REGCMPNT : A Fortran Program for Regression Models with ARIMA Component Errors

RegComponent models are time series models with linear regression mean functions and error terms that follow ARIMA (autoregressive-integrated-moving average) component time series models. Bell (2004) discusses these models and gives some underlying theoretical and computational results. The REGCMPNT program is a Fortran program for performing Gaussian maximum likelihood estimation, signal extraction, and forecasting with RegComponent models. In this paper we briefly examine the nature of RegComponent models, provide an overview of the REGCMPNT program, and then use three examples to show some important features of the program and to illustrate its application to various different RegComponent models.

LV software for supersonic flow analysis

The NASA Lewis Research Center (LeRC) maintains a leadership position in research into advanced aerospace propulsion systems. For the next generation of aircraft, engine designs continue to involve complex, high-speed flows. Performing the detailed flow diagnostics to properly evaluate these designs requires advanced instrumentation to probe these highly turbulent flows. The hostile flow environment often requires nonintrusive measurement techniques such as the laser velocimeter (LV). Since the LV is a proven instrument for nonintrusive flow measurement, it can provide quantitative velocity data with minimal interference to the flow. Based on anticipated flow conditions, laser velocimeter systems were procured from TSI, Inc. The initial system utilized counter processor technology, but later procurements this past year include a more advanced, correlator-based processor, which significantly improves the overall LV performance. To meet the needs of advanced research into propulsion, this instrument must be integrated into an existing VAX/VMS computer system for data acquisition, processing, and presentation. The work done under this grant before this period concentrated on developing the software required to setup and acquire data from the TSI MI-990 multichannel interface, and the RMR 1989 rotating machinery resolver. With the basis established for controlling the operation of the LV system, software development this past year shifted in emphasis from instrumentation control and data acquisition to data analysis and presentation. The progress of the program is reported

Collinsville solar thermal project: yield forecasting (final report)

Executive Summary 1        Introduction This report’s primary aim is to provide yield projections for the proposed Linear Fresnel Reflector (LFR) technology plant at Collinsville, Queensland, Australia.  However, the techniques developed in this report to overcome inadequate datasets at Collinsville to produce the yield projections are of interest to a wider audience because inadequate datasets for renewable energy projects are commonplace.  Our subsequent report called ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) uses the yield projections from this report to produce long-term wholesale market price and dispatch forecasts for the plant.  2        Literature review The literature review discusses the four drivers for yield for LFR technology: DNI (Direct Normal Irradiance) Temperature Humidity Pressure Collinsville lacks complete historical datasets of the four drivers to develop yield projections but its three nearby neighbours possess complete datasets, so could act as proxies for Collinsville.  However, analysing the four drivers for Collinsville and its three nearby sites shows that there is considerable difference in their climates.  This difference makes them unsuitable to act as proxies for yield calculations.  Therefore, the review investigates modelling the four drivers for Collinsville. We introduce the term “effective” DNI to help clarify and ameliorate concerns over the dust and dew effects on terrestrial DNI measurement and LFR technology. We also introduce a modified Typical Metrological Year (TMY) technique to overcome technology specific TMYs.  We discuss the effect of climate change and the El Niño Southern Oscillation (ENSO) on yield and their implications for a TMY. 2.1     Research questions Research questions arising from the literature review include: The overarching research question: Can modelling the weather with limited datasets produce greater yield predictive power than using the historically more complete datasets from nearby sites? This overarching question has a number of smaller supporting research questions: Does BoM adequately adjust its DNI satellite dataset for cloud cover at Collinsville? Given the dust and dew effects, is using raw satellite data sufficient to model yield? Does elevation between Collinsville and nearby sites affect yield? How does the ENSO cycle affect yield? Given the 2007-12 electricity demand data constraint, will the 2007-13 based TMY provide a “Typical” year over the ENSO cycle? How does climate change affect yield? Is the method to use raw satellite DNI data to calculate yield and retrospectively adjusting the calculated yield with an effective to satellite DNI energy per area ratio suitable? How has climate change affected the ENSO cycle? A further research question arises in the methodology but is included here for completeness. What is the expected frequency of oversupply from the Linear Fresnel Novatec Solar Boiler? 3        Methodology In the methodology section, we discuss the data preparation and the model selection process for the four drivers of yield.  We also discuss the development of the technology specific TMY and sensitivity analysis to address the research questions on climate change and elevation. 4        Results and analysis In the results section we present the selection process for the four driver models.  We also present the effective to satellite DNI ratio, the annual variation in gross yield, the selection of TMMs for the TMY based on monthly yield, the sensitivity analysis results on climate change and elevation, and the frequency of gross yield exceeding 30 MW. 5        Discussion We analyse the results within a wider context, in particular, we make a comparison with the yield calculations for Rockhampton to address the overarching research question.  We find that the modelling of weather at Collinsville using incomplete weather data has higher predictive performance that using the complete weather data at Rockhampton but recommend using the BoM’s one-minute solar data to improve the comparative test.  Other findings include the requirement to increase the current TMM’s selection period 2007-13 to incorporate more of the ENSO cycle.  There is less than 0.3% change in gross yield from the plant in the most likely case of climate change but there is a requirement to determine the effect of climate change on electricity demand and the ensuing change in wholesale electricity prices. 6        Conclusion In this report, we have addressed the key research questions, produced the yield projections for our subsequent report ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) and made recommendations for further research

