1
|
Ahmed D, Ebeed M, Kamel S, Nasrat L, Ali A, Shaaban MF, Hussien AG. An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty. Sci Rep 2024; 14:15558. [PMID: 38969676 PMCID: PMC11226461 DOI: 10.1038/s41598-024-65867-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 06/25/2024] [Indexed: 07/07/2024] Open
Abstract
The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.
Collapse
Affiliation(s)
- Deyaa Ahmed
- Holding Company for Water and Wastewater (HCWW), Aswan, 81542, Egypt
| | - Mohamed Ebeed
- Faculty of Engineering, Sohag University, Sohag, 82524, Egypt
- Department of Electrical Engineering, University of Jaén, EPS Linares, 23700, Linares, Jaén, Spain
| | - Salah Kamel
- Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
| | - Loai Nasrat
- Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
| | - Abdelfatah Ali
- Department of Electrical Engineering, American University of Sharjah, 26666, Sharjah, United Arab Emirates
- Department of Electrical Engineering, South Valley University, Qena, 83523, Egypt
| | - Mostafa F Shaaban
- Department of Electrical Engineering, American University of Sharjah, 26666, Sharjah, United Arab Emirates
| | - Abdelazim G Hussien
- Department of Computer and Information Science, Linköping University, Linköping, Sweden.
- Faculty of Science, Fayoum University, Fayoum, Egypt.
- MEU Research Unit, Middle East University, 11831, Amman, Jordan.
| |
Collapse
|
2
|
Habibi S, Effatnejad R, Hedayati M, Hajihosseini P. Stochastic energy management of a microgrid incorporating two-point estimation method, mobile storage, and fuzzy multi-objective enhanced grey wolf optimizer. Sci Rep 2024; 14:1667. [PMID: 38238390 PMCID: PMC10796331 DOI: 10.1038/s41598-024-51166-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 01/01/2024] [Indexed: 01/22/2024] Open
Abstract
In this study, the stochastic energy management, and scheduling of a renewable microgrid involving energy sources and dynamic storage is performed considering energy resource and demand uncertainties and demand response (DR) using the two-point estimation method (2 m + 1 PEM). The three-dimensional objective function is defined as maximizing the renewable hosting capacity and minimizing the operation cost, and emission cost minimization. The decision variables include installation location and size of the renewable resources and mobile energy storage system (MESS), determined using a multi-objective enhanced grey wolf optimizer (MOEGWO) improved based on the logistic chaotic mapping integrated with fuzzy decision-making approach. The simulations are implemented for several cases of employing MESS, DR, and uncertainties to investigate the proposed approach's efficacy. The MOEGWO performance is confirmed to solve the ZDT and CEC'09 functions according to some well-known algorithms. Then, the performance of the MOEGWO is evaluated on the stochastic energy management and scheduling of the renewable microgrid. The results indicate that considering the dynamic MESS causes reducing the operation and emission costs by 23.34% and 34.78%, respectively, and increasing the renewable hosting capacity by 7.62% in contrast to using the static MESS. Also, the stochastic problem-solving considering uncertainties showed that operation and emission costs are raised, the renewable hosting capacity is decreased, and the uncertainty impact is reduced in the condition of DR application. So, the results validated the proposed methodology's effectiveness for minimizing the operation and emission costs and maximizing the renewable hosting capacity. Moreover, the superior capability of the MOEGWO is confirmed in comparison with the multi-objective particle swarm optimization to obtain lower operation and emission costs and higher renewable hosting capacity.
Collapse
Affiliation(s)
- Serajuddin Habibi
- Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
| | - Reza Effatnejad
- Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
| | - Mahdi Hedayati
- Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
| | - Payman Hajihosseini
- Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
| |
Collapse
|
3
|
Wang F, Gan L, Zhang P. A two-stage SCUC model for distribution networks considering uncertainty and demand response. Heliyon 2023; 9:e20189. [PMID: 37810800 PMCID: PMC10556595 DOI: 10.1016/j.heliyon.2023.e20189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/05/2023] [Accepted: 09/13/2023] [Indexed: 10/10/2023] Open
Abstract
Demand response (DR) is one of the most effective and economical methods for power operators to improve network reliability in face of uncertainty and emergencies. In this paper, considering the uncertainty of wind power output and the failure of power system components, a two-stage security-constrained unit commitment (SCUC) model is established by optimizing the real-time pricing mechanism to indirectly adjust the DR. Firstly, the uncertainty of wind power output is modeled based on self-organizing map (SOM), and the component failure of the power system is modeled based on Monte Carlo. Then, a pricing scheme is proposed to stimulate users' electricity consumption behavior. Finally, the marine predator algorithm is used to solve the problem. Simulation results on IEEE-RTS system show that the proposed method can reduce the total operating cost by 10%, effectively stimulate the electricity consumption behavior of users, to improve the peak load shifting and valley filling capacity of the power system.
