They can be

named Independent Power Producers DGs (IPPDG).

4) DGs operational nature [7]:

DGs operational nature can be classified into two cases:

Stochastic nature: In case of wind-based and photovoltaic

DG units, the output power is not controllable as it depends

on a variable input resource. To overcome this nature, this

type of DG must be equipped with battery storage in order

to be able to control the output power.

Other DG technologies such as FCs and micro-turbines

have an operational dispatchable nature. They are capable

of varying their operation quickly. Therefore, in general,

VPP should include controllable loads, Energy Storage

Elements (ESE) and dispatchable DGs in order to



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



compensate the vulnerability of the stochastic nature-DG

type.

B. Energy Storage systems (ESS)

ESS and its elements play a pivotal role in bridging the gap

between the generation and demand, especially in the

presence of high penetration of stochastic generation. Energy

storage elements (ESEs) can store energy during off-peak

periods and feed it during the peak periods. It also can

optimally redistribute the output power of wind turbines and

photovoltaic arrays throughout the day. ESS can be classified

according to their applications; i.e. supplying power or energy

[8], as follows:

Energy supply class includes:

- Hydraulic Pumped Energy Storage (HPES)

- Compressed Air Energy Storage (CAES)

Power supply class includes:

- Flywheel Energy Storage (FWES)

- Super Conductor Magnetic Energy Storage (SMES)

- Super Capacitors

C. Information and Communication systems

The energy management system (EMS) represents the heart

of the information and communication system. It manages the

operation of other VPP components through communication

technologies in bidirectional ways, as shown in Fig. (1).

The EMS has the following responsibilities [9]:

Receiving information about the status of each

element inside the VPP.

Forecasting RES primary sources and output

power.

Forecasting and management of loads.

Coordinating the power flow between the VPP

elements

Controlling the operation of DGs, storage

elements, and controllable loads.

The EMS’s aim is to achieve one of the following targets:

Minimization of generation cost.

Minimization of energy losses.

Minimization of greenhouse gases.

Maximization of profit.

Improvement of voltage profile.

Enhancement of power quality.

IV. VPP FRAMEWORK

VPP is a large entity that involves a huge number of DGs,

controllable loads, and storage elements under a layer of

Information and Communication Technologies (ICT). VPP is

responsible for controlling the supply and manages the

electrical energy flow not only within its cluster but also in

exchange with the main grid. In addition, VPP can also offer

ancillary and power quality services. To achieve these

functions, VPP must own the following tools [3]:

ICT infrastructure.

Monitoring and control applications.

Smart metering and control devices installed at the

customer sites.

Software applications to forecast the power generation of

the VPP.

For the sake of specialization, VPP is subdivided into two

entities; Technical Virtual Power Plant (TVPP) and

Commercial Virtual Power Plant (CVPP). These two entities

operate together in order to achieve the VPP functions. TVPP

and CVPP functionalities and responsibilities are as follows:

A. Technical virtual power plant (TVPP)

TVPP is responsible for the correct operation of the DER

and the ESSs in order to manage the energy flow inside the

VPP cluster, and execution of ancillary services. TVPP

receives information from the CVPP about the contractual

DGs and the controllable loads, this information must

include:

The maximum capacity and commitment of each DG

unit.

The production and consumption forecast.

The location of DG units and loads.

The capacity and the locations of the energy storage

systems.

The available control strategy of the controllable loads at

all times during the day according to the contractual

obligations between the VPP and the loads.

Based on the information received from the CVPP in

addition to the detailed information about the distribution

network topology, TVPP ensures that the power system is

operated in an optimized and secure way taking physical

constraints and potential services offered by VPP into account.

The following functions are provided by the TVPP [10] and

[11]:

Managing the local system for distribution system

operators (DSO)

Providing balancing, management of the network and

execution of ancillary services.

Providing visibility of the DERs in the distribution

network to the transmission system operator (TSO)

allowing DG and demand to contribute to the

transmission system management activities.

Taking care of the DER operation according to

requirements obtained from CVPP and system status

information.

Monitoring continuously the condition for the retrieval

of equipment historical loadings.

Asset management- supported by statistical data.



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



Self-identification of system components

Determining of fault location.

Facilitating maintenance.

Optimizing of project portfolio and statistical analysis.

B. Commercial Virtual power plant (CVPP)

CVPP considers DERs as commercial entities offering the

price and amount of energy that it can deliver, optimizing

economical utilization of VPP portfolio for the electricity

market [11]. CVPP performs bilateral contracts with both the

DG units and the customers. These contracts’ information is

sent to the TVPP in order to take the amount of the contracted

power into consideration during the performance of technical

studies. Small-scale DG units are not able to participate in the

electricity market individually. Therefore, CVPP makes these

units visible to the electricity market. The CVPP

functionalities are summarized as follows [4] and [11]:

Scheduling of production based on predicted needs

of consumers.

Trading in the wholesale electricity market

Balancing and/or trading portfolios

Providing of services to the system operator

Submitting of DERs’ characteristics and costs and

maintenance

Production and consumption forecasting based on

weather forecasting and demand profiles.

Outage demand management

Constructing DER bids and submitting them to the

electricity market.

Scheduling of generation and daily optimization

Selling DER power in the electricity market.

In order to achieve the above mentioned targets, CVPP

interacts with the following entities [11]:

DER: Its main function is to bridge the gap between

demand and production. Its production must be

planned, forecasted, and transferred that

information to the TVPP.

Balance Responsible Party (BRP): It is an energy

trading entity with a property to make its own

production/consumption plan available to be used

by TVPP.

Transmission System Operator (TSO): It has a main

role in maintaining the instantaneous supply and

demand balance in the network.

TVPP: It receives information from CVPP and

takes it into consideration in optimizing the

operation of the VPP and its interaction with the

main grid.

V. VPP VERSUS MICROGRID

In regards to the power system deregulation, a new

generation of distribution networks is developed named

active distribution networks (ADN). ADN is defined as a

distribution network whose operator can remotely and

automatically control the DER units and network topology to

efficiently manage and optimally utilize the network assets

[12]. The ADN brain is the central control system that is

capable of making control actions and send control signals to

the DER units. The central control system aims to enhance the

DER controllability and the technical and economical

benefits achieved by both the DER owners and the host grid

[13] and [14].

The ADN concept radically changes the traditional

distribution system into a new distribution paradigm. Thus,

distribution systems can be decomposed into smaller,

autonomously-operated systems, called microgrid that has a

central system controller coordinating the operation of DER

units and controllable loads. Using the Virtual Power Plant

(VPP) concept, each small autonomously operated system can

be presented as aggregated controllable group. This group is

available for use in system management functions at higher

network voltage levels [14].