Collinsville solar thermal project: energy economics and dispatch forecasting (final report)

The primary aim of this report is to help negotiate a Power Purchase Agreement (PPA) for the proposed hybrid gas-Linear Frensel Reflector (LFR) plant at Collinsville, Queensland, Australia.  The report’s wider appeal is the discussion of the current situation in Australian National Electricity Market (NEM) and techniques and methods used to model the NEM’s demand and wholesale spot prices for the lifetime of the proposed plant. Executive Summary 1        Introduction This report primarily aims to provide both dispatch and wholesale spot price forecasts for the proposed hybrid gas-solar thermal plant at Collinsville, Queensland, Australia for its lifetime 2017-47.  These forecasts are to facilitate Power Purchase Agreement (PPA) negotiations and to evaluate the proposed dispatch profile in Table 3.  The solar thermal component of the plant uses Linear Fresnel Reflector (LFR) technology.  The proposed profile maintains a 30 MW dispatch during the weekdays by topping up the yield from the LFR by dispatch from the gas generator and imitates a baseload function currently provided by coal generators.  This report is the second of two reports and uses the findings of our first report on yield forecasting (Bell, Wild & Foster 2014b). 2        Literature review The literature review discusses demand and supply forecasts, which we use to forecast wholesale spot prices with the Australian National Electricity Market (ANEM) model. The review introduces the concept of gross demand to supplement the Australian Electricity Market Operator’s (AEMO) “total demand”.  This gross demand concept helps to explain the permanent transformation of the demand in the National Electricity Market (NEM) region and the recent demand over forecasting by the AEMO.  We also discuss factors causing the permanent transformation.  The review also discusses the implications of the irregular ENSO cycle for demand and its role in over forecasting demand. Forecasting supply requires assimilating the information in the Electricity Statement of Opportunities (ESO) (AEMO 2013a, 2014c).  AEMO expects a reserve surplus across the NEM beyond 2023-24.  Compounding this reserve surplus, there is a continuing decline in manufacturing, which is freeing up supply capacity elsewhere in the NEM.  The combined effect of export LNG prices and declining total demand are hampering decisions to transform proposed gas generation investment into actual investment and hampering the role for gas as a bridging technology in the NEM.  The review also estimates expected lower and upper bounds for domestic gas prices to determine the sensitivity of the NEM’s wholesale spot prices and plant’s revenue to gas prices. The largest proposed investment in the NEM is from wind generation but the low demand to wind speed correlation induces wholesale spot price volatility.  However, McKinsey Global Institute (MGI 2014) and Norris et al. (2014a) expect economically viable energy storage shortly beyond the planning horizon of the ESO in 2023-24.  We expect that this viability will not only defer investment in generation and transmission but also accelerate the growth in off-market produced and consumed electricity within the NEM region. 2.1     Research questions The report has the following overarching research questions: What is the expected dispatch of the proposed plant’s gas component given the plant’s dispatch profile and expected LFR yield? What are the wholesale spots prices on the NEM given the plant’s dispatch profile? The literature review refines the latter research question into five more specific research questions ready for the methodology: What are the half-hourly wholesale spots prices for the plant’s lifetime without gas as a bridging technology? Assuming a reference gas price of between $5.27/GJ to$7.19/GJ for base-load gas generation (depending upon nodal location;) and for peak-load gas generation of between $6.59/GJ to$8.99/GJ; and given the plant’s dispatch profile What are the half-hourly wholesale spots prices for the plant’s lifetime with gas as a bridging technology? Assuming some replacement of coal with gas generation How sensitive are wholesale spot prices to higher gas prices? Assuming high gas prices are between $7.79/GJ to$9.71/GJ for base-load gas generation (depending upon nodal location); and for peak-load gas generation of between $9.74/GJ to$12.14/GJ; and What is the plant’s revenue for the reference gas prices? How sensitive is the plant’s revenue to gas as a bridging technology? How sensitive is the plant’s revenue to the higher gas prices? What is the levelised cost of energy for the proposed plant? 