Price Scenarios Research Articles

Stochastic Planning of Integrated Energy System via Frank-Copula Function and Scenario Reduction

Uncertainty introduces both significant complexity and the high risk of suboptimal investment decisions into power system planning. Considering the multiple uncertainties of wind and solar power output, load demands, and energy prices as well as pollutant emission factors during the planning period, a multi-scenario stochastic programming model of an integrated energy system (IES) is constructed in this paper. Scenarios of wind and solar power output are generated based on non-parametric kernel density estimation and the Frank-Copula function; scenarios of load demands are generated through DeST software, and energy prices and pollutant emission factors are generated corresponding to a uniform distribution. Then the generated scenario results of wind and solar power output and load demands are reduced by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -means clustering; the generated scenarios of energy prices and pollutant emission factors are reduced by discrete approximation of continuous distribution based on Gaussian quadrature. An illustrative example with 8 cases is performed to analyze the influences of each uncertain parameter on the optimal configuration and economy of the IES.

An Application of Decision Trees Algorithm to Project Hourly Electricity Spot Price as Support for Decision Making on Electricity Trading in Brazil

Estimating the price of a financial asset or any tradable product is a complex task that depends on the availability of a reasonable amount of data samples. In the Brazilian electricity market environment, where spot prices are centrally calculated by computational models, the projection of hourly energy prices at the spot market is essential for decision-making, and with the particularities of this sector, this task becomes even more complex due to the stochastic behavior of some variables, such as the inflow to hydroelectric power plants and the correlation between variables that affect electricity generation, traditional statistical techniques of time series forecasting present an additional complexity when one tries to project scenarios of spot prices on different time horizons. To address these complexities of traditional forecasting methods, this study presents a new approach based on Machine Learning methodology applied to the electricity spot prices forecasting process. The model’s Learning Base is obtained from public information provided by the Brazilian official computational models: NEWAVE, DECOMP, and DESSEM. The application of the methodology to real cases, using back-testing with actual information from the Brazilian electricity sector demonstrates that the research is promising, as the adherence of the projections with the realized values is significant.

Design a sustainable supply chain for the textile and clothing industry with consideration of carbon emissions

The clothing industry accounts for nearly 60% of the global textile industry. It is largely responsible for the consumption of non-renewable resources. According to studies, it is responsible for nearly 10% of greenhouse gas emissions in the world (Cuc and Vidovic (2014)). The aim of this article is to propose a planning approach that integrates two decision levels (strategic and tactical), using a new mixed integer linear programming model (MILP) that integrates carbon emissions in SCMP (Supply Chain Management Planning). This approach must be used in a general way in all SCMP industries for the control of carbon flows and resources. It could be used as a decision support tool to evaluate and monitor the effects of carbon emissions. This tool offer to the industrials the best choices at different stages (selection of suppliers, plants, transportation modes, storage etc.), with lower cost and carbon emissions. For this purpose, the approach is tested on several scenarios, based on severals literature case studies in the textile and clothing sectors. The results show that carbon price uncertainty would significantly influence the economic and environmental management of the supply chain, and the repartition of the manufacturing activity (insource or outsource). Over a range of carbon price scenarios, the results show a reduction over to 36% in carbon emissions in the supply chain.

Machine learning-based estimated ultimate recovery prediction and sweet spot evaluation of shale oil

In this paper, we propose a machine learning-based, multi-discipline integrated evaluation workflow to evaluate sweet spots. We predict the estimated ultimate recovery (EUR) map and evaluate sweet spots at different oil price scenarios in the study area. The results show that the correlation coefficient between well EUR and predicted EUR is 0.9247. At the oil price of $40, $50, and $60/bbl, the sweet spot areas are 3.31 km2, 27.75 km2, and 51.61 km2, and the total economically recoverable reserves are estimated to be 2.46 × 105 t, 14.02 × 105 t, 26.91 × 105 t respectively. It is concluded that machine learning model is an excellent way to auto learn the relationship between complex shale reservoir variables with EUR. It is innovative to show the distribution of sweet spots according to oil price. This workflow can be wildly used in the shale oil exploration and development to evaluate investments and optimise well placement. [Received: August 26, 2020; Accepted: April 19, 2021]

