Abstract
The energy affordability principles aim to enhance energy accessibility and reduce electricity expenditures for low-income communities. However, the advocated affordability policies impose new requirements on system operators, as few have been translated into technical utility models. Energy storage systems (ESS) are crucial in implementing energy affordability by regulating voltage profiles and reducing power loss. This paper proposes an energy affordability-embedded robust planning (EARP) model to relieve the energy burden of low-income loads through equitable ESS deployment. The proposed model innovatively enforces implicit energy affordability (EA) constraints into technical models with two steps. First, an energy affordability slackness component (EASC) variable is introduced to evaluate the smallest gaps between the given preliminary planning decisions and the desired affordability-satisfied decisions. Then, by relaxing the dispatch model with the EASC variable, the implicit EA constraints are converted into explicitly cutting planes using the duality theorem. The proposed bi-level EARP model is solved via an improved column & constraint generation (C&CG) algorithm. Case studies on the IEEE 33-Bus and 123-Bus systems verify the model's effectiveness in relieving energy burdens and achieving better sustainability in the long term compared with conventional bill assistance programs.
| Original language | English |
|---|---|
| Pages (from-to) | 1561-1574 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Sustainable Energy |
| Volume | 17 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 1 2026 |
Funding
Received 15 April 2025; revised 13 August 2025; accepted 15 October 2025. Date of publication 22 October 2025; date of current version 24 March 2026. This work was supported in part by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) through Hydrogen Fuel Cell Technology Office, under Award DE-EE0010724 and in part by the CURENT which is a graduated NSF Engineering Research Center Headquartered at The University of Tennessee. Paper no. TSTE-00496-2025. (Corresponding author: Fangxing Li.) Sufan Jiang, Fangxing Li, Chenchen Li, Jingzi Liu, and Yan Wen are with The University of Tennessee, Knoxville, TN 37996 USA (e-mail: [email protected]).
Keywords
- Energy affordability
- column & constraint generation (C&CG) algorithm
- energy storage system (ESS)
- feasibility cutting planes
- renewable energy uncertainty
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