Building and investigating generators' bidding strategies in an electricity market

In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings

Development and evaluation of data-driven models for electricity demand forecasting in Queensland, Australia

Queensland (QLD) is the second largest state in Australia, with a growing demand for electricity, but existing studies appear to lack their ability to accurately model the consumer demand for electricity. In this Master of Science Research (MSCR) thesis, two kinds of hybrid forecasting models were developed by integrating the Extreme Learning Machines (ELM) with a Markov Chain Monte Carlo (MCMC) algorithm based bivariate copula model (ELM-MCMC) and also, a conditional bivariate copula model to probabilistically forecast the electricity demand (D). The study has incorporated statistically significant lagged electricity price (PR) datasets as a non-linear regression covariate into the final D-forecasting model. In the first objective of the MSCR thesis, the ELM model was trained using statistically significant historical electricity demand at (t–1) timesteps for the state of Queensland used as a predictor variable, derived from Partial Autocorrelation Functions (PACF). This represented historical usage patterns in the electricity demand datasets used to forecast the future usage. It was then tested against current electricity demand (D(t)) to forecast the future D values. The output (i.e., simulated and observed tested D values) from the independent test dataset of the ELM model was used as the input for the MCMC-based copula model to derive the best copula model and to further improve forecasting accuracy. This involved the adoption of twenty-six copulas (e.g., Gaussian, t, Clayton, Gumble, Frank, etc.) and enabled us to also rank the best copulas based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Maximum Likelihood (MaxL) to establish the dependence of historical D with the current and future D values. The results for the ELM-MCMC copulabased model outperformed both of its counterpart models (i.e. MCMC copula-based model and the standalone ELM model) based on vigorous statistical performance metrics. For 6 and 12-hours timescales, the MCMC-Fischer-Hinzmann copula yielded the highest Legates and McCabe Index (LM) (0.98 and 0.98), and lowest error terms including root mean square error (RMSE) (285.480 and 534.090), relative root mean square error (RRMSE) (0.348 and 0.320%), mean absolute error (MAE) (262.241 and 490.661 MW), relative mean absolute error (RMAE) (0.336 and 0.309 %), AIC (-63136.102 and -34727.466), BIC (-63125.530 and -34718.279), and MaxL ( 51570.051 and 17365.733), respectively. Similarly, for the daily timescale, the ELM-MCMC-Cuadras-Auge copula outclassed its counterpart models by displaying LM (0.98), MSE ( 482703.8 MW), RMSE (694.769 MW), RRMSE (0.220 %), MAE (638.365 MW), RMAE (0.208 %), AIC (-14514.312), BIC (-14510.412), and MaxL (7258.156). These present results indicated that the hybrid ELM-MCMC copula-based model had an excellent performance, evidenced by attaining less than 10% RRMSE and RMAE, and Legates McCabe value close to unity. This is further supported by better model fits as denoted by lower AIC and BIC values and small residual error between observed and predicted data as indicated in higher MaxL values for the respective timescales. In another phase of this study, we explored the ability of both local and global optimization techniques in achieving the best parameter estimate for the 26 copulas. It has shown that the global MCMC optimization method delivers accurate parameter estimates for 6 and 12-hours timescales whilst presenting information on the posterior distribution by computing uncertainty range of parameter values within a Bayesian framework. The local method appeared to provide better estimates of copula parameters for the daily timescale of D-forecasting. In the second objective of the MSCR thesis, this study has developed a conditional bivariate copula model to probabilistically forecast electricity demand by incorporating the significant lagged electricity price (PR) from the Australian Energy Market Operator (AEMO) as a covariate into the final D-forecasting model. The use of energy price data to predict the energy demand is an important contribution given the relationships between these variables are well established. This objective resulted in the bivariate BB7 and BB8 copulas as being ranked highly for the probabilistic forecasting of D at a timescale of 30 minutes, 1-hour, and daily. The conditional exceedance probability of electricity demand greater than 7000 MW, 14000 MW, and 360000 MW for 30-minutes, 1-hour, and daily timescales given their respective prices greater than AU$25/MWh, AU$60/MWh, and AU$165/MWh predicted to be 20$25/MWh, AU$60/MWh, and AU$165/MWh was predicted to be 80%, 72%, and 70% respectively. When benchmarked with literature, the proposed research methodologies for objective (i.e., projection of demand based on antecedent behaviour) and objective 2 (i.e., projection of demand based on antecedent energy price data) appear to be versatile tools possessing a robust predictive capability for forecasting D in Queensland, Australia. Hence, this research project is the first to develop and test these novel techniques, especially using price as regression covariate to forecast demand to achieve high forecasting accuracy, when the models are applied for multiple forecasting horizons of 30-minutes, 1-hour, 6-hourly, 12-hourly, and daily. It is noted that these timescales are relevant for stakeholders (e.g., energy utilities) to develop decision systems for better energy security, and can potentially be adopted in real power grid operations to ensure stability, cost reduction and improved efficiency whilst granting consumer satisfaction. In summary, the novel energy demand modelling techniques presented here can help address research gaps in electricity usage monitoring sector by making a significant contribution towards improved forecasting accuracy of energy demand. While this study has currently been limited to Queensland, the research findings are immensely useful for energy experts in the National Energy Markets elsewhere including supporting the work of AEMO, Energex and other companies to enhance their energy forecasting and monitoring skills. These can assist in informed decisions and addressing the growing challenges within electricity industry, through improving energy demand and price monitoring, consumer satisfaction and maximized profitability endeavours of energy companies

Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices

Recurrent neural networks (RNNs) with rich feature vectors of past values can provide accurate point forecasts for series that exhibit complex serial dependence. We propose two approaches to constructing deep time series probabilistic models based on a variant of RNN called an echo state network (ESN). The first is where the output layer of the ESN has stochastic disturbances and a shrinkage prior for additional regularization. The second approach employs the implicit copula of an ESN with Gaussian disturbances, which is a deep copula process on the feature space. Combining this copula with a non-parametrically estimated marginal distribution produces a deep distributional time series model. The resulting probabilistic forecasts are deep functions of the feature vector and also marginally calibrated. In both approaches, Bayesian Markov chain Monte Carlo methods are used to estimate the models and compute forecasts. The proposed deep time series models are suitable for the complex task of forecasting intraday electricity prices. Using data from the Australian National Electricity Market, we show that our models provide accurate probabilistic price forecasts. Moreover, the models provide a flexible framework for incorporating probabilistic forecasts of electricity demand as additional features. We demonstrate that doing so in the deep distributional time series model in particular, increases price forecast accuracy substantially

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Electrical energy demand forecasting model development and evaluation with maximum overlap discrete wavelet transform-online sequential extreme learning machines algorithms

To support regional electricity markets, accurate and reliable energy demand (G) forecast models are vital stratagems for stakeholders in this sector. An online sequential extreme learning machine (OS-ELM) model integrated with a maximum overlap discrete wavelet transform (MODWT) algorithm was developed using daily G data obtained from three regional campuses (i.e., Toowoomba, Ipswich, and Springfield) at the University of Southern Queensland, Australia. In training the objective and benchmark models, the partial autocorrelation function (PACF) was first employed to select the most significant lagged input variables that captured historical fluctuations in the G time-series data. To address the challenges of non-stationarities associated with the model development datasets, a MODWT technique was adopted to decompose the potential model inputs into their wavelet and scaling coefficients before executing the OS-ELM model. The MODWT-PACF-OS-ELM (MPOE) performance was tested and compared with the non-wavelet equivalent based on the PACF-OS-ELM (POE) model using a range of statistical metrics, including, but not limited to, the mean absolute percentage error (MAPE%). For all of the three datasets, a significantly greater accuracy was achieved with the MPOE model relative to the POE model resulting in an MAPE = 4.31% vs. MAPE = 11.31%, respectively, for the case of the Toowoomba dataset, and a similarly high performance for the other two campuses. Therefore, considering the high efficacy of the proposed methodology, the study claims that the OS-ELM model performance can be improved quite significantly by integrating the model with the MODWT algorithm

Saudi Arabia’s Solar and Wind Energy Penetration:Future Performance and Requirements

Saudi Arabia fully depends on fossil fuels such as oil and natural gas to generate its electricity. Fossil fuels may have limited life and a history of fluctuating costs, which will lead to multiple issues that can affect the energy security of this country in the long-term. Critical Infrastructure Protection (CIP) is a concept different to “energy security”, which must consider the solar and wind energy as basic sources of energy supplies in Saudi Arabia. Monte Carlo Simulation (MCS) and Brownian Motion (BM) approaches were employed to predict the future behaviour of solar and wind energy, along with long-term temperature performance, based on 69 years of historical daily data. MCS and BM were employed to provide a wide range of options for future prediction results. A validation exercise showed that the north-western region was the most highly recommended region for deployment of solar and wind energy applications due to an abundance of solar and wind energy resources with low temperature supported by a clearer sky during the year. This is followed by the southern region, which exhibited good solar and wind energy resources. This study can be considered as a roadmap to meet the climate and sustainability goals by providing a long-term overview of solar energy, wind energy, and temperature performance in some countries that have a lack of long-term future prediction analysis such as Saudi Arabia

Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

An intelligent hybrid short-term load forecasting model for smart power grids

An accurate load forecasting is always particularly important for optimal planning and energy management in smart buildings and power systems. Millions of dollars can be saved annually by increasing a small degree of improvement in prediction accuracy. However, forecasting load demand accurately is a challenging task due to multiple factors such as meteorological and exogenous variables. This paper develops a novel load forecasting model, which is based on a feed-forward artificial neural network (ANN), to predict hourly load demand for various seasons of a year. In this model, a global best particle swarm optimization (GPSO) algorithm is applied as a new training technique to enhance the performance of ANN prediction. The fitness function is defined and a weight bias encoding/decoding scheme is presented to improve network training. Influential meteorological and exogenous variables along with correlated lagged load data are also employed as inputs in the presented model. The data of an ISO New England grid are used to validate the performance of the developed model. The results demonstrate that the proposed forecasting model can provide significantly better forecast accuracy, training performances and convergence characteristics than contemporary techniques found in the literature. (C) 2016 Elsevier Ltd. All rights reserved