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
This work presents a detailed case study for the optimization of the expansion of a district energy system evaluating the investment decision timing, type of capacity expansion, and fine-scale operational modes. The study develops an optimization framework to find the investment schedule over 30 years with options of investing in traditional heating sources (boilers) or a next-generation combined heat and power (CHP) plant that provides heat and electricity. In district energy systems, the selected capacity and type of system is dependent on demand-side requirements, energy prices, and environmental costs. This work formulates capacity planning over a time horizon as a dynamic optimal control problem considering both operational modes and capital investment decisions. The initial plant is modified by the dynamic optimization throughout the 30 years to maximize profitability. The combined optimal controller and capital investment planner solves a large scale mixed integer nonlinear programming problem to provide the timing and size of the capacity investment (30 year outlook) and also guidance on the mode of operation (1 hour time intervals). The optimizer meets optimal economic, environmental, and regulatory constraints with the suggested design and operational guidance with daily cyclical load following of heat and electricity demand.
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