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Energies
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Energy Management and Optimization for New Power Systems
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Energy Management and Optimization for New Power Systems

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

Deadline for manuscript submissions: closed (30 March 2024) | Viewed by 8923

Special Issue Editors

Prof. Dr. Tianguang Lu

E-Mail Website
Guest Editor
Department of Electrical Engineering, Shandong University, Jinan 250061, China
Interests: optimization for low-carbon power systems; microgrids; artificial intelligence and data-driven analytics in smart grids; electricity market
Dr. Changsen Feng

E-Mail Website
Guest Editor
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Interests: optimization and control in power systems; game theory; reinforcement learning

Special Issue Information

Dear Colleagues,

Driven by the goals of clean energy and zero carbon emissions, the power industry is undergoing significant transformations. The government of China plans to build a new type of power system featuring a gradual increase in the proportion of new energy sources and the large-scale deployment of clean power resources nationwide. The ever-increasing penetration of uncertain inverter-based distributed energy resources (DERs), such as wind and rooftop PV, will inevitably exert a considerable influence on the power system, generating reliability, economic and resiliency concerns. Hence, it is essential to improve the comprehensive regulation capability of the power system, accelerate construction of flexible regulation power, and guide self-supplied power plants, traditional high-energy industrial loads, industrial and commercial interruptible loads, electric vehicle charging networks and virtual power plants to participate in system regulation in order to build a strong smart grid and improve grid security.

Within this context, the new control methods, new management strategies as well as new market mechanisms are highly desirable. Research into the new power system is strongly interdisciplinary, involving the state of the art in control theory, economics, artificial intelligence (AI) and information technology. This Special Issue aims to compile innovative ideas and interdisciplinary research in this direction to address the forthcoming technical challenges.

Prof. Dr. Tianguang Lu
Dr. Changsen Feng
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

  • grid protection, reliability, energy/power quality, and maintenance
  • smart metering, measurement, instrumentation, and control
  • renewable energy, wind, solar, fuel cells, and distributed generation within microgrids
  • scalable optimization and control approaches for the new power system
  • novel market architecture, valuation mechanisms for der integration
  • business models and energy policies in the new power system
  • peer-to-peer energy and service trading
  • distributed ledger technology including blockchain
  • advanced information and computing technologies (ICT)
  • big data technology and AI for renewable energies
  • energy efficiency, conservation, and savings

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Published Papers (4 papers)

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Research

16 pages, 4304 KiB  
Article
Towards Sustainable Energy Communities: Integrating Time-of-Use Pricing and Techno-Economic Analysis for Optimal Design—A Case Study of Valongo, Portugal
by Goran Dobric and Mileta Zarkovic
Energies 2024, 17(14), 3375; https://doi.org/10.3390/en17143375 - 10 Jul 2024
Viewed by 985
Abstract
This paper presents a comprehensive analysis of optimal energy community design, leveraging time-of-use pricing mechanisms and techno-economic parameters. Focusing on a case study of Valongo, Portugal, this study explores the intricate interplay between energy infrastructure planning, economic considerations, and pricing dynamics. Through a [...] Read more.
This paper presents a comprehensive analysis of optimal energy community design, leveraging time-of-use pricing mechanisms and techno-economic parameters. Focusing on a case study of Valongo, Portugal, this study explores the intricate interplay between energy infrastructure planning, economic considerations, and pricing dynamics. Through a systematic approach, various factors, such as renewable energy integration, demand–response strategies, and investment costs, are evaluated to formulate an efficient and sustainable energy community model. Time-of-use pricing schemes are incorporated to reflect the dynamic nature of energy markets and consumer behavior. By integrating techno-economic analyses, this study aims to optimize energy resource allocation while ensuring cost-effectiveness and environmental sustainability. The influence of optimized sizes of photovoltaics (PV), battery storage, and electrical vehicles (EVs) on self-sufficiency rates, self-consumption rates and CO2 savings is analyzed. The findings offer valuable insights into the design and implementation of energy communities in urban settings, highlighting the importance of adaptive strategies in the transition towards a resilient and low-carbon energy future. The novelty of this paper lies in its comprehensive approach to energy community design, which integrates time-of-use pricing mechanisms with techno-economic parameters. By focusing on the specific case of Valongo, Portugal, it addresses the unique challenges and opportunities present in urban settings. Additionally, the analysis considers the interaction between renewable energy production, demand profiles and investment costs, providing valuable insights for optimizing resource allocation and achieving both cost-effectiveness and environmental sustainability. Full article
(This article belongs to the Special Issue Energy Management and Optimization for New Power Systems)
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14 pages, 5193 KiB  
Article
Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization
by Zeyu Li and Junhua Xiong
Energies 2024, 17(10), 2316; https://doi.org/10.3390/en17102316 - 11 May 2024
Cited by 3 | Viewed by 2003
Abstract
The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. [...] Read more.
The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions. Full article
(This article belongs to the Special Issue Energy Management and Optimization for New Power Systems)
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17 pages, 3555 KiB  
Article
Artificial Intelligence for Energy Theft Detection in Distribution Networks
by Mileta Žarković and Goran Dobrić
Energies 2024, 17(7), 1580; https://doi.org/10.3390/en17071580 - 26 Mar 2024
Cited by 4 | Viewed by 3147
Abstract
The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) [...] Read more.
The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks. Full article
(This article belongs to the Special Issue Energy Management and Optimization for New Power Systems)
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18 pages, 2804 KiB  
Article
Efficiency Improvement of Permanent Magnet Synchronous Motors Using Model Predictive Control Considering Core Loss
by Lian Hou, Youguang Guo, Xin Ba, Gang Lei and Jianguo Zhu
Energies 2024, 17(4), 773; https://doi.org/10.3390/en17040773 - 6 Feb 2024
Cited by 7 | Viewed by 2253
Abstract
The highway cycle is an important consideration in the EV’s new European driving cycles (NEDCs) range, as the steady-state efficiency improvement in such conditions can be greatly beneficial. In the model predictive control (MPC) of the permanent magnet synchronous motors (PMSMs), the predicted [...] Read more.
The highway cycle is an important consideration in the EV’s new European driving cycles (NEDCs) range, as the steady-state efficiency improvement in such conditions can be greatly beneficial. In the model predictive control (MPC) of the permanent magnet synchronous motors (PMSMs), the predicted next-step feedback reference generated by the equivalent circuit model (ECM) will contribute directly to the voltage vector selection, therefore influencing the performance of the motor control. In the current MPC scheme, when the conventional ECM is applied, it only considers copper loss, and the core loss is usually disregarded. In some circumstances, such as the highway cycle of EVs, the motors are at high speed, the torque is low, and the core loss can be significant in the losses, thus affecting the accuracy of control and the efficiency of the system; hence, the introduction of core loss ECM into the MPC would be beneficial. This paper aims to investigate the steady-state efficiency improvement of a novel ECM of PMSM considering core loss ECM, and the comparison will be based on model predictive direct torque control (MPDTC) using the core loss ECM, which will be compared to MPDTC with the conventional ECM of the PMSM. The results demonstrate the proposed ECM’s efficiency improvement in various conditions, the limitations of the model and the simulation are discussed, and future work is proposed. Full article
(This article belongs to the Special Issue Energy Management and Optimization for New Power Systems)
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