The ADN can operate in two distinct modes:

Fig. (2) Schematic diagram of an ADN operating as VPP

1) Utility-Connected Mode: In this mode, the distribution

network is grid connected where the ADN operator optimally

controls the ADN components to maximize the technical and



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



economical benefits of the existing DER. One way of

achieving this is to coordinate the ADN apparatus to provide a

pre-specified performance profile at the Point of Common

Coupling (PCC), i.e., the ADN operates as a VPP that is

comparable to a centralized power plant [15] and [16]. A

conceptual representation of the VPP is given in Fig (2).

2) Islanded Mode: In this mode, the ADN operates in offgrid

autonomous mode where the ADN operator controls the ADN

components to achieve a safe and reliable operation of the

network independent of the utility, i.e., form an islanded

microgrid [17].

Table I VPP Vs Microgrid

Concept VPP Microgrid

Control

objective

Promotes market and grid

service participation from

DERs

Increases DER visibilities

to transmission and

distribution system

operators

Aggregates the capacity of

many diverse DERs, and

creates a single operating

profile from a composite

of the parameters

characterizing each DER

to facilitate DER trading

in the wholesale energy

markets.

Implements autonomous

operation of a

distribution network with

many DERs, of which

stand-alone system is of

particular interest

Infrastructure Information and communication technologies and software

interfaces

Components Distributed generators, energy storage elements,

controllable loads and energy management system.

Features Supports grid services Capable of

performing

automatic

islanding

Both VPP and microgrids can facilitate DER integration

into power system but with different aims. Microgrids focus

on network operation control concerning active and reactive

power using DER units existing within one local grid. On the

other hand while VPP concentrate on the provision of energy

and power system support services from DER units [18].

Table I compares the two concepts and highlighting their

differences and similarities [14] and [18].

VI. VPP OPTIMIZATION

The optimal VPP operation aims at enhancing its operation

and minimizing the cost of its produced energy. This section

surveys the publications in this field. VPP future optimization

studies can be divided into two main categories from the

authors’ point of view.

A. Selecting VPP structure by optimizing its components.

B. Optimizing the VPP operation.

These two categories are explained as follows:

A. Optimization of the VPP structure

VPP as an independent entity that has the ability to perform

two sets of contracts:

Bilateral contracts with DG units. These contracts

include the maximum DG units’ capacities and the

obligations of these units towards the VPP.

Contracts with customers that include the category

of the load and the possibility of controlling or even

interrupting it. The corresponding

controlling/interruption duration and initiation

times.

VPP optimization methodology depends on the power

system under study; either if it is new or existing. For a

newly-established power system, VPP has the ability to

choose the capacity and location of the DG units and ESEs,

and the locations of the loads to be controlled and the

appropriate control strategies and schedules. On the other

hand for existing power systems, these options are limited as

the location and size of the DG units and ESEs, and the

locations of the controllable loads are pre-determined.

The following ideas are proposed for VPP structure

optimization:

1) DG units’ optimal sizing and siting:

Researchers investigated various optimization techniques to

determine the optimal location and size of DG in order to

reduce power loss and improve the voltage profile of the

power system. Similarly, VPP optimization can be carried out

through optimal placing of stochastic DG units (wind and



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



photovoltaic). Other studies can be performed to select the

optimal capacity of a conventional power plant used in

collaboration with DG units as well as purchasing energy

from the electricity market to supply the required VPP energy.

2) ESEs optimal sizing and siting:

Optimal sizing and siting of ESEs helps in reducing power

loss, improving voltage profiles, and in optimizing the

generation of stochastic DGs.

3) Optimal load control scheduling:

The VPP has the authority to control or even interrupt the

loads according to their importance in order to optimize its

operation. The loads can be divided into three categories:

Critical loads (Class-A): These loads are the most

important loads. They are not interruptible or even

controllable. The price of energy supplied to these

loads must be the most expensive one but on the

other hand a big penalty should be paid by the VPP

operator in case of interruption.

Emergency loads (Class-B): These loads are less

important than the critical loads. They are also not

interruptible but they are controllable. As mentioned

before, the control procedures should be well

defined and stated in the contract.

Normal loads (Class-C): These loads are the least

important loads. They are interruptible and

controllable. The price of energy supplied to these

loads is the least one as a tribute to the possibility of

interruption. These normal loads may be further

divided into sub-categories based on the allowable

duration of interruption and control and the

corresponding time (within the peak period or

off-peak period). Undoubtedly, as the allowable

period of interruption/control increases the price of

energy decreases.

B. Optimal operation of the VPP

For an existing power system with pre-determined

capacities and locations of DG units and ESEs and with

certain allowable schedules of load control, the optimal

operation could be obtained by optimally determining the

generation of DG units, the charge and discharge rate of the

ESEs and the amount of energy to be purchased from the

electricity market.

C. Optimization of the VPP components

Although rare studies were performed to optimize the

structure and operation of the VPP as a one unit (i.e.

optimizing all the VPP components and operation

simultaneously), several studies were done to optimize each

of the VPP components of the VPP individually. The

following subsections surveyed the work done to optimize the

VPP component (i.e. DGs, ESEs and controllable loads).

C.1 Optimal sizing and placement of DGs

Distributed generators (DGs) are connected to the distribution

network for different purposes: improving the voltage profile,

reducing the power loss, enhancing of system reliability and

security, improving of power quality (supply continuity),

relieving transmission and distribution congestion, reducing

health care costs due to improved environment, reducing the

system cost, and deferral of new investments.

Optimal location and capacity of DGs plays a pivotal role in

gaining the maximum benefits from them. On the other side

non-optimal placement or sizing of DGs may cause

undesirable effects. Optimal DG sizing and siting problem

can be classified according different aspects as presented in

Fig. (3).

Fig. (3) Different classifications of DG optimization studies

C.1.1 DG optimization according to methodology

The search space of optimal location and capacity of DGs

is roomy; Different optimization methods are used in this field

for the sake of power loss minimization, cost reduction, profit

maximization and environmental emission reduction. The

optimization methods could be analytical [18-23], numerical

[24-31] and heuristic [32-46].

A) Analytical methods

The analytical method known as the “2/3 rule” was

proposed in [18] for optimal installation of a DG of 2/3

capacity of the incoming generation at 2/3 of the length of the

line. However, this technique may not be effective for

nonuniformly distributed loads. Two analytical methods for

optimal location of a single DG for radial and meshed power

systems were introduced in [19]. The first method is



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



applicable to radial and the second one to meshed power

systems based on bus admittance matrix, generation

information and load distribution of the system. In [20] a non

iterative analytical method based on the exact loss formula to

minimize power losses by the optimal placement of DG in

radial and meshed systems was presented. In [21], the

optimum size and location of DG were defined so as to

minimize total power losses based on the equivalent current

injection technique and without the use of impedance or

Jacobian matrices for radial systems. Analytical expressions

for finding optimal size and power factor of different types of

DGs were suggested in [22]. An improved analytical method

was described in [23] for allocating four types of multiple DG

units for loss reduction in primary distribution networks.

