Strategic Bidding in a Pool-Based Electricity Market under Load Forecast Uncertainty

This paper proposes a method for determining the price bidding strategies of market participants consisting of generation companies (gencos) and distribution companies (discos) in a day-ahead electricity market, while taking into consideration the load forecast uncertainty and demand response programs. the proposed algorithm tries to find a pareto optimal point for a risk neutral participant in the market. because of the complexity of the problem a stochastic method is used. in the proposed method, two approaches are used simultaneously. first approach is fuzzy genetic algorithm for finding the best bidding strategies of market players, and another one is mont-carlo method that models the uncertainty of load in price determining algorithm. it is demonstrated that with considering transmission flow constraints in the problem, load uncertainty can considerably influences the profits of companies and so using the second part of the proposed algorithm will be useful in such situation. it is also illustrated when there are no transmission flow constraints, the effect of load uncertainty can be modeled without using a stochastic model. the algorithm is finally tested on an 8 bus system.