Abstract
Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer’s personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attributes. We apply a spectral relaxation clustering approach to show distinct groups of households within the two most used dynamic pricing schemes: Time-Of-Use and Real-Time Pricing. The results indicate that a more effective design of smart home energy management systems can lead to a better fit between customer and electricity tariff in order to reduce costs, enhance predictability and stability of load and allow for more optimal use of demand flexibility by such systems.
Highlights
The energy business is going through a series of swift and radical transformations to meet the growing demands for sustainable energy
Our setting is a natural experiment, involving real-world customers of a national utility company participating via dynamic pricing contracts
Dependingto onthe andgranularity optimallyoftarget them according preferences. Asand these may vary according the contract, home energy management systems can guide the customer or act upon his preferences heterogeneity amongst customers depending on household attributes, we aim to identify behaviors of and beliefs in orderatohome optimize his demand profile,system where in monetary incentives are given by dynamic customers operating energy management dynamic price settings
Summary
The energy business is going through a series of swift and radical transformations to meet the growing demands for sustainable energy. The future of the energy sector will, to a large extent, be formed by a transformation in the electricity sector, posing challenges for traditional electrical power systems. This shift is of a complex nature, but offers ample opportunities for business and information analytics to support the transition [1]. The electricity grid faces decentralized production from renewable sources, electric mobility, and related advances. These are at odds with traditional power systems, where central large-scale generation of electricity follows inelastic consumer demand. The non-storability and volatile aspect of sustainable energy sources and the required shift from a demand-driven to a supply-driven market means the energy transition requires system flexibility from all market participants
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