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A fast computation and optimization algorithm for smart grid energy system

机译:智能电网能量系统的快速计算与优化算法

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Increasing penetration of intermittent and variable renewable energy sources (RESs) has significantly complicated smart grid operations. The uncertain nature of RESs may cause increased operating costs for committing costly reserve units or penalty costs for curtailing load demands. In addition, it is often desired to control and coordinate a battery energy storage system (BESS) in an efficient and economical way, especially for islanded microgrid. To address these issues, an approximate dynamic programming (ADP) approach is proposed to investigate the optimal operation of energy systems in islanded microgrid considering stochastic wind energy and load demands. A battery control strategy is also presented to maintain the battery state of charge in a certain range which will help to increase the battery lifetime in the future. The traditional dynamic programming (DP) approach is also implemented to validate the percentage of optimality of the proposed ADP approach for stochastic case studies. The simulation results show that the proposed ADP approach can obtain competitive percentages of optimality with around 50% less computational time compared to the traditional DP approach. Again, the proposed ADP approach is also validated on a large data sample case and achieved 18.77 times faster response than the traditional DP approach.
机译:增加间歇性和可变可再生能源(RESS)的渗透性具有显着复杂的智能电网操作。 RES的不确定性质可能会提高用于削减抵制负载需求的昂贵储备单位或罚款的运营成本。另外,通常希望以有效且经济的方式控制和协调电池储能系统(BESS),特别是对于孤岛化的微电网。为了解决这些问题,提出了一种近似的动态编程(ADP)方法,以研究考虑随机风能和负载需求的岛状微普林中的能量系统的最佳运行。还提出了一种电池控制策略以在一定范围内保持电池电量的电荷状态,这将有助于在未来增加电池寿命。还实施了传统的动态规划(DP)方法以验证所提出的ADP方法对随机案例研究的最优性百分比。仿真结果表明,与传统DP方法相比,所提出的ADP方法可以获得大约50 %的计算时间约为50 %的竞争百分比。同样,拟议的ADP方法也在大数据样本案例上验证,并且比传统的DP方法更快的响应达到18.77倍。

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