Scope and Limitations of the Study

The scope of this study is limited to assessing energy consumption of grocery stores that can be reduced. Attainment of net-zero levels in the grocery store is neither attempted nor discussed. In addition, the study is limited to a discussion on grocery store centric options for cogeneration. Other scales of community based cogeneration exist but have neither been attempted nor discussed. Finally, the economic analysis is limited to the assessment of

cogeneration systems. An economic assessment for the energy efficiency measures proposed for the grocery store was not discussed.

The limitations of this study can be categorized into the following categories:

o Limitations due to the design of building systems, which include limitations of the building system configuration being assessed and the level of detail of building system design proposed by this analysis.

o Limitations due to selection of individual design software, which include the selection of methods and software for assessing energy efficiency measures as well as CHP options in the grocery store.

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o Limitations due to the integration process, which included simplification of assessing building system components due to the integration of outputs from the eQUEST-Refrigeration program and the results from the spreadsheet analysis.

89 CHAPTER IV METHODOLOGY

4.1 Overview

In order to proceed with the analysis, the study considered two building types –a grocery store and a multifamily building, which consisted of multiple units that could be scaled up or down to absorb the surplus electricity and thermal energy generated by an on-site CHP system.

The study was broadly divided into two parts. In the first part the study evaluated the grocery store at the building level and investigated measures to reduce the overall energy consumption of the grocery store. The study evaluated the impact of implementing the measures in terms of site and source energy consumption. The second part of the analysis involved reducing the energy consumption in the grocery store by using appropriate CHP systems. Surplus energy was then shared across the boundary of the store with the surrounding residential community, which in this case, were multiples of an 8-unit multifamily building. The resultant energy consumption for the energy efficient store and the surrounding residential units were accounted for at the site as well as source levels. Figure 4-1 presents a diagrammatic view of this concept. A discussion of part 1 and part 2 are presented in the second and third section of this chapter. A detailed methodology and results from each part are presented in separate chapters of the study.

4.2 Part 1: Reducing Energy Consumption in a Grocery Store on a Building Level The first part of the analysis was conducted in two steps. In the first step, a simulation model of a grocery store was constructed and calibrated to data from an existing store in central Texas. The case-study store provided information to describe the characteristics of a base-case store in terms of building size and usage, as well as specifications for the building envelope, lighting and equipment, HVAC systems and refrigeration systems. The resultant grocery store model was constructed using eQUEST-Refrigeration (Version 3.61)¹ whole building energy simulation program specifically created for supermarket and industrial refrigeration. The model used information from the case-study store and certain other assumptions from a literature review as well as reasonable default values from the simulation program.

1 Development on the eQUEST Refrigeration software program was discontinued in 2006 due to lack of funding with the latest version being Ver. 3.61.

90 BASE-CASE

PART 1: Reducing Energy Consumption in a Grocery Store at a Building Level

PART 2: Reducing Energy Consumption in a Grocery Store as Part of a Community using CHP

Figure 4-1: Research Methodology – Overview

SOURCE

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To ensure that the simulation model was performing correctly it was found necessary to validate the model using measured data obtained from the grocery store. For this study the simulation model was validated using both hourly and monthly measured data obtained from the grocery store for a time period of one year. The time period selected for the analysis was 2009. A suitable weather file was selected to run the simulation, which was compiled using 2009

measured weather data² from a nearby weather station (Kim and Baltazar-Cervantes 2010).

The calibration process was conducted using both statistical and graphical indices.

Graphical indices included time series plots, bin plots and scatter plots. Statistical indices include RMSE, CV(RMSE) and MBE values³. Parameters selected for the iterative process represent the building systems operating in the store, which includes the building envelope, lighting and equipment, HVAC systems and refrigeration system. The overall process was cumulative with a change made to the model after each iteration. Details of this step are provided in Chapter 5 of this study.

In the second step, the modified base-case was used in the assessment of efficiency measures that could potentially be used to reduce the energy consumption in the grocery store.

The literature review presented a discussion on a number of efficiency measures that could be used in grocery store to reduce energy consumption. These measures were grouped together under several subcategories of building systems that operate in the grocery store. The subcategories included building envelope, lighting and equipment, HVAC systems and the refrigeration system. The list of energy efficiency strategies was narrowed down considering the modeling constraints imposed by eQUEST Refrigeration whole building energy consumption software that was selected for this analysis. The efficiency strategies were first assessed on an individual basis. After reviewing the results from individual runs, several high performing strategies were grouped under the above mentioned categories. The simulations were then cumulatively performed. The final simulation model included the efficiency measures for the envelope, lighting and equipment, HVAC and refrigeration. Details of this step are provided in Chapter 6 of this study. A flowchart diagram for Part I of the analysis is presented in Figure 4-2.

2 A TRY formatted weather file for College Station, TX was used for the calibration analysis (Kim and Baltazar-Cervantes 2010). The TRY weather file was packed using data downloaded from National Climatic Data Center website (NCDC), and solar radiation data (Global solar radiation) downloaded from Texas Commission on Environmental Quality (TCEQ).

3 A detailed discussion of these statistical indices is provided in the literature review of this study.

92 Figure 4-2: Research Methodology – Part I

SIMULATED ENERGY DATA

STEP 1: CALIBRATING THE SIMULATION MODEL

STEP 2: ASSESSING ENERGY EFFICIENT MEASURES A

TMY3 WEATHER

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