Marcus Hildmann

Electricity grid in-feed from renewable sources: A risk for pumped-storage hydro plants?

The ongoing large-scale deployment of new renewable energy sources (RES), mostly in the form of wind turbines and photovoltaic (PV) units, causes an increasing share of stochastic electricity in-feed, which inevitably has impacts on electricity spot markets. In this paper, an analysis of the impacts of RES in-feeds on the spot market price spread as well as its volatility is presented. The potential risk that these impacts on spot prices may pose for the business model of pumped-storage hydro plants is discussed. Furthermore, the dependency of spot prices on wind turbine and PV electricity in-feeds are studied. The presented analysis focuses on the situation in Germany with its, in relative and absolute terms, high share of stochastic electricity in-feed and its mature spot market.

Empirical Analysis of the Merit-Order Effect and the Missing Money Problem in Power Markets With High RES Shares

An on-going debate in the energy economics and power market community raises the question if energy-only power markets are increasingly failing due to growing feed-in shares from subsidized renewable energy sources (RES); the argument being essentially that the merit-order effect of RES production depresses day-ahead spot market prices, which in turn creates a situation where power plant owners cannot recover investment costs (missing-money problem). We argue in short that energy-only power markets are not failing in principle but are rather facing several market distortions that hinder proper market functioning, namely 1) the gap between the electricity volume actually traded at day-ahead spot markets versus the overall electricity consumption, and 2) the regulatory assumption that variable RES generation, i.e., wind and photovoltaic (PV), truly has zero marginal operation and grid integration costs. In this paper, we show that both effects over-amplify the well-known merit-order effect of RES power feed-in, and indirectly also the missing-money problem beyond a level that is explainable by underlying physical realities. We empirically analyze the impacts of wind and PV power feed-in on the day-ahead market for the EPEX German-Austrian market zone, a region that is already today experiencing significant feed-in tariff (FIT)-subsidized RES power feed-in (ca. 20% FIT share). Our analysis shows that, if necessary regulatory adaptations are taken, i.e., increasing the spot markets share of overall load demand and using the true marginal costs of RES units in the merit-order, energy-based power markets remain functional despite high RES power feed-in.

Modeling the Merit Order Curve of the European Energy Exchange Power Market in Germany

An updating study concerning the analysis and modeling of the European Energy Exchange power market in Germany is urgently called for due to the dramatic transformations of the market in recent years. Based on careful data research, this paper provides a consolidated and detailed view concerning the development of the market and its mechanisms. This paper proposes a new analytical model of the merit order curve of the market. The model is designed to capture the occurrences of negative prices. It considers fuel prices and CO2 prices, and by taking into account the structural changes of the market such as the increasing energy in-feed from renewable sources and the moratorium of nuclear power plants in Germany, the model is robust to these changes of market fundamentals. The proposed merit-order-curve model constitutes a critical component in realistic fundamental models for the European Energy Exchange power market.

Combined estimation and prediction of the Hourly Price Forward Curve

In deregulated electricity markets, market participants use the Hourly Price Forward Curve as a powerful tool to evaluate long term electricity contracts, derivatives and risk measures. In this paper a new framework to calculate the Hourly Price Forward Curve as a combined problem of parameter estimation and future price prediction on hourly basis is developed. The combined calculation results in higher robustness of the prediction, because the estimated parameters satisfy the future time series prediction equation. An estimator that is robust based on the least absolute deviation and the model selection framework Least Absolute Deviation with Least Absolute Shrinkage and Selection Operator is presented. The problem is implemented as a linear program. As a testing set, German electricity spot prices and German Future products are used. Discussion on the new framework and its advantages is given in the end.

Revisiting the merit-order effect of renewable energy sources

Summary form only given. An on-going debate in the energy economics and power market community has raised the question if energy only power markets are increasingly failing due to growing feed-in shares from subsidized renewable energy sources (RES). The short answer to this is: No, they are not failing. Energy-based power markets are, however, facing several market distortions, namely from the gap between the electricity volume traded at day-ahead markets versus the overall electricity consumption as well as the (wrong) regulatory assumption that variable RES generation, i.e., wind and photovoltaic (PV), truly have zero marginal operation costs.

What makes a good Hourly Price Forward Curve

While an Hourly Price Forward Curve (HPFC) plays a defining role in the profitability of all power trading, there is a lack of consensus on the a priori requirements of a suitable HPFC, as opposed to the a posteriori quality measure that executed trades be profitable. We attempt to address this issue by reviewing the methodology of HPFC construction, and recommending a suite of quality checks for the resulting prices, based on their intended application.

Modeling the wind power in-feed in Germany by data decomposition and time series analysis

The total wind power in-feed in Germany possesses highly predictable seasonal and diurnal patterns. The stochastic variations of wind power have consistent pseudo-cyclic patterns that are related to oscillations of air-pressure systems. The proposed model can replicate the wind power data with the hourly resolution and of time-span of years. The primary purpose of the model is for market applications such as pricing of forward contracts several years ahead. The results of the model may also be used in applications of power system operation such as reliability analysis, day-ahead scheduling, and hour-ahead scheduling.

A quantitative analysis of the electricity market in Switzerland

The electricity market in Switzerland began liberalisation in 2008, with the passing of the Federal Electricity Supply Act, and is still not fully liberalised. Owing to its relative immaturity, there is little in the way of centralised reporting of its stylised facts, a situation the current work attempts to rectify. As a guideline, a standard Hourly Price Forward Curve (HPFC) is developed. Since Switzerland does not yet boast a financial index for energy Futures, the implementation of this model is not straightforward. This issue is addressed by a quantitative market analysis based on regionally specific stylised facts. The resulting model also yields insights on the influence of bottom-up factors driving Swiss spot prices, in particular with regards to the relatively high share of hydro-power in Switzerland. We conclude with a discussion of this “Swiss-made” HPFC and its implications for the evolution of the Swiss market.

Modeling of electricity load for forward contract pricing

The majority of the consumed electricity in Europe is currently traded as forward contracts and therefore reliable price forecasting is crucial for market players. Several pricing methods based on fundamental models are available. They require a load model, which captures the characteristics of the electricity consumption. We propose an hourly load model for fundamental modeling of forward contract pricing and the formulation of the Hourly Price Forward Curve. This model captures deterministic patterns such as yearly and weekly seasonality, intra-day patterns including holidays, temperature effects and economic trends. These pattern and trends can be estimated with computational efficiency via Maximum Likelihood Estimator.

Hourly Price Forward Curve calculation for market coupling

In this paper we propose an algorithm to calculate the Hourly Price Forward Curve (HPFC) under market coupling conditions. The algorithm uses the supply and demand curves, transfer capacity, weather and seasonal indicators to calculate the HPFC based on the Market Coupling (MC) algorithm using implicit capacity auctions.