Demand-Side Management in a Micro-Grid with Multiple Retailers:A Coalitional Game Approach

This paper deals with the design and analysis of a novel on-line pricing mechanism based on coalitional game theory. The proposed architecture consists of a micro-grid (MG) where the power demand can be fulfilled by multiple competing energy retailers trying to attract consumers by announcing a price in a hierarchical leader-follower structure. The existence of a Stackelberg equilibrium in such game is shown, leading to a guaranteed consumption value given a price. The coalition formation is then extended to a minimum spanning tree game that affects the rational decision of the players involved. The stability analysis for the resulting coalitions is performed and the steps in the game are presented. Simulations provide a comparison of the profits generated by the proposed scheme against a more traditional single retailer scheme, while simultaneously showing convergence towards steady-state equilibrium

Incorporating forecasting and peer-to-peer negotiation frameworks into a distributed model-predictive control approach for meshed electric networks

The continuous integration of renewable energy sources into power networks is causing a paradigm shift in energy generation and distribution with regard to trading and control. The intermittent nature of renewable sources affects the pricing of energy sold or purchased. The networks are subject to operational constraints, voltage limits at each node, rated capacities for the power electronic devices, and current bounds for distribution lines. These economic and technical constraints, coupled with intermittent renewable injection, may pose a threat to system stability and performance. In this article, we propose a novel holistic approach to energy trading composed of a distributed predictive control framework to handle physical interactions, i.e., voltage constraints and power dispatch, together with a negotiation framework to determine pricing policies for energy transactions. We study the effect of forecasting generation and consumption on the overall network's performance and market behaviors. We provide a rigorous convergence analysis for both the negotiation framework and the distributed control. Finally, we assess the impact of forecasting in the proposed system with the aid of testing scenarios

Incorporating forecasting and peer-to-peer negotiation frameworks into a distributed model predictive control approach for meshed electric networks

The continuous integration of renewable energy sources into a power network has caused a paradigm shift in energy generation and distribution. The intermittent nature of renewable sources affects the prices at which energy can be sold or purchased. In addition, the network is subject to operational constraints, voltage limits at each node, rated capacities for the power electronic devices, current bounds for distribution lines; these constraints coupled with intermittent renewable injections may pose a threat to system stability and performance. We propose a distributed predictive controller to handle operational constraints while minimising generation costs, and an agent based market negotiation framework to obtain suitable pricing policies, agreed among participating agents, that explicitly considers availability of energy storage in its formulation. The controller handles the problem of coupled constraints using information exchanges with its neighbours to guarantee their satisfaction. We study the effect of different forecast accuracy have on the overall performance and market behaviours. We provide a convergence analysis for both the negotiation iterations, and its interaction with the predictive controller. Lastly, We assess the impact of the information availability with the aid of testing scenarios

Demand-Side Management in a Micro-Grid with Multiple Retailers: A Coalitional Game Approach

This paper deals with the design and analysis of a novel on-line pricing mechanism based on coalitional game theory. The proposed architecture consists of a micro-grid (MG) where the power demand can be fulfilled by multiple competing energy retailers trying to attract consumers by announcing a price in a hierarchical leader-follower structure. The existence of a Stackelberg equilibrium in such game is shown, leading to a guaranteed consumption value given a price. The coalition formation is then extended to a minimum spanning tree game that affects the rational decision of the players involved. The stability analysis for the resulting coalitions is performed and the steps in the game are presented. Simulations provide a comparison of the profits generated by the proposed scheme against a more traditional single retailer scheme, while simultaneously showing convergence towards steady-state equilibrium

Robust coalitional model predictive control with plug-and-play capabilities

This article presents a distributed implementation of a model predictive controller with information exchange to manage a distributed networked system of coupled dynamic subsystems. We propose a coalitional control method, where local controllers coalesce into clusters to improve performance, as a tool to solve plug-and-play problems. Our main contribution is a tube-based coalitional approach that employs online optimized invariant sets. These sets are instrumental in guaranteeing recursive feasibility and stability when faced with plug-and-play operations, i.e., subsystems joining or leaving the network. We also explore the inherent robustness properties to absorb disturbances not covered by the tubes without the need to group local controllers. Finally, the simulation results show the benefits of our proposed control method

Incorporating forecasting and peer-to-peer negotiation frameworks into a distributed model predictive control approach for meshed electric networks

The continuous integration of renewable energy sources into a power network has caused a paradigm shift in energy generation and distribution. The intermittent nature of renewable sources affects the prices at which energy can be sold or purchased. In addition, the network is subject to operational constraints, voltage limits at each node, rated capacities for the power electronic devices, current bounds for distribution lines; these constraints coupled with intermittent renewable injections may pose a threat to system stability and performance. We propose a distributed predictive controller to handle operational constraints while minimising generation costs, and an agent based market negotiation framework to obtain suitable pricing policies, agreed among participating agents, that explicitly considers availability of energy storage in its formulation. The controller handles the problem of coupled constraints using information exchanges with its neighbours to guarantee their satisfaction. We study the effect of different forecast accuracy have on the overall performance and market behaviours. We provide a convergence analysis for both the negotiation iterations, and its interaction with the predictive controller. Lastly, We assess the impact of the information availability with the aid of testing scenarios