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Artificial Intelligence for Power System and Renewable Energy Optimization
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Artificial Intelligence for Power System and Renewable Energy Optimization

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: closed (31 March 2023) | Viewed by 13111

Special Issue Editor

Prof. Dr. Ismail Musirin

E-Mail Website
Guest Editor
School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
Interests: artificial intelligence, optimization techniques, power system analysis, renewable energy, distributed generation and power system stability

Special Issue Information

Dear Colleagues,

This special issue aims at disseminating the latest research in new algorithms development involving optimization techniques, modelling and compensation techniques. This issue would like to promote latest optimization techniques which could be the hybridization of several traditional optimization techniques to address the setback of the current traditional techniques. Implementations in power system modellings, renewable energy and other relevant studies are encouraged to be shared in this issue. Latest development in power electronics research is also encouraged to be disseminated to the readers. The trend in the compensation schemes for power system to address loss minimization, voltage stability improvement and voltage profile enhancement can be a part of the interesting topics. Important properties which address the percentage of loss reduction and computational time can be of the interesting topic which can help the power system utilities in their future power system planning and expansion

Prof. Dr. Ismail Musirin
Guest Editor

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Keywords

  • Optimization techniques
  • Compensation schemes
  • Voltage stability
  • Loss minimization

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Published Papers (6 papers)

