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DistributedSmall-ScalePowerPlantsforDistrictEnergyApplication:DesignandImplementationofCaseStudyinBrazil.CONFERENCEPAPERAUGUST2014

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13th International Conference on Sustainable Energy technologies (SET2014) 25-28th August, 2014

Geneva Paper ID: SET2014-E10086

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Distributed Small-Scale Power Plants for District Energy Application: Design and Implementation of

Case Study in Brazil

Cesare Quintero Pica1, *, Marcos Aurlio Izumida Martins1, Felipe Pereira Cassias2, Luciano Gentilini1, Victor Maryama1, and Daniel Gomes Makohin1

1 CERTI Foundation, UFSC Campus, Sector C Trindade, Florianpolis SC Brazil, ZIP Code 88040-970

2 Celesc S.A., Itamarati Avenue - Itacorubi, Florianpolis SC - Brazil, ZIP Code 88034-495

*Corresponding email: [email protected]

ABSTRACT

This paper aims to present the design and implementation of technical solutions for the deployment of distributed power plants as

a short-term viable approach for district energy applications. A distributed power plant can be considered an integrated system

comprised of: (i) one or more small-scale energy resources (such as photovoltaic panels, microturbines and wind generators),

allowing flexibility of power generation and increased power quality; (ii) energy storage systems; (iii) controllable local loads; (iv)

communication technologies and (iv) control and energy management systems. Distributed power plants are based on the concept

of microgrids.

Specifically, this paper presents a case study of a small-scale distributed power plant, developed by the authors in Brazil. It consists

of a 13.9kW pilot power plant, comprising: 5.7kW photovoltaic system, 2.6kW wind turbine, 5.5kW diesel generator and 10kWh of

batteries, establishing a test bed where the concept of distributed power plant could be evaluated from the utilitiess point of view. Simulation and preliminary experimental results are present in this paper, including the operation of the pilot plant in both grid-

connected and island mode, and the implementation of the energy management and demand response algorithms.

KEYWORDS: Microgrids, Distributted Generation, Energy Management, Energy Storage, Demand Response

1 INTRODUCTION

As the demand for energy rises in Brazil, as well as in the rest of the world, renewable energy resources become more common in

everyday life. These power sources are mostly scattered throughout cities and properties, usually near to the loads they feeds, thus

creating a new concept called Distributed Energy Resources (DERs): small units of generation distributed in the energy grid, feeding

nearby loads in order to increase the systems energy efficiency. By using alternative ways of energy generation such as wind and photovoltaic power, developed countries have been fighting against the regular increase of power demand over the years, but this fight

if not restricted to these countries. In Brazil, net metering was made possible in 2012 [1][2] as a response to a power demand growth

rate of 3.3% per year as of 2012-2013 and the rise in generation by combustion [3].

Although the penetration of these DERs are crucial to mitigate pollution due to fossil and non-fossil fuel based power plants, its

sudden increase in generation share may cause critical problems as:

Energy quality problems (low power factor, high levels of harmonic distortion);

Safety issues regarding distributor maintenance teams;

Difficulty in reaching power balance, as the DERs are not under control of the power utilities and some are not even dispatchable.

The problems described above are not taken seriously sometimes because some argue that, for low power consumers THD, power

factor and quality issues are not very important. However, according to the data, residential and consumer consumption totals 197

TWh, which means that a 1% increase in grid efficiency is equivalent to a large amount of energy.

In order to help maintaining order within the energy grid, the deployment of Distributed Power Plants (DPPs) is proposed. The DPPs

are based on the Microgrids and Virtual Power Plants concepts, possessing elements as such as: (i) one or more small-scale energy

resources (such as photovoltaic panels, microturbines and wind generators), allowing flexibility of power generation and increased

power quality; (ii) energy storage systems; (iii) controllable local loads; (iv) communication technologies and (iv) control and energy

management systems. The combination of all these elements permits a flexible operation that relies on information exchange between

DPP and utility to give the utility access to different services like Automated Demand Response (ADR), Ancillary Services and

Metering Data Provision, rewarding the consumer in return.

This paper describes the concept and implementation of a DPP in south Brazil considering the countrys limitations regarding equipment availability, laws, energy market and generation potential. Its creation has the objective to serve as test bed for new services

and studies in topics like DERs, Microgrids and energy management in order to help utilities understand the impacts, benefits and

constraints respecting the presence of active element in the distribution grid.

