Utility Service Area DG Policy Models

Evaluate Distributed Resource Issues for Utility Service Areas, States, Regions and the Entire US

DG Marketplace provides potential DG customers with FREE estimates of energy bill savings. Online analysis shows utility bill savings available with engines, microturbines, turbines and fuel cell systems customized specifically for each site. Customer Data Makes A Difference! MAISY DG Policy Models are based on actual customer data developed from more than 800,000 US customers. Technologies are purchased by individual rather than "average customers;" therefore, the most reliable and informative results are developed with individual customer analysis. All MAISY models analyze DG issues for a sample of customers in each service area. MAISY software has been developed to complete detailed customer analysis "behind the scenes" so that model users can see as little or as much information as desired, from aggregate service area results down to individual customer detail. A Comprehensive Modeling Process is Critical! DG choices take place in a complicated environment where price impacts, rate design, demand response options, conservation and competition among energy-related technologies enter into DG economics and customer decisions. Utility DG evaluations also depend on the utility's cost of service, distribution costs, capacity expansion options, customer profitability and other issues. MAISY DG Policy models, which are based on more than two decades of Jackson Associates utility service area data development, modeling and analysis, incorporate these items in a comprehensive model. DG models are designed for use by utility staff and policy-makers to increase awareness of the benefits and costs of DG and CHP and to provide a policy tool which can contribute to the elimination of regulatory and other market barriers. DG Policy Models permit utilities, state regulatory and federal government agencies to explore and evaluate distributed energy policy issues within the framework of widely-used utility service area models and customer data resources. Evaluate the cost effectiveness of DG regulations, policies and strategies. Utility Service Area Policy Models now provide comprehensive benefit/cost analysis including : Ratepayer Impact Measure (RIM) Utility Cost Test Total Resource Cost Test (TRC) Societal Cost Test Participant Cost Test

Utility Service Area DG Policy Models provide an easy-to-use assessment of DG issues for every utility service area in the US. The Service Area DG Models are available in two forms:

1. Basic Utility Service Area DG Policy Models

Basic Utility Service Area DG Policy Models provide a summary assessment of the current economic potential of engines, microturbines, turbines and fuel cells for current customers in every utility service area. Thirty DG technology characterizations are evaluated in the models based on manufacturer and industry data. An assessment of DG potential applications is conducted including peak clipping applications, baseload systems, waste heat utilization for space heating, water heating and absorption air conditioners .

The Basic DG Policy Models have been developed to provide an easy-to-use and economical overview of DG impacts on individual service areas, states, regions and the nation. Energy and peak demand impacts are provided by customer sector, market segment and DG technology class. More detailed analysis results can be provided with a customized model developed to meet individual user's needs.

DG evaluations are conducted for each customer in the appropriate MAISY Utility Service Area Customer Databases. Users can specify alternative electric and natural gas prices as well as economic evaluation criteria, technology efficiency and cost characteristics to evaluate DG potentials and sensitivities to energy prices and future DG technology improvements. Internal model analysis uses full 8760 end-use hourly loads for each customer record in the Utility Service Area Databases providing a detailed, realistic analysis of the potential DG market. The size of the Utility Service Area Databases (between 1,500 - 3,000 records per service area) incorporates customer diversity of the utility populations, avoiding the problems encountered when using average or prototype load shapes.

The models are easy to apply: (1) users specify analysis parameters in a series of forms, (2) model software automatically conducts the detailed technology analysis for each customer in the Service Area Database and (3) results are provided in an Excel Report Workbook.

Basic Utility Service Area DG Policy Model reports include:

• Monthly kwh, peak kW, natural gas and oil use with and without DG technologies for the service area, by sector (residential, commercial and industrial) and by summary market segments (dwelling unit type, business type)
• Technology evaluation reports identifying, for each technology class (engines, microturbines, turbines and fuel cells) the number of technologies determined to be economic and the impact on monthly kWh, peak kW, natural gas and oil use
• Total expenditures on DG equipment, avoided electric costs, changes in natural gas costs, operating and maintenance costs and other cost data by technology class, sector and summary market segments
• Summary market segment reports identifying basic characteristics of customers for whom each technology class is economic including average size (square feet, number of employees) and energy use characteristics

See Web Demo of Basic Utility Service Area DG Policy Models

2. Customized Utility Service Area DG Policy Models

Customized Utility Service Area DG Policy Models extend the Basic Policy Models to include more detailed analysis, forecasts and information of interest to individual clients. Customized models can include any of the following extensions:

