Integrated offering strategy for profit enhancement of distributed resources and demand response in microgrids considering system uncertainties

Abstract Due to the uncertain nature and limited predictability of wind and PV generated power, these resources participating in most of electricity markets are subject to significant deviation penalties during market settlements. In order to balance the unpredicted wind and PV power variations, system operators need to schedule additional reserves. This paper presents the optimal integrated participation model of wind and PV energy including demand response, storage devices, and dispatchable distributed generations in microgrids or virtual microgrids to increase their revenues in the intra-market. This market is considered 3–7 h before the delivered time, so that the amount of the contracted energy could be updated to reduce the produced power deviation of microgrid. A stochastic programming approach is considered in the development of the proposed bidding strategies for microgrid producers and loads. The optimization model is characterized by making the analysis of several scenarios and simultaneously treating three kinds of uncertainty including wind and PV power, intra-market, and imbalance prices. In order to predict these uncertainty variables, a neuro-fuzzy based approach has been applied. Historic data are used to forecast future prices and wind and PV power production in the adjustment markets. Also, a probabilistic approach based on the error of forecasted and real historic data is considered for estimating the future IM and imbalance prices of wind and PV produced power. Further, a test case is applied to example the microgrid using the Spanish market rules during one week, month, and year period to illustrate the potential benefits of the proposed joint biding strategy. The simulations results, carried out by MATLAB/optimization toolbox.

[1]  Bala Venkatesh,et al.  Optimal participation and risk mitigation of wind generators in an electricity market , 2010 .

[2]  Masood Parvania,et al.  Integrating Load Reduction Into Wholesale Energy Market With Application to Wind Power Integration , 2012, IEEE Systems Journal.

[3]  Whei-Min Lin,et al.  Electricity price forecasting using Enhanced Probability Neural Network , 2010 .

[4]  P. Varaiya,et al.  Bringing Wind Energy to Market , 2012, IEEE Transactions on Power Systems.

[5]  Julio Usaola,et al.  Optimal operation of a pumped-storage hydro plant that compensates the imbalances of a wind power pr , 2011 .

[6]  Javier Contreras,et al.  A decision-making tool for project investments based on real options: the case of wind power generation , 2011, Ann. Oper. Res..

[7]  Georges Kariniotakis,et al.  Strategies for Wind Power Trading in Sequential Short-Term Electricity Markets , 2009 .

[8]  Joao P. S. Catalao,et al.  A stochastic programming approach for the development of offering strategies for a wind power producer , 2012 .

[9]  Vjekoslav Galzina,et al.  An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices , 2013, Expert Syst. Appl..

[10]  R. Harley,et al.  Increased Wind Revenue and System Security by Trading Wind Power in Energy and Regulation Reserve Markets , 2011, IEEE Transactions on Sustainable Energy.

[11]  Mohammad Kazem Sheikh-El-Eslami,et al.  Price forecasting of day-ahead electricity markets using a hybrid forecast method , 2011 .

[12]  H. Shayeghi,et al.  Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme , 2013 .

[13]  Seyed Hossein Hosseinian,et al.  Optimization of hybrid solar energy sources/wind turbine systems integrated to utility grids as microgrid (MG) under pool/bilateral/hybrid electricity market using PSO , 2012 .

[14]  Mohammad Shahidehpour,et al.  Integration of High Reliability Distribution System in Microgrid Operation , 2012, IEEE Transactions on Smart Grid.

[15]  J. Usaola,et al.  Evaluating risk-constrained bidding strategies in adjustment spot markets for wind power producers , 2012 .

[16]  Haiyan Lu,et al.  Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model , 2012 .

[17]  Zhenhai Guo,et al.  A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm , 2014 .

[18]  Andrew Kusiak,et al.  Very short-term wind speed forecasting with Bayesian structural break model , 2013 .

[19]  Robert Pitz-Paal,et al.  Methodology for optimized operation strategies of solar thermal power plants with integrated heat storage , 2011 .

[20]  Henrik Madsen,et al.  Online short-term solar power forecasting , 2009 .

[21]  M. El-Sharkawi,et al.  Coordinated Trading of Wind and Thermal Energy , 2011, IEEE Transactions on Sustainable Energy.

[22]  A. Rahimi-Kian,et al.  Aggregated wind power and flexible load offering strategy , 2011 .

[23]  N. Kumarappan,et al.  Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT , 2014 .

[24]  Georges Kariniotakis,et al.  Advanced strategies for wind power trading in short-term electricity markets , 2008 .

[25]  Rohit Bhakar,et al.  Strategic bidding for wind power producers in electricity markets , 2014 .

[26]  A. Conejo,et al.  Optimal offering strategy for a concentrating solar power plant , 2012 .