A new methodology for reducing carbon emissions using multi-renewable energy systems and artificial intelligence
Bibliographic Data
| ID | 21229375 |
|---|---|
| Authors | Bilal Naji Alhasnawi (0000-0002-2630-8455, Al-Furat Al-Awsat Technical University), Sabah Mohammed Mlkat Almutoki (Al-Furat Al-Awsat Technical University), Firas Faeq K Hussain (0000-0003-4087-5592, Iraqi University), Ambe Harrison (0000-0002-4353-1261, University of Buea), Bahamin Bazooyar (0000-0002-7341-4509, Brunel University of London, corresponding author), Marek Zanker (0000-0002-2745-4868, University of Hradec Králové), Vladimír Bureš (0000-0001-7788-7445, University of Hradec Králové) |
| Year | 2024 |
| Volume | 114 |
| Pages | 105721 |
| Publication date | 2024-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2024.105721 |
| OpenAlex | W4401304472 |
| Language | EN |
| Citations received | 5 |
| References cited | 72 |
Microgrid cost management is a significant difficulty because the energy generated by microgrids is typically derived from a variety of renewable and non-renewable sources. Furthermore, in order to meet the requirements of freed energy markets and secure load demand, a link between the microgrid and the national grid is always preferred. For all of these reasons, in order to minimize operating expenses, it is imperative to design a smart energy management unit to regulate various energy resources inside the microgrid. In this study, a smart unit idea for multi-source microgrid operation and cost management is presented. The proposed unit utilizes the Improved Artificial Rabbits Optimization Algorithm (IAROA) which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. Also, a comparison between the optimization outcomes obtained results is implemented using Honey Badger Algorithm (HBA), and Whale Optimization Algorithm (WOA). The results prove the applicability and feasibility of the proposed method for the demand management system in SMG. The price after applying HBA is 6244.5783 (ID). But after applying the Whale Optimization Algorithm, the cost is found 4283.9755 (ID), and after applying the Artificial Rabbits Optimization Algorithm, the cost is found 1227.4482 (ID). By comparing the proposed method with conventional method, the whale optimization algorithm saved 31.396 % per day, and the proposed artificial rabbit's optimization algorithm saved 80.3437 % per day. From the obtained results the proposed algorithm gives superior performance
Demand response · Electrical engineering · Electricity · Energy management · Mathematical optimization · Microgrid · Reliability engineering · Renewable energy · Smart grid · Computer Science · Energy Load and Power Forecasting · Engineering · Mathematics · Microgrid Control and Optimization · Smart Grid Energy Management
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O algoritmo é um texto
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| Citations per year | 2.5 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |