Determining the Impact of Wind on System Costs via the Temporal Patterns of Load and Wind Generation

Wind Energy, System Costs, Alternative Energy, Electricity Generation, Environmental Economics and Policy, Resource /Energy Economics and Policy, Q4, Q42, Q54,

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Resource /Energy Economics and Policy, Risk and Uncertainty,

Applying Load Factors to the Mean-Variance Analysis for Fuel Diversification

Fuel diversification implies the selection of a mix of generation technologies for long-term electricity generation. The goal is to strike a good balance between reduced costs and reduced risk. The method of analysis that has been advocated and adopted for such studies is the mean-variance portfolio analysis pioneered by Markowitz (1952). However the standard meanvariance methodology, does not account for the ability of various fuels/technologies to adapt to varying loads. Such analysis often provides results that are easily dismissed by regulators and practitioners as unacceptable, since load cycles play critical roles in fuel selection. To account for such issues and still retain the convenience and elegance of the mean-variance approach, we propose a variant of the mean-variance analysis using the decomposition of the load into various types and utilizing the load factors of each load type. We also present examples using real data for the state of Indiana and demonstrate the ability of the model in providing useful insights

Assessment of the National Prospects for Electricity Generation from

The objective of this study is to assess the national potential for generating electricity using biomass. The assessment is done for the contiguous continental United States and does not include Alaska, Hawaii or U.S. territories. The analysis involves estimating which counties in the contiguous U.S. can economically produce enough biomass annually to sustain an economically sized and operate

Diversification of fuel costs accounting for load variation

A practical mathematical programming model for the strategic fuel diversification problem is presented. The model is designed to consider the tradeoffs between the expected costs of investments in capacity, operating and maintenance costs, average fuel costs, and the variability of fuel costs. In addition, the model is designed to take the load curve into account at a high degree of resolution, while keeping the computational burden at a practical level. The model is illustrated with a case study for Indiana's power generation system. The model reveals that an effective means of reducing the volatility of the system-level fuel costs is through the reduction of dependence on coal-fired generation with an attendant shift towards nuclear generation. Model results indicate that about a 25% reduction in the standard deviation of the generation costs can be achieved with about a 20–25% increase in average fuel costs. Scenarios that incorporate costs for carbon dioxide emissions or a moratorium on nuclear capacity additions are also presented

The Projected Impacts of Carbon Dioxide Emissions Reduction Legislation on Electricity Prices in Indiana

This report estimates the impact of proposed federal regulations aimed at reductions in carbon dioxide (CO2) emissions on the projected prices of electricity and the use of electric energy in the state of Indiana. The analysis is based on the Lieberman-Warner Climate Security Act (S. 2191), which places a declining cap on greenhouse gas emissions; however, it does not attempt to model the full details of the proposed legislation. Although the bill places limits on six greenhouse gases (CO2, methane, nitrous oxide, sulfur hexafluoride, perfluorocarbons, and hydrofluorocarbons) from a number of producers, this report solely focuses on CO2 emissions from Indiana’s electric utility industry. The analysis focuses on the impacts of the legislated limitations on CO2 emissions on the electric energy sector of the economy and does not address the benefits of reduced emissions. The analysis is performed using a traditional regulation forecasting model developed by the State Utility Forecasting Group (SUFG) at Purdue University. This is a sector model that takes the overall economic activity in the state as a given (e.g., the level of gross state product, employment, etc.) and projects changes in electricity usage reflectin