Einsatz künstlicher neuronaler Netze bei der kurzfristigen Lastprognose

Um die erweiterten Möglichkeiten des Stromhandels, die sich durch die geplante Liberalisierung des Strommarktes ergeben, optimal nutzen zu können, muß die Planung zur Deckung der Stromnachfrage in Energieversorgungs- und anderen Unternehmen auf einer verläßlichen Lastprognose beruhen. Künstliche neuronale Netze, über deren Möglichkeiten bei der Lastprognose ein kurzer Überblick gegeben wird, weisen in diesem Zusammenhang, u. a. gegenüber der klassischen multiplen Regression, Vorteile auf. Anhand typischer Merkmale werden die Lastprognosesysteme mit künstlichen neuronalen Netzen, die teilweise bereits mit Erfolg eingesetzt werden, kurz charakterisiert. Darüber hinaus werden noch vorhandene Probleme im Umgang mit dieser Methode aufgezeigt, die vor allem darin bestehen, daß die Entwicklung solcher Systeme bisher weitgehend auf Versuch und Irrtum basiert. Daher wird abschließend eine entsprechende Entwicklungsmethodik vorgestellt und diskutiert, die zwar im Detail noch auszugestalten ist, auf die aber für eine breite wirtschaftliche Anwendung individuell angepaßter Systeme nicht verzichtet werden kann

Ein Rollenmodell zur Einbindung der Endkunden in eine smarte Energiewelt

The successful expansion of renewable energies requires a phase of change in the energy supply system. On the one hand solutions have to be found to ensure the system dependability in spite of the high volatility of the feeding-in from renewable sources. On the other hand the differences between feeding-in and demand, which also occurs on the regional level evermore, have to be equalized on the regional level, too. For this purpose, it is necessary to develop new control and modified market mechanisms. The role definition of the involved actors gets an increasing relevance because of the politically predetermined unbundling. However, only a slight attention was paid to the role of the final consumers in the past. For a successful rebuilding of the energy supply system it is nevertheless important to involve the consumers in this process. It could be demonstrated within the research project “MeRegio”, that the integration of the consumers into an incentive based demand side management can tap significant potentials to equalize differences between feeding-in and demand. Therefore, the focusing on the final consumers can have an important contribution to rebuild the energy supply system

Identification of the Efficiency Gap by Coupling a Fundamental Electricity Market Model and an Agent-Based Simulation Model

A reliable and cost-effective electricity system transition requires both the identification of optimal target states and the definition of political and regulatory frameworks that enable these target states to be achieved. Fundamental optimization models are frequently used for the determination of cost-optimal system configurations. They represent a normative approach and typically assume markets with perfect competition. However, it is well known that real systems do not behave in such an optimal way, as decision-makers do not have perfect information at their disposal and real market actors do not take decisions in a purely rational way. These deficiencies lead to increased costs or missed targets, often referred to as an “efficiency gap”. For making rational political decisions, it might be valuable to know which factors influence this efficiency gap and to what extent. In this paper, we identify and quantify this gap by soft-linking a fundamental electricity market model and an agent-based simulation model, which allows the consideration of these effects. In order to distinguish between model-inherent differences and non-ideal market behavior, a rigorous harmonization of the models was conducted first. The results of the comparative analysis show that the efficiency gap increases with higher renewable energy shares and that information deficits and policy instruments affect operational decisions of power market participants and resulting overall costs significantly.Funded by the German Federal Ministry for Economic Affairs and Energy, grant numbers 03ET4025A/03ET4025B

Das Kopernikus-Projekt ENavi - Die Transformation des Stromsystems mit Fokus Kohleausstieg

In diesem Bericht wird die Transformation des Stromsystems als zentrale Stellschraube zur Erreichung der Klimaziele analysiert. Dabei wird die Dekarbonisierung, insbesondere der Ausstieg aus der Kohleverstromung, in den Fokus gerückt. Anhand einer systematischen Vorgehensweise werden Transformationsszenarien für das deutsche Energiesystem identifiziert, analysiert und bewertet. Die Analyse erfolgt mithilfe unterschiedlicher computergestützter Modelle, um die Auswirkungen im gesamten System abschätzen zu können. Es werden sowohl Wechselwirkungen im Stromsystem und im Energiesystem, als auch im Wirtschaftssystem und im Bereich Ressourcen und Umwelt untersucht

Asset Profitability in the Electricity Sector: An Iterative Approach in a Linear Optimization Model

In a competitive electricity market, generation capacities can exactly cover their full costs. However, the real market deviates from this ideal in some aspects. One is the concern of non-existent or insufficient scarcity prices. We present an iterative method in a linear optimization model to investigate the profitability of assets in the absence of scarcity prices and how the system changes when this risk is incorporated into investors’ expectations. Therefore, we use a two-step optimization of capacity planning and unit commitment. Iteratively, mark-ups at the height of uncovered costs are added to investment costs. This typically leads to a system with better investment profitability while keeping the system cost increase low. The methodology is applied to a simplified brownfield generation system, targeting CO2-free power generation within 25 years. In a model with annual foresight of actors, iterations result in a generation system with significantly lower (or even no) uncovered costs for new investments within ten or fewer iterations. Our example case with full foresight shows that early-added gas (combined cycle) and wind onshore capacities are able to recover their full costs over a lifetime, even without scarcity prices. However, the contribution margin gap remains high, especially for storage and biomass

