Optimal Bidding Strategy for a Smart Microgrid in Day-Ahead Electricity Market with Demand Response Programs Considering Uncertainties

Optimal Bidding Strategy for a Smart Microgrid in Day-Ahead Electricity Market with Demand Response Programs Considering Uncertainties

This paper computes the optimal bids that the smart microgrid energy management system (SMEMS) submits to the day-ahead electricity market. This smart microgrid consists of dispatchable generation resources, renewable generation resources, storage system and the loads that can be participate in the demand response (DR) programs. In this study we intend to maximize the expected profit earned by trading in day-ahead electricity market as well as optimal scheduling of smart microgrid for energy dispatching on the operating day. The bidding problem can be difficult due to different uncertainties in generations, loads and market prices forecasts amounts. To deal with these uncertainties, two-stage stochastic programming is employed. Various stochastic scenarios are generated by Monte Carlo simulation and then a scenario reduction algorithm based on kantorovich distance is performed. Nonlinear terms of the objective function are recast into linear forms. Numerical results have confirmed the profitability of the proposed smart microgrid.

This paper computes the optimal bids that the smart microgrid energy management system (SMEMS) submits to the day-ahead electricity market. This smart microgrid consists of dispatchable generation resources, renewable generation resources, storage system and the loads that can be participate in the demand response (DR) programs. In this study we intend to maximize the expected profit earned by trading in day-ahead electricity market as well as optimal scheduling of smart microgrid for energy dispatching on the operating day. The bidding problem can be difficult due to different uncertainties in generations, loads and market prices forecasts amounts. To deal with these uncertainties, two-stage stochastic programming is employed. Various stochastic scenarios are generated by Monte Carlo simulation and then a scenario reduction algorithm based on kantorovich distance is performed. Nonlinear terms of the objective function are recast into linear forms. Numerical results have confirmed the profitability of the proposed smart microgrid.