German Day-ahead Market Research Articles

Stochastic programming of energy system operations considering terminal energy storage levels

Energy storage units offer vital balancing power for energy systems with an increasing amount of variable renewable energy (VRE) sources. The operation of such systems can be optimized by stochastic programming, which anticipates the uncertainty related to the variable renewable energy sources. However, these optimization problems can only be formulated for optimization horizons with a finite length, due to the rapidly increasing problem size and uncertainty in VRE production. Realistic valuation of the stored energy at the end of a horizon is important for long-term operation of the system. In this work, we investigate two different valuation methods, which are based on forecasted electricity prices, for storage-only and producer-oriented energy systems that participate in the day-ahead market. On a case study on the German day-ahead market, the methods yield competitive profits with reduced cycling of the energy storage unit and deviations with respect to the day-ahead trading.

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. Various market models are based on this principle when attempting to predict electricity prices, yet the principle is fraught with assumptions and simplifications and thus is limited in accurately predicting prices. In this article, we present an explainable machine learning model for the electricity prices on the German day-ahead market which foregoes of the aforementioned assumptions of the merit order principle. Our model is designed for an ex-post analysis of prices and builds on various external features. Using SHapley Additive exPlanation (SHAP) values we disentangle the role of the different features and quantify their importance from empiric data, and therein circumvent the limitations inherent to the merit order principle. We show that load, wind and solar generation are the central external features driving prices, as expected, wherein wind generation affects prices more than solar generation. Similarly, fuel prices also highly affect prices, and do so in a nontrivial manner. Moreover, large generation ramps are correlated with high prices due to the limited flexibility of nuclear and lignite plants. Overall, we offer a model that describes the influence of the main drivers of electricity prices in Germany, taking us a step beyond the limited merit order principle in explaining the drivers of electricity prices and their relation to each other.

The demand response potential in copper production

Demand response (DR) of large industrial electricity consumers is a promising option to balance the fluctuating supply by renewable energies in the electricity grid. Renewable energy technologies themselves depend on copper as a key material. At the same time, the production of copper is a power-intensive process but its DR potential has not yet been quantified in detail via process scheduling. Here, we analyze the DR potential of copper production by optimally scheduling the batch and continuous tasks of a representative copper process. To determine the optimal schedule, we formulate a mixed-integer linear program (MILP) based on the resource-task network (RTN) formulation approach. We first optimize the production volume to define a reference case and then minimize the electricity costs under time-varying electricity prices while retaining the production target. A sensitivity analysis evaluates the impact of task capacities on the production volume and DR potential. The results indicate a significant DR potential, as optimal scheduling can reduce the annual electricity costs by up to 14.2% while still producing the maximum copper output as the reference process schedule. The power-intensive electrolytic refining shows the largest potential for reducing costs. Offgas handling, air separation, and air compression further show significant cost reduction potentials. These tasks must process large material streams that are directly connected to operating the smelting and refining tasks. Our model shows the potential of considering these interlinked tasks in one scheduling model that focuses on DR. The results suggest that DR scheduling in copper production has a significant economic potential without compromising production goals. Further, the DR scheduling shifts large amounts of electricity demand by responding to fluctuating electricity prices, which enables flexibility of the demand side and can thus support the integration of renewable energy into the electric grid. • Process scheduling of copper production to assess demand response (DR) potential. • Discrete-time resource-task network model to resolve electricity demands. • DR can reduce electricity costs by up to 14% based on German day-ahead market. • Substantial load shifting potential of 16% of the annual electricity demand. • Task-specific DR potentials and impact of process debottlenecking analyzed.

Multi-Day-Ahead Electricity Price Forecasting: A Comparison of Fundamental, Econometric and Hybrid Models

Forecasting hourly electricity prices and their characteristic properties is a core challenge for energy generation companies and trading houses. The short-term marketing and purchase of electricity is usually managed with standardized products traded on different markets and with specific temporal resolution and maturity. The size and scope of the electricity price forecasting literature has grown significantly in recent years, with the majority of studies focused on short-term (intraday and day-ahead) or long-term (investment decisions) periods. However, the literature for forecasting the period beyond the day-ahead horizon, which is relevant for trading the aforementioned products or for managing assets over several days, is rather scarce. Our paper fills this gap by developing individual forecasting models covering horizons from the day ahead up to a week ahead. We introduce hybrids of a parsimonious fundamental model and various popular econometric models. In a case study for the German day-ahead market in 2016 we test and compare the different model settings by carefully considering realistic available data and limiting the calculation time to fit typical trading time constraints. We find that the best models across the individual horizons and across all horizons jointly are hybrid model approaches. They combine the strengths of autoregressive models in terms of capturing daily - even non-linear-structures with the immediate reactions of fundamental models to short-term events or fundamental changes in the market.

