Optimal stochastic bilevel scheduling of pumped hydro storage systems in a pay-as-bid energy market environment

Abstract This paper proposes a stochastic bilevel optimization approach to owners of pumped hydro storage systems (PHSSs) to participate in pay-as-bid power market and provide optimal bids and offers. The price offering of other generation units is modeled by stochastic programming. The upper-level of the proposed bilevel programming seeks maximization of the profit of the PHSS arbitrage, where the lower-level assures the optimal system dispatching (and market-clearing), and keeps the network security. The bilevel optimization is then transferred into a single-level equivalent via Karush-Kuhn-Tucker (KKT) complementarity conditions. The equations of the KKT conditions are linearly modeled using special ordered sets of type 1 (SOS1) variables. Furthermore, the bilinear objective function of the upper-level is approximated using McCormick envelopes relaxation method, in order to obtain the solutions as fast as possible and making the problem as mixed-integer linear programming. The proposed method is verified on the IEEE 24-bus reliability test system (RTS) considering different cases. The operation of a single PHSS is assessed in the network's normal and congested conditions. The results show that the PHSS achieve more revenue in a limited network as the offered prices go up to price cap in some periods. In the studied cases, the profit of energy arbitrage by the PHSS increases from $ 3302 in a normal network, to $ 8170 in a limited network. Moreover, the effect of wind generation uncertainty on the arbitrage problem is investigated using five sub-scenarios dedicated to wind generation. It is shown that the arbitrage profit is more sensitive on generation cost uncertainty rather than wind generation uncertainty. Furthermore, it is revealed that the expected profit in the presence of wind turbines is slightly lower than that without wind turbines. This is due to the fact that the wind generation power is always accepted and dispatched in the market and lowers the load demand and deteriorates the arbitrage opportunity. In this case the expected profit of the PHSS is decreased from $ 3302 to $ 3288. Furthermore, the effects of three PHSS units in the mentioned network is investigated. For a particular PHSS in the studied system, it is found that the increase of PHSS units in the network decreased the expected profit from $ 3302 to about $ 3176. Also, the location of PHSS units is deduced as an influential factor on profitability of merchant storage facilities.

[1]  H. Zareipour,et al.  A Bilevel Model for Participation of a Storage System in Energy and Reserve Markets , 2018, IEEE Transactions on Sustainable Energy.

[2]  Der-Jiunn Deng,et al.  Optimal Charging Control of Energy Storage and Electric Vehicle of an Individual in the Internet of Energy With Energy Trading , 2018, IEEE Transactions on Industrial Informatics.

[3]  B. Mohammadi-ivatloo,et al.  Electricity Market Pricing: Uniform Pricing vs. Pay-as-Bid Pricing , 2020 .

[4]  J. Slootweg,et al.  Dual technology energy storage system applied to two complementary electricity markets using a weekly differentiated approach , 2017 .

[5]  Miguel Carrión,et al.  Investments in merchant energy storage: Trading-off between energy and reserve markets , 2018, Applied Energy.

[6]  K. Afshar,et al.  Optimal bidding strategy of wind power producers in pay-as-bid power markets , 2018, Renewable Energy.

[7]  Hamed Mohsenian-Rad Optimal Bidding, Scheduling, and Deployment of Battery Systems in California Day-Ahead Energy Market , 2016, IEEE Transactions on Power Systems.

[8]  M. Parsa Moghaddam,et al.  Risk-constrained dynamic self-scheduling of a pumped-storage plant in the energy and ancillary service markets , 2009 .

[9]  Hamed Mohsenian Rad,et al.  Optimal Operation of Independent Storage Systems in Energy and Reserve Markets With High Wind Penetration , 2014, IEEE Transactions on Smart Grid.

[10]  Andreas Jossen,et al.  Model-Based Dispatch Strategies for Lithium-Ion Battery Energy Storage Applied to Pay-as-Bid Markets for Secondary Reserve , 2017, IEEE Transactions on Power Systems.

