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 Products & ServicesDSM Planning & EvaluationDRPricer     April 21, 2014  
DSM Planning & Evaluation Minimize

DRPricer

DRPricer assists demand response (DR) program designers, regulators, resource planners, programs implementers and evaluators determine the fair value of program participation options. The tool is intended to assist in the initial program design phase in screening participant value and encouraging participation, but it can also assist in the operational ranking of program dispatch. Demand response (DR) programs provide several benefits to society in general. These benefits are often compared to alternative fossil fuel generators.  Because DR programs target peak hours, the cost of a combustion turbine (CT) is often chosen as the starting point for measuring DR program value. If a DR program can eliminate the need for a future CT, the program can be given credit for the avoided cost of the CT.

Although the benefits of DR programs can be compared to alternative fossil fuel resources, the unique characteristics of customer participation have to be considered in the final valuation. Typically, program participants set limits on how and when a program can be called.


       These include:
  1. Hours available for the year.
  2. Customer notice (how far in advance do you have to tell the customer about an event to get them to curtail).
  3. MW available to curtail.
  4. Minimum duration of an interruption.
  5. Maximum duration of an interruption.
  6. Number of interruptions during the same day.
  7. Number of interruptions during a week.
  8. Eligible days for interruptions (weekdays only (holidays excluded), weekdays (holidays included), any day).
  9. Eligible months for interruptions (summer only, or available all year).
  10. Consecutive days an interruption can be called.
  11. Maximum number of interruption events for the year.

Although these options can greatly increase participation by allowing the customer to better manage the negative consequences of power interruptions, they also reduce the relative benefits of the demand response resource compared to availability of a new CT. The more limiting the set of customer options, the less likely the program will cost effectively offset alternative fossil fuel generation. 

All market participants, from technology vendors and implementers to utilities and public utility commissions, benefit from understanding the impact of customer participation limits on program cost effectiveness. DRPricer measures the negative effect on the overall program benefit due to these participation constraints. The model establishes a cost-based value for demand response programs and measures relative changes in value as customers choose different program options. The tool measures the relative change of several different program options, both independently and jointly, as options are combined to form a program best suited to the needs of the customer.

DRPricer relies on several well established pricing methods to value demand response programs and the various customer options available to program participants. Using propriety algorithms developed by Integral Analytics the model measures the optimal value of the avoided fossil generation costs attainable through an alternative demand response resource. These methods are described in this section and the key elements are discussed.

The DR pricing model follows the generally accepted idea that demand response is a function of both avoided cost and market prices. Avoided costs are used to measure DR value consistent with the long-run regulatory view of installed capacity as necessary to maintain system reliability. Energy markets on the other hand accurately value the variable avoided benefits of demand-side programs. The analysis for DR resources then considers both the value of supply-side costs and energy prices, as the total benefits are the sum of both avoided capital investment cost and avoided market-based variable energy price.

The value of a demand response program is largely determined by the value of the resources that would otherwise be needed in the absence of the program. The value depends on uncertain supply and uncertain demand through the life of the program. The interaction of supply and demand can be measured though the widening and narrowing of reserve margin. During extreme weather events, the reserve margin narrows. During reliability events, when transmission or generation resources fail, the reserve margin narrows. During these times when reserve margin narrows, demand response programs are most valuable. Although we cannot know with certainty the frequency or magnitude of resource reliability or weather events through the life of the program, we can estimate the probability of these events. Reliability is a function of the installed resources and the operational characteristics of these resources. The probability of extreme weather events can be understood by examining 30+ years of weather data. Using 30+ years of weather data, all possible weather conditions are modeled and causally simulated in the evaluation process.

The following contour charts represent the total annual system potential developed by this methodology. The charts project the relative value of peak hours for each month of service. Both the constrained and unconstrained program will operate during those hours when potential is highest, subject to the constraints imposed on the Setup page. The unconstrained program operates consistent with the operation of a CT. The constrained program operates during those hours that maximize the value of the program, subject to the operational constraints selected by the participant or program planner. In the following figure, the purple-colored area represents the hours of highest value.

                

System Value Potential Contour

DRPricer takes as input a vector of price/value pairs for each interruptible hour of each day in a set of weeks. The program also accepts as input the various customer choices. The program uses dynamic programming techniques to compute efficiently the value of the DR options given the load vector and customer choices.  The algorithm works “bottom-up” by first computing the maximum possible value of each day for each possible combination of number of interruptions and number of hours interrupted in that day. 

Next, the algorithm uses dynamic programming to convert the results for the various individual days into the maximum possible values of each week for each possible combination of number of interruptions and number of hours interrupted in that week.  Finally, dynamic programming is again used, this time to combine the results for the various individual weeks into an optimum interruption schedule for the entire year, given the customer choices/constraints.


  
 
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