Synergetic renewable generation allocation and 5G base station placement for decarbonizing development of power distribution system: A multi-objective interval evolutionary optimization approach

Abstract

The growing penetration of 5G base stations (5G BSs) is posing a severe challenge to efficient and sustainable operation of power distribution systems (PDS) due to their huge energy demand and massive quantity. To tackle this issue, this paper proposes a synergetic planning framework for renewable energy generation (REG) and 5G BS allocation to support decarbonizing development of future PDS. The problem is formulated as a multi-objective interval optimization (MOIO) model, wherein the 5G BS siting and sizing are co-optimized with the REG capacity allocation and their operating strategies to minimize the total economic cost and carbon emissions of the PDS. The potential flexibility benefits achievable from 5G BS operation (as responsive load demands to PDS) are explicitly considered in the proposed planning formulation by accounting for the effects of both transmit power control and on-site energy storage battery dispatch. Moreover, we adopt an interval approach to capture the uncertainties involved in the system (including REG output, market prices, carbon emissions factor, and power/communication load demands). In order to solve the proposed MOIO problem efficiently, a constrained interval multi-objective optimization evolutionary algorithm based on decomposition (CIMOEA/D) is developed by combining constraint handling, interval-based Tchebycheff aggregation function and interval ensemble comparison, which could achieve Pareto optimal solutions and an interval-formed Pareto front to the problem with all the uncertain information fully preserved in the decision-making process. The proposed model is demonstrated on a modified IEEE 33-bus test case, and the simulation results verified the effectiveness of the suggested approach.