Demand Side Management Option Risk Evaluator (DSMore)

The Leading Cost Effectiveness Tool for Energy Efficiency, Demand Side Management and Demand Response Programs.


How defensible is your DSM portfolio?

Does it contain the most cost effective options? Did you accurately measure the covariance between loads and costs?  Which assumptions matter more? Have you evaluated each measure across multiple years of hourly weather and avoided cost scenarios?

It’s questions like these that cause demand side planners to run countless measure analyses as they develop energy efficiency and demand response programs, variable rates, and integrated resource plans.

No matter what you’re designing, it’s essential to get your assumptions right, and the only way to know that is to test them against a wide range of participation rates, prices, loads, and weather conditions. But often even that’s not enough. Because you also need to factor in low probability/high impact events that can be costly when reality fails to conform to average forecasts.

There are a thousand things that can trip you up and lead to poor analyses, from an unusual weather pattern to a small variance in a pricing model that can erroneously result in value differences of 20% or more. And any time you base your calculations on averages instead of hourly inputs then your calculations may have even more room for error.

Fortunately there is a solution that handles all this complexity with the ease of an Excel spreadsheet.

Demand Side Management Option Risk Evaluator (DSMore) is a powerful financial analysis tool designed to evaluate the costs, benefits and risks of demand-side management (DSM) programs, including energy efficiency, demand response and smart grid programs and services. Its power lies squarely in its ability to process millions of calculations within seconds, resulting in thousands of cost-effectiveness scenarios that vary with weather and/or market prices.

By viewing DSM performance and cost-effectiveness over a wide variety of conditions, managers and regulators can more accurately measure the risks and benefits of employing DSM measures versus traditional generation capacity additions.

But the accuracy of your analysis is only as good as the data you input.

That’s why DSMore is the only modeling tool for energy efficiency, DSM and demand response that correlates weather, loads and prices on an hourly level. 

Compare for Yourself

To set up a free side-by-side comparison and demonstration to see how DSMore compares with the system you’re using now, contact us today.

Why Hourly Analysis Matters?

If you are using Excel odds are you are under-valuing your programs because you are ignoring the important covariance between hourly loads and prices due to weather.

Using average loads and prices does not capture the full value of a DSM/DR/EE measure because it misses the value at peak times. It also understates the total measure value. Using the hourly analysis captures both the peak value and the total value of the measure. The result is up to 35% more energy benefits from your program.

The DSMore application is unique in that it values DSM/DR/EE using a market-based approach – similar to supply side valuations. The relationship between prices and loads is captured at the hourly level to accurately measure risk-based DSM value. Without these key inputs, modeled over 30 to 40 years of actual weather, you will not see the important days or hours within peak seasons which can dramatically alter financial valuations.

Consider two scenarios; one using the average loads and average prices and another scenario using hourly loads and prices. In both scenarios the average load is the same (2 MW) and the average price is also the same ($50/MWh) over the time period. However, the total value of the hourly analysis is greater ($620 versus $500).


To perform this hourly analysis DSMore correlates historic loads and prices to historic weather. These relationships (or covariances) between loads and weather, and price and weather, are used to calculate about 700 different market scenarios

Test results are presented based on weather and market price conditions, allowing you to see the probability distribution over high and low avoided cost scenarios. This helps you assess the risk and value of your DSM/DR program.

How DSMore Works

DSMore uses Excel’s user interface for our inputs and outputs so the power of DSMore is available to more than just technical users, statisticians, economists, or engineers.  Of course, Excel can’t do the heavy lifting number crunching of hourly avoided costs calculations. That’s why we also use more powerful C-based calculation engines so you can be assured you are getting the most accurate and efficient calculations applied to your program valuations and decisions.

If you can use Excel, then you can use DSMore. One worksheet holds your DSM program inputs and the remaining worksheets provide your requested output. Data can be entered in a number of different formats depending on what you have available. For instance, measured savings can be entered as savings, hourly savings, or reductions off the bill. Changes are as simple as cell changes within Excel, and ad hoc additions to your inputs or outputs can be done within the same Excel file, if necessary.

This makes it easy to quickly look at different variables, e.g., incentive levels and administrative costs, to determine program risks and the opportunities for program improvements. DSMore handles all of the familiar cost effectiveness test results, including Utility Cost Test, Total Resource Cost Test, Ratepayer Impact Measure Test, and Societal Test. What’s more, these test results are provided for various weather conditions, including a mild weather and an extreme weather year. By viewing numerous test results, the potential magnitude of benefits of the program becomes apparent.

