|MAISY Smart Grid Database Applications
Smart Grid Analysis
Smart grid-related data needs and hourly load modeling are supported with MAISY Databases and forecasting models. This page describes several MAISY database and modeling support activities and includes abstracts and links to several papers that apply MAISY data and modeling to address smart grid issues.
MAISY Smart Grid Analysis
- Improving Energy Efficiency and Smart Grid Program Analysis With Agent-Based End-Use Forecasting Models
- Shining the Light on Smart Grid Investments: A Duke Energy Case Study
- Are Smart Grids a Smart Investment?
MAISY databases and models provide a comprehensive utility-specific smart grid analysis, planning and program development resource.
The Smart Grid concept includes:
- Smart devices situated throughout the electric grid that control and use electricity from generation plants to homes and businesses
- Two-way communication systems that send pricing, electricity use and other information throughout the system
- Control systems that enable utilities and utility customers to modify their use of electricity in response to information provided by the system
Utility benefits of smart grid application can be substantial and will depend largely on the extent to which utility customers respond to smart grid activities including time-of-use pricing, real-time pricing, adoption of automated load control and other options. The energy and hourly load impact of customer responses are determined both by customer potentials, customer program participation and customer responses to price signals. MAISY smart grid applications provide unparalled smart grid analysis, program planning and evaluation capabilities.
The foundation of MAISY smart grid analysis is the MAISY Utility Customer Databases providing comprehensive information on utility customer electricity use including 8,760 hourly loads for each database record with detailed information on end-use (e.g., air conditioning, lighting, etc.) electricity use and hourly loads.
Smart grid analysis is conducted with MAISY agent-based models that apply program characterizations and forecast program participation and electricity use impacts. Electricity use impacts include hourly, daily, monthly, and annual electricity use and hourly loads for as much as a twenty-year forecast horizon.
Costs and benefits of the following smart grid-related programs and analysis areas are provided by a MAISY smart grid application.
- Real-time pricing
- Time-of-use and other pricing options
- Rate design/analysis
- Distributed resources
- Demand response
- Load control
- Continuous commissioning
- Customer electricity use/cost feedback
- New technology analysis
- Program measurement/verification
- Program evaluation
- Program design
MAISY smart grid analysis begins with access to the MAISY Utility Customer Databases including psychographic, firmographic, building, occupancy, equipment and electricity use information for a statistically representative sample of customers. MAISY utility customer databases can optionally be supplemented with customer information provided by electric utility clients.
Detailed customer report information is provided visually and in written reports. MAISY utility customer electricity use report information includes hourly, daily, monthly and annual electricity use information for each customer for the current year and for a twenty year forecast period. Data are available at the customer record level, by customer segment and for the entire utility service area.
Forecasts for future years reflect electricity prices, other energy prices, economic and other variables provided as input parameters to the MAISY models.
Smart grid program parameters including utility incentives, technology installations and other activities are specified by the user and applied by the MAISY models to generate electricity use forecasts reflecting program impacts. Alternative program specifications can be applied to consider the impacts of different program strategies. This process can be repeated to analyze any number of program options, program costs and program benefits
Additional information on MAISY smart grid analysis components is provided with the following Web page links:
The combination of detailed customer information available in the MAISY Utility Customer Databases and the agent-based microsimulation modeling methodology provides a number of significant advantages compared to other smart grid analysis approaches including:
- The ability to evaluate and analyze smart grid program potential for any customer characteristic or customer segment including business type, building size, family income, and other customer variables
- The ability to develop programs and program parameters that maximize smart grid benefits and minimize program costs
- The ability to identify target markets for smart grid initiatives
- A comprehensive framework for program evaluation, development and assessment
MAISY smart grid model analysis also provides traditional twenty year forecasts of electricity use by sector, customer segments (business type, income category, etc.), and end use (space heating, air conditioning, lighting, etc.) on an hourly, daily, monthly and annual basis for a twenty-year forecast horizon.
