Modified RPS Calculator: Inputs, Updating Procedure, and Outputs

This report provides an overview of the modified version of the renewable portfolio standard (RPS) Calculator model. In this report, we describe the inputs and outputs of the modified model, show the method used for updating demand and the resulting effects on the outputs, explain the procedures to set up the model for running, and provide an approach for reinstalling the model on a new RPS Calculator version. The model estimates the benefits of Electric Program Investment Charge (EPIC) research, which lowers the cost or affects the technical parameters (e.g. capacity factor) of renewable and conventional energy generation, electricity demand levels, emissions, fuel costs, and system losses. The general logic of the model is as follows: EPIC projects affect the parameters (inputs) used inside the model; the average effect on the parameters of the model is then determined by the market penetration of the technology; values for the effects on the parameters are drawn repeatedly from one of the few statistical or empirical distributions to reflect specific estimates of penetration for each draw; finally, the model is run with the new random inputs considering all random effects, and the changes in outputs are stored. The modified version also incorporates a demand estimation procedure based on electricity prices. The original RPS Calculator model makes use of fixed demand forecasts. The EPIC version employs Short-run and Long-run demand functions that are responsive to prices, and estimates the demand endogenously. Based on new electricity prices (costs) obtained from running the RPS Calculator, all demand elements are iteratively updated using the demand functions. This report also reviews the system-wide effects of using price-responsive demand in comparison with using fixed demand forecasts. The last section of the report explains how the model can be installed on newer versions of the RPS Calculator. CPUC and Energy and Environmental Economics (E3) will update the RPS Calculator regularly. This report describes the steps needed to attach the existing Visual Basic (VB) programs and to add the EPIC’s model-specific sheets to the new RPS Calculator. The result will be a new EPIC model which can be run using the new RPS Calculator features. Also, we listed a number of important possible modifications that can be made to improve the model

The “Randomizer” Program: Procedure and Operating Methods

This report provides a detailed explanation of the “Randomizer” program. The “Randomizer” program calculates the benefits of Electric Program Investment Charge (EPIC) research projects by estimating the effects of the projects on some of the parameters incorporated in the RPS Calculator. The “Randomizer” program is written in the VB environment inside an Excel spreadsheet. The program determines the random effects of EPIC projects on the model’s parameters. Values for the effects on the parameters are drawn repeatedly from one of the few statistical or empirical distributions. Once a set of values are drawn for all the involved parameters, the modified RPS Calculator is run. The main modification is related to the demand responsiveness to electricity prices. Finally, changes in electricity prices, carbon emissions, and renewable portfolios from the base case (without EPIC projects) are reported as the outputs of the program

Impact of Value of Time (VOT) on toll roads

This paper provides a brief overview of the concept of value of time (VOT), in the context of toll road schemes. VOT analysis determines the tradeoffs travelers make between time and tolls. The analysis is very important when considering the choice between tolled and un-tolled alternatives. Using travel demand model of Fresno, CA, I provide a sensitivity analysis showing how the outcomes of tolling schemes can change with varying VOT levels

Impact of Value of Time (VOT) on toll roads

This paper provides a brief overview of the concept of value of time (VOT), in the context of toll road schemes. VOT analysis determines the tradeoffs travelers make between time and tolls. The analysis is very important when considering the choice between tolled and un-tolled alternatives. Using travel demand model of Fresno, CA, I provide a sensitivity analysis showing how the outcomes of tolling schemes can change with varying VOT levels

Road pricing: An overview

This paper offers a general overview of the road pricing concept. It first examines the common objectives used in road pricing, namely (a) congestion reduction; (b) raising profits; (c) social welfare maximization; etc. Then, it explores various types of road pricing, including two major ones: (1) road tolls and (2) congestion pricing charges. Next, general modeling approaches used for estimating the impacts of road pricing are discussed. Finally, the paper concludes with a checklist explaining how to promote a successful road pricing scheme

Queue Dissipation Shockwave Speed for Signalized Intersections

Queue formation and dissipation have been extensively studied in relation to traffic signalization, work zone operation, incident occurrence, and ramp metering. This study is an attempt to estimate the effect of vehicle mix, commute time, traffic direction, and road upgrade on queue dissipation speed (time). The data were collected at several intersections in Davis, California, U.S. and analyzed using regression models. The models were determined regressing several functional forms and considering the statistical significance and ease of interpretation of the included variables. The main findings are: 1) dissipation speed does not vary purely by location; 2) a heavy vehicle is faster to discharge than its passenger car size-equivalent is; 3) the queue in a left-turn lane discharges faster than that in a through lane; 4) an upgrade slope increases the queue dissipation time due to more rolling resistance to vehicle start-up and larger vehicle gaps for safety ; 5) morning queues generally discharge more slowly; 6) contrary to common delay estimation models, regression analysis shows that queue dissipation time is linearly related to the number of vehicles rather than quadratically or in other ways; and 7) the simple linear function performs well both in terms of explanatory power (R2) and consistency of signs

