A review of optimization techniques applied to solve unit commitment problem in microgrid

Karthik, N.; Parvathy, A. K.; Rajagopalan, Arul; Baskar, S.

doi:10.11591/ijeecs.v15.i3.pp1161-1169

Cited by 7 publications

(1 citation statement)

References 66 publications

(69 reference statements)

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“…It has Bulletin of Electr Eng & Inf ISSN: 2302-9285  Development of regional load management system based on rural, semi urban … (Ayandeep Ganguly) 69 also gained immense importance in developing load forecasting and management techniques [12]- [15]. Different techinues have been applied to propose solutions for unit commitment problem, improvement of stability and reactive power compensation [16]- [18]. However, though some of these techniques consider consumer comfort [19] but none of them consider the difficulty of applying the solution to a large consumer base.…”

Section: Introductionmentioning

confidence: 99%

Development of regional load management system based on rural, semi urban and urban loads-a critical analysis

Ganguly

Sil

2022

Bulletin EEI

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The sharp rise in population during the last half century has created immense pressure on the resources required for generation of energy essential to lead a comfortable and healthy lifestyle. The drive towards 100% electrification in developing countries like India has also contributed to this increase in demand. Till recently, fossil fuel was use to supply the bulk of this power. Now, the world is moving more and more towards renewable energy. This paper presents a model where several regions are combined together based on the demand profile of the regions segregated as urban, semi urban and rural along with the flexibility to schedule loads on the basis of availability of renewable energy sources within the area of the regions. The main focus is on detailed neural-networking based load forecasting and developing a load management system to manage load based on availability of distributed generation capacity and available tariff system. A solution is proposed in this paper based on a new approach to answer load management on the basis of region, population demographics and per capita energy consumption. A considerable amount of improvement to manage demand is intended to be attained and has been demonstrated in this research work.

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Section: Introductionmentioning

confidence: 99%

Development of regional load management system based on rural, semi urban and urban loads-a critical analysis

Ganguly

Sil

2022

Bulletin EEI

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Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm

Rajagopalan,

Nagarajan,

Bajaj

et al. 2024

Sci Rep

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The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 €ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 €ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 €ct, 160.9827 €ct and 128.2815 €ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases.

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IOT-enabled microgrid for optimization of energy consumption

Medhi,

Nagarajan,

Selvaraj

et al. 2023

AIP Conference Proceedings

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A review of optimization techniques applied to solve unit commitment problem in microgrid

Cited by 7 publications

References 66 publications

Development of regional load management system based on rural, semi urban and urban loads-a critical analysis

Development of regional load management system based on rural, semi urban and urban loads-a critical analysis

Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm

IOT-enabled microgrid for optimization of energy consumption

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