Uncertainty Of Future Conditions Research Articles

Economically optimal hydropower development with uncertain climate change

Climate change affects the planning, construction, operation and management of hydropower plants in various ways. Evaluating hydropower generation with nonstationary climate changes is important for planning electric power infrastructure development. For a new hydropower plant with climate change, specifying too large a reservoir may result in excessive investment if the future becomes drier, while too small a reservoir may cause insufficient utilization of hydropower resources. Whether to build a new hydropower station at once or build it dynamically in stages over time is challenged by uncertainty in future conditions. This study proposes a planning method for developing a flexible hydropower construction schedule to adapt to long-term uncertain hydrology with climate changes. Projected uncertain nonstationary hydrology scenarios were developed first, and Bayes’ theorem is used to update the probability of each scenario based on observed hydrologic conditions over time. A profit maximization model integrates stochastic dynamic programming and linear programming for a cascade hydropower system to evaluate hydropower plant expansion and how to build it dynamically with uncertain nonstationary climate conditions. In this, stochastic dynamic programming evaluates and prescribes inter-stage reservoir upgrades and linear programming prescribes inner-stage hydroelectric power generation. The case study of Jinsha cascade hydropower system in China’s Yunnan province illustrates the method. The intensity and trend of climate change effects on streamflow directly affect the optimized results. If streamflow decreases in the future, the best strategy is to build a small reservoir initially, without future changes. If streamflow increases in the future, it is better to build a small reservoir first, and gradually increase capacity according to observed changes in streamflow and economies of scale in capacity construction. If there is no clear trend in streamflow change, it is also better to build a small reservoir at first, then decide whether to expand the reservoir according to future observed changes in streamflow. This method with uncertain nonstationary climate change and upgrading adaptively with observed hydrology usually produced better results than optimization for a deterministic nonstationary hydrology.

Which precipitation forecasts to use? Deterministic versus coarser‐resolution ensemble NWP models

AbstractDeterministic numerical weather prediction (NWP) models and ensemble NWP models are routinely run worldwide to assist weather forecasting. Deterministic forecasts are capable of capturing more detailed spatial features, while ensemble forecasts, often with a coarser resolution, have the ability to predict uncertainty in future conditions. A comparative understanding of the performance of these two types of forecasts is valuable for both users of NWP products and model developers. Past published comparisons tended to be limited in scope, for example, for only specific locations and weather events, and involving only raw forecasts. In this study, we conduct a comprehensive comparison of the performance of a deterministic model and an ensemble model of the Australian Bureau of Meteorology in forecasting daily precipitation across Australia over a period of 3 years. The deterministic model has a horizontal grid spacing of approximately 25 km, and the ensemble model 60 km. Despite the coarser resolution, the ensemble forecasts are found to be superior by a number of measures, including correlation, accuracy and reliability. This finding holds true for both raw forecasts from the NWP models and forecasts post‐processed using the recently developed seasonally coherent calibration (SCC) model. Post‐processing is shown to greatly improve the forecasts from both models; however, the improvement is greater for the deterministic model, narrowing the performance gap between the two models. This study adds strong evidence to the general notion that coarser‐resolution ensemble NWP forecasts perform better than deterministic forecasts.

Assessing the performance of uncertainty-aware transactive controls for building thermal energy storage systems

Energy storage systems provide a wide range of technological approaches to manage the balance between energy supply and demand in the electric grid. With the increasing uncertainty and variability that comes with wide-spread adoption of grid-scale and behind-the-meter renewable energy, it is imperative to develop stochastic operational planning and control approaches that can account for uncertainty in future conditions. Although, coordination of multiple thermal energy storage resources can support the transition to low carbon energy by enabling valuable system flexibility, few stochastic planning and control approaches have been developed for coordinating building-level thermal energy storage resources. In addition, there is also a need to analyze the potential benefits of an aggregator-level stochastic control framework versus applying stochastic planning and controls at each building individually. This work addresses these needs by developing an uncertainty-aware transactive control (UA-Tx) framework for an aggregator to coordinate the thermal energy storage (TES) assets of multiple buildings. A two-stage stochastic optimization framework is formulated for day-ahead energy procurement that considers uncertainty in building occupancy patterns, weather conditions, and real-time energy prices of the following day. In the second stage, possible recourse decisions through modifying TES operation are also considered. The dispatch of TES operational strategies is implemented through transactive controls, which use market mechanisms and customer preferences to achieve changes in building demand. During real-time operation, a local demand response aggregator determines the transactive clearing prices to dispatch the flexibility enabled by TES. Simulation case studies were conducted to demonstrate the capabilities of the uncertainty-aware aggregator control framework compared to the performance of applying intelligent controllers at each individual building. Up to 3.7% energy cost savings were observed for buildings under the UA-Tx aggregator control framework. Other potential benefits of the control approach are also discussed, along with anticipated future extensions.

