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IET Generation, Transmission & Distribution Special Issue on Power Electronic Converter Systems for Integration of Renewable Energy Sources

Control of virtual power plant in microgrids: a coordinated approach based on photovoltaic systems and controllable loads

ISSN 1751-8687 Received on 30th May 2014 Revised on 30th April 2015 Accepted on 1st May 2015 doi: 10.1049/iet-gtd.2015.0392 www.ietdl.org

Yun Liu, Huanhai Xin ✉, Zhen Wang, Deqiang Gan Department of the Electrical Engineering, Zhejiang University, Hangzhou 310027, People’s Republic of China ✉ E-mail: [email protected]

Abstract: Renewable energy sources (RESs) such as solar energy are cost-effective to meet part of the energy needs. However, the inherent fluctuation and intermittence of RESs may deteriorate the stability and security of power grids. Energy storage systems can mitigate the problem, but they are very expensive. For this reason, a coordinated control method of virtual power plant (VPP), which includes photovoltaic systems (PVs) and controllable loads, is proposed in this study so that the aggregated power output of the VPP can be flexibly adjusted in a wide range. To achieve this, power output of the PVs and operational modes of controllable loads are coordinated by solving a mixed integer programming (MIP) problem. Meanwhile, with a quadratic interpolation based active power control strategy, each PV can operate in a power dispatch mode and simultaneously estimate its maximum available power, which is an input to the MIP problem. Externally, the VPP can quickly adjust the aggregated power and achieve functions important to power systems with high penetration of distributed energy resources, such as primary frequency regulation. Simulation results validate the effectiveness of VPP in providing frequency support to an island microgrid.

1

Introduction

Solar energy is a pollution-free and renewable energy source, which can effectively alleviate the pressures on power industries caused by energy crisis and environmental issues. In recent years, photovoltaic systems (PVs) are rapidly developing worldwide. It is reported by the European Photovoltaic Industry Association that the world’s cumulative PV capacity is over 138.9 GW by the end of 2013, which can save as many as 73 million tons of CO2 each year [1]. However, since the grid-connected PV capacity is not uniformly distributed, some regions in the world, for example, Germany, are already experiencing a very high local PV penetration of more than 200 kWp/km2 [2]. Considering that the cost of installation and maintenance of PVs is still relatively high and the ‘fuel’ is free [3], PVs nowadays mostly operate in maximum power point tracking (MPPT) mode to maximise the income. In such a mode, because power output of PVs directly depends on the operational conditions (e.g. solar irradiance and temperature), PVs cannot regulate their power output to provide ancillary services (e.g. peak shaving and frequency regulation) to the power grid. Therefore synchronous generators should retain their capacity to perform the functions and thus cannot be retired [4, 5], which is not cost-effective. Moreover, as the penetration of distributed energy resources (DERs) increases (especially in microgrids), several impacts on the operation of the power system will appear: (1) The power output of PVs reaches the peak at noon, which coincides with the off-peak loading period and leads to power imbalance in the electric network [6]; (2) The variability and unpredictability of power output of DERs can lead to unstable operation of electric networks, such as voltage variation and frequency fluctuation [7]; (3) The direction of power flow in the distribution/transmission networks might be changed and even lead to power congestion. These impacts restrain the development of DERs, for example, many large-scaled wind farms and PV stations are curtailed because the power cannot be properly accommodated. Nowadays, energy storage systems (ESSs) are most commonly used to address the above problem, for example, offering spinning reserve, providing frequency regulation and contributing to power

IET Gener. Transm. Distrib., 2015, Vol. 9, Iss. 10, pp. 921–928 & The Institution of Engineering and Technology 2015