Collapse
Affiliation(s)
- Fei Wang
- State Grid Hubei Jingmen Power Supply Company, Jingmen 448000, China
| | - Lei Gan
- State Grid Hubei Shiyan Power Supply Company, Shiyan, 442000, China
| | - Pengchao Zhang
- State Grid Hubei Xiangyang Power Supply Company, Xiangyang, 441000, China
| |
Collapse
|
4
|
Al-Betar MA, Awadallah MA, Makhadmeh SN, Alyasseri ZAA, Al-Naymat G, Mirjalili S. Marine Predators Algorithm: A Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:3405-3435. [PMID: 37260911 PMCID: PMC10115392 DOI: 10.1007/s11831-023-09912-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/05/2023] [Indexed: 06/02/2023]
Abstract
Marine Predators Algorithm (MPA) is a recent nature-inspired optimizer stemmed from widespread foraging mechanisms based on Lévy and Brownian movements in ocean predators. Due to its superb features, such as derivative-free, parameter-less, easy-to-use, flexible, and simplicity, MPA is quickly evolved for a wide range of optimization problems in a short period. Therefore, its impressive characteristics inspire this review to analyze and discuss the primary MPA research studies established. In this review paper, the growth of the MPA is analyzed based on 102 research papers to show its powerful performance. The MPA inspirations and its theoretical concepts are also illustrated, focusing on its convergence behaviour. Thereafter, the MPA versions suggested improving the MPA behaviour on connecting the search space shape of real-world optimization problems are analyzed. A plethora and diverse optimization applications have been addressed, relying on MPA as the main solver, which is also described and organized. In addition, a critical discussion about the convergence behaviour and the main limitation of MPA is given. The review is end-up highlighting the main findings of this survey and suggests some possible MPA-related improvements and extensions that can be carried out in the future.
Collapse
Affiliation(s)
- Mohammed Azmi Al-Betar
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
- Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Al-Huson, Irbid, Jordan
| | - Mohammed A. Awadallah
- Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
| | - Zaid Abdi Alkareem Alyasseri
- Information Technology Research and Development Center (ITRDC), University of Kufa, An Najaf, 54001 Iraq
- College of Engineering, University of Warith Al-Anbiyaa, Karbalä’, Iraq
| | - Ghazi Al-Naymat
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
| | - Seyedali Mirjalili
- Center for Artificial Intelligence Research and Optimization, Torrens University, Adelaide, Australia
| |
Collapse
|
5
|
Rai R, Dhal KG, Das A, Ray S. An Inclusive Survey on Marine Predators Algorithm: Variants and Applications. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:3133-3172. [PMID: 36855410 PMCID: PMC9951854 DOI: 10.1007/s11831-023-09897-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/08/2023] [Indexed: 05/13/2023]
Abstract
Marine Predators Algorithm (MPA) is the existing population-based meta-heuristic algorithms that falls under the category of Nature-Inspired Optimization Algorithm (NIOA) enthused by the foraging actions of the marine predators that principally pursues Levy or Brownian approach as its foraging strategy. Furthermore, it employs the optimal encounter rate stratagem involving both the predator as well as prey. Since its introduction by Faramarzi in the year 2020, MPA has gained enormous popularity and has been employed in numerous application areas ranging from Mathematical and Engineering Optimization problems to Fog Computing to Image Processing to Photovoltaic System to Wind-Solar Generation System for resolving continuous optimization problems. Such huge interest from the research fraternity or the massive recognition of MPA is due to several factors such as its simplicity, ease of application, realistic execution time, superior convergence acceleration rate, soaring effectiveness, its ability to unravel continuous, multi-objective and binary problems when compared with other renowned optimization algorithms existing in the literature. This paper offers a detailed summary of the Marine Predators Algorithm (MPA) and its variants. Furthermore, the applications of MPA in a number of spheres such as Image processing, classification, electrical power system, Photovoltaic models, structural damage detection, distribution networks, engineering applications, Task Scheduling, optimization problems etc., are illustrated. To conclude, the paper highlights and thereby advocates few of the potential future research directions for MPA.
