Periods Of High Prices Research Articles

FROM CALENDAR TIME TO BUSINESS TIME: THE CASE OF COMMODITY MARKETS

We address the problem of modeling commodity forward curves, while preserving empirical features observed in commodity markets. By letting a commodity market to “ live” in the tempo of a business rather than calendar clock, we create a model with a rich but realistic set of features, such as stochastic volatility, stochastic rate of mean reversion and various shapes of forward curves such as backwardation and contango. The model, when applied to extensive historical datasets of crude oil and natural gas forward curves, shows a remarkably good fit to the observed futures prices, also in periods of high volatility and negative prices. The model is developed in such a way that it can be used for a wide variety of applications, ranging from exotic derivatives pricing to risk management of commodity portfolios.

How Do Household Coping Strategies Evolve With Increased Food Insecurity? An Examination of Nigeria's Food Price Shock of 2015–2018

ABSTRACTFaced with a significant devaluation of its currency and a surge in food prices, the Nigerian government prohibited the use of foreign currency for food imports. This essentially blocked the importation of numerous food items under the guise of stimulating the domestic output of these staples. Consequently, food prices in Nigeria increased despite a global decline in food prices, and the incidence and severity of food insecurity escalated. This study examines the changes in the types and severity of coping mechanisms for food insecurity resulting from the food price shock caused by the oil price crash, currency devaluation, and restrictions on foreign exchange. Nigeria's General Household Survey Panel data from 2012 and 2015, during periods of high oil prices, is compared with data from 2018 when oil prices had remained low, the currency had been devalued, and the treasury had been depleted. Alongside detailed descriptive statistics, logistic and hurdle regressions are employed for statistical analysis. Findings indicate a rise in the percentage of Nigerian households grappling with food insecurity from 2015 to 2018. During this period, 68.7% of households resorted to at least one coping mechanism, 31.8% adopted six or more coping strategies, and 43.2% resorted to severe coping strategies. The issue stems not primarily from natural disasters or conflicts but from a failure in macroeconomic and agricultural economic policies. Our findings confirm that these policies come at great cost, particularly to female‐headed households, single‐parent households, households headed by elderly people, and other vulnerable populations, pushing them deeper into food insecurity.

The new era of turbulence: Peacemaking trends in post-carbon times

Based on an empirical comparison of peace processes in carbon-dependent economies over time, this article investigates the impact of decarbonization and the related declining availability of political finance on peacemaking. While the period of high oil prices in the mid-2000s saw a small number of peace deals that attempted comprehensive settlements, the decline of oil prices in the years from 2014 resulted in a new era of turbulence. The turbulence is characterized by a high number of peacemaking attempts that strongly lean toward conflict management rather than resolution. The reasons for this new turbulence are twofold: the available means for substantial “buy-in,” into a political marketplace by ruling elites in carbon-dependent conflictive political marketplaces have vanished while, at the same time, the strategic interest of international powers in geopolitical stability and their willingness and capability to invest in such stability has declined.

Enhancing economic sustainability in mature oil fields: Insights from the clustering approach to select candidate wells for extended shut-in

Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts. In periods of significant price drops, companies may consider extended duration of well shut-ins (i.e. temporarily stopping oil production) for economic reasons. For example, prices during the early days of the Covid-19 pandemic forced operators to consider shutting in all or some of their active wells. In the case of partial shut-in, selection of candidate wells may evolve as a challenging decision problem considering the uncertainties involved. In this study, a mature oil field with a long (50+ years) production history with 170+ wells is considered. Reservoirs with similar conditions face many challenges related to economic sustainability such as frequent maintenance requirements and low production rates. We aimed to solve this decision-making problem through unsupervised machine learning. Average reservoir characteristics at well locations, well production performance statistics and well locations are used as potential features that could characterize similarities and differences among wells. While reservoir characteristics are measured at well locations for the purpose of describing the subsurface reservoir, well performance consists of volumetric rates and pressures, which are frequently measured during oil production. After a multivariate data analysis that explored correlations among parameters, clustering algorithms were used to identify groups of wells that are similar with respect to aforementioned features. Using the field’s reservoir simulation model, scenarios of shutting in different groups of wells were simulated. Forecasted reservoir performance for three years was used for economic evaluation that assumed an oil price drop to $30/bbl for 6, 12 or 18 months. Results of economic analysis were analyzed to identify which group(s) of wells should have been shut-in by also considering the sensitivity to different price levels. It was observed that wells can be characterized in the 3-cluster case as low, medium and high performance wells. Analyzing the forecasting scenarios showed that shutting in all or high- and medium-performance wells altogether results in better economic outcomes. The results were most sensitive to the number of active wells and the oil price during the high-price period. This study demonstrated the effectiveness of unsupervised machine learning in well classification for operational decision making purposes. Operating companies may use this approach for improved decision making to select wells for extended shut-in during low oil-price periods. This approach would lead to cost savings especially in mature fields with low-profit margins.

