Abstract
With the increasing penetration rate of renewable energy like fluctuating wind and solar energy, the power system has to equip itself with a more reasonable reserve capacity. Therefore, how to quantify the reserve capacity needed for dealing with the uncertainty and fluctuation has turned out to be a new problem faced by the power system integrated with large-scale renewable energy. This paper proposes a flexibility based day-ahead generation–reserve bilevel decision model. In the upper level, the day-ahead unit commitment model is constrained by flexibility reserve, which is calculated in the lower level. In the lower level, taking into account various factors of uncertainty and fluctuation, e.g., wind power ramping, load ramping and random failure of conventional units, the ramping probability distribution of an equivalent system is obtained by the universal generating function method, then the quantified relationship between operating reserve and flexibility is established ultimately. If the unit commitment scheme gained from the upper level could not provide sufficient reserve, a feedback for the correction of the upper level is needed. The rationality and validity of the proposed model are verified through the simulation of a modified IEEE-118 bus system.
Highlights
Renewable energy (RE) represented by wind power and solar power has become the main player of the energy industry revolution in China
The relationship between the flexibility index Loss of flexibility probability (LOFP) and reserve capacity is established by the ramping state probability distribution of the equivalent system, thereby the reserve capacity is calculated by the given LOFP
The proposed unit commitment model in the upper level is converted into a mixed integer linear planning problem, and it was solved by the CPLEX solver on Matlab using the Yalmip toolbox [29,30]
Summary
Renewable energy (RE) represented by wind power and solar power has become the main player of the energy industry revolution in China. Paterakis et al [13] presented a multi-objective energy and reserve joint optimization model with the objective of minimum total expected cost and conditional risk value, and proposed an improved variant of the epsilon-constraint algorithm These methods add economic factors like expected outage cost in the objective function, thereby realizing the coordinated optimization of generation and reserve. Lu et al [17] presented a flexibility evaluation method for power system planning issue that has comprehensively considered system resources regulation features and renewable energy sources fluctuation based on a conditional probability convolution algorithm. Based on historical data and forecast information, the proposed model establishes the quantitative relationship between reserve and flexibility, and ensures system flexibility to meet the requirements while realizing conventional unit output scheduling.
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