A stochastic approach to determine the energy consumption and synthetic load profiles of different customer types of rural communities

被引:5
作者
Ashetehe, Ahunim Abebe [1 ]
Shewarega, Fekadu [2 ]
Gessesse, Belachew Bantyirga [1 ]
Biru, Getachew [3 ]
Lakeou, Samuel [4 ]
机构
[1] Bahir Dar Univ, Bahir Dar Inst Technol, Elect & Comp Engn, Bahir Dar, Ethiopia
[2] Univ Duisburg Essen, Elect & Comp Engn, Duisburg, Germany
[3] Addis Ababa Univ, Sch Elect & Comp Engn, Addis Ababa, Ethiopia
[4] Univ Dist Columbia, Elect & Comp Engn, Washington, DC USA
关键词
Load profile; Rural electrification; Renewable energy; Stochastic bottom-up approach; Energy demand; Developing countries; Ethiopia; BOTTOM-UP;
D O I
10.1016/j.sciaf.2024.e02172
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The electricity demand is highly stochastic and unpredictable. This is due to the fact that it is significantly impacted by a number of factors, including the type of load, weather, time of day, seasonality, economic limitations, customers' way of living, and other randomness factors. An accurate load model is one of the main inputs for the design of an economical and reliable renewable-based rural electrification system for rural communities and demand management systems. This paper presents a generic methodology for determining a rural community's energy consumption load profile, which is used to determine the most cost-effective size of renewable energy sources for rural electrification purposes. To determine the load profile parameters, such as the types of appliances used, their functioning times, functioning windows, and expected minimum and maximum cycle time, a field survey was conducted in four rural electrified Ethiopian villages. Since the survey findings will not fully explain the stochastic nature of the load profile, the load parameters are randomly generated, and a bottom-up approach is used to estimate the rural community's energy usage. In this study, household loads, public institution loads, business loads, and small industrial loads are the main consumer types taken into account. These loads are classified according to their energy usage as weekdays, weekends, and national and religious holidays. A MATLAB program is developed and implemented to obtain the load profiles of different customer groups. The results of this study are assessed in accordance with the multitier criterion and verified through the use of the well-known software HOMER Pro- and LoadProGen.
引用
收藏
页数:26
相关论文
共 37 条
[1]   Dynamic aggregated building electricity load modeling and simulation [J].
Abdelsalam, Abdelazeem A. ;
Gabbar, Hossam A. ;
Musharavati, Farayi ;
Pokharel, Shaligram .
SIMULATION MODELLING PRACTICE AND THEORY, 2014, 42 :19-31
[2]   Hybrid Top-Down and Bottom-Up Approach for Residential Load Compositions and Percentages [J].
Alahmed, Ahmed S. ;
Almuhaini, Muhammed M. .
2021 POWER SYSTEM AND GREEN ENERGY CONFERENCE (PSGEC), 2021, :1-6
[3]   Optimum unit sizing of hybrid renewable energy system utilizing harmony search, Jaya and particle swarm optimization algorithms [J].
Alshammari, Nahar ;
Asumadu, Johnson .
SUSTAINABLE CITIES AND SOCIETY, 2020, 60
[4]   Community stochastic domestic electricity forecasting [J].
Amin, Amin ;
Mourshed, Monjur .
APPLIED ENERGY, 2024, 355
[5]  
[Anonymous], 2015, Campus Master Plan Update 2015: Landscape Grounds Maintenance, P1
[6]  
[Anonymous], SDG7 DATA PROJECTION
[7]  
Ashetehe A.A., 2022, INT C EL COMP EN TEC, DOI [10.1109/ICECET55527.2022.9872635, DOI 10.1109/ICECET55527.2022.9872635]
[8]   Accuracy of energy-use surveys in predicting rural mini-grid user consumption [J].
Blodgett, Courtney ;
Dauenhauer, Peter ;
Louie, Henry ;
Kickham, Lauren .
ENERGY FOR SUSTAINABLE DEVELOPMENT, 2017, 41 :88-105
[9]   Combining bottom-up and top-down [J].
Boehringer, Christoph ;
Rutherford, Thomas F. .
ENERGY ECONOMICS, 2008, 30 (02) :574-596
[10]   High resolution stochastic generator of European household specific electricity demand load curves for decentralized power self-production applications [J].
Bouvenot, Jean-Baptiste ;
Latour, Benjamin ;
Flament, Bernard ;
Siroux, Monica .
ENERGY AND BUILDINGS, 2020, 229