Analysis of MaaS membership attributes: an agent-based approach

Mobility-as-a-service (MaaS) provides a bundle of mobility services under one plan subscription, allowing its customers to travel on a seamless multimodal system. It is envisioned that MaaS will foster more sustainable mobility, but its expected impact is still unknown, since no MaaS system has been deployed in practice, if not in few pilots. This study aims to identify potential MaaS members attributes applying an agent-based modelling approach including MaaS as alternative for the agents. In particular, we simulate a MaaS system configuration in the city of Berlin, giving accessibility to a basic MaaS plan characterized by a daily subscription fee payment. We evaluate 5 different ranges of MaaS plan price scenarios. For comparison, we simulate a Pay-as-you-go configuration, considering specific trip-based costs per each mobility service and considering the total cost of car ownership in the daily agents’ score function. Results generally show a general modal shift from walk, bike and car to MaaS services. Expectedly, the number of MaaS customers decreases as the MaaS price rises. Former public transport and free-floating car-sharing users represent the main potential MaaS customers, as they are the users who employ MaaS services for the longest time during the day. Moreover, car trips have been substituted more than 40% by public transport in MaaS scenarios suggesting that MaaS could have an overall positive impact on the environment.

Tail-GAN: Nonparametric Scenario Generation for Tail Risk Estimation

The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components, with particular importance devoted to the simulation of tail risk scenarios. Commonly used parametric models have been successful in applications involving a small number of assets, but may not be scalable to large or heterogeneous portfolios involving multiple asset classes. We propose a novel data-driven approach for the simulation of realistic multi-asset scenarios with a particular focus on the accurate estimation of tail risk for a given class of static and dynamic portfolios selected by the user. By exploiting the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES), we design a Generative Adversarial Network (GAN) architecture capable of learning to simulate price scenarios that preserve tail risk features for these benchmark trading strategies, leading to consistent estimators for their Value-at-Risk and Expected Shortfall. We demonstrate the accuracy and scalability of our method via extensive simulation experiments using synthetic and market data. Our results show that, in contrast to other data-driven scenario generators, our proposed scenario simulation method correctly captures tail risk for both static and dynamic portfolios.

Optimal ship lifetime fuel and power system selection

Alternative fuels and fuel-flexible ships are often seen as promising solutions for achieving significant greenhouse gas reductions in shipping. We formulate the selection of alternative fuels and corresponding ship power systems as a bi-objective integer optimization problem. We apply our model to a Supramax Dry-bulker and solve it for a lower bound price scenario including a carbon tax. Within this setting, the question whether bio-fuels will be available to shipping has significant effect on the lifetime costs. For the given scenario and case study ship, our model identifies LNG as a robust power system choice today for a broad range of GHG reduction ambitions. For high GHG reduction ambitions, a retrofit to ammonia, produced from renewable electricity, appears to be the most cost-effective option. While these findings are case-specific, the model may be applied to a broad range of cargo ships.

Oil-Price Based Long-Term Hourly System Marginal Electricity Price Scenario Generation

We synthesize scenarios of hourly electricity price, which is known as the system marginal price (SMP), for thirty-years based on the oil price. Hourly SMP scenarios are very important when planning new generators because the revenue and cost of new capacity margins are determined based on the SMP. Because the SMP contains both short-term and long-term periodic patterns, designing a single model based on these patterns to predict the SMP is difficult. Although oil price affects SMP, they can not be directly used in the forecasting model because the resolution of SMP is at hourly intervals, but that of oil price is at yearly intervals. To overcome these problems, we decompose the SMP into annual, monthly, and daily components, and the components are predicted based on different models. The model for the annual component (AC) is designed to predict the long-term trend based on fuel price scenarios. The model for the monthly component (MC) is designed to predict the seasonal trends based on the long short term memory (LSTM) model. The model for the daily component (DC) is designed to predict the daily SMP fluctuation. Finally, we synthesize SMP scenarios by aggregating three components. We make three types of SMP scenarios (high, reference, and low), and the performance of the scenarios is tested using previous data for two years on the basis of mean absolute error (MAE). Due to the global COVID-19 pandemic, the low type of SMP scenario is most accurate. We also verify that the reliability of long-term scenarios can be secured by using oil price while maintaining monthly and daily patterns.

