Title :
Optimal Expansion Planning of Wind-Diesel Energy System
Author :
Ming, Ding ; Yichun, Wu
Author_Institution :
Res. center for Photovoltaic Syst. Eng. Minist. of Educ., Heifei Univ. of Technol., Hefei
Abstract :
The utilization of wind energy sources in autonomous power systems can significantly reduce the system fuel costs but increase the investment costs and have adverse effect on the system safety and reliability, therefore there is a need to study rational capacity expansion planning. This paper proposes the model of the optimal expansion planning of wind-diesel energy system based on improved genetic algorithm (IGA) due to its application to the highly constrained nonlinear discrete dynamic optimization problem. The main advantage of IGA approach is that "the curse of dimensionality" in the mathematical method can be overcame. In this paper the planning objective costs take into account unserved energy costs besides investment costs and operation costs. The evaluation of probability cost and reliability in the model is performed by using Monte-Carlo method for representing the uncertainty for wind speed and loads, forced outage rate of the units, the correlation of wind speed and load series, wind generation operational constraints. The example indicates that the model and algorithm that puts forward in this paper is feasible and provides the important reference value for the planning and design of wind generation system.
Keywords :
Monte Carlo methods; diesel-electric power stations; genetic algorithms; power generation economics; power generation planning; wind power plants; Monte-Carlo method; autonomous power system; genetic algorithm; investment cost; nonlinear discrete dynamic optimization; operation cost; optimal expansion planning; probability cost; wind-diesel energy system; Capacity planning; Costs; Fuels; Investments; Power system modeling; Power system planning; Power system reliability; Safety; Wind energy; Wind speed; Monte-Carlo method; evaluation of probability cost and reliability; improved genetic algorithm; optimal expansion planning; wind-diesel energy system;
Conference_Titel :
Power System Technology, 2006. PowerCon 2006. International Conference on
Conference_Location :
Chongqing
Print_ISBN :
1-4244-0110-0
Electronic_ISBN :
1-4244-0111-9
DOI :
10.1109/ICPST.2006.321412