Pricing spark spread options in electricity markets

Spark spread – A screening parameter for combined heating and power systems

The Marcellus Shale is one of the largest natural gas reserves in the United States; it has recently been the focus of intense drilling and leasing activity. This paper describes an air emissions inventory for the development, production, and processing of natural gas in the Marcellus Shale region for 2009 and 2020. It includes estimates of the emissions of oxides of nitrogen (NOx), volatile organic compounds (VOCs), and primary fine particulate matter (≤2.5 µm aerodynamic diameter; PM2.5) from major activities such as drilling, hydraulic fracturing, compressor stations, and completion venting. The inventory is constructed using a process-level approach; a Monte Carlo analysis is used to explicitly account for the uncertainty. Emissions were estimated for 2009 and projected to 2020, accounting for the effects of existing and potential additional regulations. In 2020, Marcellus activities are predicted to contribute 6–18% (95% confidence interval) of the NOx emissions in the Marcellus region, with an average contribution of 12% (129 tons/day). In 2020, the predicted contribution of Marcellus activities to the regional anthropogenic VOC emissions ranged between 7% and 28% (95% confidence interval), with an average contribution of 12% (100 tons/day). These estimates account for the implementation of recently promulgated regulations such as the Tier 4 off-road diesel engine regulation and the U.S. Environmental Protection Agency's (EPA) Oil and Gas Rule. These regulations significantly reduce the Marcellus VOC and NOx emissions, but there are significant opportunities for further reduction in these emissions using existing technologies. Implications: The Marcellus Shale is one of the largest natural gas reserves in United States. The development and production of this gas may emit substantial amounts of oxides of nitrogen and volatile organic compounds. These emissions may have special significance because Marcellus development is occurring close to areas that have been designated nonattainment for the ozone standard. Control technologies exist to substantially reduce these impacts. PM2.5 emissions are predicted to be negligible in a regional context, but elemental carbon emissions from diesel powered equipment may be important.

The fruit market is characterized by high price volatility of a seasonal nature, resulting from supply instability and the asymmetry between supply and demand. The aim of this article is to examine the price dynamics of strawberries in selected wholesale markets during the 2023-2024 period. Daily price data were collected from the Warsaw Agricultural and Food Wholesale Market S.A. in Bronisze and the Agricultural and Food Wholesale Market S.A. in Radom. The study period corresponds to the strawberry season in Poland, covering the months from April to July in both years. The number of observations (daily price quotations per market) was 75 in 2023 and 76 in 2024.The results indicate substantial price volatility in both markets, which may be attributed to fluctuating supplies of seasonal fruit – increasing harvest volumes over the course of the season, followed by sharp declines. A detailed analysis of daily strawberry prices in 2023-2024 suggests that both average and maximum prices varied significantly depending on the day of the week. These fluctuations could reflect changes in consumer demand patterns. Notably, the highest average and maximum prices were recorded midweek, possibly indicating peak consumer demand or reduced supply levels during those days.

An efficient and provable sequential quadratic programming method for American and swing option pricing

As a result of the deregulation processes, liberalized markets, where electricity futures and derivatives are traded, have arisen all over the world. Utilities, consumers, traders and, generally, market agents must do quantitative assessments of their positions. Basic analytical data are the forward and volatility curves of the traded products. However, electricity price dynamics is very different of other commodities prices dynamics. Furthermore, electricity prices of different markets are usually very different of each other. As consequence, most analytical approaches to compute forward and volatility curves, as well as other statistics useful to risk management tasks, are very complex or do not exist. In this paper, we propose to compute the forward and volatility curves by Monte Carlo simulation. The main contribution lies in the used variance reduction techniques, needed to achieve this objective at reasonable computational cost. A case example consisting of the study of the EEX prices is also provided

In this article, we develop a novel electric power supply chain network model with fuel supply markets that captures both the economic network transactions in energy supply markets and the physical network transmission constraints in the electric power network. The theoretical derivation and analysis are done using the theory of variational inequalities. We then apply the model to a specific case, the New England electric power supply chain, consisting of six states, five fuel types, 82 power generators, with a total of 573 generating units, and 10 demand market regions. The empirical case study demonstrates that the regional electric power prices simulated by our model match the actual electricity prices in New England very well. We also compute the electric power prices and the spark spread, an important measure of the power plant profitability, under natural gas and oil price variations. The empirical examples illustrate that in New England, the market/grid‐level fuel competition has become the major factor that affects the influence of the oil price on the natural gas price. Finally, we utilize the model to quantitatively investigate how changes in the demand for electricity influence the electric power and the fuel markets from a regional perspective.The theoretical model can be applied to other regions and multiple electricity markets under deregulation to quantify the interactions in electric power/energy supply chains and their effects on flows and prices. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009

In this paper, we propose a fractional stochastic volatility jump-diffusion model which extends the Bates(1996) model. Where we model the volatility as a fractional process. Extensive empirical studies show that the distributions of the logarithmic returns of financial asset usually exhibit properties of self-similarity and long-range dependence and since the fractional Brownian motion has these two important properties, it has the ability to capture the behavior of underlying asset price. Further incorporating jumps into the stochastic volatility framework gives further freedom to financial mathematicians to fit both the short and long end of the implied volatility surface. We propose a stochastic model which contains both fractional and jump process. Then we price options using Monte Carlo simulations along with a variance reduction technique(antithetic variates). We use market data from the S&P 500 index and we compare our results with the Heston and Bates model using error measures. The results show our model greatly outperforms previous models in terms of estimation accuracy.