Subsidized and unsubsidized price competition in a multi-echelon natural gas supply chain with governmental and private members

Determining the optimal prices among supply chain members, including both governmental and private natural gas producers, processing plants (PPs), and distribution centers (DCs) is an important and challenging task. This paper analyses the competitions between the members of a multi-echelon natural gas supply chain for determining their prices, which specify the demand for their products. This study proposes a novel game theory model to investigate cooperative and non-cooperative pricing scenarios. While, in Scenario 1, all supply chain members make decisions cooperatively as a centralized model, the members compete simultaneously to obtain the Nash equilibrium in Scenario 2. The Karush-Kuhn-Tucker (KKT) conditions are defined for the non-cooperative game and the Nash equilibrium is obtained using two different methods. The proposed model is applied to a realistic case study based on a data set derived from the natural gas industry to analyze the optimal pricing decisions under the two scenarios and verify the practicality of the proposed approach. Moreover, the variability in the profits of the members with respect to the changes in the base demands and self-price sensitivity parameters are studied. The results show that the government’s total profit in the cooperative scenario is more than the non-cooperative scenario, while the profit of the private DC in the Nash equilibrium of the non-cooperative scenario is more than that of the cooperative scenario. Moreover, increasing the base demand for the private DC leads to a decrease and increase in its profit in Scenarios 1 and 2, respectively. These findings illustrate the potential sources of conflicts between governmental and private members of the natural gas supply chains when seeking collaboration to improve their profits and highlight the need for future research on incentive mechanism designs facilitating such collaboration.

An optimisation model for incentivising the development of marginal oil and gas fields amidst increasingly complex ownership patterns: UKCS case study

Many recent discoveries in the UKCS have been economically marginal and comparatively small such that they are unable to be developed on a standalone basis. Nevertheless, there is a proliferation of production facilities and transport infrastructure that can, on accepting a tariff, process hydrocarbons from these smaller-sized oil and gas fields, thereby contributing to the UK Government's strategy of maximising economic recovery (MER). However, the empirical dimensions of cost-sharing arrangements between a field owner (host field or hub) and tie-in (satellite) fields are not well examined in the literature although they influence long-term economics in matured oil provinces. In this paper, we develop a mixed integer programming optimisation model to analyse how these third-party access to infrastructure issues impact MER in the UKCS. A first-best situation with only one regional operator is empirically compared with the actual situation of multiple ownership. We then assess the impact of different infrastructure unbundling provisions via pipeline tariff choices and their interaction with the fiscal regime on the net present value (NPV) and other project metrics such as timings of hub and field shutdowns. We find that differences in field ownership, tariff choice and changes to the fiscal regime impact the overall NPV of field developments. Our results also show that having a progressive fiscal regime in a mature basin such as the UKCS is important to support continued operations under low oil prices while increasing the government's take at high prices. The cost-sharing effect shows that imposing strict cost-sharing rules does not change overall project valuations, even in a low oil price scenario. An additional contribution to the extant literature is that tariff determination could be based on cost-sharing rules that enjoin each field tieback to a hub to pay a split tariff, comprised of a fixed cost of service (access charge) and variable (marginal) costs. • Presents MIP model for analysing economic dependencies of infrastructure assets. • Ownership style, choice of tariffs, and fiscal regime impact overall field value. • Reducing marginal tax rates improves profitability, reflecting neutrality and progressivity. • Tariffs are decisive as to whether satellite or user fields are developed. • Tariff determination to maximise MER should be based on two-part cost-sharing.

Milk, Fertility and Udder Health Performance of Purebred Holstein and Three-Breed Rotational Crossbred Cows within French Farms: Insights on the Benefits of Functional Diversity.

