The objective of this research is to develop an approach to reduce price volatility while ensuring security in the future power systems with a high penetration of controllable loads. The approach is based on an expanded concept of a critical load level (CLL), where a price step change occurs, combined with a feedback control considering load response to time-delayed price signals. The research will assist the faster adoption of controllable loads and renewable energy as both financial and reliability risk would be reduced.
The intellectual merit is based on: feedback control using the concept of CLL as an indicator for system operators to manage end consumers via load-serving entities; models of consumers? behavior when there exists extensive controllable loads; coordination of price-based and direct control during intra-and-inter trading-period among operators, load-serving entities, and consumers; and correlation of economic volatility and physical security.
The broader impacts include a model to facilitate the fast integration of controllable loads, renewables and storage dispersed in distribution systems with minimum impact to security in Smart Grids. This has wider implication for integration of renewable energy sources and reduction of greenhouse gases. The education plans emphasizes new interdisciplinary collaboration and recruiting underrepresented students.
The main goal of this project is to investigate a feedback control scheme using the concept of critical load level (CLL) as the indicator of electricity price signal for system operators to manage the end consumers via load serving. At a CLL, there exists a step change of electricity price which indicates a new binding constraint, either transmission or generation, in the electricity market model. In this project, first, the impact from generation uncertainty as well as load uncertainty is considered for CLL analysis as well as various economic studies such as bidding strategy and co-optimization. Then, a probability-differentiated framework for dispatching variable generation coupled with flexible demand is explored in this project such that highly reliable generation may be matched with rigid loads while the uncertain portion of generation resources may be linked with flexible demands. Next, interval mathematics is employed to build a closed loop model for boundary analysis for demand response. The model considers system economic dispatch as a feedback control process and introduces a flexible and adjustable load cost as a controlled signal to adjust demand response to reduce the system load to a level below the existing CLL. Monte Carlo simulation is employed to benchmark the interval mathematics model regarding the boundary analysis. System operators may use the proposed model to obtain insights in demand response processes for their decision-makings regarding system load levels and operation conditions. Further, a demonstration system for controllable load, namely, smart home energy management system (SHEMS), is developed with web-based dissemination platform for research, education and outreaching activities. More than 15 REU (research experience for undergraduate) students and senior design students participated in the development of SHEMS. Since demand response is critical to the worldwide sustainability as a means to offset the variable renewable resources, the exploration of demand response in this project helps understand the mechanism and impact to smart grid operations with future high-penetration renewable energy. Thus, the project is of great importance to the development of the smart grid infrastructure and technology.