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A Reinforcement Learning Approach to Setting Multi-Objective Goals for Energy Demand Management

A Reinforcement Learning Approach to Setting Multi-Objective Goals for Energy Demand Management

Ying Guo (CSIRO ICT Centre North, Australia), Astrid Zeman (CSIRO ICT Centre North, Australia), and Rongxin Li (CSIRO ICT Centre North, Australia)
Copyright: © 2009 |Pages: 16
DOI: 10.4018/jats.2009040104
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Abstract

In order to cope with the unpredictability of the energy market and provide rapid response when supply is strained by demand, an emerging technology, called energy demand management, enables appliances to manage and defer their electricity consumption when price soars. Initial experiments with our multi-agent, power load management simulator, showed a marked reduction in energy consumption when price-based constraints were imposed on the system. However, these results also revealed an unforeseen, negative effect: that reducing consumption for a bounded time interval decreases system stability. The reason is that price-driven control synchronizes the energy consumption of individual agents. Hence price, alone, is an insufficient measure to define global goals in a power load management system. In this article we explore the effectiveness of a multi-objective, system-level goal which combines both price and system stability. We apply the commonly known reinforcement learning framework, enabling the energy distribution system to be both cost saving and stable.

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