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Advances and Optimization of Electric Energy System—2nd Edition
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Advances and Optimization of Electric Energy System—2nd Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 3869

Special Issue Editors

Dr. Yuqing Bao

E-Mail Website
Guest Editor
NARI School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
Interests: energy management of the electric power system; demand side management of the electric power system
Special Issues, Collections and Topics in MDPI journals
Dr. Zhenya Ji

E-Mail Website
Guest Editor
NARI School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
Interests: vehicle-to-grid (V2G); coordinated operations of integrated energy systems; electricity market
Special Issues, Collections and Topics in MDPI journals
Dr. Zhenyu Lv

E-Mail Website
Guest Editor
NARI School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
Interests: microgrid control; DC distribution network; application of energy storage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing penetration of renewable energy, the current electric energy system is facing change. Distributed power sources, electric vehicles, distributed energy storage, and flexible loads are becoming more and more important, and they increasingly affect the electric energy system by source–load interaction. In this context, the electric energy system is becoming more and more complex. The optimization of the power system not only needs to consider the operating characteristics of the generation resources, but also needs to take into account the uncertainty of distributed power sources, the travel rules of electric vehicles, the comfort level of electric energy users, etc. Therefore, it is necessary to fully investigate the advances of electric energy systems and design more feasible, efficient, and robust optimization strategies.

Papers in the relevant areas of Advances and Optimization of Electric Energy System, including but not limited to the following issues, are invited:

  • Modeling and optimization of electric energy system;
  • Modeling and management of flexible loads;
  • Scheduling of high renewable penetrated electric power system;
  • Load forecasting of electric energy system;
  • Electricity market design for source–load interaction;
  • Coordinated operations of integrated energy systems;
  • Vehicle-to-grid (V2G) optimation and control technologies;
  • Optimization and control of energy storage systems;
  • Review of advances in electric energy system.

Dr. Yuqing Bao
Dr. Zhenya Ji
Dr. Zhenyu Lv
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. 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

  • energy management
  • demand response
  • vehicle-to-grid (V2G)
  • integrated energy systems

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Related Special Issue

Published Papers (5 papers)

