Mark Trayer

Automated Residential Demand Response: Algorithmic Implications of Pricing Models

Smart energy management is an important problem in Smart Grid network, and demand response (DR) is one of the key enabling technologies. If each home uses automated demand response which would opportunistically schedule devices that are flexible to run at any time in a large time window, towards the slots with lower electricity prices, peaks at these slots may happen. We denote such peaks as rebound peaks. We address the potential rebound peak problems of automated DR algorithms, and provide possible solutions. We illustrate why a rebound peak is possible via the insights we obtain from the optimal automated DR algorithm. We show that if the utility electricity supply cost is assumed to be a homogeneous function in the energy consumption over a certain time span, a system of multiple homes and utility company has the lowest total electricity supply cost if the electricity consumption from all the homes is flat over the time span. We study multiple approaches to reduce the rebound peak, and accordingly propose algorithms for DR at each home. Effectiveness of the approaches is verified by numerical results.

Automated residential demand response: Algorithmic implications of pricing models

Smart energy management is an important problem in Smart Grid network, and demand response (DR) is one of the key enabling technologies. If each home uses automated demand response which would opportunistically schedule devices that are flexible to run at any time in a large time window, towards the slots with lower electricity prices, peaks at these slots may happen. We denote such peaks as rebound peaks. We address the potential rebound peak problems of automated DR algorithms, and provide possible solutions. We illustrate why a rebound peak is possible via the insights we obtain from the optimal automated DR algorithm. We show that if the utility electricity supply cost is assumed to be a homogeneous function in the energy consumption over a certain time span, a system of multiple homes and utility company has the lowest total electricity supply cost if the electricity consumption from all the homes is flat over the time span. We study multiple approaches to reduce the rebound peak, and accordingly propose algorithms for DR at each home. Effectiveness of the approaches is verified by numerical results.

Training-free non-intrusive load monitoring of electric vehicle charging with low sampling rate

Non-intrusive load monitoring (NILM) is an important topic in smart-grid and smart-home. Many energy disaggregation algorithms have been proposed to detect various individual appliances from one aggregated signal observation. However, few works studied the energy disaggregation of plug-in electric vehicle (EV) charging in the residential environment since EVs charging at home has emerged only recently. Recent studies showed that EV charging has a large impact on smart-grid especially in summer. Therefore, EV charging monitoring has become a more important and urgent missing piece in energy disaggregation. In this paper, we present a novel method to disaggregate EV charging signals from aggregated real power signals. The proposed method can effectively mitigate interference coming from air-conditioner (AC), enabling accurate EV charging detection and energy estimation under the presence of AC power signals. Besides, the proposed algorithm requires no training, demands a light computational load, delivers high estimation accuracy, and works well for data recorded at the low sampling rate 1/60 Hz. When the algorithm is tested on real-world data recorded from 11 houses over about a whole year (total 125 months worth of data), the averaged error in estimating energy consumption of EV charging is 15.7 kwh/month (while the true averaged energy consumption of EV charging is 208.5 kwh/month), and the averaged normalized mean square error in disaggregating EV charging load signals is 0.19.

Architecture model choices for a Smart Grid home network

Different architectural models for a Smart Grid home network, based on centralized and distributed control options, are proposed in order to enable the integration of smart devices into a Utilitys demand/response infrastructure. The models explore the role of the home gateway and the smart meter for enabling the Smart Grid home architecture. This paper evaluates the models using a wide range of criteria such as scalability, security, demand response control, privacy, integration of the home local area network and flexibility. Architecture issues related to standardization and regulations and their impact on the models are also discussed.