【摘要】 为了解决智能小区用电过程中存在的用电费用高、电能浪费明显的问题，在构建光伏、蓄电池数学模型基础上，对应分时电价，提出了以用电费用最低为优化目标的智能小区能量调度优化策略。针对智能小区对蓄电池的效用需要，构建了单元楼蓄电池的工作模型，同时对蓄电池运行特性进行了探究。针对小区内具有各种家用负荷、蓄电池和光伏电源的问题，提出了智能小区能量管理系统的总体架构。运用蒙特卡洛法，分别模拟出优化前后某智能小区负荷曲线，并利用仿真软件对智能小区能量调度优化策略进行验证。对应分时电价，对比了采用负荷群优化控制策略前后的用电情况，计算了采用该策略所削减的电费同时计算在运用能量调度优化策略前后智能小区所削减的电费。仿真结果表明，该策略可以在有效节约小区用电费用的同时实现小区负荷削峰填谷。更多还原

【Abstract】 To solve the problems of high electricity consumption cost and obvious waste of electricity in the process of electricity consumption in intelligent community, the optimization strategy of energy dispatching in intelligent community with the lowest electricity consumption cost as the optimization goal is proposed on the basis of constructing the mathematical model of photovoltaic and battery and corresponding to the time-sharing tariff. In view of the utility needs of the battery in the intelligent community, the working model of the battery in the community building is constructed, and the operating characteristics of the battery are also explored. The overall architecture of the energy management system of the intelligent community is proposed for the problem of having various domestic loads, batteries and photovoltaic power sources in the community. The load curve of a smart community is simulated before and after the optimization using Monte Carlo method, and the simulation software is used to verify the optimization strategy of the intelligent community. The optimization strategy of the energy dispatch is validated in accordance with the optimization objective of the intelligent community. The energy dispatch optimization strategy is verified by using simulation software. The electricity consumption before and after the adoption of the load group optimization control strategy is compared with the time-of-use tariff, and the electricity cost reduction by the adoption of the strategy is calculated, as well as the electricity cost reduction by the intelligent community before and after the adoption of the energy dispatch optimization strategy. The simulation results show that the strategy can effectively save the cost of electricity in the community while achieving peak and valley reduction.更多还原