Price Uncertainty Research Articles

Optimal adaptive heuristic algorithm based energy optimization with flexible loads using demand response in smart grid.

Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling controller (LSC) to model and solve the scheduling issue for household appliances. The LSC offers a solution to the primary problems faced during implementing demand response. The goal is to minimize peak-to-average demand ratios (PADR) and electricity bills while preserving customer satisfaction. Time-varying pricing, intermittent renewable energy, domestic appliance energy demand, storage battery, and grid constraints are all incorporated into the model. The optimal adaptive wind-driven optimization (OAWDO) method is a stochastic optimization technique designed to manage supply, demand, and power price uncertainties. LSC creates the ideal schedule for home appliance running periods using the OAWDO algorithm. This guarantees that every appliance runs as economically as possible on its own. Most appliances run the risk of functioning during low-price hours if just the real time-varying price system is used, which could result in rebound peaks. We combine an inclined block tariff with a real-time-varying price to alleviate this problem. MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. By contrasting it with other algorithms, including the genetic algorithm (GA), the whale optimization algorithm (WOA), the fire-fly optimization algorithm (FFOA), and the wind-driven optimization (WDO) algorithms, the effectiveness of the OAWDO technique is supported. Results indicate that OAWDO works better than current algorithms in terms of reducing power costs, PADR, and rebound peak formation without sacrificing user comfort.

Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties

Abstract With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of “renewable energy + pumped storage” is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system’s overall revenue. Therefore, an analysis is conducted around the operational mechanism of the “wind power–pumped storage” joint operation, and the uncertain factors faced during the system’s operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model’s robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125.

Cooperative game robust optimization control for wind-solar-shared energy storage integrated system based on dual-settlement mode and multiple uncertainties

Aiming at the problems of renewable energy output uncertainties and single scenario operation mode of energy storage systems, a cooperative game robust optimization control method for wind-solar-shared energy storage system based on dual-settlement mode of power market is proposed in this paper. A cooperative game-based energy management framework under dual settlement mode of electricity market is constructed, the profit relationship between shared energy storage under multiple application scenarios and renewable energy are extracted and the corresponding profit models are established. Considering the multiple uncertainties of renewable energy and electricity prices, combined with robust optimization theory, a multi-level two-stage robust optimization model is established to make optimal electricity trading decisions for renewable energy and shared energy storage. Additionally, the cooperative game robust optimization model is solved by i-C&CG algorithm. The effectiveness of proposed control method is verified through actual operating data of a certain power grid in China. The simulation results show that the cooperative game robust optimization model achieves the optimal operation of the wind-solar-shared energy storage system considering multiple uncertainties, which can improve the ability of the system to cope with the uncertainty risk and the reliability of the system.

Impact of Oil Price Uncertainty on Capital Structure Choice by Petroleum Companies

Impact of Oil Price Uncertainty on Capital Structure Choice by Petroleum Companies

Effects of Crude Oil Price Uncertainty on Fossil Fuel Production, Clean Energy Consumption, and Output Growth: An Empirical Study of the U.S.

Effects of Crude Oil Price Uncertainty on Fossil Fuel Production, Clean Energy Consumption, and Output Growth: An Empirical Study of the U.S.

Enhancing the Reliability of Weak-Grid-Tied Residential Communities Using Risk-Based Home Energy Management Systems under Market Price Uncertainty

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios.

A novel hybrid forecasting system for crude oil futures prices: A dual perspective of deterministic forecasting and uncertainty analysis

The crude oil market occupies a crucial place in the composition of the global economy, however, the high level of uncertainty in crude oil prices makes it a challenging task to construct reliable forecasting techniques to obtain reliable expected results. Therefore, to effectively cope with the high uncertainty and the technical limitations of existing forecasting studies, this study constructs a hybrid forecasting system from the dual perspectives of deterministic forecasting and uncertainty analysis. With the concept of “denoising-integration” as the core, the system integrates several denoising techniques, econometric methods, and artificial intelligence methods, and establishes a hybrid forecasting model with high accuracy and robustness with the assistance of multi-objective optimization theory. Furthermore, by introducing fuzzy theory onto the basis of deterministic prediction results, an interval forecasting model with a narrower interval width and better comprehensive performance is constructed under the premise of ensuring that the coverage of prediction intervals meets the requirements of the confidence level. In this study, the forecasting system is applied to four global core crude oil futures markets, and the empirical study finds that the performance is improved by more than 50 % compared with the benchmark model, which has an impressive performance advantage.

