Title :
A stochastic approach to hydroelectric power generation planning in an electricity market
Author :
Sauhats, Antans ; Varfolomejeva, Renata ; Petrichenko, Roman ; Kucajevs, Jevgenijs
Author_Institution :
Inst. of Power Eng., Riga Tech. Univ., Riga, Latvia
Abstract :
The paper discusses the planning of hydroelectric power generation. A stochastic optimization procedure is offered to solve the complex task of planning the operation of three hydroelectric power plants. The proposed stochastic optimization algorithm is based on time average revenue maximization, taking into account the random nature of the future energy prices and river water inflows. Random variables are predicted by using an algorithm based on artificial neural networks. For computing within the operational planning, the task is divided into three parts. First, middle-term planning is used to solve the water resources distribution task. The second and third parts are related to day-ahead operational and unit commitment planning; in those cases, nonlinear programming tools are used. A case study presents the results of electric power generation in the case of optimum water resource distribution in the storage basins of hydroelectric power plant´s cascade. The paper proves the workability of the developed algorithm for maximizing the income value and is intended to enable and support improved planning and decision-making for electric power producers.
Keywords :
decision making; hydroelectric power stations; neural nets; nonlinear programming; power engineering computing; power generation dispatch; power generation planning; power generation scheduling; power markets; stochastic programming; artificial neural network; day-ahead operational planning; decision making; electricity market; hydroelectric power generation planning; hydroelectric power plant operation planning; middle-term planning; nonlinear programming; stochastic optimization procedure; time average revenue maximization; unit commitment planning; water resources distribution task; Hydroelectric power generation; Optimization; Planning; Reservoirs; Stochastic processes; artificial neural networks; hydroelectric power generation; power generation planning; stochastic processes;
Conference_Titel :
Environment and Electrical Engineering (EEEIC), 2015 IEEE 15th International Conference on
Conference_Location :
Rome
Print_ISBN :
978-1-4799-7992-9
DOI :
10.1109/EEEIC.2015.7165280