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A Bi-Level Energy-Saving Dispatch in Smart Grid Considering Interaction Between Generation and Load

机译:考虑发电与负荷相互作用的智能电网双层节能调度

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The energy-saving dispatch could significantly enhance the energy consumption and carbon dioxide emission reduction, as well as the sustainable development of the socio economy in China. With the rapid growth of smart grid, the use of demand response to dispatch loads with flexible consumption time and/or quantity has been a new trend in power industry. This paper proposes a new energy-saving dispatch problem while considering energy-saving and emission-reduction potentials of generation and demand sides, as well as the interaction between the two. A bi-level optimization model is established to address the interaction between the energy-saving dispatch of thermal units and that of users. The objective function of the upper level considers both the electric power generation cost and the carbon emission cost of thermal units, while the lower level integrates both compensation and incentive costs of electricity consumers into its objective function according to the influences of their regulation-based demand response to the power grid. Moreover, user benefits of reducing downtime and avoiding frequent load restarting are also considered in the lower layer model. An iterative algorithm is proposed and the improved nondominated sorting genetic algorithm II (NSGA-II) method is used to solve the lower-layer model for seeking the optimal compromise solution on the Pareto frontier, which is derived by maximum deviations and entropy-based multiple attributes decision making method. Comparing with general NSGA-II and multiobjective genetic algorithms, the improved NSGA-II method can improve the spatial distribution of Pareto solution set and reduce the number of iterations, thus having stronger consistency among multiple objective functions and better performance.
机译:节能调度可以显着提高能源消耗和二氧化碳排放量,以及中国社会经济的可持续发展。随着智能电网的快速发展,用需求响应来分配负荷时间和/或数量灵活的负荷已成为电力行业的新趋势。本文在考虑发电侧和需求侧的节能减排潜力以及两者之间的相互作用的同时,提出了一个新的节能调度问题。建立了双层优化模型,以解决热力单元节能调度与用户节能调度之间的相互作用。上层的目标函数同时考虑了热力单元的发电成本和碳排放成本,而下层的目标函数则根据其基于法规的需求的影响,将用电者的补偿成本和激励成本整合到其目标函数中对电网的响应。此外,在较低层模型中还考虑了减少停机时间和避免频繁的负载重启的用户利益。提出了一种迭代算法,并使用改进的非支配排序遗传算法II(NSGA-II)方法求解下层模型,以最大偏差和基于熵的倍数为基础,在Pareto边界上寻求最优折衷解。属性决策方法。与一般的NSGA-II和多目标遗传算法相比,改进的NSGA-II方法可以改善Pareto解集的空间分布并减少迭代次数,从而在多个目标函数之间具有更强的一致性和更好的性能。

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