【摘要】 为应对能源结构转型中的低碳化、清洁化需求,考虑了微网同时参与日前能量市场、实时能量市场以及碳交易市场的情况。针对市场电价预测精度高、误差分布规律等特点,采用随机规划法对其不确定性进行处理。针对光伏出力的随机性与间歇性,采用动态鲁棒优化法对其进行处理。构建了考虑电价和光伏出力不确定性的微网两阶段鲁棒优化调度模型,并采用恶劣场景辨别算法将原问题分解为主问题和子问题进行迭代求解。子问题用来辨别最恶劣的光伏出力情景,并通过主问题对该情景下的单层优化模型进行求解,从而极大地削减了所需求解情景数量,提高了模型的计算效率。算例验证了算法的有效性,结果表明:微网同时参与多个电力市场可显著增加利润;采用恶劣场景辨别算法能够减轻计算负担,有效提高计算效率,且在大规模场景下的适应性更强。更多还原

【Abstract】 In order to meet the demand of low carbon and cleanliness in the transformation of energy structure, the situation that microgrid participates in the energy market, real-time energy market and carbon trading market at the same time is considered. In view of the high forecasting accuracy and error distribution law of market electricity price, the stochastic programming method is used to deal with its uncertainty. In view of the randomness and intermittence of photovoltaic output, the dynamic robust optimization method is used to deal with it. A two-stage robust optimal scheduling model of microgrid considering the uncertainty of electricity price and photovoltaic output is constructed, and the bad scene identification algorithm is used to decompose the original problem into the main problem and sub-problem iteratively. The sub-problem is used to identify the worst photovoltaic scenario, and the single-layer optimization model under this scenario is solved by the main problem, which greatly reduces the number of scenarios needed and improves the computational efficiency of the model. A numerical example is given to verify the effectiveness of the algorithm, and the results show that the microgrid participating in multiple power markets at the same time can significantly increase profits, and the use of bad scene identification algorithm can reduce the computational burden and effectively improve the computational efficiency, and it is more adaptable in large-scale scenarios.更多还原