Strategies, methods and tools for solving long-term transmission expansion planning in large-scale power systems

Abstract

Driven by several techno-economic, environmental and structural factors, the electric energy industry is expected to undergo a paradigm shift with a considerably increased level of renewables (mainly variable energy sources such as wind and solar), gradually replacing conventional power production sources. The scale and the speed of integrating such sources of energy are of paramount importance to effectively address a multitude of global and local concerns. In recent years, wind and solar power have been attracting large-scale investments in many countries, especially in Europe. Renewables, wind and solar in particular, are abundant almost everywhere although their energy intensities differ very much from one place to another. Because of this, a significant integration of such energy sources requires heavy investments in transmission infrastructures, spanning over geographically wide and large-scale networks. However, the stochastic nature of such resources, along with the size of the network systems, results in optimization problems that may become intractable in practice. Thus, the challenge addressed in this work is to design models, strategies and tools that may solve large-scale and uncertain TEP problems, being computationally efficient and reasonably accurate. Of course, the specific definition of the term “reasonably accurate” is the key issue of the thesis work since it requires deep understanding of the main cost and technical drivers of adequate TEP investment decisions. A new stochastic formulation is proposed for a long-term planning of transmission investments under uncertainty, with a multi-stage decision framework and considering a high level of renewable sources. The proposed model combines the need for short-term decisions with the evaluation of long-term scenarios, which is the practical essence of a real-world planning. Furthermore, in order to significantly reduce the combinatorial solution search space, a specific heuristic solution strategy is devised. The global solution strategy works by decomposing the original problem into successive optimization phases. Each optimization phase can be defined and solved as an independent problem; thus, allowing the use of specific decomposition techniques, or parallel computation whenever possible. One of the first modeling challenges of the type of problem addressed in this thesis is to select the right network model for power flow and congestion evaluation: complex enough to capture the relevant features but simple enough to be computationally fast. The thesis includes extensive analysis of existing and improved network models. In addition, this work analyzes alternative losses models. Some of them are already available and others are proposed as original contributions of the thesis. These models are evaluated in the context of a large-scale TEP problem subject to a significant RES integration. Another relevant contribution of this work is a domain-driven clustering process to handle operational states, allowing a more compact and efficient representation of uncertainty with little loss of accuracy. This thesis shows how the snapshot reduction can be achieved by means of clustering based on a “moments” technique, a well-known approach in Optical Pattern Recognition problems. The developed models, methods and solution strategies have been tested on small-, medium- and large-scale network systems. This thesis also presents numerical results of an aggregated 1060-node European network system obtained considering multiple RES development scenarios. Generally, test results show the effectiveness of the proposed TEP model, since—as originally intended—it contributes to a significant reduction in computational effort while fairly maintaining optimality of the solutions.