A smart electricity consumption management framework using internet of things (IoT) to optimize electricity demand

The emerging technologies in the field of the electricity consumption are focusing the strengthening of the infrastructure of the electricity trading utilities. The infrastructure includes the smart devices which can generate real-time parameters for electricity supply monitoring in an efficient manner. This paper proposes an efficient artificial intelligence-based framework for optimal electricity demand load shifting in electricity consumption to minimize power outages in instant grid load fall and reduce the peak-to-average ratio. The framework is analyzing the acquired electricity consumption data for forecasting and load shedding at real-time which can significantly reduce customers' power outage. The proposed framework for optimal electricity demand load shifting is based on the emerging IT/OT (Information Technology/Operational Technology) technologies. We first analyzed the historical consumption of the electricity segment and then developed this framework. The analysis predicts the electricity consumption, can be utilized by the utility end as well as the consumer end by accruing economic benefits through shifting the electricity demand/load away from the very high-priced periods. The model is using various sensors and actuators in IoT Device for collecting the various real-time parameters for the electrical appliances and the machine learning algorithms to optimize the load over the electricity distribution assets. The proposed methodology improves the effectiveness of load optimization across electricity distribution assets by integrating a variety of sensors and actuators in Internet of Things devices to gather real-time metrics from electrical appliances. In addition to helping to forecast patterns in power consumption, the framework's previous consumption data analysis allows utility providers and customers to save money by strategically shifting electricity demand and load away from expensive times.

On–off-Grid Optimal Hybrid Renewable Energy Systems for House Units in Iraq

This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement.

Research on the Short-Term Economic Dispatch Method of Power System Involving a Hydropower-Photovoltaic-Pumped Storage Plant

The auxiliary regulation capacity of pumped-storage power stations can be utilized as an effective method to regulate the output of a hydro-photovoltaic complementary system, further mitigating the power fluctuations of the system and enhancing the photovoltaic absorption. This study aims to minimize power fluctuations and maximize the economic benefits of electricity generation in a hydropower-photovoltaic-pumped-storage complementary system (HPPCS), which are treated as the objective functions. It explores the participation of the HPPCS in grid active power balance auxiliary services. By modulating the participation ratio of the HPPCS in the grid’s active balance service, the system output is aligned to fluctuate proportionally with the daily load curve trend. Consequently, a short-term economic dispatch model for the integrated HPPCS is developed. The case study focuses on the considerable impact of weather conditions on photovoltaic (PV) power generation. In this model, the outputs of cascading hydro-power stations and pumped-storage power stations are considered as the decision variables. A decomposition-based multi-objective evolutionary algorithm is applied to derive an optimized intra-day dispatch Pareto solution set for the cascading HPPCS in each of these scenarios. Additionally, this study compares the Pareto solution sets for the HPPCS in various extents of its participation in grid auxiliary services. The results of the case study suggest that the system is capable of timely adjustments during the peak and trough periods of load demand. Considering the economic benefits, it enables the pumped-storage station to generate electricity for the grid during periods of high electricity prices and to store energy by pumping water when prices are low.

Peasant Resistance in Times of Economic Affluence: Lessons From Paraguay

Contrary to the established belief that low agricultural prices fuel peasant rebellion, our study investigates the surge in peasant unrest during a period of high agricultural prices. The transition to capital-intensive agriculture, characterized by reduced labor demand and heightened entry barriers, prompts landowners to expand into the agricultural frontier during periods of price increases. In these frontier regions, the soils, while comparatively less suitable, become economically viable for commercial agriculture when crop prices are high. This scenario sets the stage for heightened collective resistance, particularly where organizational capacities and subsistence communities provide peasants with symbolic and material resources to resist land encroachment. We provide evidence of this argument by using unique municipal-level data from Paraguay between 2000 and 2014, a period of rising yet fluctuating prices. Our study shows how the interplay between technological advancements in agriculture and global market forces reshape the geography of peasant rebellion.

