Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid

The combined heat and power (CHP) microgrid can work both effectively and efficiently to provide electric and thermal power when an appropriate schedule and control strategy is provided. This study proposes a stochastic model predictive control (MPC) framework to optimally schedule and control the CHP microgrid with large scale renewable energy sources. This CHP microgrid consists of fuel cell based CHP, wind turbines, PV generators, battery/thermal energy storage system (BESS/TESS), gas fired boilers and various types of electrical and thermal loads scheduled according to the demand response policy. A mixed integer linear programming based energy management model with uncertainty variables represented by typical scenarios is developed to coordinate the operation of the electrical subsystem and thermal subsystem. This energy management model is integrated into an MPC framework so that it can effectively utilize both forecasts and newly updated information with a rolling up mechanism to reduce the negative impacts introduced by uncertainties. Simulation results show that the approach proposed in this paper is more efficient when compared with an open loop based stochastic day-ahead programming (S-DA) strategy and a MPC strategy. In addition, the impacts of fuel cell capacity and TESS capacity on microgrid operations are investigated and discussed.