Moreover, an approach for optimally selecting the optimal

DG power factor is also presented.

B) Numerical methods

The most common used numerical methods can be

summarized as follows

1) Linear Programming (LP): It was used to solve optimal DG

power optimization problem in [24] and [25] for achieving

maximum DG penetration and maximum DG energy

harvesting, respectively.

2) Nonlinear Programming (NLP): It was presented in [26]

for capturing the time variations of multiple renewable sites

and demands as well as the effect of innovative control

schemes

3) Gradient Search: Gradient search for the optimal sizing of

DGs in meshed networks considering fault level constraints

was proposed in [27].

4) Sequential Quadratic Programming (SQP): It was applied

in [28] to determine the optimal locations and sizes of single

and multiple DGs with specified and unspecified power

factor.

5) Dynamic Programming (DP): DP was applied to maximize

the profit of the DNO by optimal selection of DGs locations

while considering light, medium, and peak load conditions

[29].

6) Exhaustive Search: An exhaustive search was proposed for

determination of optimal DG size and locations in unbalanced

distribution networks considering the changes in the loading

conditions due to contingencies in [30] and for heavily over

loaded networks [31].

C) Heuristic methods

The most common heuristic methods used can be

summarized as follows

1) Genetic Algorithm (GA): It was applied to solve an optimal

multiple DGs sizing and siting problem with reliability

constraints in [32] where the optimization process is solved

by the combination of genetic algorithms (GA) techniques

with methods to evaluate DG impacts in system reliability,

losses and voltage profile. Authors in [33] proposed a GA

based method for optimal sizing and siting of DGs in radial as

well as networked systems for the sake of power loss

minimization. A GA was utilized in [34] to solve the

optimization problem that maximizes the profit of the system

based on nodal pricing for optimally allocating distributed

generation for profit, loss reduction, and voltage

improvement including voltage rise phenomenon. A GA

methodology was implemented to optimally allocate

renewable DG units in distribution network to maximize the

worth of the connection to the local distribution company as

well as the customers connected to the system [35]. A

value-based approach, taking into account the benefits and

costs of DGs, was developed and solved by a GA that

computes the optimal number, type, location, and size of DGs

[36].

2) Tabu Search (TS): It was used to obtain the optimal sizing

and siting of DG units simultaneously with the optimal

placement of reactive power sources in [37]. A stochastic

multiple DGs optimal sizes and locations were determined for

cost minimization by a combined TS and scatter search [38].

3) Particle Swarm Optimization (PSO): It was utilized for

optimal selection of types, locations and sizes in order to

maximize the DG penetration considering standard harmonic

limits and protection coordination constraints [39]. A PSO

based algorithm was implemented for cost minimization

through the optimal sizing and placement of multiple DG

units in [40].

4) Ant Colony Optimization: A multiobjective ant colony

system algorithm was proposed to derive the optimal recloser

and DG placement scheme for radial distribution networks in

[41]. A composite reliability index was used as the objective

function in the optimization procedure.

5) Artificial Bee Colony (ABC): DG-unit placement and

sizing process was performed with ABC algorithm in [42].

6) Harmony Search (HS): The optimal DG location is based

on loss sensitivity factors and the optimal DG size is obtained

by HS algorithm [43].

7) Cat Swarm Optimization: The authors in [44] presented a

cat swarm optimization method for optimal placement and

sizing of multiple DGs to achieve higher system reliability in

large scale primary distribution networks.

8) Big Bang Big Crunch (BB-BC): A supervised Big Bang

Big Crunch optimization method was proposed in [45] and

[46] for the optimal sizing and siting of voltage controlled

distributed generators for the sake of power loss as well as

energy losses minimization.

9) Firefly algorithm (FA): Authors in [47] proposed a firefly

based optimization algorithm for the optimal sizing and siting

of dispatchable distributed generators for power loss

reduction.

C.1.2 DG optimization according to objective



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



Optimal sizing and placement of DGs is performed by

achievement of the required objective function that could be

single or multi objective.

The single objective functions may be based on [48]:

a) Maximization of:

profit

voltage limit loadability(i.e., the maximum

loading that can be supplied by the power

distribution system while the voltages at all nodes

are kept within the limits)

b) Minimization of:

energy loss

generation cost

voltage deviations

system average interruption duration index

Multiobjective formulations can be classified as:

1) Multiobjective function with weights (the weighted sum

method), where the multiobjective formulation is converted to

a single objective function using the weighted sum of

individual objectives.

2) Goal multiobjective index, where the multiobjective

formulation is transformed into a single objective function

using the goal programming method.

3) Multiobjective formulation considering more than one

contrasting objectives and selecting the best compromise

solution in a set of feasible solutions [48].

Several Multiobjective optimization algorithms were

performed for optimal sizing and placement of DG units

[49-64], the reviewed literature and their contributions are

summarized in Table 2.

Table II Summary of multiobjective optimization literature review

Ref.

Contribution Method

[49] A multiobjective with weight algorithm

for optimal sizing and siting of multiple

DGs considering DGs uncertainty and

power quality.

GA

[50] A multiobjective algorithm for optimal

sizing and siting of multiple DGs for

minimization of network costs and

improvement of power quality.

Double trade off

method

[51] Two strategies are worked out to

achieve an integration of multiple

stochastic DG units in low-and

medium-voltage distribution grids

while optimizing several relevant

objectives.

Monte Carlo

simulation integrated

with GA

[52] A multiobjective algorithm for

placement of DG in which the

objectives are defined as minimization

of cost index, technical risks and

economic risk.

Non-dominant

sorting GA

[53] Time-varying approach is applied to

both load and generation to estimate the

benefits of DG insertion

Exhaustive Search

[54] A multiobjective algorithm for optimal

sizing and siting of multiple DGs.

GA combined with

fuzzy programming

[55] A multiobjective programming

approach is applied in order to find

configurations that maximize the

integration of distributed wind power

generation while satisfying voltage and

thermal limits.

Non-dominant

sorting GA

[44] A composite reliability index is used as

the objective function in the

optimization procedure to derive the

optimal recloser and DG placement

scheme for radial distribution networks.

Ant Colony

Algorithm

[56] A multiobjective performance

index-based size and location

determination of distributed generation

in distribution systems with different

load models.

GA

[57] An optimal proposed approach to

determine the optimal sitting and sizing

of DG with multi-system constraints to

achieve a single or multi-objectives.

GA

[58] A multiobjective with weights

algorithm is proposed to lower down

both cost and loss effectively by optimal

selection of DG size and location.

