Energy Systems Planning, Operation and Optimization in Net-Zero Emissions: 2nd Edition

Topic Information

Dear Colleagues,

Decarbonization energy transition is one of the most important measures to mitigate climate change and improve sustainability. These main investment projects for future net-zero emissions include renewables, energy storage systems (ESSs), electric vehicles (EVs), charging infrastructure, hydrogen production, recycling, etc. High penetration of renewables and large-scale deployment of EV and charging infrastructure can significantly affect the operations of energy systems, even rendering them unstable and unreliable. Therefore, revolutionary energy systems call for the advancement of smart technologies in planning, operation, and optimization. This includes highly resilient energy system architecture enabled by the Internet of Energy (IoE).

This Topic on “Energy Systems Planning, Operation and Optimization in Net-Zero Emissions” invites contributions on the most advanced and latest research developments, focusing in particular on the planning, operation, and optimization for energy system integration with high penetration of renewable energy and EVs for net-zero emissions. The topics include but are not limited to:

• Government roadmap of energy system transition for net-zero emission;
• Design, planning, and optimization of smart technologies for resilient energy system architecture and net-zero energy systems;
• Energy system operation and control under highly variable and uncertain energy sources;
• Application and optimization for the integration of EVs and ESSs in energy systems;
• Integration of IoE in net-zero energy systems;
• Environment and industry issues from the transition of net-zero energy systems and solutions thereof;
• Optimization modeling, simulation, and solution techniques for design, simulation, analysis, and operation of energy systems achieving net-zero emission;
• Hybrid microgrid design and development for net-zero energy systems;
• Energy markets enabling net-zero energy systems;
• AI and cyberphysical-system-enabled net-zero energy systems.

Prof. Dr. Jen-Hao Teng
Dr. Lakshmanan Padmavathi
Dr. Kin-Cheong Sou
Prof. Dr. Alfeu J. Sguarezi Filho
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electricity electricity	2.7	4.4	2020	25.8 Days	CHF 1200	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Sci sci	4.1	5.4	2019	28.2 Days	CHF 1400	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit
Systems systems	3.8	5.4	2013	19.8 Days	CHF 2400	Submit

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All Energies Sustainability Systems Electricity Sci

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17 pages, 3322 KB  

Open AccessArticle

Low-Carbon Robust Planning for PIESs with Multi-Time-Scale Uncertainties and Elastic DR Regulation

by Xin Huang, Shucan Zhou, Jian Xiong, Keteng Jiang, Hao Yu and Haibo Li

Energies 2026, 19(13), 3207; https://doi.org/10.3390/en19133207 - 7 Jul 2026

Abstract

With the widespread application of park integrated energy systems (PIESs), challenges of multi-energy coupling, high investment costs, and multi-type uncertainties have become increasingly prominent. Existing research often employs typical scenario generation or robust optimization for short-term uncertainties but struggles with long-term load growth [...] Read more.

With the widespread application of park integrated energy systems (PIESs), challenges of multi-energy coupling, high investment costs, and multi-type uncertainties have become increasingly prominent. Existing research often employs typical scenario generation or robust optimization for short-term uncertainties but struggles with long-term load growth uncertainties and fails to fully utilize the flexibility of demand-side resources during the planning phase. This paper proposes a robust planning method for PIESs considering dynamic demand response and multi-timescale uncertainties. First, an energy flow framework encompassing cooling, heating, electricity, gas, and hydrogen is constructed. To overcome the limitations of traditional fixed-boundary DR, a dynamic elastic DR mechanism featuring transferable, substitutable, and curtailable types is established. Transferable demand boundaries are defined by a price–demand elasticity matrix, and actual responses are dynamically adjusted in synergy with system power balance conditions for optimal configuration. Second, multivariate dynamic time warping and hierarchical clustering algorithms derive typical daily scenarios accounting for short-term uncertainties. Finally, information gap decision theory characterizes long-term load growth uncertainty, constructing a robust planning model addressing both timescales. Case studies show that flexible resources and demand response reduce lifecycle cost by 55.24% and carbon emissions by 47.75%. The proposed demand response method further cuts costs by 153,800 yuan and emissions by 11.36%. The robust planning method synergistically addresses multi-timescale uncertainties, ensuring economy while maximizing resilience to uncertain fluctuations. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions: 2nd Edition)

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