Simple SummaryWhen implementing dairy crossbreeding in purebred Holstein (HO) herds, farmers expect to improve the overall herd performance. However, they lack knowledge about how to manage and benefit from the diversity of genetic classes generated by three-breed rotational crossbreeding, which firstly refers to the cohabitation of purebred HO and first- and second-generation crosses (F1 and G2, respectively) within the herd. This study aimed to compare milk production, reproduction and udder health performance of HO, F1 and G2 cows, and to estimate how their combination in different proportions in the herd affects its profitability. We found that HO, F1 and G2 had different and complementary performance profiles, with two main trends. First, HO had higher milk yield, while F1 and G2 crosses had better fertility performance. Second, F1 had win-win trade-offs between milk production, fertility and udder health compared to HO and G2. We showed that HO-F1 or HO-F1-G2 (below 30%) mixed herds could be more profitable than purebred HO or fully crossbred herds with a conventional milk price. These findings can be used for advising purebred HO farmers who wonder about the benefits and the ways of managing the diversity of animal entities generated by the use of dairy crossbreeding in their herds.Using three-breed rotational crossbreeding in a purebred Holstein (HO) herd raises two questions: Do the different genetic classes of cows generated by crossbreeding perform differently? Are there any economic benefits of combining them within a herd? This study aimed at comparing the performance between the different genetic classes resulting from the use of three-breed rotational crossbreeding, and simulating the effect of combining them on herd profitability. Based on a dataset of 14 French commercial dairy herds using three-bred rotational crossbreeding from a HO herd over a 10-year period, we defined three genetic classes according to the theoretical value of heterosis and the percentage of HO genes. We performed linear models and estimated least square means to compare HO cows and the first and second generation of crosses (F1 and G2, respectively) on eight performance characteristics related to milk yield and solids, udder health and fertility. We used these to simulate profitability of five herd compositions differing according to HO, F1 and G2 proportions. We showed that HO, F1 and G2 cows had different and complementary performance profiles. HO had a win-lost trade-off between milk yield and fertility, G2 had the opposite trade-off and F1 had a win-win trade-off. Differences regarding milk solids and udder health were less clear-cut. We highlighted that combining HO with F1 or with both F1 and G2 (below 30%) could be more profitable than using purebred HO or crossbred herds in a conventional milk price scenario. These findings provide evidence on the benefits of functional diversity generated from the use of dairy crossbreeding in dairy herds.

Proposing Electric Motorcycle Adoption-Diffusion Model in Indonesia: A System Dynamics Approach

In 2019, the number of conventional vehicles in Indonesia reached 133,617,012 units, dominated by motorcycles of 112,771,136 units and passenger cars of 15,592,419 units. The high number of conventional motorcycle users can increase the number of pollutants and combustion emissions in the environment. This condition has encouraged the transition to a sustainable transport system that will be needed for decades to come, especially for the electric motorcycle to resolve the issue. This research aims to predict and estimate the market share of electric motorcycles by considering life cycle cost per kilometer. System dynamics simulations are developed to model the adoption-diffusion of electric motorcycles in Indonesia. This model has four main modules: an electric motorcycle module, a conventional motorcycle module, an economy module, and a consumer market module. This model shows a positive trend of EM market share from 2021-2030, with the market share value of EM is 0,411 in 2030. The development of retail price subsidy and electricity price scenarios is also carried out to determine the right policies to accelerate the adoption-diffusion process. Based on the scenario, the provision of retail price subsidy and a decrease in electricity price can increase the value of the EM Market Share.

Posted price and name-your-own-price in a product line design problem

In this research, we address Name your own price (NYOP) as a mechanism to offer products with transparent, rather than opaque, quality levels. We compare posted price (PP) and NYOP in a product line design problem from a firm’s viewpoint. We first consider the firm offering two vertically differentiated products that each can be priced by NYOP or PP. The quality level of products is considered either as a decision variable or as a fixed predetermined value for the firm. A customer correspondingly decides which product to purchase and if applicable, the bid at NYOP. We characterize both the customer’s and the firm’s decisions under four possible pricing scenarios. The results show that, it is most profitable for the firm to use PP for both products. We then consider if each product is offered by a competitive firm, where quality levels might again be decision variables or fixed. Results show that both competitive firms prefer PP to NYOP when they can create quality differentiation. The firm that offers the product with a lower quality level prefers PP to NYOP for all combinations of fixed quality levels as well. The other firm, with a higher quality level, also usually prefers PP to NYOP; However, it can be better off using NYOP when fixed quality levels are large and close enough to each other. In this case, the preference of NYOP over PP increases as customers’ willingness to pay enhances.