Improving Energy Efficiency and Smart Grid Program Analysis With Agent-Based End-Use Forecasting Models, Energy Policy, 2010
Electric utilities and regulators face difficult challenges evaluating new energy efficiency and smart grid programs prompted, in large part, by recent state and federal mandates and financial incentives. It is increasingly difficult to separate electricity use impacts of individual utility programs from the impacts of increasingly stringent appliance and building efficiency standards, increasing electricity prices, appliance manufacturer efficiency improvements, energy program interactions and other factors. This study reviews traditional approaches used to evaluate electric utility energy efficiency and smart-grid programs and presents an agent-based end-use modeling approach that resolves many of the shortcomings of traditional approaches. Data for a representative sample of utility customers in a Midwestern US utility are used to evaluate energy efficiency and smart grid program targets over a fifteen-year horizon. Model analysis indicates that a combination of the two least stringent efficiency and smart grid program scenarios provides peak hour reductions one-third greater than the most stringent smart grid program suggesting that reductions in peak demand requirements are more feasible when both efficiency and smart grid programs are considered together. Suggestions on transitioning from traditional end-use models to agent-based end-use models are provided.
WHITE PAPER: Shining the Light on Smart Grid Investments: A Duke Energy Case Study
Many utilities and state agencies are struggling to evaluate the cost effectiveness of smart grid initiatives with unreliable elasticity-based models, Much of the uncertainty surrounding estimated financial benefits can be removed by applying agent-based models.
Agent-based models provide a more accurate and insightful analysis of smart grid impacts because they reflect electricity use of individual utility customers or agents. The models simultaneously recognize and account for all important factors that determine electricity use of individual customers including income, demographics and other factors. Agent-based models are widely used in modeling applications outside the utility industry and have been an important component of MAISY predecessor models, REDMS and CEDMS since the late 1980s.
Jackson Associateâs MAISY agent-based end-use model was applied in this study to analyze peak hour electricity savings for a Duke Energy Indiana smart grid implementation. MAISY Utility Customer Databases are the source of the utility customer hourly load and other information applied in the independent study. Duke Energy did not fund or participate in the study.
Study results illustrate the advantages of MAISY smart grid agent-based end-use analysis. Results show that Duke Indiana smart grid programs can expect to achieve between 4 and 8 percent reductions in residential summer peak loads over a 15-year period.
WHITE PAPER: Are Smart Grids a Smart Investment? A First-of-Its-Kind Hourly Load Study of 800,000 utility customers Shows Utility Returns Vary Widely
This study is the first to apply individual utility customer end-use hourly electric loads to evaluate smart grid costs and benefits. Data for more than 800,000 residential and commercial utility customers in the 200 largest US utilities were applied in the study.
Before this analysis, studies, including a recently released FERC analysis, have relied on assumptions about elasticities and electricity pricing to estimate changes in broad customer-class aggregate hourly loads. Instead, this new study applies load control and pricing program impacts directly to individual customer end-use loads such as air conditioning, water heating and so on to determine utility-level impacts.
Study conclusions include:
- Total savings potential, after cost, is $48 billion for the 200 largest US utilities
- Individual utility savings range from negative savings to $3.2 billion
- One out of 10 utilities may lose money with comprehensive smart grid deployments
- One out of 10 utilities may lose money with comprehensive smart grid deployments
- Benefit/cost ratios of comprehensive smart grid systems depend on a complicated mix of factors (such as dwelling unit age and size) and vary widely across utilities
- Targeted, strategic technology deployments significantly increase benefit/cost ratios
- Customer end-use hourly load information should be used to insure economic benefits exceed costs
This study also breaks new ground in providing the first âbottom-upâ analysis of utility smart grid systems by applying the MAISY Utility Customer Hourly Loads Databases. These databases have a long history in evaluating energy technology impacts including studies of fuel cells, combined heat and power (CHP), cool storage, wind, flywheel and other technologies. MAISY clients include utilities, states, DOE research laboratories and energy technology companies including United Technologies, Carrier, Toyota, Ingersoll Rand, Aisin, Bloom, Ice Energy, IdaTech and others.