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Study mode:
on
1
Intro
2
Motivations
3
Outline
4
Energy arbitrage with storage
5
Modelling: electricity market
6
Mean Field Game Formulation
7
Numerical resolution
8
Cyclic constraints
9
Implementation
10
Case study (2)
11
Model extensions
12
Thermostatically Controlled Loads
13
Main Features
14
Modelling: Individual TCL
15
Modelling: TCL population
16
Modelling: Unit Commitment
17
Summary
18
Transmission Expansion
19
Network Representation
20
Centralized Network Expansion
21
Game with continuum of players
22
Objective function
23
Merchant planning solution
24
Conclusions
Description:
Explore the application of Mean Field Games (MFG) in distributed control solutions for future power networks in this 41-minute workshop video from the Alan Turing Institute. Delve into the basic theory of MFG and its applications to optimization problems in the energy industry and environmental sciences. Learn about energy arbitrage with storage, electricity market modeling, and numerical resolution techniques. Examine case studies on cyclic constraints and model extensions for Thermostatically Controlled Loads (TCLs). Investigate transmission expansion planning, including network representation, centralized network expansion, and merchant planning solutions. Gain insights from leading experts in the field and discover how MFG can be applied to high-dimensional distributed optimization problems in various domains, from energy production and storage optimization to climate change negotiations modeling.

Mean Field Games and Smart Grids - Distributed Control Solutions for the Future Power Network

Alan Turing Institute
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