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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v10i6.15632</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>CVaR-Based Optimal Scheduling of an Integrated Energy System with Battery Storage, Hydrogen Storage, and Demand Response</title><url>https://artdesignp.com/journal/JERA/10/6/10.26689/jera.v10i6.15632</url><author>YanDelong</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>10</volume><issue>6</issue><history><date date-type="pub"><published-time>2026-07-23</published-time></date></history><abstract>High renewable penetration improves the low-carbon performance of integrated energy systems, but it also increases scheduling uncertainty and renewable curtailment risk. This paper proposes a CVaR-based optimal scheduling model for an electric-heat-hydrogen integrated energy system with battery energy storage, hydrogen storage, and demand response. The proposed model minimizes a weighted objective that combines expected operating cost and tail-risk cost, while considering electricity purchase and sale, gas consumption, carbon emissions, battery degradation, hydrogen conversion, demand response compensation, and renewable curtailment penalty. Wind power, photovoltaic generation, and electric load uncertainty are represented by multiple scenarios, and the same scenario set is used for all comparative cases to ensure fairness. Five operation schemes are studied, including no storage, battery energy storage only, hydrogen storage only, battery-hydrogen storage, and the proposed battery-hydrogen-demand response scheme. The numerical results show that the proposed scheme achieves the lowest weighted objective, expected cost, and CVaR cost. Compared with the no-storage case, the proposed scheme reduces the weighted objective from 12337.22 to 9677.39, increases renewable utilization from 92.3% to 99.8%, and reduces expected carbon emissions from 4771.88 to 2994.13. These results indicate that coordinated scheduling of battery storage, hydrogen storage, and demand response can improve economic performance, reduce operational risk, and enhance renewable energy accommodation in high-renewable integrated energy systems.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Li B, Chen M, Zhong H, et al., 2023, A Review of Long-Term Planning of New Power Systems with Large Share of Renewable Energy. Proceedings of the CSEE, 43(2): 555–581.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Zhuo Z, Zhang N, Xie X, et al., 2021, Key Technologies and Development Challenges of Power Systems with High Proportion of Renewable Energy. Automation of Electric Power Systems, 45(9): 171–191.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Liu Z, 2026, Research on Future Development Path of Renewable Energy: Analysis Based on Renewable Energy Consumption Responsibility Weight and Green Power Direct Connection Policy. China Energy Observation, 2026(2): 92–96.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Yang Z, Cao J, Shi Y, et al., 2026, Capacity Allocation Optimization of Wind-Solar-Load-Storage for Grid-Connected Green Power Direct Supply Projects Considering Load-Side Response. Northwest Hydropower, 2026(2): 124–130.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Zhao D, An Y, Sun Z, et al., 2026, Research on Optimization and Scheduling Technology of Green Power Direct Connection System for AIDC Computing Power Base. Journal of Electric Power: 1–11. Available at: https://link.cnki.net/urlid/14.1185.TM.20260507.1437.002.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B6" content-type="article"><label>6</label><element-citation publication-type="journal"><p>Ding H, Shang H, Tao Y, et al., 2026, A Review on Scheduling Optimization of Source-Grid-Load-Storage Integrated System. Journal of Chinese Society of Power Engineering, 46(5): 50–63.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B7" content-type="article"><label>7</label><element-citation publication-type="journal"><p>Lin W, Jiang R, Ren X, 2026, Definition and Key Collaborative Technologies for Electricity-Hydrogen-Carbon Ecosystem. Power System Technology: 1–19. https://doi.org/10.13335/j.1000-3673.pst.2026.0191.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B8" content-type="article"><label>8</label><element-citation publication-type="journal"><p>Zhang B, Cai Y, Liu D, et al., 2025, An Intelligent Regulation Solution for Integrated Wind-Solar-Storage and Power-Hydrogen-Ammonia Coupling Systems. Automation Panorama, 42(10): 54–57.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B9" content-type="article"><label>9</label><element-citation publication-type="journal"><p>Huang Z, Wen Z, Wang C, 2025, Optimal Scheduling of Integrated Energy System Considering Multiple Utilization of Ammonia Energy. Modern Electronics Technique: 1–6.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B10" content-type="article"><label>10</label><element-citation publication-type="journal"><p>Wang S, Hu S, Cheng D, et al., 2026, Two-Stage Distributionally Robust Scheduling of Off-Grid Wind-Solar-Hydrogen-Ammonia Systems Adopting Multi-State Flexible Synthesis. Journal of Xi'an Jiaotong University: 1–10. Available at: https://link.cnki.net/urlid/61.1069.t.20260512.1747.005.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B11" content-type="article"><label>11</label><element-citation publication-type="journal"><p>Cheng J, Zhang B, Bao G, 2025, Two-Stage Optimal Scheduling Strategy for Hydrogen-Electric-Thermal Multi-Energy Coupled Hybrid Hydrogen Production Integrated Energy System. Southern Power System Technology: 1–12. Available at: https://link.cnki.net/urlid/44.1643.tk.20251103.1717.014.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B12" content-type="article"><label>12</label><element-citation publication-type="journal"><p>Wang R, Liu J, Ju W, et al., 2025, Coordinated Optimal Scheduling of Electric-Hydrogen System Considering Hybrid Energy Storage in the Day-Ahead and Intra-Day Stages. Journal of Shandong University (Engineering Science): 1–9.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B13" content-type="article"><label>13</label><element-citation publication-type="journal"><p>Xu Z, Sun Y, Xie D, et al., 2020, Optimal Energy Storage Configuration of Regional Integrated Energy System Considering Electric/Thermal Flexible Loads. Automation of Electric Power Systems, 44(2): 53–59.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B14" content-type="article"><label>14</label><element-citation publication-type="journal"><p>Qi N, Cheng L, Tian L, et al., 2020, Review and Prospect of Distribution Network Planning Considering Flexible Load Integration. Automation of Electric Power Systems, 44(10): 193–207.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B15" content-type="article"><label>15</label><element-citation publication-type="journal"><p>Zhang J, Lyu H, Hu S, et al., 2026, Low-Carbon Optimization Scheduling of Electric-Thermal Integrated Energy System Considering Uncertainty of Flexible Loads. Machine Building &amp; Automation, 55(1): 213–217.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