Airline dynamic pricing with patient customers using deep exploration-based reinforcement learning

This paper addresses a crucial issue in the airline industry by tackling a dynamic pricing problem in the presence of patient customers, a scenario that has gained significance due to the revenue loss of airlines caused by customers’ non-myopic decision-making. To effectively capture this non-myopic characteristic, we propose a Markov decision process (MDP) including a history of offered prices as a state variable. In contrast to previous studies, distributions of customers’ properties are assumed to be unknown in advance for a more realistic representation of real-world scenarios. To deal with the new challenges of the problem, we propose utilizing a specific learning framework (i.e., deep exploration-based RL) that is unexplored in this domain. The numerical experiments demonstrate that its performance can be improved on the MDP we designed and show that it outperforms the benchmark algorithm. The structures of pricing policies generated from the bootstrapped deep Q-network algorithm imply that airlines should offer high and low prices alternately from the beginning of the sales period rather than increasing prices as time goes on. We also ascertain that more frequent consecutive high-priced periods can increase airlines’ revenue in environments with higher customer patience levels.

Have the Chinese national oil companies paid too much in overseas asset acquisitions?

This paper provides both a quantitative and qualitative analysis of whether the Chinese national oil companies have “overpaid” than their international counterparts in overseas asset acquisitions during 2001–2017. Using a hedonic pricing framework, we find that while the Chinese companies did not pay more than international oil companies for the period of 2001–2008, there is statistically significant evidence that they overpaid during the 2009–2016 period. A counterfactual analysis was conducted to quantify the extent of the overpayment. Interviews and qualitative analysis suggest that the overpayment mainly results from a rush to gain projects in a period of high oil prices and easier bank loans after the global financial crisis rather than concerns about energy security or other political considerations.

Oil Price Volatility and Business Profitability: Evidence from The Local Market

This study seeks to investigate the impact of oil price volatility on business profitability in the Province of Guimaras. Through a mixed-methods approach, the study aims to answer important research questions such as the correlation between crude oil price changes and micro business profitability and investment, potential differences in the effect of crude oil prices during periods of high and low prices, trends in the profitability of micro businesses entrepreneurs, the role of the oil and gas upstream segment and its prospects for the future, and proposes an extension program toolkit to help micro business entrepreneurs mitigate the impact of oil price fluctuations on their businesses. The study will collect and analyze data from 200 micro business entrepreneurs operating in five municipalities within the Province of Guimaras, which represent approximately 90 percent of micro businesses in the area by population. By providing significant insights into the impact of oil price volatility on small businesses, the findings of this study can help inform policymakers and business owners to improve economic sustainability and competitiveness in the local market. The researchers recommend several strategies to help small businesses better manage the risks associated with oil price volatility. These include diversifying business operations and revenue streams, developing proactive risk management strategies, seeking financial support and education, and advocating for policies that promote energy efficiency or diversify energy sources. The researchers hope that these recommendations will help small businesses maintain profitability in the face of changing market conditions.

Evaluation of model predictive control (MPC) of solar thermal heating system with thermal energy storage for buildings with highly variable occupancy levels