Paper ID: SET2014-E10086

By Cesare Quintero Pica, Marcos Martins, Felipe Cassias, Luciano Gentilini, Victor Maryama and Daniel Makohin

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2 THE DISTRICT DISTRIBUTED POWER PLANT

In order to help the power utility to understand the impacts of active elements in its grid, the test bed needs to have as many elements

as possible present in the smart grid concept. That is why the model chosen for the creation of the District Distributed Power Plant

(DDPP) is a mix of Microgrid and Virtual Power Plant.

By definition, microgrids are a small-scale power grid where generation and consumption are packed together [4][5][6] in the presence

of a control system to maintain voltage, frequency and power factor levels as well as regulate the power flow , the Figure 1 shows a

rsum of this system. The power upper-limit for a microgrid may vary and there is no rule regarding this aspect. In [5], microgrid

examples of Kythnos (17 of kW generation + 85 kWh of storage), Netherlands (315 kW of generation), Mannheim-Wallstadt (30 kW

of generation) and CERTS (~> 2 MW of peak power demand) show the lack of a fixed upper-limit for microgrids.

Virtual power plants, on the other hand, are clusters of DERs connected to the same power grid, operating together under the control

of a central system to optimize operation [7][8], as shown in Figure 2.

Figure 1 - Microgrid comprised of generation, load and control

Figure 2 - Virtual Power Plant scheme [7]

The DDPP configuration, Figure 3, has 13.8 kW of power generation capacity, divided under three different kinds of source: 5.7 kW

of photovoltaic power, 2.6 kW of wind power and 5.5 kW of diesel power. However, due to poor energy quality, lack of control

system and CO2 emission, the diesel generator is not able to connect while the DDPP is connected to the main grid, which makes the

diesel generator a back-up power source for islanded mode. Also, the system has a battery group capable of dispatching up to 10,6

kW of active power from its 10 kWh reserves.

The DDPP has five different controllable loads: 1.9 kW for lighting, 1.6 kW for refrigeration, 4.5 kW of resistive AC loads, as well

as 2.5 kW of resistive DC loads. All of these loads are monitored through a metering system capable of getting data as voltage, current,



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frequency, THD, power factor and energy consumed. By monitoring the loads, the central controller is able to adjust power flow,

optimize operational cost or protect the grid.

All of the power elements chosen for the project were distributed as a radial grid because this configuration is much simpler to operate

than loop topology. In this sense, the grid was projected with two busses: one 220 VAC (phase-neutral) bus and one 48 VCC bus. The

220 VAC bus connects itself to the main grid through a two state switch called ATS (automatic transference switch), which executes

the anti-islanding function to prevent lack of power when the main grid is offline at the same time it connects the diesel generator to

the DDPP although not turning it on automatically. Interconnecting the AC equipment, there is an instrumented switch box called

Multicluster Box 6, whose objective is to coordinate power signal synchronization between the DDPP alternate current bus and the

main grid/diesel generation. Furthermore, a group of Sunny Island (SI) inverters controls this coordination together with the power

flow between the DC and AC bus, as well as the photovoltaic inverter SETP5000tl. This whole operation allows the AC signal to stay

synchronized in case of island operation.

In the background, the DDPP has a control system running inside a SCADA to optimize its operation. The SCADA has three functions:

gather data and store it in a database, enable the interaction between plant and operator through a HMI and supervise the operation

with algorithms for emergency load-shedding and power flow control. The software used for the SCADA is called SCADABR, an

open source supervisory software developed in Brazil. As this choice has a limited number of communication drivers, the research

team provided the necessary Modbus or DNP3 in the equipments communication interface. The control system uses an Ethernet switch to interconnect all the DDPP elements. Ethernet gateways are used in cases where the

device has no support for Ethernet port. This solution is ideal for scale-up an integration, as Ethernet is a multiprotocol solution for

information flow.

Figure 3 - District Distributed Power Plant

Figure 4 - Wind and Photovoltaic generation of the DDPP



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Besides the generation and loads, there is a full list of the elements used to build the DDPP. The Table 1 shows a list of the elements needed to build the whole system.

Table 1 - DDPP Elements

Equipments Name Function

SMA Sunny Island 6.0H (3) Interface between DC and AC busses, coordination of the multicluster box for switching purposes. Each inverter correspond to one AC phase.

SMA STP5000tl (1) Photovoltaic inverter for the solar panels.

SMA BatFuse B.03 (1) Protection fuses between DC bus and the SI inverters to avoid overcurrent.

SMA GenMan (1) A device used by the SI inverters to turn on the diesel generator.

SMA Sunny WebBox (1) Modbus/TCP interface for the SMA equipment.

SMA SensorBox (1) A weather station located near the photovoltaic panels to help the PV inverter to better control the PV generation

KVA Top One (1) Automatic transfer switch for the grid access point.

