An Adaptive Robust Scheduling Optimization Method for Virtual Power Plant
An adaptive robust, virtual power plant technology, applied in data processing applications, instruments, calculations, etc., to achieve the effects of increasing revenue, reducing risks, and simplifying the modeling process
Active Publication Date: 2021-09-07
HOHAI UNIV
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Problems solved by technology
[0004] In order to solve the technical problems raised by the above-mentioned background technology, the present invention aims to provide an adaptive robust scheduling optimization method for virtual power plants, which solves the two-stage scheduling optimization problem of virtual power plants ahead of time and in real time. The adjustment strategy of each aggregation unit and the real-time market balance reduce the risks brought about by the fluctuation of renewable energy output and increase the income of virtual power plants
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[0066] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.
[0067] The present invention designs a virtual power plant adaptive robust scheduling optimization method, such as figure 1 shown.
[0068] Hereinafter, the present invention is introduced by taking a virtual power plant as an example.
[0069] The scheduling cycle of the virtual power plant is 1 day, which is divided into 24 periods. The virtual power plant participates in the day-ahead and real-time markets at the same time, and the electricity price in the electricity market is shown in Table 1. The aggregation units of the virtual power plant include a gas turbine, a wind turbine and loads. The gas turbine adopts the TAU5670 model, and the specific data are shown in Table 2. Wind power output and load forecast data such as figure 2 shown.
[0070] The comparison results between the adaptive robust optimization method and the dete...
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The invention discloses an adaptive robust scheduling optimization method for a virtual power plant. This method uses adaptive robust optimization to deal with the uncertainty of renewable energy output, and considers the two-stage scheduling of virtual power plants ahead of time and in real time. The model built by the adaptive robust scheduling optimization method is a three-layer optimization model. In order to solve this problem, firstly, auxiliary variables are introduced to divide the model into a single-layer main problem and a two-layer sub-problem; secondly, through the dual theory , transforming the sub-problem of the two layers into a single-layer problem; finally, adopting the column and constraint generation method, the main problem and the sub-problem are solved by alternating iterations until the gap between the two problems converges to an acceptable range. Compared with static robust optimization, while ensuring system security, the present invention considers the adjustment strategy of each aggregation unit inside the virtual power plant and the balance of the real-time market after knowing the output of renewable energy, which can effectively stabilize the fluctuation of renewable energy. Improve the economics of virtual power plants.
Description
technical field [0001] The invention belongs to the field of power system dispatching, and in particular relates to an adaptive robust dispatching optimization method for a virtual power plant. Background technique [0002] In recent years, distributed power generation based on renewable energy has developed rapidly. However, the characteristics of small capacity, large quantity, scattered access and intermittent output of distributed power have limited its large-scale development. Aggregating renewable energy in the form of a virtual power plant, and controlling each aggregation unit through an energy management system, can realize the coordinated and optimized operation of the aggregation unit, thereby improving the stability of the virtual power plant and the competitiveness when participating in the electricity market, and obtaining the benefits of economies of scale. The power market that virtual power plants can participate in includes the day-ahead market, real-time m...
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IPC IPC(8): G06Q10/06G06Q50/06
CPCG06Q10/0637G06Q50/06Y04S10/50Y02P90/90
Inventor 周亦洲卫志农孙国强臧海祥朱瑛陈胜
Owner HOHAI UNIV