Probabilistic generating capacity adequacy evaluation: Research roadmap

Almutairi, Abdulaziz; Ahmed, Mohammed Hassan; Salama, M.M.A.

doi:10.1016/j.epsr.2015.07.013

Cited by 23 publications

(5 citation statements)

References 39 publications

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“…The capability of the RE system to transition into 100% and to sustainably deliver the energy system demand under various conditions and scenarios can be determined with the use of generation adequacy analysis [156][157][158][159][160][161], employing different deterministic and probabilistic techniques such as probability density function; convolution functions; correlation/regression; Markov process; frequency and duration; radial, parallel, and meshed network; point estimate; Monte Carlo; and artificial neural network techniques [162][163][164], which support the reliability evaluation of RE generation technology integrated into the national grid.…”

Section: General Adequacy Analysismentioning

confidence: 99%

Towards a Common Methodology and Modelling Tool for 100% Renewable Energy Analysis: A Review

Akpan,

Olanrewaju

2023

Energies

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Some advanced countries’ rapid population, economic growth, and energy consumption expansion contribute significantly to global CO2 emissions. And while developed countries have achieved 100% universal access to electricity, mainly from non-renewable sources, many developing countries still lack it. This presents challenges and opportunities for achieving the United Nations’ Sustainable Development Goals (SDGs) 7 and 13 of generating all energy from cleaner or low-carbon sources to reduce CO2 emissions in all countries and combating climate change consequences. Renewable energies have been widely acknowledged to greatly advance this endeavour, resulting in many studies and about 30 countries already with over 70% of their national electricity mix from RE. It has birthed a new paradigm and an emerging field of 100% RE for all purposes, recently receiving much attention from academia and in public discourse. The major challenge with this idea is that achieving such a feat requires a more diverse approach. This study emphasises the need to meet technical and non-technical requirements for working towards a 100% RE for all purposes. Therefore, our work introduces six methodological or evaluation mechanisms (herein, identified as 100% RE evaluation metrics) suitable for existing and future 100% renewable energy analysis. It then reviews energy modelling tools to identify their applicability to 100% RE analysis. The review and perspectives presented in this study will be valuable in developing a common integrated methodology and modelling tool for analysing full renewable energy adoption in countries or regions with best trade-offs, using performance indices that have not been previously used. It will also help with proper national and regional energy resources and system planning for new energy projects and installations, contributing to sustainable development.

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Section: General Adequacy Analysismentioning

confidence: 99%

Towards a Common Methodology and Modelling Tool for 100% Renewable Energy Analysis: A Review

Akpan,

Olanrewaju

2023

Energies

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“…Numerical approaches to power system reliability often take the form of MC methods. Literature has suggested that when the power system is complex – as is typical in medium to large power systems – an MC approach simplifies the reliability problem at the cost of added computational complexity [24]. MC methods are classified as either non‐sequential or sequential.…”

Section: Power System Reliabilitymentioning

confidence: 99%

Estimating the impact of ocean wave energy on power system reliability with a well‐being approach

Johnson

Cotilla‐Sanchez

2020

IET Renewable Power Generation

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Ocean wave energy is a developing industry that provides many attractive qualities for utilities to meet future energy demand. Therefore, it is important to investigate and understand the impact that ocean wave energy has on power system reliability. Reliability assessment techniques have been applied to systems with variable amounts of wind penetration, but as of yet, no study has explored the impacts that ocean wave energy may have on the reliability of power grids. The authors' approach to this problem applies a sequential Monte Carlo technique coupled with a Well-Being analysis approach to capture the seasonal variations associated with the ocean and then calculate key loss-of-load indices. The authors benchmark this method on the IEEE Reliability Test System 1996 and integrate synthesised ocean wave energy farms throughout the system. Their results suggest that wave energy results in a small decrease on the reliability of the power system, however, this change does not result in additional failure states as compared to the base system. Additionally, they are able to mitigate marginal system states with a relatively small increase in system capacity.

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“…Most meta-heuristic algorithms are derived from the simulation of biological behavior or physical properties or chemical processes, for example, Ant colony algorithm is to simulate the actual ant colony foraging process (Gambardella and Dorigo, 1997), particle swarm algorithm is derived from the bird and fish groups (Benidris and Mitra, 2014;Green et al, 2010Green et al, , 2012Hadow et al, 2010;Huang and Liu, 2013), Evolutionary Computation (EC) and Smart State Space Pruning (ISSP). Despite the series of researches on the reliability of generation system, more appropriate techniques are needed which are computationally scalable and more practical to reflect the soundness of power generation (Almutairi et al, 2015;Kadhem et al, 2017;Athraa et al, 2017). Each algorithm has its advantages and disadvantages, such as: If the cooling process is slow enough, the simulation process is long enough, the simulated annealing algorithm can almost ensure that the optimal solution is found.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system

Abdalla

Nazir

Jiang

et al. 2020

Energy Exploration & Exploitation

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Generating systems are known as adequately reliable when satisfying the load demand. Meanwhile, the efficiency of electrical systems is currently being more influenced by the growing adoption of the Wind/Solar energy in power systems compared to other conventional power sources. This paper proposed a new optimization approach called Metaheuristic Scanning Genetic Algorithm (MSGA) for the evaluation of the efficiency of power generating systems. The MSGA is based on a combination of metaheuristic scanning and Genetic Algorithm. The MSGA technique is used for evaluating the reliability and adequacy of generation systems integrated with wind/Solar energy is developed. The usefulness of the proposed algorithm was tested using Reliability Test System ‘IEEE-RTS-79’ which include both of wind power and solar power generation. The result approve the effectiveness of the proposed algorithm in improving the computation time by 85% and 2% in comparison with the particle swarm optimization (PSO) and differential evolution optimization algorithm (DEOA) respectively. In addition, the proposed model can be used to test the power capacity forecasting scheme of the hybrid power generation system with the wind, solar and storage.

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Probabilistic generating capacity adequacy evaluation: Research roadmap

Cited by 23 publications

References 39 publications

Towards a Common Methodology and Modelling Tool for 100% Renewable Energy Analysis: A Review

Towards a Common Methodology and Modelling Tool for 100% Renewable Energy Analysis: A Review

Estimating the impact of ocean wave energy on power system reliability with a well‐being approach

Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system

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