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Research

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29 pages, 9770 KiB  
Article
The Influence of Distributed Generation on the Operation of the Power System, Based on the Example of PV Micro-Installations
by Norbert Chamier-Gliszczynski, Grzegorz Trzmiel, Jarosław Jajczyk, Aleksandra Juszczak, Waldemar Woźniak, Mariusz Wasiak, Robert Wojtachnik and Krzysztof Santarek
Energies 2023, 16(3), 1267; https://doi.org/10.3390/en16031267 - 25 Jan 2023
Cited by 9 | Viewed by 1777
Abstract
This article describes the problems associated with distributed electrical power generation and the most frequent interruptions occurring in power grids. The most common methods of improving the quality of the power supply were analyzed and the possibilities offered by energy storages in this [...] Read more.
This article describes the problems associated with distributed electrical power generation and the most frequent interruptions occurring in power grids. The most common methods of improving the quality of the power supply were analyzed and the possibilities offered by energy storages in this respect were considered. The operating parameters of an exemplary PV system connected to the power grid were analyzed. For this purpose, the model implemented in the Matlab/Simulink environment was used. Based on the conducted analysis and a review of the literature, conclusions were drawn and solutions were presented which could improve the quality and the reliability of power supply. The simulations conducted focused on the co-operation of individual photovoltaic, micro-installations, with rated powers of 12.2, 19.825, and 39.65 Kw in the power grid, which also corresponds to the co-operation of several, smaller micro-installations with low density. Full article
(This article belongs to the Special Issue Artificial Intelligence for Power System and Renewable Energy Optimization)
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19 pages, 2070 KiB  
Article
Rural–Urban Differences in Solar Renewable Energy Investments Supported by Public Finance in Poland
by Joanna Rakowska, Mariusz Maciejczak, Iwona M. Batyk and Eliza Farelnik
Energies 2022, 15(22), 8476; https://doi.org/10.3390/en15228476 - 13 Nov 2022
Cited by 1 | Viewed by 1482
Abstract
The deployment of renewable energy (RE) needs to be policy-driven and supported by public funds. Hence, the aim of this study was to find out whether urban and rural areas benefit from public funds for RE deployment equally and whether factors determining other [...] Read more.
The deployment of renewable energy (RE) needs to be policy-driven and supported by public funds. Hence, the aim of this study was to find out whether urban and rural areas benefit from public funds for RE deployment equally and whether factors determining other types of investments also determine investments in RES. To do so, we carried out: (i) comparative analyses of qualitative and quantitative data describing 2642 investments in solar RE supported by the European Union funds and carried out in Poland under operational programmes in 2014–2020; (ii) multiple linear regressions, evaluating the predictions. Findings showed that principles of supporting solar RE investments were the same for all kinds of beneficiaries in both urban and rural areas. However, in rural areas, most RE investments cumulated in eastern, north-eastern and south-eastern parts of Poland, and depended only on few socio-economic characteristics. RE investments in urban areas were dispersed all over the country rather evenly and did not depend on any of the socio-economic characteristics. Individual households appeared to be important silent partners to RE investments carried out by local governments. Thus, future policies should focus on them more to increase the deployment and use of solar RE. Full article
(This article belongs to the Special Issue Artificial Intelligence for Power System and Renewable Energy Optimization)
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17 pages, 4466 KiB  
Article
Maximum Power Point Tracking for Photovoltaic Systems Operating under Partially Shaded Conditions Using SALP Swarm Algorithm
by Lilia Tightiz, Saeedeh Mansouri, Farhad Zishan, Joon Yoo and Nima Shafaghatian
Energies 2022, 15(21), 8210; https://doi.org/10.3390/en15218210 - 3 Nov 2022
Cited by 12 | Viewed by 1945
Abstract
This article presents a new method based on meta-heuristic algorithm for maximum power point tracking (MPPT) in photovoltaic systems. In this new method, the SALP Swarm Algorithm (SSA) is used instead of classic methods such as the Perturb and Observe (P&O) method. In [...] Read more.
This article presents a new method based on meta-heuristic algorithm for maximum power point tracking (MPPT) in photovoltaic systems. In this new method, the SALP Swarm Algorithm (SSA) is used instead of classic methods such as the Perturb and Observe (P&O) method. In this method, the value of the duty cycle is optimally determined in an optimization problem by SSA in order to track the maximum power. The objective function in this problem is maximizing the output power of the photovoltaic system. The proposed method has been applied on a photovoltaic system connected to the load, taking into account the effect of partial shade and different atmospheric conditions. The SSA method is compared with the Particle Swarm Optimization (PSO) algorithm and P&O methods. Additionally, we evaluated the effect of changes in temperature and radiation on solving the problem. The results of the simulation in the MATLAB/Simulink environment show the optimal performance of the proposed method in tracking the maximum power in different atmospheric conditions compared to other methods. To validate the proposed algorithm, it is compared with four important indexes: ISE, ITSE, IAE, and ITAE. Full article
(This article belongs to the Special Issue Artificial Intelligence for Power System and Renewable Energy Optimization)
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22 pages, 2040 KiB  
Article
Big Data Analysis for Optimising the Decision-Making Process in Sustainable Energy Action Plans: A Multi-Criteria Evaluation Approach Applied to Sicilian Regional Recovery and Resilience Plans
by Umberto Di Matteo and Sofia Agostinelli
Energies 2022, 15(20), 7487; https://doi.org/10.3390/en15207487 - 11 Oct 2022
Cited by 1 | Viewed by 1512
Abstract
