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| Research on Interactive Integration Planning of Data Centers and Distribution Network Driven by Carbon Emission Reduction |
| Zhang Yuying1,2, Zeng Bo1, Zhou Yinyu1, Xu Hao1, Liu Wenxia1 |
1. School of Electrical and Electronic Engineering North China Electric Power University Beijing 102206 China;
2. CHN Energy New Energy Technology Research Institute Co. Ltd Beijing 102209 China |
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Abstract With the proliferation of data centers (DC) in the digital economy, the scientific planning of DC grid connection and the use of its demand response (DR) capability are crucial to the secure, economical, and sustainable operation of future power systems. However, most of the existing studies have only explored the DC integration problem from the perspective of adverse-effect mitigation, while the potential value of DCs for supporting low-carbon operation of the grid has not been fully explored. As such, this paper proposes an interactive planning framework to enable synergetic integration of DC and distribution network under the carboncarbon-neutrality background.
Firstly, synthesizing the impacts of equipment characteristics, environmental temperature and other factors, a DC demand response model considering heat dissipation constraints is established. Secondly, given the impact of uncertainties such as power consumption/ data demand and renewable energy output on system objectives, the concept of carbon-emission-reduction loss risk in the distribution network with DC is defined by introducing conditional risk value (CVaR) theory. Then, based on the two-stage stochastic optimization method, a carbon-emission-reduction driven collaborative optimization model for DC and distribution network planning is proposed, which aims to minimize the total costs associated with system investment operating costs, carbon emission taxes and total CVaR losses (carbon emission reduction and economic risk). By considering the planning and operation control of source-grid-load components simultaneously, the proposed model could co-optimize the economic and low-carbon benefits of the system under controllable system risk. The model is reformulated into a mixed integer linear programming problem and resolved by the Gurobi commercial solver.
A modified IEEE-33 node system is used as an example for simulation analysis. In order to reveal the benefits of implementing DC and distribution network interactive integration planning, four scenarios with different DC integration modes and response characteristics are set up and their resulting optimal planning solutions and cost-benefits are compared and analyzed. It is found that DC and distribution network integration planning taking into account spatial and temporal adjustable characteristics, i.e., the method proposed in this paper, minimizes capacity allocation requirements and allows the system to make greater use of renewable energy sources and reduce external power purchases, thus achieving optimal economic and carbon reduction benefits. In addition, the impact of DC-specific thermal constraints and uncertainties on the achievement of these objectives is analyzed in detail.
The following conclusions can be drawn from the simulation analysis: (1) Compared with traditional self-regulated planning, IIP can effectively improve the efficiency of the distribution network with DC and promote the use of renewable energy, thereby helping the power system to reduce carbon emissions. (2) The expected benefits of implementing IIP are influenced by various factors such as the spatio-temporal adjustability of DC data load, demand characteristics and environment temperature. By considering the thermodynamic characteristics in the DC modeling, it helps to capture the real-time interactive responsivity of DC more accurately, thus ensuring the effectiveness of the final planning scheme. (3) Selecting cities with low average annual temperatures or high daytime temperature variations to implement IIP can save system construction and operation costs and better utilize the flexibility value of DC resources. (4) CVaR can intuitively quantify the risk of the uncertainty on the carbon emission reduction, and effectively balance the inherent contradiction between the expected benefits and risk losses according to the decision-maker's preference, thus having a better engineering practical value.
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Received: 29 August 2022
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2023, Vol. 38 
