Kelly Kissock

Measuring Progress with Normalized Energy Intensity

Energy standard ISO 50001 will require industries to quantify improvement in energy intensity to qualify for certification. This paper describes a four-step method to analyze utility billing, weather, and production data to quantify a company‟s normalized energy intensity over time. The method uses 3-pararameter change-point regression modeling of utility billing data against weather and production data to derive energy signature equations. The energy signature equation is driven by typical weather and production data to calculate the „normal annual consumption‟, NAC, and divided by typical production to calculate „normalized energy intensity” NEI. These steps are repeated on sequential sets of 12 months of data to generate a series of „sliding‟ NEIs and regression coefficients. The method removes the effects of changing weather and production levels, so that the change in energy intensity is a sole function of changing energy efficiency. Deficiencies of other methods of calculating NEI are identified. The method is demonstrated in a case study example.

Cost Optimization of Net-Zero Energy House

Environmental and resource limitations provide increased motivation for design of net-zero energy or net-zero CO2 buildings. The optimum building design will have the lowest lifecycle cost. This paper describes a method of performing and comparing lifecycle costs for standard, CO2 -neutral and net-zero energy buildings. Costs of source energy are calculated based on the cost of photovoltaic systems, tradable renewable certificates, CO2 credits and conventional energy. Building energy simulation is used to determine building energy use. A case study is conducted on a proposed net-zero energy house. The paper identifies the least-cost net-zero energy house, the least-cost CO2 neutral house, and the overall least-cost house. The methodology can be generalized to different climates and buildings. The method and results may be of interest to builders, developers, city planners, or organizations managing multiple buildings.© 2007 ASME

Understanding Industrial Energy Use Through Lean Energy Analysis

This paper describes a simple statistical method to statistically disaggregate industrial energy use into production-dependent, weatherdependent and independent components. This simple statistical disaggregation has many uses, including improving model calibration, quantifying non-productive energy use and identifying energy efficiency opportunities. The process is called Lean Energy Analysis (LEA) because of its relationship to Lean Manufacturing, which seeks to reduce non-productive activity. This paper describes the statistical models, discusses the application of the LEA approach to over 40 industrial facilities, and provides case study examples of the benefits.

Cost-Benefit Analysis of Net Zero Energy Campus Residence

In response to both global and local challenges, the University of Dayton is committed to building a net-zero energy student residence, called the Eco-house. A unique aspect of the Ecohouse is its cost effectiveness. This paper discusses both the design and cost-benefit analysis of a net-zero energy campus residence. Energy use of current student houses is presented to provide a baseline for determining energy savings. The use of the whole-system inside-out approach to guide the overall design is described. Using the inside-out method, the energy impacts of occupant behavior, appliances and lights, building envelope, energy distribution systems and primary energy conversion equipment are discussed. The designs of solar thermal and solar photovoltaic systems to meet the hot water and electricity requirements of the house are described. Ecohouse energy use is compared to the energy use of the existing houses. Cost-benefit analysis is first performed on house components and then on the whole house. At a 5% discount rate, 5% borrowing rate for a 20 year mortgage, a 35 year lifetime, and an annual fuel escalation rate of 4%, the Ecohouse can be constructed for no additional lifetime cost.Copyright

Optimizing Compressed Air Storage for Energy Efficiency

Compressed air storage is an important, but often misunderstood, component of compressed air systems. This paper discusses methods to properly size compressed air storage in load-unload systems to avoid short cycling and reduce system energy use. First, key equations relating storage, pressure, and compressed air flow are derived using fundamental thermodynamic relations. Next, these relations are used to calculate the relation between volume of storage and cycle time in load-unload compressors. It is shown that cycle time is minimized when compressed air demand is 50% of compressor capacity. The effect of pressure drop between compressor system and storage on cycle time is discussed. These relations are used to develop guidelines for compressed air storage that minimize energy consumption. These methods are demonstrated in two case study examples. INTRODUCTION In the United States, nearly 90 billion kWh are consumed each year by compressed air systems at a cost of about

Understanding Industrial Energy Use through Sliding Regression Analysis

4.5 billion [Lawrence Berkeley National Laboratory, 1999]. Compressed air storage is an important, but often misunderstood, component of these compressed air systems. System storage is especially important in systems with compressors that operate in load/unload operation where the compressor cycles between periods of compressed air generation and idling. This paper establishes the relationship of storage, pressure, and compressed air flow through fundamental thermodynamic relations. Theoretical minimum cycle time is calculated to be 50% of compressor capacity. The effects of blowdown time and pressure drops before storage are discussed in relation to cycle time and energy use. These relations are used to develop guidelines for compressed air storage that minimize energy consumption. Other considerations that affect proper storage sizing are also discussed. The use of these relations and guidelines are demonstrated with two examples from the industrial sector. UTILIZING COMPRESSED AIR STORAGE FOR ENERGY MINIMIZATION FUNDAMENTAL STORAGE RELATIONS Consider a compressed air system operating in load-unload mode between two pressures, P1 and P2. The system contains a storage tank of volume V, and air with mass, m and temperature, T. According to the ideal gas law, the mass in the tank is described by Equation 1.