Impact of Tidal Energy on Battery Sizing in Standalone Microgrids: A Case Study

El-Bidairi, Kutaiba S.; Nguyen, Hung D.; Jayasinghe, Shantha Gamini; Mahmoud, Thair S.; Penesis, Irene

doi:10.1109/eeeic.2018.8493999

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…The integration of DG units with Renewable Energy Resources (RES) like solar PV, Wind Energy Conversion Systems (WECS), and small hydro-turbine systems is affected by physical and climate situations [2]. DG components using non-RES sources, such as electrochemical cells, small-scale gas turbines, and diesel gensets (DiGs), are linked to the network at various locations, taking into consideration the cost of fuel [3]. In the realm of electrical power systems, the electrical power distribution is a complex network that plays a vital role in delivering power from the source to end customers.…”

Section: Introductionmentioning

confidence: 99%

Distribution Generators Optimal Power Flow with Renewable Resources Network with Grey Wolf Optimization

Tentu,

Kundala,

Darimireddy

2024

Preprint

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The integration of diverse distributed generation (DG) units, encompassing both Renewable Energy Sources (RES) and non-RES technologies delves into voltage levels, current distributions, and power flows, aiming to optimize and effectively manage the distribution network. An RES-based DGs such as photovoltaic systems and wind turbines, alongside non-RES units like fuel cells and micro turbines. This study proposes a hybrid approach the Multi-Objective Genetic Algorithm - Grey Wolf Optimizer (MOGA-GWO). By merging the strengths of multi-objective genetic algorithms with the efficiency of the Grey Wolf Optimizer, this advanced algorithm seeks semi-optimal solutions for multi-objective optimization problems. The algorithm aims to improve convergence speed without compromising solution quality, offering a promising solution for optimizing the characteristics of DG and RES integration in distribution networks compared to Cuckoo Search and Simulation Annealing Techniques. Key algorithmic operations include individual representation, initialization, fitness function computation, selection, crossover, mutation, and ending conditions. The DG optimal power flow problem is explored with a focus on minimizing generation costs, improving voltage profiles, minimizing losses, enhancing reliability, integrating renewable energy, reducing environmental impact, managing congestion, and ensuring compliance with operational constraints. Beyond traditional considerations, the study emphasizes neglected objectives and constraints, such as cyber-security concerns, real-time operation, regulatory frameworks, social acceptance, lifecycle environmental impact, grid resilience, data privacy, aging infrastructure compatibility, and economic viability.

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Section: Introductionmentioning

confidence: 99%

Distribution Generators Optimal Power Flow with Renewable Resources Network with Grey Wolf Optimization

Tentu,

Kundala,

Darimireddy

2024

Preprint

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“…Kalla et al [26] propose an adaptive sliding mode control algorithm for the control of voltage source converters and regulates the frequency and voltage of the standalone MG in addition to harmonic current mitigation. On the other hand, El‐Bidairi et al [27] solve a multi‐objective optimisation problem designed for a standalone MG operation and investigates the impact of tidal energy addition on the size of the storage system.…”

Section: Introductionmentioning

confidence: 99%

Impact of scaled fitness functions on a floating‐point genetic algorithm to optimise the operation of standalone microgrids

Batool

Shahnia

Islam

2019

IET Renewable Power Generation

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Standalone hybrid remote area power systems, also known as microgrids (MGs), can provide reasonably priced electricity in geographically isolated and the edge of grid locations for their operators. To achieve the reliable operation of MGs, whilst consuming minimal fossil fuels and maximising the penetration of renewables, the voltage and frequency should be maintained within acceptable limits. This can be accomplished by solving an optimisation problem. Floating-point genetic algorithm (FP-GA) is a heuristic technique that has a proven track record of effectively identifying the optimal solutions. However, in addition to needing appropriate operators, the solver needs a fitness function to yield the most optimal control variables. In this study, a suitable fitness function is formulated, by including the operational, interruption and technical costs, which are then solved with an FP-GA, with different combinations of operators. The developed fitness function and the considered operators are tested for the non-linear optimisation problem of a 38-bus MG. Detailed discussions are provided on the impact, which different operators have upon the outcomes of the fitness function. Ratio DDER ratio of power supplied by each DDER S BSS , S DDER , S load , S line , S NDER apparent power of BSS, DDER, load, line and NDER SoC, SoC min , SoC max SoC and its minimum and maximum values v wind speed V , V min , V max , V nom , ΔV voltage magnitude and its lower, upper, and nominal values and its deviation from the nominal value Y bus Y-bus of the network ΔT time period η gearbox efficiency λ tip-speed ratio θ blade pitch angle ρ standard atmosphere air density

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