• Detailed descriptions of individual technologies and technology combinations by detailed customer segments
• Detailed descriptions of customer characteristics by detailed technology and technology combinations
• Analysis of tradeoffs of distributed energy resources, demand management, and other energy-conservation
• Full 8760 hourly loads analysis before and after DG technology implementation
• Environmental analysis including emission impacts for NOx SOx, PM-10 and CO2
• Analysis of Electric system reliability,transmission/distribution cost savings, rate structures/rate reform
• Incorporation of advanced technologies including photovoltaic systems, hybrid fuel cells, etc.
• Detailed DG technology characteristics including forecasts of future system efficiencies and cost
• Detailed market penetration analysis of individual technologies over a twenty-year forecast horizon
• DER technology competition and market penetration
• Detailed service area forecasts of residential commercial and industrial customer populations
• Integration of the utility client's utility billing data including 8760 hourly load extensions for monthly-billed customers
• Detailed geographic analysis customer load impacts
• Automated rate structure and pricing analysis
• Impacts of alternative energy price, technology development, incentive programs, economic growth and other variables over a 20 year forecast horizon
• National, regional, utility, customer cost/benefit analysis

Individual customer results are also available for each customer in the customer databases for each year in the customized Policy Models permitting access and evaluation of any detail of the analysis through standard MAISY Database software and detailed Excel Workbook files.

Utility Service Area DG Policy Model Features

MAISY Utility Service Area DG Policy Models apply the same data that distributed generation manufacturers have been using to support technology design and market strategy. Policy Models incorporate the microsimulation modeling and analysis framework developed in the widely-used CEDMS and REDMS End Use Forecasting Models. DG Policy models permit utilities, state regulatory and federal government agencies to explore and evaluate distributed energy policy issues within the framework of widely-used utility service area models and customer data resources.

The Utility Service Area DG Policy Models reflect a technology-specific application of the more general MAISY Energy Policy Models. Some of the most important characteristics of this modeling approach are described below.

Actual Customer Data With Utility Rates Structures - By using MAISY Utility Customer Databases as a platform for the Utility Service Area DG Policy Models and applying utility rates, results of policy analysis reflect real world situations rather than abstract characterizations based on average loads and average energy prices.

Microsimulation Modeling Process

The concept of microsimulation is both simple and attractive. The first step in this process is to collect information on a sample of representative residential, commercial and industrial customers for the application area. The microsimulation model then tracks each customer over the next 23 (or more) years and replicates choices that will actually be made by that customer. Existing equipment wears out and is replaced, fuel choice is made with respect to some decisions, changes in fuel prices will prompt changes in operation of equipment and, in some cases, fuel switching. New customers are added to the databases to reflect new construction.

The impacts of new technologies such as fuel cells can be evaluated by including fuel cells in the set of choices available to customers along with fuel cell costs and operating characteristics. Information on each customer including his/her investment criteria, valuation of power quality and outage protection and a risk premium associated with a fuel cell as well as the impact of the fuel cell on the customer's hourly loads and avoided electricity costs will determine which of the customers in the representative sample will select a fuel cell in each year of the forecast period.

The microsimulation modeling framework applied in the MAISY Distributed Energy Policy Models is based on the widely-used CEDMS and REDMS End-Use Forecasting Models which have been applied by Jackson Associates (JA) for analysis and evaluations of distributed energy resource technologies, new energy-using technologies, conservation programs, DSM, integrated resource planning, utility energy and hourly load forecasting and other applications for more than two-dozen clients since 1982. Model results have been been used in dozens of state/utility regulatory hearings and in a variety of utility, state and federal policy analyses. JA policy modeling approaches provide the basis for EPRI and state energy policy models including the California Energy Commission.

Maintaining Customer Diversity with Real Customer Data - Utility Service Area DG Policy Models have, as a foundation, the MAISY Utility Customer Databases permitting analysis of real buildings rather than engineering prototype or "average" buildings. Incorporating this customer diversity is important because, for instance, what may be an unattractive DG application for an "average" customer within a market segment can hide the fact that the application is attractive for many of the individual customers within that segment. In statistical terminology this result is caused by aggregation bias inherent in using average customer characteristics rather than individual customers for analysis. The MAISY Databases and the Utility Service Area DG Policy Models were developed specifically to avoid these aggregation problems.

MAISY Databases reflect customer diversity based on information from more than 800,000 individual utility customers throughout the US and provide extensive customer building structure, equipment, occupancy and energy use data required to assess specific policy issues. 8760 Hourly loads for each customer in the MAISY Databases support detailed load and utility rate analysis required to evaluate many energy policy issues.

Simultaneity - The greatest deficiency of most existing policy models and analysis is the inability to simultaneously consider the most important issues surrounding even a single policy topic. For instance, potential impacts of a particular distributed energy technology depend on the competition that it faces from other technologies as well as the changes in factors which influence distributed energy technology choices over time. Consider, for example, the market potential for fuel cells. Changes in fuel prices, changes in electricity rate structures including net metering and standby pricing, incentive programs and potential changes in the price and technology characteristics of microturbines will all have an impact on fuel cell market potential. Only by using an analytical structure that supports these and other influences and supports various "what-if" analyses, can a reliable evaluation of fuel cell market penetration be developed.

