Editorial

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2 pages, 195 KiB

Open AccessEditorial

Planning and Operation of Electrical Energy Systems under Uncertainties

by Marcos Tostado-Véliz, Salah Kamel and Abd Elnaby Kabeel

Appl. Sci. 2023, 13(19), 10872; https://0-doi-org.brum.beds.ac.uk/10.3390/app131910872 - 30 Sep 2023

Viewed by 441

Abstract

The electricity sector is evolving dramatically [...] Full article

(This article belongs to the Special Issue Planning and Operation of Electrical Energy Systems under Uncertainties)

Research

Jump to: Editorial

31 pages, 4846 KiB

Open AccessArticle

An Improved Cheetah Optimizer for Accurate and Reliable Estimation of Unknown Parameters in Photovoltaic Cell and Module Models

by Zulfiqar Ali Memon, Mohammad Amin Akbari and Mohsen Zare

Appl. Sci. 2023, 13(18), 9997; https://0-doi-org.brum.beds.ac.uk/10.3390/app13189997 - 05 Sep 2023

Cited by 4 | Viewed by 1246

Abstract

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. [...] Read more.

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. The ICO algorithm’s search phase is controlled based on the leader’s position. The step length is adjusted following the sorted population. As a result of this updated operator, the algorithm can perform global and local searches. Furthermore, the interaction factor during the attack phase is adjusted based on the position of the prey, and a random value controls the turning factor. Single-, double-, and PV module models are investigated to test the ICO’s parameter estimation performance. Statistical analysis uses the minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in the test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared with other state-of-the-art optimization algorithms, the CO and ICO algorithms are proven to be highly reliable and accurate when identifying PV parameters. According to the results, the ICO and CO obtained the first- and second-best sum ranking results for the studied PV models among 12 applied algorithms. Despite this, the ICO algorithm reduces the CO’s computation time by 40% on average. Additionally, ICO’s convergence speed is high, reaching an optimal solution in less than 25,000 function evaluations in most cases. Full article

(This article belongs to the Special Issue Planning and Operation of Electrical Energy Systems under Uncertainties)

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25 pages, 3476 KiB

Open AccessArticle

Optimal Power Flow of Hybrid Wind/Solar/Thermal Energy Integrated Power Systems Considering Costs and Emissions via a Novel and Efficient Search Optimization Algorithm

by Ali S. Alghamdi

Appl. Sci. 2023, 13(8), 4760; https://0-doi-org.brum.beds.ac.uk/10.3390/app13084760 - 10 Apr 2023

Cited by 8 | Viewed by 1773

Abstract

The OPF problem has significant importance in a power system’s operation, planning, economic scheduling, and security. Today’s electricity grid is rapidly evolving, with increased penetration of renewable power sources (RPSs). Conventional optimal power flow (OPF) has non-linear constraints that make it a highly [...] Read more.

The OPF problem has significant importance in a power system’s operation, planning, economic scheduling, and security. Today’s electricity grid is rapidly evolving, with increased penetration of renewable power sources (RPSs). Conventional optimal power flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimization problem. This complex problem escalates further with the integration of renewable energy resource (RES), which are generally intermittent in nature. This study suggests a new and effective improved optimizer via a TFWO algorithm (turbulent flow of water-based optimization), namely the ITFWO algorithm, to solve non-linear and non-convex OPF problems in energy networks with integrated solar photovoltaic (PV) and wind turbine (WT) units (being environmentally friendly and clean in nature). OPF in the energy networks is an optimization problem proposed to discover the optimal settings of an energy network. The OPF modeling contains the forecasted electric energy of WT and PV by considering the voltage value at PV and WT buses as decision parameters. Forecasting the active energy of PV and WT units has been founded on the real-time measurements of solar irradiance and wind speed. Eight scenarios are analyzed on the IEEE 30-bus test system in order to determine a cost-effective schedule for thermal power plants with different objectives that reflect fuel cost minimization, voltage profile improvement, emission gases, power loss reduction, and fuel cost minimization with consideration of the valve point effect of generation units. In addition, a carbon tax is considered in the goal function in the examined cases in order to investigate its effect on generator scheduling. A comparison of the simulation results with other recently published algorithms for solving OPF problems is made to illustrate the effectiveness and validity of the proposed ITFWO algorithm. Simulation results show that the improved turbulent flow of water-based optimization algorithm provides an effective and robust high-quality solution of the various optimal power-flow problems. Moreover, results obtained using the proposed ITFWO algorithm are either better than, or comparable to, those obtained using other techniques reported in the literature. The utility of solar and wind energy in scheduling problems has been proposed in this work. Full article

(This article belongs to the Special Issue Planning and Operation of Electrical Energy Systems under Uncertainties)

