Abstract
Many new demand response strategies are emerging for energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable appliances to minimize smart appliance energy costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable costs are negligible.
Highlights
Smart grids are modern electricity infrastructure networks
This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer Intelligent Decision Support Systems (IDSSs)
This paper has presented details of an extensive study into a heuristic scheduling algorithm for use in a consumer IDSS for minimizing smart appliance energy costs
Summary
Smart grids are modern electricity infrastructure networks. They cost-effectively integrate the actions and behaviors of all the connected users in order to ensure safe, sustainable and reliable electricity supply [1]. The emerging smart grid, by use of an Advanced Metering Infrastructure (AMI)—a two way communication infrastructure—can deliver real-time prices of electricity to consumers and simultaneously send back their consumption data to the utility service companies for billing and other purposes [2] This enables consumers to manage energy distribution efficiently by modifying their consumption behavior in line with the pricing signals. Most household consumers buy electricity on flat rate tariff and have no demand response incentives to encourage shifting energy consumption from peak to off peak period Smart pricing mechanisms such as RTP, critical time pricing (CPP), and TOUP could lead to cost-reflective consumption, driven by aspects of the entire supply chain involved in delivering electricity during a certain period of time in a given quantity at a specific location [3]. An automated energy management system or Intelligent Decision Support
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