Collapse
Affiliation(s)
- Rebika Rai
- Department of Computer Applications, Sikkim University, Sikkim, India
| | - Krishna Gopal Dhal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
| | - Arunita Das
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
| | - Swarnajit Ray
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal India
| |
Collapse
|
6
|
Abd El-Hameid AM, Elbaset AA, Ebeed M, Abdelsattar M. Optimal Allocation of Distributed Energy Resources Using Modern Optimization Techniques. ENHANCEMENT OF GRID-CONNECTED PHOTOVOLTAIC SYSTEMS USING ARTIFICIAL INTELLIGENCE 2023:73-99. [DOI: 10.1007/978-3-031-29692-5_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
|
7
|
Abd El-Hameid AM, Elbaset AA, Ebeed M, Abdelsattar M. Literature Review and Power Quality Issues. ENHANCEMENT OF GRID-CONNECTED PHOTOVOLTAIC SYSTEMS USING ARTIFICIAL INTELLIGENCE 2023:5-37. [DOI: 10.1007/978-3-031-29692-5_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
|
8
|
Abd El-Hameid AM, Elbaset AA, Ebeed M, Abdelsattar M. Results and Discussion. ENHANCEMENT OF GRID-CONNECTED PHOTOVOLTAIC SYSTEMS USING ARTIFICIAL INTELLIGENCE 2023:101-161. [DOI: 10.1007/978-3-031-29692-5_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
|
9
|
Chen L, Xu Y, Xu F, Hu Q, Tang Z. Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03875-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
|
10
|
Gami F, Alrowaili ZA, Ezzeldien M, Ebeed M, kamel S, Oda ES, Mohamed SA. Stochastic optimal reactive power dispatch at varying time of load demand and renewable energsy resources using an efficient modified jellyfish optimizer. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07526-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
11
|
Kanagasabai L. Chaotic implanted opposition-based-quantum equipoise state and Ascidiacea algorithms for loss lessening and power permanence enrichment. Soft comput 2022. [DOI: 10.1007/s00500-022-07232-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
12
|
Turbulent Flow of Water-Based Optimization for Solving Multi-Objective Technical and Economic Aspects of Optimal Power Flow Problems. MATHEMATICS 2022. [DOI: 10.3390/math10122106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The optimal operation of modern power systems aims at achieving the increased power demand requirements regarding economic and technical aspects. Another concern is preserving the emissions within the environmental limitations. In this regard, this paper aims at finding the optimal scheduling of power generation units that are able to meet the load requirements based on a multi-objective optimal power flow framework. In the proposed multi-objective framework, objective functions, technical economical, and emissions are considered. The solution methodology is performed based on a developed turbulent flow of a water-based optimizer (TFWO). Single and multi-objective functions are employed to minimize the cost of fuel, emission level, power losses, enhance voltage deviation, and voltage stability index. The proposed algorithm is tested and investigated on the IEEE 30-bus and 57-bus systems, and 17 cases are studied. Four additional cases studied are applied on four large scale test systems to prove the high scalability of the proposed solution methodology. Evaluation of the effectiveness and robustness of the proposed TFWO is proven through a comparison of the simulation results, convergence rate, and statistical indices to other well-known recent algorithms in the literature. We concluded from the current study that TFWO is efficient, effective, robust, and superior in solving OPF optimization problems. It has better convergence rates compared with other well-known algorithms with significant technical and economical improvements. A reduction in the range of 4.6–33.12% is achieved by the proposed TFWO for the large scale tested system. For the tested system, the proposed solution methodology leads to a more competitive solution with significant improvement in the techno-economic aspects.
Collapse
|
13
|
Rai R, Das A, Dhal KG. Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review. EVOLVING SYSTEMS 2022; 13:889-945. [PMID: 37520044 PMCID: PMC8859498 DOI: 10.1007/s12530-022-09425-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/15/2022] [Indexed: 12/14/2022]
Abstract
Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019-2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.
