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Optimal Economical Schedule of Hydrogen-Based Microgrids With Hybrid Storage Using Model Predictive Control

机译:基于模型预测控制的混合存储氢微电网的最优经济调度

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The electricity market rules determine the energy prices in the day-ahead market, matching offers from generators to bids from consumers. The unpredictability of renewable energy combined with the penalty deviations used in the regulation market makes it difficult for clean energy to play an important role in the electricity market. The high density of hydrogen as an energy storage system (ESS) appears to be one solution to the problems outlined. There is still not a perfect ESS, everyone has different limitations from the point of view of time autonomy, time response, degradation issues, or acquisition cost. The design of a hybrid energy storage management system emerges as a technological solution to the problems commented. The development of an optimal control for renewable energy microgrids with hybrid ESS is carried out using model predictive control (MPC). The MPC techniques allow maximizing the economical benefit of the microgrid, minimizing the degradation causes of each storage system, and fulfilling the different system constraints. In order to capture both continuous/discrete dynamics and switching between different operating conditions, the plant is modeled with the framework of mixed logic dynamic. The MPC problem is solved within mixed-integer quadratic programming.
机译:电力市场规则决定了日间市场中的能源价格,将发电机的报价与消费者的报价相匹配。可再生能源的不可预测性以及监管市场中使用的罚款偏差使得清洁能源难以在电力市场中发挥重要作用。作为能量存储系统(ESS)的高密度氢似乎是解决上述问题的一种方法。尚无完善的ESS,从时间自治,时间响应,降级问题或购置成本的角度来看,每个人都有不同的限制。混合储能管理系统的设计作为解决上述问题的技术解决方案而出现。使用模型预测控制(MPC)进行了具有混合ESS的可再生能源微电网的最优控制的开发。 MPC技术可以最大程度地发挥微电网的经济效益,最大程度地减少每个存储系统的退化原因,并满足不同的系统约束。为了捕获连续/离散动态以及在不同操作条件之间的切换,工厂采用了混合逻辑动态框架。 MPC问题在混合整数二次规划中得以解决。

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