No abstract

Coalitional control is concerned with the management of multi-agent systems where cooperation cannot be taken for granted (due to, e.g., market competition, logistics). This paper proposes a model predictive control (MPC) framework aimed at large-scale dynamically-coupled systems whose individual components, possessing a limited model of the system, are controlled independently, pursuing possibly competing objectives. The emergence of cooperating clusters of controllers is contemplated through an autonomous negotiation protocol, based on the characterization as a coalitional game of the benefit derived by a broader feedback and the alignment of the individual objectives. Specific mechanisms for the cooperative benefit redistribution that relax the cognitive requirements of the game are employed to compensate for possible local cost increases due to cooperation. As a result, the structure of the overall MPC feedback can be adapted online to the degree of interaction between different parts of the system, while satisfying the individual interests of the agents. A wide-area control application for the power grid with the objective of minimizing frequency deviations and undesired inter-area power transfers is used as study case.

No abstract

This paper presents OPEN, an open-source software platform for integrated modelling, control and simulation of smart local energy systems. Electric power systems are undergoing a fundamental transition towards a significant proportion of generation and flexibility being provided by distributed energy resources. The concept of 'smart local energy systems' brings together related strategies for localised management of distributed energy resources, including active distribution networks, microgrids, energy communities, multi-energy hubs, peer-to-peer trading platforms and virtual power plants. OPEN provides an extensible platform for developing and testing new smart local energy system management applications, helping to bridge the gap between academic research and industry translation. OPEN combines features for managing smart local energy systems which are not provided together by existing energy management tools, including multi-phase distribution network power flow, energy market modelling, nonlinear energy storage modelling and receding horizon optimisation. The platform is implemented in Python with an object-oriented structure, providing modularity and allowing it to be easily integrated with thirdparty packages. Case studies are presented, demonstrating how OPEN can be used for a range of smart local energy system applications due to its support of multiple model fidelities for simulation and control. Highlights • Presents the Open Platform for Energy Networks (OPEN), github.com/EPGOxford/OPEN • Integrated modelling, control & simulation framework for smart local energy systems • The object-oriented approach offers modularity, code reuse & extensibility • Development has been motivated by four industry-academic demonstration projects • Case studies demonstrate how OPEN can be extended for new applications

Motivated by electric vehicle charging control problems, we consider multi-agent noncooperative games where, following a data driven paradigm, unmodeled externalities acting on the players' objective functions are represented by means of scenarios. Building upon recent developments in scenario-based optimization, based on the evaluation of the computed solution, we accompany the Nash equilibria of the uncertain game with an a posteriori probabilistic robustness certificate, providing confidence on the probability that the computed solution remains unaffected when a new uncertainty realisation is encountered. The latter constitutes, to the best of our knowledge, the first application of the so-called scenario approach to multi-agent Nash equilibrium problems. The efficacy of our approach is demonstrated in simulation for the charging coordination of an electric vehicle fleet.

No abstract

No abstract

This paper presents a novel receding horizon framework for the power scheduling of flexible electric loads performing heterogeneous periodic tasks. The loads are characterized as price-responsive agents and their interactions are modelled through an infinite-time horizon aggregative game. A distributed control strategy based on iterative better-response updates is proposed to coordinate the loads, proving its convergence and global optimality with Lyapunov stability tools. Robustness with respect to variations in the number and tasks of players is also ensured. Finally, the performance of the control scheme is evaluated in simulation, coordinating the daily battery charging of a large fleet of electric vehicles.