The presence or absence of occupants in a building has a direct effect on its energy use, as it influences the operation of various building energy systems. Buildings with high occupancy variability, such as universities, where fluctuations occur throughout the day and across the year, can pose challenges in developing control strategies that aim to balance comfort and energy efficiency. This situation becomes even more complex when such buildings are integrated with renewable energy technologies, due to the inherently intermittent nature of these energy source. To promote widespread integration of renewable energy sources in such buildings, the adoption of advanced control strategies such as model predictive control (MPC) is imperative. However, the variable nature of occupancy patterns must be considered in its design. In response to this, the present study evaluates a price responsive MPC strategy for a solar thermal heating system integrated with thermal energy storage (TES) for buildings with high occupancy variability. The coupled system supplies the building heating through a low temperature underfloor heating system. A case study University building in Nottingham, UK was employed for evaluating the feasibility of the proposed heating system controlled by MPC strategy. The MPC controller aims to optimize the solar heating system’s operation by dynamically adjusting to forecasted weather, occupancy, and solar availability, balancing indoor comfort with energy efficiency. By effectively integrating with thermal energy storage, it maximizes solar energy utilization, reducing reliance on non-renewable sources and ultimately lowering energy costs. The developed model has undergone verification and validation process, utilizing both numerical simulations and experimental data. The result shows that the solar hot water system provided 63% heating energy in total for the case study classroom and saved more than half of the electricity cost compared with that of the original building heating system. The electricity cost saving has been confirmed resulting from the energy shifting from high price periods to medium to low price periods through both active and passive heating energy storages.

The impact of the war in Ukraine on energy prices: Consequences for firms’ financial performance

We analyze the impact of the shock to energy prices induced by the war in Ukraine on the financial performance of the major European firms listed in the Eurostoxx 600 index. We find that equity returns (CDS spreads) decreased (increased) more substantially for firms characterized by high energy intensity and carbon emission intensity. We then present a method, based on a VAR model, to produce forecasts of firms’ CDS spreads conditional on a 1 or 3-month stress period of high electricity prices and we document a non-negligible increase in the number of firms with a CDS-implied non-investment rating.

Hierarchical transactive power exchange method on expressways for EV energy supplement

Electric vehicle (EV) users’ driving requirement is restricted by the long charging period and high cost. In this paper, a hierarchical transactive power exchange method on expressways is proposed to eliminate range anxiety faced by EV users and further reduce their cost. When EVs are driven on expressways, battery swapping is considered the suitable power exchange mode due to high efficiency and adjustability. EVs are scheduled to supplement energy in battery swapping stations (BSSs) according to the remaining energy and battery swapping cost. Then, the power exchange among batteries and the power grid in BSSs is optimized for reducing the operation cost. In the optimization process, battery-to-battery and battery-to-grid modes are considered for reducing the power cost in the high electric price period. Some EVs release battery energy in designated BSSs and supplement energy in other BSSs. It reduces fast charging power of the battery and operation costs in designated BSSs. Several case studies are presented to validate the effectiveness and economy of the proposed method.

Enhancing energy system models using better load forecasts

AbstractSince energy system models require a large amount of technical and economic data, their quality significantly affects the reliability of the results. However, some publicly available data sets, such as the transmission system operators’ day-ahead load forecasts, are known to be biased and inaccurate, leading to lower energy system model performance. We propose a time series model that enhances the accuracy of transmission system operators’ load forecast data in real-time, using only the load forecast error’s history as input. We further present an energy system model developed specifically for price forecasts of the short-term day-ahead market. We demonstrate the effectiveness of the improved load data as input by applying it to this model, which shows a strong reduction in pricing errors, particularly during periods of high prices and tight markets. Our results highlight the potential of our method the enhance the accuracy of energy system models using improved input data.

Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector

Electrification of space heating combined with electricity generation through renewable resources has a significant potential to reduce greenhouse gas emissions, especially when coupled with advanced control strategies that utilize building energy flexibility. This study analyses data from approximately 7,800 houses in Ontario and Québec, Canada to investigate the most suitable data length, data interval and calibration horizon of building models for assessment of Model Predictive Control strategies on a large scale. Overall, models with calibration horizon of 24 h, data length of 7 days and time interval of 15 min provided the best balance between accuracy and computational resources. The calibrated models were used to compare a simple deadband controller to a Model Predictive Controller that minimizes the cost of electricity for a cold day in January. The results showed that the Model Predictive Controller can systematically reduce the high-price energy consumption, while improving thermal comfort, by successfully preheating before the high-price periods. Preheating shifts the peak power consumption, reducing it by 15 % for Ontario and 30 % for Québec. If just 1,000 houses adopt optimal strategies, the grid could see an average reduction in energy consumption of up to 15 MWh and 11 MWh during high-price periods in Ontario and Québec respectively.

Are climate change policies politically costly?