The impact of renewables on electricity prices in Germany - An update for the years 2014–2018

Increasing electricity prices for private consumers manifested the idea of price boosting renewable energy sources in Germany's public opinion. Literature however widely concurs that wind and photovoltaics have a reducing effect on spot market electricity prices. The underlying merit order effect led to historically low exchange prices in the past years. To quantify this effect, this study analyses historic demand and supply curves from the German day-ahead market to reconstruct electricity prices without the feed-in of wind and photovoltaics from 2014 to 2018. Results show that renewable energy sources considerably reduced electricity prices by between 2.89 ct/kWh in 2014 to 8.89 ct/kWh in 2017. This allowed German end-consumers to save a total of 40 billion € from 2014 to 2018. Additionally, wind and PV contributed significantly to security of supply. Demand could not have been met by domestic conventional and nuclear generation capacities in up to 424 hours in 2018. The study highlights the necessity to further speed-up the expansion of wind and photovoltaics.

Modelling German electricity wholesale spot prices with a parsimonious fundamental model – Validation & application

In this paper, we introduce a parsimonious fundamental model for the German day-ahead market. The methodology approximates the supply stack by a piecewise linear function and considers fundamental information, e.g. power plant availabilities, must-run production and cross-border exchange. We reduce complexity by considering technology classes, uncoupled time periods and only one market area. The model accurately reproduces the hourly historical prices and electricity production volumes for most thermal production units in Germany. In a case study, we investigate the effects of the German nuclear phase-out decision. We find an additional price decrease and reduced electricity imports in the counterfactual scenario.

The evolution of electricity price on the German day-ahead market before and after the energy switch

Germany is a forerunner in developing renewable energies. It is therefore of considerable interest to investigate the impact of switch to renewables on the market during transition era. The aim of this study is in two parts: 1) Investigating the volatility; and 2) Conducting a descriptive study on the evolution of daily profiles and emergence of non-positive prices. In terms of volatility quantification, the following characteristics of EPEX prices should be recognized: 1) Covering the whole year (24/7); 2) Taking non-positive values; 3) Depending on calendar information; and 4) Changing according to demand and supply availability. We, therefore, propose a robust and generic approach to account for diurnal or seasonal patterns of human activities in volatility analysis. An important distinction of our work is in introducing an alternative representation (as matrices) for quasi-periodic price data. We, herein, propose a new notion of volatility using a matrix decomposition technique, namely the singular value decomposition (SVD). Our observations indicate price volatility reduction, in the recent years. The second part of this article provides evidences of effect of renewables on daily price profiles – emergence of non-positive prices and also shifts of peak price values to hours where solar is less available.

Residual Demand Modeling and Application to Electricity Pricing

A model for residual demand is proposed, which extends structural electricity price models to account for renewable infeed in the market. Infeed from wind and solar is modeled explicitly and withdrawn from total demand. The methodology separates the impact of weather and capacity. Efficiency is modeled as a stochastic process. Installed capacity is a deterministic function of time. The residual demand model is applied to the German day-ahead market. Price trajectories show typical features seen in market prices in recent years. The model is able to closely reproduce the structure and magnitude of market prices. Using simulations it is found that renewable infeed increases the volatility of forward prices in times of low demand, but can reduce volatility in peak hours. The merit-order effect of increased wind and solar capacity is calculated. It is found that under current capacity levels in the German market wind has a stronger overall effect than solar, but both are even in peak hours.

Residual Demand Modeling and Application to Electricity Pricing

A model for residual demand is proposed, which extends structural electricity price models to account for renewable infeed in the market. Infeed from wind and solar is modelled explicitly and withdrawn from total demand. The methodology separates the impact of weather and capacity. Efficiency is modelled as a stochastic process. Installed capacity is a deterministic function of time. The residual demand model is applied to the German day-ahead market. Price trajectories show typical features seen in market prices in recent years. The model is able to closely reproduce the structure and magnitude of market prices. Using simulations it is found that renewable infeed increases the volatility of forward prices in times of low demand, but can reduce volatility in peak hours. The merit-order effect of increased wind and solar capacity is calculated. It is found that under current capacity levels in the German market wind has a stronger overall effect than solar, but both are even in peak hours.