Optimal Operation Strategy of Energy Storage System
for Grid-Connected Wind Power Plants
Zhen Shu, Student Member, IEEE, and Panida Jirutitijaroen, Senior Member, IEEE
Abstract—This paper proposes an adaptive optimal policy for
hourly operation of an energy storage system (ESS) in a grid-
connected wind power company. The purpose is to time shift wind
energy to maximize the expected daily profit following uncertainties
in wind generation and electricity price. A stochastic dynamic
programming (SDP) framework is adopted to formulate this prob-
lem, and an objective function approximation method is applied to
improve the SDP computational efficiency. Case studies on the
Electric Reliability Council of Texas demonstrate that the resultant
profits from SDP-based operation policy are considerably higher
than those from deterministic policy, and comparable to those from
the perfect information model. It is concluded that the presented SDP
approach can provide operation policy highly adaptive to uncer-
tainties arising from wind and price. The proposed framework can
help the wind company optimally manage its generation with ESS.
Index Terms—Energy storage system (ESS), optimization,
stochastic dynamic programming (SDP), storage operation, wind
generation.
NOMENCLATURE
Indices:
Time index.
Terminal time index.
Sets:
Convex set of continuous feasible decisions.
Set of operation policies.
Set of the entire admissible policies.
Parameters:
Operating cost of storage ($/MWh).
Transmission capacity (MW).
Initial and final energy levels of storage (MWh).
Energy capacity of storage (MWh).
Power capacity of storage (MW).
Ratio of total generated energy to the energy
consumed from compressed air.
Energy conversion efficiency of charging.
Energy conversion efficiency of discharging.
Variables:
Random wind power output at time (MW).
Random electricity price at time ($/MWh).
Energy level of storage at time (MWh).
Charging ( > ) or discharging energy
(
< ) of storage during hour (MWh).
Discarded power during hour (MW).
I. I
NTRODUCTION
I
NTEGRATION of large-scale renewable energy sources
brings new challenges to power system operation due to
their high intermittency. The energy storage system (ESS) is a
viable option to mitigate variability of renewable generation
[1]–[3]. System operators use ESS for reliability improvement
[4], ancillary services [5], and transmission congestion relief [6].
Renewable energy producers apply ESS for capacity firming
and energy time shifting [2], [7]–[9]. For wind power plants,
using ESS for energy time shifting may result in higher profits
thus making wind integration more attractive [2]. In the Electric
Reliability Council of Texas (ERCOT), the wind power output
is usually high at night and low during daytime, whereas the
electricity load and price are usually low at night and become
higher during daytime. If wind energy is stored during low-price
periods and discharged back to the grid during high-price
periods, higher profit can be achieved and peak load can also
be reduced to help alleviate transmission congestions.
For wind power companies, the operation strategy of ESS is
very important in achieving optimal tradeoff between operation
cost and revenue growth. This operat ion problem is challenging
due to stochastic behaviors of wind power and market prices.
The issue of coupling wind power plants with ESS for energy
time shifting has been studied in some literature in both planning
and operation aspects [8]– [14]. However, these techniques are
neither optimal [10]–[12] nor applicable to a large number of
wind and price scenarios [9], [10], [12].
In [12], a number of sample paths for uncertain price need
to be obtained beforehand. A profit maximization problem is
formulated and separately solved to find optimal daily operation
for each path. An operation strategy is then found from an
envelope of those daily operations, which provides a preferable
operation outline, but can neither indicate operations accurately
under various scenarios nor guarantee optimality. Moreover, as
the number of random variables grows or time horizon increases,
the size of scenarios will grow substantially, which makes the
problem computationally intractable. The objective of [13] is to
minimize the cost from thermal generator operation as well as
ESS installation without considering ESS operating cost. The
operation policies are over-optimistic since future uncertainties
are assumed known, which is not valid in practice. ESS operations
are found as functions of system states with dynamic program-
ming [9], [14]; however, a deterministic model in [9] is not
Manuscript received March 22, 2013; revised July 13, 2013; accepted August
10, 2013. Date of publication September 30, 2013; date of current version
December 12, 2013. This work was supported by Ministry of Education
Academic Research Fund, Grant R-263-000-691-112.
The authors are with the Department of Electrical and Computer Engineering,
National University of Singapore, 119077, Singapore (e-mail: a0017032@nus.
edu.sg; elejp@nus.edu.sg).
Color versions of one or more of the figures in this paper are available online at
http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TSTE.2013.2278406
190 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 5, NO. 1, JANUARY 2014
1949-3029 © 2013 IEEE