Abstract
Abstract This paper presents the study of a large-scale integrated energy system (LSIES) consisting of distributed district heating and cooling units (DHCs) and wind power generation interconnected via a power grid. A multi-objective group search optimizer with adaptive covariance and Levy flights (MGSO-ACL) is developed to optimize the power dispatch of the LSIES with the selected objectives compromising the competing benefits of both the power grid and the DHCs. Furthermore, a decision making method based on an evidential reasoning (ER) approach is used to determine a final optimal solution from the Pareto-optimal solutions obtained by the MGSO-ACL. The ER takes into account both the multiple objectives and the multiple evaluation criteria representing the economy and reliability interests of the power grid and the DHCs. Simulation studies are conducted on an IEEE 30-bus system, which is modified to include distributed DHCs and wind power generation, to verify the effectiveness of the MGSO-ACL and ER. The results demonstrate that the MGSO-ACL can obtain superior Pareto-optimal solutions in terms of convergence and diversity, and the ER is pragmatic in handling the complex decision making problem, in particular for determining the final optimal solution of the power dispatch of the LSIES.
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