Price Uncertainty Research Articles

An IGDT-WDRCC based optimal bidding strategy of VPP aggregators in new energy market considering multiple uncertainties

This study addresses the volatility and uncertainty challenges in managing renewable energy within electricity markets, particularly focusing on the role of Virtual Power Plant (VPP) aggregators. Recognizing the risks these uncertainties pose to the revenue and stability of power systems, the paper presents a novel information gap decision theory (IGDT)-Wasserstein metric based distributionally robust chance constraint (WDRCC) approach to devise an optimal bidding strategy for VPP operators. It involves a data-driven distributionally robust optimization framework, leveraging the worst-case scenario from the distributed resource uncertainties, guided by an ambiguity set rooted in the Wasserstein metric. Furthermore, the distributionally robust chance constraint modeling is introduced ensuring that uncertainty constraints of distributed resources meet a predefined risk level. Although this method shows promising out-of-sample performance, it relies on forecasted energy prices, a notable limitation given the price volatility and information inadequacy in the newly-opened market. To address this, the risk-averse bidding strategy, grounded in IGDT, is proposed simulataneously to safeguard the operator’s expected returns against price uncertainties, implementing an advanced piecewise linear approximation technique, ”nf4l,” for linearizing the bi-linear term from IGDT. The effectiveness of this approach is empirically validated through a comprehensive case study and sensitivity analysis.

Degradation-infused energy portfolio allocation framework: Risk-averse fair storage participation

This work proposes a novel degradation-infused energy portfolio allocation (DI-EPA) framework for enabling the participation of battery energy storage systems in multi-service electricity markets. The proposed framework attempts to address the challenge of including the rainflow algorithm for cycle counting by directly developing a closed-form of marginal degradation as a function of dispatch decisions. Further, this closed-form degradation profile is embedded into an energy portfolio allocation (EPA) problem designed for making the optimal dispatch decisions for all the batteries together, in a shared economy manner. We term the entity taking these decisions as ‘facilitator’ which works as a link between storage units and market operators. The proposed EPA formulation is quipped with a conditional-value-at-risk (CVaR)-based mechanism to bring risk-averseness against uncertainty in market prices. The proposed DI-EPA problem introduces fairness by dividing the profits into various units using the idea of marginal contribution. Simulation results regarding the accuracy of the closed-form of degradation, effectiveness of CVaR in handling uncertainty within the EPA problem, and fairness in the context of degradation awareness are discussed. Numerical results indicate that the DI-EPA framework improves the net profit of the storage units by considering the effect of degradation in optimal market participation.

Distributionally robust CVaR optimization for refinery integrated production–maintenance scheduling under uncertainty

In the petroleum refining industry, efficient production planning and maintenance scheduling are crucial for economic performance and operational efficiency. Moreover, the production processes face significant uncertainties stemming from market fluctuations and equipment failures. However, traditional optimization methods often treat production and maintenance independently and neglect the risk management associated with uncertainties in the production process, leading to unreliable plans and suboptimal execution. To address these issues, this paper proposes an innovative data-driven distributionally robust conditional value-at-risk (DRCVaR) method to tackle the integrated production–maintenance optimization problem under crude oil price uncertainty. By constructing confidence sets with L2 norm constraints based on historical data, our approach directly links the model’s conservatism to the amount of available data, effectively managing risk. In addition, we propose robust linear transformation to simplify the min–max nonlinear problem into a conic constraint problem, enhancing solution efficiency and ensuring better operational stability. Refinery case studies demonstrate that the proposed DRCVaR consistently achieves a practical and acceptable solution, significantly outperforming state-of-the-art approaches.

Data driven multi-objective economic-environmental robust optimization for refinery planning with multiple modes under uncertainty

In the context of the global energy transition, the refining industry is facing a major challenge between economic profitability and environmental protection. Planning refineries using traditional methods ignores the complex interactions between market dynamics and environmental factors, which causes unsatisfactory decisions. This study proposes a novel model-driven multi-objective robust optimization framework designed to tackle both economic and environmental challenges faced by the refining industry during its transformation process. The framework aims to maximize refinery profitability while reducing price uncertainty and achieving low carbon emissions and university energy use. Firstly, we introduce a nonlinear mechanism model of key production units to construct a multi-objective planning model that aims to simultaneously maximize profits and minimize carbon emissions and energy consumption. Secondly, to capture market dynamics uncertainty, we construct an uncertainty set based on the principal component analysis-robust kernel density estimation technique. Thirdly, using robust optimization theory, we transform this uncertainty set into a solvable binary problem. Finally, a case study is presented to demonstrate the effectiveness of this optimization framework in real-world applications, where we can keep CO2 emissions constant and still make the refinery profitable up to 95% in the worst-case uncertainty optimization. Additionally, uncertainty optimization’s profitability increases as conservatism decreases, illustrating the framework’s flexibility in responding to changing markets. Using this framework for decision support can assist the refining industry in protecting the environment while safeguarding economic profitability.

A Lagrangian relaxation approach for algae-based biofuel supply chain network design under uncertainty and pricing issue.

