Impact of industrial virtual power plant on renewable energy integration

Runze Liu1*,Yu Liu1,Zhaoxia Jing1

1.School of Electric Power,South China University of Technology,Guangzhou 510641,P.R.China

Abstract:An industrial park is one of the typical energy consumption schemes in power systems owing to the heavy industrial loads and their abilities to respond to electricity price changes.Therefore,energy integration in the industrial sector is significant.Accordingly,the concept of industrial virtual power plant (IVPP) has been proposed to deal with such problems.This study demonstrates an IVPP model to manage resources in an eco-industrial park,including energy storage systems,demand response (DR) resources,and distributed energies.In addition,fuzzy theory is used to change the deterministic system constraints to fuzzy parameters,considering the uncertainty of renewable energy,and fuzzy chance constraints are then set based on the credibility theory.By maximizing the daily benefits of the IVPP owners in day-ahead markets,DR and energy storage systems can be scheduled economically.Therefore,the energy between the grid and IVPP can flow in both directions:the surplus renewable electricity of IVPP can be sold in the market;when the electricity generated inside IVPP is not enough for its use,IVPP can also purchase power through the market.Case studies based on three wind-level scenarios demonstrate the efficient synergies between IVPP resources.The validation results indicate that IVPP can optimize the supply and demand resources in industrial parks,thereby decarbonizing the power systems.

Keywords:IVPP,Virtual power plants,Industrial loads,Renewable energy integration,Fuzzy chance constraint,Credibility theory.

0 Introduction

The energy crisis is becoming severe with the rapid growth of electricity demand.The quality of the environment in fast-growing economies is also deteriorating,particularly in industrial parks with heavy loads.Therefore,it is essential to utilize distributed renewable energies.Simultaneously,energy storage systems and controllable loads are introduced into the electricity systems to compensate for the inherent intermittency and uncertainty of renewable energies.The concept of virtual power plant (VPP) has been proposed as a successful method to aggregate distributed renewables,demand response (DR),and energy storage systems,to maximize daily revenue in the electricity market [1].

Research studies on the optimal dispatch of VPP falls into two categories:centralized and decentralized.In centralized modes,the VPP integrates diverse generating resources via the control coordination center [2-6].Although the centralized mode is a hotspot,the concept of VPP can be extended to multiple geographical areas,which is suitable for decentralized modes.Moreover,different logically or physically separated components of VPP can be controlled hierarchically via the multiagent system (MAS) [7],[8].

The mathematical optimization methods of VPP are based on linear programming (LP) [9],[10],mixed integer linear programming (MILP) [11],[12],intelligent algorithms [13],[14],and others.Reference [9]uses LP to facilitate the integration and management in a decentralized manner.Reference [10]proposes an inexact two-stage stochastic LP method to support sustainable management in the presence of uncertainties.The optional dispatch problem can also be described as a MILP model.Reference[11]introduces a MILP model to minimize conventional power plant costs due to poorly forecasted RESs (renewable energies) power output.Furthermore,intelligent algorithms can be utilized to solve the problems [6],[13].Reference[6]uses genetic algorithms to configure the power of each distributed generation.Reference [13]proposes a bi-level fuzzy chance constrained programming by combining a pattern-search algorithm and artificial bee colony algorithm to solve the VPP dispatch problem.

Wind-power forecasting has unpredictable volatility and intermittency [15].Algorithms used in models with fluctuating wind power include fuzzy optimization [16-18],robust optimization [19],[20],and stochastic optimization[21],[22].Reference [16]indicates that compared with stochastic optimization and robust optimization,fuzzy optimization has several advantages,such as no statistical errors and conservative and adjustable forecast results,when dealing with wind power forecasting problems.Therefore,this study chooses fuzzy optimization.

VPP can be applied to numerous situations.Based on bidding,VPP can gain flexible access to the electricity energy market and auxiliary market via bids.Reference[23]states that VPP can contribute in the frequency control market and provide peak shaving service.In addition,reference [5]has reported that a VPP multicomponent system participates in energy and reserve electricity markets as a single entity.Moreover,reference [2]studies the possibility of VPP owners participating in the green certificate market and in the day-ahead market simultaneously.

