A procedure for the simplified modeling of distributed generation (DG) systems and finding optimal control and dispatch schedules is presented. Mixed integer linear programming can be used to develop optimized equipment dispatch schedules for a wide range of constraints and objective functions. For the monetary objective function considered in this study, optimal control of distributed generation equipment can reduce operating costs by reducing the amount of energy purchased from the grid, reducing demand charges, and reducing the heating energy cost through the implementation of a CHP plant. The procedure can easily be modified for alternative objective functions considering site energy, primary energy, or emissions and the resultant dispatch schedules used to derive operational guidelines. Moreover, a wide range of operational constraints can be modeled. Results are presented for a healthcare outpatient care center in Richmond, Indiana for the cases of single-day and multi-day demand management using on/off control of emergency generation equipment, continuous control of the emergency generation equipment between a nonzero minimum and a maximum part-load ratio, and combined heat and power systems with thermal storage. Nonlinearities in the objective function due to demand charges or nonzero lower bounds on the part-load operation of the DG equipment have been avoided by properly formulating substitute problems. The emphasis of this article is on the model development process rather than the specific dispatch schedules generated.
- Advanced Energy Systems Division and Solar Energy Division
Modeling and Optimal Control of Distributed Generation Systems for Demand and Energy Management
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Yosten, AJ, & Henze, GP. "Modeling and Optimal Control of Distributed Generation Systems for Demand and Energy Management." Proceedings of the ASME 2009 3rd International Conference on Energy Sustainability collocated with the Heat Transfer and InterPACK09 Conferences. ASME 2009 3rd International Conference on Energy Sustainability, Volume 2. San Francisco, California, USA. July 19–23, 2009. pp. 109-119. ASME. https://doi.org/10.1115/ES2009-90142
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