Learn other advantages to using DSMore

Why Use DSMore?

DSMore is designed to be easy to use while at the same time producing informative and detailed results. These major features are essential for effective program design and evaluation.

  • Analyzes market-based and cost-based avoided costs results at the same time so you can compare results
  • Aligns prices and loads at hourly level, by day-type, month, leap years, holidays, etc., and by region
  • Customizes avoided costs to specific customer load shapes and unique weather sensitivities
  • Calculates a range of results under different weather and price assumptions for each test simultaneously
  • Reflects more accurate valuations by including weather effects hourly by weather station
  • Values “low probability, high consequence events”
  • Calculates all standard cost effectiveness tests
  • Creates appropriate hourly end use load savings, without costly meters
  • Fast results–measure screening is less than 30 seconds
  • User-friendly Excel interface all you to change DSM program parameters and reevaluate in seconds

Additional Functionality

In addition to these core features, IA is continually improving the DSMore application based on customer feedback. DSMore’s customer base, currently in over 30 states, drives continual improvement in the application.

  • Batch tool for easy and fast processing of multiple measures in one step
  • Aggregation tool to group results by measures types, programs, or portfolios without recalculating
  • Functionality to interface with @Risk and Crystal Ball
  • Interfaces with DOE-2 to simplify the evaluation of weather-dependent measures
  • Calculates Greenhouse Gas savings and values based on plant dispatch to get accurate impacts by measure
  • Analyzes the latest technologies such as customer sited renewables, electric vehicles and ice storage

But don’t take our word for it.

Learn what our customers say.

What Do Our Users Say?

Joe O’Donnell, Manager, Market Intelligence, Kansas City Power and Light

“I’ve used DSMore for five years and I really appreciate the speed with which I can evaluate a measure.

We’re putting together a demand side resource plan for implementation in 2012 and we have a 60 page set of requirements that we need to cover, including cash flow, costs and benefits of avoided energy, and all that. When we packaged the programs and presented them for review an intervener asked why we didn’t include LEDs. To accommodate the request I found a run on CFLs and made some changes to the assumptions and within 15 minutes we had the LED results.

DSMore also allows you to quickly vary the underlying assumptions to see what the impacts might be.

For example air conditioning typically runs May through September. But in May and late September you might only see three percent usage, while July and August will account for 70 percent of total kilowatt hours used. So you can accurately evaluate a measure using a defined load shape.

You can also check assumptions for variable time of use rates.

Soon after we started using DSMore I was working with a consultant from an engineering firm who worked up a methodology to construct a time of use model where summer off-peak prices were only 6 cents down from 11 cents, while the on-peak price was 36 cents for four hours on week days. During this four hour period they’d pay 36 cents, while every other hour they’d get the discount. The consultant modeled this using a spreadsheet that showed that the amount of added revenue from the load at 36 cents more than covered the amount lost during the discounted hours. But when I modeled it in DSMore, it showed me the proposed rate structure was highly sensitive to the percentage of load that needed to shift. The consultant used a fixed percentage in his spreadsheet and if the assumption was off by more than 5 percent then we would end up losing 20 percent revenue. Needless-to-say, we changed the consultant’s rate plan.

What’s more, DSMore gives us credibility and defensibility with agencies like public utility commissions, offices of public council, interveners who scrutinize our results.

Once they see the model and our input assumptions they don’t challenge the results. Plus we can also easily accommodate specific regulatory requirements. For example, the Kansas commission required us to evaluate all measures with an assumed 2 percent reduction in performance every year. This was an easy calculation since degradation of savings is a readily available option in DSMore. It also looks at things like net to gross ratios, free ridership, and tax benefits, so you can look at all sorts of factors to get a better understanding.

I admit there have been times when I’ve needed an ability to input something that wasn’t in the current version.

But I’ve found Integral Analytics to be very responsive to my needs. I recently talked to them about the fact that I could only enter a single weekend price for time of use rates, but I need a summer and winter aspect to the rate. So I discussed it with Jason Crabtree and he agreed to incorporate it into the next version. They do this based on other people’s input as well, such as earlier request to add the ability to input different discount rates for societal, or participant, or utility. Now you can easily value the costs and benefits at different discount rates.

It takes a while to learn how to use it, but I’ve never had any issues.

The service model is more than adequate and is always responsive to any questions or issues. Any time I’ve had an issue I always get an answer within a day. Plus there is an active user community that can help you with ideas as well.

DSMore is a great tool with a lot of flexibility.

I definitely recommend the product and the company.”

—Joe O’Donnell, Manager, Market Intelligence, Kansas City Power and Light