PORTFOLIO OPTIMIZATION IN ELECTRICITY TRADING WITH LIMITED LIQUIDITY

In principle, portfolio optimization in electricity markets can make use of the standard mean-variance model going back to Markowitz. Yet a key restriction in most electricity markets is the limited liquidity. Therefore the standard model has to be adapted to cope with limited liquidity. An application of this model shows that the optimal hedging strategy for generation portfolios is strongly dependent on the size of the portfolio considered as well as on the variance-covariancematrix used and the liquidity function assumed.optimization; electricity, liquidity; electricity trading; mean-variance-model

Baseball Portfolio Optimization

Portfolio optimization techniques are methods used to determine the best set of stocks in which to invest. Mean-variance optimization, one method of portfolio optimization, attempts to find the set of portfolios that have the maximum expected return at each level of risk (Jorion, 1992). Another technique, Monte Carlo simulation, uses random number generation to create a probability distribution of potential returns (Kwak & Ingall, 2007). This can be used to determine the risk of potential investments not returning a certain desired amount (Thompson & McLeod, 2009). Though traditionally used in the world of finance, these tools can also be utilized by professional sports teams, such as those in Major League Baseball, to make more efficient investments in personnel and increase their likelihood of reaching the postseason. This research effort explores strategies to optimize the allocation of a baseball team’s resources in the free agent market. In this effort, we use a portfolio optimization approach and explore a variety of baseball performance metrics. A prototype optimization model is created and evaluated. This model is designed to assemble the team with the highest likelihood of making the playoffs while accounting for various budget and roster constraints faced by Major League Baseball teams. The prototype is utilized to create an optimized 2015 roster for three teams: the Boston Red Sox, Kansas City Royals, and San Diego Padres. These optimized rosters are then compared to each team’s actual 2015 opening day roster. Several iterations of this model are discussed in an attempt to find the option that returns the most value. After multiple alternatives are analyzed, three different options are identified that compare favorably to the teams’ actual opening day rosters with regards to 2015 performance of the players selected. Weaknesses of the model are then discussed, as well as ways in which it can be improved. Keywords: portfolio optimization, mean-variance optimization, Monte Carlo simulation, expected returns, risk, performance metrics, stochastic optimization, linear optimizatio

Medium-term planning for thermal electricity production

In the present paper, we present a mid-term planning model for thermal power generation which is based on multistage stochastic optimization and involves stochastic electricity spot prices, a mixture of fuels with stochastic prices, the effect of CO $$_2$$ 2 emission prices and various types of further operating costs. Going from data to decisions, the first goal was to estimate simulation models for various commodity prices. We apply Geometric Brownian motions with jumps to model gas, coal, oil and emission allowance spot prices. Electricity spot prices are modeled by a regime switching approach which takes into account seasonal effects and spikes. Given the estimated models, we simulate scenario paths and then use a multiperiod generalization of the Wasserstein distance for constructing the stochastic trees used in the optimization model. Finally, we solve a 1-year planning problem for a fictitious configuration of thermal units, producing against the markets. We use the implemented model to demonstrate the effect of CO $$_2$$ 2 prices on cumulated emissions and to apply the indifference pricing principle to simple electricity delivery contracts

Optimal GENCO bidding strategy

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming.;The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed.;A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies\u27 (GENCOs) bidding strategies.;After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system

Optimizing daily fantasy sports contests through stochastic integer programming

Master of ScienceDepartment of Industrial & Manufacturing Systems EngineeringTodd W. EastonThe possibility of becoming a millionaire attracts over 200,000 daily fantasy sports (DFS) contest entries each Sunday of the NFL season. Millions of people play fantasy sports and the companies sponsoring daily fantasy sports are worth billions of dollars. This thesis develops optimization models for daily fantasy sports with an emphasis on tiered contests. A tiered contest has many different payout values, including the highly sought after million-dollar prize. The primary contribution of this thesis is the first model to optimize the expected payout of a tiered DFS contest. The stochastic integer program, MMIP, takes into account the possibility that selected athletes will earn a distribution of fantasy points, rather than a single predetermined value. The players are assumed to have a normal distribution and thus the team’s fantasy points is a normal distribution. The standard deviation of the team’s performance is approximated through a piecewise linear function, and the probabilities of earning cumulative payouts are calculated. MMIP solves quickly and easily fits the majority of daily fantasy sports contests. Additionally, daily fantasy sports have landed in a tense political climate due to contestants hopes of winning the million-dollar prize. Through two studies that compare the performance of randomly selected fantasy teams with teams chosen by strategy, this thesis conclusively determines that daily fantasy sports are not games of chance and should not be considered gambling. Besides creating the first optimization model for DFS tiered contests, this thesis also provides methods and techniques that can be applied to other stochastic integer programs. It is the author’s hope that this thesis not only opens the door for clever ways of modeling, but also inspires sports fans and teams to think more analytically about player selection