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Research

21 pages, 2124 KiB  
Article
Optimal Scheduling and Compensation Pricing Method for Load Aggregators Based on Limited Peak Shaving Budget and Time Segment Value
by Hanyu Yang, Zhihao Sun, Xun Dou, Linxi Li, Jiancheng Yu, Xianxu Huo and Chao Pang
Energies 2024, 17(22), 5759; https://doi.org/10.3390/en17225759 - 18 Nov 2024
Viewed by 303
Abstract
Load-side peak shaving is an effective measure to alleviate power supply–demand imbalance. As a key link between a vast array of small- and medium-sized adjustable resources and the bulk power system, load aggregators (LAs) typically allocate peak shaving budgets using fixed pricing methods [...] Read more.
Load-side peak shaving is an effective measure to alleviate power supply–demand imbalance. As a key link between a vast array of small- and medium-sized adjustable resources and the bulk power system, load aggregators (LAs) typically allocate peak shaving budgets using fixed pricing methods based on peak shaving demand forecasts. However, due to the randomness of supply and demand, fluctuations in peak shaving demand occur, making it a significant technical challenge to meet peak shaving needs under limited budget allocations. To address this issue, this paper first conducts a clustering analysis of various adjustable load characteristics to derive typical electricity consumption curves, and then proposes a differentiated calculation method for the value of multi-time-segment peak shaving. Subsequently, an optimization model for LA scheduling and compensation pricing is established based on the limited peak shaving budget and time-segment peak shaving value. While ensuring the economic benefits of LAs, the model also analyzes the impact of different peak shaving budget allocations on the scale of peak shaving that can be achieved. Finally, case studies demonstrate that, compared to traditional fixed compensation pricing, the proposed pricing method reduces scheduling costs by an average of 16.5%, while significantly improving the overall satisfaction of adjustable users. Full article
(This article belongs to the Special Issue Advances and Optimization of Electric Energy System—2nd Edition)
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12 pages, 1525 KiB  
Article
Multi-Objective Optimization and Reconstruction of Distribution Networks with Distributed Power Sources Based on an Improved BPSO Algorithm
by Dan Lu, Wenfeng Li, Linjuan Zhang, Qiang Fu, Qingtao Jiao and Kai Wang
Energies 2024, 17(19), 4877; https://doi.org/10.3390/en17194877 - 28 Sep 2024
Viewed by 616
Abstract
The continuous integration of distributed power into the distribution network has increased the complexity of the distribution network and created challenges in distribution-network reconfiguration. In order to make the distribution network operate in the optimal mode, this paper establishes a multi-objective reconfiguration-optimization model [...] Read more.
The continuous integration of distributed power into the distribution network has increased the complexity of the distribution network and created challenges in distribution-network reconfiguration. In order to make the distribution network operate in the optimal mode, this paper establishes a multi-objective reconfiguration-optimization model that takes into account active network loss, voltage offset, number of switching actions and distributed power output. For a distribution network with a distributed power supply, it is easy for the traditional binary particle swarm optimization algorithm to fall into a local optimum. In order to improve the convergence speed of the algorithm and avoid premature convergence, this paper adopts an improved binary particle swarm optimization algorithm to solve the problem. The IEEE33 node system is used as an example for simulation verification. The experimental results show that the algorithm improves the convergence speed and global search ability, effectively reduces the system network loss, and greatly improves the voltage level of each node. It improves the stability and economy of distribution-network operation and can effectively solve the problem of multi-objective reconfiguration. Full article
(This article belongs to the Special Issue Advances and Optimization of Electric Energy System—2nd Edition)
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14 pages, 2460 KiB  
Article
A Snake Optimization Algorithm-Based Power System Inertia Estimation Method Considering the Effects of Transient Frequency and Voltage Changes
by Yanzhen Pang, Feng Li, Haiya Qian, Xiaofeng Liu and Yunting Yao
Energies 2024, 17(17), 4430; https://doi.org/10.3390/en17174430 - 4 Sep 2024
Viewed by 550
Abstract
Inertia is the measure of a power system’s ability to resist power interference. The accurate estimation and prediction of inertia are crucial for the safe operation of the power system. To obtain the accurate power system inertia provided by generators, this paper proposes [...] Read more.
Inertia is the measure of a power system’s ability to resist power interference. The accurate estimation and prediction of inertia are crucial for the safe operation of the power system. To obtain the accurate power system inertia provided by generators, this paper proposes an estimation method considering the influence of frequency and voltage characteristics on the power deficit during transients. Specifically, the traditional swing equations-based inertia estimation model is improved by embedding linearized frequency and voltage factors. On this basis, the snake optimization algorithm is utilized to identify the power system inertia constant due to its strong global search ability and fast convergence speed. Finally, the proposed inertia estimation method is validated in four test systems, and the results show the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advances and Optimization of Electric Energy System—2nd Edition)
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28 pages, 5625 KiB  
Article
A Multiparadigm Approach for Generation Dispatch Optimization in a Regulated Electricity Market towards Clean Energy Transition
by Suroso Isnandar, Jonathan F. Simorangkir, Kevin M. Banjar-Nahor, Hendry Timotiyas Paradongan and Nanang Hariyanto
Energies 2024, 17(15), 3807; https://doi.org/10.3390/en17153807 - 2 Aug 2024
Viewed by 805
Abstract
In Indonesia, the power generation sector is the primary source of carbon emissions, largely due to the heavy reliance on coal-fired power plants, which account for 60% of electricity production. Reducing these emissions is essential to achieve national clean energy transition goals. However, [...] Read more.
In Indonesia, the power generation sector is the primary source of carbon emissions, largely due to the heavy reliance on coal-fired power plants, which account for 60% of electricity production. Reducing these emissions is essential to achieve national clean energy transition goals. However, achieving this initiative requires careful consideration, especially regarding the complex interactions among multiple stakeholders in the Indonesian electricity market. The electricity market in Indonesia is characterized by its non-competitive and heavily regulated structure. This market condition often requires the PLN, as the system operator, to address multi-objective and multi-constraint problems, necessitating optimization in the generation dispatch scheduling scheme to ensure a secure, economical, and low-carbon power system operation. This research introduces a multiparadigm approach for GS optimization in a regulated electricity market to support the transition to clean energy. The multiparadigm integrates multi-agent system and system dynamic paradigms to model, simulate, and quantitatively analyze the complex interactions among multiple stakeholders in the Indonesian regulated electricity market. The research was implemented on the Java–Madura–Bali power system using AnyLogic 8 University Researcher Software. The simulation results demonstrate that the carbon policy scheme reduces the system’s carbon emissions while increasing the system’s cost of electricity. A linear regression for sensitivity analysis was conducted to determine the relationship between carbon policies and the system’s cost of electricity. This research offers valuable insights for policymakers to develop an optimal, acceptable, and reasonable power system operation scheme for all stakeholders in the Indonesian electricity market. Full article
(This article belongs to the Special Issue Advances and Optimization of Electric Energy System—2nd Edition)
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13 pages, 1234 KiB  
Article
Distribution Network Reconfiguration Optimization Using a New Algorithm Hyperbolic Tangent Particle Swarm Optimization (HT-PSO)
by David W. Puma, Y. P. Molina, Brayan A. Atoccsa, J. E. Luyo and Zocimo Ñaupari
Energies 2024, 17(15), 3798; https://doi.org/10.3390/en17153798 - 2 Aug 2024
Viewed by 866
Abstract
This paper introduces an innovative approach to address the distribution network reconfiguration (DNR) challenge, aiming to reduce power loss through an advanced hyperbolic tangent particle swarm optimization (HT-PSO) method. This approach is distinguished by the adoption of a novel hyperbolic tangent function, which [...] Read more.
This paper introduces an innovative approach to address the distribution network reconfiguration (DNR) challenge, aiming to reduce power loss through an advanced hyperbolic tangent particle swarm optimization (HT-PSO) method. This approach is distinguished by the adoption of a novel hyperbolic tangent function, which effectively limits the rate of change values, offering a significant improvement over traditional sigmoid function-based methods. A key feature of this new approach is the integration of a tunable parameter, δ, into the HT-PSO, enhancing the curve’s adaptability. The careful optimization of δ ensures superior control over the rate of change across the entire operational range. This enhanced control mechanism substantially improves the efficiency of the search and convergence processes in DNR. Comparative simulations conducted on 33- and 94-bus systems show an improvement in convergence, demonstrating a more exhaustive exploration of the search space than existing methods documented in the literature based on PSO and variations where functions are proposed for the rate of change of values. Full article
(This article belongs to the Special Issue Advances and Optimization of Electric Energy System—2nd Edition)
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