304 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 31, NO. 1, JANUARY 2016
Optimal Storage Planning in Active Distribution
Network Considering Uncertainty of Wind
Power Distributed Generation
Mahdi Sedghi, Ali Ahmadian, Student Member, IEEE, and Masoud Aliakbar-Golkar
Abstract—The penetration of renewable distributed generation
(DG) sources has been increased in active distribution networks
due to their unique advantages. However, non-dispatchable DGs
such as wind turbines raise the risk of distribution networks.
Such a problem could be eliminated using the proper application
of energy storage units. In this paper, optimal planning of bat-
teries in the distribution grid is presented. The optimal planning
determines the location, capacity and power rating of batteries
while minimizing the cost objective function subject to technical
constraints. The optimal long-term planning is based on the
short-term optimal power flow considering the uncertainties. The
point estimate method (PEM) is employed for probabilistic op-
timal power flow. The batteries are scheduled optimally for several
purposes to maximize the benefits. A hybrid Tabu search/particle
swarm optimization (TS/PSO) algorithm is used to solve the
problem. The numerical studies on a 21-node distribution system
show the advantages of the proposed methodology. The proposed
approach can also be applied to the realistic sized networks when
some sensitive nodes are considered as candidate locations for
installing the storage units.
Index Terms—Distribution network, optimal planning, point es-
timate method, storage, wind power uncertainty.
NOMENCLATURE
Cost objective function ($).
Investment cost of storage units ($).
Total operation cost ($).
Total reliability cost ($).
Penalty factor.
Installation cost related to capacity of storage ($).
Installation cost related to the power rating of
storage ($).
Replacement cost related to the capacity of
storage ($).
Replacement cost related to the power rating of
storage ($).
Manuscript received June 15, 2014; revised September 08, 2014, December
11, 2014, and January 26, 2015; accepted February 12, 2015. Date of publication
March 02, 2015; date of current version December 18, 2015. Paper no. TPWRS-
00811-2014.
The authors are with the Faculty of Electrical Engineering, K. N. Toosi
University of Technology, Tehran, Iran (e-mail: Meh.sedghi@gmail.com;
Ali.ahmadian.1988@ieee.org; Golkar@eetd.kntu.ac.ir).
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/TPWRS.2015.2404533
Capacity of the th storage unit (kWh).
Power rating of the th storage unit (kVA).
Number of all storage units.
Total operation cost in th season ($).
Duration of the th season in one year (days).
Inflation rate.
Interest rate.
Period of the project (years).
Operation and maintenance cost function of
storage units ($).
Operation cost of the th HV/MV substation at
time of the th season ($).
Active energy price at th hour of the th season
($/MWh).
Injected active power in th HV/MV substation at
time of the th season (MW).
Reactive energy price at th hour of the th season
($/MVARh).
Injected reactive power in th HV/MV substation
at time of the th season (MVAR).
Number of all HV/MV substations.
Average failure rate of the th event (f/year).
Binary variable associated to the outage of th
load node due to th failure event.
Outage cost function ($).
Power of interrupted load in th node (kW).
Type of the th load node.
Duration of outage due to th failure event (hour).
Number of all failure events.
Number of all load nodes.
Power of equipment (kVA).
Allowed maximum power of equipment (kVA).
Number of all the equipment.
Voltage magnitude in th node at time (p.u.).
Allowed minimum voltage magnitude (p.u.).
Allowed maximum voltage magnitude (p.u.).
Power of the th HV/MV substation at th hour
(kVA).
Power of th DG unit at th hour (kVA).
Number of all DG units.
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