Real time economic model for behind the meter wind generation installation in a government facility
Moon, Dale E
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Wind energy is a renewable resource that will always be available. In the United States, the wind market is fast growing and one of the largest in the world. The force to drive national greenhouse gas reductions is at the forethought of the Executive Order (EO) 13693. EO 13693, “Planning for Federal Sustainability in the Next Decade, ensures that Federal Agencies shall where life-cycle cost-effective, beginning in fiscal year 2016 at not less than 10 percent, unless otherwise specified, ensure the total amount of building electric energy and thermal energy shall be clean energy, accounted for by renewable electric energy and alternative energy” (EO 2015, pp 20). Ensuring that the total amount of building electric energy consumed by the agency is at least 25 percent renewable electric energy in the next decade has driven the federal government to place a policy that agencies will have to increase efficiency in order to meet the required standard. They will also have to improve their environmental performance. This research developed a model to determine if wind generation can be economically constructed by a government plant given the restrictions for use of government funding, and overheads associated with construction on federal land, and compare this same type of installation for commercial application. This development model is utilized for wind projects that have the generators connected directly to the customer’s load behind the utility meter. Where this research is unique is that it considers the modeling of real time generation against real time facility loading and real time energy market pricing to provide a more accurate method of determining the economics of wind generation installed on the customer side of the meter. This model used comparative accurate financial information in determining that detailed analysis using real time pricing providing improved accuracy in the economic viability of wind energy when connected to the customer side of the meter. This model allows the user the flexibility of tailoring each site specific conditions to analyze the project NPV. With the case study project, which represents a pilot study project for the DOE, the government will help reduce future wind farm development expenses by validating that wind is a viable resource for use at a government facility when connected behind the utility meter. A significant part of the initial base case site analysis expenses were in developing the high level analysis for the site. This model provides a detailed upfront economic analysis based on existing site conditions. The initial base case project management and development and took approximately 2 years to complete at the expense of approximately $1M. This model enables the government to do future analysis quickly and with a great deal more accuracy saving both time and money. Wind energy has a good potential to compete in retail electrical markets, and the value of Renewable Energy Credits (RECs) allow government facilities to meet EO 13693. The value of RECs is another less know factor in this analysis. By adding in the current site market value of the RECs produced by this project, the economics of this project increased significantly. Although the initial analysis indicated that the base case project is not economical for the government at this time, it is economical in a commercial application where government subsidies in the form of tax credits are added to the analysis. There are unknown factors such as utility escalation rates, interest rates and REC values that influence the outcome and are inputs for the final developer in their acceptance of risk in a project. The base case was completed using 100 model runs with random interest and utility escalation rates resulted in an average Net Present Value (NPV) of -$900,407. To test the sensitivity of the independent variables in this base case, three additional models were ran using the same random numbers generated as inputs for interest and utility escalation rates. The following changes were made to test the sensitivity of the independent variables. When the interest was held constant at two percent (best case) and randomized utility escalation, the average NPV for these model runs was -$751,455. The next two comparisons held the utility escalation rates at zero percent (worst case) and five percent (best case) with randomized interest rates. When the escalation was five percent the model runs resulted in the average NPV of positive $5,328,136. When the escalation rate is held at zero percent the average NPV was -$5,974,901. The base case analysis for a commercial application yields a positive NPV of $13,741,956. The addition of RECs into the annual cash flows for a government installation resulted in a positive NPV of $1,037,723. The post hoc analysis of real time pricing versus average pricing for excess kilowatt production sent to the grid resulted in a decrease from -$4,008,472 to -$3,530,653 for the base case analysis for the test site government facility which is a 12% error in the base case analysis without real time pricing. The results for the base case study vary with the biggest factor being the utility escalation rate. The government interest rate variance had minimal effect on the final NPV outcome. The behind the meter installation at this case study plant was at a location where the price per kWh for energy is approximately three cents. This is relatively low for most locations across the U.S. where wind energy is prevalent and locations where utility prices are higher make this model even more financially attractive. The result also shows the true value of tax incentives in renewable energy development for commercial applications. If the market for RECs increases across the U.S. the value of renewable energy development becomes even more attractive, and government subsidies of renewable energy can decrease. The results of this behind the meter model indicate that a more economical way to install small renewable energy projects by matching plant load to connected renewable generation. The value of real time pricing also adds increased accuracy to the model results.