balance in microgrids. However, the cost of the device and its maintenance is still relatively high, especially when the required capacity is large [8]. In [9], within the framework of frequency regulation, three major approaches to reduce fluctuation of power output of PVs are investigated in terms of their economic effect: (1) installing and operating an ESS while keeping the PVs in the MPPT mode (see [10, 11], and the references therein), (2) installing and operating a dump load bank to absorb surplus energy [12] and (3) operating a PV in a power dispatch mode by some deliberate power curtailment strategies. The conclusion is that, within a normal power fluctuation range, the power curtailment and dump load approaches can be more economical than installing ESSs. This motivates our previous research [13], which proposes a power dispatch strategy for frequency regulation by direct PV power curtailment. In this method, each PV is deloaded to some extent so that it can regulate its power upward or downward. However, a compromise has to be made between the frequency regulation capability and economic loss. The concept of virtual power plant (VPP) is first proposed in [5], which aggregates multiple DERs and can be viewed as a single entity in the power market. VPP can improve the visibility and controllability of DERs to system operator, which meets the requirement of future smart grid. In [14, 15], the concept of VPP is extended to demand response, which is used to provide ancillary services, such as frequency reserve. However, to the best of our knowledge, VPP composed of both DERs and demand-side devices has never been studied in the literature. In this paper, a centralised control method of VPP comprising multiple PVs and controllable loads is proposed. The power output of the PVs and power consumption of the controllable loads is coordinated by solving a mixed integer programming (MIP) problem, so that the aggregated power output of the VPP can be flexibly adjusted in a wide range. Controllable loads have fast response to the control signal and can achieve step change of the power consumption, which is beneficial to bridge the power gap between supply and demand instantaneously after disturbance. PVs, adapting a method similar to that described in [16], can continuously change the power output of the VPP and attain precise track of the reference power output. When the capacity of

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ESSs is not enough, VPPs can provide the system with additional power regulation capability, which facilitates the accommodation of DERs with a higher penetration level [17]. In comparison with frequency regulation by direct PV power curtailment [13], the proposed VPP design decreases the solar power to be curtailed, has an extended power regulation range, and thus can provide better ancillary services required for system support and security activities, for example, primary frequency regulation. This paper is organised as follows: Section 2 presents the main tasks of the paper. In Section 3, the coordination of PVs and controllable loads is formulated as an optimisation problem, and to solve the problem, the quadratic interpolation based PV power control strategy is redesigned so that the maximum available power can be simultaneously estimated when a PV operates in the power dispatch mode. Then, the proposed VPP is used to provide primary frequency regulation to the power grid. Simulation results and conclusions are presented in Sections 4 and 5, respectively.

2

Problem formulation

The VPP is fulfilled by the coordination of PVs and controllable loads (in this paper, ice machines are used as an example because of their wide application in island microgrid and heat storage property). By flexibly adjusting the power output of the PVs and power consumption of the controllable loads, the aggregated power output of the VPP can track a given reference value. Assume that a VPP is composed of n PVs and N ice machines (in the rest of the paper, the terms controllable load and ice machine might be used interchangeably). Suppose that a controllable load has three different operational modes, that is, power-off, slow ice-making and fast ice-making modes, whose corresponding power consumptions are Poff, Pslow and Pfast, respectively. So as to meet the requirement of ice production and system maintenance etc., certain number of controllable loads is designated to operate in specific mode. The rest of the controllable loads, which can be dispatched to operate in any mode to adjust the aggregated power output of the VPP, are called flexible loads. The average lifespan of the flexible loads can be substantially reduced because of frequent mode changes, hence the customers should be compensated for providing ancillary services. Harbo and Biegel [16] study the incentives to encourage consumers to participate in demand response, and suggest two ways of compensation, that is, a flat rate compensation comes in the form of annual payment or electricity discount; while a flex rate compensation depends on the frequency a flexible load is activated. It should be noted that the aggregated power of the VPP Pvpp can be continuously adjusted if and only if the aggregated available power of the PVs is greater than the step power change of a single controllable load. Therefore considering that the aggregated maximum available power of PVs is proportional to the capacity of PV arrays, a higher PV capacity increases the probability that the power output of the VPP can be continuously adjusted. The main tasks of this paper are twofold Task 1: Coordinate the power output of the PVs and power consumption of the ice machines by curtailing certain amount of PV power and adjusting the number of controllable loads operating in the three modes so that PVPP can track its reference ref , while the economic benefit is also considered. power PVPP Generally, there are more than one scheme satisfying the above requirement. For example, PVPP could be equal if both the power output of the PVs and power consumption of the controllable loads are high or vice versa. However, to maximise the economic benefit (e.g. generating more ice cubes for sale), it is preferred that more flexible loads operate in slow or fast ice-making mode, so that the curtailed power of PVs is minimised. Moreover, when PVPP cannot be continuously adjusted because of a low solar ref as irradiance level, it should converge to a value as close to PVPP possible. The key to solve this problem, as will be illustrated in detail in the next section, is

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Fig. 1 P-V characteristic curve of the ith PV

