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

Highlights: • Modelling mathematical integration of the proposed central bidding strategy for microgrids. • Considering and modelling the intra-market for adjusting the energy imbalances. • Analyzing effect of uncertainty of demand response and imbalance prices in profit of MG components. - 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