考虑风-光-荷时空相关性的分布式电源和广义储能联合规划

在分布式电源(DG)和广义储能(GES)联合规划中,针对运行与规划如何紧密耦合、在获取大量分散资源行为规律的基础上如何预测其在规划周期内的关联特性的难题,提出了多能互补发电系统中DG与GES的双层优化规划方法.上层以新能源历史出力为输入,建立了考虑综合成本、DG承载能力和系统综合运行风险的上层规划模型,决策得到DG和GES的安装方案;下层考虑运行成本以平衡各储能系统的充放电功率,决策得到GES的调度策略.提出了可描述多维风-光-荷时空相关性的MD-K2贝叶斯网络模型,用于实现多元数据驱动下DG和GES的协同优化.算例测试结果验证了所提模型和方法的合理性以及有效性.

Joint planning of distributed generation and generalized energy storage considering spatial-temporal correlation of wind-photovoltaic-load

In the joint planning of DG(Distributed Generation) and GES(Generalized Energy Storage),a two-level optimal planning method of DG and GES in multi-energy complementary power generation system is proposed to solve the problems of how to couple operation and planning closely and how to predict the correlation characteristics of a large number of decentralized resources in the planning cycle on the basis of obtaining their behavior rules. Taking the historical output of new energy as input, the upper level planning model considering the comprehensive cost, DG carrying capacity and system comprehensive operation risk is established, and the installation scheme of DG and GES is obtained. The lower level considers the operation cost to balance the charging and discharging power of each energy storage system, and obtains the scheduling strategy of GES. The MD-K2 Bayesian network model, which can describe the spatial-temporal correlation of multi-dimensional wind-photovoltaic-load, is proposed to realize the collaborative optimization of DG and GES driven by multi-variate data. The rationality and effectiveness of the proposed model and method are verified by the test results of a numerical example.