Are policies designed to avert climate change (Climate Change Policies, or CCPs) politically costly? Using data on governmental popular support and the OECD's Environmental Stringency Index covering 30 countries between 2001 and 2015, our results show that CCPs are not necessarily politically costly: policy design matters. First, in contrast to non-market-based CCPs (such as emission limits), only market-based CCPs (such as emission taxes) entail political costs for the government. Second, the effects are only present when CCPs are adopted during periods of high oil prices, prior to elections, or in countries depending strongly on non-green (dirty) energy sources. Third, CCPs are only politically costly when inequality is high and/or social insurance/transfer does not sufficiently address the regressivity of CCPs. Our results are robust to numerous robustness checks including to address concerns related to endogeneity issues.

Technology Focus: Mature Fields and Well Revitalization (January 2023)

Capitalizing on untapped potential and quantifying risk is the key to success in a turbulent commodity industry. Mature fields have seen it all. Periods of high oil price have invited increased investment and pushed boundaries, while low-oil-price periods have had a severe effect on the fields’ sustainability and development. Mature fields have existed through a time of great technological change and stand on the cusp of their ultimate fate—either to maximize recovery with the lowest possible expenditures amid tightening environmental regulations or to close the taps and prepare for painful abandonments. In a capital-intensive industry, mistakes often are unforgiving and the costs of uncertainty and error are great. Fluctuating technological, political, and business influences add to the volatility and risk in selecting the next big idea for mature fields. The framework and success of future opportunities is reliant on the data used to quantify them, and this is something of which all mature fields have plenty. As they have delivered value throughout their life, the uncertainty associated with them reduces, and the new ideas discussed here come to light. Alternative use of wells for geothermal energy generation, carbon sequestration in depleted reservoirs, and the use of machine learning to maximize recovery are solutions that provide insight into how diverse the scope of mature field rejuvenation ideas can be. These solutions have something in common, however; they aim to capitalize on data, modularize problems, and structure a sustainable solution. These solutions are scalable, upgradable, and, most importantly, cost-effective for large and small operators. The upstream sector has long valued efficiency and accuracy. Small improvements in mature fields can make a significant economic difference with an established infrastructure in place. Value in large mature fields still exists, but one has to know where to look and what lenses to use. Recommended additional reading at OnePetro: www.onepetro.org. IPTC 21436 Permeability Prediction Using Rock Typing, Flow-Zone Indicator, and Machine-Learning Techniques in a Brownfield Offshore Malaysia by Budi Priyatna Kantaatmadja, Petronas, et al. SPE 208204 Generating Value From Mature Gas Fields by Quantifying Well-Integrity Assurance With a Critical Analysis of Multiple Logs and Retrieved Tubular Surface Inspection by Christna Golaco, Sharjah National Oil Corporation, et al. SPE 209988 Successful Reservoir Management and Optimization of Mature Steamflood Projects Using Artificial Intelligence by Andrei Popa, Chevron, et al.

The Importance of Primary Commodities in the Foreign Trade of Brazil in the 21st Century

This paper aims to identify the importance of primary commodities in Brazil's foreign trade and define the commodity groups in whose exports Brazil has the highest comparative advantages. At the same time, we want to find out how these comparative advantages have changed and developed in the period from 2001 to 2020, i.e., in the period of high prices of primary commodities (2003 – 2012) and the period after until the outbreak of the global pandemic COVID-19. To achieve this goal, we used the Lafay index of international specialization using data at the four-digit level of the Harmonised System. The results of the Lafay index indicate that Brazil achieves comparative advantages, especially in exports of agricultural products, food, and some minerals. A significant comparative advantage is achieved in the soybean and iron ore commodity groups.

Designing and analysis of index-based long-term electricity market contract considering recent surge of coal price in China

Under the background that the spot market has not yet been fully established, the risk of coal price fluctuation lacks an effective mechanism to transmit to users. During the period of high coal prices, coal-fired units suffered serious losses and the willingness to generate electricity declined extremely, which was not conducive to the stable growth of the national economy. To solve the above problems, after investigating domestic and foreign solutions, this paper conceived a designing idea for the index-based long-term electricity market contract suitable for the current electricity market. Based on the actual data from a province in China, the specific parameters of index-based contract were designed and the profit of units were analyzed. The results show that the profit fluctuations of the coal-fired units will be significantly reduced after the trading contract adopted. In the end, the key issues of trading contract and the impact on market entities were discussed.