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Energies
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Research on Operation Optimization of Integrated Energy Systems
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Research on Operation Optimization of Integrated Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 5 May 2026 | Viewed by 2465

Special Issue Editor

Dr. Maksymilian Kochanski

E-Mail Website
Guest Editor
Research and Innovation Centre Pro-Akademia, 95-050 Konstantynów Łódzki, Poland
Interests: energy efficiency; big data analytics; smart grids; energy systems; decarbonisation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Increased demand for sustainable energy technologies has brought integrated energy systems (IESs) to the forefront of modern energy research. These systems, which combine various forms of energy generation, storage, and distribution, are critical for enhancing the reliability, resilience, and overall performance of energy conversion and use. The complexity of IESs, with their intertwined electrical, thermal, and, sometimes, chemical processes, presents unique challenges in operation optimization, demanding innovative approaches and methodologies.

This Special Issue will showcase the latest research advancements in operation optimization for integrated energy systems. We will gather cutting-edge contributions that address the multifaceted challenges associated with optimizing the performance, efficiency, and sustainability of IESs in various contexts, including urban environments, industrial applications, and renewable energy integration.

Potential paper topics include, but are not limited to, the following:

  • Optimization algorithms and methodologies for IES operation;
  • Integration of renewable energy sources into IESs;
  • Demand-side management and its impact on IES optimization;
  • Energy storage solutions within IESs and their operational strategies;
  • Economic dispatch and unit commitment in IESs;
  • Multi-energy flow modeling and optimization;
  • Advanced control strategies for the real-time optimization of IESs;
  • Decentralized and distributed optimization approaches for IES;
  • Uncertainty management in IES operation optimization;
  • Case studies of IESs in urban, industrial, or rural settings;
  • Smart grids and their interaction with IES;
  • Artificial intelligence and machine learning applications in IES optimization;
  • Lifecycle analysis and sustainability assessments in IES operation.

We invite researchers and practitioners from academia, industry, and governmental bodies to contribute to this Special Issue by submitting original research, reviews, or case studies that advance the knowledge and practice of operation optimization in integrated energy systems.

We look forward to receiving your submissions and increasing our collective understanding of and technological progress in this critical area of energy research.

Dr. Maksymilian Kochanski
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-energy flow management
  • renewable energy integration
  • smart grid control strategies
  • decentralized energy optimization

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Published Papers (3 papers)

