## Relating DR to Occupancy

How can we begin to understand individual occupant behavior in a building? In what context is modeling individual behavior needed or is aggregated behavior more practical? Literature suggests in households, modeling an individual’s activity stream can capture the logical structure of energy behavior. Averaged schedules and consumption behaviors are insufficient and may not incorporate covariant end uses. In the case of low-energy or passive buildings especially, occupants completely drive the internal gains and hence energy demands. Simulations of daily behavior (extendable to multiple occupants) can then replicate daily household energy consumption.

For defining a DR potential model, Hae Young and I talked about how to distinguish “flexible” from “inflexible” behaviors, and how the data we can currently obtain might reflect that. Because “flexibility” is more of a spectrum and can be altered by the price, the relationships are not necessarily a hierarchy. Rather, I think putting it in this chart can help organize how we can think about the ends of the spectrum, understand sensor data, and interpret some meaning.

To explain the chart, the energy consumption data that would be collected from every measured load could show a range of activity, from showing strong, fixed patterns to appearing completely random in time and duration. Thus, splitting these into two categories of extremes, we can look at its relationship to occupancy information. Based on how correlated the energy consumption is to the occupancy level or status could mean many things about the flexibility of that load. Correlation could entail active occupancy is linked to the activity or the response could be delayed, where an activity or action does not consume energy until some time later or persists after the occupant(s) have left, such as running a laundry machine. Therefore, different ways of correlating activity is necessary.

In the first category, fixed consumption patterns could be a result of either a strong preference to use that device or appliance at some fixed time or because the load is really a baseload that is essentially inflexible. Of course, if such a device or appliance were inefficient, modifying its operation would be possible and be a low-cost source of DR potential. In a perfect system, these inefficiencies may be assumed to have already been removed. Although in reality, there are many barriers that may make temporarily reducing inefficiencies more viable than permanent reductions. Therefore, controlling loads with fixed consumption patterns would appear to translate into a function of cost. In the correlated case, that cost would be related to the user’s cost of comfort whereas in the other case, the cost is tied to long-term, user-aggregated, or overall building costs. An example would be the cycling of the refrigerator which has potential long-term maintenance costs and distributed impacts to all who share its usage.

In the second category, seemingly random consumption of an appliance or device can similarly be divided into its relationship to occupancy. Correlations with occupant behavior indicate it is dependent on user demand (e.g. lighting) and hence its flexibility will be price-driven. The discomfort cost is higher for some than others and will depend on the value of the activity itself. However, in the uncorrelated case, the usage of the device of appliance may not at all matter to the occupant and hence is likely to have a low impact on comfort and should be highly flexible. To be careful, though, the costs may not be linear in the sense that although doing laundry in 1 hour vs 5 hours makes little difference, not having it done by the next day at a certain time may in fact incur high discomfort costs. Capturing and predicting such preferences will be a challenge given that current appliances generally do not have such an option to express this preference. The main challenge in assessing DR potential in the uncorrelated case is predicting when such random activities occur so that they can be shifted.

As a next task, can experimental data fit such a representation?  Would such a representation be rational and useful down the road?

## Another article about writing

Today I read an old article that a friend had sent me on technical writing. Given all the journal writing ahead, I thought it was extremely relevant. Like the previous links we had gotten about the necessity of practicing writing, I think my writing muscle has indeed gotten lazy and atrophied. It’s not as if I hadn’t previously cranked out reports and such recently, but I definitely did not go over them with as much intricate thought and detail as I might have attempted to in the past. Anyway, thought it would be useful to share.

• #### Mario Bergés 1:52 pm on September 18, 2012 Permalink | Reply

Looks like a very useful article. I’ll add it to my to-read list.

## Hello world!

Welcome to INFER Lab Sites. This is your first post. Edit or delete it, then start blogging!

• #### Mr WordPress 4:16 pm on July 18, 2012 Permalink | Reply

Hi, this is a comment.
To delete a comment, just log in and view the post's comments. There you will have the option to edit or delete them.

## Introduction to Distribution Planning Costing

I think we often take for granted the work that occurs on the ground when it comes to electricity service. Ever wonder who pays for the crew to come out to build new lines, hook-up new connections, or fidget with the substations? We aren’t sent itemized bills with the cost of trimming the two trees that blocked the way or the long line that had to be pulled from the street to get to the house with the large setback. How does this massive infrastructure get funded despite all the situational complexity?

It is no surprise that marginal T&D capacity costs are area-specific and vary greatly. A costing methodology study by Energy & Environmental Economics, Inc. and Pacific Energy Associates looked at four utilities and obtained estimates from $73 to$556 per kW, and these are just averages of much larger ranges for each utility. An important reason is because costs are driven by peak loads in areas that need congestion relief. In other words, T&D costs are essentially price-driven since they translate into the rates charged for energy that generate income.

