Open Access

ARTICLE

Robust Optimal Scheduling of Integrated Energy Systems Considering Waste Heat Recovery from Power-to-Ammonia and Ammonia Cofiring Substitution

Xingzuo Pan¹, Yi Ding², Zhilong Wei³, Tonglin Liu⁴, Jianxin Ni⁵, Yupeng He^1,*

1 Department of Electrical Engineering, Northeast Electric Power University, Jilin, 132012, China
2 Zhejiang Zheneng Zhongmei Zhoushan Coal Power Co., Ltd., Zhoushan, 316131, China
3 CGN Cangnan Nuclear Power Co., Ltd., Wenzhou, 325800, China
4 NARI RELAYS Electric Co., Ltd., Nanjing, 210000, China
5 China Resources Power Xiantao Company, Xiantao, 433000, China

* Corresponding Author: Yupeng He. Email:

(This article belongs to the Special Issue: Innovative Energy Engineering for Resilient and Green Systems)

Energy Engineering 2026, 123(6), 8 https://doi.org/10.32604/ee.2025.072849

Received 04 September 2025; Accepted 11 November 2025; Issue published 27 May 2026

Abstract

Wind and photovoltaic generation integration into power systems has steadily increased in recent years. To mitigate increasing renewable curtailment and deteriorating operational economics associated with high penetrations of wind and PV, this paper develops a robust optimal scheduling framework for integrated energy systems that integrates waste-heat recovery from power-to-ammonia (P2A) processes and ammonia cofiring as a substitution strategy. First, the energy transfer pathways of electricity–heat, ammonia, and the heat release characteristics of the entire P2A process are analyzed, enabling waste heat recovery throughout the conversion process. Second, considering the low-carbon characteristics of ammonia cofiring in gas turbine units, the combustion mechanism of ammonia–natural gas blends is examined. Subsequently, an energy-curtailment-driven carbon capture control strategy is developed by introducing electricity-heat flexible loads, and a collaborative operation model coupling carbon capture equipment with ammonia cofiring is constructed. Finally, a high-dimensional scenario set representing wind and photovoltaic fluctuations is generated via Latin hypercube sampling, clustered, and embedded into a two-stage distributionally robust optimization model. The proposed method is solved using the IBM solver, and simulation results verify its stability under extreme wind and photovoltaic volatility, achieving a 37.2% reduction in total cost and a 68.05% reduction in carbon emissions compared to the baseline scenario.

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