3        Methodology In the methodology section, we discuss the following items: dispatch forecasting for the proposed plant; supply capacity for the years 2014-47 for the NEM; demand forecasting using a Typical Meteorological Year (TMY); and wholesale spot prices calculation using ANEM, supply capacity and total demand define three scenarios to address the research questions: reference gas prices; gas as a bridging technology; and high gas prices. The TMY demand matches the solar thermal plant’s TMY yield forecast that we developed in our previous report (Bell, Wild & Foster 2014b).  Together, these forecasts help address the research questions. 4        Results In the results section we will present the findings for each research question, including the TMY yield for the LFR and the dispatch of the gas generator given the proposed dispatch profile in Table 3; Average annual wholesale spot prices from 2017 to 2047 for the plant’s node for: Reference gas prices scenario from $18/MWh to$38/MWh Gas as a bridging technology scenario from $18/MWh to$110/MWh High gas price scenario from $20/MWh to$41/MWh The combined plants revenue without subsidy given the proposed profile: Reference gas price scenario $36 million Gas as a bridging technology scenario$52 million High gas price scenario $47 million 5        Discussion In the discussion section, we analyse: reasons for the changes in the average annual spot prices for the three scenarios; and the frequency that the half-hourly spot price exceeds the Short Run Marginal Cost (SRMC) of the gas generator for the three scenarios for: day of the week; month of the year; and time of the day. If the wholesale spot price exceeds the SRMC, dispatch from the gas plant contributes towards profits.  Otherwise, the dispatch contributes towards a loss.  We find that for both reference and high gas price scenarios the proposed profile in Table 3 captures exceedances for the day of the week and the time of the day but causes the plant to run at a loss for several months of the year.  Figure 14 shows that the proposed profile captures the exceedance by hour of the day and Figure 16 shows that only operating the gas component Monday to Friday is well justified.  However, Figure 15 shows that operating the gas plant in April, May, September and October is contributing toward a loss.  Months either side of these four months have a marginal number of exceedances.  In the unlikely case of gas as a bridging scenario, extending the proposed profile to include the weekend and operating from 6 am to midnight would contribute to profits. We offer an alternative strategy to the proposed profile because the proposed profile in the most likely scenarios proves loss making when considering the gas component’s operation throughout the year.  The gas-LFR plant imitating the based-load role of a coal generator takes advantage of the strengths of the gas and LFR component, that is, the flexibility of gas to compensate for the LFR’s intermittency, and utilising the LFR’s low SRMC.  However, the high SRMC of the gas component in a baseload role loses the flexibility to respond to market conditions and contributes to loss instead of profit and to CO2 production during periods of low demand. The alternative profile retains the advantages of the proposed profile but allows the gas component freedom to exploit market conditions.  Figure 17 introduces the perfect day’s yield profile calculated from the maximum hourly yield from the years 2007-13.  The gas generator tops up the actual LFR yield to the perfect day’s yield profile to cover LFR intermittency.  The residual capacity of the gas generator is free to meet demand when spot market prices exceed SRMC and price spikes during Value-of-Lost-Load (VOLL) events.  The flexibility of the gas component may prove more advantageous as the penetration of intermittent renewable energy increases. 6        Conclusion We find that the proposed plant is a useful addition to the NEM but the proposed profile is unsuitable because the gas component is loss making for four months of the year and producing CO2 during periods of low demand.  We recommend further research using the alternative perfect day’s yield profile. 7        Further Research We discuss further research compiled from recommendation elsewhere in the report. 8        Appendix A Australian National Electricity Market Model Network This appendix provides diagrams of the generation and load serving entity nodes and the transmission lines that the ANEM model uses.  There are 52 nodes and 68 transmission lines, which make the ANEM model realistic.  In comparison, many other models of the NEM are highly aggregated. 9        Appendix B Australian National Electricity Market Model This appendix describes the ANEM model in detail and provides additional information on the assumptions made about the change in the generation fleet in the NEM during the lifetime of the proposed plant