Oil Price Uncertainty and Consumer Sentiment in Advanced Economies

How does oil price uncertainty affect consumer sentiment in advanced economies? Is the response of consumer sentiment to exogenous positive and negative oil price shocks symmetric or asymmetric? Motivated to answer these questions, this paper provides a comprehensive examination of the effects of real oil price shocks on consumer sentiment in the G7 economies in the context of two classes of empirical models. With the application of a bivariate structural GARCH-in-Mean VAR model we find that (in general) oil price uncertainty has a negative and statistically significant effect on consumer sentiment in the G7 countries. Moreover, using a test of symmetry, we find that the relationship between oil prices and consumer sentiment is in general asymmetric. JEL Classification: C32, D12, Q43, O57

A Financial Analysis and Valuation of Shell

In todays era of global energy transition, the strategic choices made by energy giants like Shell carry immense significance, shaping not only their own future blueprint but also profoundly influencing the overall energy sectors trends. The article provides a comprehensive analysis of Shell plc with liquidity, solvency, profitability, and valuation analysis of 2023 with its main competitors in the oil and gas industry. Despite facing challenges from fluctuating commodity prices and regulatory changes, Shell maintains a competitive advantage and favorable market position. The company demonstrates robust financial health, with strong liquidity, solvency, and profitability ratios compared to industry peers. Nevertheless, the analysis reveals the companys profitability ratios lag behind competitors, indicating room for improvement. Although facing uncertainties in future commodity prices and regulatory environments, the analysis highlights Shells commitment to balancing environmental goals with shareholder value, as evidenced by its energy transition strategy. Shells stock is still fairly valued according to the analysis, presenting a compelling investment opportunity with its well-constructed financial framework. Overall, Shells strategic plans and adjustments aim to ensure resilience and adaptability, promising investors sustainable development amidst evolving market dynamics.

Carbon Taxation and Electricity Price Dynamics: Empirical Evidence from the Australian Market

AbstractIn this paper, we study the change of Australian electricity price dynamics that was observed before, during and after the two-year period in which a Carbon Pricing Mechanism was in force. We fit a two-states Markov Switching Model, representing a high- and a low-volatility state of the world. To avoid the interference due to periodic patterns, a deseasonalization process accounting for short- and long-term seasonality is carried out prior to the study of volatility. Estimation results highlight that, during the period when the carbon tax applies, the volatility level is lower for both the states of the world. Furthermore, the persistence in the low-volatility state is increased in the presence of the carbon tax. This conclusion is particularly relevant for macroeconomic and investment considerations because the increased uncertainty in electricity prices can significantly influence firms’ investment decisions and shape inflation expectations.

Robust wind power capacity planning under fuel price uncertainty using conic duality theory and piecewise McCormick relaxation

Increasing the proportion of renewable energy sources (RESs) in power generation is crucial due to fossil fuel depletion and rising environmental pollution. In this regard, identifying the most lucrative sites and sizes for installing wind farms, as one of the fastest-growing types of RESs, is imperative. This paper presents a robust profit-oriented wind power capacity planning (WPCP) considering long- and short-term uncertainty. The model forms a two-stage min-max-min hierarchical structure. The first stage minimizes the investment cost plus maximum regret, while the second stage maximizes the profit under the worst-case uncertainty realization. Unlike the existing approaches where uncertainty in fossil fuel prices is neglected, we model fuel price uncertainty using both polyhedral and ellipsoidal uncertainty sets. In this respect, the third level is formulated as a bi-level program, with the upper level being the profit maximization and the lower level being the locational marginal price (LMP) calculation. In the case of the ellipsoidal set, the conic duality theory is employed to dualize the lower level. The piecewise McCormick relaxation (PMR) technique linearizes the bilinear terms. The nested column-and-constraint generation (NCCG) technique solves the formulated problem. A clarifying case study is employed to demonstrate the efficacy of the proposed model.

A Survey of Data-Driven Construction Materials Price Forecasting

The construction industry is heavily influenced by the volatility of material prices, which can significantly impact project costs and budgeting accuracy. Traditional econometric methods have been challenged by their inability to capture the frequent fluctuations in construction material prices. This paper reviews the application of data-driven techniques, particularly machine learning, in forecasting construction material prices. The models are categorized into causal modeling and time-series analysis, and characteristics, adaptability, and insights derived from large datasets are discussed. Causal models, such as multiple linear regression (MLR), artificial neural networks (ANN), and the least square support vector machine (LSSVM), generally utilize economic indicators to predict prices. The commonly used economic indicators include but are not limited to the consumer price index (CPI), producer price index (PPI), and gross domestic product (GDP). On the other hand, time-series models rely on historical price data to identify patterns for future forecasting, and their main advantage is demanding minimal data inputs for model calibration. Other techniques are also explored, such as Monte Carlo simulation, for both price forecasting and uncertainty quantification. The paper recommends hybrid models, which combine various forecasting techniques and deep learning-advanced time-series analysis and have the potential to offer more accurate and reliable price predictions with appropriate modeling processes, enabling better decision-making and cost management in construction projects.

