An Optimal Energy Storage Control Strategy for Grid-connected Microgrids

Malysz, Pawel; Sirouspour, Shahin; Emadi, Ali

doi:10.1109/tsg.2014.2302396

Cited by 215 publications

(104 citation statements)

References 21 publications

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“…However, when there are several available energy resources to supply the demand, the energy supplied by each one should be scheduled to get an optimal dispatch regarding specific objectives such as economical, technical and environmental aspects [2], [3]. Particularly, storage-based microgrids can provide economic benefits without the need of shifting or shedding loads with the inclusion of a energy management system (EMS) [4] which is essential for critical loads that must be uninterruptedly fed [2].…”

Section: Introductionmentioning

confidence: 99%

“…Optimization techniques have been researched as linear and nonlinear approaches [3], even when these methods are often used offline. Recently, some online energy management system for microgrids (MG-EMS) have been implemented as in [4], but it does not considered safety ranges for the operation of the ESS which ensures longer ESS lifetime [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online energy management system for distributed generators in a grid-connected microgrid

Luna

Díaz

Graells

et al. 2015

2015 IEEE Energy Conversion Congress and Exposition (ECCE)

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Abstract-A microgrid is an energy subsystem composed of generation units, energy storage, and loads that requires power management in order to supply the load properly according to defined objectives. This paper proposes an online energy management system for a storage based grid-connected microgrid that feeds a critical load. The optimization problem aims to minimize the operating cost while maximizing the power provided by the renewable energy sources. The power references for the distributed energy resources (DER) are scheduled by using CPLEX solver which uses as input current measurements, stored data and adjusted weather forecast data previously scaled in each iteration considering the current status. The proposed structure is tested in a real time simulation platform (dSPACE 1006) for the microgrid model, by using Labview to data acquisition and Matlab to implement the energy management system. The results show the effectiveness of the proposed energy management system for different initial conditions of the storage system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online energy management system for distributed generators in a grid-connected microgrid

Luna

Díaz

Graells

et al. 2015

2015 IEEE Energy Conversion Congress and Exposition (ECCE)

View full text Add to dashboard Cite

show abstract

“…Reference [11] presents an online optimal energy/power control strategy for the operation of energy storage in grid-connected microgrids. The approach is based on a mixed-integer-linear-programming formulated over a rolling horizon window, considering predicted future electricity load demands and renewable energy generations.…”

Section: Introductionmentioning

confidence: 99%

Optimal energy management for industrial microgrids with high-penetration renewables

Eseye

Zhang

et al. 2017

Prot Control Mod Power Syst

165

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This paper presents a day-ahead optimal energy management strategy for economic operation of industrial microgrids with high-penetration renewables under both isolated and grid-connected operation modes. The approach is based on a regrouping particle swarm optimization (RegPSO) formulated over a day-ahead scheduling horizon with one hour time step, taking into account forecasted renewable energy generations and electrical load demands. Besides satisfying its local energy demands, the microgrid considered in this paper (a real industrial microgrid, "Goldwind Smart Microgrid System" in Beijing, China), participates in energy trading with the main grid; it can either sell power to the main grid or buy from the main grid. Performance objectives include minimization of fuel cost, operation and maintenance costs and energy purchasing expenses from the main grid, and maximization of financial profit from energy selling revenues to the main grid. Simulation results demonstrate the effectiveness of various aspects of the proposed strategy in different scenarios. To validate the performance of the proposed strategy, obtained results are compared to a genetic algorithm (GA) based reference energy management approach and confirmed that the RegPSO based strategy was able to find a global optimal solution in considerably less computation time than the GA based reference approach.

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“…To address solar generation uncertainties, stationary and mobile storages can be connected to the solar generation as buffers [2], [3]. Even though Battery Energy Storage System (BESS) and gridable Electrical Vehicles (EVs) can compensate for the solar generation fluctuation, a better prediction of the solar generation helps to reduce the size of the BESS and the number of EVs.…”

Section: Introductionmentioning

confidence: 99%

Two-tier prediction of solar power generation with limited sensing resource

Wang

Huang

et al. 2016

2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)

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Abstract-This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods that are not suitable for applications requiring longer term prediction. We propose a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hour) solar generation prediction using only the historical power data. In global-tier, we examine two popular heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN). In local-tier, the global-tier results are adaptively updated using real-time analytical residual analysis. The proposed method is validated using the UCLA Microgrid with 35kW of solar generation capacity. Experimental results show that the proposed two-tier prediction method achieves higher accuracy compared to day-ahead predictions while providing the same prediction length. The difference in the overall prediction performance using either weighted k-NN based or NN based in the global-tier are carefully discussed and reasoned. Case studies with a typical sunny day and a cloudy day are carried out to demonstrate the effectiveness of the proposed two-tier predictions.

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An Optimal Energy Storage Control Strategy for Grid-connected Microgrids

Cited by 215 publications

References 21 publications

Online energy management system for distributed generators in a grid-connected microgrid

Online energy management system for distributed generators in a grid-connected microgrid

Optimal energy management for industrial microgrids with high-penetration renewables

Two-tier prediction of solar power generation with limited sensing resource

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