Switch Dell PowerConnect 2816 (1) Ethernet Switch for communication purposes.

Conversor Schneider Electric EGX100 (1) Ethernet gateway for devices, which operate under Modbus/RTU through RS485.

Medidor de Qualidade de Energia ION8650C (1) Metering device for energy quality monitoring. Operates with Modbus/TPC.

Protection and automation panel (1) A group of devices needed to interconnect the whole system. Inside the panel, elements like circuit breakers, contactors, PLC, metering devices and relays are present.

HP Proliant Server (1) A Linux Cent OS based server used to run the SCADABR software.

No-Break (1) No-break used to hold essential loads (from the automation system) in case of grid failure.

Operational Modes of the DDPP

As an active player of the power system, the DDPP can operate under many modes, in both connected and islanded mode. This section describes the many operational modes of the DDPP.

Mode 1 connected to the main grid with generation surplus: in this operational mode, the DDPP is connected to the utility grid and is renewable generation more power than its needed. In this case, it deliveries passively the unnecessary amount to the main grid and get a compensation through the net metering system. In addition, in this operation, the battery does not dispatch any power.

Figure 5 - DDPP mode 1, exporting energy (red arrows as the active power flow)

Mode 2 connected to the main grid with insufficient generation: this operation is very similar to the first mode, but here, the

renewable generation is not enough to power the loads active in the systems. Thus, there is a need for extra power taken from the grid. When the battery recharges under connected mode, the operation falls under this mode, as the battery, acts as a load.



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Figure 6 DDPP mode 2, importing energy

Mode 3 islanded operation without diesel generation: when there is a fault in the main grid, or the energy quality in this grid is

not good enough, an islanding operation takes place. This operation starts with the ATS switching between the main grid and the diesel generator (without turning it on automatically), and then, as a cascade effect, the Multicluster switches off the external bus. After these two actions, the DDPP starts to act alone and the SI inverters become the power signal reference for the renewable sources. The islanding operation is deemed critical because sometimes, the renewable energy will not be enough to power the loads in the system. In this case, there are two possible actions, depending on the operators choice, for the central controller to take: battery dispatch or load shedding. If the operator chooses to power all the loads even at island operation, the battery will be dispatched to match the power needed to feed the loads, but if he chooses to prioritize some loads or if the battery power cannot feed the loads, the load shedding system gives an order to turn some loads off.

Figure 7 DDPP mode 3, islanded operation with load shedding

Mode 4 islanded operation with diesel generation: when the DDPP is under islanded mode and the battery is near minimum

discharge point, the central controller triggers an order to turn the diesel generator on to recharge the battery or simply feed the high priority loads, also depending on the strategy the operator chooses. There is a transient time between mode 3 and mode 4, because the diesel generator needs to achieve its steady state. When the generators power signal is stable, the SI inverters together with the multicluster start to synchronize the generator and the internal buss AC signal until both signals are matched and the switch is turned on. Under this mode, the diesel generator gives the reference AC signal to the inverters, just like in grid-connected operation.



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Figure 8 DDPP mode 4, islanded operation with diesel generator online

Mode 5 grid resynchronization: when the DDPP is operating under islanded mode, and the main grid is reestablished, the central controller starts to resynchronize its grid to the main one. This action occurs differently according to the diesel generators state. If the generator is offline, it means that both the ATS and the Multicluster are switched off, thus, the ATS simply switches back to the main grid and the SI inverters perform a synchronization function as they do at Mode 4. If the diesel generator is online, the operation gets more complex, as this kind of generator and ATS (for small-scale generation purposes) have no synchronization function embedded. Because of this lack of functionality, when the islanded mode includes diesel generation, the following procedures must be followed in order to reestablish a connection to the main grid:

The central controller acknowledges the return of the main grid through the ATS; The central controller orders the diesel generator to stop and dispatch the battery to match the loads (or shed the excessive

loads). This cause the Multicluster to switch off again;

The ATS is ordered to switch to the main grid, allowing a power signal to achieve the Multicluster; The SI inverters and Multicluster proceed to synchronize and reconnect.

Figure 9 DDPP mode 5, recharging battery after islanded operation

Mode 6 connected to the main grid and attending to a demand response signal: this operational mode is very similar to the Mode

1 and 2, as it is a connected mode with a controlled power flow to match the needs of the utility according to the ADR contract. More details regarding the ADR system are present in section 3.

Load Shedding System

To help the operation run smoothly, the central controller has a load-shedding algorithm embedded in order to pre-set an external controller Programmable Logic Controller (PLC), as in [9], to discard loads in case of need, or send an order to shut down a load to maintain the power flow in ADR mode.

