考虑数据中心需求响应的城市电网阻塞管理

Congestion Management of Urban Power Grid Considering Demand Response of Data Center

随着"大云物移智"技术的快速推进,大型数据中心迅速发展,地理位置上分散的互联网数据中心(internetdata centers,IDCs)是大数据平台的物理基础。在数据流爆炸的背景下,用电容量巨大的IDCs可作为需求响应的一种,成为智能电网的重要互动资源。利用IDCs网络与城市电网动态关联特性,使数据负荷在IDCs网络中的迁移代替电能在电网中的转移。构建负荷转供方法与IDCs需求响应方法协同管控城市电网的双层优化模型,将城市电网静态容量转变为动态容量,解决城市电网阻塞问题与需求响应中2个独立主体运行效益问题。首先,构建电网公司与IDCs运营商双层协同优化模型,上层模型目标函数为电网公司阻塞管理成本最小、下层模型目标函数为IDCs运营商参与需求响应的收益最大,通过基于优惠券激励的需求响应决策模型,协调二者运行效益。其次,基于Benders分解思想构建算法的整体框架与流程,对模型进行求解。最后,通过算例验证:当IDCs达到一定规模时,协同优化模型阻塞管理效果优于传统负荷转供模型,在解决阻塞问题的同时,既能降低电网公司阻塞管理成本也能保障IDCs运营商参与需求响应收益。

With the rapid development of the technology of "Big data, cloud computing, Internet of things, mobile Internet, artificial intelligence", large data centers are developing rapidly. Distributed Internet data centers(IDCs) are the physical basis of big data platform. Under the background of data flow explosion, IDCs with huge power consumption capacity can be used as a kind of demand response and become an important interactive resource of smart grid. The purpose of this paper is to make use of the dynamic correlation between the internet data centers(IDCs) Network and the urban power grid to replace the power transfer in the power grid with the data load transfer in the IDCs Network. Therefore a two-level optimization model is proposed with the coordination of the load transfer method and the IDCs demand response method to manage and control the urban power grid. It will transform the static capacity of the urban power grid into the dynamic capacity, solving the problems of the power congestion and the demand response of the urban power grid. First of all, a bilevel optimization model with cooperation between the grid companies and the IDCs operators is constructed. The objective function on the upper layer of the model is to minimize the congestion management cost for the grid companies, and the one of the lower layer is to maximize the benefits of the IDCs operators’ participation in demand response. Through the demand response decision-making model based on coupon incentive, the operating benefits on both the two layers are coordinated. Secondly, based on Benders Decomposition idea, the overall framework and process of the algorithm are constructed to solve the model. Finally, an example is given to verify that when the IDCs reaches a certain scale, the congestion management effect of the collaborative optimization model is better than that of the traditional load transfer model. While solving the congestion problem, it can not only reduce the congestion management cost of the power grid companies, but also guarantee the demand response revenue of the IDCs operators.