Abstract
Transmission expansion planning is a problem of considerable complexity where classical optimization techniques are unable to handle large case studies. Decomposition and divide-and-conquer strategies have been applied to this problem. We propose an alternative approach based on agent-based modeling (ABM) and inspired by the behavior of the Plasmodium mold, which builds efficient transportation networks as result of its search for food sources. Algorithms inspired by this mold have already been applied to road-network design. We modify an existing ABM for road-network design to include the idiosyncratic features of power systems and their related physics, and test it over an array of case studies. Our results show that the ABM can provide near-optimal designs in all the instances studied, possibly with some further interesting properties with respect to the robustness of the developed design. In addition, the model works in a decentralized manner, using mostly local information. This means that computational time will scale with size in a more benign way than global optimization approaches. Our work shows promise in applying ABMs to solve similarly complex global optimization problems in the energy landscape.
Highlights
Transmission expansion planning (TEP) decides the optimal reinforcements for the transmission grid
We present an agent-based modeling (ABM) inspired by the behavior of the slime mold to solve TEP
We start out by comparing the newly developed mold-inspired ABM to classical optimization methods applied to the transmission expansion planning problem
Summary
Transmission expansion planning (TEP) decides the optimal reinforcements for the transmission grid. Solving this problem presents considerable computational complexity, given the very high number of discrete variables that result from taking into account all the possible reinforcements. This issue has been exacerbated by the increase in renewable penetration. The variability of renewables could be balanced by using long-range transmission across geographical regions This means that larger areas need to be planned in a coordinated manner. This is highly problematic, as computing time scales badly with size for these optimization problems. We propose an alternative approach: agent-based modeling (ABM)
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