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23 pages, 2775 KiB

Open AccessArticle

Energy Hub Optimal Scheduling and Management in the Day-Ahead Market Considering Renewable Energy Sources, CHP, Electric Vehicles, and Storage Systems Using Improved Fick’s Law Algorithm

by Ali S. Alghamdi, Mohana Alanazi, Abdulaziz Alanazi, Yazeed Qasaymeh, Muhammad Zubair, Ahmed Bilal Awan and Muhammad Gul Bahar Ashiq

Appl. Sci. 2023, 13(6), 3526; https://0-doi-org.brum.beds.ac.uk/10.3390/app13063526 - 09 Mar 2023

Cited by 2 | Viewed by 1550

Abstract

Coordinated energy scheduling and management strategies in the energy hub plan are essential to achieve optimal economic performance. In this paper, the scheduling and management framework of an energy hub (EH) is presented with the aim of energy profit maximization in partnership with [...] Read more.

Coordinated energy scheduling and management strategies in the energy hub plan are essential to achieve optimal economic performance. In this paper, the scheduling and management framework of an energy hub (EH) is presented with the aim of energy profit maximization in partnership with electricity, natural gas, and district heating networks (EGHNs) considering the coordinated multi-energy management based on the day-ahead market. The optimum capacity of EH equipment, including photovoltaic and wind renewable energy sources, a combined heat and power system (CHP), a boiler, energy storage, and electric vehicles is determined in the day-ahead market using the improved Fick’s law algorithm (IFLA), considering the energy profit maximization and also satisfying the linear network and hub constraints. The conventional FLA is inspired by the concept of Fick’s diffusion law, and, in this study, its performance against premature convergence is improved by using Rosenbrock’s direct rotational method. The performance of the IFLA when applied to EH coordinated scheduling and management problems with the aim of profit maximization is compared with the conventional FLA, particle swarm optimization (PSO), and manta ray foraging optimization (MRFO) methods. The results show that the proposed scheduling and multi-energy management framework achieves more energy profit in the day-ahead electricity, gas, and heating markets by satisfying the operation and EH constraints compared to other methods. Furthermore, according to the findings, the increased (decreased) demand and the forced outage rate caused a decrease (increase) in the EH profit. The results show the effectiveness of the proposed framework to obtain the EH maximum energy profit in the day-ahead market. Full article

(This article belongs to the Special Issue Planning and Operation of Electrical Energy Systems under Uncertainties)

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35 pages, 5084 KiB

Open AccessArticle

Techno-Economics and the Identification of Environmental Barriers to the Development of Concentrated Solar Thermal Power Plants in India

by Seepana Praveenkumar, Ephraim Bonah Agyekum, Abhinav Kumar, Jeffrey Dankwa Ampah, Sandylove Afrane, Fahd Amjad and Vladimir Ivanovich Velkin

Appl. Sci. 2022, 12(20), 10400; https://0-doi-org.brum.beds.ac.uk/10.3390/app122010400 - 15 Oct 2022

Cited by 14 | Viewed by 2774

Abstract

India is endowed with a lot of solar radiation as a result of its location. The Indian government therefore intends to maximize the usage of its solar energy resources through the development of solar power plants across the country. The concentrated solar power [...] Read more.

India is endowed with a lot of solar radiation as a result of its location. The Indian government therefore intends to maximize the usage of its solar energy resources through the development of solar power plants across the country. The concentrated solar power plant (CSP) is one of the technologies that rely on solar energy for its electricity generation. The type of condenser model in the CSP technology has the potential to affect its techno-economic viability. In this paper, a 100 MW solar tower power plant (STPP) with two different condenser models, i.e., the dry-cooled STPP and wet-cooled STPP models, are studied using the System Advisor Model (SAM) at six different geographical areas in India. The study employed the optimization of the thermal energy storage and the solar field size to identify the minimum levelized cost of electricity (LCOE) for all six locations. Results from the simulation show that the LCOE will range between 13 and 17 cents/kWh under the optimization conditions for the STPP dry-cooled condenser model, while that of the wet-cooled condenser model will range between 12.40 and 12.96 USD cents/kWh for the study locations. It was also observed that the optimized solar multiple (SM) for the dry-cooled STPP model ranges between 1.4 and 1.8, whereas that of the wet-cooled model ranges between 1 and 1.8. The study identified Bhopal as the best location for installing the STPP plant for both condenser models. In addition, this paper also discusses major potential barriers and government policies that are needed to develop CSP technologies in India. The outcome of the study is expected to help both government and other stakeholders in decision making and policy formulation for the sector. Full article

(This article belongs to the Special Issue Planning and Operation of Electrical Energy Systems under Uncertainties)

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24 pages, 1487 KiB

Open AccessArticle

A Hybrid Firefly–JAYA Algorithm for the Optimal Power Flow Problem Considering Wind and Solar Power Generations

by Ali S. Alghamdi

Appl. Sci. 2022, 12(14), 7193; https://0-doi-org.brum.beds.ac.uk/10.3390/app12147193 - 17 Jul 2022

Cited by 16 | Viewed by 1308

Abstract

Optimal power flow (OPF) is widely used in power systems. This problem involves adjusting variables such as online capacity, generator output, power stability, and bus voltage to reduce production costs. This paper presents HFAJAYA, a combined evolution method using the Firefly and JAYA [...] Read more.