A simple

conventional iterative

search technique

along with Newton

Raphson method

[59] A multiobjective with weights

algorithm is used for optimal placement

of DG considering electricity market

price fluctuation.

Mixed integer

nonlinear

programming

[60] An effective method to determine the

optimal size and best locations of DG

sources taking into account the system

constraints, maximizes the system

loading margin as well as the profit of

the distribution companies over the

planning period.

GA

[61] Transform the original objectives and

constraints into a fuzzy weighted

single-objective function in order to

optimize different types of DGs

Fuzzy set theory and

genetic algorithm

[62] A mathematical model of the chance

constrained programming is developed

and solved with the minimization of the

DGs’ investment cost, operating cost,

maintenance cost, network loss cost, as

well as the capacity adequacy cost as the

objective, security limitations as

constraints, and the siting and sizing of

DGs as optimization variables.

Monte Carlo

simulation-embedde

d genetic-algorithm-

based approach

[63] A hybrid genetic algorithm and particle

swarm is suggested for optimal sizing

and siting of DGs.

Combination of GA

and PSO

[64] A novel application of multiobjective

optimization with the aim of

determining the optimal DGs places,

sizes, and their generated power

contract price taking into consideration

improving the voltage profile and

stability, power-loss reduction,

reliability enhancement and economic

issues.

PSO



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



Table 3- Review of ESE literature

Ref.

Contribution Objective

[65] An open-loop optimal control

scheme to incorporate the

operating constraints of the

battery storage, such as state of

charge limits, charge/discharge

current limits, and lifetime.

Provide as much smoothing

as possible, so that the wind

power can be dispatched on

an hourly basis based on the

forecasted wind conditions.

[66] An ESE mathematical model was

developed

based on stochastic

programming, in which the

forecast error of wind power is

taken into consideration. Hybrid

intelligent algorithm based on GA

was introduced to solve the

stochastic model.

Decrease the bid

imbalance. Shift energy

from the cheapest to the

most expensive.

[67] A matrix real-coded GA

methodology for optimal

allocation and economic analysis

of ESS in microgrids.

Maximize the total net

profit achieved during the

system operational lifetime

period.

[68] The unit commitment problem

with spinning reserve for

microgrid was formulated as

mixed linear integer problem.

Time series and feed forward

neural network techniques were

used for forecasting the wind

speed and solar radiations

respectively taking into

consideration the forecasting

errors.

Determine the optimal size

of ESE connected to

microgrid based on cost

benefit analysis

[69] An optimal energy management

scheme for active hybrid ESS.

Minimize the

magnitude/fluctuation of

the current flowing in and

out of the battery and the

energy loss

[70] An economical cost-benefit

analysis has been performed

taking advantage of the increased

capabilities given by the

combined use of RES and storage

devices

Enhance wind generation

performances and adapt it

to demand

[71] Optimal operation of distribution

networks with wind-based

embedded generation and ESEs

Minimize the energy losses

[72] A model for calculating the

optimal size of an ESS in a

microgrid considering reliability

criterion where the ESS

investment cost and microgrid

operating cost were taken into

account.

Minimize the investment

cost of the ESS, and

expected microgrid

operating cost.

[73] A concept in which a DNO

controls the output of the ESEs of

commercial customers during a

specific time period in exchange

for providing a subsidy covering a

part of the initial cost of the

storage system. Further, a model

Solve the voltage

fluctuation problem in

distribution networks

that allows customers to

optimally ESSs was developed

[74] A simple scenario in which

independent storage either

cooperates with an intermittent

energy producer or competes in

reserve markets.

Obtain an optimal storage

scheduling strategy

[75] A solution strategy that uses a

convex optimization based

relaxation to solve the optimal

control problem then use this

framework to illustrate the effects

of various levels of energy storage

along with both time-invariant

and demand-based cost functions.

Investigate the effects of

different energy storage

capacities on generation

costs and peak-shaving

[76] A new algorithm to optimize the

day-ahead thermal and electrical

scheduling of a large scale VPP

using mixed-integer linear

programming

Calculate the daily optimal

operation of energy storage

devices and dispatchable

generation.

[77] The optimal control of the

microgrid’s energy storage

devices. The suggested method

computed the globally optimal

power flow, in both the network

and time domains.

Control stored energy is

controlled to balance power

generation of RES to

optimize overall power

consumption at the

microgrid PCC.

[78] A stochastic framework to

enhance the reliability and

operability of wind integration

using optimally placed and sized

ESSs.

Minimize the sum of

operation and interrupted

load costs over a planning

period

[79] A double fuzzy logic control

strategy optimizing the

management of the

superconducting magnetic energy

storage was proposed by

combining the wind power

forecasting and the real-time

control of the wind power system

Smooth the power

fluctuations of wind turbine

and prevent the

superconducting magnetic

energy storage from

occurring of the state of

over-charge/deep-discharg

e

[80] An adaptive optimal policy for

hourly operation of ESS in a grid

connected wind power company

to achieve the optimal operation

of ESS for wind energy time

shifting.

Maximize the expected

daily profit following

uncertainties in wind

generation and electricity

price through time shift

wind energy.

[81] A Fuzzy PSO was presented to

determine the optimal sizing and

siting decisions for ESS through a

cost-benefit analysis method.

Track the forecasted net

demand curve, reduce the

energy exchanged at

distribution substation and

mitigate power output

fluctuations by installing

ESS at DG sites

[82] The optimal operation of a ship

electric power system comprising

full electric propulsion and ESS

was analyzed.

Minimize the operation

cost and limit greenhouse

gas emissions

[83] A probabilistic method was

proposed to determine optimal

size of ESS for wind farm.

Smooth the wind power

output and make it more

dispatchable

[84] A new evolutionary technique

named improved bat algorithm

that is used for developing

corrective strategies and for

Perform least cost

dispatches



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



optimal sizing of ESEs.

[85] A unit commitment formulation

for micro-grid and the optimal

sizes of different energy storage

devices are determined in the

operation problem.

Minimize generation cost

C.2 Optimal sizing and placement of ESEs

Integration of RES into power systems is a must in order to

face the swelling demand and the soaring fuel prices. Wind

and solar powers are the most dynamically growing

renewable technologies due to their primary power source

availability. However, the mounting penetration of these

resources increases the network uncertainties due to their

stochastic nature.

RES such as wind and photovoltaic (PV) power are

difficult to be accurately simulated because they are strongly

correlated to the climate, ambient temperature, season, time,

and geography. Thus, RES increase the uncertainties in the

power system operation.

In order to secure proper system operation while considering

the uncertainties of the RES and due to their importance and

developing technologies, the ESEs were integrated

extensively into power systems.

The ESE studies include [65-86]:

Increasing the RES penetration [65], [76], and [83].

