Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

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.

Multiobjective Scheduling of an Active Distribution Network Based on Coordinated Optimization of Source Network Load

“…In the two-objective optimization problem, the nondominated sorting genetic algorithm 2 (NSGA2) with the crowding distance strategy is usually adopted [42][43][44]. However, in the face of multiobjective optimization problems of three or more objectives, if we continue to use the crowding distance of NSGA2, the convergence and diversity of the algorithm will be problematic, such as an uneven distribution of the Pareto solution on the nondominated layer, resulting in the algorithm falling into a local optimum.…”

Multi-Stage Transmission Network Planning Considering Transmission Congestion in the Power Market

“…The transmission network model based on scenario analysis describes the uncertainties as multiple individual probabilistic scenarios, which reduces the difficulty of solving while considering the coupling of uncertain risks [38]. The uncertainties of renewable generation and load demand are represented by a set of scenarios through rough fuzzy clustering in the flexible transmission network expansion planning of [39]. In [40], the scenario identification index is defined to determine the important scenarios for solving the stochastic transmission expansion planning problem with N-1 contingency analysis.…”

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

“…It should be noted that apart from extracting information about demand patterns, load profiling is an important tool that has been employed in various applications such as load forecasting, retailer profit maximization, scenarios generation for optimization problems, demand side management implementation, load dispatching and others [78,[83][84][85][86][87][88]. The combination of clustering and forecasting system is a promising approach [84].…”