• Energy Research

Abstract Electricity markets in Europe become increasingly interconnected due to new grid connections and market coupling regulations. This paper examines the interdependencies between the Swiss electricity market and those of neighbouring countries. The Swiss market serves as a good example for a smaller electricity market which is increasingly affected by developments in the large neighbouring countries. To study these cross-border effects, especially those on Swiss electricity prices, we apply two different methodologies, an econometric and a Nash-Cournot equilibrium model. The analyses show that the Swiss electricity price correlates strongly with the German electricity price in the summer, but tends to follow the French electricity price in the winter. Another finding is that gas prices and the electricity load of neighbouring countries have a significant influence on prices. In particular, the load of France and Italy is driving up Swiss prices in the winter, while the German electricity demand and renewable energy generation have a larger influence on Swiss prices in the summer.

With rising shares of fluctuating renewable energy generation, the role of electricity storage becomes increasingly important. However, European pumped storage plants (PSPs) are currently confronted with economic challenges and their potential for smoothing the residual load curve is consequently not fully exploited. In this paper, the long-term effects of load-smoothing and the typical price-based PSP operation are compared with an emphasis on the development of the wholesale electricity prices and the generation adequacy. The study is conducted using an agent-based simulation model of the day-ahead electricity market that has been extended by a methodology to heuristically dispatch the PSPs in Germany. The results show, that the contribution load-smoothing PSP operation can make to improving generation adequacy is rather small. Putting a strategic reserve in place reduces the differences between price-based and load-smoothing PSP operation even further. Moreover, load-smoothing PSP operation goes along with slightly increased wholesale electricity prices as compared to price-based operation.

Abstract This paper evaluates different financial price and time series models, such as mean reversion, autoregressive moving average (ARMA), integrated ARMA (ARIMA) and general autoregressive conditional heteroscedasticity (GARCH) process, usually applied for electricity price simulations. However, as these models are developed to describe the stochastic behaviour of electricity prices, they are extended by a separate data treatment for the deterministic components (trend, daily, weekly and annual cycles) of electricity spot prices. Furthermore price jumps are considered and implemented within a regime-switching model. Since 2008 market design allows for negative prices at the European Energy Exchange, which also occurred for several hours in the last years. Up to now, only a few financial and time series approaches exist, which are able to capture negative prices. This paper presents a new approach incorporating negative prices. The evaluation of the different approaches presented points out that the mean reversion and the ARMA models deliver the lowest mean root square error between simulated and historical electricity spot prices gained from the European Energy Exchange. These models posses also lower mean average errors than GARCH models. Hence, they are more suitable to simulate well-fitting price paths. Furthermore it is shown that the daily structure of historical price curves is better captured applying ARMA or ARIMA processes instead of mean-reversion or GARCH models. Another important outcome of the paper is that the regime-switching approach and the consideration of negative prices via the new proposed approach lead to a significant improvement of the electricity price simulation.

AbstractStructural changes in the energy sector have created considerable challenges for regulators, energy consumers, and suppliers. Energy researchers rely on quantitative modeling approaches to address these challenges. At the Karlsruhe Institute of Technology, we have developed several models to analyze today's and the future's energy markets to address the challenges for electricity networks as a result of intermittent renewable and decentralized power and heat supply and to find solutions for integrating demand response. This paper presents a survey of these approaches and discusses further challenges. The developed models have proved to be suitable for answering a range of research questions related to structural changes. As the energy sector remains in a constant state of flux, these models need to be further developed. This means coupling different energy sectors and markets and improving their technical accuracy as well as their spatial and temporal resolutions.

In Europe, the ongoing renewable expansion and delays in the planned grid extension have intensified the discussion about an adequate electricity market design. Against this background, we jointly apply an agent-based electricity market model and an optimal power flow model to investigate the long-term impacts of splitting the German market area into two price zones. Our approach allows capturing long-term investment and short-term market behavior under imperfect information. We find strong impacts of a German market splitting on electricity prices, expansion planning of generators and required congestion management. While the congestion volumes decrease significantly under a market split in the short term, the optimal zonal configuration for 2020 becomes outdated over time due to dynamic effects like grid extension, renewable expansion and new power plant investments. Policymakers and regulators should therefore regularly re-assess bidding zone configurations. Yet, this stands in contrast to the major objective of price zones to create stable locational investment incentives.

Purpose With the growing deployment of variable renewable energy sources, such as wind and PV and the increasing interconnection of the power grid, multi-regional energy system models (ESMs) are increasingly challenged by the growth of model complexity. Therefore, the need for developing ESMs, which are realistic but also solvable with acceptable computational resources without losing output accuracy, arises. The purpose of this study is to propose a statistical approach to investigate asynchronous extreme events for different regions and then assess their ability to keep the output accuracy at the level of the full-resolution case. Design/methodology/approach To extract the extreme events from the residual demands, the paper focuses on analyzing the tail of the residual demand distributions by using statistical approaches. The extreme events then are implemented in an ESM to assess the effect of them in protecting the accuracy of the output compared with the full-resolution output. Findings The results show that extreme-high and fluctuation events are the most important events to be included in data input to maintain the flexibility output of the model when reducing the resolution. By including these events into the reduced data input, the output's accuracy reaches the level of 99.1% compared to full resolution case, while reducing the execution time by 20 times. Originality/value Moreover, including extreme-fluctuation along with extreme-high in the reduced data input helps the ESM to avoid misleading investment in conventional and low-efficient generators.

Abstract The effectiveness of the energy-only market (EOM) in providing sufficient incentives for investments is intensively discussed in Europe. While supporters claim that an improved EOM can guarantee generation adequacy, energy suppliers in particular favor the introduction of a capacity market to finance power plant investments. However, there is a lack of quantitative assessment of market design options taking into account individual decisions of market players. Existing studies mainly include a system view based on a central planner optimization. This paper on the other hand is based on an agent-based simulation model for the German electricity market. This method can explicitly incorporate individual investment decisions and aggregate them to present a holistic view of the system. Our results show that an EOM extended with a strategic reserve can incentivize investments, and guarantee supply security in a market with high share of renewable energies. However, the generation adequacy can be more easily achieved with a capacity market. Furthermore, the cost advantage of an EOM diminishes in the long-term, as scarcity prices in the EOM lead to similar system costs as with a capacity market.

Although decarbonisation is one of the most important macro-trends of this century, electricity generation from coal power plants is still broadly common. The main goal of this study is to evaluate the impact of a premature coal power plants phase-out on the Italian day-ahead electricity market. For this purpose, two electricity price forecasts, related to different scenarios between 2019 and 2030, and two different hypotheses for the creation of electricity spot price, were compared. The results from the different scenarios show that coal power plants phase-out determines a small variation in electricity price when bid-up is not considered; instead, when operators’ bid-up is included in the study, the price variation becomes relevant.