Collapse
Affiliation(s)
- Rebika Rai
- Department of Computer Applications, Sikkim University, Sikkim, India
| | - Arunita Das
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
| | - Krishna Gopal Dhal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
| |
Collapse
|
14
|
|
15
|
Durmus A. The Concentric Elliptical Antenna Array Patterns Synthesis Using Marine Predators Algorithm. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05351-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
16
|
On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06199-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
17
|
Techno-Economic Assessment and Operational Planning of Wind-Battery Distributed Renewable Generation System. SUSTAINABILITY 2021. [DOI: 10.3390/su13126776] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Electrical energy and power demand will experience exponential increase with the rise of the global population. Power demand is predictable and can be estimated based on population and available historical data. However, renewable energy sources (RES) are intermittent, unpredictable, and environment-dependent. Interestingly, microgrids are becoming smarter but require adequate and an appropriate energy storage system (ESS) to support their smooth and optimal operation. The deep discharge caused by the charging–discharging operation of the ESS affects its state of health, depth of discharge (DOD), and life cycle, and inadvertently reduces its lifetime. Additionally, these parameters of the ESS are directly affected by the varying demand and intermittency of RES. This study presents an assessment of battery energy storage in wind-penetrated microgrids considering the DOD of the ESS. The study investigates two scenarios: a standalone microgrid, and a grid-connected microgrid. The problem is formulated based on the operation cost of the microgrid considering the DOD and the lifetime of the battery. The optimization problem is solved using non-linear programming. The scheduled operation cost of the microgrid, the daily scheduling cost of ESS, the power dispatch by distributed generators, and the DOD of the battery storage at any point in time are reported. Performance analysis showed that a power loss probability of less than 10% is achievable in all scenarios, demonstrating the effectiveness of the study.
Collapse
|
18
|
Liang P, Fu Y, Ni S, Zheng B. Modeling and optimization for noise-aversion and energy-awareness disassembly sequence planning problems in reverse supply chain. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021:10.1007/s11356-021-14124-w. [PMID: 34014476 DOI: 10.1007/s11356-021-14124-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 04/21/2021] [Indexed: 06/12/2023]
Abstract
Nowadays, the reverse supply chain management receives much attention because of its critical role in environmental protection and economic development. Disassembly is very important in the reverse supply chain. It aims at dismantling valuable components from end-of-life products which are then remanufactured into like-new ones after reprocessing and reassembly operations. To efficiently organize and manage the remanufacturing process from the perspective of sustainable development, this work proposes a stochastic disassembly sequence planning problem with consideration of noise pollution and energy consumption to achieve disassembly profit maximization. A chance-constrained programming model is formulated to describe it mathematically. Then, a discrete marine predators algorithm combined with a stochastic simulation approach is specially designed. By conducting simulation experiments on some real-life instances and comparing the designed approach with two popularly known methods in literature, we mainly find that the proposed model and approach can make better disassembly plan for the investigated problem with maximal profit subject to the given noise pollution and energy consumption constraints. The results demonstrate that the proposed method can efficiently and effectively handle the considered problem, which contributes to reaching the highly reliable and environmentally sustainable disassembly process.
Collapse
Affiliation(s)
- Pei Liang
- School of Business, Qingdao University, Qingdao, 266071, China
| | - Yaping Fu
- School of Business, Qingdao University, Qingdao, 266071, China.
| | - Songyuan Ni
- College of Civil Engineering and Architecture, Qingdao Agricultural University, Qingdao, 266109, China
| | - Bing Zheng
- School of Business Administration, Zhejiang Gongshang University, Hangzhou, 310018, China
| |
Collapse
|
19
|
Sizing and Sitting of DERs in Active Distribution Networks Incorporating Load Prevailing Uncertainties Using Probabilistic Approaches. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11094156] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this study, a microgrid scheme encompassing photovoltaic panels, an energy storage system, and a diesel generator as a backup supply source is designed, and the optimal placement for installation is suggested. The main purpose of this microgrid is to meet the intrinsic demand without being supplied by the upstream network. Thus, the main objective in the design of the microgrid is to minimize the operational cost of microgrid’s sources subject to satisfy the loads by these sources. Therefore, the considered problem in this study is to determine the optimal size and placement for generation sources simultaneously for a microgrid with the objectives of minimization of cost of generation resources along with mitigation of power losses. In order to deal with uncertainties of PV generation and load forecasting, the lognormal distribution model and Gaussian process quantile regression (GPQR) approaches are employed. In order to solve the optimization problem, the lightning attachment procedure optimization (LAPO) and artificial bee colony (ABC) methods are employed, and the results are compared. The results imply the more effectiveness and priority of the LAPO approach in comparison with ABC in convergence speed and the accuracy of solution-finding.