Geographic Detail - DG policy issues have a geographic dimension requiring at least utility service area focus (e.g., variation in rate structures); analysis of many issues (e.g., transmission and distribution costs, microgrids) require even greater geographic disaggregations. MAISY Databases contain information down to the county and zip code level. MAISY Distributed Energy Model results can also be provided for smaller geo-coded geographic areas.

Regulated Utility Applications - Integration and Extensions of Billing File Data - Individual customer information can be integrated in MAISY Distributed Energy Policy Models by applying MAISY Customer Load Profiling Models to utility billing file records. These models use monthly utility billing data, weather data , customer SIC codes and a proprietary statistical-engineering process to estimate 8760 hourly loads for each customer. This process extracts weather sensitive HVAC information from monthly energy, demand and weather data and hourly loads of similar customers in the Utility Customer Databases for the specific utility.

Customer Energy Technology Investment Preferences - are based on a survey of over 20,000 customers.

Integrated Resource Planning Benefit/Cost Analysis including :

• Ratepayer Impact Measure (RIM)
• Utility Cost Test
• Total Resource Cost Test (TRC)
• Societal Cost Test
• Participant Cost Test

MAISY Utility Service Area DG Policy Models Can Address Issues and Answer Questions Missed by Other DG Models

Other energy policy models use representations of market segments that apply aggregate representations to a greater or lessor extent (e.g., average commercial customers, average office buildings , average small office buildings and/or average 8760 hourly load profiles). This reliance on aggregate customer characterizations misses diversity that exists within each aggregate segment. This homogenization of utility customers can introduce significant aggregation error in analysis results of these models.

Problems in using Average Customers or Market Segments

For example, evaluating DG economics for an "average restaurant" in a multistate region will provide a single result, ignoring the fact that because individual restaurants vary with respect to hourly loads, end use fuel uses, operating hours, electric and gas prices and other characteristics, DG economics will also vary for individual restaurants. Thus an accurate analysis would provide a range of DG economics results reflecting actual customer DG economics diversity. Some models attempt to address this aggregation problem by applying a distribution to the "average restaurants" results to get a distribution of results. While this "fix" makes the results look more realistic, the results are arbitrary since only an analysis of a sample of customers reflecting all of the factors can empirically determine the appropriate distribution to be applied. However, if developers of these models could actually perform analysis of a sample of customers to develop accurate distributions, then these results would be used initially rather than using averages and distributions. Interestingly, when the distribution of individual customers within a segment are evaluated, the shape of the distribution depends on a number of variables whose values are typically part of the analysis (e.g., electricity and natural gas prices). Thus applying a distribution to an average result to get "reasonable results" actually requires a series of distributions for different analysis variables - obviously not a feasible approach.

Traditional logit or S-shaped penetration curves are also based on this average-distribution approach.

Problems in using Average 8760 Load Profiles

Another serious aggregation error is introduced by applying prototype load profiles to "spread" energy use to 8760 hours of the year. 8760 hourly load data is required to compute detailed DG economics and to determine avoided electricity costs. A typical DG application is to apply a generic load profile for a market segment (e.g., non-electric space heated, air conditioned ,medium office buildings) to annual or monthly energy use. These load profiles are typically developed with a heat load model like DOE2 and may even be calibrated to the average of a sample of customers.

The implications of this methodology are that any two customers with the same energy use will also have identical hourly load profiles and will exhibit exactly the same DG economics. Furthermore customers in the same segment with different energy use will reflect hourly load profiles which are simply shifted higher or lower to reflect differences in energy use.

In reality, two customers with the same monthly energy use can exhibit drastically different hourly loads just as customers within the same segment can exhibit significantly different load shapes. For example, the chart below shows week-day hourly loads in July for 23 medium-sized air-conditioned office buildings in Houston along with the average of all 23 buildings. To provide a meaningful comparison, each of the loads has been normalized so that the sum of kW loads over the 24 hours equals 1.0. Differences in load shapes of the individual customers from the average show the inaccuracies inherent in using average or prototype load profiles. Accurately representing variations in individual customer hourly loads is important both in evaluating onsite generation and waste heat utilization and in determining avoided electricity costs.

MAISY 8760 hourly load data are developed individually for each customer record in the database using statistical and engineering analysis applied to the customer's billing data.

MAISY Utility Service Area DG Policy Models avoid these aggregation problems by modeling energy-related decisions of each customer in the representative sample of customers where each customer's 8760 hourly loads reflect the unique characteristics of that customer. The advantage of this approach is especially obvious when one considers customer diversity that exists across customers in a large state like New York. Individual samples of customers in each of the seven major utility service areas reflect climate and customer populations in those areas (building size, building age, operating and other characteristics vary by service area). In addition to the obvious differences that exists between customer populations in Consolidated Edison (New York City) and Rochester Gas and Electric (city of Rochester), electric and natural gas structures vary significantly between the two service areas. Once modeling results are determined for individual customers, the results can be aggregated to any level without introducing aggregation bias.