[PDF] Stochastic Price Generation For Evaluating Wholesale Electricity Market Bidding Strategies eBook

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Page : 0 pages
File Size : 49,19 MB
Release : 2023
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This work presents a novel method for generating electricity price scenarios from statistical properties of past electricity prices using a hybrid statistical and reduced-form stochastic model. Previous work in applying stochastic differential equations (SDE) to model electricity prices has focused on daily average prices. To extend stochastic price generation methods to hourly or sub-hourly pricing, we address several weaknesses in the state-of-the-art: (1) we replace the mean-reversion component of the SDE with an ARIMA process that is better able to characterize the daily and weekly trends; (2) we extend the price-spike, or jump process to account for conditional probabilities of price spikes occurring in consecutive time steps by replacing the traditional Poisson process for modeling jumps with a generalized point process model inspired by brain neuron models; and (3) we replace the traditional method of estimating spike intensity with empirical variance with a Markov process based on observed price spike intensity transitions. The method is demonstrated with electricity prices from the US ERCOT market and a use-case example is provided for bidding an energy storage unit into the day-ahead and real-time energy markets of ERCOT using stochastic optimization methods. Results show that the the synthetic price model out performs a (naive) persistence forecast model by resulting in 24% to 47% more in profits over 168 simulated days.

Author : Dongliang Xiao
Publisher :
Page : 0 pages
File Size : 31,90 MB
Release : 2019
Category : Electricity
ISBN : 9781392534564

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The short-term electricity markets in the United States have a two-settlement structure, which includes a day-ahead (DA) and a real-time (RT) markets. Virtual bidding is a financial tool available for the participants to earn profits by utilizing the price difference between the DA and RT markets. To better utilize this financial tool to help with the electricity market operation, it is necessary to develop decision-making models for virtual bidders to generate optimal virtual bidding strategies while considering the uncertainties related to the electricity prices and the participants' physical assets. In this dissertation, stochastic optimization-based decision-making models were developed for generating optimal virtual bidding strategies for different types of market participants, and a hybrid electricity price scenario generation method was proposed to improve the virtual bidders' profits.Firstly, bilevel stochastic optimization models were developed for generating the virtual bidding strategies used by two types of physical participants, i.e., a wind power producer and an electricity retailer, respectively. The proposed models considered the participants' risk preferences, the impacts of other participants' bidding strategies on the market clearing processes, and that the physical participants would use virtual bidding at multiple buses, which were not limited to the locations of their generating units or demands, to improve their market power. Case studies were carried out to validate the proposed models for a strategic wind power producer and a retailer, respectively, and the simulation results showed that virtual bidding improved their expected profits. Next, a hybrid electricity price scenario generation method using a seasonal autoregressive integrated moving average (SARIMA) model and historical data was proposed. In the proposed method, the spikes contained in the historical data of the electricity prices were firstly identified by using an outlier detection method; then, the historical data were decomposed into base and spike components; next, the base and spike component scenarios were generated by using the SARIMA- and historical data-based methods, respectively; finally, the electricity price scenarios were obtained by combining the base and spike component scenarios. Case studies were carried out for a virtual bidder in the Pennsylvanian-New Jersey-Maryland (PJM) electricity market to validate the proposed method.

Author : Tiago Pinto
Publisher : Academic Press
Page : 474 pages
File Size : 48,27 MB
Release : 2021-07-03
Category : Business & Economics
ISBN : 0128226668

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Local Electricity Markets introduces the fundamental characteristics, needs, and constraints shaping the design and implementation of local electricity markets. It addresses current proposed local market models and lessons from their limited practical implementation. The work discusses relevant decision and informatics tools considered important in the implementation of local electricity markets. It also includes a review on management and trading platforms, including commercially available tools. Aspects of local electricity market infrastructure are identified and discussed, including physical and software infrastructure. It discusses the current regulatory frameworks available for local electricity market development internationally. The work concludes with a discussion of barriers and opportunities for local electricity markets in the future. Delineates key components shaping the design and implementation of local electricity market structure Provides a coherent view on the enabling infrastructures and technologies that underpin local market expansion Explores the current regulatory environment for local electricity markets drawn from a global panel of contributors Exposes future paths toward widespread implementation of local electricity markets using an empirical review of barriers and opportunities Reviews relevant local electricity market case studies, pilots and demonstrators already deployed and under implementation