Leveling demand curve [70], [75], [77], and [81].

Minimizing operation cost and maximizing the

profit [67], [78], [80], [82], and [85].

Improving voltage fluctuations [73].

Deferring network upgrades [72].

Minimizing the system losses [71].

Relieving the system congestion [70] and [74].

Shifting of energy time [66], [75], [77], and [81].

Covering the forecast error of renewable based DGs

[68], [69], and [79].

Ancillary services [82].

Table 3 lists a summary of ESEs literature review and their

contributions.

C.3 Optimal load controlling schedules

Unlike the two previous topics, the optimal load controlling

studies are rare. Very few publications considered the load

control problem in order to enhance the power system

performance.

The load control studies are divided into two major

categories:

1) The first category of load control studies considers the load

management from the customer point of view in order to

minimize the electricity bill [90] - [92].

2) The second category of the load control studies, which are

the most common studies, adopts the system point of view in

controlling the demand. The most common load management

program of the second sector is end-use equipment control

known as Direct Load Control (DLC). The purpose of DLC is

to shape the load curve by cycling customers’ large current

drawing appliances. A number of DLC schemes have been

developed to reach both peak load shaving and operating cost

saving [87]-[90], [94] - [101].

The main objectives of the load control studies are:

Minimizing the electricity bill [91]

Minimizing the cost [87], [88], [92], [97], [99], and

[100]

Leveling demand curve [88], [95], [98], [99], and

[101]

Improving system reliability [92] and [93]

Social welfare maximization [94]

The authors in [87] presented a mixed integer linear

programming formulation for load-side control of electrical

energy demand in order to minimize the net cost of load

shedding. In [88] a multiple-block fuzzy logic based

water-heater demand side management strategy was proposed

to shift the high electricity demand to off-peak hours. A

Relaxed Dynamic Programming (RDP) algorithm was

suggested in [89] to generate a daily control scheduling for

optimal or near-optimal air conditioner loads. The

computational scheme aids customers in restraining peak load

demand and in saving electricity costs. Authors in [90]

determined the optimal control schedules that an aggregator

should apply to the VPP controllable devices in order to

optimize load reduction over a specified control period. In

[91], an automated optimization-based residential load

control scheme in a retail electricity market was introduced.

The main goal of the control scheme is to minimize the

household’s electricity bill by optimally scheduling the

operation and energy consumption for each appliance, subject

to the special needs indicated by the users. Authors in [92]

proposed a fuzzy logic-based DLC scheme of air conditioning

loads considering nodal reliability characteristics and

considering the effect of the transmission network reliability

on the DLC scheme, fuzzy dynamic programming was

utilized to determine the optimal DLC scheme which achieves

a good tradeoff among peak load shaving, operating cost

reduction and system reliability improvement. In [93] a

control scheme was proposed. This scheme is based on the

nodal interrupted energy assessment rate which considers

both nodal reliability and customer willingness to pay for his

reliability to encourage the air conditioning loads customers

to participate in the DLC program. In [94], social welfare

maximization for energy scheduling between a utility

company and residential end-users where the utility company

adopts a cost function representing the cost of providing

energy to end-users was presented. In [95], a mixed-integer



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



linear programming decision model to implement DLC on

battery charging processes at electric vehicle charging points

located at parking areas was introduced. A practical strategy

for large-scale control of domestic refrigerators for demand

peak reduction in distribution systems was proposed in [96].

Its common idea is to take advantage of the thermodynamics

of refrigerators in order to accumulate energy during a short

time interval and releasing this energy in another appropriate

interval, during which the refrigerator remains off

contributing for energy consumption reduction. A

mixed-integer programming model was presented in [97] and

applied for load shifting to minimize the overall cost of

power. Authors in [98] suggested a decentralized optimal load

control scheme that provides contingency reserve in the

presence of sudden generation drop. In [99], a control strategy

for the electricity price-load-overload was formed; the main

objective of the study is to apply demand response control

strategies to relief the distribution-line overload and to realize

improvements in terms of electricity cost. An optimization

strategy via load scheduling and control was implemented in

[100] based on PSO in order to decrease the electricity cost.

Authors in [101] concentrated on proposing a new strategy

from the perspective of an aggregator that optimally

schedules residential loads during the next day. Gaussian

copula function and Gaussian mixture model were

investigated as new efficient tools to estimate the aggregate

power demand of specific domestic appliances.

VII. CONCLUSION

VPP is a relatively new and yet an attractive concept that

needs thorough research to facilitate its implementation. This

paper presents a comprehensive literature review for the

different VPP definitions, components, and the relation

between these components. Moreover, the VPP framework is

explained and the functionalities of the TVPP and CVPP are

stated for better understanding of the VPP concept. A survey

of the different optimization techniques aims to optimize

either the VPP structure or the VPP operation discussed. The

optimization of the VPP structure included the optimal sizing

and siting of DG units and the ESEs, the optimal load control,

and the optimal measurement devices location. The required

objective functions, and optimizing algorithms for the sake of

VPP optimal operation are highlighted. The presented survey

helps the researchers in better understanding of the VPP

framework and operation and in finding the optimization tools

and objectives required to realize the VPP concept.

REFERENCES

[1] K. Dielmann, Alwin and van der Velden, Virtual power plant (VPP) a new perspective for energy generation? Proceedings of 9th IEEE

international scientific and practical conference of students, post-graduates and Young scientists, (2003) 18 - 20.

[2] D. Pudjianto, C. Ramsay and G. Strbac, Virtual power plant and system integration of distributed energy resources IET renewable power generation , 1 (2007) 10-16.

[3] K.E.Bakari and W.L.Kling, Virtual power plant: An answer to increasing distributed generation IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), Gothenburg, Sweden, (2010) 1-6.

[4] C.Tarazona, M.Muscholl, R.Lopez and GC. Passelergue, Integration of Distributed Energy Resources in The Operation of Energy Management Systems IEEE PES/IAS conference on Sustainable Alternative Energy (SAE),Valencia, Spain, (2009) 1-5

[5] P. Lombardi, M. Powalko, and K. Rudion, Optimal Operation of a Virtual Power Plant Power &Energy Society General Meeting, Calgary, Canada, (2009) 1-6.

[6] Daniel Hropko, Ján Ivanecký, and Ján Turček, Optimal Dispatch of Renewable Energy Sources Included in Virtual Power Plant Using Accelerated Particle Swarm Optimization ELEKTRO, Rajeck Teplice,(2012) 196-200.

[7] E.Mashhour and S.M. Moghadda-Tafreshi, Trading Models for Aggregating Distributed Energy Resources into Virtual Power Plant 2nd international conference on Adaptive science & technology, Accra, Ghana, (2009) 418-421.

