Economic and environmental operation of power systems including combined cooling, heating, power and energy storage resources using developed multi-objective grey wolf algorithm

Increasing energy demand, energy generation costs, and environmental concerns are among the reasons driving governments to develop technologies in order to save energy and reduce greenhouse gas emissions. The combined cooling, heating, and power systems are among the technologies developed to reduce primary energy consumption, costs, and greenhouse gas emissions. Combined cooling, heating, and power systems generate power on-site by a primary actuator. The difference between these systems and traditional power plants is that these systems use the heat dissipated from the primary actuator to meet consumers' heating and cooling demands. These systems convert the waste heat in the power generation sector into the heating and cooling energy required by users to improve performance. This study aims to develop a multi-objective model considering three objective functions in energy, economic, and environmental areas as well as different practical and nonlinear constraints to effectively implement this system in the power grid. Given that these functions are inherently contradictory, an optimized multi-objective grey wolf algorithm is proposed as a solution. The proposed multiobjective algorithm is a model developed based on non-dominated sorting theory, variable detection, memorybased strategy selection, and fuzzy theory to select the optimal Pareto from the set of solutions, which has powerful performance in solving the above problem. The results obtained from the figures and tables in different time periods indicated the proposed method increased by about 2% in summer and 2% in winter compared to other methods. In addition, the computation time for energy system planning was acceptable.

Automated summary

The study aims to develop a multi-objective model considering energy, economic, and environmental factors, using an optimized multi-objective algorithm to effectively implement combined cooling, heating, and power systems. The results show that the system not only improves performance but also saves energy costs, demonstrating advantages in solving conflicting objectives.