Saturday, August 17, 2013
Saturday, August 3, 2013
No Wind for Northwind
Northwind plant, located at the northern part of Luzon grid in the Philippines has a capacity factor of 16.2% from March to July 2013. And that is six months.
This means the said wind farm has an average dependable capacity of 4 MW out of total of 25.5 MW maximum capacity which is very low compared to the global wind power capacity factor of 28% and to the US wind power capacity factor of 31.8%.
Significantly, this means that for a Luzon peak load of 6,800 MW we need about 42,000 MW of wind power capacity to supply Luzon grid on wind power alone, and that's a lot!
This means the said wind farm has an average dependable capacity of 4 MW out of total of 25.5 MW maximum capacity which is very low compared to the global wind power capacity factor of 28% and to the US wind power capacity factor of 31.8%.
Significantly, this means that for a Luzon peak load of 6,800 MW we need about 42,000 MW of wind power capacity to supply Luzon grid on wind power alone, and that's a lot!
 |
| Capacity factor and daily output of Northwind power plant from March to July 2013. |
Tuesday, March 5, 2013
Electric Vehicle Charging Station Location using Fuzzy Optimization
Electric vehicle charging
station location is a basic problem in integrating electric vehicles in
electric power systems. An electric vehicle plugged into the electric
distribution system may absorb or produce active and/or reactive power [1-4]
depending on the need of the electric power system, Table 1 below.
 |
| Electric vehicle charger operating modes [4]. |
When finding the location of EV charging stations in order
to support the electric distribution systems, the cost of EV charging, the
impact on distribution system losses and voltage profile of the system are
parameters needed to be considered. These variables are to be looked into when
plugged in EV is either acting as a generator or a load given a system demand
level.
Recent studies have solved this EV charging station location
problem. In [5], a mixed integer programming solution was developed with site
accessibility, local jobs and population densities and trip attributes as main
constraints. A genetic programming approach is utilized in [6] for simulation
of electric vehicles on a real map of a European city where the optimal
solution of the charging infrastructure is derived based on mean trip times of
electric vehicles. A two step procedure is proposed in [7] where the authors
included environmental factors and service radius of EV charging stations in
the screening first step and built a modified primal-dual interior point
algorithm (MPDIPA) for optimal sizing of EV charging stations with the
minimization of total cost associated with EV charging stations to be planned
as the objective function with losses and voltage profile included in the
problem. Reference [8] introduces an optimization process for sizing and siting
of EV charging stations, modeling the charging demand and the structure of road
network to where the solution approach was graph theory. Level 1 and level 2
charging stations are discussed in [9] and how to allocate them for residential
EV users using simulation-optimization strategy.
Recent studies do not consider uncertainties and imprecision
which can be captured using fuzzy optimization. Fuzzy set theory can provide a
simpler yet powerful solution for allocating EV charging stations in electric
distribution systems. The Civanlar test system [11] will be utilized for the
study and assuming that capital investment of the EV charging station is the
same for all distribution system candidate nodes while considering time of use
(TOU) electricity tariff, distribution system losses and voltage profiles.
References
[1] Chenye Wu, Hamed Mohsenian-Rad, and Jianwei Huang, “PEV-based Reactive Power Compensation for Wind DG Units: A Stackelberg Game Approach”, in Proc. of the IEEE Conference on Smart Grid Communications (SmartGridComm’12), Tainan City, Taiwan, October 2012.
[2] Chenye Wu, Hamed Mohsenian-Rad, Jianwei Huang, Juri Jatskevich, “PEV-Based Combined Frequency and Voltage Regulation for Smart Grid”, the 3rd IEEE Innovative Smart Grid Technologies Conference, Washington DC, Jan 2012.
[3] M. Kisacikoglu, B. Ozpineci, L. M. Tolbert, "V2G Reactive Power Compensation Using a PHEV Bidirectional Charger Interface Rated at Level 1, 2, and 3 Charging Standards," IEEE Energy Conversion Congress and Exposition, Atlanta, Georgia, Sept. 12-16, 2010.
[4] M. Kisacikoglu, B. Ozpineci, L. M. Tolbert, "Examination of a PHEV Bidirectional Charger System for V2G Reactive Power Compensation," IEEE Applied Power Electronics Conference, Palm Springs, California, Feb. 21-25, 2010, pp. 458-465.
[5] Chen, T. D., et al, “The Electric Vehicle Charging Station Location Problem: A Parking-Based Assignment Method for Seattle”, on-line: http://www.caee.utexas.edu/
[6] Hess, A. Et al, “Optimal Deployment of Charging Stations for Electric Vehicular Networks”, on-line:http://conferences.sigcomm.
[7] Liu, Zhipeng, Wen, F. and Ledwich, G. F. , “Optimal Planning of Electric-Vehicle Charging Stations in Distribution Systems”, IEEE Transactions on Power Delivery, Jan. 2013, Vol. 28 , Issue 1.
[8] Jia, L., Hu, Z., Song, Y., Luo, Z., “Optimal siting and sizing of electric vehicle charging stations”, 2012 IEEE International Electric Vehicle Conference (IEVC), 4-8 March 2012
[9] Xi, X., et al, “Simulation-Optimization Model for Location of a Public Electric Vehicle Charging Infrastructure”, on-line:http://www.ise.osu.edu/
[10]“Siting Electric Vehicle Charging Stations”, on-line: http://www.
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