Optimal management of renewable energy sources by virtual power plant

Abstract In recent years, due to lack of sufficient quantity of fossil fuel, the need of Renewable Energy Sources (RESs) has become an important matter. In addition to the shortage of fossil fuel, global warming is another concern for many countries and companies. These issues have caused a large number of RESs to be added into modern distribution systems. Nevertheless, the high penetration of RESs beside the intermittent nature of some resources such as Wind Turbines (WT) and Photovoltaic (PV) cause the variable generation and uncertainty in power system. Under this condition, an idea to solve problems due to the variable outputs of these resources is to aggregate them together. A collection of Distributed Generators (DGs), Energy Storage Systems (ESSs) and controllable loads that are aggregated and then are managed by an Energy Management System (EMS) which is called Virtual Power Plant (VPP). The objective of the VPP in this paper is to minimize the total operating cost, considering energy loss cost in a 24 h time interval. To solve the problem, Imperialist Competitive Algorithm (ICA), a meta-heuristic optimization algorithm is proposed to determine optimal energy management of a VPP with RESs, Battery Energy Storage (BSS) and load control in a case study.

[1]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[2]  S. M. Moghaddas-Tafreshi,et al.  Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part I: Problem Formulation , 2011, IEEE Transactions on Power Systems.

[3]  Mohammad Kazem Sheikh-El-Eslami,et al.  Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method , 2013 .

[4]  Igor Kuzle,et al.  Virtual power plant mid-term dispatch optimization , 2013 .

[5]  Christof Wittwer,et al.  Decentralised optimisation of cogeneration in virtual power plants , 2010 .

[6]  I. G. Moghaddam,et al.  Risk-averse profit-based optimal operation strategy of a combined wind farm–cascade hydro system in an electricity market , 2013 .

[7]  Henrik W. Bindner,et al.  Utilization of Flexible Demand in a Virtual Power Plant Set-Up , 2015, IEEE Transactions on Smart Grid.

[8]  Behrooz Vahidi,et al.  Bacterial foraging solution based fuzzy logic decision for optimal capacitor allocation in radial di , 2011 .

[9]  Oya Ekin Karasan,et al.  Cost and emission impacts of virtual power plant formation in plug-in hybrid electric vehicle penetrated networks , 2013 .

[10]  Ozan Erdinc,et al.  The importance of detailed data utilization on the performance evaluation of a grid-independent hybr , 2011 .

[11]  Zita Vale,et al.  Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming , 2010 .

[12]  Taher Niknam,et al.  Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants , 2012 .

[13]  Alfred J. Cavallo,et al.  Controllable and affordable utility-scale electricity from intermittent wind resources and compressed air energy storage (CAES) , 2007 .

[14]  Andreas Sumper,et al.  A review of energy storage technologies for wind power applications , 2012 .

[15]  Miadreza Shafie-khah,et al.  Development of a virtual power market model to investigate strategic and collusive behavior of market players , 2013 .

[16]  M. Ehsan,et al.  Possibilistic Evaluation of Distributed Generations Impacts on Distribution Networks , 2011, IEEE Transactions on Power Systems.

[17]  Taher Niknam,et al.  Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel , 2011 .

[18]  Miloš Pantoš,et al.  Stochastic optimal charging of electric-drive vehicles with renewable energy , 2011 .

[19]  M. Kurrat,et al.  Virtual power plants with combined heat and power micro-units , 2005, 2005 International Conference on Future Power Systems.

[20]  Mohammad Kazem Sheikh-El-Eslami,et al.  The design of a risk-hedging tool for virtual power plants via robust optimization approach , 2015 .

[21]  H. Morais,et al.  Scheduling distributed energy resources in an isolated grid — An artificial neural network approach , 2010, IEEE PES General Meeting.

[22]  Pedro Faria,et al.  Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling , 2013, IEEE Transactions on Smart Grid.

[23]  Haidar Samet,et al.  Consideration effect of uncertainty in power system reliability indices using radial basis function network and fuzzy logic theory , 2011, Neurocomputing.

[24]  Jinde Cao,et al.  Bi-level optimal dispatch in the Virtual Power Plant considering uncertain agents number , 2015, Neurocomputing.

[25]  Mehdi Ehsan,et al.  Probabilistic dynamic multi-objective model for renewable and non-renewable distributed generation planning , 2011 .

[26]  Stavros A. Papathanassiou,et al.  Optimum sizing of wind-pumped-storage hybrid power stations in island systems , 2014 .

[27]  Antonio J. Conejo,et al.  Offering model for a virtual power plant based on stochastic programming , 2013 .

[28]  S. M. Moghaddas Tafreshi,et al.  Mathematical modeling of electrochemical storage for incorporation in methods to optimize the operational planning of an interconnected micro grid , 2010, Journal of Zhejiang University SCIENCE C.

[29]  R. Belmans,et al.  Impact of residential demand response on power system operation: A Belgian case study , 2014 .

[30]  Zhen Wang,et al.  Control of virtual power plant in microgrids: a coordinated approach based on photovoltaic systems and controllable loads , 2015 .

[31]  T. G. O’Flaherty.  Mixed Integer Linear Programming – A Working Algorithm , 1971 .

[32]  Ali Kaveh,et al.  Imperialist Competitive Algorithm , 2014 .

[33]  J. Morales,et al.  Point Estimate Schemes to Solve the Probabilistic Power Flow , 2007, IEEE Transactions on Power Systems.

[34]  Evangelos Rikos,et al.  Implementing agent-based emissions trading for controlling Virtual Power Plant emissions , 2013 .

[35]  Abdollah Kavousi-Fard,et al.  Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices , 2013 .

[36]  Marco Giuntoli,et al.  Optimized Thermal and Electrical Scheduling of a Large Scale Virtual Power Plant in the Presence of Energy Storages , 2013, IEEE Transactions on Smart Grid.