Work

Distributed optimal scheduling of multi-microgrids considering ammonia energy storage and energy-reserve sharing

Year: 2026

Type: article

Abstract: Traditional multi-microgrid typically focuses only on energy trading while overlooking reserve trading, and the high uncertainty of renewable generation makes it challenging to assess spinning reserve requirements. To this end, this paper proposes a distributed optimal method for multi-microgrids incorporating ammonia energy storage and the sharing of both energy and spinning reserves. First, we propose a data-driven distributionally robust upper and lower bound model to evaluate the spinning reserve requirements of microgrids under uncertainty. By applying conditional value‑at‑risk and duality theory, the model is reformulated into a linear program and solved in advance, effectively avoiding the computational burden that grows with the sample size. Then, considering flexible resources such as ammonia‑blended gas turbines and ammonia energy storage, we establish a Nash bargaining‑based energy and spinning reserve sharing model among multiple microgrids. Finally, to fairly allocate the shared energy, spinning reserves, and their associated costs among microgrids, the original problem is decomposed via the inequality of arithmetic and geometric means into a coalition cost minimization subproblem and an energy-reserve trading payment bargaining subproblem. These subproblems are then solved in a distributed manner using the alternating direction method of multipliers. Case studies demonstrate that the proposed distributionally robust upper and lower bound model offers high computational efficiency, while the proposed algorithm enables both the efficient sharing of spinning reserve resources and the fair allocation of costs among microgrids.

Source: Electric Power Systems Research

DOI: 10.1016/j.epsr.2026.114244

Authors: Jiaqi Chang, Yuanyu Ge, Jun Xie, Yingji Chen, Zixuan Liu, Zhangwei Wang

Institution: Hohai University

Language: English

Topic: Electric Power System Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,810