Collapse
|
20
|
Scenario-Based Stochastic Framework for Optimal Planning of Distribution Systems Including Renewable-Based DG Units. SUSTAINABILITY 2021. [DOI: 10.3390/su13063566] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Renewable energy-based distributed generators are widely embedded into distribution systems for several economical, technical, and environmental tasks. The main concern related to the renewable-based distributed generators, especially photovoltaic and wind turbine generators, is the continuous variations in their output powers due to variations in solar irradiance and wind speed, which leads to uncertainties in the power system. Therefore, the uncertainties of these resources should be considered for feasible planning. The main innovation of this paper is that it proposes an efficient stochastic framework for the optimal planning of distribution systems with optimal inclusion of renewable-based distributed generators, considering the uncertainties of load demands and the output powers of the distributed generators. The proposed stochastic framework depends upon the scenario-based method for modeling the uncertainties in distribution systems. In this framework, a multi-objective function is considered for optimal planning, including minimization of the expected total power loss, the total system voltage deviation, the total cost, and the total emissions, in addition to enhancing the expected total voltage stability. A novel efficient technique known as the Equilibrium Optimizer (EO) is actualized to appoint the ratings and locations of renewable-based distributed generators. The effectiveness of the proposed strategy is applied on an IEEE 69-bus network and a 94-bus practical distribution system situated in Portugal. The simulations verify the feasibility of the framework for optimal power planning. Additionally, the results show that the optimal integration of the photovoltaic and wind turbine generators using the proposed method leads to a reduction in the expected power losses, voltage deviations, cost, and emission rate and enhances the voltage stability by 60.95%, 37.09%, 2.91%, 70.66%, and 48.73%, respectively, in the 69-bus system, while in the 94-bus system these values are enhanced to be 48.38%, 39.73%, 57.06%, 76.42%, and 11.99%, respectively.
Collapse
|
21
|
Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm. ENERGIES 2021. [DOI: 10.3390/en14051222] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The optimal reactive power dispatch (ORPD) problem represents a fundamental concern in the efficient and reliable operation of power systems, based on the proper coordination of numerous devices. Therefore, the ORPD calculation is an elaborate nonlinear optimization problem that requires highly performing computational algorithms to identify the optimal solution. In this paper, the potential of metaheuristic methods is explored for solving complex optimization problems specific to power systems. In this regard, an improved salp swarm algorithm is proposed to solve the ORPD problem for the IEEE-14 and IEEE-30 bus systems, by approaching the reactive power planning as both a single- and a multi- objective problem and aiming at minimizing the real power losses and the bus voltage deviations. Multiple comparison studies are conducted based on the obtained results to assess the proposed approach performance with respect to other state-of-the-art techniques. In all cases, the results demonstrate the potential of the developed method and reflect its effectiveness in solving challenging problems.
Collapse
|
22
|
A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM. MATHEMATICS 2021. [DOI: 10.3390/math9030291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.
Collapse
|
23
|
Oda ES, Hamed AMAE, Ali A, Elbaset AA, Sattar MAE, Ebeed M. Stochastic Optimal Planning of Distribution System Considering Integrated Photovoltaic-Based DG and DSTATCOM Under Uncertainties of Loads and Solar Irradiance. IEEE ACCESS 2021; 9:26541-26555. [DOI: 10.1109/access.2021.3058589] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
|
24
|
Reactive Power Optimization and Price Management in Microgrid Enabled with Blockchain. ENERGIES 2020. [DOI: 10.3390/en13236179] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The energy market is gradually changing from centralized trading to peer-to-peer trading due to the tremendous increase in a microgrid with green energy resources. When more generating units are included in the microgrid, the possibilities of more reactive power flows exist in the system that leads to high transmission loss which has to be optimized. The reactive power is one of the essential ancillary services in the microgrid towards preserving the voltage in the transmission and distribution line. The major contribution of the paper is towards managing the ancillary service in the distributed energy network economically and technically. This study aims to estimate and optimize the power loss, reactive power, and price management as well. Towards optimization, the self-balanced differential evolution algorithm (SBDE) is used in this study. A distribution system operator is involved in coordinating the sellers and buyers. The proposed layered microgrid architecture uses the blockchain technology for reactive power price management by providing transparency and security among peers. The process of converging various transactions into a block and adding in the distributed blockchain is illustrated. Multiple transactions are performed by using the proposed methodology, giving efficient energy transaction. The results show that the power loss is minimized using SBDE algorithm for different cases. Additionally, the study has demonstrated the price allocation of the optimal reactive power obtained from providers. The blockchain technology embedded in reactive power pricing will play a significant role in the evolution of traditional power distribution systems to active distribution networks.
Collapse
|