Demand response of grid-connected microgrid based on metaheuristic optimization algorithm

Singh, Arvind R.; Ding, Lei; Raju, D. Koteswara; Kumar, Ranjan; Raghav, L. Phani

doi:10.1080/15567036.2021.1985654

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…To overcome the uncertainty associated with solar and wind power output, Singh et al used a stochastic-based scenario development and reduction strategy [28]. Unlike other techniques, the flexible load responsive model is developed for each DR programme in order to quantify the sensitivity of consumer engagement.…”

Section: Nature-inspired Algorithm Based Dr Managementmentioning

confidence: 99%

Artificial Intelligence-Based Demand-Side Response Management of Renewable Energy

Hanna

WANG

et al. 2022

WIT Transactions on Ecology and the Environment

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Renewable energy (RE) sources will aid in the decarbonization of the energy sector, which would assist in alleviating the negative consequences of climate change. However, using RE resources for hybrid power generation has two technological challenges, uncertainty and variability owing to RE features, making estimating generated power availability difficult. Artificial intelligence techniques have been used in a variety of applications in power systems, but demand-side response (DR) is just lately receiving major research interest. The DR is highlighted as one of the most promising ways of providing the electricity system with demand flexibility; as a result, many system operators believe that growing the scale and breadth of the DR programme is critical. There are many different sorts of demand reduction programmes, and the most common classification is dependent on who begins the demand reduction. There are two types of DR schemes: (1) price-based programmes and (2) incentive-based programmes.

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Section: Nature-inspired Algorithm Based Dr Managementmentioning

confidence: 99%

Artificial Intelligence-Based Demand-Side Response Management of Renewable Energy

Hanna

WANG

et al. 2022

WIT Transactions on Ecology and the Environment

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“…These algorithms can explore the search space using multiple variables and constraints, thus achieving high-quality solutions. Some of the most popular metaheuristic algorithms are Particle Swarm Optimization (PSO) [23], Genetic Algorithms [24], Cuckoo Search Algorithm [25], Whale Optimization Algorithm (WOA) [26], Tabu Search Algorithm [27], Grey Wolf Optimizer [28], Black Widow Optimization (BWO) [29], Self-Adaptive Elephant Herd Optimization (SA-EHO) [30], Mixed Integer Distributed Ant Colony Optimization (MIDACO) [31] and Grasshopper Optimization Algorithm (GHA) [32]. Intelligent EMS strategies are nonlinear computational algorithms generally based on Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) [12,33,34].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing of Renewable Energy Communities: A Multiple Swarms Multi-Objective Particle Swarm Optimization Approach

Faria,

Marques,

Pombo

et al. 2023

Energies

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Renewable energy communities have gained popularity as a means of reducing carbon emissions and enhancing energy independence. However, determining the optimal sizing for each production and storage unit within these communities poses challenges due to conflicting objectives, such as minimizing costs while maximizing energy production. To address this issue, this paper employs a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with multiple swarms. This approach aims to foster a broader diversity of solutions while concurrently ensuring a good plurality of nondominant solutions that define a Pareto frontier. To evaluate the effectiveness and reliability of this approach, four case studies with different energy management strategies focused on real-world operations were evaluated, aiming to replicate the practical challenges encountered in actual renewable energy communities. The results demonstrate the effectiveness of the proposed approach in determining the optimal size of production and storage units within renewable energy communities, while simultaneously addressing multiple conflicting objectives, including economic viability and flexibility, specifically Levelized Cost of Energy (LCOE), Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR). The findings also provide valuable insights that clarify which energy management strategies are most suitable for this type of community.

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A demand response-based optimal scheduling framework considering renewable sources and energy storage: a deterministic approach

Ramesh,

Khedkar,

Kotha

et al. 2024

Electr Eng

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Demand response of grid-connected microgrid based on metaheuristic optimization algorithm

Cited by 6 publications

References 30 publications

Artificial Intelligence-Based Demand-Side Response Management of Renewable Energy

Artificial Intelligence-Based Demand-Side Response Management of Renewable Energy

Optimal Sizing of Renewable Energy Communities: A Multiple Swarms Multi-Objective Particle Swarm Optimization Approach

A demand response-based optimal scheduling framework considering renewable sources and energy storage: a deterministic approach

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