Distributed Generation Use and Control in Buildings
Abstract
The increasing commercial development and deployment of fuel cells
in distributed power applications has given rise to the need for
novel control and dispatch strategies. Recognizing that consumer
interest in fuel cell deployment will be largely economically motivated,
a novel cost-minimization control strategy has been developed. The
novel controller is designed to control operation of a variety of
distributed energy resources, including fuel cells, reciprocating
engines, and micro turbine generators (MTG). The control and dispatch
algorithm is designed to continuously minimize energy costs by monitoring
utility prices and building demand, while working within the context
of the physical limitations and capabilities of the fuel cell and
other distributed power devices.
Using Matlab Simulink, dynamic empirical models of each of the prime
movers (e.g., fuel cells), energy conversion devices (e.g., absorption
chillers), and energy storage devices (e.g., thermal energy storage)
have been developed. Measurements of building electrical and thermal
demand were made by the UC Irvine team on a 90,000 ft2 two-story
commercial building. These dynamic load profiles were then used
to analyze the dynamic performance of the several fuel cell systems
as controlled and dispatched by the novel algorithm. The economics,
efficiencies, and emissions of fuel cell system design and load
scenarios are analyzed to highlight the key deployment needs and
opportunities.
Introduction
- Dynamic component models developed for a variety of Distributed Energy Resources in order to analyze novel control strategies, system configurations, and utility scenarios
Background
- Empirical models of generators and energy conversion devices used to enhance simulation performance and more closely reflect reality
- Component models developed in Matlab Simulink programming environment, allowing for high-resolution, full year simulations in the order of hours
- Actual commercial building electrical and thermal demand data used for scenario analysis
- Novel cost minimization controller developed with the following target function
- Example instantaneous minimized solution (red dot below) for specific building demand and utility price scenario for a system with (1) 250kW HTFC and (1) 60kW MTG
- For this study, the following utility costs and capital costs were assumed:
Results and Conclusions
- Peak electrical load (without air conditioning) of building examined is approximately 250kW; accordingly, three DER scenarios were analyzed:
- 1-250kW HTFC with 25TR Absorption Chiller
- 4-60kW MTGs with 100TR Absorption Chiller
- 1-125kW HTFC and 2-60kW MTGs with 63TR Absorption Chiller
- Heating not considered in this study due to low heating requirements of So. California location
Note: Assumes 93% availability for MTGs and HTFCs. Installed capital
costs
(per kW) of DG: $3000 for HTFC and $1500 for MTG. Installed capital
cost (per TR) of chillers: $2000 for absorption chiller and $500
for electric chiller.
Note: Grid efficiency including generation, transmission and distribution
is assumed to be 35%. NOx and CO2 calculations are based US EPA
eGRID data (2) and DG emission values of 7e-4 lbs/kWh NOx and
1.5 lbs/kWh CO2 for MTGs, 7e-5 lbs/kWh NOx and 0.85 lbs/kWh CO2
for HTFC.
- Electrical grid impact varies greatly with system configuration
- Optimum configuration for this scenario w.r.t. payback is combination HTFC-MTG system.
- HTFC system alone provides greatest fuel savings and emissions reduction
Recent Publications
Meacham, J.R., Brouwer, J., Jabbari, F., and Samuelsen, G.S., "Simulation of Control and Dispatch Scenarios for Distributed Energy Resources," First Industrial Conference on Power Electronics for Distributed and Co-Generation, Irvine, CA, March 22-24, 2004.
Personnel
Investigators: J. Brouwer, F. Jabbari, and G.S. Samuelsen
Staff: J. Brouwer, S.W. Lee, V.G. McDonell, J.L. Mauzey
Students: J.R. Meacham
Sponsors
U.S. Department of Defense
U.C. Office of the President