Optimal sizing of hybrid energy resources for electrifying distant rural areas of Iran

2012 ◽  
Author(s):  
A. Vahabzadeh ◽  
F. Separi ◽  
M. Samakush ◽  
M. Jafari
Keyword(s):  
Rural Areas ◽  
Energy Resources ◽  
Optimal Sizing ◽  
Hybrid Energy

scholarly journals Techno-Economic Evaluation of Hybrid Energy Systems Using Artificial Ecosystem-Based Optimization with Demand Side Management

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 204
Author(s):  
Hammed Olabisi Omotoso ◽  
Abdullah M. Al-Shaalan ◽  
Hassan M. H. Farh ◽  
Abdullrahman A. Al-Shamma’a
Keyword(s):  
Renewable Energy ◽  
Rural Areas ◽  
Search Algorithm ◽  
Demand Side Management ◽  
Demand Side ◽  
Energy Fraction ◽  
Optimal Sizing ◽  
Hybrid Energy ◽  
Simulation And Optimization ◽  
Artificial Ecosystem

Electrification of remote rural areas by adopting renewable energy technologies through the advancement of smart micro-grids is indispensable for the achievement of continuous development goals. Satisfying the electricity demand of consumers while adhering to reliability constraints with docile computation analysis is challenging for the optimal sizing of a Hybrid Energy System (HES). This study proposes the new application of an Artificial Ecosystem-based Optimization (AEO) algorithm for the optimal sizing of a HES while satisfying Loss of Power Supply Probability (LPSP) and Renewable Energy Fraction (REF) reliability indices. Furthermore, reduction of surplus energy is achieved by adopting Demand Side Management (DSM), which increases the utilization of renewable energy. By adopting DSM, 28.38%, 43.05%, and 65.37% were achieved for the Cost of Energy (COE) saving at 40%, 60%, and 80% REF, respectively. The simulation and optimization results demonstrate the most cost-competitive system configuration that is viable for remote-area utilization. The proposed AEO algorithm is further compared to Harris Hawk Optimization (HHO) and the Future Search Algorithm (FSA) for validation purpose. The obtained results demonstrate the efficacy of AEO to achieve the optimal sizing of HES with the lowest COE, the highest consistent level, and minimal standard deviation compared with HHO and FSA. The proposed model was developed and simulated using the MATLAB/code environment.

Renewable Energy Development – Challenges for Bangladesh

2007 ◽  
Vol 18 (3-4) ◽  
pp. 421-430 ◽  
Author(s):  
Md. Tarik-ul-Islam ◽  
Sara Ferdousi
Keyword(s):  
Renewable Energy ◽  
Rural Development ◽  
Rural Areas ◽  
Energy Development ◽  
Lessons Learned ◽  
Energy Resources ◽  
Great Promise ◽  
Renewable Energy Resources ◽  
Sustainable Rural Development ◽  
Renewable Energy Development

In Bangladesh, annual per capita energy consumption is approximately 200 KgOE3, which is among the lowest in the world. Presently, 70% of the population does not have access to electricity in Bangladesh (GoB, 2004). The average system loss is 20.97% (GoB, 2006). The demand for power is estimated to increase 300 MW annually (GoB, 1996a). In contrast, concerns have been raised about the conventional energy production from fossil fuels that significantly contributes to environmental degradation at the local, regional and global levels. This situation calls for adoption of sustainable energy strategies that permeate every level of the economy and can provide rural dwellers with the services that they want and need. With this backdrop, Bangladesh has been experiencing a gradual shift towards exploring renewable energy resources as a means to fuel rural development in the off-grid areas. The country is endowed with abundant renewable energy resources. The Local Government Engineering Department (LGED), with its mandate for sustainable rural development has embarked on a program for demonstration and transfer of renewable energy technologies in the off-grid rural areas. The project “Sustainable Rural Energy (SRE)‘ has been conceived within the overall framework of ‘Sustainable Environment Management Programme (SEMP)’ with financial support from United Nations Development Programme (UNDP). This project has already completed a number of renewable energy installations demonstrating diversified community applications of these technologies for livelihood and socio-economic improvement of the people living in the remote off-grid villages. The lessons learned from these demonstration plants reveal that, with careful forward planning, renewable energy can provide far-reaching economic and social benefits to people living in remote rural areas in Bangladesh. The private sectors and NGOs (Non-government organizations) have started to take part in the process of renewable energy development with great promise and enthusiasm. However, the process encounters policy, institutional and technological barriers, which are critical for continued development in this sector.

