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Scenario based stochastic optimal operated for hybrid energy system with random drift swarm optimization

机译:随机漂移群优化随机漂移系统的混合能量系统运行的基于方案的随机优化

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To deal with the uncertainties of wind power and solar energy, the theory of stochastic programming is introduced, and this paper proposes an optimal model of economic dispatch of power system which the prediction error of wind power and photovoltaic output power is considered. Firstly, the stochastic probability distribution model of wind power and solar energy is analyzed. And then the scenarios are constructed by Latin hypercube sampling(LHS). To reduce the complexity of the model, the secenarios reduction method is used to reduce the similarity and low probability scenarios. On the basis of the above analysis, the costs of thermal power unit fuel and energy storage system operation are comprehensively considered, and the random drift particle swarm optimization algorithm which is used to obtain the minimum expected total cost of the hybrid system in the research period is applied. The energy storage system is introduced to reduce the impact of the prediction error of wind power and solar energy on power system stability. The case study indicates the rationality and effectiveness of the proposed model.
机译:为了解决风电和太阳能的不确定性,随机规划的理论引入,并提出其中风力发电和光伏输出功率的预测误差被认为是电力系统的经济调度的优化模型。首先,风能和太阳能的随机概率分布模型进行了分析。然后场景拉丁超立方抽样(LHS)构成。为了降低模型的复杂性,减少secenarios方法用于降低相似性和低概率的情况。上述分析的基础上,火力发电单元的燃料和能量存储系统的操作的成本综合考虑,并且它是用来获得最小的随机漂移粒子群优化算法在研究期间所期望的混合动力系统的总成本被申请;被应用。所述能量存储系统被引入以减小风功率和电力系统的稳定的太阳能的预测误差的影响。案例研究表明该模型的合理性和有效性。

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