Abstract
This paper presents a flexible transmission network expansion planning (TNEP) approach considering uncertainty. A novel hybrid clustering technique, which integrates the graph partitioning method and rough fuzzy clustering, is proposed to cope with uncertain renewable generation and load demand. The proposed clustering method is capable of recognizing the actual cluster distribution of complex datasets and providing high-quality clustering results. By clustering the hourly data for renewable generation and load demand, a multi-scenario model is proposed to consider the corresponding uncertainties in TNEP. Furthermore, due to the peak distribution characteristics of renewable generation and heavy investment in transmission, the traditional TNEP, which caters to rated renewable power output, is usually uneconomic. To improve the economic efficiency, the multi-objective optimization is incorporated into the multi-scenario TNEP model, while the curtailment of renewable generation is considered as one of the optimization objectives. The solution framework applies a modified NSGA-II algorithm to obtain a set of Pareto optimal planning schemes with different levels of investment costs and renewable generation curtailments. Numerical results on the IEEE RTS-24 system demonstrated the robustness and effectiveness of the proposed approach.
Highlights
With the rapid development of renewable energy recently, renewable generation will be extensively utilized in the smart grid of the near future to mitigate environmental pollution and promote sustainable development
Owing to the stochastic volatility and intermittency of renewable energy, the integration of renewable energy sources would possibly exacerbate the uncertainty of power systems, which brings a series of new challenges to transmission network expansion planning (TNEP)
It is necessary to present an economic and robust TNEP method to deal with renewable energy integration in the smart grid
Summary
With the rapid development of renewable energy recently, renewable generation will be extensively utilized in the smart grid of the near future to mitigate environmental pollution and promote sustainable development. Owing to the stochastic volatility and intermittency of renewable energy, the integration of renewable energy sources would possibly exacerbate the uncertainty of power systems, which brings a series of new challenges to transmission network expansion planning (TNEP). It is necessary to present an economic and robust TNEP method to deal with renewable energy integration in the smart grid. [1] employed methods of stochastic programming and probabilistic constraints for generation and transmission expansion planning with consideration of uncertainties in power system operation. In [2], a chance-constrained programming method was proposed to deal with uncertain load and wind power in TNEP. The stochastic optimization generally requires the accurate probability density function (PDF) of each uncertain parameter as well as the deterministic form of constraints associated with uncertainties, which are
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