Due to the peaky nature of loads, utilities must be sensitive to location, time of year, and time of day when evaluating planning decisions. Demand response is ideally suited as a potential solution, given its similar dependencies. The difficult task is finding the proper match between demand and potential resource that will provide net benefits for the utility, resource owner, and any other involved parties. On the utility side, current practice for T&amp;D planning generally involves (1) identifying problem areas, (2) developing and evaluating potential solutions, and (3) allocating budget for the best projects. For a demand response (DR) program vs. a traditional technical solution (e.g. capital budgeting for a new line), understanding and possibly modifying the methodology for evaluating alternatives should be considered.

There are generally five cost tests that can be applied to utility projects. The Utility Cost Test (UCT) differs from the Rate Impact Measure (RIM) in that it measures as costs all the expenses that affect the ratebase, rather than impact the rate itself. The rate is the ratebase divided by sales, thus projects that impact sales will create different results between the two tests. The Total Resource Cost (TRC) considers the cost to the utility and customer as a whole, so transfers of cost between them aren’t recognized. The Societal Cost Test (SCT) extends the TRC to consider externalities. Of course, the participant alone can be considered, who would see incentives, revenue, and net costs of whatever program being subscribed to.

The elements that go into cost evaluations can be difficult to quantify. Environmental effects are usually monetized into cost per pound of air emissions or per kWh of energy. Power quality is translated into outage costs, repair costs, and/or value if quality-differentiated rates exist. Power reliability are usually treated as constraints, so meeting reliability index criteria become important. Even more difficult to quantify is managing risk (i.e. low freq and high impact events, probability estimation, cost estimation) and option/strategic value, which is from the flexibility to respond to by situation with limited information. Advanced methods that can be used include dynamic programming, game theory, contingent claims analysis, financial derivatives, and decision analysis.

The study also lists these as major cost drivers: location, load growth, load shape, equipment characteristics, operational details, financial parameters, synergies, environmental considerations, PQR, uncertainty, and intangibles (e.g. public relations, learning experience). They are not mutually exclusive of one another and can share some dependencies.

In terms of ranking and selecting projects, criteria and feasibility constraints must be met. Criteria refers to the usual financial measures of PV of cost, NPV, levelized cost, IRR, payback time, and benefit-cost ratio, as well as engineering standards, incremental measures of those technical aspects, and utility function. Feasibility refers to constraints placed by technology, budget, regulation, social and political, and the participants. Evaluating these with respect to an individual project, simple portfolio, interdependent portfolio, or even better, dynamically programmed, provide valuable decision support. Still, senior management decision-making based on simply experience and judgement is common.

Costs should be allocated by location and time, and methods exist for primarily measuring area- and time-specific marginal costs (ATSMC). Area-specific analysis is applicable for expansion plans, where budget is allocated based on engineering-defined boundaries, if possible. If not, zones are used to differentiate approximate costs. Facility sharing can be dealt with based on how load is allocated, for which there are several indices used in literature, but requires hourly load data and is thus historical and not necessarily predictive. As for time dependency, attributing costs to “peak block” shares and applying allocation factors is used. A peak period or block can be the top XX or all number of hours above a threshold. Allocation factors are more advanced and divide costs into each hour of the year. Two key examples are the loss-of-load-probability and peak capacity above a threshold level.

Finally, the study recognizes that costing is not able to address some related issues if the aim is to to incorporate DR. For example, some DR alternatives need longer lead-times to implement. Reducing the review process of evaluating programs thus would enable more alternatives. Load forecasting affects some aspects of costing, but due to different methods there can be biases that planners may need to be aware of. Also, the growing awareness of DG has resulted in creative proposals from customers and customer-utility partnerships that offer risks and benefits that may not be easy to incorporate into the costing. Insuring if DR alternatives are actually clean is another issue. For public policy, the goal should be to incentivize DR by internalizing differences between stakeholders so that costing can effect socially desirable outcomes. Costing in itself is subject to public policy and thus only evaluates utility projects on existing guidelines.

The common costing framework and methodology of where costs are derived, allocated, and evaluated is an interesting process that varies from utility to utility. Depending on existing practices, there are many aspects where improvements can be made, or challenges to be found with the expanding array of alternatives for distribution planning.

Sources:
[1] The Energy Foundation. Prepared by Energy & Environmental Economics, Inc. and Pacific Energy Associates. “Costing Methodology for Electric Distribution System Planning.” Nov. 9 2000.

[2] Chernick, Paul and Patrick Mehr. “Electricity Distribution Costs: Comparisons of Urban and Suburban Areas.” Lexington Electric Utility Committee. Oct. 28 2003.

[3] Filippini, Massimo and Jörg Wild. “Regional Differences in Electricity Distribution Costs and their Consequences for Yardstick Regulation of Access Prices.” May 2000.

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