Optimization and sustainability analysis of PV/wind/diesel hybrid energy system for decentralized energy generation

Nsafon, Benyoh Emmanuel Kigha; Owolabi, Abdulhameed Babatunde; Butu, Hemen Mark; Roh, Jong Wook; Suh, Dongjun; Huh, Jeung-Soo

doi:10.1016/j.esr.2020.100570

Cited by 46 publications

(16 citation statements)

References 22 publications

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“…In a hybrid system, a dispatch strategy is employed when renewable resources cannot satisfy the load demand. A dispatch strategy is simply a control algorithm used to switch between the generator and battery bank when the available renewable energy sources cannot satisfy the load demand (Nsafon et al, 2020). By default, HOMER has two control strategies: load following (LF) and cycle charging (CC) (HOMER, 2021).…”

Section: Control Strategy/energy Managementmentioning

confidence: 99%

“…Load following is a strategy in which the generator meets the immediate load demand when other generating sources are insufficient to satisfy the required demand. In this case, charging the battery is the sole responsibility of other generating sources (Nsafon et al, 2020). Renewable energy sources are reserved for lower priority tasks like recharging the storage bank or supplying the deferrable load.…”

Section: Control Strategy/energy Managementmentioning

confidence: 99%

“…Their results indicate that LF produced the best performance based on the renewable fraction. Nsafon et al (2020) carried out an optimization and sustainability analysis of a PV-wind-diesel hybrid energy system for decentralized energy generation using HOMER. They varied the PV slope and wind turbine hub height under the LF and CC dispatch strategies to supply the load.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of renewable-based sustainable micro-grid under predictive management control strategy: A case study of Gado refugee camp in Cameroon

et al. 2022

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The recent use of hybrid renewable energy systems (HRESs) is considered one of the most reliable ways to improve energy access to decentralized communities because of their techno-economic and environmental benefits. Many distant locales, such as camps in war-torn nations, lack basic necessities like power. This study proposes a remedy for power outages in these areas; by designing an HRES and a control system for monitoring, distributing, and managing the electrical power from sustainable energy sources to supply the load. Hence, providing affordable, reliable, and clean energy for all (Sustainable Development Goal 7). In this study, the feasibility and techno-economic performance of an HRES for a refugee camp was evaluated under load following (LF), cycle charging (CC), and predictive control strategy (PS). The optimization results revealed that the PS was the most suitable, as it had the lowest cost and was more eco-friendly and energy-efficient. The predictive control strategy had a 48-h foresight of the load demand and resource potential and hence could effectively manage the HRES. The total net present cost (NPC) for the electrification of this refugee camp was $3,809,822.54, and the cost of electricity generated for every kWh is $0.2018. Additionally, 991,240.32 kg of emissions can be avoided annually through the hybridization of the diesel generator under the PS.

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Section: Control Strategy/energy Managementmentioning

confidence: 99%

Section: Control Strategy/energy Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of renewable-based sustainable micro-grid under predictive management control strategy: A case study of Gado refugee camp in Cameroon

et al. 2022

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“…Nevertheless, the condition is to keep the original optimisation set of (i = 1), subject to l i ≤ X i ≤ u i , where l i and u i are the vectors of minimum and maximum thresholds for i. Therefore, we used the GRG algorithm of sensitivity analysis for the problem denoted in Formulas ( 4)-( 6) to optimise the sequence of problems of Formulas (11) and (12). To contribute to this, one needs to provide all the values y(x) of the basic variables of the end-user demand.…”

