Wei-Peng Chen

Customer-centric energy usage data management and sharing in smart grid systems

A recent study revealed that fine-grained energy usage data could potentially leak sensitive information about an electricity customer. On the other hand, a number of online service providers utilizing such data have emerged and improved effectiveness of smart grid technologies (for instance, demand-response aggregators), and therefore sharing of data is getting popular. In this work, we propose a customer-centric framework to manage, store, and share energy usage data in a privacy-enhanced way. We present a mechanism to enable customers to flexibly control the amount of energy usage information disclosed while still allowing third-party service providers to be convinced of the authenticity of data. A prototype implementation using the widely used Green Button data model is presented and evaluated. We further discuss the design of a demand-response aggregation service on top of the proposed framework.

Enhancing Demand Response signal verification in automated Demand Response systems

Demand Response (DR) is a promising technology for meeting the worlds ever increasing energy demands without corresponding increase in energy generation, and for providing a sustainable alternative for integrating renewables into the power grid. As a result, interest in automated DR is increasing globally and has led to the development of OpenADR, an internationally recognized standard. In this paper, we propose security-enhancement mechanisms to provide DR participants with verifiable information that they can use to make informed decisions about the validity of received DR event information.

Residential demand response system framework leveraging IoT devices

While large-scale residential demand response (DR) has been considered promising because of the significant flexibility for controlling electricity demand, customer acquisition and engagement are the major barriers hindering its penetration. To overcome the challenges, we propose a comprehensive DR system framework both for utilities to support multiple DR programs and a wide range of controllable resources, i.e., a variety of Internet of Things (IoT) devices, in residential households and for customers to enjoy two-way, timely communication with utility companies and centralized control over heterogeneous IoT devices in their households (i.e., smart appliances), which facilitates their DR participation. As the key enabler of these advantages, we propose the use of mobile app DR client. Pilot-ready prototypes of the mobile app and DR server are demonstrated and evaluated.

SPOT: a smartphone-based control app with a device-agnostic and adaptive user-interface for IoT devices

The recent progress of IoT technologies, including broad penetration of smart appliances such as remotely controllable lights, thermostats and cameras, have changed the way we interact with the appliances in our homes and perform our daily activities. However, the significant heterogeneity in the emerging IoT devices has led to fragmented smart-home systems in which each single appliance vendor provides proprietary solution for appliance specific connectivity and user experience. One of the desired solutions in smart-home systems is to have a unified smartphone app that can control any arbitrary IoT appliances. In this extended abstract we focus on the design of such an app only from the user interaction viewpoint. In particular, we present SPOT app, a smartphone-based platform for multi-vendor smart-home appliances, that features an adaptive and device-agnostic user interface enabled by a novel device driver mechanism. To validate the flexibility and feasibility of our design, we have built a SPOT prototype based on 8 real IoT devices and present the quality of generating such adaptive graphical user interface by the measure of screen smoothness in the prototyped android app.

Optimizing economic operations for residential demand response programs

Residential demand response (DR) services have the great potential of growth, yet they are not adopted extensively. A comprehensive residential DR framework proposed in [2] connects utilities with DR participants via mobile apps, supports multiple DR programs, and enables fine-granulated control. In this paper, we focus on mathematical modeling and solutions for economically optimal operation for popular DR programs so that utilities can appropriately select and combine those programs to meet their needs. Performances of multiple DR programs are evaluated via simulations based on real-world data.