[8] P. Lombardi, M. Stötzer, Z. Styczynski, and A. Orths, Multi-criteria optimization of an energy storage system within a Virtual Power Plant architecture Power and Energy Society General Meeting, San Diego, USA, (2011) 1-6.

[9] Roberto Caldon, Andrea Rossi Patria and Roberto Turri, OPTIMISATION ALGORITHM FOR A VIRTUAL POWER PLANT OPERATION 39th international Universities Power Engineering Conference, Bristol, UK, 2 (2004) 1058-1062.

[10] H .Saboori,, M. Mohammadi, and R. Taghe, Virtual Power Plant (VPP), Definition, Concept, Components and Types Power and Energy Engineering Conference Asia-Pacific, Wuhan, China, (2011) 1-4.

[11] Slobodan Lukovic, Igor Kaitovic, Marcello Mura and Umberto Bondi, Virtual Power Plant as a bridge between Distributed Energy Resources and Smart Grid 43rd Hawaii international conference on system science, Honolulu, USA, (2010)1-8.

[12] CIGRE Task Force C6.11, Development and Operation of Active Distribution Networks Final Report, (2010).

[13] F. Pilo, G. Pisano, and G. Soma, Digital Model of a Distribution Management System for the Optimal Operation of Active Distribution Systems Proceeding of 20th International Conference on Electricity Distribution - Part 1, CIRED, Prague, Czech Republic, (2009) 1–5.

[14] D. Pudjianto, C. Ramsay, and G. Strbac, Microgrids and Virtual Power Plants: Concepts to Support the Integration of Distributed Energy Resources Proceeding of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 7 (2008) 731 – 741.

[15] Omid Palizban, Kimmo Kauhaniemi, and Josep M. Guerrero Microgrids in active network management—Part I: Hierarchical control, energy storage, virtual power plants, and market participation Renewable and Sustainable Energy Reviews 36 (2014) 428-439.

[16] T. Werner and R. Remberg, Technical, Economical, and Regulatory Aspects of Virtual Power Plants 3rd International Conf. on Electric Utility Deregulation and Restructuring and Power Technologies, Nanjing, China (2008) 2427-2433.

[17] F. Kateraei, M. Iravani, and P. Lehn, Micro-grid Autonomous Operation During And Subsequent To Islanding Process IEEE Transactions on Power Delivery, 20 (2005) 248–257.



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



[18] H. L. Willis, Analytical methods and rules of thumb for modeling DG-distribution interaction Proceeding of IEEE Power Energy Society Summer Meeting, (2000) 1643–1644.

[19] C. Wang and M. H. Nehrir, Analytical approaches for optimal placement of distributed generation sources in power systems IEEE Transactions on Power Systems, 19 (2004) 2068–2076.

[20] N. Acharya, P. Mahat, and N.Mithulananthan, An analytical approach for DG allocation in primary distribution network International Journal of Electric Power and Energy Systems, 10 (2006) 669–678.

[21] T. Gözel and M. H. Hocaoglu, An analytical method for the sizing and siting of distributed generators in radial systems Electric Power System Research, 6 (2009) 912–918.

[22] D. Q. Hung, N. Mithulananthan, and R. C. Bansal, Analytical expressions for DG allocation in primary distribution networks IEEE Transactions on Energy Conversion, 3 (2010) 814–820.

[23] D. Q. Hung and N. Mithulananthan, Multiple distributed generators placement in primary distribution networks for loss reduction IEEE Transactions on Industrial Electronics, 4 (2013) 1700–1708.

[24] A. Keane and M. O’Malley, Optimal allocation of embedded generation on distribution networks IEEE Transactions on Power Systems, 3 (2005) 1640–1646.

[25] A. Keane and M. O’Malley, Optimal utilization of distribution network for energy harvesting IEEE Transactions on Power Systems, 1 (2007) 467–475.

[26] L. F. Ochoa and G. P. Harrison, Minimizing energy losses: Optimal accommodation and smart operation of renewable distributed generation IEEE Transactions on Power Systems, 1 (2011) 198–205.

[27] P. Vovos and J. Bialek, Direct incorporation of fault level constraints in optimal power flow as a tool for network capacity analysis IEEE Transactions on Power Systems, 4 (2005) 2125–2134.

[28] M. F. AlHajri, M. R. AlRashidi, and M. E. El-Hawary, Improved sequential quadratic programming approach for optimal distribution generation deployments via stability and sensitivity analyses Electric Power Components and Systems, 14 (2010) 1595–1614.

[29] N. Khalesi, N. Rezaei, and M.R. Haghifam, DG allocation with application of dynamic programming for loss reduction and reliability improvement International Journal of Electric Power and Energy Systems 2 (2011) 288–295.

[30] S. Kotamarty, S. Khushalani, and N. Schulz, Impact of distributed generation on distribution contingency analysis Electric Power System Research, 9 (2008) 1537–1545.

[31] H. Khan and M. A. Choudhry, Implementation of distributed generation (IDG) algorithm for performance enhancement of distribution feeder under extreme load growth International Journal of Electric Power and Energy Systems 9 (2010) 985–997.

[32] C. L. T. Borges and D. M. Falcão, Optimal distributed generation allocation for reliability, losses, and voltage improvement International Journal of Electric Power and Energy Systems 6 (2006) 413–420.

[33] R. K. Singh and S. K. Goswami, Optimum siting and sizing of distributed generations in radial and networked systems Electric Power Components and Systems, 2 (2009) 127–145.

[34] R. K. Singh and S. K. Goswami, Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue International Journal of Electric Power and Energy Systems 6 (2010) 637–644.

[35] M. F. Shaaban, Y. M. Atwa, and E. F. El-Saadany, DG allocation for benefit maximization in distribution networks IEEE Transactions on Power Systems, 2 (2013) 639-649.

[36] J. H. Teng, Y.-H. Liu, C.Y. Chen, and C.F. Chen, Value-based distributed generator placements for service quality improvements International Journal of Electric Power and Energy, 3 (2007) 268–274.

[37] M. E. H. Golshan and S. A. Arefifar, Optimal allocation of distributed generation and reactive sources considering tap positions of voltage regulators as control variables European Transactions on Electric Power, 3 (207) 219–239.

[38] C. Novoa and T. Jin, Reliability centered planning for distributed generation considering wind power volatility Electric Power System Research, 8 (2011) 1654–1661.

[39] V. R. Pandi, H. H. Zeineldin, and W. Xiao, Determining optimal location and size of distributed generation resources considering harmonic and protection coordination limits IEEE Transactions on Power Systems, 2 (2013) 1245-1254.

[40] M. Gomez-Gonzalez, A. López, and F. Jurado, Optimization of distributed generation systems using a new discrete PSO and OPF Electric Power System Research, 1 (2012) 174–180.

