Thermal energy storage to minimize cost and improve efficiency of a polygeneration district energy system in a real-time electricity market. (15th October 2016)

• Record Type: Journal Article
• Authors: Powell, Kody M.; Kim, Jong Suk; Cole, Wesley J.; Kapoor, Kriti; Mojica, Jose L.; Hedengren, John D.; Edgar, Thomas F.
• Is Part Of: Energy. Volume 113(2016)
• Page Start: 52
• Page End: 63
• Publication Date: 2016-10-15
• Subjects: Polygeneration -- Thermal energy storage -- Combined heat and power -- Dynamic optimization -- District energy systems; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.07.009
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: District energy systems can produce low-cost utilities for large energy networks, but can also be a resource for the electric grid by their ability to ramp production or to store thermal energy by responding to real-time market signals. In this work, dynamic optimization exploits the flexibility of thermal energy storage by determining optimal times to store and extract excess energy. This concept is applied to a polygeneration distributed energy system with combined heat and power, district heating, district cooling, and chilled water thermal energy storage. The system is a university campus responsible for meeting the energy needs of tens of thousands of people. The objective for the dynamic optimization problem is to minimize cost over a 24-h period while meeting multiple loads in real time. The paper presents a novel algorithm to solve this dynamic optimization problem with energy storage by decomposing the problem into multiple static mixed-integer nonlinear programming (MINLP) problems. Another innovative feature of this work is the study of a large, complex energy network which includes the interrelations of a wide variety of energy technologies. Results indicate that a cost savings of 16.5% is realized when the system can participate in the wholesale electricity market. Highlights: A district energy system with central cooling, heating, and electricity generation is studied. The system is optimized over 24 h using thermal energy storage to shift loads. A novel static/dynamic decomposition is used to solve the dynamic optimization problem. Scenarios with buying and selling electrical power in a real-time market are considered. An annual savings of 16.5% is achieved going from base case to optimal with market participation.