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Felix Rey

Bio: Felix Rey is an academic researcher from ETH Zurich. The author has contributed to research in topics: Model predictive control & Resource allocation. The author has an hindex of 6, co-authored 16 publications receiving 81 citations. Previous affiliations of Felix Rey include Boston Consulting Group & Karlsruhe Institute of Technology.

Papers
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Proceedings ArticleDOI
12 Jun 2018
TL;DR: A model predictive control framework for a group of agents with linear dynamics with convex state and input constraints is considered and an algorithm that, if applied by all agents, mediates individual objectives while satisfying constraints is developed.
Abstract: We utilize the alternating direction method of multipliers (ADMM) to devise a communication and control protocol for fully decentralized coordination of moving agents. In particular, we consider a model predictive control (MPC) framework for a group of agents. Each agent has linear dynamics with convex state and input constraints. Nonconvex collision avoidance constraints constitute inter-agent coupling. We develop an algorithm that, if applied by all agents, mediates individual objectives while satisfying constraints. The resulting procedure exhibits several attractive features, including (i) fully decentralized, parallel, and aggregator-free operation, where each agent is only aware of its closest neighbors; (ii) adaptive linearization for handling the nonconvex collision avoidance constraints; and (iii) the treatment of uncooperative agents.

25 citations

Proceedings ArticleDOI
10 Jul 2019
TL;DR: In this paper, the authors consider a resource allocation problem over an undirected network of agents, where edges of the network define communication links, and derive two methods by applying the alternating direction method of multipliers (ADMM) for decentralized consensus optimization.
Abstract: We consider a resource allocation problem over an undirected network of agents, where edges of the network define communication links. The goal is to minimize the sum of agent-specific convex objective functions, while the agents' decisions are coupled via a convex conic constraint. We derive two methods by applying the alternating direction method of multipliers (ADMM) for decentralized consensus optimization to the dual of our resource allocation problem. Both methods are fully parallelizable and decentralized in the sense that each agent exchanges information only with its neighbors in the network and requires only its own data for updating its decision. We prove convergence of the proposed methods and demonstrate their effectiveness with a numerical example.

25 citations

Journal ArticleDOI
TL;DR: In this article, an aggregation of buildings that places a joint bid on a reserve market is modeled as a large-scale optimization problem, where the aggregation can make its decision in a computationally efficient and conceptually meaningful way, using the alternating direction method of multipliers.
Abstract: In a power grid, the electricity supply and demand must be balanced at all times to maintain the system’s frequency. In practice, the grid operator achieves this balance by procuring frequency reserves in an ahead-of-time market setting. During runtime, these reserves are then dispatched whenever there is an imbalance in the grid. Recently, there has been an increasing interest in engaging electricity consumers, such as plug-in electric vehicles or buildings, to offer such frequency reserves by exploiting their flexibility in power consumption. In this paper, we focus on an aggregation of buildings that places a joint bid on a reserve market. The resulting shared decision is modeled as a large-scale optimization problem. Our main contribution is to show that the aggregation can make its decision in a computationally efficient and conceptually meaningful way, using the alternating direction method of multipliers. The proposed approach exhibits several attractive features that include: ${(i)}$ the computational burden is distributed between the buildings; ${(ii)}$ the setup naturally provides privacy and flexibility; ${(iii)}$ the iterative algorithm can be stopped at any time, providing a feasible (though suboptimal) solution; and ${(iv)}$ the algorithm provides the foundation for a reward distribution scheme that strengthens the group.

24 citations

Journal ArticleDOI
01 Jan 2019
TL;DR: A novel matrix approach is introduced, namely the generalized Peano–Baker series, which is comparable to the transition matrix in the case of ordinary systems, and the solution of the time-variant fractional pseudo state space equation is derived.
Abstract: Time-variant fractional systems have many applications. For example, they can be used for system identification of lithium-ion batteries. However, the analytical solution of the time-variant fractional pseudo state space equation is missing so far. To overcome this limitation, this letter introduces a novel matrix approach, namely the generalized Peano-Baker series, which is comparable to the transition matrix in the case of ordinary systems. Using this matrix, the solution of the time-variant fractional pseudo state space equation is derived. The initialization process is taken into account, which has been proven to be a crucial part for fractional operator calculus. Following this initialization, a modified definition of a fractional pseudo state is presented.