A Fundamental Economic Assessment of Recovering Rare Earth Elements and Critical Minerals from Acid Mine Drainage Using a Network Sourcing Strategy

In recent years, acid mine drainage (AMD) has emerged as a promising unconventional source of rare earth elements (REEs) and other critical minerals (CMs) such as cobalt and manganese. In this regard, AMD provides a natural heap leaching effect that extracts and concentrates REE/CM from the host strata creating a partially enriched feedstock suitable for downstream extraction, separation, and recovery. While several prior studies have described processes and approaches for the valorization of AMD, very few have described the supply chain and infrastructure requirements as well as the associated economic assessment. To that end, this paper provides a fundamental economic assessment of REE/CM recovery from AMD using a network sourcing strategy in addition to a robust, flexible feedstock separations and refining facility. The methodology of this paper follows that of a typical techno-economic analysis with capital and operating costs estimated using AACE Class IV (FEL-2) guidelines. To demonstrate the range of possible outcomes, four pricing scenarios were modeled including contemporary prices (September, 2021) as well as the minimum and maximum prices over the last decade. In addition, five production scenarios were considered reflecting variations in the product suite, ranging from full elemental separation to magnet REE and CM production only (i.e., Pr, Nd, Tb, Dy, Y, Sc, Co, and Mn). The results of this analysis show that, with the exception of the minimum price scenario, all operational configurations have positive economic indicators with rates of return varying from 25% to 32% for the contemporary price scenario. The optimal configuration was determined to be production of Co, Mn, and all REEs except for mischmetal, which is not recovered. Sensitivity analysis and Monte Carlo simulation show that capital cost and HCl consumption are the two major factors influencing rate of return, thus indicating opportunities for future technology development and cost optimization. Implications of the study and a cooperative profit-sharing model for sourcing are also described.

A machine learning-based price state prediction model for agricultural commodities using external factors

In recent times of noticeable climate change the consideration of external factors, such as weather and economic key figures, becomes even more crucial for a proper valuation of derivatives written on agricultural commodities. The occurrence of remarkable price changes as a result of severe changes in these factors motivates the introduction of different price states, each describing different dynamics of the price process. In order to include external factors we propose a two-step hybrid model based on machine learning methods for clustering and classification. First, we assign price states to historical prices using K-means clustering. These price states are also assigned to the corresponding data of external factors. Second, predictions of future price states are then obtained from short-term predictions of the external factors by means of either K-nearest neighbors or random forest classification. We apply our model to real corn futures data and generate price scenarios via a Monte Carlo simulation, which we compare to Sørensen (J Futures Mark 22(5):393–426, 2002). Thereby we obtain a better approximation of the real futures prices by the simulated futures prices regarding the error measures MAE, RMSE and MAPE. From a practical point of view, these simulations can be used to support the assessment of price risks in risk management systems or as decision support regarding trading strategies under different price states.

Derivation of operating rule curves for cascade hydropower reservoirs considering the spot market: A case study of the China's Qing River cascade-reservoir system

The spot price varies in deregulated markets, and is important for the revenue of cascade hydropower reservoirs. However, the conventional operating rules maximize hydropower generation without considering the spot price. To address this issue, this study focuses on deriving operating rule curves to maximize revenue by integrating reservoir operations and a spot market. Spot prices are simulated by aggregating supply bids and estimating demand. A parameter-simulation-optimization approach is then used to maximize the hydropower revenue and reliability, and minimize spill water simultaneously. With China's Hubei province as the market boundary, the Qing River cascade-reservoir system was selected as the case study. The spot price is significantly affected when the Qing River cascade-reservoir system participates, compared with the market without the cascade system. The spill water reduction (1.33%) and average water head increase (0.56%) contributes to the 1.74% hydropower generation increment of the derived rule curves compared with the conventional operating rules, resulting in hydropower revenue increase by 2.25% with the mean spot price raised by 0.36%. The firm-output operational zone is expanded during the refill period, compared with that derived in an independent pricing scenario. The derived operating rule curves are effective to maximize hydropower revenue for long-term operation.

