Optimal Scheduling of Wastewater Treatment Plant Integrated Energy Systems Using an Enhanced Grey Wolf Optimization Algorithm

Wastewater treatment plants (WWTP), as common integrated energy systems (IES), efficiently manage electrical and thermal demands while utilizing renewable energy and biomass resources. This paper proposes an optimal scheduling framework for WWTP, covering gas turbines, gas boilers, electric boilers, photovoltaics, wind turbines, biomass generators, and energy storage devices. An enhanced Grey Wolf Optimization (GWO) algorithm is compared with the Mixed-Integer Linear Programming (MILP) method solved by CPLEX. Simulation results show that the total cost achieved by the GWO algorithm is 0.43% lower than that of the MILP algorithm, with the operating cost reduced by 6.39% and the carbon cost reduced by 10.95%. Although the utilization rate of renewable energy is lower than that of MILP, GWO demonstrates higher cost efficiency, flexibility in handling complex constraints, accessibility without commercial solvers, and robustness to uncertainties. These characteristics make GWO a practical and effective solution for optimizing energy management in WWTP.