Energy Initiatives

The modeling report is designed to assist Clean Cities personnel to better set policy and programs that are impacted by transportation-generated pollution. The effectiveness of policies to mitigate ground level emissions through the replacement of conventionally fueled vehicles with electric vehicles depends on many factors, including the type of vehicle and the usage pattern of that vehicle. Certain types of vehicles or vehicles with certain usage patterns (e.g., vehicles that travel more than 200 miles per day, beyond the range of most electric vehicles) are not well-suited for this program, and identifying the optimal vehicles to replace is crucial for the efficacy of the program.

The data gathering stage involves the identification of the number of replaceable vehicles in broad vehicle classes, as well as the usage characteristics of these vehicles. Average usage per vehicle is not instructive in this case, and so the classification of vehicles into cohorts is necessary. In this manner, the report can address all sizes of vehicles, and the programs can be targeted to each individual community.

Electric vehicles are characterized by the significant fixed costs and relatively lower variable costs required to operate them. The benefits from operating these vehicles have a number of uncertain input parameters driving them, and the concept of "expected" or "average" revenue associated with them is typically not very meaningful. However, it is typically the concept of "expected revenue" that is used as the "benefit" in traditional cost-benefit analyses. This may be a serious limitation to most cost-benefit analyses that do not incorporate uncertainty. These types of analyses either ignore the idea of risk completely, or simply assume that the risk associated with a particular program is symmetric. That is, the likelihood of earning some amount greater than the average is equal to the likelihood of earning some amount less than the average. This is almost certainly not true, but stochastic analysis is necessary to determine the characteristics of the revenue distribution. Stochastic analysis, or Monte Carlo simulation, requires the construction of a computer model that allows for the analysis of possible outcomes that may result from uncertainty in input values. The value of an electric vehicle at some point in the future depends on many factors, or variables, such as the cost required to purchase the vehicle, the price of the off peak electricity required to charge the vehicle, the avoided cost of the on peak electricity that can be either used by the vehicle or sold back to the electric grid, the costs of fossil fuels, the avoided cost of any emissions, the availability of future generating technologies, and the growth and shape of electric load, among other factors. The interaction between all of these inputs is instrumental in determining the program value, yet none of these can be known with certainty today. Therefore, it is important to use a model that will allow for uncertainty in these input parameters and still generate meaningful results.

Transparency and consistency of the process are critical to ensuring that program evaluation is as equitable as possible. The computer model developed will be publically accessible with transparent algorithms, to ensure that these goals are met. The model works by running a number of iterations, or distinct views of the future. For each iteration, a value for every uncertain input is randomly drawn from a statistical distribution. If the value of the input is known with a high degree of certainty, then this distribution is very narrow, and the value of this input does not change appreciably from iteration to iteration. For inputs with a greater degree of uncertainty, this distribution is very wide and the value of this input may change appreciably with every iteration. A large number of iterations are run (anywhere from 10,000 to 1,000,000), and the result is a distribution of future program revenues, electricity usage, avoided emissions, and many other variables. The expected value of this revenue distribution can be calculated, but in addition, the width of this distribution can be analyzed and the uncertainty of the program value assessed. Therefore, the benefit from this program may be expressed in the expected value, but in relative risk associated with the project as well. Potential financing agencies, then, will be able to quantify the probability that a reasonable return on their investment will be earned, and will have more information on which to base financing decisions. That is, in these times of constrained capital markets, it is not sufficient to simply address whether a project will produce a return in the expected case. A potential financier should be able to see whether the probability of earning a given return on its investment is 50%, 80%, or 20%. Such information, critical in the decision to finance a project, is accessible only under a simulation approach.

For more information, please contact Ted Kury, PURC Director of Energy Studies.

At the conclusion of the 2007 summit in Miami, Governor Charlie Crist signed Executive Order 07-127. It establishes several policies that will directly affect the electric utility industry and electric utility customers:

1. A cap on carbon dioxide emissions from the electric utility industry of Year 2000 emissions levels by 2017; Year 1990 emissions levels by 2025; and 20% of Year 1990 levels by 2050. The rules to achieve these goals are to be developed by the Department of Environmental Protection (DEP).
2. The Public Service Commission (PSC) is to initiate a rulemaking to set a 20% renewable portfolio standard (RPS) with a strong focus on wind and solar resources.
3. The PSC is to initiate a rulemaking to reduce the cost of connecting solar and other renewable energy technologies to Florida's power grid by adopting uniform statewide interconnection standard for all utilities.
4. The PSC is to initiate a rulemaking to facilitate net metering for customers using on-site renewable energy generation technologies which allows customers to run their meter backwards to offset consumption charges during a billing period.

One potential policy choice for meeting the Governor's carbon dioxide emissions limits will likely be a cap-and-trade emissions trading mechanism. A cap-and-trade scheme caps the aggregate level of emissions (as has already been done in the Governor's Executive Order), allocates the rights to pollute in some manner to pollution sources, and then allows those sources to trade their emissions rights if they choose to do so. (The primer below is available through the research papers search engine.)