15 citations

Proceedings ArticleDOI
TL;DR: In this article, the authors consider a resource allocation problem over an undirected network of agents, where edges of the network define communication links, and derive two methods by applying the alternating direction method of multipliers (ADMM) for decentralized consensus optimization.
Abstract: We consider a resource allocation problem over an undirected network of agents, where edges of the network define communication links. The goal is to minimize the sum of agent-specific convex objective functions, while the agents' decisions are coupled via a convex conic constraint. We derive two methods by applying the alternating direction method of multipliers (ADMM) for decentralized consensus optimization to the dual of our resource allocation problem. Both methods are fully parallelizable and decentralized in the sense that each agent exchanges information only with its neighbors in the network and requires only its own data for updating its decision. We prove convergence of the proposed methods and demonstrate their effectiveness with a numerical example.

12 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, a large-scale demand response demonstration involving a population of more than 300 residential buildings with heat pumps is presented, and the results of various experiments illustrate that load reductions of 40-65% of the total load can be achieved by throttling the heat pumps, and that these load reductions can be delivered precisely with a median absolute percentage error of below 7%.

44 citations

Journal ArticleDOI
TL;DR: The results from this evaluation confirm the effectiveness of the proposed bidding strategies and the AI-based bidding optimization framework in terms of cumulative revenue generation, leading to an increased availability of frequency reserves.

32 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a community-based market mechanism, in which prosumers can pool their heat and electricity production and consumption, and coordinate their participation in the wholesale markets.
Abstract: In an increasingly decentralized energy system with tight interdependencies with heat and electricity markets, prosumers, who act both as consumers and producers at the interface between the markets, are becoming important operational flexibility providers. Policy-makers and market operators need a better understanding of the motivations and behavior of prosumers in order to harness their underlying flexibility. In that context, existing market mechanisms must be accompanied by decision-making tools which enable direct participation of prosumers and cooperation among them towards a social choice. This work focuses on designing a community-based market mechanism, in which prosumers can pool their heat and electricity production and consumption, and coordinate their participation in heat and electricity wholesale markets. A central research question that is addressed, is to understand how this mechanism can affect the outcomes of the interactions among individuals towards a social choice, and in particular incentivize cooperation among prosumers. Game-theoretic concepts are used to analyze the properties of the proposed market mechanism with different allocation schemes, namely uniform pricing, Vickrey-Clarke-Groves, Shapley value, and nucleolus. This analysis shows that it is beneficial for the community as a whole to cooperate and that there exists a set of stable allocations for the proposed mechanism. Additionally, although no allocation can satisfy all fundamental desirable market properties, this study demonstrates that the proposed mechanism based on a nucleolus allocation can provide an interesting trade-off between stability, efficiency, and incentive compatibility. Finally, the concepts and properties discussed in this work are illustrated in a case study.

30 citations

Proceedings ArticleDOI
12 Jun 2018
TL;DR: A model predictive control framework for a group of agents with linear dynamics with convex state and input constraints is considered and an algorithm that, if applied by all agents, mediates individual objectives while satisfying constraints is developed.
Abstract: We utilize the alternating direction method of multipliers (ADMM) to devise a communication and control protocol for fully decentralized coordination of moving agents. In particular, we consider a model predictive control (MPC) framework for a group of agents. Each agent has linear dynamics with convex state and input constraints. Nonconvex collision avoidance constraints constitute inter-agent coupling. We develop an algorithm that, if applied by all agents, mediates individual objectives while satisfying constraints. The resulting procedure exhibits several attractive features, including (i) fully decentralized, parallel, and aggregator-free operation, where each agent is only aware of its closest neighbors; (ii) adaptive linearization for handling the nonconvex collision avoidance constraints; and (iii) the treatment of uncooperative agents.

25 citations

Proceedings ArticleDOI
10 Jul 2019
TL;DR: In this paper, the authors consider a resource allocation problem over an undirected network of agents, where edges of the network define communication links, and derive two methods by applying the alternating direction method of multipliers (ADMM) for decentralized consensus optimization.
Abstract: We consider a resource allocation problem over an undirected network of agents, where edges of the network define communication links. The goal is to minimize the sum of agent-specific convex objective functions, while the agents' decisions are coupled via a convex conic constraint. We derive two methods by applying the alternating direction method of multipliers (ADMM) for decentralized consensus optimization to the dual of our resource allocation problem. Both methods are fully parallelizable and decentralized in the sense that each agent exchanges information only with its neighbors in the network and requires only its own data for updating its decision. We prove convergence of the proposed methods and demonstrate their effectiveness with a numerical example.

25 citations