Abstract
•Solving an optimal scheduling problem for CHP-based MG with reconfigurable capability.•Analyzing economic and environmental operation of CHP-based RMG based on multi-objective problem.•Consider incentive-based DR and tri-state CAES as emerging sources to provide more flexibility.•Proposing a two-stage scenario-based stochastic approach to handle the fluctuation.
Microgrids (MGs) are solutions to integrate high shares of variable renewable energy which can contribute to more economical and environmental benefits, as well as improving the energy supply efficiency. One significant potential of MGs is an expanded opportunity to use the waste heating energy from the conversion of the primary fuel (such as natural gas) to generate electricity. The use of waste heat in combined heat and power (CHP)-based MG is more efficient to meet local load and decrease the emission pollution. Hence, this paper elaborates on optimal multi-objective scheduling of CHP-based MG coupled with compressed air energy storage (CAES), renewable energy, thermal energy storage (TES), and demand response programs through shiftable loads, which considers a reconfiguration capability. The embedded CAES, in addition to the charging/discharging scheme, can operate in a simple cycling mode and serve as a generation resource to supply local load in an emergency condition. The daily reconfiguration of MG will introduce a new generation of MG named reconfigurable microgrid (RMG) that offers more flexibility and enhances system reliability. The RMG is coupled with TES to facilitate the integration of the CHP unit that enables the operator to participate in the thermal market, in addition to the power market. The main intents of the proposed multi-objective problem are to minimize the operation cost along with a reduction in carbon emission. The epsilon-constraint technique is used to solve the multi-objective problem while fuzzy decision making is implemented to select an optimal solution among all the Pareto solutions. The electricity prices and wind power generation variation are captured as random variables in the model and the scenario-based stochastic approach is used to handle them. Simulation results prove that the simultaneous integration of multiple technologies in CHP-based RMG decreases the operation cost and emission up to 3 % and 10.28 %, respectively.