Energy Cost Reduction with Optimized Resource Usage

A major university issued a Request for Proposal (RFP) in 2003 to help the Facilities staff manage their energy costs.

Sterling Energy submitted a proposal and won the contract to develop a tool in the form of a Microsoft Excel spreadsheet to give the staff the hands-on ability to manage energy costs.

The campus energy requirements consist of electricity, heating, and chilling needs. Electricity is provided by two on-site cogeneration units and two Power Purchase agreements (PPAs) with the local utility. Natural gas is provided under two separate contracts with the regional gas utility. The natural gas is used in the cogenerators as well as in stand-alone boilers. Lastly, chilling needs are fulfilled by three banks of electric chillers located on campus.

In order to reduce energy costs, the university staff needed a flexible tool to determine the optimum resource mix to serve any given campus load (a combination of electricity, heating, and chilling).

Sterling Energy thus created a tool that would provide the optimum mix of cogeneration vs. PPA electricity, boiler heating vs. GT steam, and multi-bank chiller operations. The model output consists of equipment operating levels and power and gas purchase amounts to minimize the hourly variable energy cost.

The inputs for this energy “dispatch” model are the following:

• Technical data: This data includes the relevant performance specifications of the cogeneration units, boilers, and chillers
• Contract data: PPA electricity pricing as well as natural gas tariffs are the primary inputs. However, all other variable operating costs are included here.
• Availibility and load data: This includes instantaneous resource availability (from 0 to 100% for each component) as well as the energy needs on the campus.

The model was designed for hands-on use by the staff who need not be thoroughly versed in either spreadsheet use or all aspects of energy management. This is achieved by:

• Employment of a graphical user interface such that energy usage, flows, and costs are related to the campus geographical sources and uses of electricity, heat, and chilling.
• Model layout in a “compartmentalized” fashion such that each of the required input disciplines is concentrated in print-scaled areas for quick editing/updating.

The model determines the optimum mix of resources by first determining the total variable operating cost under each of dozens of scenarios (representing all operating resource mix possibilities). Then these scenarios are ranked by operating cost, and the lowest-cost scenario is returned in the form of an operating profile.

Furthermore, the operating profiles may be scrolled through from best to worst to illustrate the cost benefits of one resource mix as compared to other possibilities.

The spreadsheet is relatively compact (on the order of 500 kb) and operates without the use of macros or databases so that it may also serve as a foundation for the development of future applications:

• A “real-time” controls system such that current energy use can be monitored and fed directly into the spreadsheet code to constantly update optimum operations
• A long-term energy cost management model which will forecast energy cost on an hourly/monthly/annual basis. This will allow for budgeting as well as benchmarking or economic analysis of capital spending projects.

Location:

  Major University

Project Description:

   Energy Resource Management

Delivered Product:

Client-based program to manage and analyze campus energy costs