[41] L. Wang and C. Singh, Reliability-constrained optimum placement of reclosers and distributed generators in distribution networks using an ant colony system algorithm IEEE Transactions on Power Systems, 6 (2008) 757–764.

[42] A.A. Seker and M.H. Hocaogla Artificial Bee Colony algorithm for optimal placement and sizing of distributed generation 8th international conference on electrical and electronic engineering, Bursa, Turkey, (2013) 127-131.

[43] R. S. Rao, K. Ravindra, K. Satish, and S. V. L. Narasimham, Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation IEEE Transactions on Power Systems,1 (2014) 317-325.

[44] S. Deepak Kumar, R. Samantaray, I. Kamwa, and N.C.Sahoo Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators in Power Distribution Network Using Cat Swarm Optimization Electric Power Components and Systems, 2 (2014) 149–164.

[45] M. M. Othman, W. El-Khattam, Y. G. Hegazy and A. Y. Abdelaziz, Optimal Placement and Sizing of Distributed Generators in Unbalanced Distribution Systems Using Supervised Big Bang Big Crunch Method IEEE Transactions on Power Systems, to be published.

[46] A. Y. Abdelaziz, Y. G. Hegazy, W. El-Khattam and M. M. Othman, A Multiobjective Optimization for Sizing and Placement of Voltage Controlled Distributed Generation Using Supervised Big Bang Big Crunch Method Electric Power Components and Systems, 1 (2015) 105-117.

[47] A. Y. Abdelaziz, Y. G. Hegazy, W. El-Khattam and M. M. Othman, Optimal Planning of Distributed Generators in Distribution Networks Using Modified Firefly Method Electric Power Components and Systems, 3 (2015) 320-333.

[48] Pavlos S. Georgilakis and Nikos D. Hatziargyriou Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods, and Future Research IEEE Transactions on Power Systems, 3 (2013) 3420-3428.

[49] G. Caprinelli, G. Celli, F. Pilo, and A. Russo, Embedded generation planning under uncertainty including power quality issues European Transactions on Electric Power, 6 (2003) 381–389.

[50] G. Caprinelli, G. Celli, S. Mocci, F. Pilo, and A. Russo, Optimisation of embedded generation sizing and siting by using a double trade-off method IEE Proceeding Generation, Transmission Distribution, 4 (2005) 503–513.

[51] E. Haesen, J. Driesen, and R. Belmans, Robust planning methodology for integration of stochastic generators in distribution grids IET Renewable Power Generation, 1 (2007) 25–32.

[52] M.R. Haghifam, H. Falaghi, and O. P. Malik, Risk-based distributed generation placement IET renewable power generation, 2 (2008) 252–260.



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



[53] L. F. Ochoa, A. Padilha-Feltrin, and G. P. Harrison, Evaluating distributed time-varying generation through a multiobjective index IEEE Transactions on Power Delivery, 2 (2008) 1132–1138.

[54] K. H. Kim, K. B. Song, S. K. Joo, Y. J. Lee, and J.-O. Kim, Multiobjective distributed generation placement using fuzzy goal programming with genetic algorithm European Transactions on Electric Power 3 (2008) 217–230.

[55] L. F. Ochoa, A. Padilha-Feltrin, and G. P. Harrison, Time-series-based maximization of distributed wind power generation integration IEEE Transactions on Energy Conversion, 3 (2008) 968–974.

[56] D. Singh, D. Singh, and K.S. Verma, Multiobjective optimization for DG planning with load models IEEE Transactions on Power Systems, 1 (2009) 427–436.

[57] A. A. A .El-Ela, S. M. Allam, and M. M. Shatla, Maximal optimal benefits of distributed generation using genetic algorithms Electric Power System Research, 7 (2010) 869–877.

[58] S. Ghosh, S. P. Ghoshal, and S. Ghosh, Optimal sizing and placement of distributed generation in a network system International Journal of Electric Power and Energy, 8 (2010) 849–856.

[59] S. Porkar, P. Poure, A. Abbaspour-Tehrani-Fard, and S. Saadate, Optimal allocation of distributed generation using a two-stage multi-objective mixed-integer-nonlinear programming European Transactions on Electric Power, 1 (2011) 1072–1087.

[60] M. F. Akorede, H. Hizam, I. Aris, and M. Z. A. Abd- Kadir, Effective method for optimal allocation of distributed generation units in meshed electric power systems IET Generation, Transmission, Distribution 2 (2011) 276–287.

[61] K. Vinothkumar and M. P. Selvan, Fuzzy embedded genetic algorithm method for distributed generation planning Electric Power Components and Systems, 4 (2011) 346–366.

[62] Z. Liu, F. Wen, and G. Ledwich, Optimal siting and sizing of distributed generators in distribution systems considering uncertainties IEEE Transactions on Power Delivery, 4 (2011) 2541–2551.

[63] M. H. Moradi and M. Abedini A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems International Journal of Electric Power and Energy systems, 1 (2012) 66–74.

[64] A. Ameli, S. Bahrami, F. Khazaeli, and M. Haghifam, A Multiobjective Particle Swarm Optimization for Sizing and Placement of DGs from DG Owner’s and Distribution Company’s Viewpoints IEEE Transactions on Power Delivery, 4 (2014) 1831–1840.

[65] S.Teleke, M. E. Baran, S, Bhattacharya, and Alex Q. Huang Optimal Control of Battery Energy Storage for Wind Farm Dispatching IEEE Transactions on Power Delivery, 3 (2010) 787-794.

[66] Y. Yuan, Q. Li1, and W. Wang, Optimal operation strategy of energy storage unit in wind power integration based on stochastic programming, IET Renewable Power Generation 2 (2011) 194–201.

[67] C. Chen, S. Duan, T. Cai, B. and L. G. Hu, Optimal Allocation and Economic Analysis of Energy Storage System in Microgrids IEEE Transactions on electronics, 10 (2011) 2762-2773.

[68] S. X. Chen, H.B.Gooi, and M. Q. Wang, Sizing of Energy Storage for Microgrids IEEE Transactions on smart grid, 1 (2012) 142-151.

[69] M. Choi, S. Kim, and S. Seo, Energy Management Optimization in a Battery/Super capacitor Hybrid Energy Storage System IEEE Transactions on smart grid, 1 (2012) 463-472.

[70] S. Grillo, M. M. Massucco, and F. Silvestro, Optimal Management Strategy of a Battery-Based Storage System to Improve Renewable Energy Integration in Distribution Networks IEEE Transactions on Smart grid, 2 (2012) 950-958.

[71] A. Gabash, and P. Li, Active-Reactive Optimal Power Flow in Distribution Networks With Embedded Generation and Battery Storage IEEE Transactions on Power Systems, 4 (2012) 2026-2035.

[72] S. Bahramirad, W. Reder, and A. Khodaei, Reliability-Constrained Optimal Sizing of Energy Storage System in a Microgrid IEEE Transactions on Smart grid, 4 (2012) 2056-2062.

[73] H. Sugihara, K. Yokoyama, O. Saeki, and K. T. T. Funaki, Economic and Efficient Voltage Management Using Customer-Owned Energy Storage Systems in a Distribution Network With High Penetration of Photovoltaic Systems IEEE Transactions on Power Systems, 1 (2013) 102-111.

[74] J. A. Taylor, D. S. Callaway, and K. Poolla, Competitive Energy Storage in the Presence of Renewables IEEE Transactions on Power Systems, 2 (2013) 985 -996.

[75] D. Gayme, and U. Topcu, Optimal Power Flow with Large-Scale Storage Integration IEEE Transactions on Power Systems, 2 (2013) 709- 717.

[76] M. Giuntoli and D. Poli, Optimized Thermal and Electrical Scheduling of a Large Scale Virtual Power Plant in the Presence of Energy Storages IEEE Transactions on Smart grid, 2 (2013) 942-955.

[77] Y. Levron, J. M. Guerrero, and Y. Beck, Optimal Power Flow in Microgrids With Energy Storage IEEE Transactions on Power Systems, 3 (2013) 3226-3234.

[78] M. Ghofrani, A. Arabali, M. Etezadi-Amoli, and M. S. Fadali, Energy Storage Application for Performance Enhancement of Wind Integration IEEE Transactions on Power Systems, 4 (2013) 4803 -4811.

[79] K. Zhang, C. Mao, J. Lu, D. Wang, X. Chen, and J. Zhang, Optimal control of state-of-charge of superconducting magnetic energy storage for wind power system IET Renewable Power Generation, 1 (2014) 58–66.

[80] Z. Shu, and P. Jirutitijaroen, Optimal Operation Strategy of Energy Storage System for Grid-Connected Wind Power Plants IEEE Transactions on sustainable Energy, 1 (2014) 190 -199.

[81] Y. Zheng, , Z. Y. Dong, F. J. Luo, K. Meng, , J. Qiu and K. P. Wong Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations IEEE Transactions on Power Systems, 1 (2014) 212 -220.

[82] F. D. Kanellos, Optimal Power Management With GHG Emissions Limitation in All-Electric Ship Power Systems Comprising Energy Storage Systems IEEE Transactions on Power Systems, 1 (2014) 330 -339.

[83] J. Wu, B. Zhang, Hang Li, Z. Li and X. Miao, Statistical distribution for wind power forecast error and its application to determine optimal size of energy storage system International Journal of Electric Power and Energy systems, 2 (2014) 100–107.

[84] B. B. Firouzi , and R. A. Abarghooee, Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm International Journal of Electric Power and Energy systems, 56 (2014) 42–54.

[85] S. Mohammadi and A. Mohammadi, Stochastic scenario-based model and investigating size of battery energy storage and thermal energy storage for micro-grid International Journal of Electric Power and Energy systems, 61 (2014) 531–546.

[86] S. F. Mekhamer, A. Y. Abdelaziz, M.A.L. Badr, and M. A. Algabalawy “Hybrid Power Generation Systems:A Holistic View” International Electrical Engineering Journal (IEEJ), 6 (2015) 1905-1912.

[87] Z. Luo , R. Kumar , J. Sottile, and J. C. Y. An MILP formulation for load-side demand control Electric Power Components and Systems, 9 (1998) 935-949.

[88] M. H. Nehrir and B.J. LaMeres, A multiple-block fuzzy logic-based electric water heater demand-side management strategy for leveling distribution feeder demand profile Electric Power System Research, 3 (2000) 225-230.

[89] T. Lee, M. Cho, Y. Hsiao, P. Chao, and F. Fang, Optimization and Implementation of a Load Control Scheduler Using Relaxed Dynamic



Vol. 6 (2015) No.9, pp. 2010-2024

ISSN 2078-2365



Programming for Large Air Conditioner Loads IEEE Transactions on Power Systems, 2 (2008) 691 -702.

[90] N. Ruiz, I. Cobelo, and J. Oyarzabal, A Direct Load Control Model for Virtual Power Plant Management IEEE Transactions on Power Systems, 2 (2009) 959-966.

[91] A. H. Mohsenian-Rad, and A. L. Garcia, Optimal Residential Load Control with Price Prediction in Real-Time Electricity Pricing Environments IEEE Transactions Smart grid, 2 (2010) 120 -133.

[92] L. Goel, Q. Wu, P. Wang, Fuzzy logic-based direct load control of air conditioning loads considering nodal reliability characteristics in restructured power systems Electric Power System Research, 1 (2010) 98-107.

[93] Q. Wu, P. Wang, and L. Goel, Direct Load Control (DLC) Considering Nodal Interrupted Energy Assessment Rate (NIEAR) in Restructured Power Systems IEEE Transactions on Power Systems, 3 (2010) 1449 -1456.

[94] N. Gatsis, and G. B. Giannakis, Residential Load Control: Distributed Scheduling and Convergence with Lost AMI Messages IEEE Transactions Smart grid, 2 (2012) 770 -786.

[95] P. S. Martin, G. Sanchez, and G. M. España, Direct Load Control Decision Model for Aggregated EV Charging Points IEEE Transactions on Power Systems, 3 (2012) 1577 -1584.

[96] G. Niroa, D. Salles, M. V. P. Alcântarab, L. C. P. da Silva, Large-scale control of domestic refrigerators for demand peak reduction in distribution systems Electric Power System Research, 1 (2013) 34 -42.

[97] A. Croft, J. Boys, G. Covic, and A. Downward, Benchmarking Optimal Utilization of Residential Distributed Generation with Load Control International Conference on Renewable Energy Research and Applications, Madrid, Spain, (2013).

[98] C. Zhao, U. Topcu, and S. H. Low, Optimal Load Control via Frequency Measurement and Neighborhood Area Communication IEEE Transactions on Power Systems, 4 (2013) 3576 -3587.

[99] M. T. Bina, D. A. Aggregate domestic demand modeling for the next day direct load control applications IET Generation, Transmission, Distribution 7 (2014) 1306–1317.

[100] W. Kong, T. Chai, J. Ding, and S. Yang, Multifurnace Optimization in Electric Smelting Plants by Load Scheduling and Control IEEE Transactions on Automation science and engineering, 3 (2014) 850 -862.

[101] L. Chena, X. Xub, L. Yaob and Q. Xu, Study of a Distribution Line Overload Control Strategy Considering the Demand Response Electric Power Components and Systems, 9 (2014) 935-949.


Documents

A Review of Virtual power plant Definitions, … Review of Virtual power plant Definitions, Components, Framework and Optimization Abstract- This paper presents a comprehensive survey