Section: Stage 2 Optimisation Simulation: (Sa)mentioning

confidence: 99%

Energy Loss Impact in Electrical Smart Grid Systems in Australia

Zaghwan

Gunawan

2021

Sustainability

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This research draws attention to the potential and contextual influences on energy loss in Australia’s electricity market and smart grid systems. It further examines barriers in the transition toward optimising the benefit opportunities between electricity demand and electricity supply. The main contribution of this study highlights the impact of individual end-users by controlling and automating individual home electricity profiles within the objective function set (AV) of optimum demand ranges. Three stages of analysis were accomplished to achieve this goal. Firstly, we focused on feasibility analysis using ‘weight of evidence’ (WOE) and ‘information value’ (IV) techniques to check sample data segmentation and possible variable reduction. Stage two of sensitivity analysis (SA) used a generalised reduced gradient algorithm (GRG) to detect and compare a nonlinear optimisation issue caused by end-user demand. Stage three of analysis used two methods adopted from the machine learning toolbox, piecewise linear distribution (PLD) and the empirical cumulative distribution function (ECDF), to test the normality of time series data and measure the discrepancy between them. It used PLD and ECDF to derive a nonparametric representation of the overall cumulative distribution function (CDF). These analytical methods were all found to be relevant and provided a clue to the sustainability approach. This study provides insights into the design of sustainable homes, which must go beyond the concept of increasing the capacity of renewable energy. In addition to this, this study examines the interplay between the variance estimation of the problematic levels and the perception of energy loss to introduce a novel realistic model of cost–benefit incentives. This optimisation goal contrasted with uncertainties that remain as to what constitutes the demand impact and individual house effects in diverse clustering patterns in a specific grid system. While ongoing effort is still needed to look for strategic solutions for this class of complex problems, this research shows significant contextual opportunities to manage the complexity of the problem according to the nature of the case, representing dense and significant changes in the situational complexity.

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“…Multiple fuel options were proposed for islanded areas in Egypt (Kharga, Quseer, and Saint Catherine) to minimize the total costs with reasonable GHG values. Nsafon et al [13] studied the techno-economic issues and sustainability of a hybrid PV/wind/diesel planned for decentralized power supplies. Several designs were thus investigated by variable PV slope and the wind turbine hub's height at Cameron location with several dispatches.…”

Section: Introductionmentioning

confidence: 99%

Optimal Economic and Environmental Indices for Hybrid PV/Wind-Based Battery Storage System

Elnozahy

Yousef

Ghoneim

et al. 2021

J. Electr. Eng. Technol.

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This paper shows an application of hybrid PV/wind energy and battery storage in the islanded area. This work's main target allows the distributed energy resources to contribute efficiently in the economic feasibility and enhance the environmental impact of the hybrid renewable energy source. Several factors such as levelized cost of energy (COE), greenhouse gas (GHG) emissions, and loss of power supply probability are studied. A combined solution is to compromise the economic and environmental aspects via the Utopia point approach is investigated. The optimal configuration of the hybrid PV/wind along with battery-storage and diesel engine as secondary source is obtained via meta-heuristic optimizers: Genetic Algorithm (GA) and Particle-Swarm Optimization (PSO) and impartial comparison of the results with HOMER software. The results of Utopia point solution show that the PV (about 46%) and wind turbine (about 13%) are shared significantly as primary renewable sources and battery storage (about 39%) as storage options. Meanwhile, the diesel engine (about 3%) has insignificant sharing in feeding the demand load. The optimal COE and GHG, which are achieved via GA and PSO optimization techniques are 0.182$/kWh and 12076 kg/year, agansit 0.343$/kWh and 17618 kg/year that are obtained via HOMER software, respectively. This corssponing to 47% and 31% reduction in COE and GHG, respectively. Sensitivity studies demonstrate that the variation of the wind energy sharing from 50 to 150% shows that the wind energy has a slight effect considering the GHG emissions. Contrarily, lower PV sharing ratios undesirably raises the levelized COE, however, reduces the GHG emissions. KeywordsPhotovoltaic (PV) • Hybrid renewable generations (HRGs) • GA • PSO • GHG • Cost of energy (COE) • Utopia point Abbreviations AD Autonomy days COE Cost of energy ($/kWh) DOD Depth of discharge (%) GHG Green-house gas HRGs Hybrid renewable energy system GA Genetic algorithm PSO Particle swarm optimization NOCT Normal operating cell temperature (°C) NPC Net present cost * Saad A.

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Optimization and sustainability analysis of PV/wind/diesel hybrid energy system for decentralized energy generation

Cited by 46 publications

References 22 publications

Performance evaluation of renewable-based sustainable micro-grid under predictive management control strategy: A case study of Gado refugee camp in Cameroon

Performance evaluation of renewable-based sustainable micro-grid under predictive management control strategy: A case study of Gado refugee camp in Cameroon

Energy Loss Impact in Electrical Smart Grid Systems in Australia

Optimal Economic and Environmental Indices for Hybrid PV/Wind-Based Battery Storage System

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