Hybrid robust-stochastic energy scheduling of multi-carrier systems integrated with ice storage and heat storage systems: A multi-solver approach

Abstract

This paper focuses on the short-term operation management of small-scale energy systems. The proposed model integrates the cooling, heating, and electrical sections to provide more flexibility, efficiency, and economic benefits for the system. To this end, an optimization framework is performed to minimize the total costs of different sectors considering the marginal costs of local resources, emission costs, incentive cost for demand response programs, and cost of purchasing energy from the main grid. We apply the stochastic, robust, and hybrid robust-stochastic approaches to consider the uncertainty of non-controllable energy resources, loads, and market prices to get more accurate results. In the proposed model, the probabilistic behaviors of photovoltaic and wind energy are considered by stochastic optimization. Also, the load and market prices are modeled by a robust approach to consider the worst condition in the operation and ensure robustness. To get the best operation scheduling, the proposed model is integrated with multiple solvers such as CPLEX, CBC, LINDO, MOSEK, GUROBI, and BDLMP. The proposed multi-solver model ensures that the solution found is optimal and robust. The proposed model is tested on a general case study and results show that demand response programs and electric vehicles can reduce short-term costs by $ 37.88. Also, the sensitivity analysis shows that the electrical demand response program and wind energy can reduce the operation costs of the system by 6.22 % and 19.17 %, respectively. Besides, the thermal demand response program can reduce the operation cost of the system by $ 12.52.