Risk‐sensitive planning for conserving coral reefs under rapid climate change

AbstractCoral reef ecosystems are seriously threatened by changing conditions in the ocean. Although many factors are implicated, climate change has emerged as a dominant and rapidly growing threat. Developing a long‐term strategic plan for the conservation of coral reefs is urgently needed yet is complicated by significant uncertainty associated with climate change impacts on coral reef ecosystems. We use Modern Portfolio Theory to identify coral reef locations globally that, in the absence of other impacts, are likely to have a heightened chance of surviving projected climate changes relative to other reefs. Long‐term planning that is robust to uncertainty in future conditions provides an objective and transparent framework for guiding conservation action and strategic investment. These locations constitute important opportunities for novel conservation investments to secure less vulnerable yet well‐connected coral reefs that may, in turn, help to repopulate degraded areas in the event that the climate has stabilized.

A risk-based framework for water resource management under changing water availability, policy options, and irrigation expansion

Long-term water resource management requires the capacity to evaluate alternative management options in the face of various sources of uncertainty in the future conditions of water resource systems. This study proposes a generic framework for determining the relative change in probabilistic characteristics of system performance as a result of changing water availability, policy options and irrigation expansion. These probabilistic characteristics can be considered to represent the risk of failure in the system performance due to the uncertainty in future conditions. Quantifying the relative change in the performance risk can provide a basis for understanding the effects of multiple changing conditions on the system behavior. This framework was applied to the water resource system of the Saskatchewan River Basin (SaskRB) in Saskatchewan, Canada. A “bottom-up” flow reconstruction algorithm was used to generate multiple realizations for water availability within a feasible range of change in streamflow characteristics. Consistent with observed data and projected change in streamflow characteristics, the historical streamflow was perturbed to stochastically generate feasible future flow sequences, based on various combinations of changing annual flow volume and timing of the annual peak. In addition, five alternative policy options, with and without potential irrigation expansion, were considered. All configurations of water availability, policy decisions and irrigation expansion options were fed into a hydro-economic water resource system model to obtain empirical probability distributions for system performance – here overall and sectorial net benefits – under the considered changes. Results show that no one specific policy can provide the optimal option for water resource management under all flow conditions. In addition, it was found that the joint impacts of changing water availability, policy, and irrigation expansion on system performance are complex and resemble nonlinear functions of changes in individual drivers. The proposed risk-based framework can be linked to any water resource system assessment scheme to quantify the risk in system performance under changing conditions, with the larger goal of proposing alternative policy options to address future uncertainties and management concerns.

Global change and conservation triage on National Wildlife Refuges

Johnson, F. A., M. J. Eaton, G. McMahon, R. Nilius, M. R. Bryant, D. J. Case, J. Martin, N. J. Wood, and L. Taylor. 2015. Global change and conservation triage on National Wildlife Refuges. Ecology and Society 20(4):14. https://doi.org/10.5751/ES-07986-200414

Assessing Effects of Variation in Global Climate Data Sets on Spatial Predictions from Climate Envelope Models

Abstract Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

Bounded ranges of variation as a framework for future conservation and fire management

Alterations in natural fire patterns have negatively affected fire-prone ecosystems in many ways. The historical range of variability (HRV) concept evolved as a management target for natural vegetation composition and fire regimes in fire-prone ecosystems. HRV-based management inherently assumes that ecosystem resilience is reflected in observed ranges of past vegetation and fire dynamics, typically without knowledge of where thresholds exist beyond these dynamics. Given uncertainty in future conditions, some have argued that HRV may not adequately reflect ecosystem resilience to future fire activity. We suggest a refinement that includes concepts from the thresholds of potential concern (TPC) framework, which emphasizes tipping points at the extremes of ecosystem dynamics and other socially unacceptable outcomes. We propose bounded ranges of variation (BRV), an approach focused on building resilience by using historical information, but also by identifying socio-ecological thresholds to avoid and associated management action triggers. Here, we examine nonnative species and carbon sequestration as examples of how the BRV framework could be used in the context of conservation and fire management.

