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As renewable power penetration gradually increases in hierarchical distribution networks, certain regions have started to lack the ability to consume. How to improve the consumption capacity of a hierarchical distribution network through optimal dispatching has become a hot topic in the current research on distribution system operation. Firstly, th...
Citations
... However, in multiobjective optimization problems, there is often a complex coupling relationship between multiple objective functions, which poses signifcant challenges for the multilevel decomposition process. Moreover, this approach generally results in a trade-of among multiple objective functions, lacking an analysis of the importance level between diferent objective functions [21]. ...
... Processing method for multiobjective function [9] PV and ESS Cost of power exchanged with other local microgrids and the grid Sum of the cost method [10] DG, BESS, and microgrid Determine the lifetime of BESS and the cost of microgrids functioning Weighted summation method [12] DGs and grid Active power loss, reactive power loss, and the voltage deviation index Weighted summation method [18] DG, PV, ESS, EV, HVACs, and other RLs Maximizing TVPP's day-ahead and intraday proft in the fexible market Two-stage adaptive robust optimization [19] DN, WIND, PV, ESS, PEMFC, EB, GB, TR, and HS Carbon trading cost, operational cost, maintenance cost, and rescheduling cost Two-stage distributionally robust optimization [21] DG, renewable energy, ES, and grid Line loss rate, renewable energy abandonment power penalty item, and ESS residual penalty item Multistage decoupling structure based on extreme scenario evaluation [26] PV, ESS, EV, and grid Maximizing the proft and minimizing the peak-to-valley diference of the load and loss rate of ESS MOMUS + TOPSIS [28] PV, WIND, BESS, EV, TL, IL, and grid Maximizing the benefts of GS and maximizing the benefts of LS International Transactions on Electrical Energy Systems 3 ...
To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems.