Seasonal flexibilisation: A solution for biogas plants to improve profitability

It has been shown that the power demand-oriented operation of biogas plants contributes to the challenges of future energy systems with high shares of fluctuating renewable energies. This flexibilisation is usually short-term and can improve profitability. Long-term seasonal flexibilisation could further overcome present low heat utilisation rates due to the low heat demand of district heating in summer and constant biogas production across the year. To assess the benefits of seasonal flexibilisation for different biogas plants, we apply an existing model approach for analysing biogas repowering and optimising the combined heat and power (CHP) despatch. Plant-specific constraints and historical spot market data are used to determine power revenues, heat utilisation rates, greenhouse gas emissions and the profitability of the biogas plants. For different setups of CHP units and heat demand levels, the seasonal operation mode is compared with the non-seasonal reference mode. The economic benefit of seasonal flexibilisation is on average 10 €/MWhel when comparing the same rated power output and varies with plant size and CHP-setup. Increases in the heat utilisation rate are the main driver. Benefits increase with additional installed CHP capacities and rising heat prices. As gas production changes over the year, higher CHP capacities increase flexibility in winter and align the power market revenues of seasonal and non-seasonal operation. Very high heat prices even offset economies of scale. However, seasonal flexibilisation does not allow economic operation in Germany under current market conditions. The dependence on other sources of revenues and extended support schemes therefore remains

Impact of long-term water inflow uncertainty on wholesale electricity prices in markets with high shares of renewable energies and storages

Renewable energy shares in electricity markets are increasing and therefore also require an increase in flexibility options. Conventional electricity price modelling with optimisation models in thermally dominated markets is not appropriate in markets with high shares of renewable energies and storages because price structures are not adequately represented. Previous research has already identified the impact of uncertainty in renewable energy feed-in on investment and dispatch decisions. However, we are not aware of any work that investigates the influence of uncertainties on price structures by means of optimisation models. Appropriate modelling of electricity price structures is important for investment and policy decisions. We have investigated the influence of uncertainty concerning water inflow by applying a second stage stochastic dual dynamic programming approach in a linear optimisation model using Norway as an example. We found that the influence of uncertainty concerning water inflow combined with high shares of storages has a strong impact on the electricity price structures. The identified structures are highly influenced by seasonal water inflow, electricity demand, wind, and export profiles. Additionally, they are reinforced by seasonal primary energy source prices and import prices. Incorporating uncertainties in linear optimisation models improves the price modelling and provides, to a large extent, an explanation for the seasonal patterns of Norwegian electricity market prices. The paper explains the basic pricing mechanisms in markets with high shares of storages and renewable energies which are subject to uncertainty. To identify these fundamental mechanisms, we focused on uncertainty regarding water inflow, but the basic results hold true for uncertainties regarding other renewable energies as well

Policy Brief: Folgen des Kohleausstiegs und der Energiewende für die Haushalte in Deutschland

Der aktuell diskutierte „Kohleausstieg“ sowie das geplante Klimaschutzgesetz verursachen Kosten. Die Bepreisung von Kohlendioxyd (CO2) ist in diesem Zusammenhang als kosteneffizientes Instrument zu beurteilen und daher aus ökonomischer Sicht vorteilhaft. Durch die CO2-Bepreisung entstehen einerseits Kosten für den Systemumbau, andererseits werden staatliche Einnahmen generiert. Werden diese Mehreinnahmen jedoch nicht zur Entlastung der Verbraucher genutzt, so kommt es auf Haushaltsebene zu erheblichen Mehrbelastungen. Um diese Mehrbelastungen zu vermeiden, sind flankierende Ausgleichsmaßnahmen unbedingt notwendig

A more realistic heat pump control approach by application of an integrated two-part control

Heat pumps are a vital element for reaching the greenhouse gas (GHG) reduction targets in the heating sector, but their system integration requires smart control approaches. In this paper, we first offer a comprehensive literature review and definition of the term control for the described context. Additionally, we present a control approach, which consists of an optimal scheduling module coupled with a detailed energy system simulation module. The aim of this integrated two-part control approach is to improve the performance of an energy system equipped with a heat pump, while recognizing the technical boundaries of the energy system in full detail. By applying this control to a typical family household situation, we illustrate that this integrated approach results in a more realistic heat pump operation and thus a more realistic assessment of the control performance, while still achieving lower operational costs

Long-term distributional impacts of European cap-and-trade climate policies: a CGE multi-regional analysis

Carbon pricing is a policy with the potential to reduce CO2 emissions in the household sector and support the European Union in achieving its environmental targets by 2050. However, the policy faces acceptance problems from the majority of the public. In the framework of the project Role of technologies in an energy efficient economy–model-based analysis of policy measures and transformation pathways to a sustainable energy system (REEEM), financed by the European Commission under the Horizon 2020 program, we investigate the effects of such a policy in order to understand its challenges and opportunities. To that end, we use a recursive-dynamic multi-regional Computable General Equilibrium model to represent carbon pricing as a cap-and-trade system and calculate its impacts on consumption of energy goods, incidence of carbon prices, and gross income growth for different income groups. We compare one reference scenario and four scenario variations with distinct CO2 reduction targets inside and outside of the EU. The results demonstrate that higher emission reductions, compared to the reference scenario, lead to slower Gross Domestic Product growth, but also produce a more equitable increase of gross income and can help reduce income inequalities. In this case, considering that the revenues of carbon pricing are paid back to the households, the gross income of the poorest quintile grows as much as, or even more in some cases, than the gross income of the richest quintile