Adaptive machine learning for automated modeling of residential prosumer agents

An efficient participation of prosumers in power system management depends on the quality of information they can obtain. Prosumers actions can be performed by automated agents that are operating in time-changing environments. Therefore, it is essential for them to deal with data stream problems in order to make reliable decisions based on the most accurate information. This paper provides an in-depth investigation of data and concept drift issues in accordance with residential prosumer agents. Additionally, the adaptation techniques, forgetting mechanisms, and learning strategies employed to handle these issues are explored. Accordingly, an approach is proposed to adapt the prosumer agent models to overcome the gradual and sudden concept drift concurrently. The suggested method is based on triggered adaptation techniques and performance-based forgetting mechanism. The results obtained in this study demonstrate that the proposed approach is capable of constructing efficient prosumer agents models with regard to the concept drift proble

Prevision and planning for residential agents in a transactive energy environment

Transactive Energy (TE) has brought exciting opportunities for all stakeholders in energy markets by enabling management decentralization. This new paradigm empowers demand-side agents to play a more active role through coordinating, cooperating, and negotiating with other agents. Nevertheless, most of these agents are not used to process market signals and develop optimal strategies, especially in the residential sector. Accordingly, it is indispensable to create tools that automate and facilitate demand-side participation in TE systems. This paper presents a new methodology for residential automated agents to perform two key tasks: prevision and planning. Specifically, the proposed method is applied to a forward market where agents' planning is a fundamental step to maintain the dynamic balance between demand and generation. Since planning depends on future demand, agents' prevision of consumption is an inevitable part of this step. The procedures for automating the targeted tasks are developed in a general way for residential prosumers and consumers, interacting at the distribution level. These players are managed by a demand aggregator as the leader by means of the Stackelberg game. The suggested process results in a TE setup for multi-stage single-side auctions, useful to manage future Smart Energy Markets. Through simulated transactions, this paper examines the market clearing mechanism and the convenience of agents' planning. The results show that customers with higher price-elasticity leverage lower costs periods. However, they make it harder to reduce the peak-to-average ratio of the aggregated demand profile since a unique price signal can create prisoner's dilemma conditions

Consensus and disagreement in collective homing problems: a mean field games formulation

Abstract — Inspired by successful biological collective decision mechanisms such as honey bees searching for a new colony or the collective navigation of fish schools, we consider a mean field games (MFG) scenario producing decentralized homing decisions in large multi-agent systems. For our setup, we show that given an initial distribution of the agents, many strategies exist, with each one of them defining an −Nash equilibrium. These strategies, on which the processes of consensus and dis-agreement within the group depend, collapse into one strategy as the number of agents goes to infinity. I