Abstract
With the sustained growth in renewable energy penetration, it is important to incorporate the interval prediction information of the wind and photovoltaic power into the monthly unit commitment model and introduce the system reliable rate as an indicator to measure the system reliability, which make an important contribution to deal with the volatility and randomness of the wind and photovoltaic power and ensure the economy and reliability of the monthly unit commitment. To enhance the practicality of the model and improve the solving ability, the multiobjective function composed by operating cost and reliable rate is transformed into a single-objective function by using the evaluation function based on geometric weighting method. An adaptive genetic algorithm (AGA) is used to solve the above problem when the prohibiting inbreeding strategy is adopted to replace the mutation operator, avoiding the hybridization between close relatives and containing the diversity of the population. Finally, the testing systems verify the validity and accuracy of the proposed model and algorithm.
Highlights
In order to cope with the global warming and environmental pollution, the proportion of renewable energy replacing traditional fossil energy generation has increased year by year
In order to ensure that the monthly unit commitment (UC) problem has the ability to cope with the uncertainty of the renewable energy generation and balance the economy and reliability of the system operation as well as improve the solving efficiency, the main work done in this paper is as follows: (1) Based on the interval prediction information of the mid-term power output of wind farms and photovoltaic power stations, the positive and negative spinning reserves and power balance of wind-photovoltaic-thermal power generation are processed
The system reliable rate which is integrated by power supply margin and unit start-off statistics is introduced
Summary
In order to cope with the global warming and environmental pollution, the proportion of renewable energy replacing traditional fossil energy generation has increased year by year. Regarding the establishment of the objective functions, most of the traditional UC problems [5, 6] mainly consider system economy; that is, the target of monthly UC is the total operating cost of the system. The priority list realizes simple calculation speed but the obtained result tends to deviate greatly from the optimal solution in [13]; the dynamic programming in [14] is easy to fall into the “dimensional disaster” when the problem scale increases; the Lagrange relaxation in [15] limits its further application due to the problems of “dual gap” and “relaxation constraint”; there are two solving ways of the particle swarm optimization algorithm in [16]: optimizing the start-off status and the power output of the units simultaneously and decomposing the UC problem into two subproblems of unit start-off and economic dispatching optimization; in [17] GA is essentially an unconstrained optimization algorithm whose efficiency is greatly affected by how to deal with constraints; as it is a stochastic optimization algorithm, it requires a large amount of computation and a long time to hardly obtain a local optimal solution. The practicality and effectiveness of the proposed model and algorithm are verified by power systems of 10∼100 units
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