Demand response (DR) is considered one of the most reliable and cost-effective solutions for smoothing the electric demand curve of systems under stress. DR programs encourage customers to make changes in power consumption habits in response to electricity price incentives. A well designed autonomous scheduling system for households that are part of the smart grid can result in numerous benefits to all the players in the electricity market.
Distribution intelligence can be used to anticipate and moderate electricity usage, resulting in lowered production costs. When using this communication network, each entity may send and receive local and global data in a timely fashion, enabling customers to monitor their own electricity usage. Within a smart home, the energy management system is connected to smart appliances, thermostats, and other devices via a home area network (HAN). The HAN balances the electricity demand within the household and prioritizes between appliances and electric devices to modulate electricity usage and to ultimately reduce costs.
With a collection of rich and timely data, players in the power system can make better decisions to improve reliability, to optimize energy usage, and to reduce energy costs for themselves and for the system. Advanced metering infrastructure (AMI) creates ample opportunities to effectively address peak demand periods using pricing incentives, such as in DR programs and time-of-use (ToU) pricing, which ultimately reduce utilities operating costs. Electricity usage is thus reduced during peak hours with appliances and devices operating at other times, ensuring that electricity production is more evenly distributed throughout the day.
This dissertation presents a smart home energy management system (SHEMS) using a limited memory algorithm for bound constrained problems known as L-BFGS-B, along with time-of-use (ToU) pricing to optimize appliance scheduling in a 24-hour period. The allocation of energy resources for each appliance is coordinated by a smart controllable load (SCL) device embedded in the household's smart meter. SCL guarantees automation of the proposed SHEMS and prevents manual participation of customers in demand response (DR) programs. The model is simulated on a population of 247 residential prosumers with solar photovoltaic (PV) systems based on 15-min interval electric load data from a residential community in Austin, TX. After clustering households based on their electricity profiles, the proposed optimization model is performed. Simulation results showed that the proposed autonomous scheduling system reduced cumulative energy consumption for customers across the different clusters. In addition, when households were grouped based on their respective category according to the ToU pricing scheme, the simulation reported a notable decrease in total energy consumption from 65.771 kWh to 44.295 kWh; as well as a reduction in the cumulative cost of energy from $6.550 to $4.393 per day. Simulation results confirmed that the proposed algorithm effectively improved the operational efficiency of the distribution system, reduced power congestion at key times, and decreased electricity costs for prosumers.
|Advisor:||Tanju, Bereket, Eveleigh, Timothy J.|
|Commitee:||Etemadi, Amir, Malalla, Ebrahim, Murphree, Edward L.|
|School:||The George Washington University|
|School Location:||United States -- District of Columbia|
|Source:||DAI-B 79/08(E), Dissertation Abstracts International|
|Subjects:||Engineering, Systems science, Energy|
|Keywords:||Appliance scheduling, Big data, Demand response, Energy management, L-BFGS-B, Time of use|
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