Optimal Management and Sizing of Energy Storage Under Dynamic Pricing for the Efficient Integration of Renewable Energy

We address the optimal energy storage management and sizing problem in the presence of renewable energy and dynamic pricing associated with electricity from the grid. We formulate the problem as a stochastic dynamic program that aims to minimize the long-run average cost of electricity used and inve...

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Bibliographic Details
Published in:IEEE transactions on power systems Vol. 30; no. 3; pp. 1164 - 1181
Main Authors: Harsha, Pavithra, Dahleh, Munther
Format: Journal Article
Language:English
Published: IEEE 01.05.2015
Subjects:
Dynamic programming/optimal control
Electricity
Energy storage
Equations
infinite horizon
Investment
management
operations
Pricing
renewable energy
Renewable energy sources
Storage management
ISSN:0885-8950, 1558-0679
Online Access:Get full text
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Summary:We address the optimal energy storage management and sizing problem in the presence of renewable energy and dynamic pricing associated with electricity from the grid. We formulate the problem as a stochastic dynamic program that aims to minimize the long-run average cost of electricity used and investment in storage, if any, while satisfying all the demand. We model storage with ramp constraints, conversion losses, dissipation losses and an investment cost. We prove the existence of an optimal storage management policy under mild assumptions and show that it has a dual threshold structure. Under this policy, we derive structural results, which indicate that the marginal value from storage decreases with its size and that the optimal storage size can be computed efficiently. We prove a rather surprising result, as we characterize the maximum value of storage under constant prices and i.i.d. net-demand processes: if the storage is a profitable investment, then the ratio of the amortized cost of storage to the constant price is less than 1/4. We further perform sensitivity analysis on the size of optimal storage and its gain via a case study. Finally, with a computational study on real data, we demonstrate significant savings with energy storage.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2014.2344859