International Journal of Applied Power Engineering (IJAPE)最新文献

{"title":"Leveraging PSO algorithms to achieve optimal stand-alone microgrid performance with a focus on battery lifetime","authors":"Vicky Andria Kusuma, Aji Akbar Firdaus, S. S. Suprapto, D. F. U. Putra, Y. Prasetyo, Firillia Filliana","doi":"10.11591/ijape.v12.i3.pp293-299","DOIUrl":"https://doi.org/10.11591/ijape.v12.i3.pp293-299","url":null,"abstract":"This research endeavors to increase the lifespan of a battery utilized in a standalone microgrid system, a self-sufficient electrical system that consists of multiple generators that are not connected to the main power grid. This type of system is ideal for use in remote locations or areas where the grid connection is not possible. The sources of energy for this system include photovoltaic panels, wind turbines, diesel generators, and batteries. The state of charge (SOC) of the battery is used to determine the amount of energy stored in it. The particle swarm optimization (PSO) method is applied to minimize energy generation costs and maximize battery life. The results show that battery optimization can decrease energy generation costs from Rp 5,271,523.03 ($338.64 in USD) to Rp 13,064,979.20 ($839.30 in USD) while increasing the battery's lifespan by 0.42%, with losses of 7.22 kW and 433.29 kVAR, and also a life loss cost of Rp 5,499/$0.35.","PeriodicalId":340072,"journal":{"name":"International Journal of Applied Power Engineering (IJAPE)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122719045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using machine learning prediction to design an optimized renewable energy system for a remote area in Italy","authors":"Ali Rezaei, Afshin Balal, Yaser Pakzad Jafarabadi","doi":"10.11591/ijape.v12.i3.pp331-340","DOIUrl":"https://doi.org/10.11591/ijape.v12.i3.pp331-340","url":null,"abstract":"Due to the lack of fossil fuels, there is a significant demand to employ renewable energy systems (RES) worldwide. This paper proposes designing an optimized RES for a remote microgrid that relies solely on solar and wind sources. The proposed RES aims to provide reliable and efficient energy to the microgrid by using machine learning algorithms to forecast the power output of the solar and wind sources. This forecasting will help the system to anticipate and adjust to changes in the weather patterns that may affect the availability of solar and wind. In addition, the system advisor model (SAM) software is used to design the hybrid solar/wind system, considering factors such as the size of the microgrid and the available resources. The system comprises a 60-kW wind system of ten turbines and a 100-kW PV system spread out over the houses. The results show that random forest regression (RFR) models achieved a high level of accuracy in predicting solar power generation, as evidenced by their low mean squared error (MSE) and high R² values. Additionally, a proposed hybrid system can generate enough energy to meet the area's needs.","PeriodicalId":340072,"journal":{"name":"International Journal of Applied Power Engineering (IJAPE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114899466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}