For the pre-set load shedding action, the system reads the available generation and demand of the DDPP, compares both of them and choses which loads should be turned off in case of an islanding operation. Through the comparison the algorithm searches, from the lowest priority load to the highest priority one, which of the loads must be turned off, and then it looks for lower priority that can be maintained with the power surplus the shedding brings. This avoids excessive shedding in cases where lower priority loads have lower demands, thus maintaining the highest number of loads online as possible.



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3 AUTOMATED DEMAND RESPONSE SERVICE

As an active system, the DDPP is able to interact with the utility, adding value to the operation. One of the services the DDPP can offer is the Automated Demand Response (ADR). The ADR is an interface where the utility has access to the amount of power a user is willing to relieve/generate in exchange for a reward. This kind of operation falls under the services known as ancillary service [10].

The ADR system works as an optimization problem where the central controller has to calculate the optimal price the utility must pay for each demand response scenario. The central controller calculates an optimal price for each demand response scenario (e.g. 1 kW, 2 kW, etc.) based on the Generation and Load-shedding cost, as well as the Battery Cost. For the DDPP demand response system, the equation (1) is must be optimized for the lowest possible operational cost. For this equation, FC(P) express the operational cost in function of the generation cost, the load-shedding cost, the utilitys power cost and the battery cost. In this case, the PUtility element represents cost of the power flow between DDPP and Utility. For each ADR scenario calculated, its value changes according to the amount of power the utility should request.

() = (() + )

=

=1

+(() + )

=

=1

() ()

(1)

The optimization problem has some restrictions for the equation in order for it to respect the physical constraints of the electrical operation, the generation limits, the maximum and minimum battery charge and dispatchable power are all constraints for the optimization model. This function is optimized periodically, giving the best prices for a DR signal from the utility for each scenario. In addition to the constraints, the problem also uses a payback method to establish generation costs (as used for most renewable power sources [11]), a battery utilization cost based on its life time relation to the discharge as well as an estimative for the value of each load.

Although the optimization problem already uses generation data and forecast (based on past weather and consumption data), the power generation and demand changes as the time passes. Because of this, a power flow control must run in a narrower time window when compared to the optimization period. The equation (2) represents the basic equation for the power flow control, where the total sum of active power from the loads (P_Load), generators (P_Gen) and Utility (PUtility) must be zero. For this algorithm, the utility power is constant in order to fulfill the DR contract between player and utility. It is comprised of the power before the ADR signal plus the amount contracted by the ADR system, in addition to a hysteresis amount of 10% for power fluctuations between the algorithm iterations, as shown in [12].

= _

=0

+ _

=0

+ = 0 (2)

As the sum for the equation must be zero, and the utility power is fixed, the controller must adjust generation and consumption to match the needed active power. In the DDPP system, the only dispatchable power available comes from the battery, as the wind and photovoltaic sources are intermittent. The loads are also treated as dispatchable, but they operate under full or empty capacity. That is why the first parameter adjusted by the controller is the battery active power, to avoid turning the loads off. After the system dispatches the battery, it starts to evaluate the need for load shedding, and if it necessary, they are switched off until the DDPP can provide the contracted active power.

4 GENERATION SIMULATION

The DDPP generation was also simulated by the research team to foresee how the DDPP generation would behave itself. The simulation uses a simulation software developed by the team at CERTI Foundations center for sustainable energy. The software has models corresponding to the equipment in Table 2. (i) the wind and photovoltaic conversion were based on manufacturers information and (ii) the Sunny Island battery manager was modeled to control the diesel generator based on battery level turned on when the battery is discharged and turned off when the battery is charged.

Equipment Model Nominal Capacity

Diesel Generator Toyama TD7000SGE3 5,5 kVA

Batteries Heliar Freedom DF4001 240 Ah

Photovoltaic Panels Kyocera KD240GX-LFB 5,76 kW

Wind Generator Skystream Marine 2,4 kW

DDPP Manager Sunny Island 6.0H (3x) 18 kW Table 2 - Modeled equipment

Demand and Renewable Resources

The demand profile used to represent the DDPP power demand is a typical commercial load profile during the week as seen at Figure 10. The data used for wind and photovoltaic generation was gathered at a weather station during a year at the site where the



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DDPP is installed. The team has corrected the wind speed profile to match a 20 m high point, as the station only 3 m high. The Wind speed and solar irradiation are visible in Figure 11 and Figure 12.

