Thermal energy storage to minimize cost and improve efficiency of a polygeneration district energy system in a real-time electricity market

Abstract

Distributed 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-hour 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 hours 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  Overall, a savings of 16.5% is achieved for a one-year period

Comparison of Direct and Indirect Active Thermal Energy Storage Strategies for Large-Scale Solar Heating Systems

“…Although the potential benefits of TES in renewable district heating have been widely recognized [41], technology choice and storage sizing guidance are to be addressed in this work. Particularly, the comparison between water-based TES and PCM-based indirect one, regarding the charging/discharging limitation, may offer guidance on the proper choice of TES.…”

“…Meanwhile, we do consider heat loss characteristic in TES. In this regard, the adopted methodology does not reflect the dynamic characteristics of the system such as flexibility, control strategy, which requires more detailed dynamic optimization shown in the literatures [41,[43][44][45][46].…”

Optimal combined long-term facility design and short-term operational strategy for CHP capacity investments

“…District energy systems yield more optimization opportunities by simultaneously providing for electrical, heating, and cooling demands [28,31]. However, the new demands and constraints of these systems offer more complex problems and require a more diverse range of system models.…”

Predictive Optimization of the Heat Demand in Buildings at the City Level

“…Fang and Lahdelma [15], Short et al [16], and Powell et al [17] all optimized heat production in the district heating system assuming a central heat storage unit. In this work, the aim was to study how the heat demand can be optimized and how much peak loads can be cut at the city level by modelling each individual building to enable the utilization of their thermal storage capacity.…”