Furthermore,VPP can be applied to several fields,such as industrial and commercial,as well as for residential users.This study focuses on the economic arrangement of industrial VPP (IVPP) in day-ahead electricity markets.The remainder of this paper is organized as follows.Section 2 introduces the different concepts of VPPs and proposes the corresponding model.The two-way flow IVPP model is established in section 3,including solar photovoltaic,wind power,energy storage,and DR resources.Subsequently,fuzzy theory is used to change the deterministic system constraints to the system parameters subject to the values of the fuzzy parameters.Results from case studies in three renewable energy scenarios are provided and analyzed in section 4.Section 5 summarizes and brings forth proposals about the application of the IVPP.

1 Concept of virtual power plant

The concept of VPP was first conceived by Awerbuch as “a virtual utility” [1]that provides insights into the paradigm shift of the restructured utility industry.Since then,the concept of VPP has been extended by different scholars.Basically,VPP consists of several types of power generation resources,such as wind,photovoltaic,or thermal.Reference [2]composes the VPP of distributed energy resources,energy storage systems,and dispatchable classical generators.Other scholars extended the concept of VPP to consumers.Reference [24]includes the DR resources as interruptible loads in VPP.

VPP can be segmented by three customer applications,engaging commercial,industrial,and residential consumers.When modeling VPP,it should be considered that different consumers may have different attitudes and responses toward provided load shedding incentives.For example,industrial users usually consume considerable energy every day.For some factories,moderate load reduction may not substantially affect their production.This is referred to as “the price sensitive type.” Consequently,industrial consumers may receive low incentives but generate highload reductions.However,residential consumers may require a high incentive to be willing to make an extremely low-level load reduction because the reduction of the demand may greatly compromise the comfort or satisfaction of residential consumers.

In this study,IVPP is defined as a VPP applied to an industrial park that aggregates various energy resources,including distributed wind power generation and photovoltaic,storage facilities,and loads.According to the characteristics of different loads,the industrial loads are further divided into two categories:nonreducible and reducible.The IVPP structure and interactions with electricity markets are shown in Fig.1.

2 IVPP model

Fig.1 IVPP structure

In this study,it is supposed that the IVPP participates in a centralized electricity market.IVPP owners maximize the daily profit of IVPP by integrating all resources in the industrial park.It is considered that the cost of wind power and photovoltaics is minimal (considered as zero).Thus,this model only considers the cost of DR.When the power generation in VPP is greater than the power consumption,the power will be sold to the grid;however,when the power consumption in IVPP is greater than the power generation,the power will be purchased from the grid.

2.1 Industrial load modeling considering power consumption characteristics

There are numerous types of industrial loads,such as the textile industry,machinery and equipment manufacturing,nonferrous metal industry,food industry,and petroleum refining.As the second largest industrial city in China,Suzhou has a complex industrial structure and a strong demand for electricity.In this study,three types of typical industrial loads of an industrial park in Suzhou are selected:machinery and equipment manufacturing industry,textile industry,and the nonferrous metal industry.The typical daily load characteristics of the three types of industrial loads are shown in Fig.2.

Fig.2 Typical daily load curves of three types of industrial loads

As shown in Fig.2,the textile industry load is relatively high from 8:00 to 18:00 and corresponds to the working hours of workers.The daily load characteristics of nonferrous metals industry are relatively stable with minor fluctuations.The load of machinery and equipment manufacturing industry is generally stable and fluctuates within a certain range.

In addition,the DR intensity of different load types is different.Generally,industrial loads do not accept load interruption.Thus,this study only considers load shedding.The textile industry is more sensitive to prices and has a certain capacity of load reduction.Most of the electrical equipment in the machinery and equipment manufacturing industry does not have high and continuous requirements.For the nonferrous metal industry,electrolysis is one of its primary production links that belongs to the continuous production process with a large downtime loss.Thus,its response capacity is small.

To simplify the model,the aforementioned three types of loads are divided into two categories according to the power consumption characteristics and load shedding capacity,namely which represents the reducible amount of industrial load i in time t,and which represents the fixed power consumption of industrial load i in time t.

