5 research outputs found
An Extended Mean Field Game for Storage in Smart Grids
We consider a stylized model for a power network with distributed local power
generation and storage. This system is modeled as network connection a large
number of nodes, where each node is characterized by a local electricity
consumption, has a local electricity production (e.g. photovoltaic panels), and
manages a local storage device. Depending on its instantaneous consumption and
production rates as well as its storage management decision, each node may
either buy or sell electricity, impacting the electricity spot price. The
objective at each node is to minimize energy and storage costs by optimally
controlling the storage device. In a non-cooperative game setting, we are led
to the analysis of a non-zero sum stochastic game with players where the
interaction takes place through the spot price mechanism. For an infinite
number of agents, our model corresponds to an Extended Mean-Field Game (EMFG).
In a linear quadratic setting, we obtain and explicit solution to the EMFG, we
show that it provides an approximate Nash-equilibrium for -player game, and
we compare this solution to the optimal strategy of a central planner.Comment: 27 pages, 5 figures. arXiv admin note: text overlap with
arXiv:1607.02130 by other author
Regression Monte Carlo for Microgrid Management
We study an islanded microgrid system designed to supply a small village with
the power produced by photovoltaic panels, wind turbines and a diesel
generator. A battery storage system device is used to shift power from times of
high renewable production to times of high demand. We introduce a methodology
to solve microgrid management problem using different variants of Regression
Monte Carlo algorithms and use numerical simulations to infer results about the
optimal design of the grid.Comment: CEMRACS 2017 Summer project - proceedings
Regression Monte Carlo for microgrid management
We study an islanded microgrid system designed to supply a small village with the power produced by photovoltaic panels, wind turbines and a diesel generator. A battery storage system device is used to shift power from times of high renewable production to times of high demand. We build on the mathematical model introduced in [14] and optimize the diesel consumption under a “no-blackout” constraint. We introduce a methodology to solve microgrid management problem using different variants of Regression Monte Carlo algorithms and use numerical simulations to infer results about the optimal design of the grid
Regression Monte Carlo for microgrid management
We study an islanded microgrid system designed to supply a small village with the power produced by photovoltaic panels, wind turbines and a diesel generator. A battery storage system device is used to shift power from times of high renewable production to times of high demand. We build on the mathematical model introduced in [14] and optimize the diesel consumption under a “no-blackout” constraint. We introduce a methodology to solve microgrid management problem using different variants of Regression Monte Carlo algorithms and use numerical simulations to infer results about the optimal design of the grid