Task 2: The power output of a PV can be adjusted by changing the terminal voltage of the PV array. A PV power control strategy is to be designed so that the power output of the ith PV PPVi (∀i ∈ [1,…, n]) ref , which is derived by can rapidly track its reference power PPVi solving Task 1. In the meantime, the PV should simultaneously MPP with satisfactory estimate its maximum available power PPVi precision, which is to be used as a variable in the constraint of Task 1. For the ith PV array, its power output PPVi is a function of the terminal voltage of the PV array UPVi, which can be expressed as [18] PPVi = PV(UPVi , b)

(1)

where β denotes a vector of parameters such as the solar irradiance and temperature. The general shape of the P-V characteristic curve is shown in Fig. 1. However, since it is difficult to measure β, and the expression of (1) is only approximate, it is impossible to precisely acquire the P-V characteristic curve in real time. Consequently, the only solution is to iteratively estimate UPVi ref 1 2 corresponding to PPVi (denoted as UPVi or UPVi in Fig. 1), for example, using the revised perturb and observe method or the quadratic interpolation based method [19]. Based on the latter, an improved quadratic interpolation based PV power control method is to be proposed to solve task 2.

3 3.1

Coordinated control design of the VPP Optimal power dispatch of the VPP

ref Given the aggregated reference power PVPP and the maximum MPP (∀i ∈ [1, …, n]) as input signals, the available power of PVs PPVi optimal power dispatch aims to acquire the reference power of ref and the numbers of ice machines operating in each each PV PPVi ref as possible. It mode, so that PVPP can converge as close to PVPP can be summarised as the MIP problem below

max R =

n

ref ref PPVi − P · PVPP − PVPP

(2)

i=1

s.t. PVPP =

n

ref PPVi − noff Poff − nslow Pslow − nfast Pfast

(3)

i=1 ref MPP 0 ≤ PPVi ≤ Pesti

i = 1, . . . , n

(4)

ref MPP ref MPP a∗ = PPV1 /Pest1 = · · · = PPVn /Pestn

(5)

noff + nslow + nfast = N

(6)

(nfast − nfast0 )(noff − noff 0 ) = 0

(7)

noff ≥ noff0 ,

nslow ≥ nslow0 , and nfast ≥ nfast0

(8)


Fig. 2 Enumeration method

where P is a sufficiently large penalty factor (P = 100 in the MPP MPP (∀i [ [1, . . . , n]) is the estimate of PPVi , which simulations). Pesti is to be illustrated in the next section. ref ref T is the decision vector. x = [PPV 1 , . . . , PPV n , noff , nslow , nfast ] noff [ N, nslow [ N, and nfast [ N denote the numbers of controllable loads operating in power-off, slow and fast ice-making modes, respectively. noff0 [ N, nslow0 [ N, and nfast0 [ N denote the minimum numbers of controllable loads that are ref is determined with required to operate in the three modes. PVPP centralised or decentralised schemes [20] to provide necessary ancillary services, for example, primary frequency regulation. The objective function (2) is to optimise the revenue by maximising the power output of the PVs. The penalty term is to ref when it can be guarantee that PVPP should precisely track PVPP continuously adjusted; otherwise, it should converge as close to ref ref as possible, that is, PVPP − PVPP should be minimised. PVPP Constraint (3) means that PVPP equals the total power output of the PVs minus the total power consumption of the ice machines. Constraint (4) indicates that the reference power of each PV should be less than or equal to its maximum available power. Constraint (5) means that each PV operates in the same utilisation MPP , which results in an equal share of profit ratio a∗ = PPVi /Pesti among PV owners and a same rate of wear-and-tear. Constraint (6) indicates that the numbers of ice machines operating in the three modes sum up to N. Constraint (7) indicates that flexible loads can be turned to fast ice-making mode only when no flexible load is in power-off mode. ref is The feasible range of PVPP min ref max ≤ PVPP ≤ PVPP PVPP

where

(9)