In this article,Pi ,t DR is a decision variable.The objective function is to maximize the total profit of IVPP,wherein the cost of interruptible load can be expressed as follows,

where represents the amount of DR resource of industrial load i in time t,and represents the corresponding cost.

2.2 Fuzzy model of wind power

When forecasting wind power,the forecast valuecan be expressed by the forecast base value forecast errorand forecast error coefficient etw[25],

Most wind power output distributions are singlepeak distribution curves.This article selects the Gaussian distribution that is the most commonly used distribution to express the forecast error of wind power.In this case,etw obeys the normal distributionandthen obeys the normal distributionThe probability of68.82%,and the probability of its function value is the largest;the probability ofis 31.61%,the probability is 0.38%,and the probability of this part (0.38%) could be neglected.According to the characteristic of the normal distribution and the physical meaning of the membership function,the fuzzy number could be used to fit the wind power output.In the dispatch period,the fuzzy parameter of the wind power output forecast value can be expressed by a trapezoidal function,

where is a membership function that can be represented by a four-tuple:

2.3 Objective function

Assuming that the electricity market is relatively mature and accurate,and the number of market entities is sufficient,the IVPP will participate in the electricity market as a price taker.As shown in (1),the objective is to maximize the daily profit of the IVPP.The total profit comes from the incomes by selling surplus power in the day-ahead market,and the cost considers the load-shedding service of controllable loads in the industrial park.

where is the amount of electricity sold in the day-ahead market.If IVPP buys electricity,Ptspot is negative.If IVPP sells electricity to the market,Ptspot is positive.λt is the spot price at time t during a day,and CtDR is the cost of the load response at time t.

2.4 Constraints

Energy balancing fuzzy constraints of IVPP considering renewable energy uncertainty

Based on the interaction with the day-ahead market,IVPP maintains an energy balancing at each time period during a day.If the uncertainty of renewable energy is excluded,the balance constraint of electricity generation and consumption can be expressed as follows,

where and the total amount of wind and solar power generations of the IVPP during each time period t,respectively.The load is divided into two categories:fixed load (uncontrollable) and DR resource (controllable).Additionally, and represent the amounts of fixed load and power generation reduced by DR of industrial load i at each time t,respectively.Ptspot indicates the total electricity purchased (negative) or sold (positive) by IVPP in the day-ahead market at time period t.

To represent the state of the battery,aj ,t is introduced as a variable in the range of 0-1.In this case,the value of zero indicates that the battery j charges at time t,and the value of one indicates that the battery j discharges at time t.When charging,will be equal to zero;when discharging, will be zero.

When the uncertainty of intermittent wind power output is expressed with fuzzy parameters,the constraints of system power balance under deterministic conditions are meaningless.A new method is used such that the power balance constraints can be established with a certain credibility confidence level a.This means that the decisionmaker believes that the predicted value of the intermittent power output is not sufficiently accurate for various reasons;therefore,the absolute balance of the system power cannot be achieved.The volatility should be considered in the scheduling arrangement to approximately balance the system power,and the credibility confidence level is used to measure this balanced level.The credibility opportunity constraint of the VPP power balance is expressed as,

where is the fuzzy parameter of the wind power output in the dispatching period t,and is the fuzzy parameter of wind power output in the dispatching period t.

Clear equivalence classes for power balance constraints

When solving the optimal problem with fuzzy chance constraints,the key is to deal with chance constraints.One method is fuzzy simulation that uses random sampling inspection to obtain the credibility of the chance constraint according to the principle of large numbers,thereby assessing the advantages and disadvantages of the decision variable value [26],[27].However,the simulation result is only a statistical estimate that is inaccurate and requires a time-consuming process.Another method is to convert the opportunity constraints into clear equivalence classes,thereby solving the model [28-30].In this study,the power balance constraints are transformed to corresponding clear equivalence classes.The clear equivalence class of power balance constraints subject to the step fuzzy parameters is,

Demand response constraints

As stated in Section 3.1,considering the particularity of industrial loads,the load is divided into two types:controllable and uncontrollable.The DR of controllable customers is paid according to the amount of their power reduction.Therefore,considering the actual situation of each industrial load,the DR is constrained as follows,

whererepresent the maximum and minimum capacities of the DR load i,respectively.

