Semi-Cooperative Learning in Smart Grid Agents
Striving to reduce the environmental impact of our growing energy demand creates tough new challenges in how we generate and use electricity. We need to develop Smart Grid systems in which distributed sustainable energy resources are fully integrated and energy consumption is efficient. Customers, i.e., consumers and distributed producers, require agent technology that automates much of their decision-making to become active participants in the Smart Grid. This thesis develops models and learning algorithms for such autonomous agents in an environment where customers operate in modern retail power markets and thus have a choice of intermediary brokers with whom they can contract to buy or sell power. In this setting, customers face a learning and multiscale decision-making problem – they must manage contracts with one or more brokers and simultaneously, on a finer timescale, manage their consumption or production levels under existing contracts. On a contextual scale, they can optimize their isolated selfinterest or consider their shared goals with other agents. We advance the idea that a Learning Utility Management Agent (LUMA), or a network of such agents, deployed on behalf of a Smart Grid customer can autonomously address that customer’s multiscale decision-making responsibilities. We study several relationships between a given LUMA and other agents in the environment. These relationships are semi-cooperative and the degree of expected cooperation can change dynamically with the evolving state of the world. We exploit the multiagent structure of the problem to control the degree of partial observability. Since a large portion of relevant hidden information is visible to the other agents in the environment, we develop methods for Negotiated Learning, whereby a LUMA can offer incentives to the other agents to obtain information that sufficiently reduces its own uncertainty while trading off the cost of offering those incentives. The thesis first introduces pricing algorithms for autonomous broker agents, time series forecasting models for long range simulation, and capacity optimization algorithms for multi-dwelling customers. We then introduce Negotiable Entity Selection Processes (NESP) as a formal representation where partial observability is negotiable amongst certain classes of agents. We then develop our ATTRACTIONBOUNDED- LEARNING algorithm, which leverages the variability of hidden information for efficient multiagent learning. We apply the algorithm to address the variable-rate tariff selection and capacity aggregate management problems faced by Smart Grid customers. We evaluate the work on real data using Power TAC, an agent-based Smart Grid simulation platform and substantiate the value of autonomous Learning Utility Management Agents in the Smart Grid.
- Machine Learning
- Doctor of Philosophy (PhD)