min PVPP = −noff0 Poff − nslow0 Pslow − (N − noff0 − nslow0 )Pfast

and max = PVPP

n

MPP PPVi − (N − nslow0 − nslow0 )Poff − nslow0 Pslow − nfast0 Pfast

i=1


This feasible range varies under different solar irradiance. Specifically, in the daytime when solar irradiance is abundant, PVPP can be adjusted continuously in a wide range; while at night, PVPP can only be adjusted discretely in a narrow range. However, the frequency fluctuation generally happens in the daytime because of power fluctuation of DERs and load switching etc.; while at night, the general load curve is smoother. Therefore in general, more primary reserve is required in the daytime, while less primary reserve is required at night, which is in accordance with the primary reserve the proposed scheme can provide. Since there are only three integer decision variables, as long as MPP can be properly estimated, the MIP problem can be solved PPVi by Fig 2, which enumerates every possible combination of operational modes of the ice machines. Specifically, the outer loop enumerates different number of flexible loads in slow ice-making mode; in the inner loop, all the rest of the flexible loads are dispatched to either power-off mode or fast ice-making mode because of constraint (7). Therefore the maximum number of iteration is ITRmax = 2(N − noff0 − nslow0 − nfast0 ) The objective function value is calculated in every iteration, and the maximum one corresponds to the optimal combination of PV power outputs and operational modes of the ice machines. Fig 2 only determines the number of flexible loads operating in each mode. To determine the operational mode of a specific flexible load, first-in first-out method is adopted, which is frequently used for organising and manipulating a data buffer in computer science [21]. Specifically, each flexible load is assigned a time stamp. When the number of flexible loads in a certain mode decreases, the load with the maximum time value changes to some other mode, and its time value is reset to zero. In this way, the time interval between two consecutive mode changes in every flexible load is maximised, which relieves the possible electromechanical stress on it.

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Fig. 3 Quadratic interpolation method with the left solution cannot forecast the maximum available power

3.2 Quadratic interpolation based PV power control strategy

Fig. 4 Diagram of the improved Newton quadratic interpolation method after convergence

tolerable error, as depicted in Fig. 4. The value can be calculated as

Liu et al., [19] indicates that for the ith PV, given three sampling points on the P-V characteristic curve as (U1i, P1i), (U2i, P2i) and (U3i, P3i), the quadratic curve passing these points can be used to fit the P-V characteristic curve, which can be written as POUTi (Ui ) = ai Ui2 + bi Ui + ci

(10)

MPP = ci − Pesti

ref )=0 ai Ui2 + bi Ui + (ci − PPVi

1MPPi =

(11)

Owing to constraint (4), only the power dispatch mode of PVs is considered, which corresponds to two feasible operational points on the P-V characteristic curve, as illustrated in Fig. 1. For the left solution (located on the uphill section of the curve), besides the drawbacks mentioned in [19], the quadratic curve can only fit the uphill section of the P-V characteristic curve when the three points are monotonically increasing. Therefore the maximum available power cannot be estimated, as illustrated in Fig. 3. For this reason, the right solution (located on the downhill section of the curve) is selected as the reference terminal voltage, which can be expressed as ref UPVi

=

bi +

ref b2i − 4ai (ci − PPVi ) 2 ai

(12)

(13)

Define the error rate of estimate as

where (see equation at the bottom of the page) ref , a quadratic Equating POUTi (Ui) with the reference power PPVi equation can be acquired as

b2i 4ai

MPP MPP PPVi − Pesti × 100% MPP PPVi

Fig. 5 shows the error rate of estimate under different utilisation ratio after convergence when the solar irradiance level is 1000 W/m2 and the temperature is 25°C. It indicates that: (i) the error rate reaches its peak of 8% when the utilisation ratio is about 0.4, which is precise enough; (ii) the error rate is always positive, thus the estimate is always conservative. Note that a conservative estimate is always ref MPP may take a value greater than PPVi , preferred, otherwise, PPVi which is actually not attainable. 3.3

VPP for primary frequency regulation

In power systems, a synchronous generator participating in primary frequency regulation always operates in the frequency droop control mode, that is, its power output depends on system frequency fsys. Specifically, during the transient when secondary frequency control is not activated [24], a generator should decrease its power output below the nominal power Pnom when fsys is above the nominal value fnom, and vice versa.

The prototype of the proposed control strategy is based on Muller’s method in [22], its convergence property can be summarised into the following theorem. Theorem 1: According to the iteration rule stated in [19], the terminal voltage of the ith PV array converges to a value, whose ref . The order of convergence is corresponding power output is PPVi 1.84. Proof: please see [22, 23] for the details.

