Energy storage resources management: Planning, operation, and business model
Kaile ZHOU(), Zenghui ZHANG, Lu LIU, Shanlin YANG
School of Management, Hefei University of Technology, Hefei 230009, China; Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education, Hefei University of Technology, Hefei 230009, China
With the acceleration of supply-side renewable energy penetration rate and the increasingly diversified and complex demand-side loads, how to maintain the stable, reliable, and efficient operation of the power system has become a challenging issue requiring investigation. One of the feasible solutions is deploying the energy storage system (ESS) to integrate with the energy system to stabilize it. However, considering the costs and the input/output characteristics of ESS, both the initial configuration process and the actual operation process require efficient management. This study presents a comprehensive review of managing ESS from the perspectives of planning, operation, and business model. First of all, in terms of planning and configuration, it is investigated from capacity planning, location planning, as well as capacity and location combined planning. This process is generally the first step in deploying ESS. Then, it explores operation management of ESS from the perspectives of state assessment and operation optimization. The so-called state assessment refers to the assessment of three aspects: The state of charge (SOC), the state of health (SOH), and the remaining useful life (RUL). The operation optimization includes ESS operation strategy optimization and joint operation optimization. Finally, it discusses the business models of ESS. Traditional business models involve ancillary services and load transfer, while emerging business models include electric vehicle (EV) as energy storage and shared energy storage.
You et al. (2014)Nick et al. (2014)Nick et al. (2018)
An isolated section of the power grid
Improve frequency smoothing
Bat optimization algorithm
Ramírez et al. (2018)
Transmission system
Reduce the economic cost
Stochastic mixed-integer linear programming
Fernández-Blanco et al. (2017)
Power grid
Improve the ability of stabilizing voltage fluctuation of ESS
Genetic algorithmSimulated annealing algorithm
Crossland et al. (2014)
Microgrid
Reduce the total cost
Bi-objective optimization model -constraint methodFuzzy satisfying technique
Nojavan et al. (2017)
Transmission system and distribution network
Improve the technical performance of ESS
Complex-valued neural networks and time domain power flowEconomic dispatch
Motalleb et al. (2016)
Reduce the economic costImprove the system voltage profiles
Hybrid multi-objective PSO
Wen et al. (2015)
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