Quantifying the spillover effects of U.S. economic policy uncertainty on emerging market economies using GMM-PVAR model

This paper quantifies the spillover effects of economic policy uncertainty (EPU) in the United States on emerging market economies (EMEs). Using a generalized method of moments (GMM) estimation of a panel vector autoregression (PVAR) model on a dataset­ of 39 EMEs from 2005 to 2019, we find that increased U.S. EPU significantly raises the consumer price index (CPI) and negatively impacts the real GDP of these economies. Additionally, heightened U.S. EPU leads to a depreciation of emerging market currencies and a reduction in short-term interest rates. We employ a news-based EPU index developed by Baker et al. (2016) and conduct robustness checks using forward orthogonal transformation, an alternative EPU index, and by addressing the potential endogeneity of the oil price uncertainty (OPU) index. Our findings highlight the adverse effects of U.S. economic policy uncertainty on key macroeconomic variables in emerging markets, underscoring the importance of stable economic policies and robust institutions to mitigate these impacts.

Quantifying the value of probabilistic forecasts when trading renewable hybrid power parks in day-ahead markets: A Nordic case study

Renewable hybrid power parks (HPPs) that combine wind power, solar photovoltaic (PV) power and storage have emerged as promising electricity generation resources. However, HPPs face operational challenges due to the uncertainty in power production and electricity prices, which is why probabilistic forecasts that capture the uncertainty associated with forecast errors have gained attention. While the research community has proposed several methods to improve the accuracy of probabilistic forecasts, the question on how these forecasts can improve decision-making over deterministic forecasts is rarely quantified. This study assesses the value of probabilistic forecasts and analyze the improvement compared to deterministic forecasts in day-ahead markets. The value is quantified using almost two years of data from an operational HPP in Sweden. Results show that: (i) high grid connection capacities leverage the value of probabilistic models, (ii) a deterministic model is preferable for parks with a ratio of battery energy capacity to installed nominal power of the renewable power park equal to 0.6 MWh/MW, (iii) a probabilistic model allows utilizing the energy storage more effectively by reducing the energy throughput of the battery with 61%–87%, and (iv) a probabilistic model increases the unit profit when the forecast errors of the regulating price are higher than the spot price, (v) a simple probabilistic benchmark model, which is worse in terms of forecast accuracy, increases the unit profit compared to the analyzed deterministic models, and (vi) the more advanced probabilistic model analyzed in this study does not provide a significant improvement over a simple probabilistic benchmark model.

Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways

Stochastic techno-economic analysis (TEA) is pivotal in assessing the financial viability and risks inherent in biofuel production processes. In this method, the Monte Carlo approach entails the random sampling of input variables and multiple runs of the TEA model to create probability distributions of economic metrics. However, traditional Monte Carlo TEA, reliant on iterative calls to process simulation, is resource-intensive and time-consuming, hindering widespread adoption. To address these challenges, we present an accessible framework that harnesses machine learning methods to estimate techno-economic uncertainty in biofuel production pathways. Our approach streamlines the conventional simulation process by automating dataset generation and machine learning model training. These trained models enable rapid predictions of minimum fuel selling prices at any scale, accommodating randomized input variables based on their defined distributions. We illustrate the efficacy of our framework through examples from sustainable aviation fuel production pathways. Our research entails identifying the primary factors influencing uncertainties in minimum selling prices, exploring the synergistic effects of pathway inputs, and assessing how price variability is impacted by financial, technical, and supply chain factors. These examples underscore the framework's effectiveness in addressing breakeven price uncertainties in biofuel production across diverse input scenarios.

Does oil price uncertainty affect corporate total factor productivity? Evidence from China

In the era of high-quality economic development, oil price volatility (OVX) and the total factor productivity (TFP) of corporations are pivotal issues for both policymakers and scholars. This study leverages the implied volatility of oil prices and financial data from Chinese listed industrial companies spanning 2010 to 2022 to investigate the influence of OVX on firms’ TFP and the underlying mechanisms. The findings reveal that OVX substantially dampens firms’ TFP, with corporate leverage and financialization identified as key channels through which this impact occurs. Further heterogeneity analysis indicates that the negative impact of OVX on TFP is particularly pronounced in firms operating in industries with low concentration and among small and medium-sized enterprises (SMEs). The extension analysis suggests a threshold effect in the relationship between OVX and corporate TFP, with the suppressive effect of OVX on TFP intensifying as the level of corporate financialization increases. Consequently, it is imperative for policymakers to closely monitor oil price fluctuations and implement timely strategies to mitigate the risks associated with OVX.