□

MPP can be estimated In the sequel, it is to be demonstrated that PPVi with the maximum value of POUTi (Ui) conservatively with a

Fig. 5 Relation between utilisation ratio and the error rate of estimate

P1i P2i P3i + + (U1i − U2i )(U1i − U3i ) (U2i − U1i )(U2i − U3i ) (U3i − U1i )(U3i − U2i ) P1i (U2i + U3i ) P2i (U1i + U3i ) P3i (U1i + U2i ) b=− + + (U1i − U2i )(U1i − U3i ) (U2i − U1i )(U2i − U3i ) (U3i − U1i )(U3i − U2i ) a=

c=

924

P1i U2i U3i P2i U1i U3i P3i U1i U2i + + (U1i − U2i )(U1i − U3i ) (U2i − U1i )(U2i − U3i ) (U3i − U1i )(U3i − U2i )


Fig. 6 Droop curve of the VPP under different solar irradiance level

It is beneficial that a VPP provides primary frequency regulation to contribute to frequency stability in the power grid. To this end, a droop control can also be designed for VPP so that PVPP changes according to frequency deviation. Since frequent mode change of a flexible load may decrease its lifespan, a frequency tolerance band Δtb is considered in the droop design so that PVPP remains unchanged as long as frequency is within normal range, as

ref PVPP

⎧ ⎨ Pnom − kd ( fmea − fnom + Dtb ) = Pnom ⎩ Pnom − kd ( fmea − fnom − Dtb )

Fig. 7 Island microgrid for simulations

if fmea , fnom − Dtb if fmea − fnom ≤ Dtb if fmea . fnom + Dtb (14)

where kd is a constant droop gain; fnom = 50 Hz is the nominal system frequency; Pnom is the nominal VPP power output determined by considering the demand of the consumers, maximum available power of the PVs and desired primary frequency reserve etc. fmea is the measured frequency equalling to fsys passing through a low-pass filter as fmea =

1 f 1 + sTf sys

(15)

where Tf is a filter time constant. The frequency of load mode changes can be substantially decreased if Tf can be carefully tuned, as demonstrated in the simulations. An example of the frequency droop curve of a VPP is shown in Fig. 6 with fnom = 50 Hz and Pnom = −0.5 MW. It can be seen that in the daytime when the solar irradiance is sufficient, the droop curve is continuous and covers a wide range of system frequency (denoted as the blue line); during night when there is no solar irradiance, the droop curve becomes stepwise (denoted as the red line).

4

Simulation results

To validate the effectiveness of the VPP in providing primary frequency support, numerical simulations are performed in DIgSILENT simulation software based on a model of island microgrid, as shown in Fig. 7. Fig 2 is implemented with DIgSILENT simulation language. The microgrid consists of two 1.0 MW diesel generators (each with a droop of 0.25 MW/Hz), two 0.2 MW (Load1-2) and two 0.3 MW(Load3-4) uncontrollable loads (modelled as 50% constant power and 50% constant impedance load) and the VPP. The VPP consists of two 0.3 MW PVs and eight ice machines (CL1-CL8, modelled as constant power load). The solar irradiance level is 1000 W/m2, the temperature is 25°C, and all ice machines are flexible unless otherwise stated. The power consumptions of the power-off, slow and fast ice-making modes are 0 MW, 0.1 MW, and 0.2 MW, respectively. The nominal operational point of the VPP is set to (−0.5 MW, 50 Hz), with kd = 0.5 MW/Hz, Δtb = 0.2 Hz and Tf = 2.5 s unless otherwise stated (the corresponding droop curve is shown in Fig. 6). In what follows, three different operational


