Optimal Allocation of Distributed Energy Resources for Reducing Energy Burden in Electrical Distribution Networks
Open Access Depositeda least-cost Business-as-Usual (BAU) baseline and two Equitable DER (EDER) cases with moderate (θ = 0.5) and full (θ = 1.0) prioritization of energy burden reduction. The EDER θ = 0.5 scenario achieves a 56% reduction in average energy burden-benefiting over 13,711 households-while maintaining investment costs comparable to BAU ($33.5M vs. $32M ). Equity-integrated planning improves DER accessibility across household types, mitigating the spatial disparities seen under BAU. Additionally, limited grid reinforcementis required under moderate equity prioritization. The proposed model offers a scalable decision support tool for policymakers to align DER deployment with local community needs. Results show that modest equity-focused adjustments can yield immense social returns without compromising economic viability, advancing a socially beneficial clean energy transition.
Existing power distribution system planning prioritizes cost and technical efficiency, often overlooking the energy burden faced by low- and middle-income (LMI) households. As renewable distributed energy resources (DERs) become increasingly central to decarbonization strategies, their spatial allocation presents a technical, economic, and social opportunity. This thesis introduces a multi-objective Mixed Integer Second Order Cone Program (MISOCP) model for optimizing the siting, sizing, and timing of community-owned PV, wind, and green hydrogen systems in radial power distribution networks, explicitly embedding energy burden reduction into DER and network expansion planning. The model is applied to the IEEE 37-bus test system modeled for Washington, D.C., under three planning scenarios
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