Keeping the global temperature rise below 2 degrees Celsius, as foreseen by the Paris Agreement, requires a new global roadmap for the energy transition. For this reason, the European Commission decided to directly involve local municipalities in reaching these objectives through multilevel, bottom-up [...] Read more.
Keeping the global temperature rise below 2 degrees Celsius, as foreseen by the Paris Agreement, requires a new global roadmap for the energy transition. For this reason, the European Commission decided to directly involve local municipalities in reaching these objectives through multilevel, bottom-up actions for sustainable energy. The Covenant of Mayors is a very concrete demonstration of this trend of development and adoption of sustainable energy action plans (SEAP), rethinking the way cities operate and bringing them closer to energy self-sufficiency, with measures favouring local economic development and improving citizens’ quality of life. The numerous RES/RUE actions included in SEAPs at the regional level have led both to the request for huge funding and to increased complexity for regional managers to identify the best projects to be financed. To manage the multitude of data (emissions, energy consumption, cost, etc.) present in the SEAPs at a regional level, a web-based platform called Lex-energetica was developed. In this context, this paper aims to present a participatory supportive framework for the decision-making process involved in financing the SEAPs’ actions, considering the selection of sustainable Renewable Energy Sources (RES) and Rational Use of Energy (RUE) technologies. This study proposes a methodology based on two macro-phases: the first phase consists of a ranking evaluation of categories of areas of intervention based on the analytic hierarchy process, while the second identifies nine criteria, according to the domains corresponding to the three pillars of sustainability, to compare the most appropriate RES/RUE actions. Full article
(This article belongs to the Special Issue Artificial Intelligence for Power System and Renewable Energy Optimization)
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17 pages, 8013 KiB  
Article
Optimal Allocation of Renewable Energy Sources and Battery Storage Systems Considering Energy Management System Optimization Based on Fuzzy Inference
by Marinko Barukčić, Toni Varga, Tin Benšić and Vedrana Jerković Štil
Energies 2022, 15(19), 6884; https://doi.org/10.3390/en15196884 - 20 Sep 2022
Cited by 5 | Viewed by 1653
Abstract
The main problem in planning the optimal operation of renewable energy sources and battery storage systems is the amount of data that must be considered to cover an entire observation period. If the observation period is one year, the characteristic days or averaged [...] Read more.
The main problem in planning the optimal operation of renewable energy sources and battery storage systems is the amount of data that must be considered to cover an entire observation period. If the observation period is one year, the characteristic days or averaged data (daily, weekly or monthly averages) are considered to reduce the number of data. Since the average values of the entered data differ from the actual values, it is better to work with hourly or 15-min data at the annual level. The study presents a framework for solving the problem of the optimal allocation and operation of renewable energy sources and battery storage systems. The proposed method simultaneously solves the optimal allocation and energy management problem considering hourly data at the annual level. The fuzzy inference-based system is proposed for scheduling optimal profiles of battery storage systems and renewable energy sources. The developed fuzzy inference system manages the power factors of the photovoltaic and wind power systems, the power factor and output of the biogas plant, and the operating status of the battery storage system. The presented method simultaneously finds the optimal parameters of the energy management system and the optimal allocation and operation of the renewable energy sources and the battery storage system. The developed method is based on the calculation of steady-state power flow. The proposed method is to be used in the design phase for the installation of various renewable energy sources and battery storage systems. In addition, the method is intended to be used to optimally control the power output of energy sources and the operation of energy storage systems during steady-state operation in order to operate the distribution network with minimum annual active energy losses. The developed method is applied to the test distribution system IEEE with 37 nodes. The reduction in annual energy losses in the tested distribution system is about 80% compared to the base case without renewable energy sources and battery storage system. Full article
(This article belongs to the Special Issue Artificial Intelligence for Power System and Renewable Energy Optimization)
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Review

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41 pages, 2591 KiB  
Review
Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources
by Adam Krechowicz, Maria Krechowicz and Katarzyna Poczeta
Energies 2022, 15(23), 9146; https://doi.org/10.3390/en15239146 - 2 Dec 2022
Cited by 17 | Viewed by 4016
Abstract
Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, [...] Read more.
Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, scheduling, managing inertia and frequency response during contingency events. The recent three years have proved that Machine Learning (ML) models are a promising solution for forecasting electricity generation from RES. In this review, the 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database. Statistic analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown. The results indicate that (1) Extreme Learning Machine and ensemble methods were the most popular methods used for electricity generation forecasting from RES in the last three years (2020–2022), (2) most of the research was carried out for wind systems, (3) the hybrid models accounted for about a third of the analyzed works, (4) most of the articles concerned short-term models, (5) the most researchers came from China, (6) and the journal which published the most papers in the analyzed field was Energies. Moreover, strengths, weaknesses, opportunities, and threats for the analyzed ML forecasting models were identified and presented in this paper. Full article
(This article belongs to the Special Issue Artificial Intelligence for Power System and Renewable Energy Optimization)
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