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"Electric utilities -- Mathematical models"
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Electricity markets and power system economics
\"With the theories and rules of electricity markets developing rapidly, it's difficult for beginners to start learning and difficult for those in the field to keep up. Bringing together information previously scattered among various journals and scholarly articles, this book provides a comprehensive overview of the current state of development in the electricity market. It introduces the fundamental principles of power system operation so that even those with a basic understanding can benefit from the book. It includes a series of consistent mathematical models of market operation of power systems, original cases, and MATLAB programming examples with solutions\"-- Provided by publisher.
Book
Stochastic modelling of electricity and related markets
The markets for electricity, gas and temperature have distinctive features, which provide the focus for countless studies. For instance, electricity and gas prices may soar several magnitudes above their normal levels within a short time due to imbalances in supply and demand, yielding what is known as spikes in the spot prices. The markets are also largely influenced by seasons, since power demand for heating and cooling varies over the year. The incompleteness of the markets, due to nonstorability of electricity and temperature as well as limited storage capacity of gas, makes spot-forward hedging impossible. Moreover, futures contracts are typically settled over a time period rather than at a fixed date. All these aspects of the markets create new challenges when analyzing price dynamics of spot, futures and other derivatives.
eBook
Dynamic Noncooperative Game Models for Deregulated Electricity Markets
Intro -- DYNAMIC NONCOOPERATIVE GAMEMODELS FOR DEREGULATEDELECTRICITY MARKETS -- DYNAMICNONCOOPERATIVEGAMEMODELSFORDEREGULATEDELECTRICITYMARKETS -- Contents -- List of Tables -- List of Figures -- Preface -- Restructuring in the ElectricityIndustry -- 1.1. History of Restructuring in the Electricity Industry -- 1.1.1. Motivation and History -- 1.1.2. Structural Changes within the Industry -- 1.2. Prevailing Market Models -- 1.2.1. Pool Model -- 1.2.2. \"Pure\" Market Model -- 1.2.3. Mixed Pool/Bilateral Market -- 1.3. Electricity Deregulation in the U.S. -- 1.3.1. Power Crisis in California -- 1.3.2. Successful Reform in Pennsylvania -- 1.3.3. Encouraging Start in Texas -- 1.3.4. Transmission Restructuring -- 1.4. Future Trends -- 1.4.1. Distributed Generation (DG) -- 1.4.2. Renewable Energy -- 1.4.3. Smart Grid -- 1.4.4. Energy Efficiency -- Game Theory and Strategic Bidding -- 2.1. History of Game Theory -- 2.2. Strategic Bidding in the Competitive Electricity Market -- 2.3. Review of Adaptive Control -- Adaptation for N-Person Games -- 3.1. Introduction -- 3.2. Formulation of N-person Noncooperative Games -- 3.2.1. Mathematical Models -- 3.2.2. Steady State Control Strategies -- 3.3. One-Sided Adaptation Design for N-person Games -- 3.3.1. Adaptive Mechanism Design -- 3.3.2. Persistent Excitation and Parameter Convergence -- 3.3.3. Adaptation for Nash or Other Control Strategies -- 3.3.4. Computational Iterations -- 3.3.5. Numerical Examples -- 3.4. Two-sided Adaptation for Two-person Games -- 3.4.1. Adaptation Mechanism Design -- 3.5. Multiple-sided Adaptation for N-person Games -- 3.5.1. Adaptation Mechanism Design -- 3.6. Summary -- Sensitivity Analysis of Uncertainties -- 4.1. Introduction -- 4.2. Two-person Games with Uncertain Objectives -- 4.2.1. Multi-modeling Formulation with Unknown Parameters in the PerformanceIndices.
eBook
Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU
Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for every five-minute interval on Amherst weather power station. These models were evaluated using mean absolute error root means square error, mean square error, and mean absolute percentage error. It was observed that horizontal solar irradiance and water saturation deficiency had a highly proportional relationship with Solar PV electricity generation. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed solar farm. Bi-LSTM has performed the best among all models with 0.0135, 0.0315, 0.0012, and 0.1205 values of MAE, RMSE, MSE, and MAPE, respectively. Comparison with the existing methods endorses the use of our proposed RNN variants for higher efficiency, accuracy, and robustness. Multistep-ahead solar energy prediction is also carried out by exploiting hybrids of LSTM, Bi-LSTM, and GRU.
Journal Article
Multistage Adaptive Robust Optimization for the Unit Commitment Problem
The growing uncertainty associated with the increasing penetration of wind and solar power generation has presented new challenges to the operation of large-scale electric power systems. Motivated by these challenges, we present a multistage adaptive robust optimization model for the most critical daily operational problem of power systems, namely, the unit commitment (UC) problem, in the situation where nodal net electricity loads are uncertain. The proposed multistage robust UC model takes into account the time causality of the hourly unfolding of uncertainty in the power system operation process, which we show to be relevant when ramping capacities are limited and net loads present significant variability. To deal with large-scale systems, we explore the idea of simplified affine policies and develop a solution method based on constraint generation. Extensive computational experiments on the IEEE 118-bus test case and a real-world power system with 2,736 buses demonstrate that the proposed algorithm is effective in handling large-scale power systems and that the proposed multistage robust UC model can significantly outperform the deterministic UC and existing two-stage robust UC models in both operational cost and system reliability.
