In this study, a phased operation optimization method for active distribution network with energy storage system is proposed for the operation optimization problem of active distribution network. The proposed model co...
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In this study, a phased operation optimization method for active distribution network with energy storage system is proposed for the operation optimization problem of active distribution network. The proposed model considers the PV and storage system output, the number of regulation equipment action, network loss and node voltage deviation for multi-objectiveoptimization of the active distribution network. To achieve multi-objectiveoptimization of the proposed optimal model, this study proposes a multi-objective coati optimization algorithm based on integrated crowding distance ranking, Bernoulli chaotic mapping, external archiving and nondominated ranking mechanism. The proposed algorithm exhibits good performance as tested by ZDT and UF test functions, and obtaining optimal values in 75 % of the results. In the test of the improved IEEE30 and IEEE33 node system, the proposed method is improved by 21.55 %, 56.10 % and 4.12 % in the three aspects of network loss, node voltage deviation and minimum voltage of the whole network compared with the unoptimized case. Therefore, the proposed method in this study has a good application value for the optimization of active distribution network operation and can support the grid-connected operation of distributed new energy sources.
As the main energy consumption part of the central air-conditioning systems, the energy saving of the chilled water system is particularly crucial. This system realizes heat exchange with indoor air by delivering chil...
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As the main energy consumption part of the central air-conditioning systems, the energy saving of the chilled water system is particularly crucial. This system realizes heat exchange with indoor air by delivering chilled water to air-conditioning units, effectively regulating indoor temperature and humidity to ensure thermal comfort. In this article, an improved multi-objective coati optimization algorithm (IMOCOA) is used to optimize the operating parameters and thermal comfort environment parameters of chilled water systems to improve thermal comfort and reduce energy consumption. The algorithm introduces chaotic mapping to enhance search diversity, balances global and local search capabilities through Levy flight and Gauss variation strategies, and uses location greedy choices to help coatis jump out of local optima. To verify the optimization effect of IMOCOA, a multi-objectiveoptimization model was established, combining the energy consumption model of the chilled water system and the simplified thermal comfort model. Key parameters, including chilled water supply temperature, pump speed ratio, indoor temperature, and relative humidity, are optimized. The simulation results from the experiments show that the average energy-saving rate of the chilled water system using IMOCOA is 7.8% and thermal comfort is improved by 19.6%. Compared to other optimizationalgorithms, this method demonstrates a better optimization effect.
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