Optimal power flow (OPF) is widely used in power systems. This problem involves adjusting variables such as online capacity, generator output, power stability, and bus voltage to reduce production costs. This paper presents HFAJAYA, a combined evolution method using the Firefly and JAYA algorithms to solve the OPF problem effectively and efficiently. While considering renewable energy, including solar energy and wind energy systems, the problem is regarded as a single-objective and multi-objective function. It considers power losses, emissions, emissions taxes, the total cost of fuel, and voltage deviation as objective functions of the problem. I have successfully implemented all simulations with different scenarios on a standard 30-bus IEEE network. A comparison of the results obtained from the HFAJAYA simulation with results from other well-known works has been undertaken to confirm the efficiency of the recommended HFAJAYA method. Full article

(This article belongs to the Special Issue Planning and Operation of Electrical Energy Systems under Uncertainties)

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24 pages, 3745 KiB

Open AccessArticle

The Optimal and Economic Planning of a Power System Based on the Microgrid Concept with a Modified Seagull Optimization Algorithm Integrating Renewable Resources

by Zhigao Wang, Zhi Geng, Xia Fang, Qianqian Tian, Xinsheng Lan and Jie Feng

Appl. Sci. 2022, 12(9), 4743; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094743 - 08 May 2022

Cited by 5 | Viewed by 1683

Abstract

In the past, planning to develop an electricity generation capacity supply of consumable load, an acceptable level of reliability, and minimum cost has played significant roles. Due to technological development in energy and the support of energy policymakers to make the most of [...] Read more.

In the past, planning to develop an electricity generation capacity supply of consumable load, an acceptable level of reliability, and minimum cost has played significant roles. Due to technological development in energy and the support of energy policymakers to make the most of these clean and cheap resources, a significant amount of research has been conducted to make the most of such energy. Constraints such as low capacity, output power uncertainty, and sustainability problems have made using distributed energy sources costly and complex. Theoretically, capacity development planning in a power system is part of macro-energy planning. It is generally based on specific development policies in each country’s national interest. In addition to being economical, the purpose of this planning was to find the best capacity development plan commensurate with the amount of consumption so that the development plan does not go beyond the permissible limits of reliability, environmental issues, and other constraints. On the other hand, due to the considerable growth of divided production, especially energy sources, it is essential to use microgrids. Accordingly, in this research study, in the process of solving the problem of planning and providing load growth by the distributed generation units to maximize reliability and minimize investment costs, the creation of smaller networks was investigated. To optimize zoning, the weighted graph theory method, in which the weight of the edges is the apparent power passing through the lines, was adopted. In addition, reactive power reliability was included in the calculations to improve the economic aspects. Probabilistic modeling for the presence of renewable resources was employed to bring the model to reality. Since the above problem is very complex, a Seagull-based algorithm and chaos theory were utilized to solve this matter. Finally, the suggested method for the sample system is discussed in different scenarios, indicating an improvement in the system’s performance. According to the numerical results, the NSGA, SPEA, and MOPSO have mean values of 68.3%, 50.2%, and 48.3%, which are covered by the proposed optimization algorithm. Full article

(This article belongs to the Special Issue Planning and Operation of Electrical Energy Systems under Uncertainties)

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15 pages, 1221 KiB

Open AccessFeature PaperArticle

A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems

by Eman S. Ali, Ragab A. El-Sehiemy, Adel A. Abou El-Ela, Marcos Tostado-Véliz and Salah Kamel

Appl. Sci. 2022, 12(2), 623; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020623 - 10 Jan 2022

Cited by 5 | Viewed by 1421

Abstract

Power system operation and planning studies face many challenges with increasing of renewable energy sources (RESs) penetration. These challenges revolve around the RESs uncertainty and its applications on probabilistic forecasting, power system operation optimization and power system planning. This paper proposes a novel [...] Read more.

Power system operation and planning studies face many challenges with increasing of renewable energy sources (RESs) penetration. These challenges revolve around the RESs uncertainty and its applications on probabilistic forecasting, power system operation optimization and power system planning. This paper proposes a novel and effective criterion for uncertainties modeling of the RESs as well as system loads. Four sorting stages are applied for the proposed uncertainty cases reduction. Added to that, it proposes three different uncertainty reduction strategies for obtaining different accuracy and speed options. The proposed reduction strategies are tested on medium and large scale distribution systems; IEEE 69-bus and 118-bus systems. The obtained results verify the effectiveness of the proposed criterion in uncertainties modeling in distribution systems with acceptable level of accuracy. Full article

(This article belongs to the Special Issue Planning and Operation of Electrical Energy Systems under Uncertainties)

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