scenarios are to be studied, that is, responses to power variation of uncontrollable loads, variation of the solar irradiance and the change of operational modes of the ice machines. So as to highlight the effectiveness of the proposed control strategy, some severe scenarios are considered here. It should be pointed out that the VPP can also be used to accommodate large-scale wind farms. However because of the limit of the space, it will not be illustrated in detail. First, the responses to variation of power consumption of uncontrollable loads are studied: a 0.6 MW Load5, which accounts for 60% of the uncontrollable load, is connected to Bus 1 of the microgrid at 5 s. The simulation results are illustrated in Fig. 8, where (b)–(e) are performed with Tf = 2.5 s and ( f ) is performed with Tf = 0.3 s. Figs. 8a–c illustrate that after a new steady state is reached, system frequency decreases by 0.67 Hz, and the power output of diesel generators and VPP increases by 0.32 MW and 0.24 MW, respectively. This demonstrates the effectiveness of the VPP in providing frequency support: if the PVs operate in the MPPT mode and all the loads are uncontrollable, the diesel generators bear all the power imbalance, and consequently frequency decreases more. Figs. 8d and e indicate that by decreasing flexible loads in fast ice-making mode and increasing those in power-off and slow ice-making mode, their power consumption decreases by 0.3 MW, achieving the ‘coarse tune’ of PVPP; each PV decreases its power output by 0.03 MW, achieving the ‘fine tune’ of PVPP. Collectively, PVPP increases by 0.24 MW precisely. Besides, corresponding to the change of operational modes of the flexible loads (see Fig. 8e), a power spike can be ref is observed in PVPP. This is because the variation of PVPP continuous, when a certain flexible load experiences a mode change, so will the reference power of the PVs [see Fig. 8d]. However as the actual power of the PVs cannot track the reference ref instantly either. Fortunately, as instantly, PVPP cannot track PVPP the step response of the PV power control strategy to a changing reference power is rather fast [19], the power spike in PVPP is small and thus will have neither obvious electromechanical stress on the diesel generators nor negative impact on system stability. Fig. 8a, e and f study the influence of filter time constant on system dynamics. Specifically, a small Tf increases the speed that a VPP can respond to a disturbance, but results in frequent mode changes of flexible loads in the transient. Therefore there is a tradeoff between dynamic performance and electromechanical stress on the flexible loads. Similarly, when Load3 is disconnected from the microgrid at 5 s, the dynamic responses of the system are depicted in Fig. 9.

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Fig. 8 Responses of the system when Load5 is connected to the microgrid a System frequency b Power output of the synchronous generators c Power output of the VPP d Power output of PV1 e Flexible loads in different modes (Tf = 2.5s) f Flexible loads in different modes (Tf = 0.3s)

Then, the responses to variation of solar irradiance are studied: solar irradiance of PV1 decreases to 650 W/m2 at 5 s, and solar irradiance of PV2 decreases to 450 W/m2 at 10 s. The simulation results are illustrated in Fig. 10. As the operational condition of the microgrid except the VPP does not change, the system frequency in steady state remains to be 50 Hz [see Fig. 10a], with

the trivial fluctuation in the transient also because of the fact that the power output of the PVs cannot track the reference immediately. When solar irradiance decreases, both the maximum available power and power output of the PVs decrease at once [see Fig. 10c]. As PVPP in steady state remains −0.5 MW, the power consumption of the controllable loads also decreases. Thus, after

Fig. 9 Responses of the system when Load3 is disconnected from the microgrid a System frequency b Power output of the VPP c Power output of the PV1 d Flexible loads in different modes

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Fig. 10 Responses of the system when the solar irradiance changes a System frequency b Power output of the VPP c Power output of the PV1 d Flexible loads in different modes

Fig. 11 Responses of the system when the operational modes of the ice machines are changed a System frequency b Power output of the VPP c Power output of the PV1 d Flexible loads in different modes

10 s, two flexible loads switch from fast ice-making to power-off and slow ice-making modes, respectively (see Fig. 10d). Finally, the responses to compulsory mode changes of the ice machines are studied: assume that at 5 s, 5 flexible loads switch to power-off mode because of operational failure or system maintenance, that is, noff0 = 5, nslow = 0 and nfast0 = 0. The simulation results are illustrated in Fig. 11. When the operational modes of the ice machines change, system frequency can reach as high as 50.4 Hz before recovering to the steady state. Moreover, note that before PV power output converges (i.e. from 5 s to 11 s), the estimate of the maximum available power fluctuates and is not precise [see Fig. 11c]. Fortunately, it is precise enough after convergence and as such the VPP can operate in the optimal solution of the MIP problem.


5

Conclusions

This paper proposes the design of VPP by coordinating multiple PVs and controllable loads. Based on the fact that the power output of PVs can be flexibly adjusted and the power consumption of the controllable loads can be discretely changed in a wide range, the aggregated power of the VPP can be precisely and quickly controlled, which can provide ancillary services, for example, primary frequency reserve, to microgrids when the capacity of ESSs is not enough. Simulation results demonstrate the effectiveness of VPPs in providing primary frequency support to an island microgrid even under severe conditions. Moreover, this idea offers a more economical way to accommodate a high penetration of DERs.

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6

Acknowledgments

This work was jointly supported by the National High Technology Research and Development Program of China (grant no. 2015AA0500695) and the National Natural Science Foundation of China (grant no. 51177146).

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