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Research

27 pages, 2391 KB  
Article
Gradient Revision Method for Demand Response Stimulus Parameters of the Integrated Energy System
by Kaiyu Zhou, Lirong Xie and Yifan Bian
Energies 2026, 19(7), 1742; https://doi.org/10.3390/en19071742 - 2 Apr 2026
Viewed by 361
Abstract
Integrated Demand Response (IDR) enhances the operational flexibility of Integrated Energy Systems (IES) and promotes renewable energy integration. However, limited interaction between the Integrated Energy Operator (IEO) and users during actual energy transactions can lead to biases in IDR planning, compromising user response [...] Read more.
Integrated Demand Response (IDR) enhances the operational flexibility of Integrated Energy Systems (IES) and promotes renewable energy integration. However, limited interaction between the Integrated Energy Operator (IEO) and users during actual energy transactions can lead to biases in IDR planning, compromising user response effectiveness. To address this, this paper proposes a method for revising IDR stimulus parameters in IES based on gradient descent within a Stackelberg game framework. First, an IDR model based on consumer psychology principles is constructed to establish an IES Stackelberg game, in which the IEO acts as the leader and the load aggregator acts as the follower. Subsequently, during the game, the IEO utilizes users’ energy consumption strategies to adjust the stimulus threshold parameters of the dead zone and saturation zone along the negative gradient direction, thereby updating its decision for the next round. Furthermore, a response adjustment mechanism is designed to refine the IDR plan, enhancing its feasibility. Finally, comparative analyses across diverse scenarios demonstrate that the proposed method reduces deviations in planned IDR, thereby enhancing the low-carbon performance and renewable energy integration capacity of IES. Full article
(This article belongs to the Special Issue Research on Operation Optimization of Integrated Energy Systems)
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20 pages, 2608 KB  
Article
A Stackelberg Game Approach for Collaborative Operation and Interest Balancing in Community-Based Integrated Energy Microgrids
by Zhenxing Wen, Yutao Zhou, Dingming Zhuo, Chong Li, Hui Luo and Dongguo Zhou
Energies 2026, 19(3), 837; https://doi.org/10.3390/en19030837 - 5 Feb 2026
Viewed by 460
Abstract
To address the limitation of traditional microgrid operator-led optimization models that compromise user-side benefits, this paper proposes a novel method for the collaborative optimal operation strategy of community-based integrated energy microgrids and diversified flexible resources. The method deeply integrates user-side flexibility resources into [...] Read more.
To address the limitation of traditional microgrid operator-led optimization models that compromise user-side benefits, this paper proposes a novel method for the collaborative optimal operation strategy of community-based integrated energy microgrids and diversified flexible resources. The method deeply integrates user-side flexibility resources into the decision-making process. Unlike existing research that only considers electro-heat coupling, our model integrates shared energy storage services into an integrated energy system, reflecting a more realistic future application. A Stackelberg game framework is then established with the microgrid operator (MGO) as the leader and the user aggregator as the follower. The user aggregator optimizes its energy strategy by coordinating user demand response, thereby increasing the profits of both itself and the shared energy storage operator. Meanwhile, this model guides the MGO’s pricing decisions for electricity and heat, balancing interests of all stakeholders. To solve the model, a hierarchical approach that merges the Harris Hawks Optimization algorithm with the CPLEX solver is employed. Finally, simulation results demonstrate that the proposed model and strategy significantly enhance user-side revenue and flexibility, achieve a win-win outcome for the user aggregator and MGO, and lay the foundation for future shared energy storage service providers to participate in market pricing as key game entities. Full article
(This article belongs to the Special Issue Research on Operation Optimization of Integrated Energy Systems)
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18 pages, 1239 KB  
Article
Optimized Demand Side Management for Refrigeration: Modeling and Case Study Insights from Kenya
by Josephine Nakato Kakande, Godiana Hagile Philipo and Stefan Krauter
Energies 2025, 18(13), 3258; https://doi.org/10.3390/en18133258 - 21 Jun 2025
Viewed by 1104
Abstract
According to the International Institute of Refrigeration (IIR), 20% of worldwide electricity consumption is for refrigeration, with domestic refrigeration appliances comprising a fifth of this demand. As the uptake of renewable energy sources for on-grid and isolated electricity supply increases, the need for [...] Read more.
According to the International Institute of Refrigeration (IIR), 20% of worldwide electricity consumption is for refrigeration, with domestic refrigeration appliances comprising a fifth of this demand. As the uptake of renewable energy sources for on-grid and isolated electricity supply increases, the need for mechanisms to match demand and supply better and increase power system flexibility has led to enhanced attention on demand-side management (DSM) practices to boost technology, infrastructure, and market efficiencies. Refrigeration requirements will continue to rise with development and climate change. In this work, particle swarm optimization (PSO) is used to evaluate energy saving and load factor improvement possibilities for refrigeration devices at a site in Kenya, using a combination of DSM load shifting and strategic conservation, and based on appliance temperature evolution measurements. Refrigeration energy savings of up to 18% are obtained, and the load factor is reduced. Modeling is done for a hybrid system with grid, solar PV, and battery, showing a marginal increase in solar energy supply to the load relative to the no DSM case, while the grid portion of the load supply reduces by almost 25% for DSM relative to No DSM. Full article
(This article belongs to the Special Issue Research on Operation Optimization of Integrated Energy Systems)
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