A looming revolution: Implications of self-generation for the risk exposure of retailers. ESRI WP597, September 2018

Managing the risk associated with uncertain load has always been a challenge for retailers in electricity markets. Yet the load variability has been largely predictable in the past, especially when aggregating a large number of consumers. In contrast, the increasing penetration of unpredictable, small-scale electricity generation by consumers, i.e. self-generation, constitutes a new and yet greater volume risk. Using value-at-risk metrics and Monte Carlo simulations based on German historical loads and prices, the contribution of decentralized solar PV self-generation to retailers’ load and revenue risks is assessed. This analysis has implications for the consumers’ welfare and the overall efficiency of electricity markets

Decision-making under uncertainty in short-term electricity markets

In the course of the energy transition, the share of electricity generation from renewable energy sources in Germany has increased significantly in recent years and will continue to rise. Particularly fluctuating renewables like wind and solar bring more uncertainty and volatility to the electricity system. As markets determine the unit commitment in systems with self-dispatch, many changes have been made to the design of electricity markets to meet the new challenges. Thereby, a trend towards real-time can be observed. Short-term electricity markets are becoming more important and are seen as suitable for efficient resource allocation. Therefore, it is inevitable for market participants to develop strategies for trading electricity and flexibility in these segments. The research conducted in this thesis aims to enable better decisions in short-term electricity markets. To achieve this, a multitude of quantitative methods is developed and applied: (a) forecasting methods based on econometrics and machine learning, (b) methods for stochastic modeling of time series, (c) scenario generation and reduction methods, as well as (d) stochastic programming methods. Most significantly, two- and three-stage stochastic optimization problems are formulated to derive optimal trading decisions and unit commitment in the context of short-term electricity markets. The problem formulations adequately account for the sequential structure, the characteristics and the technical requirements of the different market segments, as well as the available information regarding uncertain generation volumes and prices. The thesis contains three case studies focusing on the German electricity markets. Results confirm that, based on appropriate representations of the uncertainty of market prices and renewable generation, the optimization approaches allow to derive sound trading strategies across multiple revenue streams, with which market participants can effectively balance the inevitable trade-off between expected profit and associated risk. By considering coherent risk metrics and flexibly adaptable risk attitudes, the trading strategies allow to substantially reduce risk with only moderate expected profit losses. These results are significant, as improving trading decisions that determine the allocation of resources in the electricity system plays a key role in coping with the uncertainty from renewables and hence contributes to the ultimate success of the energy transition

The price elasticity of electricity demand in South Australia

In this paper, the price elasticity of electricity demand, representing the sensitivity of customer demand to the price of electricity, has been estimated for South Australia. We first undertake a review of the scholarly literature regarding electricity price elasticity for different regions and systems. Then we perform an empirical evaluation of the historic South Australian price elasticity, focussing on the relationship between price and demand quantiles at each half-hour of the day. This work attempts to determine whether there is any variation in price sensitivity with the time of day or quantile, and to estimate the form of any relationship that might exist in South Australia.Electricity demand; Price elasticity

The use of computational intelligence techniques for mid-term electricity price forecasting

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementWe currently live in a world ruled by large amounts of data. Organizations’ success is highly determined by the way they foresee and assess changes occurring in the future. Predictive data analytics is the art of building and using models that create forecasts based on patterns extracted from historical data. So, it is a process of making projections about a specific event which the outcome is still unknown in the present. One of the main applications is price prediction (Kelleher, Namee, & D’Arcy, 2015). Price prediction can be applied in innumerous types of business, including the energy sector. Additionally, Big Data has created opportunities for development of new energy services and bears a promise of better energy management and conservation (Grolinger, L’Heureux, Capretz, & Seewald, 2016). Whenever prediction deals with time-series data, it can be designated as forecasting. The electricity spot prices (ESP) represent the result of the market bidding prices outcome, in the electric wholesale market. Predicting these prices is an important and impactful task for market participants, like producers, consumers and retailers, since the principal objective for such players is to achieve the lowest cost in comparison with competitors. ESP play a huge role in energy market’s decision making. It is important both for developing proper bidding strategies as well as for making conscient and sustainable investment decisions (Keynia & Heydari, 2019). Additionally, it impacts the decision of the technologies to use, for example, choosing between renewable energy generators or classic gas turbines. Furthermore, the topic of electricity prices forecasting is extremely relevant for both developed and developing countries. Developed countries search for their economic prospect’s improvement. Electric energy efficiency is a crucial metric for that improvement. Electric energy efficiency can decrease the electricity prices thanks to the reduction of consumption, thus decreasing the need of having new expensive power generation and diminishing the pressure on energy resources. Therefore, ESP behavior is an important factor in their economy. Regarding developing economies, which have faced problems to take the populations out of poverty, the electricity sector restructuring has been fundamental for helping increase the levels of economic development (Ebrahimian, Barmayoon, Mohammadi, & Ghadimi, 2018)

