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Flexible Financial Credit Agreements is a broad term used to describe a suite of solar products with innovative features not currently offered in traditional solar financing programs. This brief focuses on businesses providing financial and structural support to allow their employees the opportunity to benefit from solar PV generation.
This case study describes how Healthcare Realty partnered with a renewable energy advisor to establish its solar program and make progress towards its energy and GHG emissions reduction goals; all of the projects in the pipeline will be financed through third-party ownership, via power purchase agreements or through a feed-in-tariff program.
This report introduces an innovative framework to assist policymakers to quantitatively compare the impact of different public instruments to promote renewable energy. The report identifies the need to reduce the high financing costs for renewable energy in developing countries as an important task for policymakers acting today. The framework is structured in four stages: (i) risk environment, (ii) public instruments, (iii) levelised cost and (iv) evaluation. To illustrate how the framework can support decision-making in practice, the report presents findings from illustrative case studies in four developing countries. It then draws on these results to discuss possible directions for enhancing public interventions to scale-up renewable energy investment. UNDP is also releasing a financial tool for policymakers to accompany the framework. The financial tool is available for download on the UNDP website.
As a Global Environment Facility (GEF) founding implementing agency, UNDP has worked on over 230 GEF-supported clean energy projects in close to 100 developing countries since 1992. About 100 of these projects in 80 countries have focused on renewable energy, supported by approximately US $ 293 million in GEF funds and leveraging US $1.48 billion in associated co-financing from national governments, international organizations, the private sector and non-governmental organizations. As part of UNDP efforts to codify and share lessons learnt from these initiatives, this report addresses how scarce public resources can be used to catalyze larger private financial flows for renewable energy. It provides an overview of UNDP-GEF’s extensive work supporting development of national renewable energy policies such as feed-in tariffs. In these activities UNDP-GEF assists developing countries to assess key risks and barriers to technology diffusion and then to identify a mix of policy and financial de-risking measures to remove these barriers and drive investment. This approach is illustrated through three case studies in Uruguay, Mauritius and Kazakhstan. This report is complemented by a companion publication presenting an innovative UNDP financial modeling tool to assist policymakers in appraising different public instruments to promote clean energy.
In 2010, the NEA in conjunction with the International Energy Agency produced an analysis of the Projected Costs of Electricity for almost 200 power plants, covering nuclear, fossil fuel and renewable electricity generation. That analysis used lifetime costs to consider the merits of each technology. However, the lifetime cost analysis is less applicable in liberalised markets and does not look specifically at the viewpoint of the private investor. A follow-up NEA assessment of the competitiveness of nuclear energy against coal- and gas-fired generation under carbon pricing has considered just this question. The economic competition in electricity markets is today between nuclear energy and gas-fired power generation, with coal-fired power generation not being competitive as soon as even modest carbon pricing is introduced. Whether nuclear energy or natural gas comes out ahead in their competition depends on a number of assumptions, which, while all entirely reasonable, yield very different outcomes. The analysis in this study has been developed on the basis of daily data from European power markets over the last five-year period. Three different methodologies, a Profit Analysis looking at historic returns over the past five years, an Investment Analysis projecting the conditions of the past five years over the lifetime of plants and a Carbon Tax Analysis (differentiating the Investment Analysis for different carbon prices) look at the issue of competitiveness from different angles. They show that the competitiveness of nuclear energy depends on a number of variables which in different configurations determine whether electricity produced from nuclear power or from CCGTs generates higher profits for its investors. These are overnight costs, financing costs, gas prices, carbon prices, profit margins (or mark-ups), the amount of coal with carbon capture and electricity prices. This paper will present the outcomes of the analysis in the context of a liberalised electricity market, looking at the impact of the seven key variables and provide conclusions on the portfolio that a utility would be advised to maintain, given the need to limit risks but also to move to low carbon power generation. Such portfolio diversification would not only limit financial investor risk, but also a number of non-financial risks (climate change, security of supply, accidents). (authors)
In this new working paper, the authors focus specifically on what would be necessary to achieve High Level Panel-style goals and targets for water, energy and transport, if these were to be eventually adopted by world leaders. In all three cases, much of the advocacy - and the proposed High Level Panel goals - have emphasized the need to strive for universal and sustainable access to at least basic levels of services from these sectors. Many of the proposals for post-2015 goals and targets appear ambitious, but what would it take to achieve them? This paper assesses what is needed to achieve goals for universal and sustainable access to infrastructure, specifically water, energy and transport. Using illustrative goals and targets, the paper reviews the development challenges in each sector, and what will be necessary to overcome the barriers to universal and sustainable access to water, energy and transport infrastructure services, in the areas of governance, finance, capacity development and environmental protection. The paper ends with general conclusions about infrastructure in the post-2015 development agenda.
