A Multi-Objective Interval Optimization Approach to Expansion Planning
of Active Distribution System with Distributed Internet Data Centers and
Renewable Energy Resources
Abstract
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 network. To
this end, this paper fully considers the multiple flexibility of DC and
its impact on the distribution network, and establishes a collaborative
planning model of DC and distribution network. In this model, the
interval method is utilized to capture the inherent uncertainties within
the system (such as the renewable energy source (RES) output, wholesale
market price, power load demand, carbon emission factor and workloads),
and the planning model is transformed into a multi-objective
optimization problem with interval parameters problem (IMOP) to minimize
economic cost and carbon emission. On this basis, an interval
multi-objective optimization evolutionary algorithm based on
decomposition (IMOEA/D) is proposed to solve the IMOP and obtain the
Pareto optimal solution while retaining all the uncertainty information.
Finally, an improved IEEE 33-node distribution network is utilized as an
example for simulation and analysis to confirm the efficacy of the
proposed approach.