Optimum battery depth of discharge for off-grid solar PV/battery system

Izdin.Hlal, Mohamad; Ramachandaramurthy, Vigna K.; Sarhan, Ameen; Pouryekta, Aref; Subramaniam, Umashankar

doi:10.1016/j.est.2019.100999

Cited by 88 publications

(28 citation statements)

References 25 publications

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“…Meanwhile, other studies used heuristic techniques in order to find the optimal size of a SAPV system using techno-economic objective functions such as artificial bee colony (ABC) [41], genetic algorithm (GA) [42,43], generalized regression neural network (GRNN) [44], firefly (FL) [45], and particle swarm optimization (PSO) [46]. The main advantage of these searching algorithms is the ability to converge the optimal solution in a short time.…”

Section: Optimization Methodsmentioning

confidence: 99%

“…Thus, heuristic techniques can overcome the drawback of numerical algorithms. In addition to that, some of the studies combined two or more in order to enhance the converge to the optimal values and reduce the execution time, such as the research works presented in [43,47,48]. Hussein et al [46] proposed an optimal design of the SAPV system using multi-objective particle swarm optimization (MOPSO) in Malaysia.…”

Section: Optimization Methodsmentioning

confidence: 99%

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Optimum Design of a Standalone Solar Photovoltaic System Based on Novel Integration of Iterative-PESA-II and AHP-VIKOR Methods

et al. 2020

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Solar energy is considered one of the most important renewable energy resources, and can be used to power a stand-alone photovoltaic (SAPV) system for supplying electricity in a remote area. However, inconstancy and unpredictable amounts of solar radiation are considered major obstacles in designing SAPV systems. Therefore, an accurate sizing method is necessary to apply in order to find an optimal configuration and fulfil the required load demand. In this study, a novel hybrid sizing approach was developed on the basis of techno-economic objectives to optimally size the SAPV system. The proposed hybrid method consisted of an intuitive method to estimate initial numbers of PV modules and storage battery, an iterative approach to accurately generate a set of wide ranges of optimal configurations, and a Pareto envelope-based selection algorithm (PESA-II) to reduce large configuration by efficacy obtaining a set of Pareto front (PF) solutions. Subsequently, the optimal configurations were ranked by using an integrated analytic hierarchy process (AHP) and vlsekeriterijumskaoptimizacija i kompromisonoresenje (VIKOR). The techno-economic objectives were loss of load probability, life cycle cost, and levelized cost of energy. The performance analysis results demonstrated that the lead–acid battery was reliable and more cost-effective than the other types of storage battery.

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Section: Optimization Methodsmentioning

confidence: 99%

Section: Optimization Methodsmentioning

confidence: 99%

Optimum Design of a Standalone Solar Photovoltaic System Based on Novel Integration of Iterative-PESA-II and AHP-VIKOR Methods

et al. 2020

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“…Lately, the newest electrochemical energy storage devices, e.g., Li-ion batteries, Li-S batteries, and supercapacitors, have been recognized as having high potential in energy storage [58][59][60].…”

Section: Energy Storage Systems In Distribution Networkmentioning

confidence: 99%

“…Depending on the battery power and voltage and different types of power electronic, DC-to-DC converters and inverters/rectifiers are required. Different technical solutions for topologies are possible for grid interconnection, which may vary depending on the efficiency of energy consumption [60].…”

Section: Energy Storage Systems In Distribution Networkmentioning

confidence: 99%

“…Energy efficiency is the main indicator of the storage systems of battery energy operation, and it is compared among various scenarios. Since connection to networks require different conversions, high energy losses occur [60]. The battery is able to store and extract energy in high frequencies, therefore ensuring stability of frequency and voltage, and it is able to do that for a long time, that way ensuring the efficient optimization of energy control of renewable energy systems [61].…”

Section: Energy Storage Systems In Distribution Networkmentioning

confidence: 99%

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Review of Voltage and Reactive Power Control Algorithms in Electrical Distribution Networks

Stanelytė

Radziukynas

2019

Energies

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The traditional unidirectional, passive distribution power grids are rapidly developing into bidirectional, interactive, multi-coordinated smart grids that cover distributed power generation along with advanced information communications and electronic power technologies. To better integrate the use of renewable energy resources into the grid, to improve the voltage stability of distribution grids, to improve the grid protection and to reduce harmonics, one needs to select and control devices with adjustable reactive power (capacitor batteries, transformers, and reactors) and provide certain solutions so that the photovoltaic (PV) converters maintain due to voltage. Conventional compensation methods are no longer appropriate, thus developing measures are necessary that would ensure local reactive and harmonic compensation in case an energy quality problem happens in the low voltage distribution grid. Compared to the centralized methods, artificial intelligence (heuristic) methods are able to distribute computing and communication tasks among control devices.

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Deep reinforcement learning for resilient microgrid expansion planning with multiple energy resource

Pang

Zhou

Tsianikas

et al. 2022

Quality & Reliability Eng

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Microgrid has attracted more and more attention to provide backup power for customers in the case of power grid outages. Microgrid expansion planning is significant to handle the increasing customer demand and to enhance power resilience. Current research about long‐term microgrid expansion planning rarely if ever considered the uncertainties associated with energy storage and power generation units, for example, battery cycle degradation. These factors have important influence on the performance of microgrid expansion planning in reality. In this paper, a long‐term microgrid expansion planning model with multiple energy resource is presented. Deep reinforcement learning method is used to obtain the cost‐effective microgrid expansion policies to enhance power resilience. In the case study, optimal microgrid expansion planning is achieved based on the proposed model. The impacts of battery degradation and resilience constraint on microgrid expansion policy optimization are also investigated. The simulation results prove the effectiveness of the proposed method on economic and resilient microgrid expansion planning.

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Optimum battery depth of discharge for off-grid solar PV/battery system

Cited by 88 publications

References 25 publications

Optimum Design of a Standalone Solar Photovoltaic System Based on Novel Integration of Iterative-PESA-II and AHP-VIKOR Methods

Optimum Design of a Standalone Solar Photovoltaic System Based on Novel Integration of Iterative-PESA-II and AHP-VIKOR Methods

Review of Voltage and Reactive Power Control Algorithms in Electrical Distribution Networks

Deep reinforcement learning for resilient microgrid expansion planning with multiple energy resource

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