Steady–state detection is of vital importance for experiments and simulations in chemical engineering, as well as also other fields of science, engineering, and finance—particularly when the full timescale of interest cannot be measured or simulated. We present a breakthrough for estimating the number of data points required before successful steady–state detection is feasible. Using an initial window of data, the method enables predicting the prerequisites for steady state detection (ppSSD), given as a number of data points. The method is shown to be accurate for data with realistic distributions (uniform, normal, and sine–wave), and data from actual kinetic Monte Carlo simulations. In conclusion, users need only to use the algebraic equations derived and provided in this work to estimate the required number of data points for relevant steady–state detection.
Second generation biofuel production facilities have been slower to reach large-scale production than was anticipated a few years ago even though in Europe, the Renewable Energy Directive has incentives for, and in the United States there are also financial incentives and, the Energy Independence and Security Act of 2007 has targets for second generation biofuels. There have been a number of facilities that have been cancelled, but starting in 2013 it appears that significant quantities of second generation biofuels will be produced. A variety of conversion processes, thermal and biological, as well as hybrids of the two is being utilized. There will be a variety of fuels ethanol, drop-in fuels (e.g. gasoline, diesel), biodiesel, steam, electricity, bio-oil, sugars, and chemicals; and a variety of feedstocks crop residues, wood, wood wastes, energy crops, waste oils and municipal solid waste (MSW). One approach to reducing the risk of moving from first to second generation biofuel production has been to take incremental steps such as converting the cellulosic part of grains into ethanol in addition to the starch portion. Many of the second generation biofuel facilities are co-located with first generation biofuel production facilities to share infrastructure as well as trade by-products (e.g. excess steam). One of the challenges has been financing, but both private and government sources are being utilized. Private sources include internal corporate funds and debt offerings, and venture capital. Furthermore, government sources include the U.S. federal government, the European Union, European national governments, and state and local governments.
Accurate solar radiation measured by radiometers depends on instrument performance specifications, installation method, calibration procedure, measurement conditions, maintenance practices, location, and environmental conditions. This study addresses the effect of different calibration methodologies and resulting differences provided by radiometric calibration service providers such as the National Renewable Energy Laboratory (NREL) and manufacturers of radiometers. Some of these methods calibrate radiometers indoors and some outdoors. To establish or understand the differences in calibration methodologies, we processed and analyzed field-measured data from radiometers deployed for 10 months at NREL's Solar Radiation Research Laboratory. These different methods of calibration resulted in a difference of ±1% to ±2% in solar irradiance measurements. Here by analyzing these differences will ultimately assist in determining the uncertainties of the field radiometer data and will help develop a consensus on a standard for calibration. Further advancing procedures for precisely calibrating radiometers to world reference standards that reduce measurement uncertainties will help the accurate prediction of the output of planned solar conversion projects and improve the bankability of financing solar projects.
The US residential solar photovoltaic (PV) installation grew from fewer than 100 firms in 2000 to about 3000 firms by 2016. The rapid expansion of this emerging industry prompts questions about the installation industry's market structure, such as how market shares are distributed among installers and how interinstaller competition affects PV deployment. This article leverages a rich dataset to describe trends in the evolution of the US residential PV installation industry. The data show that most residential PV installers are relatively small companies, with about half of installers installing fewer than 5 systems. At the same time, a subset of high-volume installers have accumulated market share, especially from 2010 to 2016. Furthermore causes of this recent market concentration are explored, with an emphasis on the role of the emergence of alternative customer financing options in the United States.
Artificial intelligence (AI) is rapidly changing the world, from completely controlling routine or mundane tasks like text and image generation, to powering advanced algorithms that control critical systems. The recent advances in generative AI quickly overwhelmed multiple industries from education to finance as first adopters rushed (and continue to rush) to take advantage of the technology. The expanding AI ecosystem presents novel risks and ethical challenges that must be handled to ensure that technology is leveraged fairly and ethically. There are intertwined risks and ethical challenges stemming from the stochastic nature of AI (i.e., intrinsic risks), as well as from specific applications (i.e., extrinsic risks). Appropriately regulating AI requires a systems-approach to develop an integrated solution to these dependent challenges. Thus far, however, questions of risk, ethics and regulation appear to occupy separate spaces. This paper reviews the risks and ethical implications of AI and proposes a system-level approach to integrating ethics and regulation for the nascent industry.
Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non-uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. In conclusion, we show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.
The design and financing of commercial-scale floating offshore wind projects require a better understanding of how power generation differs between newer floating turbines and well-established fixed-bottom turbines. In floating turbines, platform mobility causes additional rotor motion that can change the time-averaged power generation. In this work, OpenFAST simulations examine the power generated by the National Renewable Energy Laboratory's 5-MW reference turbine mounted on the OC3-UMaine spar and OC4-DeepCWind semisubmersible floating platforms, subjected to extreme irregular waves and below-rated turbulent inflow wind from large-eddy simulations of a neutral atmospheric boundary layer. For these below-rated conditions, average power generation in floating turbines is most affected by two types of turbine displacements: an average rotor pitch angle that reduces power, caused by platform pitch; and rotor motion upwind-downwind that increases power, caused by platform surge and pitch. The relative balance between these two effects determines whether a floating platform causes power gains or losses compared to a fixed-bottom turbine; for example, the spar creates modest (3.1%–4.5%) power gains, whereas the semisubmersible creates insignificant (0.1%–0.2%) power gains for the simulated conditions. Furthermore, platform surge and pitch motions must be analyzed concurrently to fully capture power generation in floating turbines, which is not yet universal practice. Finally, a simple analytical model for predicting average power in floating turbines under below-rated wind speeds is proposed, incorporating effects from both the time-averaged pitch displacement and the dynamic upwind-downwind displacements.
Energy and water nexus stress drives innovation in technology, financing models, business models, and partnerships. There are new platforms to accelerate nexus innovation that leverage the skills and experience of successful entrepreneurs from outside the energy and water sectors. New business and financial models are needed to solve energy and water nexus stress challenges. New partnership models are emerging to foster cross-industry and entrepreneurial platform collaboration.
Although Tribal land represents more than 5% of the solar photovoltaic technical potential in the United States, this resource is largely untapped due to a range of barriers, including complex project economics, Tribal technical and human resource capacity, project funding and financing obstacles, and regulatory challenges. To identify and better understand the regulatory barriers, the National Renewable Energy Laboratory (NREL) and the Midwest Tribal Energy Resources Association (MTERA) engaged Tribes, utilities, and regulators. Funded by the U.S. Department of Energy, the 3-year effort seeks to address regulatory challenges or barriers that affect Tribal solar projects differently - specifically or disproportionately because they are located on Tribal lands. This paper, largely excerpted from a comprehensive (draft) guidebook released by NREL and MTERA, provides an overview of 13 key regulatory barriers identified through this research, as well as potential short- and long-term solutions. In addition, the paper points to potential pathways for addressing key barriers through case studies highlighting successful Tribal solar projects along with considerations for stakeholders working with Tribes. These resources can support stakeholders in creating meaningful relationships and pursuing workable solar projects.
Cubature on Wiener space (Lyons and Victoir in Proc. R. Soc. Lond. A 460(2041):169-198, 2004) provides a powerful alternative to Monte Carlo simulation for the integration of certain functionals on Wiener space. More specifically, and in the language of mathematical finance, cubature allows for fast computation of European option prices in generic diffusion models.We give a random walk interpretation of cubature and similar (e.g. the Ninomiya-Victoir) weak approximation schemes. By using rough path analysis, we are able to establish weak convergence for general path-dependent option prices.
We suggest a numerical approximation for an optimization problem, motivated by its applications in finance to find the model-free no-arbitrage bound of variance options given the marginal distributions of the underlying asset. A first approximation restricts the computation to a bounded domain. Then we propose a gradient projection algorithm together with the finite difference scheme to solve the optimization problem. We prove the general convergence, and derive some convergence rate estimates. Finally, we give some numerical examples to test the efficiency of the algorithm.
This work is concerned with stochastic optimal control for a running maximum cost. A direct approach based on dynamic programming techniques is studied leading to the characterization of the value function as the unique viscosity solution of a second order Hamilton–Jacobi–Bellman (HJB) equation with an oblique derivative boundary condition. A general numerical scheme is proposed and a convergence result is provided. Error estimates are obtained for the semi-Lagrangian scheme. These results can apply to the case of lookback options in finance. Moreover, optimal control problems with maximum cost arise in the characterization of the reachable sets for a system of controlled stochastic differential equations. Some numerical simulations on examples of reachable analysis are included to illustrate our approach.