Optimal Operation of Stand-Alone Microgrid Considering Emission Issues and Demand Response Program Using Whale Optimization Algorithm

Tahmasebi, Mehrdad; Pasupuleti, Jagadeesh; Mohamadian, Fatemeh; Shakeri, Mohammad Taghi; Guerrero, Josep M.; Khan, M. Reyasudin Basir; Nazir, Muhammad Shahzad; Safari, Abdolreza; Bazmohammadi, Najmeh

doi:10.3390/su13147710

Cited by 21 publications

(6 citation statements)

References 37 publications

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“…In Equation ( 29), k ∈ P i j and the cluster of neighborhood of j th search agents at time i is derived as P i j ¼ fk∶dis i j,k < u i,j disj ; S i j < S i k g. conducted for the proposed model to show the efficiency of the recommended model while distinguishing with traditional algorithms like grey wolf optimization algorithm (GWO), [38] whale optimization algorithm (WOA), [39] IBSMFO, [18] TSO, [32] and GSO [35] techniques. The experimental analysis was done by considering the balanced PV irradiation level and unbalanced PV irradiation level by estimating the power level respective with time for the RESs inclusive of battery, fuel cell, wind turbine, and PV system.…”

Section: End Whilementioning

confidence: 99%

Enhancing Power Quality in Microgrids with Hybrid Tuna‐Glowworm Swarm Optimization Strategy for Renewable Energy Sources

Nanda,

Muni,

Jena

2024

Energy Tech

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The technical issues in the microgrids (MG) are predicted and the fluctuation in the MG is controlled precisely by the inclusion of distinct renewable energy sources (RESs) into the MG system. The voltage stability and distortionless harmonic are the two main factors that need to be tackled in the MG system. By solving the above‐mentioned two factors, the overall power quality (PQ) of the MG is enhanced. Multiple pieces of equipment are introduced and developed especially to resolve the issues regarding the load's nonlinearity and the source's uncertainty. Hence, a novel “hybrid tuna‐glowworm swarm optimization (HT‐GWSO)” algorithm is introduced to resolve the uncertainties in the MG and to enhance the PQ in this paperwork. The proposed algorithm is a combination of “glowworm swarm optimization (GWO) with the tuna swarm optimization (TSO)” algorithms. This algorithm is utilized in tuning the parameters of the controllers used in the MG. The reactive power is varied from the active power to enhance the PQ in this proposed scheme. The controller's parameters are optimized so that the variation in power and the error while the function is minimized in the MG system. An expensive simulation is done to prove the superiority of the suggested scheme.

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Section: End Whilementioning

confidence: 99%

Enhancing Power Quality in Microgrids with Hybrid Tuna‐Glowworm Swarm Optimization Strategy for Renewable Energy Sources

Nanda,

Muni,

Jena

2024

Energy Tech

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“…In Ref. [16], the optimal operation of renewable energy sources in standalone micro-grids was investigated. Moreover, an energy management model was tested with two different scenarios that resulted in minimizing the costs of operation and emissions with a focus on using the whale meta-heuristic algorithm to manage microgrids.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Operation of Sustainable Virtual Power Plant Considering the Amount of Emission in the Presence of Renewable Energy Sources and Demand Response

Darvishi,

Tahmasebi,

Shokouhmand

et al. 2023

Sustainability

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One of the significant environmental issues is global warming, and governments have changed their procedures to reduce carbon emissions. Sustainability is commonly described as having three dimensions: environmental, economic, and social. There are numerous environmental impacts associated with energy systems and the significance of energy for living standards and economic development. Therefore, the movement towards intelligent energy systems and virtual power plants (VPPs) is being pursued more rapidly due to economic and environmental issues. The VPP is one of the technologies used to increase the entire system’s efficiency. Moreover, because of environmental pollution, increased greenhouse gas production, and global warming, countries’ policies have changed towards reducing the use of fossil fuels and increasing the penetration of renewable energy sources (RESs) in distribution networks. However, RESs, such as wind turbines (WT) and photovoltaic (PV) panels, exhibit uncertain behavior. This issue, coupled with their high penetration, poses challenges for network operators in terms of managing the grid. Therefore, the sustainable virtual power plant (SVPP) is a suitable solution to overcome these problems and reduce the emissions in power systems. This study examines the cost of optimal operating of the SVPP and the amount of produced pollution in four different scenarios in the presence of a demand response program (DRP), energy storage system (ESS), etc., and the results are compared. The results indicate that the simultaneous implementation of DRPs and utilization of ESS can lead to a decrease in costs and pollution associated with SVPPs by 1.10% and 29.80%, respectively. Moreover, the operator can resolve the shortage and excess power generation that occurs during some hours.

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“…Liu et al [24] aimed to achieve optimal economic cost by establishing a micro-grid dispatching model with distributed energy, which takes into account the impact of grid connection and incorporates an adaptive weight strategy and Levy flight trajectory to enhance WOA's global search capability. Tahmasebi et al [25] applied the whale meta-heuristic algorithm to the operation management of microgrid, which greatly reduced the operation cost and emissions. Nadimi-Shahraki et al [26] proposed a hybrid algorithm based on the whale optimization algorithm and the improved moth flame optimization algorithm to solve the optimal power flow problem.…”

Section: Introductionmentioning

confidence: 99%

“…Tahmasebi et al. [25] applied the whale meta‐heuristic algorithm to the operation management of microgrid, which greatly reduced the operation cost and emissions. Nadimi‐Shahraki et al.…”

Section: Introductionmentioning

confidence: 99%

Economic optimization scheduling of multi‐microgrid based on improved genetic algorithm

He,

Han,

Sun

et al. 2023

IET Generation Trans & Dist

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In order to solve the collaborative optimization scheduling of multi‐microgrid under the high penetration rate of new energy, this paper considered the energy interaction between micro‐grids in multi‐microgrid and the relationship between new energy consumption and electricity cost, constructed a collaborative scheduling model considering both micro‐grid load and main grid wind and optical energy storage, proposed objective function based on economic cost, and improved Genetic Algorithm (GA). The elite thought and catastrophe thought are used to optimize the selection operation, and the particle swarm optimization algorithm is used to optimize the mutation operation. Furthermore, three scenarios were selected to verify the effectiveness of the proposed model and the improved GA. The results show that the proposed model can ensure the stable operation of the multi‐microgrid system with a high proportion of new energy access and reduce the operation cost. In addition. the improved algorithm solves the problems of easy to fall into local optimum and slow iteration speed, and has better performance in solving the model.

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Optimal Operation of Stand-Alone Microgrid Considering Emission Issues and Demand Response Program Using Whale Optimization Algorithm

Cited by 21 publications

References 37 publications

Enhancing Power Quality in Microgrids with Hybrid Tuna‐Glowworm Swarm Optimization Strategy for Renewable Energy Sources

Enhancing Power Quality in Microgrids with Hybrid Tuna‐Glowworm Swarm Optimization Strategy for Renewable Energy Sources

Optimal Operation of Sustainable Virtual Power Plant Considering the Amount of Emission in the Presence of Renewable Energy Sources and Demand Response

Economic optimization scheduling of multi‐microgrid based on improved genetic algorithm

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