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

Abstract

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.

GEKKO Optimization Suite

“…Additional example problems are shown in the back matter, with an example of an artificial neural network in Appendix A and several dynamic optimization benchmark problems shown in Appendix B. Since the GEKKO Fortran backend is the successor to APMonitor [37], the many applications of APMonitor are also possible within this framework, including recent applications in combined scheduling and control [46], industrial dynamic estimation [43], drilling automation [47,48], combined design and control [49], hybrid energy storage [50], batch distillation [51], systems biology [44], carbon capture [52], flexible printed circuit boards [53], and steam distillation of essential oils [54].…”

Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts

“…MHE is often used in conjunction with MPC, which uses the current system parameters regressed by MHE to predict future values given a set of control moves [33]. Dynamic Optimization, MPC, and MHE have wide application across a broad range of industries including continuous chemical process optimization [62][63][64], cryogenic carbon capture [65,66,[66][67][68], energy system capacity planning [69], and drilling automation [70][71][72]. The optimal control over the future prediction horizon is determined by dynamic optimization.…”

Integrated scheduling and control in discrete-time with dynamic parameters and constraints

“…There are two main types of models for scheduling of chemical processes: discrete-time and continuous-time [63]. The majority of previous work on integrated scheduling and control utilizes continuous-time scheduling formulations with integrated process dynamics to enable dynamic optimization of both scheduling and control [13,19,20,21,22,23,28,29,30,32,33,36,39,64,65,66]. Recent work also demonstrates the possibilities of using discrete-time formulations to integrate scheduling and control [67,68,69].…”