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.

Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Souhil Mouassa ◽  
Marcos Tostado-Véliz ◽  
Francisco Jurado
Keyword(s):  
Energy Demand ◽  
Supply And Demand ◽  
Optimization Technique ◽  
Smart Homes ◽  
Demand Side Management ◽  
Time Interval ◽  
Demand Side ◽  
Artificial Ecosystem ◽  
Pricing Schemes ◽  
Operational Time

Abstract With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.

A Review on Optimization Modeling of Hybrid Energy Systems

2020 ◽  
pp. 29-62
Author(s):  
Marwa Mallek ◽  
Jalel Euchi ◽  
Yacin Jerbi
Keyword(s):  
Solar Wind ◽  
Renewable Energy ◽  
Rural Areas ◽  
Renewable Energy Sources ◽  
Energy Systems ◽  
Health Impact ◽  
Health Impacts ◽  
Energy Sources ◽  
Hybrid Energy ◽  
Hybrid Energy Systems

Hybrid energy systems (HESs) are an excellent solution for electrification of remote rural areas where the grid extension is difficult or not economical. Usually, HES generally integrate one or several renewable energy sources such as solar, wind, hydropower, and geothermal with fossil fuel powered diesel/petrol generator to provide electric power where the electricity is either fed directly into the grid or to batteries for energy storage. This chapter presents a review on the solution approaches for determining the HES systems based on various objective functions (e.g. economic, social, technical, environmental and health impact). In order to take account of environmental and health impacts from energy systems, several energy optimization model was developed for minimizing pollution and maximizing the production of renewable energy.

Sign in / Sign up

Export Citation Format

Share Document