23d (edited) • 💻 Applied Modeling
New videos: Electric Vehicles demand, costs & minimax regret
5 new videos are now inside the Classroom; specifically inside online course 120. This course focuses on electricity distribution planning under an uncertain number of electric vehicles (EV) in the grid.
The course applies the Stochastic Planning and the Minimax Regret frameworks to this case study. Both frameworks are used in practice for finding the optimal ways to reinforce the electricity grid when there is uncertainty.
Each video comes with downloadable Powerpoint slides, Excel files etc.
A brief description of each of the 5 new videos follows:
- The first video (11 minutes) shows how to calculate the peak electricity demand caused by electric vehicles, in an electricity distribution grid. The calculations, the assumptions, and the structure are identical to what was used in practice. As a reminder, course 120 examines Stochastic Optimization, and Minimax Regret, which are known as "planning frameworks" and are used for finding the optimal investment decisions needed in the grid, when there is uncertainty.
- The second video (13 minutes) focuses on the social cost. Specifically , costs are of two types: investment cost and operational cost. The operational cost includes a cost of unserved energy i.e. the welfare loss incurred when electricity demand cannot be served because electricity network lines lack sufficient capacity. Because this cost reflects the welfare loss borne by end-users, we refer to it as a social cost of unserved energy. In this course we focus on the social cost associated with unserved EV charging demand. This cost can be expressed equivalently in £/MWh (per unit of unserved charging electricity) or in £/EV (per vehicle unable to charge). The two units are related through the per-EV charging energy requirement; this video shows how to convert between them.
- The third video (11 minutes) shows how to find the number of EVs that cannot be charged with electricity due to network constraints. The social cost of unserved EV charging (in £/MWh) is an input parameter to the optimization. When it is set to a low value, the weight placed on avoiding unserved demand in the objective becomes small relative to the marginal cost of network reinforcement. The optimizer then finds it cost-optimal to defer some line upgrades and accept a positive level of unserved EV charging demand at the optimum; conversely, a high social cost drives earlier and more extensive reinforcement. The video shows for example that in a bus, a total of 0.91 MW of EV peak demand is unserved and it shows how to find that this corresponds to 131 EVs unable to charge.
- The fourth and fifth videos explain the minimax regret framework and how to calculate the table of economic regrets. The idea is that the network planner assumes that a specific scenario will happen in the future (e.g. lots of EVs in the system) and makes investments according to it (e.g. spends much money to reinforce the grid in expectation of the many EVs), only to see that another scenario occurred (e.g. only a few EVs actually in the system) and so, most investments turn out to be unnecessary . This extra cost is the 'economic regret' and this is the basis of the minimax regret framework.
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New videos: Electric Vehicles demand, costs & minimax regret
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