Collinsville solar thermal project: yield forecasting (draft report)

The final report has been published and is available here. Executive Summary 1        Introduction This report’s primary aim is to provide yield projections for the proposed Linear Fresnel Reflector (LFR) technology plant at Collinsville, Queensland, Australia.  However, the techniques developed in this report to overcome inadequate datasets at Collinsville to produce the yield projections are of interest to a wider audience because inadequate datasets for renewable energy projects are commonplace.  The subsequent report called ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) uses the yield projections from this report to produce long-term wholesale market price and dispatch forecasts for the plant.  2        Literature review The literature review discusses the four drivers for yield for LFR technology: DNI (Direct Normal Irradiance) Temperature Humidity Pressure Collinsville lacks complete historical datasets of the four drivers to develop yield projects but its three nearby neighbours do possess complete datasets, so could act as proxies for Collinsville.  However, analysing the four drivers for Collinsville and its three nearby sites shows that there is considerable difference in their climates.  This difference makes them unsuitable to act as proxies for yield calculations.  Therefore, the review investigates modelling the four drivers for Collinsville. We introduce the term “effective” DNI to help clarify and ameliorate concerns over the dust and dew effects on terrestrial DNI measurement and LFR technology. We also introduce a modified TMY technique to overcome technology specific Typical Metrological Year (TMY).  We discuss the effect of climate change and the El Nino Southern Oscillation (ENSO) on yield and their implications for a TMY. 2.1     Research questions Research question arising from the literature review include: The overarching research question: Can modelling the weather with limited datasets produce greater yield predictive power than using the historically more complete datasets from nearby sites? This overarching question has a number of smaller supporting research questions: Is BoM’s DNI satellite dataset adequately adjusted for cloud cover at Collinsville? Given the dust and dew effects, is using raw satellite data sufficient to model yield? Does elevation between Collinsville and nearby sites affect yield? How does the ENSO affect yield? Given the 2007-2012 constraint, will the TMY process provide a “Typical” year over the ENSO cycle? How does climate change affect yield? A further research question arises in the methodology but is included here for completeness. What is the expected frequency of oversupply from the Linear Fresnel Novatec Solar Boiler? 3        Methodology In the methodology section, we discuss the data preparation and the model selection process for the four drivers of yield. 4        Results and analysis In the results section we present the four driver models selected and the process that was undertaken to arrive at the models. 5        Discussion We analyse the extent to which the research questions are informed by the results. 6        Conclusion In this report, we have identified the key research questions and established a methodology to address these questions.  The models for the four drivers have been established allowing the calculation of the yield projections for Collinsville

Stochastic Optimal Prediction with Application to Averaged Euler Equations

Optimal prediction (OP) methods compensate for a lack of resolution in the numerical solution of complex problems through the use of an invariant measure as a prior measure in the Bayesian sense. In first-order OP, unresolved information is approximated by its conditional expectation with respect to the invariant measure. In higher-order OP, unresolved information is approximated by a stochastic estimator, leading to a system of random or stochastic differential equations. We explain the ideas through a simple example, and then apply them to the solution of Averaged Euler equations in two space dimensions.Comment: 13 pages, 2 figure

ASRM case insulation design and development

This paper describes the achievements made on the Advanced Solid Rocket Motor (ASRM) case insulation design and development program. The ASRM case insulation system described herein protects the metal case and joints from direct radiation and hot gas impingement. Critical failure of solid rocket systems is often traceable to failure of the insulation design. The wide ranging accomplishments included the development of a nonasbestos insulation material for ASRM that replaced the existing Redesigned Solid Rocket Motor (RSRM) asbestos-filled nitrile butadiene rubber (NBR) along with a performance gain of 300 pounds, and improved reliability of all the insulation joint designs, i.e., segmented case joint, case-to-nozzle and case-to-igniter joint. The insulation process development program included the internal stripwinding process. This process advancement allowed Aerojet to match to exceed the capability of other propulsion companies

Horrendous Evils and the Problem of Representation

Marilyn McCord Adams’s work on the problem of evil offers one of the most creative and sophisticated religious theodicies. Adams relies upon specifically Christian resources to explain how God is able to defeat horrendous evils within the context of an individual’s life by integrating the participant’s experience of suffering into their relationship with God. This strategy rests upon the claim that God has participated in a representative sample of human horrors. I argue that traditional interpretations of the relevant Christian beliefs do not support the idea that Christ experienced the disintegration of his meaning-making capacities characteristic of horrendous evils. I contend that for Adams’s strategy to be successful, a scenario of ‘God hidden from God’ must be posited. Genuine divine solidarity with human horrors means that God somehow experiences the anguish and existential confusion associated with divine hiddenness. To elucidate this claim and defend its plausibility, I employ Stump’s framework for understanding the nature of union in love. Part of my aim is to distinguish and clarify notions of presence, abandonment, and hiddenness central to this discussion, and argue—contra Stump—that the mutual closeness enjoyed between Christ and God can be constrained without implying a moral fault on the part of God or Christ

In Search of the Grail: The Poetic Development of T.S. Eliot

In Poets of Reality, Joseph Hillis Miller seeks to establish T.S. Eliot as a precursor of the modern movement towards romantic. subjectivism. By applying his phenomenological critique, Miller claims that several major modern writers, including Eliot, adopt aesthetics based on various forms of philosophical monism. The point underlying this thesis is that Eliot stands opposed to any such position and, until 1930, breaks with philosophy, monistic or otherwise. His art from this period is instead characterized by a search for solution in poetic artifice, a pure art. However, with Ash Wednesday, the poet once again enters fully into the realm of ideas, and by Four Quartets has achieved a synthesis of art and idea that is clearly dualistic in nature and affirms the importance of a progressive, and not destructive tradition. All of this he finally undergirds with a logocentric belief in language as a vehicle to be purified, far from the linguistic nihilism of Miller\u27s Yale School colleague, Jacques Derrida