Author : Fred Espen Benth
Publisher : World Scientific
Page : 352 pages
File Size : 37,14 MB
Release : 2008
Category : Business & Economics
ISBN : 981281230X

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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.This book provides a concise and rigorous treatment on the stochastic modeling of energy markets. Ornstein?Uhlenbeck processes are described as the basic modeling tool for spot price dynamics, where innovations are driven by time-inhomogeneous jump processes. Temperature futures are studied based on a continuous higher-order autoregressive model for the temperature dynamics. The theory presented here pays special attention to the seasonality of volatility and the Samuelson effect. Empirical studies using data from electricity, temperature and gas markets are given to link theory to practice.

Author : Fred Espen Benth
Publisher : World Scientific
Page : 352 pages
File Size : 34,91 MB
Release : 2008-04-14
Category : Business & Economics
ISBN : 9814471313

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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.This book provides a concise and rigorous treatment on the stochastic modeling of energy markets. Ornstein-Uhlenbeck processes are described as the basic modeling tool for spot price dynamics, where innovations are driven by time-inhomogeneous jump processes. Temperature futures are studied based on a continuous higher-order autoregressive model for the temperature dynamics. The theory presented here pays special attention to the seasonality of volatility and the Samuelson effect. Empirical studies using data from electricity, temperature and gas markets are given to link theory to practice.

Author : Ansgar Steland
Publisher : Springer
Page : 479 pages
File Size : 13,68 MB
Release : 2015-02-04
Category : Mathematics
ISBN : 3319138812

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This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.

Author : Petter L. Skantze
Publisher :
Page : 61 pages
File Size : 39,22 MB
Release : 2000
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The bid based model developed in this report is intended as a fundamental model for electricity price dynamics, to be used in a wide range of applications. The emphasis was placed on incorporating the unique characteristics of electricity prices, including seasonality on multiple time scales, lack of load elasticity, stochastic supply outages, strong mean reversion, and stochastic growth of load and supply. Principal component analysis is applied in the model in order to capture intra-day dynamics, while at the same time greatly reducing the computational complexity. The model is calibrated on actual load and price data form the New England ISO. We also propose extensions of the model to deal with instances of multiple spot markets connected by transmission lines. Through simulations we illustrate how the model can be used to estimate the value of transmission rights in a two-market environment. It is also shown how the model can be used by a for-profit transmission provider in order to make optimal investment decisions in new transmission capacity. Finally, an extension of the model is proposed to simulate the interaction between technical innovation and long-term price dynamics in electricity markets.

Author : Petr Spodniak
Publisher :
Page : pages
File Size : 38,20 MB
Release : 2019
Category :
ISBN :

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Electricity wholesale markets are undergoing rapid transformation due to the increasing share of distributed and variable renewable energy sources (vRES) penetrating the market. The increasing shares of stochastic wind generation bring along greater deviations between the real time power generation and the day-ahead forecasts of power supply. It is therefore reasonable to assume that trading activity is shifting more from the traditionally dominant day-ahead market into the intra-day and regulating power markets. This is because predicting vRES power generation closer to the actual delivery is more reliable and because power generators are motivated to avoid high imbalance costs. We study price spreads between day-ahead, intra-day and regulating power markets in three Nordic countries (Denmark, Sweden and Finland) during 2013-2017. We estimate vector autoregressive (VAR) models to study the interrelationships between the price spreads and the effects of wind forecast and demand forecast errors, and other exogenous variables, such as transmission congestions, and hydrological conditions, on price spreads in different Nord Pool bidding areas. We use the variation in the shares of wind power between bidding areas to analyse the impacts of increased shares of wind power on different market places. We find that wind forecast errors do affect price spreads in areas with large shares of wind power generation. Moreover, demand forecast errors have an impact on almost all price spreads, except in areas with relatively low consumption. Our results indicate that increasing shares of wind power are, indeed, changing the relevance of different market places. Markets closer to real time are playing more important role than in the past.