Figure 10- Commercial demand through a week

Figure 11 Wind speed profile (in a week)

Figure 12 Solar irradiation profile (for a week)

By using the teams weather station and the models programmed in the software, the total energy output for the photovoltaic and

wind generation was 7.8 MWh and 0,59 MWh, respectively. In terms of power, the peak and average power output were 5.73 kW and 0.89 kW for the photovoltaic generation, as well as 2.42 kW and 0.067 kW for the wind generation.

Grid Connection

The DDPP microgrid is a system capable of operation under many scenarios, depending on weather, load profile and utilitys grid connection as energy supply. In this sense, some important scenarios were chosen for the simulation.

In the first scenario, there is an operation with the DDPP connected to the main grid. In this case, as the main grid is an unlimited supply, the diesel generator and the batteries are not necessary. Consequently, the batteries are only maintained at full charge for emergency purposes and are not dispatched. For this reason, the batteries and the diesel generator have no active role in the connected mode.

The Figure 13 shows the photovoltaic and wind generation together with the demand, as well as the power flow between the main grid and the DDPP, which amounts to in 19 MWh of energy consumption, from the utility and 1.53 MWh of exported energy. This scenario also results in several conclusions:

The wind power potential for this DDPP is too small;



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On working days the consumption is far greater than the renewable generation; On weekends the DDPP tends to export energy.

Also, for different load scenarios, the energy consumption and exportation are respectively 7.77 MWh and 2.23 MWh, for 5 kW of peak load, and 31.58 MWh and 1.14 MWh for 20 kW of peak load.

Figure 13- Connected mode operation for 10 kW of loads

Load of 5 kW at Islanded Mode

In this scenario, the main grid is offline, for reasons unknown, and the DDPP microgrid must be supplied by its own generation and storage. As the fuel is limited, the diesel generation usage is limited to the cases where the battery is near empty and the loads must operate. Also, because of the lack of a utility grid, there is no power exchange in cases of excessive generation/consumption, which leads the DDPP to activate the diesel generation or limit the renewable generation through power electronics.

The SI inverters together with the central controller control the diesel generation dispatch according to the battery charge level. When the charge level of the battery reaches a pre-established level, the control system activates the diesel generation, unless a load shedding strategy is running. The diesel generation then works until the battery is fully charged. For this control strategy, the boundaries for charge and discharge are 90 % and 60 % of the battery level to avoid deep discharges, which compromises the batteries, as well as to allow some energy absorption when there is excessive renewable generation.

Figure 14 - Islanded operation for 5 kW of load



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The Figure 14 shows the scenery where the DDPP is operating under islanded mode for a peak load of 5 kW. The results show that the generatior must operate to charge the batteries and offer power to the loads at working time. It is also observable that the diesel generator is less used when the photovoltaic generation produces more energy. On the other hand, on weekends, there is a over frequency signal generated by the excess of power, which implies that the DDPP needs to limit the generation through power electronics or through load dumping.

5 CONCLUSIONS

This paper show that it is feasible to build a distributed power plant with commercially available equipment, even in countries with technology restriction. Based on microgrids and distributed power plant models outside of Brazil, the team could develop a grid topology able to join the aspects of both DPP and microgrids in a new concept called District Distributed Power Plant, capable of offering services to the utilities. In addition, this work will provide information to the utilities about the impacts of active players connected to the power grid.

As the DDPP is at its final stage of building, it was concluded that this kind of system is feasible from the technical point of view, but that there are still challenges to overcome when building DDPPs in Brazil. Firstly, there are only a few vendors for high-end power electronics, renewable generation and automation devices, and this situation rises the prices for these equipment types. Secondly, but as important, is the regulatory aspect regarding operation issues. Nowadays, the law forbids islanded operation, mainly because of safety issues. By building the proposed system, it might be possible to prove it is a wrong assumption to prohibit this operation mode.

Finally, this work presents simulations results that guided the DDPP implementation regarding generation issues. These results gave the research team a deep understanding of how the local weather would influence the DDPP operation. This new understanding which will impact the experimentation and implementation of the control strategies.

All of the work presented in this paper is the starting point for the experimentation in microgrid and distributed power plants in Brazil. The next steps regarding the District Distributed Power Plant include the validation of the many operational modes and their impact on the utility grid, the creation of an interface for ADR systems between the utility and player for both active and reactive power. This will allow the utility to use the DDPP to monitor and control its own busses through the power system in the near future.

ACKNOWLEDGMENT

This paper was produced under the title Usina Distrital de Gerao Distribuda de Energia Renovvel (Renewable Energy based District Distributed Power Plant) project, executed by the Center for Sustainable Energy from CERTI Foundation and funded by the energy utility Ceslesc S.A through the P&D ANEEL fund for research and development regarding energy issues.

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