Social welfare analysis of HOV to HOT conversion

In this article, I will examine the social welfare effects of implementing High Occupancy Toll (HOT) lanes, and Managed Lanes in general. HOT lanes, in contrast to existing general purpose (GP) lanes, allow motorists to use these express lanes if they either pay a toll or have a certain minimum number of occupants in their vehicle. HOT lanes are often implemented by either converting existing High Occupancy Vehicle (HOV) lanes to HOT lanes or by constructing new managed lanes (MLs) in the median strip of an existing highway. They offer two major benefits over HOV lanes: (i) mitigating inefficiencies arising from the underutilization of HOV and GP lanes; and (ii) generating new revenue while preserving user satisfaction. Despite such potential benefits from HOT lane conversions and ML adoption, a comprehensive study rigorously estimating social welfare benefits and costs has not yet been undertaken. This paper reviews and provides a guideline for HOV to HOT lane conversions, form the social welfare perspective

Queue Dissipation Shockwave Speed for Signalized Intersections

Queue formation and dissipation have been extensively studied in relation to traffic signalization, work zone operation, incident occurrence, and ramp metering. This study is an attempt to estimate the effect of vehicle mix, commute time, traffic direction, and road upgrade on queue dissipation speed (time). The data were collected at several intersections in Davis, California, U.S. and analyzed using regression models. The models were determined regressing several functional forms and considering the statistical significance and ease of interpretation of the included variables. The main findings are: 1) dissipation speed does not vary purely by location; 2) a heavy vehicle is faster to discharge than its passenger car size-equivalent is; 3) the queue in a left-turn lane discharges faster than that in a through lane; 4) an upgrade slope increases the queue dissipation time due to more rolling resistance to vehicle start-up and larger vehicle gaps for safety ; 5) morning queues generally discharge more slowly; 6) contrary to common delay estimation models, regression analysis shows that queue dissipation time is linearly related to the number of vehicles rather than quadratically or in other ways; and 7) the simple linear function performs well both in terms of explanatory power (R2) and consistency of signs

Revenue Risk Mitigation Options for Toll Roads

The major risk associated with the provision of toll facilities results from uncertain future demand/revenue generated from the facilities. In this paper, I examine various options for mitigating toll revenue risk and provide a set of recommendations as to how revenue risk mitigation should be pursued. In addition to conducting more careful traffic revenue studies and risk analyses, policy makers can provide more flexible tolling schedules, adopt advanced toll collection technology, and limit the non-compete clause included in many toll road deals with private operators

Modified RPS Calculator: Inputs, Updating Procedure, and Outputs

This report provides an overview of the modified version of the renewable portfolio standard (RPS) Calculator model. In this report, we describe the inputs and outputs of the modified model, show the method used for updating demand and the resulting effects on the outputs, explain the procedures to set up the model for running, and provide an approach for reinstalling the model on a new RPS Calculator version. The model estimates the benefits of Electric Program Investment Charge (EPIC) research, which lowers the cost or affects the technical parameters (e.g. capacity factor) of renewable and conventional energy generation, electricity demand levels, emissions, fuel costs, and system losses. The general logic of the model is as follows: EPIC projects affect the parameters (inputs) used inside the model; the average effect on the parameters of the model is then determined by the market penetration of the technology; values for the effects on the parameters are drawn repeatedly from one of the few statistical or empirical distributions to reflect specific estimates of penetration for each draw; finally, the model is run with the new random inputs considering all random effects, and the changes in outputs are stored. The modified version also incorporates a demand estimation procedure based on electricity prices. The original RPS Calculator model makes use of fixed demand forecasts. The EPIC version employs Short-run and Long-run demand functions that are responsive to prices, and estimates the demand endogenously. Based on new electricity prices (costs) obtained from running the RPS Calculator, all demand elements are iteratively updated using the demand functions. This report also reviews the system-wide effects of using price-responsive demand in comparison with using fixed demand forecasts. The last section of the report explains how the model can be installed on newer versions of the RPS Calculator. CPUC and Energy and Environmental Economics (E3) will update the RPS Calculator regularly. This report describes the steps needed to attach the existing Visual Basic (VB) programs and to add the EPIC’s model-specific sheets to the new RPS Calculator. The result will be a new EPIC model which can be run using the new RPS Calculator features. Also, we listed a number of important possible modifications that can be made to improve the model