Joint Arbitrage and Operating Reserve Scheduling of Energy Storage Through Optimal Adaptive Allocation of the State of Charge

Energy storage can become one of the portfolio solutions in modern power systems by increasing the grid's resilience. The intensive capital cost of storage, however, is one of the most important barriers to its proliferation. To alleviate the impact of its sizable capital expenditure, exploitin...

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Bibliographic Details
Published in:IEEE transactions on sustainable energy 2019-10, Vol.10 (4), p.1705-1717
Main Authors: Khani, Hadi, Farag, Hany E. Z.
Format: Article
Language:English
Subjects:
Algorithms
Arbitrage
Capital expenditures
electricity market
Electricity supply industry
Energy charge
Energy storage
financial analysis
Lower bounds
Markets
operating reserve
Optimal scheduling
Optimization
Reserve scheduling
Resource management
Scheduling
Signal processing
Slack variables
State of charge
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Summary:Energy storage can become one of the portfolio solutions in modern power systems by increasing the grid's resilience. The intensive capital cost of storage, however, is one of the most important barriers to its proliferation. To alleviate the impact of its sizable capital expenditure, exploiting the full benefit of storage should be targeted through joint applications. The economic efficiency of jointly scheduled storage, however, is seriously undermined if its capacity is not optimally allocated for each application. This paper unveils a new storage scheduling algorithm for the joint arbitrage and operating reserve (OPR) as merchant functions. The optimization slack variables are employed as fictitious capacities to adapt the upper and lower bounds of the state of charge (SOC). The storage SOC is, then, optimally allocated for the arbitrage and OPR. Via an adaptive penalizing mechanism and soft constraints, OPR signals are incorporated into the optimization process. Anew index is formulated to quantify the storage participation toward OPR. Market price modulation factors are presented for financial analysis of the storage contribution to the OPR market. Numerical studies are conducted over the storage operation using historical market data to validate the efficacy of the proposed model.
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2018.2869882