Medium-Term Operation for an Industrial Customer Considering Demand-Side Management and Risk Management

Under a deregulated market environment, industrial customers can participate in multiple markets with different time ranges to purchase electricity. Transactions in different markets make the industrial customer involve in different levels of cost uncertainties and risks. To solve this energy procurement portfolio problem, a medium-term operation model is proposed. The risk term is measured and managed by a mean-variance approach. The uncertainties in the proposed model are characterized by stochastic day-ahead and real-time prices generated based on ERCOT historical data. A sample case study is provided to illustrate and verify the proposed model.

[1]  Mohammad Shahidehpour,et al.  Market operations in electric power systems , 2002 .

[2]  Jun Xu,et al.  Power Portfolio Optimization in Deregulated Electricity Markets With Risk Management , 2004, IEEE Transactions on Power Systems.

[3]  Ye Tang,et al.  Research into possibility of smart industrial load participating into demand response to supply the power system , 2010, CICED 2010 Proceedings.

[4]  R. I. Karimov,et al.  The efficient frontier for spot and forward purchases: an application to electricity , 2004, J. Oper. Res. Soc..

[5]  T. Funabashi,et al.  Electricity Price Forecasting for PJM Day-Ahead Market , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[6]  L. Yaan,et al.  Purchase Allocation and Demand Bidding in Electric Power Markets , 2002, IEEE Power Engineering Review.

[7]  M.A. Farahat Long-term industrial load forecasting and planning using neural networks technique and fuzzy inference method , 2004, 39th International Universities Power Engineering Conference, 2004. UPEC 2004..

[8]  Le Xie,et al.  Coupon Incentive-Based Demand Response: Theory and Case Study , 2013, IEEE Transactions on Power Systems.

[9]  A. Salo,et al.  Optimization of Electricity Retailer's Contract Portfolio Subject to Risk Preferences , 2010, IEEE Transactions on Power Systems.

[10]  P. Weil The equity premium puzzle and the risk-free rate puzzle , 1989 .

[11]  Charles A. Holt,et al.  Risk Aversion and Incentive Effects , 2002 .

[12]  Z.C. Pan,et al.  Detection and Location of Ground Faults Using a Discernible Signal , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[13]  L. Hansen,et al.  Stochastic Consumption, Risk Aversion, and the Temporal Behavior of Asset Returns , 1983, Journal of Political Economy.

[14]  Salman Mohagheghi,et al.  Managing Industrial Energy Intelligently: Demand Response Scheme , 2014, IEEE Industry Applications Magazine.

[15]  Salman Mohagheghi,et al.  Dynamic demand response solution for industrial customers , 2013, 2013 IEEE Industry Applications Society Annual Meeting.

[16]  X. Guan,et al.  Purchase allocation and demand bidding in electric power markets , 2002 .

[17]  M. Carrion,et al.  Forward Contracting and Selling Price Determination for a Retailer , 2007, IEEE Transactions on Power Systems.

[18]  Chi-Keung Woo,et al.  Managing electricity procurement cost and risk by a local distribution company , 2004 .

[19]  M. Rabin Risk Aversion and Expected Utility Theory: A Calibration Theorem , 2000 .

[20]  Lin Niu,et al.  Day-ahead Generation Scheduling with Demand Response , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[21]  A. Conejo,et al.  A Bilevel Stochastic Programming Approach for Retailer Futures Market Trading , 2009, IEEE Transactions on Power Systems.

[22]  Salman Mohagheghi,et al.  Intelligent demand response scheme for energy management of industrial systems , 2012, 2012 IEEE Industry Applications Society Annual Meeting.

[23]  K. Zare,et al.  Risk-Based Electricity Procurement for Large Consumers , 2011, IEEE Transactions on Power Systems.

[24]  Randall P. Sadowski,et al.  Simulation with Arena , 1998 .

[25]  Duan Li,et al.  Optimal Dynamic Portfolio Selection: Multiperiod Mean‐Variance Formulation , 2000 .

[26]  Shun-Hsien Huang,et al.  Demand response — An assessment of load participation in the ERCOT nodal market , 2012, 2012 IEEE Power and Energy Society General Meeting.

[27]  Peng Wang,et al.  Descriptive Models for Reserve and Regulation Prices in Competitive Electricity Markets , 2014, IEEE Transactions on Smart Grid.

[28]  Dingguo Chen Post-market computation engine toward CAISO market settlements , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[29]  R. Jones Midwest ISO experience with reliability unit commitment , 2006, 2006 IEEE Power Engineering Society General Meeting.

[30]  B. Han,et al.  Development of hardware simulator for DC micro-grid operation analysis , 2012, 2012 IEEE Power and Energy Society General Meeting.

[31]  Joanna Janczura,et al.  Regime-switching models for electricity spot prices: Introducing heteroskedastic base regime dynamics and shifted spike distributions , 2009, 2009 6th International Conference on the European Energy Market.

[32]  Ning Zhang,et al.  Generators Bidding Behavior in the NYISO Day-Ahead Wholesale Electricity Market , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[33]  J. Yu,et al.  Evolution of ERCOT Market , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[34]  S.N. Singh,et al.  Fuzzy Adaptive Particle Swarm Optimization for Bidding Strategy in Uniform Price Spot Market , 2007, IEEE Transactions on Power Systems.

[35]  M. Ma,et al.  FOUNDATIONS OF PORTFOLIO THEORY , 1990 .

[36]  Wei-Jen Lee,et al.  Comparative study on load forecasting technologies for different geographical distributed loads , 2011, 2011 IEEE Power and Energy Society General Meeting.

[37]  M Harry,et al.  MARKOWITZ, . Foundations of Portfolio Theory, Journal of Finance, , . , 1991 .