Strategies, Methods and Tools for Solving Long-term Transmission Expansion Planning in Large-scale Power Systems

Abstract: Driven by a number of factors, the electric power industry is expected to undergo a paradigm shift with a considerably increased level of variable energy sources. A significant integration of such sources requires heavy transmission investments over geographically wide and large-scale networks. However, the stochastic nature of such sources, along with the sheer size of network systems, results in problems that may become intractable. Thus, the challenge addressed in this work is to design efficient and reasonably accurate models, strategies and tools that can solve large-scale TEP problems under uncertainty. A long-term stochastic network planning tool is developed, considering a multi-stage decision framework and a high level integration of renewables. Such a tool 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. This works by decomposing the original problem into successive optimization phases.One of the modeling challenges addressed in this work 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. Another relevant contribution is a domain-driven clustering process of snapshots which is based on a “moments” technique. Finally, the developed models, methods and solution strategies have been tested on standard and real-life systems. This thesis also presents numerical results of an aggregated 1060-node European network system 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.