Abstract
The present paper aims to present a comprehensive multi-objective optimization model for energy management in local multi-energy systems in the presence of plug-in electric vehicles (PEVs), seeking to achieve the maximized profit of the operators of the local multi-energy systems and minimized CO2 emission at the same time. This problem involves technology transfer in local multi-energy systems and finding the PEVs charge/discharge optimization strategies in order to maximize the operators' profit and, in the meantime, reduce the CO2 emission. It can be solved by formulating a multi-purpose objective and can be dealt with by formulating a multi-objective programming problem through accurate modeling of mutual dependencies between the energy carriers. To solve this problem, a Modified Group Search Optimization (MGSO) algorithm is used based on the decomposition system. By the proposed structure, the local and global search is improved significantly. As indicated by the results of the effectiveness test of the optimization framework for maximizing the operator's profit and, meanwhile, reducing the CO2 emission, this objective is achievable through optimal coordination of multiple energy carriers in local multi-energy systems and effective management of the flexibility collected at both supply and demand sides.The simulation has been investigated in different scenarios. Obtained numerical analysis shows that the base case signifies the best case in terms of maximization of the local multi-energy systems operator’s profit through the optimized charging and discharging approaches of the PEVs. Also, in all scenarios, the profit of operator reductions by 5%–15% as compared with the base case. On the other hand, the best case scenario from an environmental point of view is provided by a scenario categorized with electric vehicles excluding under environmental optimization. In fact, CO2 emissions are reduced 7% compared to the emissions of base case.
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