考虑碳交易成本及区域能源优化的配电网扩展规划研究

随着《碳排放权交易管理办法》的正式实施,加速了碳排放权交易的进度,在此背景下考虑区域综合能源系统(regional integrated energy system, RIES)接入配网后需求响应、可再生资源消纳和碳排放交易成本的影响,提出一种新的配电网规划方法。首先,对区域综合能源系统结构进行概述,介绍典型区域综合能源系统的构成及特点;其次,将碳交易成本、弃风弃光成本和需求响应引入配电网扩展模型当中,综合考虑系统的碳排放、弃风弃光和需求侧响应对区域综合能源系统的影响,促进系统整体的优化运行。在上述基础上构建考虑区域综合能源系统优化的配网扩展优化双层模型,采用改进粒子群算法(improved particle swarm optimization, IPSO)和预测校正内点法(prediction correction interior point method, PCIP)求解规划层和运行层模型。最后,运用算例系统对构建的模型和方法进行验证。结果表明：配电网扩展规划时,充分考虑区域综合能源系统优化,能够提升可再生能源的消纳,降低整体的扩展规划成本。

Distribution network expansion planning considering carbon trading cost and regional energy optimization

With the formal implementation of the administrative measures for carbon emission trading, the progress of carbon emission trading has been accelerated. Under the above background, considering the impact of demand response, renewable resource consumption and carbon emission trading cost after ries is connected to the distribution network, a new distribution network planning method is proposed. Firstly, the structure of regional integrated energy system is summarized, and the composition and characteristics of typical regional integrated energy system are introduced. Secondly, the carbon trading cost, wind and light abandonment cost and demand response are introduced into the distribution network expansion model to comprehensively consider the impact of system carbon emissions, wind and light abandonment and demand side response on regional integrated energy system, so as to promote the optimal operation of the whole system . On the basis of the above, a two-level model of distribution network expansion optimization considering regional integrated energy system optimization is constructed, and the improved particle swarm optimization (IPSO) algorithm and prediction correction interior point method (PCIP) are used to solve the planning level and operation level models. Finally, an example system is used to verify the model and method, and the results show that the regional integrated energy system optimization can improve the renewable energy consumption and reduce the overall expansion planning cost.