Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization

Mohammad Dolatabadi; Alireza Zakariazadeh; Alberto Borghetti; Pierluigi Siano

doi:10.17775/CSEEJPES.2023.06270

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Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization

Regular Papers | 更新时间：2026-10-15

- Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization
- Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第3期页码：942-952
- 作者机构：
  
  1. Department of Management & Innovation Systems, University of Salerno,Salerno,Italy
  2. University of Science and Technology of Mazandaran, Behshahr,Iran
  3. Department of Electrical, Electronic, and Information Engineering, University of Bologna,Bologna,Italy
  4. Department of Power Systems, National University of Science and Technology POLITEHNICA Bucharest, Bucharest,Romania
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2023.06270
  中图分类号：
- 纸质出版：2024-05-01
- 稿件说明：
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Mohammad Dolatabadi, Alireza Zakariazadeh, Alberto Borghetti, 等. Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(3):942-952.

Mohammad Dolatabadi, Alireza Zakariazadeh, Alberto Borghetti, et al. Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization[J]. CSEE Journal of Power and Energy Systems, 2024, 10(3): 942-952.
Mohammad Dolatabadi, Alireza Zakariazadeh, Alberto Borghetti, 等. Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(3):942-952. DOI： 10.17775/CSEEJPES.2023.06270.

Mohammad Dolatabadi, Alireza Zakariazadeh, Alberto Borghetti, et al. Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization[J]. CSEE Journal of Power and Energy Systems, 2024, 10(3): 942-952. DOI： 10.17775/CSEEJPES.2023.06270.

摘要

Abstract

Encouraging citizens to invest in small-scale renewable resources is crucial for transitioning towards a sustainable and clean energy system. Local energy communities (LECs) are expected to play a vital role in this context. However

energy scheduling in LECs presents various challenges

including the preservation of customer privacy

adherence to distribution network constraints

and the management of computational burdens. This paper introduces a novel approach for energy scheduling in renewable-based LECs using a decentralized optimization method. The proposed approach uses the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method

significantly reducing the computational effort required for solving the mixed integer programming (MIP) problem. It incorporates network constraints

evaluates energy losses

and enables community participants to provide ancillary services like a regulation reserve to the grid utility. To assess its robustness and efficiency

the proposed approach is tested on an 84-bus radial distribution network. Results indicate that the proposed distributed approach not only matches the accuracy of the corresponding centralized model but also exhibits scalability and preserves participant privacy.

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