|Speaker :||Diego Kiedanski|
|Time:||3:00 pm - 6:00 pm|
To meet carbon reduction goals in Europe but worldwide too, a large number of renewable distributed energy resources (DER) still need to be deployed.
Aiming at mobilizing private capital, several plans have been developed to put end-customers at the heart of the energy transition, hoping to accelerate the adoption of green energy by increasing its attractiveness and profitability.
Some of the proposed models include the creation of local energy markets where households can sell their energy to their neighbors at a higher price than what the government would be willing to pay (but lower than what other customers normally pay), shared investment models in which consumers own a carbon-free power plant such as a wind turbine or a solar farm and they obtain dividends from its production to collective auto-consumption models in which several families are ‘hidden’ behind the same smart meter, allowing them to optimize their aggregated consumption profile and therefore maximizing the value of their DER.
One of the main objectives of the thesis is to understand these different incentives as they will play a crucial role in tackling climate change if correctly implemented. To do so, we design a framework for ‘local energy trading’ that encompasses a large number of incentives.
In the context of local energy trading, we study the interactions of prosumers (consumers with generation capabilities) located in the same Low Voltage network, possibly behind the same feeder. These prosumers will still be connected to the main power grid and they will have the option, as they do today, to buy and sell to/from their utility company at a fixed price (a flat rate or a Time-of-Use, for example). For these agents to fully benefit from the advantages of local energy trading, we shall assume that they own appliances (such as batteries) that, without changing their perceived energy demand, can enable them to change their net energy demand as seen from outside their homes. Modeling prosumers as rational utility maximizers, they will schedule their battery to decrease the cost associated with their net energy demand (as their perceived demand remains unchanged).
In the first part of the thesis, we investigate competitive models in which prosumers sell their surplus to their neighbors via a local energy market. We analyze different strategies that players could use to participate in these markets and their impact on the normal operation of the power grid and the Distribution System Operator. In this regard, it is shown that sequential markets can pose a problem to the system and a new market mechanism that exploits domain knowledge is proposed to increase the efficiency of the local trades.
In the second part of the thesis, we delve into incentives that can be implemented through cooperation. In this regard, we use cooperative game theory to model the shared investment into energy storage and photovoltaic panels (PV) by a group of prosumers. For the studied model we show that a stable solution (in the core of the game) exists in which all participants cooperate and we provide an efficient algorithm to find it. Furthermore, we also show that cooperation is stable for participants that already own batteries and PVs but prefer to operate them in coordination to increase their value, effectively implementing collective auto-consumption.
Finally, we demonstrate how to integrate both models: the shared investment and the cooperative control of existing resources into a single cooperative framework which also enjoys the existence of stable outcomes. For this later model, we propose to decouple the return over investments (ROI) obtained between the ROI produced by the investment in hardware and the ROI obtained by cooperation itself. By doing so, we can offer the former profit to external investors to raise the required capital (although nothing forbids the member of the coalition to contribute) and the latter to the actual consumers.
Here’s the streaming link to follow: https://telecom-paris.zoom.us/j/99499019699?pwd=anB2STVlWWZ2YnUzZ015bEh1dU5CUT09
– ID de réunion : 994 9901 9699
– Code secret : 371226