Using DNPV to determine the economic viability of residential photovoltaic systems in Germany: Is the investment still worth it?

The conditions for photovoltaic investments have changed significantly in recent times. Declining subsidies and rising investment costs make these investments less attractive. In addition, electricity price uncertainty has risen sharply, making the economic benefit of using self-produced electricity from residential photovoltaic systems risky. This paper provides a novel framework to determine the financial attractiveness of such investments using the Decoupled Net Present Value (DNPV) method, which allows for a systematic and consistent consideration of the individual risks inherent in these investments. Furthermore, an extensive survey of the relevant current market parameters is performed to validate the common opinion that residential photovoltaic investments are generally advantageous. It turns out that in the current market environment in Germany, residential photovoltaic systems are not economically viable under most conditions. The system evaluated in this case study with a common size of 10 kWp leads to a negative DNPV of -1,664 €. This results in a compelling need for action for political decision-makers in order not to jeopardize the ambitious goals for the expansion of power generation from renewable energy sources.

Dinamika Harga Beras di Kota Kendari: Analisis Fluktuasi 2015-2023

Food price stability is one of the essential issues in the direction of government policy. Price uncertainty at the farm level and the guarantee of food price stability at the consumer level are unresolved problems. This study aims to analyze rice price fluctuations in Kendari City from 2015 to 2024. The research was conducted in Kendari City in March - May 2024. The research used secondary data, namely time series data on rice prices in Kendari City from 2015 to 2023. The research data collection technique is a literature study, namely collecting time series data on rice prices in Kendari City sourced from the Central Bureau of Statistics and related agencies. The research variable is the price of rice in Kendari City from 2015 to 2023. Data analysis used to analyze rice price fluctuations in Kendari City is the Coefficient of Variation (CV). The results showed that rice price fluctuations in Kendari City from 2015 to 2023 were low, with a coefficient of variation (CV) of 3%. The highest rice price condition in Kendari City occurred in 2023, which amounted to IDR 13,321 per kilogram. At the same time, the lowest rice price condition occurred in 2015, which amounted to IDR 9,738 per kilogram. Therefore, government policy intervention is needed to stabilize food prices, especially rice in Kendari City.

Global economic policy uncertainty and oil price uncertainty: Which is more important for global economic activity?

Using monthly data spanning from January 1997 to November 2022, this paper investigates the influence of global economic policy uncertainty (GEPU) and oil price uncertainty (OPU) on global economic activity using a structural vector autoregressive (SVAR) model. The findings from the baseline model reveal the adverse effects of both GEPU and OPU shocks on the global economy. The counterfactual analysis highlights that the negative impact of OPU shocks primarily stems from direct effects, while approximately half of the adverse impact of GEPU shocks is indirectly transmitted through amplifying OPU. Further examination of the indirect effects of these shocks uncovers that lagged effects primarily drive the indirect impact of OPU, whereas the indirect impact of GEPU is mainly driven by common shocks. These findings suggest a more substantial and independent impact of OPU shocks compared to GEPU shocks. Additionally, employing a time-varying vector autoregressive with stochastic volatility (TVP-VAR-SV) model, this paper explores the same dataset. The results of time-varying impulse responses illustrate a diminishing effect of GEPU shocks over the sample period, whereas the impact of OPU shocks initially decreases before subsequently increasing.

The Impact of Business Uncertainty on the Performance of Small Enterprises During the Economic Crises in Sri Lanka

Small enterprises often regarded as the backbone of economies, are currently confronted with unprecedented challenges due to the uncertain conditions arising from the COVID-19 pandemic and subsequent economic crisis in Sri Lanka. Therefore, the study attempts to assess the impact of uncertainty in the business environment on the business performance of small enterprises in Sri Lanka during the economic crisis. The study is a deductive and explanatory approach in quantitative design. The primary data was collected for a structured questionnaire from 185 owners of small enterprises in Kalutare district, Sri Lanka. The data was analyzed by using Partial Least Square Structural Equation Modelling. The results revealed that demand uncertainty, supply uncertainty, price uncertainty, policy uncertainty, and behavioral uncertainty have negatively affected business performance while technological uncertainty does not have a significant impact on business performance. Thus, the study empirically exposed that the business performance of small enterprises is severely blocked by business uncertainty during the economic crisis. Therefore, small enterprises should plan business activities to make rational business decisions based on reliable information to achieve better business performance by avoiding uncertainty during the economic crisis.