Journal Article
Hybrid optimal-FOPID based UPQC for reducing harmonics and compensate load power in renewable energy sources grid connected system
Integration of renewable energy sources (RES) to the grid in today’s electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment. Unified Power Quality Conditioner (UPQC) is widely employed for solving issues with the distribution grid caused by anomalous voltage, current, or frequency. To enhance UPQC performance, Fractional Order Proportional Integral Derivative (FOPID) is developed; nevertheless, a number of tuning parameters restricts its performance. The best solution for the FOPID controller problem is found by using a Coati Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA) are combined to make a hybrid optimization CO-OA algorithm approach to mitigate these problems. This paper proposes an improved FOPID controller to reduce PQ problems while taking load power into account. In the suggested model, a RES is connected to the grid system to supply the necessary load demand during the PQ problems period. Through the use of an enhanced FOPID controller, both current and voltage PQ concerns are separately modified. The pulse signal of UPQC was done using the optimal controller, which analyzes the error value of reference value and actual value to generate pulses. The integrated design mitigates PQ issues in a system at non-linear load and linear load conditions. The proposed model provides THD of 12.15% and 0.82% at the sag period, 10.18% and 0.48% at the swell period, and 10.07% and 1.01% at the interruption period of non-linear load condition. A comparison between the FOPID controller and the traditional PI controller was additionally taken. The results showed that the recommended improved FOPID controller for UPQC has been successful in reducing the PQ challenges in the grid-connected RESs system.
Journal Article
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing
An innovations state space modeling framework is introduced for forecasting complex seasonal time series such as those with multiple seasonal periods, high-frequency seasonality, non-integer seasonality, and dual-calendar effects. The new framework incorporates Box-Cox transformations, Fourier representations with time varying coefficients, and ARMA error correction. Likelihood evaluation and analytical expressions for point forecasts and interval predictions under the assumption of Gaussian errors are derived, leading to a simple, comprehensive approach to forecasting complex seasonal time series. A key feature of the framework is that it relies on a new method that greatly reduces the computational burden in the maximum likelihood estimation. The modeling framework is useful for a broad range of applications, its versatility being illustrated in three empirical studies. In addition, the proposed trigonometric formulation is presented as a means of decomposing complex seasonal time series, and it is shown that this decomposition leads to the identification and extraction of seasonal components which are otherwise not apparent in the time series plot itself.
Journal Article
Generalized additive models for large data sets
We consider an application in electricity grid load prediction, where generalized additive models are appropriate, but where the data set's size can make their use practically intractable with existing methods. We therefore develop practical generalized additive model fitting methods for large data sets in the case in which the smooth terms in the model are represented by using penalized regression splines. The methods use iterative update schemes to obtain factors of the model matrix while requiring only subblocks of the model matrix to be computed at any one time. We show that efficient smoothing parameter estimation can be carried out in a well-justified manner. The grid load prediction problem requires updates of the model fit, as new data become available, and some means for dealing with residual auto-correlation in grid load. Methods are provided for these problems and parallel implementation is covered. The methods allow estimation of generalized additive models for large data sets by using modest computer hardware, and the grid load prediction problem illustrates the utility of reduced rank spline smoothing methods for dealing with complex modelling problems.
Journal Article
ELECTRICITY COST AND FIRM PERFORMANCE
Using data on Indian firms, I provide evidence on how electricity prices affect a firm’s industry choice and productivity growth. I construct an instrument for electricity price as the interaction between coal price and the share of thermal generation in a state’s total electricity generation capacity. I find that in response to an exogenous increase in electricity price, firms switch to less electricity-intensive production processes within narrowly defined industries, reduce their machine intensity, and have lower output and productivity growth rates. Thus, electricity constraints may limit a country’s growth by leading firms to operate in industries with fewer productivity-enhancing opportunities.
Journal Article
Predictive Model of Avian Electrocution Risk on Overhead Power Lines
Electrocution on overhead power structures negatively affects avian populations in diverse ecosystems worldwide, contributes to the endangerment of raptor populations in Europe and Africa, and is a major driver of legal action against electric utilities in North America. We investigated factors associated with avian electrocutions so poles that are likely to electrocute a bird can be identified and retrofitted prior to causing avian mortality. We used historical data from southern California to identify patterns of avian electrocution by voltage, month, and year to identify species most often killed by electrocution in our study area and to develop a predictive model that compared poles where an avian electrocution was known to have occurred (electrocution poles) with poles where no known electrocution occurred (comparison poles). We chose variables that could be quantified by personnel with little training in ornithology or electric systems. Electrocutions were more common at distribution voltages (≤33 kV) and during breeding seasons and were more commonly reported after a retrofitting program began. Red‐tailed Hawks (Buteo jamaicensis) (n = 265) and American Crows (Corvus brachyrhynchos) (n = 258) were the most commonly electrocuted species. In the predictive model, 4 of 14 candidate variables were required to distinguish electrocution poles from comparison poles: number of jumpers (short wires connecting energized equipment), number of primary conductors, presence of grounding, and presence of unforested unpaved areas as the dominant nearby land cover. When tested against a sample of poles not used to build the model, our model distributed poles relatively normally across electrocution‐risk values and identified the average risk as higher for electrocution poles relative to comparison poles. Our model can be used to reduce avian electrocutions through proactive identification and targeting of high‐risk poles for retrofitting. Modelo Predictivo del Riesgo de Electrocución de Aves en Líneas Eléctricas Elevadas
Journal Article