ELECTRIC POWER BID DETERMINATION AND EVALUATION FOR PRICE TAKER UNITS UNDER PRICE UNCERTAINTY

Power companies aim to maximize their profit which is highly related to the bidding strategies used. In order to sell electricity at high prices and maximize their profit, power companies need suitable bidding models that consider power operating constraints and price uncertainty within the market. Price taker units have no power to affect the prices but need to determine their best bidding strategy to maximize their profit assuming a quadratic cost function and uncertain market prices. Price taker units also need to evaluate their bidding strategy under different price scenarios. In this paper, we first model the bidding problem for a price taker unit and then propose quadratic programming, nonlinear programming and marginal cost based bidding models under price uncertainty. We use case studies to study the computation burden and limitation to reach a solution. We also propose a simulation methodology to evaluate the performance of each bidding strategy for different market prices in an effort to help decision makers to assess their bidding decisions.Keywords: Bidding, nonlinear programming, quadratic programming, simulation, electricity marketsJEL Classifications: L94, D44, D47, E17DOI: https://doi.org/10.32479/ijeep.11382

Cordon Pricing, Daily Activity Pattern, and Exposure to Traffic-Related Air Pollution: A Case Study of New York City

Road pricing is advocated as an effective travel demand management strategy to alleviate traffic congestion and improve environmental conditions. This paper analyzes the impacts of cordon pricing on the population’s daily activity pattern and their exposure to particulate matter by integrating activity-based models with air quality and exposure models in the case of New York City. To estimate changes in public exposure under cordon pricing scenarios, we take a sample of employees and study their mobility behavior during the day, which is mainly attributed to the location of the work and the time spent at work. The selection of employees and their exposure during the duration of their work is due to the unavailability of exact activity patterns for each individual. We show that the Central Business District (CBD) experiences a high concentration of PM2.5 emissions. Results indicate that implementing cordon pricing scenarios can reduce the population-weighted mean of exposure to PM2.5 emissions by 7% to 13% for our sample and, in particular, by 22% to 28% for those who work in the CBD. Furthermore, using an experimental model and assuming constant conditions, we point out the positive influence on indoor exposure for two locations inside and outside the CBD in response to cordon pricing. Considering the correlation between long-term exposure to fine particulate matter and the risks of developing cardiovascular disease and lung cancer, our findings suggest that improved public health conditions could be provided by implementing cordon pricing in the New York City CBD.

Energy policy implications of carbon pricing scenarios for the Brazilian NDC implementation

This study assesses the expansion of the Brazilian energy system across three GHG emissions pathways simulating the achievement of the Brazilian Nationally Determined Contribution. In the Reference scenario, the NDC is achieved through command-and-control policies. We then compare this pathway to an Emissions Pricing Scenario (EPS), which simulates a carbon pricing scheme. The Sensitive Fuels Exemption Scenario (SFE) is similar to the latter, but gasoline, diesel and liquefied petroleum gas are exempted from pricing, strengthening political buy-in to the mechanism.An integrated modelling approach combines bottom-up models representing energy demand and supply and a macroeconomic framework to ensure consistency across them. The adoption of carbon pricing schemes enables the use of a large potential of offsets (from the restoration of native vegetation) at a limited cost. This allows meeting NDC targets with bounded use of expensive mitigation actions comprised in command-and-control tools in the reference scenario. This study shows that a carbon pricing policy can increase the effectiveness of meeting climate commitments in Brazil, reducing GDP losses against business-as-usual trends. However, the mechanism's scope and sectoral coverage are key to ensure that decarbonisation is pursued in all economic sectors and in line with climate targets beyond the NDC horizon.

A Comparison of Consumer Attitudes Toward Dynamic Pricing Strategies in the Theme Park Context

ABSTRACT Dynamic pricing is the practice of changing the price for a product or service in different time periods, in different sale points, or for different market segments. The strategy is applied in many economic sectors, including the tourism and hospitality industry; however, consumer reactions to dynamic pricing in the theme park industry have not been examined. This study assessed consumers’ trust, perceived value, and brand loyalty under different pricing strategies using six hypothetical pricing scenarios. The results indicated that theme park visitors had the best positive attitudes and loyalty when they pay less and know that they paid less. Theme park visitors’ reaction toward paying price premiums does not change substantially when they know that they pay more for charitable causes or sustainable practices. Implications are discussed.