Risk modelling in energy contracts between host utilities and BOT plant investors

Investor owned generation is being encouraged all over the world by the de-regulatory climate and the need to defray the heavy capital demands of plant. Contracts between the investor and the host utility determine the price of electricity and include penalties for nondelivery and compensation for failure to accept contracted energy. While it is widely appreciated that uncertainty of future conditions has an important bearing on the terms of the contract, especially in developing countries, a theoretical framework for examining these factors has not been developed. A theoretical model for linking the degree of future uncertainty, the expected production costs of the investor and the expected production cost of the host utility, with the electricity prices and penalty charges, is developed in this paper. The insight gained from these mathematical models will be useful in contractual decision making. The financial strategy employed is known as Build Operate and Transfer (BOT).

Modelling risk in energy contracts with investor owned generation

Investor owned generation capacity construction is being encouraged all over the world by both the current deregulatory climate and the need to defray the heavy capital demands of plant. Such schemes require contracts between investor and host utility on the price of electricity, and may include penalties for nondelivery, and compensation for failure to accept contracted energy. While it is widely appreciated that uncertainty of future conditions has an important bearing on what the terms of the contract should be, a theoretical framework for examining the influence of uncertainty has not, as yet, been developed for this problem. This paper proposes a theoretical model for linking the degree of future uncertainty, the expected production costs of investor plant and the expected production cost of host utility, with the electricity prices and penalty charges which are stipulated in the energy contract between the two parties. The insight gained from these mathematical models will be useful in contractual decision making.

Budgeting in small manufacturing firms in South Africa

This paper reports on a general investigation into budgeting in small manufacturing firms in South Africa. The study was undertaken using 18 semi-formal interviews and 175 questionnaires addressed to the Chief Executive Officers (CEOs) of small manufacturing firms in six industry sectors in the greater Johannesburg region. The 63 usable responses (a response rate of 36%) were analysed using the SAS statistical package. Several results and conclusions were reached. A wide range of budgetary processes and procedures are used by small manufacturing firms. Budgeting is short-term, informal, simple and flexible, and is considered an essential element of management. There is room for much improvement in certain areas of budgeting. Although accounting reports are perceived to be adequate, CEOs may lack the necessary knowledge to develop more effective reporting systems. The degree of budgeting used is probably influenced by the nature of the business, and the background and training of the CEO. CEOs are completely involved in budgeting, but there is little participation by managers and staff. The major benefits of budgeting are perceived to be its use in planning and control, particularly of cash, and the major problems relate to the uncertainty of future conditions.

A theory of electricity tariff design for optimal operation and investment

This study addresses the problem of balancing supply- and demand-side operation and investment activities. Existing theory is extended to cover electricity industry models with uncertainty in future conditions and intertemporal linking such as storage and investment. An optimal pricing structure which takes these into account is presented. It would induce participants (suppliers and consumers) to make profit-maximizing investment and operation decisions which are socially optimal. The structure contains two terms: a short-run marginal cost pricing as well as a new incentive term to account for the interaction of participants at different time points. A probabilistic forecast of pricing structures at future time is required. A justification of the result and three simple illustrative examples are given.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The matching of assets to liabilities

1.1. The concept of the matching of assets to liabilities is fundamental in matters of finance. In its broadest sense matching is relevant both to the investment of life office and pension funds and to actuarial calculations in relation to those funds.1.2. Matching is inherently connected with the uncertainty of future conditions, as measured by such indicators as rates of interest, inflation and currency exchange. It is when conditions do not work out as hoped for or expected that the risks associated with mis-matching can materialize. Whether considering investment policy or an actuarial calculation it is important that the nature of a portfolio which minimizes these risks be appreciated, and yet it is often difficult to specify such a portfolio with precision. This may not be a source of difficulty in practice when the judgement of the actuary is applied to the problem in hand, but there is evident scope for refinement of the concepts.