This CRISP project addresses the challenges associated with the rapid evolution of the electricity grid, from one with a few large centralized generators providing power to millions of users to a transactive grid with numerous distributed energy resources. These energy resources or systems (e.g., rooftop photovoltaic systems, electric cars, smart thermostats or ventilation systems and energy efficient controllable appliances) play a vital role managing energy demand. The keystone of this research is the transformation of power distribution feeders, from relatively passive channels that deliver electricity from the transmission grid to acquiescent customers, to distribution microgrids, which are highly intelligent entities that actively manage production, storage and use of electricity, where individuals are considered as active participants. Distribution microgrids make it possible to combine the advantages of the traditional electricity grid, including the ability to transfer renewable energy across wide geographical areas, with the advantages of emerging technologies, including the ability to produce and use power locally in the event of major grid outages. The project investigates the role of customers and their associated social-psychological and environmental factors on residential energy consumption and demand response (DR), and the cyber-communications infrastructure delivers information to human and artificial stakeholders to enable rational and optimal decision-making on various aspects of energy utilization. The outcome of the combination of these research areas is twofold: (1) a unique analysis tool to explore the performance and dynamics of the complex interactive infrastructures underlying future smart grids, and (2) a theoretical basis for their fundamental understanding of customers energy behaviors, which provide important policy implications.

Some of the main findings include the development of a cybersecurity resilience framework relating to the correlation of alarms and security events for a trustworthy application in distribution control center. Also, a novel distributed control strategy has been developed based on the Hamiltonian Surface Shaping Power Flow Control (HSSPFC) method. This approach allows optimization of renewable sources on a microgrid that is easier to synchronize and inter-connect to other utilities or to create a networked microgrid.

We also developed the algorithms for analyzing controlling, shifting or reducing energy load for the behaviors of heating, cooling, laundry, water usage, lighting and so on. In addition, solar generation and electric storage capability were added to the analysis, for the purpose of simulating the interaction of local generation and storage with residential flexible loads. Residential energy behaviors are different based on residents’ motivations and attitudes in different conditions. For example, the ability to effectively reduce and control loads during an emergency situation is a function of demographics, risk perception, expectations of neighbors’ cooperation, perceived efficiency of one’s own energy saving behaviors during emergency situation, giving control to utilities to automatically control heating or cooling appliances, environmental concern, importance of ample energy supply when needed, need for air conditioning, concern for cost of electricity bills, and trust in utilities.

This project also found that low-income households (LIHs) have lower participation rates in many energy efficiencies programs and own fewer energy efficiency appliances and smart grid technologies. Additionally, thermostat control strategies are different across income levels. LIHs tend to set one fixed temperature, even when they own a programmable thermostat, which is less energy efficient. LIHs engage in more energy practices throughout the daytime than their counterparts and show the least pronounced morning and evening energy demand peaks, indicating a relatively inflexible schedule and barriers to accepting DR programs. Our study concludes with policy implications for making energy more affordable, accessible, flexible, and better for the environment, while being fair to those often-underserved community members. In order to better estimate the capability and the expense of peak load reduction through DR, we investigated the relationships among household appliance activities (e.g., electric water heater and air conditioner), load profiles, and incentive-based demand response (IBDR) participation for peak load curtailment through financial reward payment. We have developed the daily load profiles of major home appliances in a typical home. Additionally, we estimate the expense of reducing the yearly-peak of the local grid load. This project also found social-psychological factors such as trust in utility companies, environmental and cost concerns, perceive behavioral control, social norms, gender, income and political orientation affecting DR participation and energy saving potential. This project provides useful suggestions to utility companies and policy makers when implementing DR programs. More importantly, university students involved in this project are exposed to a unique combination of skills from the interdisciplinary training of engineering, data analysis and social science; such training will prepare them for leadership roles in the emerging energy economy of tomorrow with a global perspective. Importantly, this project has reached out to pre-college students and teachers to promote the knowledge and professions relating to renewable energy, social-technological integration and electrical and mechanical engineering and computer science.

Last Modified: 11/28/2019
Modified by: Laura E Brown