The past decade has seen a sudden surge in shale gas production in the United States, and a dramatic drop in natural gas prices. The shale gas boom has major implications for the future of nuclear power, the author writes. Projections for the cost of electricity suggest that natural gas will maintain an advantage over nuclear power for many years to come in the United States, and there is reason to believe that overseas markets could eventually enjoy some of the benefits of the shale gas boom. Uncertainties about the size of shale resources and the long-term productivity of new wells could affect the future price of natural gas in the United States but are unlikely to make nuclear power competitive with gas, the author argues. However, public fears about environmental contamination could drive up the cost of natural gas. The author urges companies and policy makers to address these fears in ways that build public confidence. Stringent climate policy probably would not eliminate the cost advantage of natural gas over nuclear before 2030, he writes, but could tip the balance after that. The biggest question mark looming over natural gas in a carbon-constrained world is the prospect of applying carbon-capture-and-sequestration technology to the resource.

Increasing concerns about greenhouse gas (GHG) emissions in the United States have spurred interest in alternate low carbon fuel sources, such as natural gas. Life cycle assessment (LCA) methods can be used to estimate potential emissions reductions through the use of such fuels. Some recent policies have used the results of LCAs to encourage the use of low carbon fuels to meet future energy demands in the U.S., without, however, acknowledging and addressing the uncertainty and variability prevalent in LCA. Natural gas is a particularly interesting fuel since it can be used to meet various energy demands, for example, as a transportation fuel or in power generation. Estimating the magnitudes and likelihoods of achieving emissions reductions from competing end-uses of natural gas using LCA offers one way to examine optimal strategies of natural gas resource allocation, given that its availability is likely to be limited in the future. In this study, the uncertainty in life cycle GHG emissions of natural gas (domestic and imported) consumed in the U.S. was estimated using probabilistic modeling methods. Monte Carlo simulations are performed to obtain sample distributions representing life cycle GHG emissions from the use of 1 MJ of domestic natural gas and imported LNG. Life cycle GHG emissions per energy unit of average natural gas consumed in the U.S were found to range between -8 and 9% of the mean value of 66 g CO(2)e/MJ. The probabilities of achieving emissions reductions by using natural gas for transportation and power generation, as a substitute for incumbent fuels such as gasoline, diesel, and coal were estimated. The use of natural gas for power generation instead of coal was found to have the highest and most likely emissions reductions (almost a 100% probability of achieving reductions of 60 g CO(2)e/MJ of natural gas used), while there is a 10-35% probability of the emissions from natural gas being higher than the incumbent if it were used as a transportation fuel. This likelihood of an increase in GHG emissions is indicative of the potential failure of a climate policy targeting reductions in GHG emissions.

Understanding Natural Gas Futures Trading Through Data Analysis

Optimal design and operation for supply chain system of multi-state natural gas under uncertainties of demand and purchase price

Bangladesh has significant natural gas reserves, and total demand has climbed substantially in recent years. The study uses the autoregressive distributed lag (ARDL) model for cointegration and the vector autoregressive(VAR) Granger causality model to analyze a long-run link between natural gas (NG) consumption, economic development, urbanization, and CO2 emissions. The objective is to investigate the relationship between the environmental Kuznets curve (EKC) and Bangladesh's NG consumption using data from the years 1990 to 2018. According to the ARDL model, economic growth, urbanization, and NG consumption, all have a positive and significant influence on CO2 emissions. Despite having a negative coefficient, the square of economic development has a significant impact on CO2 emissions. In the long run, it verifies the EKC hypothesis in Bangladesh. Both linear and nonlinear economic development determinants display statistically significant positive and negative signals in the short run. From Bangladesh's perspective, this also demonstrates the presence of an EKC. The impact of NG consumption in the short run is insignificant; nevertheless, urbanization has a significant effect. The VAR Granger causality demonstrates that economic development and urbanization have a bidirectional response; however, NG consumption and CO2 emissions have just one-way causality. The key policy implication of the study is that NG use is expected to raise emissions. Increasing the share of clean energy in the energy utilization system, such as nuclear power and renewable energy, is a plausible policy choice.

Power distribution network design considering dynamic and differential pricing, buy-back, and carbon trading

Due to the reason that the default events occurred constantly and still continue taking place, empirical log returns exhibit fat tail and excess kurtosis, this paper evaluates convertible bonds under Levy process with default risk using the reduced-form approach. Under the Levy process, the underlying stock prices are set to be normal inverse Gaussian (NIG) and variance Gamma (VG) model to capture the jump components. In the empirical analysis, we use the maximum likelihood method to estimate the parameters of Levy distributions, and apply the least squares Monte Carlo Simulation to price convertible bonds. Five examples are shown in pricing convertible bonds using the traditional model and Levy model. The empirical results show that the performance of Levy model is better than the traditional one.

Vapour–liquid equilibrium (VLE) and volumetric data of multicomponent mixtures are extremely important for natural gas production and processing, but it is time consuming and challenging to experimentally obtain these properties. An alternative tool is provided by means of molecular simulation. Here, Monte Carlo (MC) simulations in the Gibbs ensemble are used to compute the VLE of multicomponent natural gas mixtures. Two multicomponent systems, one containing a mixture of six components (, , , S, and ), and the other containing a mixture of nine components (, , , S, , , , and ) are simulated. The computed VLE from the MC simulations is in good agreement with available experimental data and the GERG-2008 equation of state modelling. The results show that molecular simulation can be used to predict properties of multicomponent systems relevant for the natural gas industry. Guidelines are provided to setup Gibbs ensemble simulations for multicomponent systems, which is a challenging task due to the increased number of degrees of freedom.