Online Algorithms for Optimal Energy Distribution in Microgrids

Wang, Yu; Mao, Shiwen; Nelms, R.M.

doi:10.1007/978-3-319-17133-3

Cited by 17 publications

(5 citation statements)

References 3 publications

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“…Proposed EMS converged faster, and it verified on real microgrid in Guangdong province, China. The authors in [122] showed the offline algorithm for electricity distribution in a grid connected MG. First, the authors constructed offline convex problem using customer load profile, utility cost, energy provisioning cost, and dynamic pricing, then the same prior information is used to formulate an online algorithm, to show that it also asymptotically optimal. The method is also compared with real-time pricing algorithm as a benchmark.…”

Section: Ed and Ucmentioning

confidence: 99%

Energy management system, generation and demand predictors: a review

Rafique¹,

Zhang

2018

IET Generation, Transmission & Distribution

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Section: Ed and Ucmentioning

confidence: 99%

Energy management system, generation and demand predictors: a review

Rafique¹,

Zhang

2018

IET Generation, Transmission & Distribution

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“…Considerable works have been focused on the design and management of MGs [8], [66]. Recently, the Interconnected Multi-microgrids (IMMGs) system has become an integrated, flexible network that incorporates multiple individual microgrids (MGs), which are often geographically close and connected to a distribution bus.…”

Section: Introductionmentioning

confidence: 99%

“…and reliability of the entire system [1]- [4]. The IMMG has become an increasingly important problem in the current SG research, where the key is high efficient utilization and sharing of renewable energy (RE), especially distributed renewable energy (DRE) [5]- [8], and promoting the efficient use of distributed energy and consumption [4], [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Energy Management in Interconnected Multi-Microgrids

et al. 2019

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An interconnected multi-microgrids (IMMGs) system takes advantage of various complementary power sources and effectively coordinates the energy sharing/trading among the MGs and the main grid to improve the stability, reliability, and energy efficiency of the system. The core of this structure is to achieve the optimal distribution of energy sharing through proper strategies. However, the volatility and intermittent characteristics of renewable resources, time-varying loads in the MGs, their correlated power generations, and the coupled energy among the MGs during energy trading, all bring about new challenges to achieving a stable operation and optimal scheduling in the power system. Many solutions have been proposed to solve these problems. In this paper, we provide an overview of the current energy management systems (EMS) in IMMGs, focusing on the IMMG structure, EMS objectives, timescales, and scheduling optimization structure. We then provide a review of the distributed optimization algorithms in IMMGs. We conclude this survey with a discussion of future directions. INDEX TERMS Smart grid, microgrid (MG), interconnected multi-microgrids (IMMG), energy sharing, energy management.

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“…In recent years, as the growing electrical power load demand and the claim on reducing greenhouse gases generated by the exhaustion of fossil fuels, Smart Grid (SG) and Microgrid (MG) featuring renewable energy (RE) have developed quickly, to satisfy consumption and mitigate pollution [1,2]. However, renewable energy is typically intermittent and weather-dependent, such as in the case of wind and solar power, which is a big challenge to the planning, operation, unit commitment and energy scheduling of MG or SG [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Solar Power Forecasting based on Machine Learning Methods

et al. 2018

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Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.

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Online Algorithms for Optimal Energy Distribution in Microgrids

Cited by 17 publications

References 3 publications

Energy management system, generation and demand predictors: a review

Energy management system, generation and demand predictors: a review

A Survey of Energy Management in Interconnected Multi-Microgrids

Adaptive Solar Power Forecasting based on Machine Learning Methods

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