A multi‐objective interval optimization approach to expansion planning of active distribution system with distributed internet data centers and renewable energy resources - Zhang, Yuying; Liang, Chen; Wang, Han; Zhang, Jiayi; Zeng, Bo; Liu, Wenxia (IET generation, transmission & distribution, 2024)
With the development of the digital economy, the power demand for data centers (DCs) is rising rapidly, which presents a challenge to the economic and low‐carbon operation of the future distribution system. To this end, this paper fully considers the multiple flexibility of DC and its impact on the active distribution network, and establishes a collaborative planning model of DC and active distribution network. Differing from most existing studies that apply robust optimization or stochastic optimization for uncertainty characterization, this study employs a novel interval optimization approach to capture the inherent uncertainties within the system (including the renewable energy source (RES) generation, electricity price, electrical loads, emissions factor and workloads). Subsequently, the planning model is reformulated as the interval multi‐objective optimization problem (IMOP) to minimize economic cost and carbon emission. On this basis, instead of using a conventional deterministic‐conversion approach, an interval multi‐objective optimization evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the proposed IMOP, which is able to fully preserve the uncertainty inherent in interval‐typed information and allow to obtain an interval‐formed Pareto front for risk‐averse decision‐making. Finally, an IEEE 33‐node active distribution network is utilized for simulation and analysis to confirm the efficacy of the proposed approach. This article addresses the increasing power demand of data centers (DCs) in the context of a growing digital economy, proposing a collaborative planning model that integrates DC flexibility with distribution network considerations. It introduces an interval multi‐objective optimization evolutionary algorithm (IMOEA/D) to tackle the resulting interval parameters problem (IMOP), effectively balancing economic costs and carbon emissions while accounting for uncertainties such as renewable energy output and market prices. The model's effectiveness is demonstrated through simulations on an enhanced IEEE 33‐node distribution network.