Forecasting Electricity Prices in Deregulated Wholesale Spot Electricity Market - A Review

In the new framework of competitive electricity markets, all power market participants need accurate price forecasting tools. Electricity price forecasts characterize significant information that can help captive power producer, independent power producer, power generation companies, power distribution companies or open access consumers in careful planning of their bidding strategies for maximizing their profits, benefits and utilities from long term, medium term and short term perspective. Short term spot electricity price forecasting techniques are either inspired from electrical engineering literature (i.e. load forecasting) or from economics literature (i.e. game theory models and the time-series econometric models). In this study we investigate the emergence of spot electricity markets with particular emphasis on Indian electricity market which has never been done before and review selected finance and econometrics inspired literature and models for forecasting electricity spot prices in deregulated wholesale spot electricity markets. Keywords: spot price; electricity; forecasting; power market JEL Classifications: C01; C22; C5

Forecast of the demand for hourly electric energy by artificial neural networks

Obtaining an accurate forecast of the energy demand is fundamental to support the several decision processes of the electricity service agents in a country. For market operators, a greater precision in the short-term load forecasting implies a more efficient programming of the electricity generation resources, which means a reduction in costs. In the long term, it constitutes a main indicator for the generation of investment signals for future installed capacity. This research proposes a prognostic model for the demand of electrical energy in Bogota, Colombia at hourly level in a full week, through Artificial Neural Network

Modeling daily electricity load curve using cubic splines and functional principal components

Forecasting electricity load is very important to the electric utilities as well as producers of power because accurate predictions can cut down costs by avoiding power shortages or surpluses. Of specific interest is the 24-hour daily electricity load profile, which provides insight into periods of high demand and periods where the use of electricity is at a minimum. Researchers have proposed many approaches to modeling electricity prices, real-time load, and day-ahead demand, with varying success. In this dissertation three new approaches to modeling and forecasting the 24-hour daily electricity load profiles are presented. The application of the proposed methods is illustrated using hourly electricity load data from the Atlantic City Electric (AE) zone, which is part of the Pennsylvania, New Jersey, and Maryland (PJM) electricity market. The first approach that is proposed can be used to make short-term forecasts of electricity load. This approach employs a hybrid technique utilizing autoregressive moving average method (ARMA) and cubic spline models. The second approach is suitable for obtaining long-term forecasts of the daily electricity load and employs cubic splines with time varying coefficients. These coefficients are modeled as a multivariate time series using a vector autoregressive model with exogenous variables to forecast the average daily electricity load profile for a future month. The last approach uses functional principal components to model the daily electricity load profile for each day as a linear combination of three eigenfunctions, with the coefficients of the day-specific linear combinations modeled as univariate time series using transfer functions. The fitted models from the three approaches were applied to data from a subsequent year and the results show that these models perform quite well --Abstract, page iii

Short-term Load Forecasting with Distributed Long Short-Term Memory

With the employment of smart meters, massive data on consumer behaviour can be collected by retailers. From the collected data, the retailers may obtain the household profile information and implement demand response. While retailers prefer to acquire a model as accurate as possible among different customers, there are two major challenges. First, different retailers in the retail market do not share their consumer's electricity consumption data as these data are regarded as their assets, which has led to the problem of data island. Second, the electricity load data are highly heterogeneous since different retailers may serve various consumers. To this end, a fully distributed short-term load forecasting framework based on a consensus algorithm and Long Short-Term Memory (LSTM) is proposed, which may protect the customer's privacy and satisfy the accurate load forecasting requirement. Specifically, a fully distributed learning framework is exploited for distributed training, and a consensus technique is applied to meet confidential privacy. Case studies show that the proposed method has comparable performance with centralised methods regarding the accuracy, but the proposed method shows advantages in training speed and data privacy.Comment: 5 pages, 4 figures, under revie