Charging and discharging battery constraints

At time t,the energy of battery j can be expressed as follows,

where ηj represents the charge and discharge efficiency of battery j,represent the charging power and discharging power of battery j at time t respectively.The energy of each energy storage system j shall meet the upper and lower limit constraints,considering the parameters of batteries.

In addition,the charging and discharging powers of each battery should meet the upper and lower limit constraints,

where Ej_minand Ej_max represent the upper and lower limits of the energy level of battery j,respectively;represent the upper limits of the charging and discharging powers of battery j,respectively.

3 Case Studies

It is assumed that in an industrial park,there are multiple distributed wind and solar power resources,three reducible industrial loads,and one energy storage system.Model optimization is conducted within 24 h using the Yalmip toolbox in the MATLAB environment.Assuming that the electricity market is mature and the market structure is reasonable,IVPP gains profits according to the LMP(locational marginal price) of the day-ahead market as the price taker.

According to the fuzzy model of wind power generation in this study,the predicted value of wind power can be expressed by the prediction base value,prediction error,and prediction error coefficient.The predicted base value is based on the data on the onshore wind power data on the wind power website,and three scenarios were selected in March 2019 (higher level wind power generation),July 2019 (lower level wind power generation),and October 2019 (medium level wind power generation).Furthermore,considering the particularity of solar power generation,the design data of solar power generation is shown in Fig.3.

Fig.3 Solar power generation

3.1 Case 1:March (high-level-wind-power generation)

In March,the level of wind power generation was high.The battery charge and discharge are demonstrated in the Fig.4.In this case,the internal power supply of IVPP meets the total load demand.The battery is charged and discharged within one day to meet the changing wind and solar power supply.

On a typical day in March,the total power generation of IVPP (including wind power,solar power,corresponding demand,and battery power) is as shown in Fig.5.It can be observed that the reducible load responds several times during the train of the day.At 5:00 and 9:00,owing to the lack of power generation,IVPP purchased a small amount of power from the day-ahead market.

Fig.4 Daily charging and discharging status of the battery

Fig.5 Total generation and energy exchange in the dayahead market in March

3.2 Case 2:October (medium-level wind power generation)

The power supply of IVPP is insufficient due to the decrease of wind power generation in October.As a result,the number of times when IVPP purchases electricity from the power market (during the times the solar power is reduced) also increases at 5,9,11,and 19,as shown in Fig.6.

Fig.6 Total generation energy exchange in the day-ahead market in October

3.3 Case 3:July (low-level-wind-power generation)

In July,wind power generation was at a low level.During this period,the internal power generation of IVPP is often unable to meet the industrial load demand.Consequently,the amount of electricity purchased from the day-ahead market is also increased compared with that in October.Moreover,the amount of load shedding is also increased compared with the data in March and October.

The relationship between the amount of electricity purchased by IVPP from the electricity market and the controllable load and total energy (capacity) of batteries is presented in Fig.8.It can be observed from the overall trend that the larger the capacity of the DR load is and the larger the capacity of the energy storage system is,the better is the effect of IVPP in integrating renewable energies.The impact of DR’s capacity can be reflected in the reduction of the purchased power from the day-ahead market.With the increase of battery capacity,the amount of power purchased from the day-ahead market fluctuates,which is the result of the most profitable decision of IVPP using energy storage facilities.

Fig.7 Total generation and energy exchange in the dayahead market in July

Fig.8 Relationship between the purchased electricity,controllable loads,and battery energy constraints

4 Conclusions

This study proposed an IVPP optimization model,which integrates the supply and demand-side resources of an industrial park that participates in the day-ahead market.The proposed model considered renewable generation,different industrial loads,energy storage facilities,and their constraints.To simplify the model,three types of industrial loads of the Suzhou were defined into two categories according to their typical daily power consumption curves,load shedding capacity,and corresponding costs.The IVPP model is analyzed based on three scenarios of wind production.Case studies show the important role of different levels of DR resources and energy storage facilities in renewable energy integration.With the increase in battery capacity,the overall trend of power purchased from the day-ahead market is on the rise due to the increase in the amount of charge and discharge.IVPP uses batteries to charge during the low electricity price period,discharge during the high electricity price period,and sell it to the market at a high price.

