Energy Management and Optimization Methods for Grid Energy Storage Systems

Byrne, Raymond H.; Nguyen, Tu A.; Copp, David A.; Chalamala, Babu; Gyuk, Imre

doi:10.1109/access.2017.2741578

Cited by 271 publications

(126 citation statements)

References 161 publications

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“…MILP is a programming method where some of the objective variables are constrained to be integer while additional variables are permissible to be noninteger. 188 When the system is subjected to dynamics with discrete decisions variables and nonlinearity, MINLP is helpful to find an optimal solution that combines the function of NLP and MILP (as subproblem). 189 Table 9 provides a comparative analysis of different programming method used for achieving an optimal solution in an EMS.…”

Section: Mathematical Programming-based Ems (Equation-based)mentioning

confidence: 99%

Energy management system for smart grid: An overview and key issues

2020

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Summary Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug‐in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug‐in electric vehicles, the electricity price, and the time‐varying load bring new challenges to the power engineers to achieve demand‐supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand‐side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting‐edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.

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Section: Mathematical Programming-based Ems (Equation-based)mentioning

confidence: 99%

Energy management system for smart grid: An overview and key issues

2020

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“…The hybrid AC / DC system with interregional wind power which enhances the system operation under the receiving-side pressure [15]. Similarly, [16][17][18][19][20] explains the various control features used for energy management system in hybrid power network. The power electronic converter is playing a vital role in effective control of the EMS [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy Using ANFIS Approach for Hybrid Power System

Suruli¹,

Ila²

2020

Teh. vjesn

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Renewable Energy Sources are the promising hopes of upcoming years as they are abundant in nature and available free of cost. In addition to this, these sources are pollution-free which makes them a perfect substitute for fossil fuels. A Hybrid Power System (HPS) is one that has multiple power generating sources like Photo Voltaic (PV) system, Wind turbine, Fuel cell, etc. interconnected to supply electric power for varying demand requirements with / without energy storage backup. This paper concentrates on the automation for control and integration of Renewable energy systems Viz. PV system, Solid Oxide Fuel Cell (SOFC) with Nickel-Metal-Hydride (Ni-MH) battery together with a variable load. The Proposed HPS mainly focuses on the use of PV which is 100% clean in nature with no toxic emissions on power generation. Here, the solar photovoltaic system with power extracting maximum by algorithm used as the major supply contributor in the HPS to meet with variable load demands. If there is a deficit of power supply from PV, the power from the Ni-MH battery / SOFC is utilized to meet the varying load demands. On the other hand, if there is excess supply from PV system, the excess energy will be stored in the Ni-MH battery. For efficient supply-demand balance, the HPS makes use of various control strategies namely Proportional Integral (PI) and Adaptive Neuro Fuzzy Inference System (ANFIS).

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“…2 The energy storage attributes required to facilitate increased integration of PV in electricity grids are not generally well understood. While load shifting and peak shaving of residential PV generation [13][14][15][16][17] may be achieved using batteries with relatively low power rates, power generation from solar PV can change unpredictably on sub-second time scales [18][19][20][21][22] and destabilize electricity networks. In the absence of technological solutions, this unpredictable intermittence can limit penetration levels of PV and indeed also other intermittent resources, such as wind.…”

Section: Introductionmentioning

confidence: 99%

“…In the absence of technological solutions, this unpredictable intermittence can limit penetration levels of PV and indeed also other intermittent resources, such as wind. However, there is another view that use of wind and PV in concert with parallel grid-forming inverters [23][24][25][26] and rapidly responding frequency stabilization services that utilize energy storage 17,22,[27][28][29][30][31][32][33] can enable more flexible and resilient electricity grids. 2,17 With the rapid reduction in costs of PV that are occurring, reduced and sustainable electricity prices may be possible and the full advantages of transport electrification can be realized.…”

Section: Introductionmentioning

confidence: 99%