عرضه، تقاضا، و پیشنهاد قیمت در بازار برق ایران

English Abstract: In this paper, I review the supply and demand side of the electricity market in Iran. I review the potential bidding strategies that are in place in this market. I evaluate the optimality of bidding and introduce a new bidding strategy that could raise the profits of the firms. In the market of this study, firms are allowed to bid step-wise and are constrained to bid ten steps per bid. The market dispatcher estimates the market demand and based on the cumulated supply functions clears the market at one specific price. Those steps that are below the market clearing price will be allowed to produce and sell in the market. I argue that a continuous supply function is optimal in this setting and it is at the profit of the firms to use a supply function as close to a continuous supply as possible, i.e. using all ten steps. Persian Abstract: این مقاله به بررسی عرضه و تقاضا در بازار برق ایران می‌پردازد. ابتدا ساختارهای پیشنهاد قیمت در بازار برق ایران بررسی می‌شود. سپس، بهینگی هر کدام از این ساختارهای پیشنهاد قیمت و ارتباط آن با بازار برق در ایران بررسی می‌شود. در بازار مورد مطالعه، هر تولیدکننده می‌تواند تا ده پله پیشنهاد قیمت بدهد. شرکت برق سپس با تحمین تقاضا و جمع عرضه‌ها در بازار قیمت نهایی را اعلام می‌کند. پله‌هایی که پایین‌تر از قیمت نهایی باشد اجازه‌ی تولید خواهند داشت. من در این مقاله به طور نظری اثبات می‌کنم که استفاده از هر ده پله برای تولیدکننده بهینه است. بنابراین، جهت بیشینه کردن سود، هر تولیدکننده باید تا حد ممکن شبیه تابعی پیشنهاد قیمت بدهد که پیوسته باشد

عرضه، تقاضا، و پیشنهاد قیمت در بازار برق ایران

English Abstract: In this paper, I review the supply and demand side of the electricity market in Iran. I review the potential bidding strategies that are in place in this market. I evaluate the optimality of bidding and introduce a new bidding strategy that could raise the profits of the firms. In the market of this study, firms are allowed to bid step-wise and are constrained to bid ten steps per bid. The market dispatcher estimates the market demand and based on the cumulated supply functions clears the market at one specific price. Those steps that are below the market clearing price will be allowed to produce and sell in the market. I argue that a continuous supply function is optimal in this setting and it is at the profit of the firms to use a supply function as close to a continuous supply as possible, i.e. using all ten steps. Persian Abstract: این مقاله به بررسی عرضه و تقاضا در بازار برق ایران می‌پردازد. ابتدا ساختارهای پیشنهاد قیمت در بازار برق ایران بررسی می‌شود. سپس، بهینگی هر کدام از این ساختارهای پیشنهاد قیمت و ارتباط آن با بازار برق در ایران بررسی می‌شود. در بازار مورد مطالعه، هر تولیدکننده می‌تواند تا ده پله پیشنهاد قیمت بدهد. شرکت برق سپس با تحمین تقاضا و جمع عرضه‌ها در بازار قیمت نهایی را اعلام می‌کند. پله‌هایی که پایین‌تر از قیمت نهایی باشد اجازه‌ی تولید خواهند داشت. من در این مقاله به طور نظری اثبات می‌کنم که استفاده از هر ده پله برای تولیدکننده بهینه است. بنابراین، جهت بیشینه کردن سود، هر تولیدکننده باید تا حد ممکن شبیه تابعی پیشنهاد قیمت بدهد که پیوسته باشد

