Day ahead scheduling of generation and storage in a microgrid considering demand Side management

Kumar, K. Prakash; Saravanan, B.

doi:10.1016/j.est.2018.11.010

Cited by 106 publications

(37 citation statements)

References 29 publications

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“…Kumar and Saravanan [55] proposed an algorithm based on the demand prediction over 24 h in a MG using the artificial fish swarm optimization method. Thus, the demand can be planned in advance, considering both renewable and non-renewable generation.…”

Section: Energy Management Based On Metaheuristic Methodsmentioning

confidence: 99%

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

2019

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Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed.

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Section: Energy Management Based On Metaheuristic Methodsmentioning

confidence: 99%

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

2019

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“…UP marks the participation of the DERs in the sharing of load throughout the day [56]. It is calculated using the formula below:…”

Section: Utilization Percentage (Up)mentioning

confidence: 99%

Economic emission dispatch on unit commitment-based microgrid system considering wind and load uncertainty using hybrid MGWOSCACSA

Dey

Bhattacharyya

Raj

et al. 2020

Journal of Electrical Systems and Inf Technol

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Economic emission dispatch (EED) of a three-unit stand-alone microgrid system supported by a wind farm is percolated in this paper. The adverse effects of stochastic and uncertainty nature of wind energy in raising the generation cost of the microgrid system are studied in this article. Unit commitment (UC) of the generating units is taken into account which helps in reducing the generation cost and provides relaxation time to the generation units. Three cases are contemplated for the study. For the first two cases, the generation cost of the test system was minimized without and with the involvement of wind power, respectively. The third case considered the involvement of wind power along with the UC of the conventional generation units. A novel hybrid of recently developed superior optimization algorithms, viz. grey wolf optimizer (GWO), sine–cosine algorithm (SCA) and crow search algorithm (CSA), is implemented to perform EED, and the results are compared with basic GWO and other hybrid algorithms. Results are then analysed to compare and contrast among these cases and justify the reliable and profitable one. Statistical analysis claims the superiority of the proposed hybrid MGWOSCACSA over other hybrids and GWO.

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“…The need for local management of demand and supply of management of energy has assumed greater importance in the currently prevailing Covid-19 syndrome. To satisfy this energy security demand and to ensure a cleaner environment, renewable energy source integrated microgrid [1] , [2] , [3] , [4] , [5] , [6] , [7] system claims to be a potential community-scale solution. Considering the intermittency of renewable energy [RE] sources, multiple energy sources such as solar PV, biomass along with battery energy storage system (BESS) forming a hybrid microgrid [8] can be a potential solution.…”

Section: Introductionmentioning

confidence: 99%

Internet of things based smart energy management in a vanadium redox flow battery storage integrated bio-solar microgrid

Samanta

Bhattacharjee

Pramanik

et al. 2020

Journal of Energy Storage

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Highlights First time the solar-biogas-VRFB storage integrated IoT based smart microgrid is implemented. Optimized energy management has been demonstrated for the local community. Raspberry-Pi based low cost smart communication platform is used to monitor and control microgrid operation in real time. Available solar PV power is effectively utilized even during sudden grid outage at day time. 24 × 7 energy security is ensured by intelligent scheduling of RE sources and storage.

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Day ahead scheduling of generation and storage in a microgrid considering demand Side management

Cited by 106 publications

References 29 publications

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

Economic emission dispatch on unit commitment-based microgrid system considering wind and load uncertainty using hybrid MGWOSCACSA

Internet of things based smart energy management in a vanadium redox flow battery storage integrated bio-solar microgrid

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