Open Access

REVIEW

Source-Load-Storage Flexible Resource Planning for Low-Carbon Urban Distribution Networks: Models, Metrics, Optimization Methods, and Future Pathway

Xin Li^1,*, Lixiang Lin¹, Tianyang Zhao², Qilin Zhou¹, Chang Xu¹, Zixuan Guo¹, Qiong Cui³, Ya Chen¹, Shuitian Li¹
1 Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou, China
2 School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China
3 Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou, China
* Corresponding Author: Xin Li. Email:
(This article belongs to the Special Issue: Application of Artificial Intelligence in Energy Systems: Toward Low-Carbon and Economic Sustainability)

Energy Engineering https://doi.org/10.32604/ee.2026.089831

Received 25 July 2026; Accepted 27 August 2026; Published online 31 August 2026

Abstract

The increasing penetration of distributed low-carbon generation, growing demand-side flexibility, and large-scale deployment of energy storage systems are driving urban distribution network planning from single-resource allocation toward coordinated source-load-storage (SLS) planning. However, existing reviews often address resource modelling, cross-resource coupling, planning evaluation, and optimization methods separately, limiting their ability to support integrated planning decisions. Accordingly, this review systematically examines SLS flexible-resource planning for low-carbon urban distribution networks (LCUDNs). First, modelling approaches and planning and operational boundaries are summarized for source-side low-carbon generation, load-side flexible loads, and storage-side energy storage systems, corresponding to low-carbon supply, demand adjustment, and intertemporal regulation, respectively. Second, cross-resource coupling relationships are categorized into spatiotemporal matching-capacity configuration coupling, planning-operation variable coupling, and multi-objective-network constraint coupling, thereby clarifying how SLS resources interact through operational states and distribution-network constraints. Third, a six-dimensional evaluation framework is established covering economic performance, low-carbon performance, reliability, security, flexibility, and adaptability. Planning methods are organized into six top-level categories: deterministic coordinated optimization, uncertainty-oriented planning, multi-objective optimization, bilevel/game-theoretic optimization, distributed optimization, and intelligent and data-driven optimization. Because uncertainty-oriented planning is further divided into stochastic, robust, and distributionally robust optimization, the hierarchical taxonomy comprises eight specific method families. Their modelling characteristics, advantages, limitations, and applicability are systematically compared. Finally, the limitations of the review are discussed and future research directions are proposed, providing a structured reference for flexible-resource allocation and planning-method selection in LCUDNs.

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Keywords

Low-carbon urban distribution network; source-load-storage coordination; flexible resources; coordinated planning; coupling modelling; optimization methods

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