Solving the Energy Management Problems Using Thermal Exchange Optimization

This paper proposes a new metaheuristic optimization algorithm, namely thermal exchange optimization (TEO), to solve the optimal power flow (OPF) problems. Various conflict objective functions, such as the total fuel cost (TFC), the total power loss, total emission gas (TEG), and the total voltage deviation have been optimized individually and simultaneously. The proposed TEO is validated on the electric test system Institute of Electrical and Electronics Engineers 30-Bus. The optimization results achieved by the proposed method in solving single-objective functions were more effective in finding the optimal solution compared to several well-known algorithms. The results clearly show the superiority of the proposed method in the majority of the case studies, with a better solution and competitive computational time. In contrast, the proposed multi-objective TEO (MOTEO) based OPF is investigated to solve the multi-objective OPF. It can be noticed from the results obtained that the proposed MOTEO achieved the better optimum compromise solution with a TFC value of 822.4796 $/h and a TEG value of 0.26939 ton/h, which yields a competitive total cost (970.8219 $/h) compared to those obtained by other algorithms. Moreover, the statistical analysis proves that the proposed MOTEO needs a lower number of trials to locate the best solution, also the standard deviation required to solve the single-objective problems is 0.03361, which is better compared to other techniques. The simulation results achieved by this method compared with other competitive algorithms proved the superiority of MOTEO in finding better solutions while also producing a high-quality Pareto front with appropriate precision. Index Terms--Meta-heuristic algorithm, thermal exchange optimization (TEO), optimal power flow (OPF), IEEE 30-bus test system