[11]  Garth P. McCormick,et al.  Computability of global solutions to factorable nonconvex programs: Part I — Convex underestimating problems , 1976, Math. Program..

[12]  Nima Amjady,et al.  Pay-as-bid based reactive power market , 2010 .

[13]  Daniel S. Kirschen,et al.  Optimal scheduling of energy storage under forecast uncertainties , 2017 .

[14]  J. Catalão,et al.  Optimal Single Wind Hydro-Pump Storage Bidding in Day-Ahead Markets Including Bilateral Contracts , 2016, IEEE Transactions on Sustainable Energy.

[15]  N. Amjady,et al.  Risk-Constrained Bidding and Offering Strategy for a Merchant Compressed Air Energy Storage Plant , 2017, IEEE Transactions on Power Systems.

[16]  A. Conejo,et al.  Decision making under uncertainty in electricity markets , 2010, 2006 IEEE Power Engineering Society General Meeting.

[17]  H. Vincent Poor,et al.  Scheduling Power Consumption With Price Uncertainty , 2011, IEEE Transactions on Smart Grid.

[18]  Chongqing Kang,et al.  Optimal Bidding Strategy of Battery Storage in Power Markets Considering Performance-Based Regulation and Battery Cycle Life , 2016, IEEE Transactions on Smart Grid.

[19]  Shiming Liu,et al.  Data-Driven Distributionally Robust Energy-Reserve-Storage Dispatch , 2018, IEEE Transactions on Industrial Informatics.

[20]  S. Gabriel,et al.  An SOS1-Based Approach for Solving MPECs with a Natural Gas Market Application , 2013 .

[21]  T. Brijs,et al.  Quantifying electricity storage arbitrage opportunities in short-term electricity markets in the CWE region , 2019, Journal of Energy Storage.

[22]  Hamed Mohsenian-Rad Coordinated Price-Maker Operation of Large Energy Storage Units in Nodal Energy Markets , 2016, IEEE Transactions on Power Systems.

[23]  José Fortuny-Amat,et al.  A Representation and Economic Interpretation of a Two-Level Programming Problem , 1981 .

[24]  B. Hobbs,et al.  Price-Based Unit Commitment Electricity Storage Arbitrage with Piecewise Linear Price-Effects , 2016 .

[25]  Zhi Zhou,et al.  Energy Storage Arbitrage Under Day-Ahead and Real-Time Price Uncertainty , 2018, IEEE Transactions on Power Systems.

[26]  Kazem Zare,et al.  Optimal bidding and offering strategies of merchant compressed air energy storage in deregulated electricity market using robust optimization approach , 2018 .

[27]  Pedro M. Castro,et al.  Tightening piecewise McCormick relaxations for bilinear problems , 2015, Comput. Chem. Eng..

[28]  Kazem Zare,et al.  A hybrid stochastic-robust optimization approach for energy storage arbitrage in day-ahead and real-time markets , 2019, Sustainable Cities and Society.

[29]  Pierre Pinson,et al.  Price-Taker Offering Strategy in Electricity Pay-as-Bid Markets , 2018, IEEE Transactions on Power Systems.

[30]  G. Papaefthymiou,et al.  Using Copulas for Modeling Stochastic Dependence in Power System Uncertainty Analysis , 2009 .

[31]  Pandelis N. Biskas,et al.  Demand Response in a Real-Time Balancing Market Clearing With Pay-As-Bid Pricing , 2013, IEEE Transactions on Smart Grid.

[32]  Hamidreza Zareipour,et al.  Developing Bidding and Offering Curves of a Price-Maker Energy Storage Facility Based on Robust Optimization , 2019, IEEE Transactions on Smart Grid.

[33]  Daniel S. Kirschen,et al.  Look-Ahead Bidding Strategy for Energy Storage , 2017, IEEE Transactions on Sustainable Energy.

[34]  Hamidreza Zareipour,et al.  Operation Scheduling of Battery Storage Systems in Joint Energy and Ancillary Services Markets , 2017, IEEE Transactions on Sustainable Energy.