Sotkiewicz, Paul M. 2007. "Emissions Trading." In Encyclopedia of Energy Engineering and Technology Vol.1, ed. Barney Capehart, 430-37. New York: CRC Press, Taylor and Francis.

There are lessons to be learned from existing cap-and-trade programs such as the Sulfur Dioxide Trading Program. In particular, electric utilities are regulated by state public utility commissions (PUCs), and PUCs dictate cost recovery rules for compliance options. How PUCs allow for cost recovery can affect the costs of achieving compliance with the cap-and-trade program. The two papers below outline the potential additional costs PUC regulation might add to the sulfur dioxide cap-and-trade emissions program. (They are available through the research papers search engine.)

Sotkiewicz, Paul M. 2003. "The Impact of State-Level Public Utility Commission Regulation on the Market for Sulfur Dioxide Allowances, Compliance Costs, and the Distribution of Emissions." PhD dissertation, University of Minnesota.

Sotkiewicz, Paul M. and Lynne Holt. 2005. "Public Utility Commission Regulation and Cost Effectiveness of Title IV: Lessons for CAIR." The Electricity Journal, 18(8): 68-80.

Distributed generation is generally defined as generation resources that are small and are connected on-site or close to the point where the electricity is used. The main purpose of distributed generation (DG) is to reduce line losses and/or to serve as a source of back-up power for reliability purposes. DG does not necessarily have to be a renewable energy resource, but can also include diesel generators, fuel cells, and combined heat and power (CHP) applications. (The primer below is available through the research papers search engine.)

Sotkiewicz, Paul M., and Jesus Mario Vignolo. 2007. "Distributed Generation." In Encyclopedia of Energy Engineering and Technology Vol. 1, ed. Barney Capehart, 296-302. New York: CRC Press, Taylor and Francis.

Dr. Sotkiewicz and Mr. Vignolo have written a series of papers looking at the role of distribution rate design and cost recovery on the revenues of DG resources. These papers examine the nodal pricing of losses, mw-mile methods for the recovery of fixed network costs, and the effects of the prices paid by consumers of these rate designs and the use of DG resources. (They are available through the research papers search engine.)

Sotkiewicz, Paul M., and Jesus Mario Vignolo. 2006. "Nodal Pricing for Distribution Networks: Efficient Pricing for Efficiency Enhancing Distributed Generation." IEEE Transactions on Power Systems, 21(2): 1013-14.

Sotkiewicz, Paul M., and Jesus Mario Vignolo. 2004. "Allocation of Fixed Costs in Distribution Networks with Distributed Generation." IEEE Transactions on Power Systems, 21(2): 639-652.

Sotkiewicz, Paul M., and Jesus Mario Vignolo. 2007. "Towards a Cost Causation-Based Tariff for Distribution Networks with DG." IEEE Transactions on Power Systems, 22(3): 1051-1060.

Sotkiewicz, Paul M., and Jesus Mario Vignolo. 2006. "The Value of Intermittent Wind DG under Nodal Prices and Amp-mile Tariffs." University of Florida, Department of Economics, PURC Working Paper.

PURC Research Associate Joshua Kneifel has written about state level policies designed to encourage utility scale renewable energy deployment. He finds that renewable portfolio standards (RPS), public benefits funds (PBF), and a requirement that utilities offer their consumers the option to purchase so-called green power are the biggest drivers behind utility scale renewable energy deployment. (His paper is available through the research papers search engine.)

Kneifel, Joshua. 2007. "Effects of State Government Policies on Electricity Capacity from Non-Hydropower Renewable Sources." University of Florida, Department of Economics, PURC Working Paper.

Another possible way in which carbon dioxide emissions can be reduced is through energy efficiency. One problem that has been cited by environmental groups has been rate designs and regulatory schemes in electricity that encourage utilities to sell more power in order to ensure the recovery of fixed infrastructure costs and to increase profitability. One potential answer to this dilemma is revenue decoupling which separates utility profits and infrastructure cost recovery from sales. Of course, there are many possible ways to implement revenue decoupling, and the idea is not without controversy.

Workshop for Research in Electricity Infrastructure Hardening

June 9, 2006

The University of Florida's hurricane simulator was featured on ABC's Good Morning America June 3, 2008.

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UF Hurricane Simulator on the CNN network
The University of Florida's hurricane simulator was featured May 30, 2007 on the CNN network. The device, which is the world's largest portable hurricane wind and rain simulator, is being used to test the durability of structures in the face of hurricane force wind and rain. An earlier version of the hurricane simulator was featured at the June 9, 2006, workshop on hurricane hardening research. Additional information on the hurricane simulator, which is part of a larger hurricane research effort in the Florida Coastal Monitoring Program, can be found on the UF News web site. The hurricane simulator was also featured in an article in the Gainesville Sun.

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