Determination of reliability constrained optimal resource mix for an autonomous hybrid power system using Particle Swarm Optimization

Paliwal, Priyanka; Patidar, N. P.; Nema, Rajesh Kumar

doi:10.1016/j.renene.2013.09.003

Cited by 131 publications

(73 citation statements)

References 25 publications

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“…FEE is net charge accumulated in the BESS at the end of time period of analysis and is expressed as the difference between the charge in BESS at the end and beginning of the analysis, which can be calculated by (10) [36] . They can be evaluated using (11)(12)(13)(14).…”

Section: Final Excess Energy (Fee)mentioning

confidence: 99%

“…Katsigiannis et al [11] have used Simulated Annealing (SA) and Tabu Search (TS) techniques to find optimum scheduling of small autonomous power system to reduce the cost of energy. Paliwal et al [12] have carried out the reliability analysis of isolated HRES in a rural area using PSO. Koutroulis et al [13] used Genetic Algorithm (GA) for optimization of a HES comprising of PV, WECS and diesel generator feeding a desalination system.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of an Autonomous Hybrid Renewable Energy System Using Reformed Electric System Cascade Analysis

Singh

Bansal

2019

IEEE Trans. Ind. Inf.

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This paper presents reformed electric system cascade analysis (RESCA) technique for optimization of hybrid renewable energy system (HRES) comprising of wind energy conversion system (WECS), photo-voltaic system (PV), battery energy storage system (BESS) and non-intermittent source (NIS). Optimization constraints of final excess energy (FEE), energy generation ratio (EGR), cost of energy (COE), net present cost (NPC) and renewable energy fraction (REF) are considered for the optimization of the system. This optimization is realized for an isolated load comprising of the 10 households in Malaysia with a daily consumption of 84.5 kWh. On successful implementation of RESCA with different constraints it is found that the optimum size of energy sources for the HRES varies depending on the chosen constraints. Also, the net present cost (NPC) of the system can be considerably reduced on introduction of NIS to the HRES. RESCA provides the user with better insight into the influence of variation of PV and WECS power generation while identifying the optimal size of energy storage unit. The optimized results obtained are also compared with benchmark HOMER software to validate the methodology.Index Terms-Batteries, design optimization, hybrid power system, iterative algorithm, solar energy, wind energy.

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Section: Final Excess Energy (Fee)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of an Autonomous Hybrid Renewable Energy System Using Reformed Electric System Cascade Analysis

Singh

Bansal

2019

IEEE Trans. Ind. Inf.

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“…A reliability index is usually treated as either an objective [3] or a constraint [2], [4], [7], which can be evaluated using analytical methods [7]- [8], or Monte Carlo simulation [9], [10]. To consider the impacts of the volatility of power outputs of the distributed generators on the system reliability, multistate models are developed in [11], [12]- [13].…”

Section: Introductionmentioning

confidence: 99%

“…However, these studies have paid little attention to modeling of the BESS used to balance power for RESs. A probabilistic model for BESS is introduced in [7], [8], considering multiple states of state of charge (SOC) and probability associated with each state.…”

Section: Introductionmentioning

confidence: 99%

“…Then, an approximate analytical model for reliability evaluation of BESS is demonstrated, cooperating with multistate models for DGs [16] and WTGs [12], to assess the reliability performances of various system configurations. The proposed model for BESS takes into account the forced outage rates (FORs) of equipment, which is different from the one developed in [7], [8].…”

Section: Introductionmentioning

confidence: 99%

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Optimal planning of stand-alone microgrids incorporating reliability

Wang

Jiao

Guo

et al. 2014

J. Mod. Power Syst. Clean Energy

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This paper proposes a new method for the planning of stand-alone microgrids. By means of clustering techniques, possible operating scenarios are obtained considering the daily patterns of wind and load profiles. Then, an approximate analytical model for reliability evaluation of battery energy storage system is developed in terms of the diverse scenarios, along with multistate models for wind energy system and diesel generating system. An optimal planning model is further illustrated based on the scenarios and the reliability models, with the objective of minimizing the present values of the costs occurring within the project lifetime, and with the constraints of system operation and reliability. Finally, a typical stand-alone microgrid is studied to verify the efficiency of the proposed method.

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Artificial intelligence implementation framework development for building energy saving

et al. 2020

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In this study, artificial intelligence (AI) control tools were developed to construct an AI implementation framework for energy saving for buildings. Although numerous AI studies related to energy conservation have been conducted, most of them have reported computing algorithms and control effects for single objects. This is the first study to use a framework to integrate five-category AI control tools to execute three-level building energy conservation; the three levels consist of equipment-level control, facility-level control, and whole building energy saving. Energy-saving effects were tested in a real building. The complex three-floor building primarily with a total area of 9072 m 2 serves as an office space and a semiconductor production line. Seventy percent energy consumption comes from air conditioning system and motor power. Twenty percent is lighting system and the other 10% is plug power and office automation equipment. Before implementation, the yearly energy cost reached US$1004339. In 2018, an AI implementation framework was introduced to systematically deploy AI at the site. A total of 47.5%, 37%, and 36.9% of energy was saved at equipment, facility, and whole building levels; up to US$385203 was saved. These energy savings proved the feasibility of the implementation framework. Furthermore, unmet demands of AI studies were met, and an approach to fill the research gap is discussed. K E Y W O R D S artificial intelligence (AI), artificial intelligence implementation framework (AIif), building energy saving, equipment-level control, facility-level control List of Symbols, Abbreviations, and Notation: ω i , weighting coefficients; GS T , the sum of the global similarities between the selected m cases; MV i , the mean difference of the variable i; P j , proportion of the prediction; V 1 , variation of control parameter y; y ic , the neural outputs of variable i for the control; y ip , the neural outputs of variable i for past cases; AI

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Determination of reliability constrained optimal resource mix for an autonomous hybrid power system using Particle Swarm Optimization

Cited by 131 publications

References 25 publications

Optimization of an Autonomous Hybrid Renewable Energy System Using Reformed Electric System Cascade Analysis

Optimization of an Autonomous Hybrid Renewable Energy System Using Reformed Electric System Cascade Analysis

Optimal planning of stand-alone microgrids incorporating reliability

Artificial intelligence implementation framework development for building energy saving

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