scholarly journals Optimal Scheduling of Residential Electricity Demand Based on the Power Management of Hybrid Energy Resources

2020 ◽  
Vol 24 (1) ◽  
pp. 580-603
Author(s):  
Abozar Hashemi ◽  
Ghasem Derakshan ◽  
M. R. Alizadeh Pahlavani ◽  
Babak Abdi
Keyword(s):  
Decision Making ◽  
Power Management ◽  
Energy Resources ◽  
Hybrid Energy ◽  
Management Algorithm ◽  
Residential Electricity ◽  
Resource Cost ◽  
Uncertainty Parameters ◽  
Demand Response Programs ◽  
The Impact

Abstract The present study sought to address the scheduling of the grid-connected hybrid energy resources under uncertainty of renewable sources, and load in the residential sector. After introducing hybrid resources, scheduling model was implemented through a power management algorithm in an attempt to optimize resource cost, emissions, and energy not supplied (ENS). The stated problem consists of two decision-making layers with different weight coefficients based on the prioritization of each objective function. The proposed algorithm is selected for energy optimal management based on technical constraints of the dispatchable and non-dispatchable resources, uncertainty parameters and day ahead real time pricing (RTP). Furthermore, the impact of demand response programs (DRP) on the given algorithm was investigated using load shedding and load shifting techniques. Finally, the results obtained led to the optimization of the functions in all decision-making layers with different modes of operation.

Optimum sizing and simulation of hybrid renewable energy system for remote area

2021 ◽  
pp. 0958305X2110301
Author(s):  
Animesh Masih ◽  
HK Verma
Keyword(s):  
Renewable Energy ◽  
Reliability Index ◽  
Energy System ◽  
Energy Resources ◽  
Total System ◽  
Renewable Energy Resources ◽  
Optimal Sizing ◽  
Remote Areas ◽  
Grasshopper Optimization Algorithm ◽  
Hybrid Renewable Energy

In current scenario, people tend to move towards outskirts and like to settle in places that are close to nature. But, due to urban lifestyle and to fulfill the basic needs, demand of electricity remains the same as in urban areas. This demand of electricity can be only fulfilled by using hybrid renewable energy resources, which is easily available in outskirts. Renewable energy resources are unreliable and more expensive. Researchers are working to make, it more reliable and economic in terms of utilization. This article proposes a metaheuristic grasshopper optimization algorithm (GOA) for the optimal sizing of hybrid PV/wind/battery energy system located in remote areas. The proposed algorithm finds the optimal sizing and configuration of remote village load demand that includes house electricity and agriculture. The optimization problem is solved by minimization of total system cost at a desirable level of loss of power supply’s reliability index (LPSRI). The results of GOA are compared with particle swarm optimization (PSO), genetic algorithm (GA) and hybrid optimization of multiple energy resources (HOMER) software. In addition, results are also validated by modeling and simulation of the hybrid energy system and its configurations at different weather conditions-based results. Hybrid PV/wind/battery is found as an optimal system at remote areas and sizing are[Formula: see text] with cost of energy (COE) (0.3473$/kWh) and loss of power supplies reliability index (LPSRI) (0%). It is clear from the results that GOA based methods are more efficient for selection of optimal energy system configuration as compared to others algorithms.

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