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Applied Sciences
Special Issues
Planning and Operation of Integrated Energy Systems with Uncertainties
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Planning and Operation of Integrated Energy Systems with Uncertainties

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1775

Special Issue Editors

Dr. Jiehui Zheng

E-Mail Website
Guest Editor
School of Electric Power Engineering, South China University of Technology, Guangzhou 520641, China
Interests: integrated energy systems; multi-objective optimization; decision making
Prof. Dr. Sheng Chen

E-Mail Website
Guest Editor
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Interests: integrated energy systems; electricity market; green hydrogen
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is concerned with research on the key fundamental issues of integrated energy systems (IESs) when planning and operating under uncertainties, which consist of three parts: (1) modeling of IESs, in addition to modeling the dynamic behaviors of various energy devices as well as their connections to form an IES model; (2) development of high-dimensional multi-objective stochastic optimization algorithms; (3) development of decision-making support for determination of the final optimal solution for the planning and operation of IESs under uncertainties, selected from the Pareto sets of a multi-objective optimization computation; and (4) mechanism design for distributed IESs to participate in the electricity market under various interest entities.

The referred methods could be applied to design power network-based IESs and investigate the economy and reliability of IESs under uncertainties, which could be achieved by using distributed CHP and CCHP; heat storage, cool storage and hydrogen storage; and investigating the smooth peaks and valleys of power generation and loads, respectively. This Special Issue aims to publish high-quality, original research papers in the overlapping fields of:

  • Modeling for integrated energy systems;
  • Energy storage technologies;
  • Uncertainties management;
  • Multi-objective optimization;
  • Decision-making support;
  • Net-zero-carbon emissions from IESs;
  • Economy and reliability evaluation;
  • Mechanism design for IESs.

Dr. Jiehui Zheng
Prof. Dr. Sheng Chen
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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 2400 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

  • integrated energy systems
  • planning and operation
  • modeling, optimization and decision making
  • energy storage

Published Papers (2 papers)

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Research

17 pages, 5417 KiB  
Article
Day-Ahead PV Generation Scheduling in Incentive Program for Accurate Renewable Forecasting
by Hwanuk Yu, Jaehee Lee and Young-Min Wi
Appl. Sci. 2024, 14(1), 228; https://doi.org/10.3390/app14010228 - 26 Dec 2023
Viewed by 626
Abstract
Photovoltaic (PV) power can be a reasonable alternative as a carbon-free power source in a global warming environment. However, when many PV generators are interconnected in power systems, inaccurate forecasting of PV generation leads to unstable power system operation. In order to help [...] Read more.
Photovoltaic (PV) power can be a reasonable alternative as a carbon-free power source in a global warming environment. However, when many PV generators are interconnected in power systems, inaccurate forecasting of PV generation leads to unstable power system operation. In order to help system operators maintain a reliable power balance, even when renewable capacity increases excessively, an incentive program has been introduced in Korea. The program is expected to improve the self-forecasting accuracy of distributed generators and enhance the reliability of power system operation by using the predicted output for day-ahead power system planning. In order to maximize the economic benefit of the incentive program, the PV site should offer a strategic schedule. This paper proposes a PV generation scheduling method that considers incentives for accurate renewable energy forecasting. The proposed method adjusts the predicted PV generation to the optimal generation schedule by considering the characteristics of PV energy deviation, energy storage system (ESS) operation, and PV curtailment. It then maximizes incentives by mitigating energy deviations using ESS and PV curtailment in real-time conditions. The PV scheduling problem is formulated as a stochastic mixed-integer linear programming (MILP) problem, considering energy deviation and daily revenue under expected PV operation scenarios. The numerical simulation results are presented to demonstrate the economic impact of the proposed method. The proposed method contributes to mitigating daily energy deviations and enhancing daily revenue. Full article
(This article belongs to the Special Issue Planning and Operation of Integrated Energy Systems with Uncertainties)
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20 pages, 840 KiB  
Article
Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
by Jiehui Zheng, Mingming Tao, Zhigang Li and Qinghua Wu
Appl. Sci. 2023, 13(18), 10247; https://doi.org/10.3390/app131810247 - 12 Sep 2023
Viewed by 792
Abstract
The conventional optimal power flow (OPF) is confronted with challenges in tackling more than three objectives and the stochastic characteristics due to the uncertainty and intermittence of the RESs. However, there are few methods available that simultaneously address high-dimensional objective optimization and uncertainty [...] Read more.
The conventional optimal power flow (OPF) is confronted with challenges in tackling more than three objectives and the stochastic characteristics due to the uncertainty and intermittence of the RESs. However, there are few methods available that simultaneously address high-dimensional objective optimization and uncertainty handling. This paper proposes a set-based group search optimizer (SetGSO) to tackle the stochastic many-objective optimal power flow (MaOPF) of power systems penetrated with renewable energy sources. The proposed SetGSO depicts the original stochastic variables by set-based individuals under the evolutionary strategy of the basic GSO, without using repeated sampling or probabilistic information. Consequently, two metrics, hyper-volume and average imprecision, are introduced to transform the stochastic MaOPF into a deterministic bi-objective OPF, guaranteeing a much superior Pareto-optimal front. Finally, our method was evaluated on three modified bus systems containing renewable energy sources, and compared with the basic GSO using Monte Carlo sampling (GSO-MC) and a set-based genetic algorithm (SetGA) in solving the stochastic MaOPF. The numerical results demonstrate a saving of 90% of the computation time in the proposed SetGSO method compared to sampling-based approaches and it achieves improvements in both the hyper-volume and average imprecision indicators, with a maximum enhancement of approximately 30% and 7% compared to SetGA. Full article
(This article belongs to the Special Issue Planning and Operation of Integrated Energy Systems with Uncertainties)
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