Electrical Infrastructure Adaptation for a Changing Climate

In recent years, global climate change has become a major factor in long-term electrical infrastructure planning in coastal areas. Over time, accelerated sea level rise and fiercer, more frequent storm surges caused by the changing climate have imposed increasing risks to the security and reliability of coastal electrical infrastructure systems. It is important to ensure that infrastructure system planning adapts to such risks to produce systems with strong resilience. This dissertation proposes a decision framework for long-term, resilient electrical infrastructure adaptation planning for a future with the uncertain sea level rise and storm surges in a changing climate. As uncertainty is unavoidable in real-world decision making, stochastic optimization plays an essential role in making robust decisions with respect to global climate change. The core of the proposed decision framework is a stochastic optimization model with the primary goal being to ensure operational feasibility once uncertain futures are revealed. The proposed stochastic model produces long-term climate adaptations that are subject to both the exogenous uncertainty of climate change as well as the endogenous physical restrictions of electrical infrastructure. Complex, state-of-the-art simulation models under climate change are utilized to represent exogenous uncertainty in the decision-making process. In practice, deterministic methods such as scenario-based analyses and/or geometric-information-system-based heuristics are widely used for real-world adaptation planning. Numerical experiments and sensitivity analyses are conducted to compare the proposed framework with various deterministic methods. Our experimental results demonstrate that resilient, long-term adaptations can be obtained using the proposed stochastic optimization model. In further developing the decision framework, we address a class of stochastic optimization models where operational feasibility is ensured for only a percentage of all possible uncertainty realizations through joint chance-constraints. It is important to identify the significant scalability limitations often associated with commercial optimization tools for solving this class of challenging stochastic optimization problems. We propose a novel configuration generation algorithm which leverages metaheuristics to find high-quality solutions quickly and generic relaxations to provide solution quality guarantees. A key advantage of the proposed method over previous work is that the joint chance-constrained stochastic optimization problem can contain multivariate distributions, discrete variables, and nonconvex constraints. The effectiveness of the proposed algorithm is demonstrated on two applications, including the climate adaptation problem, where it significantly outperforms commercial optimization tools. Furthermore, the need to address the feasibility of a realistic electrical infrastructure system under impacts is recognized for the proposed decision framework. This requires dedicated attention to addressing nonlinear, nonconvex optimization problem feasibility, which can be a challenging problem that requires an expansive exploitation of the solution space. We propose a global algorithm for the feasibility problem\u27s counterpart: proving problem infeasibility. The proposed algorithm adaptively discretizes variable domains to tighten the relaxed problem for proving infeasibility. The convergence of the algorithm is demonstrated as the algorithm either finds a feasible solution or terminates with the problem being proven infeasible. The efficiency of this algorithm is demonstrated through experiments comparing two state-of-the-art global solvers, as well as a recently proposed global algorithm, to our proposed method

CONTROL SYSTEM MODEL FOR ANALYSIS OF ELECTRICITY MARKET BIDDING PROCESS

This dissertation proposes a closed-loop control system model to facilitate mathematical analysis and promote operational efficiency of the dynamic bidding process. Electricity market deregulation has brought an innovation of the market structure and changed the electric power production from the old monopolistic way to a competitive market environment. Electricity is treated as a commodity and being traded among the market participants. The analysis of electricity market behavior becomes increasingly important and challenging. This dissertation develops a control-theoretic model to analyze and predict electricity market behavior. The model is based on the perspective of the power generation side (GENCOS) and ISO. The purpose is to achieve a rational profit maximizing behavior for GENCOS during the day-ahead bidding process and to improve the wholesale market efficiency. The control-theoretic model uses the game theory embedded with the learning ability as the major bidding strategy, which allows GENCOS to adjust their next-day bidding in the form of supply function equilibrium (SFE) through market observations. Recursive least square (RLS) method based on two ARMA models is introduced for demand and price forecasting in order to maximize the GENCO’s profit. This method is implemented into the bidding strategy of SFE with learning process. In order to better capture the demand and price dynamics beforehand, this dissertation also introduces an adaptive multiresolution prediction algorithm. This algorithm establishes a systematic structure to hierarchically decompose the original demand and price data into subtasks with different time frames, within which the data are able to be trained separately and efficiently. The real market data from New York Independent System Operator and PJM interconnection are used to demonstrate the effectiveness of the proposed model and training algorithm

Contract Selection Problem in Singapore Electricity Market

Master'sMASTER OF ENGINEERIN