Introducing system-based spatial electricity load forecasting

The main motivation of this research is to help reduce the Green House Gases (GHG) emissions of the electricity sector, and counteract the effects on nature and people. Traditional methods of power planning are not optimised to achieve this, and only consider Capital Expenditure (Capex) and Operational Expenditure (Opex) reduction as their main objectives. Minimising GHG emissions is now an additional objective of power planning. One way of achieving this is by optimising the distance of generators to the loads to reduce the transmission losses, and also by harnessing the available regional sources of renewable energies and increasing their integration in the network. Efficient load forecasting methods, capable of describing the regional behaviours of the electricity consumption are developed in this research, and can provide priceless input to electricity planners. Such forecasting methods, known as spatial forecasting, can be used to extract short-term and medium-term information of the electricity consumption of different regions. This work also provides tools for making decisions about the most accurate way of pre-processing consumption data and choosing the most efficient forecasting procedure. Chapter 1 talks about emissions of GHGs and their adverse effect on the nature. It introduces electricity sector as one of the major contributors of human made GHG emissions. It then describes the components of electrical power network and the planning of it. Finally the chapter concludes that an efficient spatial load forecasting method is required to help with spatial planning of power networks. The spatial planning can include more regional components like proximity of generation components to consumers, or the levels of harnessed renewable energy in each area. In such an approach, GHG reduction can be also considered along with Capex and Opex minimisation to plan the future of power networks. Chapter 2 provides definitions on power network components and the load forecasting methods. It starts with definition of power systems and explanation on how electrical energy is superior to all other forms of energy from end user point of view. Electricity generation systems and the sources of energy to produce electricity are described next. Typical generation unit sizes in MW, continuity of the supply, and also its predictability are summarised in a table at the end of this section. Thereafter, transmission lines and distribution systems are described, as other component of electrical power networks. Importance of having an accurate forecast of electricity demand and the common ways to do it are presented next. At the end of this chapter, the deficiencies of current forecasting methods are highlighted and one major goal is defined for this work. It is to overcome the deficiencies of individual forecasting methods by combining them and using them only where it performs efficient. It also mentions that the work is going to closely look at the behaviour of input data to the forecasting method to seek better methods for preparing them. Chapter 3 describes South West Interconnected System (SWIS) as the case study for this work. The reasons for selecting SWIS as the case study are mentioned, followed by a quick history of it and how it has been expanded over the last hundred years. To be able to complete spatial forecasting, the area under study needs to be divided into regions. SWIS is then divided into eight regions for this purpose. A visual presentation of the eight regions on the map is presented at the end of this chapter for more clarity. Chapter 4 performs a short forecasting method on one of the SWIS regions. The selected region is called Metro East. Metro East region is mainly composed of residential consumers. Unlike commercial and industrial consumers, the residential ones are not following a working schedule. That's why it makes them to behave differently and more randomly comparing to the other two. This means more complicated demand to forecast. This is the main reason that Metro East is selected to be studied on this chapter. One of the main components of this chapter is to introduce the methods that have been used for pre-processing of input data. The pre-processing stages include data resolution adjustment, replacement of missing data, removing outliers, clustering and signal reconstruction. A well pre-processed set of data is critical component of any forecasting strategy. The second component of chapter 4 is to generate one day ahead and seven day ahead forecasts of Metro East electricity consumption, using three different training methods. The forecasted results are comparable to other studies done on short term load forecasting. However the author questions the accuracy of classic approach of load forecasting. Classic approach is basically what have been done in the field of load forecasting for decades, which is very similar to the works done in chapter 4. In classic approach, a method gets tested on a case study with an acceptable level of accuracy. Then that method gets introduced as a very accurate tool to be applied on demand forecasting purposes. This work is showing that such accurate method cannot be accurate at all when being applied to other different case studies. Future chapters study this in further details, and come up with some guidelines on how to have accurate load forecast based on the nature of the case study in hand. Chapter 5 applies the methods of load forecasting developed in chapter 4 onto eight different case studies. By doing this, it can be seen that there is no single method of forecasting that can be accurate for all case studies out there. Temperature sensitivity and distribution of the load data of all the regions is closely studied for fifteen years of data. A load type determination criterion is presented in Table 5. By using this table, and preparing Rayleigh, Generalised Pareto, and Generalised Extreme Value distributions of the load data under study, anyone will be able to say whether their load under study is mainly commercial, residential or industrial. The outdoor temperature is one of the main inputs of short term electricity forecasting. Same chapter shows that residential loads are having a greater temperature sensitivity comparing to the other two. The results of one day and seven day ahead forecasts of the eight regions are presented at the end of chapter 5, using two methods of neural networks and decision trees. The results suggest that the two methods need to be used alternatively based on the characteristics of the case study and ambient temperature to achieve the best result. Chapter 6 explains the system based medium term load forecasting. The approach to medium term forecasting is completely different to the one developed for the short term one. Two main differences between Short-Term Load Forecasting (STLF) and Medium-Term Load Forecasting (MTLF) are the availability of weather data and the forecasting objectives. Because of the nature of the weather, temperature forecasts of a year ahead are completely impossible. Also in medium term load forecasting the focus of planners is mainly on peak load and energy consumption forecasts. The forecasting method presented in this chapter is achieved by superimposing annual trend, annual seasonality and forecasted residuals by neural networks and decision trees. Similar to chapter 5, the forecasting strategy is applied to eight different case studies for comparison. It is concluded that based on the case under study, the accuracy of the methods changes. It also provides some advices on the best practices to perform medium load forecasting, considering the characteristics of the load. For instance, it conclude that for industrial regions regression trees performs better than neural network based methods. The same applies to CBD region where commercial load dominates. For some residential areas neural networks behave better. This is because of higher nonlinearity of residential load. The major contributions of this work can be summarised as below: - The topic of the study, i.e. spatial load forecasting and the potential of using it in efficient power planning, is relatively a new topic in the electricity market literature. Moreover, many of the known spatial load forecasting methods have not yet been widely used because of the size, variety, and availability of the data required. The methodology proposed in this study can successfully be applied to spatial forecasting. - While conventional methods are useful for short-term predictions with acceptable accuracy, they fail when medium-to-long term load forecasting is dealt with. The methodology conceived and implemented in this thesis is significantly better than those known as state-of-the-art and can give very satisfactory results for medium-term predictions. - The load analysis criterion, particularly using Q-Q (Quantile vs. Quantile) plots is a unique and original finding of this work. While Q-Q plots are largely used in traditional statistics to compare two samples of data, it has never been applied before for electricity load forecasting purposes. Based on its definition and use, an electricity planner can understand which part of the load is the dominating factor (i.e. whether it is residential, commercial or industrial). And then, based on this, he/she can decide how to go ahead with choosing the most effective forecasting method. Based on this, the thesis provides a very useful criterion for decision making in the energy market. - One of the major findings of the thesis is that there is no one optimum way of forecasting electricity load in different scenarios. The results presented in the thesis have shown that a method that can accurately forecast the load on a system (3% error for a year ahead) can perform completely different in forecasting another system (observed errors of around 14%). This study demonstrates that a method which is claimed to have a given accuracy can be considerably inaccurate when applied on a different case study. - Using an ambient temperature-based criterion (i.e. the average maximum temperature of the month) to choose the correct forecasting method is another major finding of the study. In fact, the author has demonstrated that for a temperature sensitive load, different forecasting methods should be used and then combined to get the most accurate result

Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting

Forecasting price has now become essential task in the operation of electrical power system. Power producers and customers use short term price forecasts to manage and plan for bidding approaches, and hence increasing the utility’s profit and energy efficiency as well. The main challenge in forecasting electricity price is when dealing with non-stationary and high volatile price series. Some of the factors influencing this volatility are load behavior, weather, fuel price and transaction of import and export due to long term contract. This paper proposes the use of Least Square Support Vector Machine (LSSVM) with Genetic Algorithm (GA) optimization technique to predict daily electricity prices in Ontario. The selection of input data and LSSVM’s parameter held by GA are proven to improve accuracy as well as efficiency of prediction. A comparative study of proposed approach with other techniques and previous research was conducted in term of forecast accuracy, where the results indicate that (1) the LSSVM with GA outperforms other methods of LSSVM and Neural Network (NN), (2) the optimization algorithm of GA gives better accuracy than Particle Swarm Optimization (PSO) and cross validation. However, future study should emphasize on improving forecast accuracy during spike event since Ontario power market is reported as among the most volatile market worldwide

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn

Energy consumption forecasting using machine learning

Forecasting electricity demand and consumption accurately is critical to the optimal and costeffective operation system, providing a competitive advantage to companies. In working with seasonal data and external variables, the traditional time-series forecasting methods cannot be applied to electricity consumption data. In energy planning for a generating company, accurate power forecasting for the electrical consumption prediction, as a technique, to understand and predict the market electricity demand is of paramount importance. Their power production can be adjusted accordingly in a deregulated market. As data type is seasonal, Persistence Models (Naïve Models), Seasonal AutoRegressive Integrated Moving Averages with eXogenous regressors (SARIMAX), and Univariate Long-Short Term Memory Neural Network (LSTM) is used to explicitly deal with seasonality as a class of time-series forecasting models. The main purpose of this project is to perform exploratory data analysis of the Spain power, then use different forecasting models to once-daily predict the next 24 hours of energy demand and daily peak demand. To split the electricity consumption data from 2015 to 2018 into training and test sets, the first three years from 2015 and 2017 were used as the training set, while values from 2018 were used as the test set. The obtained results showed that the machine learning algorithms proposed in the recent literature outperformed the tested algorithms. Models are evaluated using root mean squared error (RMSE) to be directly comparable to energy readings in the data. RMSE has calculated two ways. First to represent the error of predicting each hour at a time (i.e. one error per-hourly slice). Second to represent the models’ overall performance. The results show that electricity demand can be modeled using machine learning algorithms, deploying renewable energy, planning for high/low load days, and reducing wastage from polluting on reserve standby generation, detecting abnormalities in consumption trends, and quantifying energy and cost-saving measures

Explainable Artificial Intelligence and Deep Learning for Analysis and Forecasting of Complex Time Series: Applications to Electricity Prices

A rapid energy transition from fossil fuel based generation to renewable energy sources is vital for the mitigation of climate change but requires complex market structures to manage the coordination of generation and demand. In particular, the German day-ahead market reacts to short-term forecasts one day prior to delivery and is driven by various external drivers. Its understanding and forecasting are essential for the energy transition as it allows renewable energy operators to make profits and promotes key technologies for a stable grid operation, such as battery storage. In this work, we analyze the German day-ahead electricity market using eXplainable Artificial Intelligence (XAI) and forecast electricity prices using deep neural networks. We investigate the application of SHapley Additive exPlanations (SHAP) to study the driving factors of electricity prices. The dataset includes several power system features such as load or renewable forecasts but also fuel prices. Our analysis suggests that load, wind and solar generation are the central external features driving prices, as expected, wherein wind generation affects prices more than solar generation. Simi- larly, fuel prices also highly affect prices in a nontrivial manner. Moreover, large generation ramps are correlated with high prices due to the limited flexibility of nuclear and lignite plants. Based on the results from the XAI method, we establish Long Short-Term Memory (LSTM) networks to forecast electricity prices. We introduce a probabilistic forecast as output, increas- ing the applicability of the model. The LSTM model is able to outperform models from related works and enables additional applications using the predicted standard deviation