A novel enviro-economic three-stage market-based energy management considering energy storage systems and demand response programs for networked smart microgrids

Today's power market has been influenced by the introduction of smart microgrids (MGs) to the electricity infrastructure. Furthermore, the operation of the power system has already been affected by exploiting the demand response (DR) programs and make more use of the energy storage. Therefore, the study of the simultaneous presence of the power market models, DR programs, and energy storage in networked smart microgrids is crucial. In this paper, a novel multi-stage optimization model is presented to indicate the effects of DR programs on the market-based scheduling of the smart networked microgrids' performance. In the first stage, optimal energy management has been carried out, and each microgrid system operator in the environmental smart grid proposes its power prices and power quantities to participate in the power market. Then, in the second stage, the market-based energy model is implemented, and the independent system operator (ISO) clears the market. The market-clearing stage is led to specify the prices and the amounts of energy that each microgrid can exchange. Also, in the last stage, energy management has been implemented based on the output parameters, which are submitted from the second stage. The objective function is defined as the mixed-integer nonlinear programming (MINLP) model, which has been implemented in the GAMS software and using BARON as the solver. The results generally show that the bidding strategy of the MGs can effectively control the final operation cost and the emission. However, enjoying these benefits requires accurate pricing of MGs. It also reveals that the DR programs are useful in emission mitigation programs. As well, DR programs are promising for the market in which the bidding strategy of an MG is not successful.