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Modified RPS Calculator: Inputs, Updating Procedure, and Outputs

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  • Omid, M. Rouhani

Abstract

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.

Suggested Citation

  • Omid, M. Rouhani, 2013. "Modified RPS Calculator: Inputs, Updating Procedure, and Outputs," MPRA Paper 53679, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:53679
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    File URL: https://mpra.ub.uni-muenchen.de/53679/1/MPRA_paper_53679.pdf
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    References listed on IDEAS

    as
    1. Dahl, Carol A., 1993. "A survey of energy demand elasticities in support of the development of the NEMS," MPRA Paper 13962, University Library of Munich, Germany.
    2. Poorzahedy, Hossain & Rouhani, Omid M., 2007. "Hybrid meta-heuristic algorithms for solving network design problem," European Journal of Operational Research, Elsevier, vol. 182(2), pages 578-596, October.
    3. Rouhani, Omid M. & Niemeier, Debbie & Knittel, Christopher R. & Madani, Kaveh, 2013. "Integrated modeling framework for leasing urban roads: A case study of Fresno, California," Transportation Research Part B: Methodological, Elsevier, vol. 48(C), pages 17-30.
    4. Espey, James A. & Espey, Molly, 2004. "Turning on the Lights: A Meta-Analysis of Residential Electricity Demand Elasticities," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 36(1), pages 1-17, April.
    5. Ali Mirchi & Saeed Hadian & Kaveh Madani & Omid M. Rouhani & Azadeh M. Rouhani, 2012. "World Energy Balance Outlook and OPEC Production Capacity: Implications for Global Oil Security," Energies, MDPI, vol. 5(8), pages 1-26, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    RPS Calculator; renewables; electricity prices; California.;
    All these keywords.

    JEL classification:

    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • H32 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Firm
    • Q20 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - General
    • Q21 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Demand and Supply; Prices
    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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