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Peer-to-peer market design for future energy communities
In this project we aim at designing a peer-to-peer market for future energy communities. We will assess its capability to provide ancillary services to the main grid (i.e. demand response, peak shaving) and compare it to current market design in order to provide suggestions to policy makers.
Keywords: Peer-to-peer market design - Distributed optimization over network - Game theory
The increasing integration of renewable energy sources and energy storage technologies in
the power network is calling for a rethinking of the structure of the electricity markets and,
subsequently, of the interactions among prosumers, DSOs and TSOs. Recently a bottomup paradigm, called energy community, has emerged as a way to foster environmental,
economic and social benefits in the power grid. An energy community is composed of a
group of prosumers, i.e. distributed energy sources, which may exchange energy both locally via P2P energy trading, and globally ’interacting’ with the main grid. With respect
to the hierarchical centralized and profit-centric view of the actual energy market, energy
communities call for a decentralized social-centric energy market. Moreover, new types of
interaction between the entities are required to assure the stability of the grid. In particular, the new users involved in the communities have to provide some ancillary services to
the main grid, such as participating in demand response programs, i.e., providing flexibility
when needed in exchange for a reward. It is interesting to study the different effects generated by supplying these services as a community, i.e., via cooperation, or as individuals,
i.e., as non-cooperating individuals. In this thesis, we will investigate these novel concepts
via a game-theoretic framework which can account for P2P trading inside the community,
as well as, for energy exchange between communities and main grid. To account for the computational aspect and the users’ privacy, we aim at developing distributed algorithms to solve the arising games. The different types of interactions are modelled via
different network structures. We assume the presence of community
managers that directly communicate with the DSO and may be used to enforce coordination among prosumers. Extensive numerical simulations will be performed to compare the
various market structures and evaluate whether they are suitable for solving the demand
response problem. Then, their fairness and efficiency is assessed. These results will be of
great value for a policy maker that has the task of designing the rules of the market.
The increasing integration of renewable energy sources and energy storage technologies in the power network is calling for a rethinking of the structure of the electricity markets and, subsequently, of the interactions among prosumers, DSOs and TSOs. Recently a bottomup paradigm, called energy community, has emerged as a way to foster environmental, economic and social benefits in the power grid. An energy community is composed of a group of prosumers, i.e. distributed energy sources, which may exchange energy both locally via P2P energy trading, and globally ’interacting’ with the main grid. With respect to the hierarchical centralized and profit-centric view of the actual energy market, energy communities call for a decentralized social-centric energy market. Moreover, new types of interaction between the entities are required to assure the stability of the grid. In particular, the new users involved in the communities have to provide some ancillary services to the main grid, such as participating in demand response programs, i.e., providing flexibility when needed in exchange for a reward. It is interesting to study the different effects generated by supplying these services as a community, i.e., via cooperation, or as individuals, i.e., as non-cooperating individuals. In this thesis, we will investigate these novel concepts via a game-theoretic framework which can account for P2P trading inside the community, as well as, for energy exchange between communities and main grid. To account for the computational aspect and the users’ privacy, we aim at developing distributed algorithms to solve the arising games. The different types of interactions are modelled via different network structures. We assume the presence of community managers that directly communicate with the DSO and may be used to enforce coordination among prosumers. Extensive numerical simulations will be performed to compare the various market structures and evaluate whether they are suitable for solving the demand response problem. Then, their fairness and efficiency is assessed. These results will be of great value for a policy maker that has the task of designing the rules of the market.
1. The student will perform a literature overview on peer-to-peer market trading and on
the concept of energy community.
2. The student will develop distributed algorithm schemes for the local energy market of
energy communities considering different network structures.
3. The student will implement the algorithms in simulation for demand response purposes.
4. The student will compare the performance of the various network in terms of profit
maximization, capability of providing ancillary services to the main grid, fairness and
efficiency. We will use a centralized algorithm as benchmark case.
5. The student will write the report and prepare a presentation
1. The student will perform a literature overview on peer-to-peer market trading and on the concept of energy community.
2. The student will develop distributed algorithm schemes for the local energy market of energy communities considering different network structures.
3. The student will implement the algorithms in simulation for demand response purposes.
4. The student will compare the performance of the various network in terms of profit maximization, capability of providing ancillary services to the main grid, fairness and efficiency. We will use a centralized algorithm as benchmark case.
5. The student will write the report and prepare a presentation
Interested Master students with solid mathematical foundations and good programming
skills (either in Matlab or Python) are encouraged to apply. Familiarity with game theory
and distributed optimization is considered a plus.
To apply send CV and updated transcripts of records (BSc and MSc) to:
Dr. Carlo Cenedese (ccenedese@ethz.ch)
Marta Fochesato (mfochesato@ethz.ch)
Interested Master students with solid mathematical foundations and good programming skills (either in Matlab or Python) are encouraged to apply. Familiarity with game theory and distributed optimization is considered a plus.
To apply send CV and updated transcripts of records (BSc and MSc) to: