Browsing by Author "Ware, Antony Frank"

Now showing 1 - 17 of 17
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    Analysis of Financial Transmission Rights Obligations and Hourly Congestion Prices in PJM Markets
    (2019-09-17) Arablou, Zahra; Ware, Antony Frank; Swishchuk, Anatoliy V.; Wu, Jingjing
    In this research we analyze patterns of FTR obligation contracts using hourly data from January 1st, 2015 through December 31st, 2018 between PJM Western and AEP Dayton hubs and concluded that there are positive net profits in FTR obligation contracts if someone were to buy and hold the contracts until they were actualized. We applied Schwartz’s one-factor log-model to changes in congestion prices, but our tests showed that residuals of the model are not normally distributed. We then applied OU process to outright congestion prices and used recursive approach proposed by Clewlow and Strickland (2000) to remove jumps from OU residuals. We concluded that non-jump and jump data sets are not normally distributed in both PJM Western and AEP Dayton hubs. Given congestion prices showed fat-tails and non-normal distributions for both jumps and non-jumps data points, we applied Johnson’s Unbounded Distribution to congestion prices and calibrated its parameters for both congestion prices. We defined a variable to filter out hourly data depending on how much of transmission interface were used in different hours and we recalibrated sets of parameters for those cases.
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    Compact High-order Finite Difference Schemes for Acoustic Wave Equations
    (2021-01-05) Li, Keran; Liao, Wenyuan; Lamoureux, Michael P.; Liao, Wenyuan; Braverman, Elena; Liang, Dong; Ware, Antony Frank
    This study developed three compact high-order finite difference schemes for acoustic wave equations. Benefiting from the compactness, the new schemes require less layers of boundary conditions than conventional finite difference schemes. All the three schemes work for acoustic wave equations with variable coefficients in homogeneous media, with the third one also being applicable to the case of heterogeneous density media. The first scheme is based on Padé approximation which is formally a product of the inverse of a finite difference operator and the conventional 2nd-order finite difference operator, thus some algebraic manipulation is necessary to discuss the product of operators. The second scheme is based on so-called combined finite difference method, which needs the boundary conditions for the second spatial derivatives and the needed boundary conditions can be derived by using the wave equation and usual Dirichlet boundary conditions themselves. The third scheme is also based on combined finite difference method, and it generalizes the second scheme so that it can also work in heterogeneous density media case, i.e., the Laplacian in the wave equations being divergence form. The stability of the first two schemes are established by an energy method, while the stability of the last scheme is obtained by an analogy of von Neumann analysis. All of these new schemes are proven to be conditionally stable with given Courant-Friedrichs-Lewy (CFL) numbers. Numerical experiments are conducted to verify the efficiency, accuracy and stability of the new schemes. It is expected that these new schemes will find extensive applications in both research and engineering areas.
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    Crude Oil Hedging - An Application of Currency Translated Options to Canada's Oil
    (2012-11-22) Obour, Paul; Ware, Antony Frank
    This study presents a review of pricing and hedging currency translated options. It is intended for Canadian Oil producers seeking to mitigate their production and F/X risks. Currency translated options are options based upon a foreign asset but with a payout that occurs in another currency. Different types of currency translated options are covered: Flexos, Compos or joint options and Quantos. A special emphasis is placed on the comparison between the first and the third versions of this product since the latter is the only version that completely eliminates the currency risk to the commodity/equity investor. Any oil producer or consumer can diversify its risks by transforming its complete dependence on spot oil prices into a variety of exposures to forward, futures, and options markets. In the light of these transformations, we analyze the efficiency of linearly delta hedging with Flexos and Quantos and further examine the hedging implications if any.
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    Effect of harmonic inflow perturbation on the two-degree-of-freedom vortex-induced vibration of a cylinder
    (2022-12-09) Hassanpour, Maziyar; Morton, Chris; Martinuzzi, Robert; Ramirez-Sarrano, Alejandro; Ware, Antony Frank
    The wake dynamics, structural response, and their coupling are investigated for a circular cylinder undergoing vortex-induced vibration (VIV) near a plane boundary at Re = 200. The effects of inflow perturbations on the structural response are explored, and three regimes of response that do not occur for VIV in a uniform flow have been identified. It was found that the interaction of a symmetric instability via perturbations in the wake and natural instabilities in the wake is the underlying physical mechanism resulting in the presence of the newly identified regimes of response. In Regime I, high drag values were observed; however, the amplitudes of vibrations remain close to the unperturbed case. Regime II is a high excitation regime characterized by a highly modulated response. In Regime III, the natural shedding mode and its second harmonic are reinforced by perturbations. In this Regime, sub-harmonic resonance occurs, resulting in irregular wake dynamics and a highly excited structural response. Regimes II and III fundamentally alter the coupling mechanism between the wake dynamics and cylinder response. To capture the observed dynamics in these regimes as well as the unperturbed response, a new approach to VIV modeling is proposed. In particular, a coupled structural response and wake dynamics system are modeled for the first time using non-isochronous oscillators. The inter-dependency of the oscillation amplitude and frequency of oscillations, and frequency entrainment are identified as the main characteristics of the flow that this model can capture.
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    Fundamental Modeling of the Alberta Power Market
    (2016) Elham, Negahdary; Ware, Antony Frank; Swishchuk, Anatoliy; Zinchenko, Yuriy; Davison, Matt
    In this project, we identify the primary price drivers and characterize their dynamics in an engineering-based bottom-up model. This fundamental model is based on the economic theory of supply and demand. The power price is determined by the intersection of demand with the dynamic characteristics of the generators’ supply functions. The dynamics of power prices will be represented naturally while satisfying operational constraints. In view of the uncertainty around the future values, we model independent exogenous variables such as fuel prices, outages, and load, as stochastic processes. A broad analysis of levels of aggregation and model simplification will be a comprehensive reference for future modelers. The downside of this methodology is the onerous data and input parameter requirements and burdensome computational costs.
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    Learning in Smooth Rate and Non-Smooth Spiking Networks
    (2024-07-08) Newton, Thomas Robert; Ware, Antony Frank; Nicola, Wilten; Rios, Cristian; Braverman, Elena
    This thesis investigates the differences between spiking and firing-rate based neural networks, focusing on learning. Understanding these differences can enhance spiking neural network learning techniques for engineering tasks and contribute to the modeling of learning in biological neural systems, such as in neuromorphic computing applications. The FORCE technique uses an online update to a linearly decoded reservoir network to train both spiking and non-spiking networks to perform dynamical systems tasks. We demonstrate that the FORCE technique effectively trains networks of leaky integrate-and-fire (LIF) neurons and their equivalent instantaneous firing-rate neurons to perform various dynamical systems tasks with correlated neural bases. Our findings indicate that while both network types perform correlated neural computations, firing-rate networks achieve more finely tuned solutions that are not always transferable to LIF networks. However, the solution learned by the LIF networks were largely transferable to the firing-rate networks. We explore error scaling as a function of network size, revealing that FORCE trained spiking networks likely use a noisy rate encoding, while firing-rate networks utilize the neural basis more efficiently. Analyzing the time-averaged cross-trial variance and bias squared in these networks, we find that spiking networks’ output error is primarily due to variability in the spiking neural basis, whereas firing-rate networks are limited by the expressiveness of their neural basis and decoder. Our results suggest that to avoid approximating a cross-trial averaged firing rate in LIF networks, stabilizing spike times relative to the learning task or using an error function different from the L2 norm is necessary. Additionally, we examine the effects of noisy conditions on spike-train decoding error. Precise and reliable spike trains can achieve efficient encoding, but perturbations in spike timing or spike failures disrupt this efficiency. Reducing spike time jitter linearly in network size and failure rates at least in the square root of network size is necessary to maintain efficient error scaling.
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    Market Power in Electricity Markets
    (2019-12) Chan, Erik; Ware, Antony Frank; Qiu, Jinniao; Badescu, Alexandru M.
    Electricity markets exhibit unique price dynamics not found elsewhere in other commodity markets. Characteristics such as limited storability, highly inelastic demand, and physical laws requiring continuous production to match consumption cause erratic price dynamics and short periods of extremely high prices known as spikes. We create a diffusion model under the assumption that a participating firm controls sufficient production capacity can significantly increase the spot price by bidding according to a strategy defined by a decision curve. We then extend Barlow's diffusion model to incorporate the decision curve and show results.
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    Numerical approximations of coupled forward-backward SPDEs with applications
    (2020-09-10) Molla, Md Hasib Uddin; Qiu, Jinniao; Ware, Antony Frank; Swishchuk, Anatoliy V.
    We introduce a new scheme combining the finite element method and machine learning techniques for the numerical approximations of coupled forward-backward stochastic partial differential equations (FBSPDEs) with homogeneous Dirichlet boundary conditions. For the FBSPDE, the finite element method in the spatial domain leads to approximations by finite-dimensional forward-backward stochastic differential equations (FBSDEs) in the temporal domain. We then approximate the solution of FBSDE by some existing machine learning schemes. Strong convergence results for spatial discretization of FBSPDEs are addressed.
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    On Battery Energy Storage Systems as Transmission Assets in Modern Power Grids
    (2020-08-08) Arteaga Lango, Juan Fernando; Zareipour, Hamidreza; Pahlevani, Majid; Venkatesh, Bala; Knight, A. M.; Ware, Antony Frank
    The electrical power industry has experienced a transition toward clean, sustainable generation. Increased percentages of non-dispatchable variable generation have a negative impact on grid stability and reliability. Energy storage is a natural fit to address this problem since it increases dispatchability on variable generation and it could be used to provide several services to the system. To further deploy grid-scale battery energy storage systems (BESSs), economic feasibility must be achieved. This thesis is focused on the large-scale integration of BESSs to the grid by providing optimization models that could help build a business case for BESSs in competitive electricity markets. First, a review of the working principles of a lithium-ion battery is provided and the desired characteristics on the chemistries for grid-scale applications are discussed. Also, the commercially available cathode - anode combinations are compared and ranked by their suitability for grid-scale applications. Then, an optimal market participation model is developed for a BESS that participates in multiple market segments. The proposed model enables the BESS to split it available capacity between multiple products and to be able to arbitrage its available energy between markets. To further increase the economic feasibility of BESSs along with the value they provide to the grid, I investigate the financial gains and risks of stacking market revenues with transmission services revenues for a stand alone merchant energy storage facility. For this study, two cases are analysed. A monetizing strategy for an existing BESS that is offered the possibility of adding transmission services to its market operation. And, an optimal sizing model for the planning of a new BESS that pretends to stack both sources of revenue. Simulations are conducted with real market data to show the validity of the models and to quantify to what extent the addition of more sources of revenue could help improve BESS's business case; thus, their widespread integration to power systems.
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    Optimal Finite Difference Schemes for the Helmholtz Equation with PML
    (2019-12-17) Dastour, Hatef; Liao, Wenyuan; Ware, Antony Frank; Lamoureux, Michael P.; Zinchenko, Yuriy
    An efficient and accurate numerical scheme for solving the seismic wave equations is a key part in seismic wave propagation modeling. The pollution effect of high wavenumbers (the accuracy of the numerical results often deteriorates as the wavenumber increases) plays a critical role in the accuracy of these numerical schemes and it is inevitable in two and three dimensional Helmholtz equations. Optimal finite difference methods can offer a remedy to this problem; however, the numerical solution to a multi-dimensional Helmholtz equation can be troublesome when the perfectly matched layer (PML) boundary condition is implemented. This study develops a number of optimal finite difference schemes for solving the Helmholtz equation in the presence of PML. In doing so, we implement two common strategies, derivative-weighting and point-weighting strategies, for constructing these schemes. Furthermore, a challenge for developing such methods is being consistent with the Helmholtz equation with PML. Thus, analytical and numerical proofs are provided to show the consistency of the schemes. Moreover, for each developed optimal finite difference method, error analysis for the numerical approximation of the exact wavenumber is provided. Based on minimizing the numerical dispersion, some optimal parameters strategies for each optimal finite difference schemes are recommended. Furthermore, several examples are provided to illustrate the accuracy and effectiveness of the new methods in reducing numerical dispersion.
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    Optimal Portfolios of Natural Gas Futures
    (2021-01-28) Li, Xiang; Ware, Antony Frank; Swishchuk, Anatoliy V.; Qiu, Jinniao
    In this thesis we investigate portfolios consisting of a collection of positions in natural gas futures. The logarithms of the futures prices follow correlated Ornstein-Uhlenbeck processes which are mean-reverting. Under the assumption that the portfolios are constant and short-selling is allowed, formulae for the portfolio that is optimal in terms of minimizing Capital at Risk (CaR) in a continuous-time context are obtained following an adapted version of the theory in [14]. We show that the results in this paper hold with small modifications in our situation and we apply them to portfolios of positions in AB NIT natural gas futures contracts. Furthermore, we allow the portfolio to be reoptimized periodically, showing dramatically better performance of portfolio with regard to the final wealth. And finally we consider the contract size limitation so that the portfolio is tradeable, and demonstrate the unsurprising underperformance of the portfolio that results.
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    Option Pricing Using Neural Networks
    (2019-08-30) Que, Danfeng; Badescu, Alexandru M.; Ware, Antony Frank; Swishchuk, Anatoliy V.
    Due to the properties of large transaction volumes, high innovation and positive promotion to the financial market development, options play an essential role. However, the flexible design of options leads to complicated pricing, which makes accurate option pricing a challenging task for a long time. In this paper, two types of neural networks - feed-forward networks such as the radial basis function (RBF), multilayer perceptron (MLP), Modular network and a recurrent deep learning network Long short-term memory (LSTM) - and three stochastic process pricing models (Black-Scholes-Merton model, Heston stochastic volatility model and Merton Jump-diffusion model) are proposed so as to predict European call option prices. Firstly, it generates simulated option prices data from three stochastic process models to test the effectiveness and approximation ability of the neural networks. Secondly, effective factors such as moneyness, time to maturity and greeks via the Black-Scholes-Merton formula are used as input variables for neural networks. Historical data of S&P 500 European option prices empirically analyzes validity and stability of the neural networks. The performance measures R2, statistical error of the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of two types of pricing models. It shows that the MLP network with two hidden layers performs best. In addition, neural networks do outperform the pricing ability of stochastic process pricing models.
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    Preconditioned Iterative Solvers on GPU and an In-Situ Combustion Simulator
    (2020-01) Yang, Bo; Chen, Zhangxing; Liao, Wenyuan; Lu, Qingye; Azaiez, Jalel; Sun, Pengtao; Ware, Antony Frank
    This thesis consists of two parts: preconditioned iterative solvers on GPU and an in-situ combustion simulator. The purpose of the first research is to develop a new parallel solution platform based on GPU features. An application of HPC (high-performance computing) technology to reservoir simulation has become an inevitable trend. As a platform for HPC, GPU can provide an effective solution for personal computers and workstations. In this research, not only a series of special CPR (constrained pressure residual) preconditioned solvers are developed for black oil models, but also a variety of other preconditioned solvers are completed as contrast solvers. The numerical experiments verify a significant improvement in the parallel performance of the solvers on GPU. They also provide an overall comparison among the combinations of different GPUs, solvers, and preconditioners. The results demonstrate that the CPR developed has excellent advantages in both parallelism and convergence for the solution of a benchmark reservoir model. The purpose of the second research is to develop a new comprehensive ISC (in-situ combustion) simulator with the PER (pseudo equilibrium ratio) method and to compare functions with those of a benchmark simulator with the VS (variable substitution) method. ISC is considered a promising recovery method because of its low cost and less environmental impact. However, an ISC simulator is regarded as one of the most complex simulators to develop. The PER method can reduce the complexity of simulator development because it lowers the influence of the phase disappearance and appearance on the mathematical system of reservoir simulation. The ISC simulator in this study is developed with comprehensive typical functions of the ISC process. For the verification of the equivalence in numerical results between the PER method and the VS method, the numerical experiments are carried out in an omnidirectional range. Because the results show a very close match, the research provides reliable experimental support for popularizing the use of the PER method to develop an in-situ combustion simulator.
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    Regime Switching Models for Gas Prices
    (2016) Hao, Kunlin; Ware, Antony Frank; Swishchuk, Anatoliy; Sick, Gordon Arthur
    This thesis is mainly concerned with a two-state regime switching model with two Ornstein-Uhlenbeck processes and its application to modelling natural gas prices. We start from analyzing Hamilton's (2005) model, a two-state regime switching model in a discrete time setting, and his recursive filtering approach to parameter calibration. Then we discuss the efficiency of models by adding seasonality to the long term mean. We also develop a recursive Bayesian filtering approach and compare it with Hamilton's (2005) filtering approach. This Bayesian calibration approach is applicable to regime switching models in both discrete and continuous time settings.
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    A Stochastic Model for Power Prices in the Alberta Electricity Market
    (2022-05-09) Ansari Esfeh, Zahra; Ware, Antony Frank; Swishchuk, Anatoliy; Sezer, Ayse Deniz
    While energy companies commit to provide the required amount of electricity to meet the power consumers’ demand, several factors can affect the demand for electricity and consequently, affect the electricity pricing. Example of these factors are weather conditions, cost of power generation, and government tax policies. Therefore, energy companies must deal with the problem of hedging load and price risk. Power prices typically exhibit some characteristics that are crucial to be considered in modelling power prices. In Alberta, periodicity, mean-reversion, and sudden power price spikes are the most common characteristics of power prices that can be explained by changes in supply and demand for electricity. The other significant feature of power prices in Alberta is the strong link between power prices and fuel prices. Therefore, it is important to obtain a power price model which shows the stochastic dynamics of fuel prices and energy demand (i.e., load) in Alberta. For this purpose, we propose a power price model considering the strong link between power price and load which facilitates the energy companies’ hedging purposes. We use the structural model for power price modelling in which the power spot price is assumed to be a parametric function of the influential factors, such as, fuel prices and load, and the dynamic features of power prices are specified by the stochastic processes of these influential factors. Incorporating these factors in the power price model allows to capture the significant features of power prices. This model also provides the opportunity of finding a closed form formula for power forward prices.
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    The Influence of Diffusion Strategies on the Competition of Two Spatially Distributed Populations in Heterogeneous Environment
    (2016-01-22) Kamrujjaman, Md.; Braverman, Elena; Liao, Wenyuan; Ware, Antony Frank; Zinchenko, Yuriy; Vamosi, Steven Michael; Vries, Gerda De
    We study reaction-diffusion equations describing population dynamics of single harvested species and of two competing species. The main aim of the thesis is to study the roles of two different diffusion strategies: the regular diffusion and the directed diffusion. In directed diffusion, rather than the population itself, its ratio to either locally available resources (carrying capacity) or to a positive distribution function diffuses. We focus on how directed diffusion, especially, carrying capacity driven dispersion in the habitat influences selection. For single species, we present comparative numerical results between carrying capacity driven diffusion and regular diffusion for Gilpin-Ayala type growth and harvesting. For two competing species, we study the interaction between different types of dispersal: one of them is subject to a regular diffusion while the other moves in the direction of most per capita available resources. If spatially heterogeneous carrying capacities coincide, and intrinsic growth rates are proportional then competitive exclusion of a regularly diffusing population is inevitable. When the resource function of a regularly diffusing population is higher than of the other species, the two populations may coexist. For symmetric growth, we consider the case when the ideal free distribution is attained as a combination of the two strategies adopted by the two species. Then there is an ideal free pair, and the relevant coexistence equilibrium is a global attractor. In the event that only one of the diffusion strategies is proportional to the carrying capacity, we prove the competitive exclusion of the other species. In case of weak competition, both species can coexist even only one of the species adopts the ideal free dispersal strategy. When one of the species is following the directed dispersal strategy and the other is dispersing regularly, there is a unique coexistence solution if the difference between the carrying capacity and the directed function is a positive constant. Coexistence can be a result of the interplay of different diffusion coefficients or growth rates.
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    Valuation of Crude Oil Futures, Options and Variance Swaps
    (2016-01-27) Shahmoradi, Akbar; Swishchuk, Anatoliy; Ware, Antony Frank; Badescu, Alexandru; Moran Villar, Pablo
    In this research we provide a set of practical approaches to value crude oil futures, especially long dated ones given crude oil spot prices. Throughout the research we change the reference point for our data sets from calendar dates to time to expiry and all our models are analyzed based on time to expiry. We use a set of Levy processes to value crude oil options by calibrating parameters using Fast Fourier Transform algorithm and solving an objective function using Particle-Swap Optimization. In order to help market participants to use available crude oil storage and refinery data in pricing futures contracts and the spreads between them, we provide a framework that helps crude oil market participants to get fair value of futures and run scenario analysis if a physical factor such as level of inventories at Cushing\Oklahoma or in the US changes. We also investigated variance risk premia in crude oil prices using information obtained from crude oil option prices. Our results indicate that “usually” there is a negative risk premium in crude oil prices but that does not necessarily provide trading opportunity for market participants because excess return of shorting the variance swap show huge losses when crude oil market is in turmoil.