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F i n a n c i a l C o n s u l t i n g S e r v i c e s. Energy Resource Optimization: Campus Facility. Client Profile. Large domestic university Multiple energy demands: Electricity Steam heating Chilled water loop
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F i n a n c i a l C o n s u l t i n g S e r v i c e s Energy Resource Optimization: Campus Facility
Client Profile • Large domestic university • Multiple energy demands: • Electricity • Steam heating • Chilled water loop • Diverse resource mix including on-site generation, chillers, boilers, third party power and natural gas contracts
Engagement Objectives • Reduction of client energy costs • Client tutorial on energy cost drivers • Implementation of energy cost management theory and practice • Development of energy cost management as a client strategic objective
Energy Cost Model • Overview of energy sources and uses • Information required for analysis • Equipment technical data (annual) • Contract pricing data (monthly) • Operating load and availability data (hourly) • Model calculation methodology • Program implementation
Information Requirements • Equipment technical specifications • Gas turbines, steam turbines, chillers, boilers • Capacity, efficiency, availability • Contract pricing and delivery terms • Utility energy contracts • Natural gas delivery contracts • Operating load and availability data
Operating Load Data • Energy diagram input • Campus 1 and Campus 2 • Non-chiller electric (MW) • Cooling required (tons/hr) • Heating load (kpph steam)
Equipment Availability Data • Energy diagram input • Campus 1 and Campus 2 • Cogen (GT) Availability • Cogen (ST) Availability • Chiller Availability • Boiler Availability
Model Calculation Methodology • Determines possible resource mixes to meet prescribed loads (electric, heating, chilling) • Many individual scenarios are generated • Energy deliveries by resource • Total scenario cost determination • Scenario evaluation and cost ranking
Model Demonstration • User-oriented • Diagram input panel • Graphical results • Calculation details
Program Implementation • Demonstration of model and capabilities to client staff • Refinement of input data • Training of client staff in model functionality and application to practical energy resource management procedures