Biofuel has gained significant attention as a potential source to meet fuel demands instead of fossil fuel. The price of biofuel and alternative fuel have a considerable impact on biofuel demand. Thus, it is important to design a biofuel supply chain network that incorporates the biofuel price into an elastic demand. More precisely, a variable demand, including customer importance level (to the environment), biofuel price, and substituted fuel price, is considered in this research. Furthermore, this research presents a bi-objective mixed-integer quadratic formulation that aims to maximize the total profit of the supply chain and carbon absorption in harvesting areas. The problem is solved by the ε-constraint and lagrangian relaxation methods due to its complexity. Moreover, substituted fuel price uncertainty is addressed by two-stage stochastic programming. Finally, a real case study utilizing the data envelopment analysis approach is applied to assess the efficiency and currency of the addressed model. Several consequences are illustrated in the case study, such as rich areas for exporting algae, suggesting hub locations for biofuel production, etc.

Sensitivity Analysis of a Nuclear Hybrid Energy System With Thermal Energy Storage in Deregulated Electricity Markets Considering Time Series Uncertainty in Electricity Price

ABSTRACTAdding thermal energy storage to nuclear power plants has been proposed as a way to allow nuclear plants to operate more flexibly and potentially be more competitive in deregulated electricity markets. The economics of these systems in deregulated markets are subject to uncertainties in capital costs, operating costs, and revenue. This study quantifies the uncertainty in the net present value of a nuclear power plant with integrated thermal energy storage in three U.S. deregulated electricity markets considering these sources of uncertainty and quantifies, for the first time, the relative contributions each source makes to the overall uncertainty. To accomplish this, a computationally efficient block bootstrap method is introduced to quantify uncertainty contributions from the stochastic time series of electricity prices, achieving a two order of magnitude decrease computational time compared to the model‐based methods used in previous works while also relaxing several strict assumptions made by the model‐based approach. Up to 18.5% of the overall variance in net present value is attributable to variance in the electricity price stochastic process, with this sensitivity varying significantly across markets.

Robust optimization model for traceable procurement of construction materials considering contract claims

In claim contracts, project owners and contractors set negotiated prices and exemption amounts for price adjustments to deal with the uncertainty of material prices, which is often overlooked in the optimization of procurement strategies. Therefore, considering contract claims, this paper constructs an optimization model for contractors’ traceable procurement strategies to address the multi-stage, multi-source procurement issue. A robust optimization model is used to consider the uncertainty of procurement prices, and the robust parameter is set to flexibly control the robustness of the solutions. The results indicate that contractors with different risk attitudes have the same preference for the exemption amount, but they exhibit varying sensitivities to the exemption amount and also have different preferences regarding the negotiated price. Furthermore, the study reveals that negotiated price exerts an anchoring effect on the procurement strategies of contractors.

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.

A tri-level hybrid stochastic-IGDT dynamic planning model for resilience enhancement of community-integrated energy systems

In this paper, a resilience-oriented dynamic planning framework is developed for optimal sizing of the community-integrated energy system components, including photovoltaic system, wind turbine, boiler, power to gas technology, combined heat and power, and storage devices. The proposed framework is formulated as a tri-level linear programming that performs Community-Integrated Energy Systems design under normal conditions at the first level while evaluating system operation during disastrous conditions at the second level. In the third level, re-planning is done based on information-gap decision theory to enhance community-integrated energy systems’ resilience against various natural disasters. Stochastic programming is also employed at all levels to address the uncertainty of electricity market price, energy demand, solar radiation, and wind speed. A detailed P2G system including, a methanation device, electrolysis, and hydrogen storage is designed to improve the resilience of the system. In addition, the power to gas proposed in this model is coupled with a carbon capture unit to mitigate carbon emission by reusing emitted carbon from the flue gas of the boiler and combined heat and power. Various economic metrics and technical constraints are also considered to achieve a realistic design. Numerical simulation results demonstrate that the positive interplay of renewable energy resources and energy storage technologies, specifically P2G, assisted the CIES in maintaining a stable and uninterrupted energy supply during extreme events. The results exhibit that increasing only 10 % of the resilience budget can decrease >93 % of unserved demand and helps reduction of >37 % of carbon emissions.

Optimal forward contract design in a supply chain with asymmetric cost information

In a buyer-led supply chain, the buyer utilizes a forward contract to effectively cope with the risk brought by uncertainty in demand and raw material prices. However, the production cost, as private information of the supplier, will inevitably affect the formulation of a forward contract. This study constructs a procurement system consisting of a buyer, a supplier, and an imperfect spot market to explore how the buyer designs the forward contract under asymmetric cost information. By applying the principal-agent theory, this study allows the buyer to maximize profits while disclosing the supplier's cost information. The results obtained indicate that in order to screen the cost type, the forward order quantity from the high-cost supplier is downward distorted. Asymmetric cost information will reduce the buyer's profits, but bring additional information rent to the supplier. Interestingly, it is also observed that asymmetric material cost information can discourage the buyer from engaging in speculative activities in the spot market. While the existence of a well-developed raw material spot market with a reduced price provides the buyer with an alternative way to make procurement, which mitigates the negative impacts of his cost information disadvantages.

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.

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.

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

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.

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.