Understanding electricity prices beyond the merit order principle using explainable AI

Electricity prices in liberalized markets are determined by the supply and demand for electric power, which are in turn driven by various external influences that vary strongly in time. In perfect competition, the merit order principle describes that dispatchable power plants enter the market in the order of their marginal costs to meet the residual load, i.e. the difference of load and renewable generation. Many market models implement this principle to predict electricity prices but typically require certain assumptions and simplifications. In this article, we present an explainable machine learning model for the prices on the German day-ahead market, which substantially outperforms a benchmark model based on the merit order principle. Our model is designed for the ex-post analysis of prices and thus builds on various external features. Using Shapley Additive exPlanation (SHAP) values, we can disentangle the role of the different features and quantify their importance from empiric data. Load, wind and solar generation are most important, as expected, but wind power appears to affect prices stronger than solar power does. Fuel prices also rank highly and show nontrivial dependencies, including strong interactions with other features revealed by a SHAP interaction analysis. Large generation ramps are correlated with high prices, again with strong feature interactions, due to the limited flexibility of nuclear and lignite plants. Our results further contribute to model development by providing quantitative insights directly from data.Comment: 13 pages, 6 figure

Short-term Risk Management for Electricity Retailers Under Rising Shares of Decentralized Solar Generation

Electricity retailers face increasing uncertainty due to the ongoing expansion of unpredictable, distributed generation in the residential sector. We analyze how increasing levels of households\u27 solar PV self-generation affect the short-term decisionmaking and associated risk exposure of electricity retailers in day-ahead and intraday markets. First, we develop a stochastic model accounting for correlations between solar load, residual load and price in sequentially nested wholesale spot markets across seasons and type of day. Second, we develop a computationally tractable twostage stochastic mixed-integer optimization model to investigate the trading portfolio and risk optimization problem faced by retailers. Through conditional value-at-risk we assess retailers\u27 profitability and risk exposure to different levels of PV self-generation by assuming different retail tariff schemes. We find risk-hedging trading strategies and tariffs to have greater impact in Summer and with low levels of residual load in the system, i.e. when the solar generation uncertainty affect more the households demand to be served and the wholesale spot prices. The study is innovative in unveiling the potential of dynamic electricity tariffs, which are indexed to spot prices, to sustain a high penetration of renewable energy source while promoting risk sharing between customer and retailer. Our findings have implications for electricity retailers facing load and revenue risks in wholesale spot markets, likewise for regulators and policy-makers interested in electricity market design

Hybrid forecast and control chain for operation of flexibility assets in micro-grids

Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets

Residential Demand Response using Electricity Smart Meter Data

The electricity industry is currently undergoing changes in a transitioning period characterised by Energy 3D: Digitalisation, Decentralisation, and Decarbonisation. Smart meters are the vital infrastructure necessary to digitalise the energy system as well as enable advancements in decentralisation and decarbonisation. As of today, more than 500 million smart meters have been installed worldwide, with that number expected to rise to several billion installations over the decade. Smart meters enable electricity load to be measured with half-hourly granularity, providing an opportunity for demand-side management innovations that are likely to be advantageous for both utility companies and customers. Among these innovations, time-of- use (TOU) tariffs are widely considered to be the most promising solution for optimising energy consumption in the residential sector, however actual use is still limited. The objective of this thesis is to investigate opportunities and problems related to TOU tariffs utilising smart meter data at the national level. The authors have identified four major research gaps which need to be filled in order to expand commercial applications of TOU tariffs. These gaps are the described and addressed in the following chapters: the "TOU load adaptation forecasting problem", the "TOU winner detection problem", the "TOU public dataset problem", and the "excess generation forecasting problem". This thesis demonstrates three modelling approaches and one new TOU dataset (CAMSL). A significant contribution to the field is through the discover of new summary statistical features (statistical moments) and assesses the capacity of these to encapsulate other more widely used explanatory variables of demand response. The thesis is concluded by discussing future works and policy implications, such as the necessity of the more tailored modelling works and public live-stream of smart meter data, which could accelerate the roll-out of the demand side management at the residential sector.EPC

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