Industrial parks with abundant distributed renewable energy are suitable for applying IVPP.Through the energy interaction between IVPP and the power market,the surplus renewable electricity can participate in the market.On one hand,it can increase the income of IVPP;on the other hand,it can also promote carbon reduction of the power system.According to its composition,the IVPP forms a type of collaborative business ecosystem with an increased number of interactions and interdependencies among IVPP resources.IVPPs will eventually help overcome the stochastic nature of RESs that will result in a more stable smart power grid and decarbonize the electricity supply of industrial parks.

In the proposed model,VPP only participates in the electricity energy market.Future study will address the consideration of IVPP participating in different markets,such as regulation market,reserve market and other ancillary service markets.The modeling of industrial load will also be refined to consider the response time of different loads,risk preference or political factors.


This work was supported by Department of Science and Technology of Guangdong Province(Project 2019B0909011001).

Declaration of Competing Interest

We declare that we have no conflict of interest.


[1]Shimon A,Preston A (1997) The virtual utility,first edn.Springer,Boston

[2]Lazaroiu GC,Dumbrava V,Roscia M,Zaninelli D (2015).Energy trading optimization of a virtual power plant on electricity market.Paper presented at the 9th international symposium on advanced topics in electrical engineering,Bucharest,Romania,7-9 May 2015

[3]Zamani AG,Zakariazadeh A,Jadid S (2016) Day-ahead resource scheduling of a renewable energy based virtual power plant.Applied Energy 169:324-340

[4]Yin S,Ai Q,Li Z,Zhang Y,Lu T (2020) Energy management for aggregate prosumers in a virtual power plant:A robust Stackelberg game approach.International journal of electrical power and energy systems 117:105605.1-105605.11

[5]Baringo A,Baringo L,Arroyo JM (2018) Day-ahead self scheduling of a virtual power plant in energy and reserve electricity markets under uncertainty.IEEE Trans Power Systems,99:1-1

[6]Chao H,Quan D,Yang T (2018) Multi-time scale simulation of optimal scheduling strategy for virtual power plant considering load response.Paper presented at the 2018 international conference on power system technology (POWERCON),Guangzhou,China,6-8 Nov.2018

[7]Chen W,Ai X,Fan Y (2014) Replicator dynamics algorithm based equilibrium dispatching strategy for distributed energy resources in virtual power plant.Power System Technology 38(03):589-595

[8]Yu J,Jiao Y,Wang X (2015) Bi-level optimal dispatch in the virtual power plant considering uncertain agents number.Neurocomputing 167(C):551-557

[9]Salmani MA,Anzalchi A,Salmani S (2010) Virtual power plant:New solution for managing distributed generations in decentralized power systems.Paper presented at the 2010 international conference on management and service science,Wuhan,China,24-26 Aug.2010

[10]Zhou C,Huang G,Chen J (2018) Planning of electric power systems considering virtual power plants with dispatchable loads included:An inexact two-stage stochastic linear programming model.Mathematical Problems in Engineering.2018

[11]Kuzle I,Zdrilić M,Pandžić H (2011) Virtual power plant dispatch optimization using linear programming.Paper presented at the 2011 10th International Conference on Environment and Electrical Engineering,Rome,Italy,8-11 May 2011

[12]Ghorbankhani E,Badri A (2018) A bi-level stochastic framework for VPP decision making in a joint market considering a novel demand response scheme.International Transactions on Electrical Energy Systems 28(1):e2473

[13]Pian D,Jianquan Z,Mingbo L (2016) Optimal dispatch of virtual power plant based on bi-level fuzzy chance constrained programming.J Electro Tech 31(9):58-67

[14]Liwei Ju,et al.(2019) A CVaR-robust-based multi-objective optimization model and three-stage solution algorithm for a virtual power plant considering uncertainties and carbon emission allowances.International Journal of Electrical Power &Energy Systems 107:628-643

[15]Shi L,Sun S,Yao L,Ni Y,Bazargan M (2014) Effects of wind generation intermittency and volatility on power system transient stability.IET Renewable Power Generation 8:509-521

[16]Zhou R,Zhang J,Zhang H,et al.(2018) Bi-level stochastic programming and dispatch of wind integrated power system considering dual market.Journal of Power System Automation 30(1):15-21

[17]Zhu H,Xu J,Wang X (2009) Active power output calculation of wind farms connected to power grids based on fuzzy chance constrained programming.Paper presented at the 2009 4th IEEE Conference on Industrial Electronics and Applications,Xi'an,2009,pp.2141-2144

[18]Yaosuo Xue,Bala Venkatesh,Liuchen Chang (2008) Bidding wind power in short-term electricity market based on multipleobjective fuzzy optimization.Paper presented at the 2008 Canadian Conference on Electrical and Computer Engineering,Niagara Falls

[19]Xiong P,Jirutitijaroen P,Singh C (2017) A Distributionally robust optimization model for unit commitment considering uncertain wind power generation.IEEE Transactions on Power Systems 32(1):39-49,Jan.2017

[20]Lorca Á,Sun X.A.(2015) Adaptive robust optimization with dynamic uncertainty sets for multi-period economic dispatch under significant wind.IEEE Transactions on Power Systems,30(4):1702-1713

[21]Wang X,Hu Z,Zhang M,Hu M (2016) Two-stage stochastic optimization for unit commitment considering wind power based on scenario analysis.Paper presented at the 2016 China International Conference on Electricity Distribution (CICED),Xi'an,China

[22]Wang Q,Wang J,Guan Y (2011) Wind power bidding based on chance-constrained optimization.Paper presented at the 2011 IEEE Power and Energy Society General Meeting,Detroit,MI,USA

[23]Soltani M,Raoofat M,Rostami MA (2012) Optimal reliable strategy of virtual power plant in energy and frequency control markets.Paper presented at the 2012 proceedings of the 17th conference on Electrical Power Distribution,Tehran,Iran,2-3 May 2012

[24]Shabanzadeh M,Sheikh-El-Eslami M-K,Haghifam M-R (2015)The design of a risk-hedging tool for virtual power plants via robust optimization approach.Applied Energy 155:766-77

[25]Zhang T et al.(2020) Energy system scheduling considering demand response and wind power uncertainty.Journal of Zhejiang University 54:1562-1572

[26]Zhao S,Li Y,Wang C (2011) Transmission network expansion planning based on credibility theory.Transaction of China Electro technical Society 26(6):166-171

[27]Xu X,Zha X,Lin T (2010) A credibility theory based method for small signal stability analysis.Automation Electric Power System 34(12):8-13

[28]Ai X,Liu X (2011) Dynamic economic dispatch for wind farms integrated power system based on credibility theory.Proc CSEE 31(Supplement):12-18

[29]Liu B,Zhao R,Wang G (2008) Uncertainty planning and applications.Tsinghua University Press,Beijing

[30]Ai X,Liu X,Sun C (2011) A fuzzy chance constrained decision model for unit commitment of power grid containing large-scale wind farm.Power System Technology 35(12):202-207

Scan for more details

Received:17 June 2020/Accepted:27 August 2020/Published:25 December 2020

Runze Liu

Yu Liu

Zhaoxia Jing

2096-5117/© 2020 Global Energy Interconnection Development and Cooperation Organization.Production and hosting by Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.This is an open access article under the CC BY-NC-ND license (


Runze Liu received bachelor’s degree at South China University of Technology,Guangzhou,in 2019.She is working towards master’s degree at South China University of Technology,Guangzhou.Her research interests include electricity market,electricity financial products.

Yu Liu received bachelor’s degree at Wuhan University of Science and Technology,Wuhan,in 2018.He working towards master’s degree at South China University of Technology,Guangzhou.His research interests include electricity market,transmission rights.

Zhaoxia Jing received her bachelor’s degree,master’s degree and Huazhong University of Science and Technology,Wuhan,in 1997,1999 and 2003 respectively.She is working at South China University of Technology,Guangzhou.Her research interests include electricity market,electric vehicles,power system operation and control,integrated energy system optimization.

(Editor Dawei Wang)