In 2022, the average end-use electricity price in the United States stood at around 12.2 U.S. cents per kilowatt-hour. This figure is projected to decrease in the coming three decades, to reach some 11 U.S. cents per kilowatt-hour by 2050.

Dataset Description Title: Electricity Market Dataset for Long-Term Forecasting (2018–2024)

Overview: This dataset provides a comprehensive collection of electricity market data, focusing on long-term forecasting and strategic planning in the energy sector. The data is derived from real-world electricity market records and policy reports from Germany, specifically the Frankfurt region, a major European energy hub. It includes hourly observations spanning from January 1, 2018, to December 31, 2024, covering key economic, environmental, and operational factors that influence electricity market dynamics. This dataset is ideal for predictive modeling tasks such as electricity price forecasting, renewable energy integration planning, and market risk assessment.

Features Description Feature Name Description Type Timestamp The timestamp for each hourly observation. Datetime Historical_Electricity_Prices Hourly historical electricity prices in the Frankfurt market. Continuous (Float) Projected_Electricity_Prices Forecasted electricity prices (short, medium, long term). Continuous (Float) Inflation_Rates Hourly inflation rate trends impacting energy markets. Continuous (Float) GDP_Growth_Rate Hourly GDP growth rate trends for Germany. Continuous (Float) Energy_Market_Demand Hourly electricity demand across all sectors. Continuous (Float) Renewable_Investment_Costs Investment costs (capital and operational) for renewable energy projects. Continuous (Float) Fossil_Fuel_Costs Costs for fossil fuels like coal, oil, and natural gas. Continuous (Float) Electricity_Export_Prices Prices for electricity exports from Germany to neighboring regions. Continuous (Float) Market_Elasticity Sensitivity of electricity demand to price changes. Continuous (Float) Energy_Production_By_Solar Hourly solar energy production. Continuous (Float) Energy_Production_By_Wind Hourly wind energy production. Continuous (Float) Energy_Production_By_Coal Hourly coal-based energy production. Continuous (Float) Energy_Storage_Capacity Available storage capacity (e.g., batteries, pumped hydro). Continuous (Float) GHG_Emissions Hourly greenhouse gas emissions from energy production. Continuous (Float) Renewable_Penetration_Rate Percentage of renewable energy in total energy production. Continuous (Float) Regulatory_Policies Categorical representation of regulatory impact on electricity markets (e.g., Low, Medium, High). Categorical Energy_Access_Data Categorization of energy accessibility (Urban or Rural). Categorical LCOE Levelized Cost of Energy by source. Continuous (Float) ROI Return on investment for energy projects. Continuous (Float) Net_Present_Value Net present value of proposed energy projects. Continuous (Float) Population_Growth Population growth rate trends impacting energy demand. Continuous (Float) Optimal_Energy_Mix Suggested optimal mix of renewable, non-renewable, and nuclear energy. Continuous (Float) Electricity_Price_Forecast Predicted electricity prices based on various factors. Continuous (Float) Project_Risk_Analysis Categorical analysis of project risks (Low, Medium, High). Categorical Investment_Feasibility Indicator of the feasibility of energy investments. Continuous (Float) Use Cases Electricity Price Forecasting: Utilize historical and projected price trends to predict future electricity prices. Project Risk Classification: Categorize projects into risk levels for better decision-making. Optimal Energy Mix Analysis: Analyze the balance between renewable, non-renewable, and nuclear energy sources. Policy Impact Assessment: Study the effect of regulatory and market policies on energy planning. Long-Term Strategic Planning: Provide insights into investment feasibility, GHG emission reduction, and energy market dynamics. Acknowledgment This dataset is based on publicly available records and market data specific to the Frankfurt region, Germany. The dataset is designed for research and educational purposes in energy informatics, computational intelligence, and long-term forecasting.

Wholesale electricity prices in the United Kingdom hit a record-high in 2022, reaching **** British pence per kilowatt-hour that year. Projections indicate that prices are bound to decrease steadily in the next few years, falling under **** pence per kilowatt-hour by 2030.

Electricity prices in Europe are expected to remain volatile through 2025, with Italy projected to have some of the highest rates among major European economies. This trend reflects the ongoing challenges in the energy sector, including the transition to renewable sources and the impact of geopolitical events on supply chains. Despite efforts to stabilize the market, prices still have not returned to pre-pandemic levels, such as in countries like Italy, where prices are forecast to reach ****** euros per megawatt hour in September 2025. Natural gas futures shaping electricity costs The electricity market's future trajectory is closely tied to natural gas prices, a key component in power generation. Dutch TTF gas futures, a benchmark for European natural gas prices, are projected to be ***** euros per megawatt hour in July 2025. The reduced output from the Groningen gas field and increased reliance on imports further complicate the pricing landscape, potentially contributing to higher electricity costs in countries like Italy. Regional disparities and global market influences While European electricity prices remain high, significant regional differences persist. For instance, natural gas prices in the United States are expected to be roughly one-third of those in Europe by March 2025, at **** U.S. dollars per million British thermal units. This stark contrast highlights the impact of domestic production capabilities on global natural gas prices. Europe's greater reliance on imports, particularly in the aftermath of geopolitical tensions and the shift away from Russian gas, continues to keep prices elevated compared to more self-sufficient markets. As a result, countries like Italy may face sustained pressure on electricity prices due to their position within the broader European energy market. As of August 2025, electricity prices in Italy have decreased to ****** euros per megawatt hour, reflecting ongoing volatility in the market.

US Electricity Prices and Sales by State, Sector, and Year

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8734253%2Fdba0dac3571f37e79f2891a6ffd80d5c%2Fus%20electric%20flag.png?generation=1712518711362350&alt=media" alt=""> This comprehensive dataset offers a detailed look at the United States electricity market, providing valuable insights into prices, sales, and revenue across various states, sectors, and years. With data spanning from 2001 onwards to 2024, this dataset is a powerful tool for analyzing the complex dynamics of the US electricity market and understanding how it has evolved over time.

The dataset includes eight key variables: | Column Name | Description | |-------|-------| | year | The year of the observation | | month | The month of the observation | | stateDescription | The name of the state | | sectorName | The sector of the electricity market (residential, commercial, industrial, other, or all sectors) | | customers | The number of customers (missing for some observations) | | price | The average price of electricity per kilowatt-hour (kWh) in cents | | revenue | The total revenue generated from electricity sales in millions of dollars | | sales | The total electricity sales in millions of kilowatt-hours (kWh) |

By providing such granular data, this dataset enables users to conduct in-depth analyses of electricity market trends, comparing prices and consumption patterns across different states and sectors, and examining the impact of seasonality on demand and prices.

One of the primary applications of this dataset is in forecasting future electricity prices and sales based on historical trends. By leveraging the extensive time series data available, researchers and analysts can develop sophisticated models to predict how prices and demand may change in the coming years, taking into account factors such as economic growth, population shifts, and policy changes. This predictive power is invaluable for policymakers, energy companies, and investors looking to make informed decisions in the rapidly evolving electricity market.

Another key use case for this dataset is in investigating the complex relationships between electricity prices, sales volumes, and revenue. By combining the price, sales, and revenue data, users can explore how changes in prices impact consumer behavior and utility company bottom lines. This analysis can shed light on important questions such as the price elasticity of electricity demand, the effectiveness of energy efficiency programs, and the potential impact of new technologies like renewable energy and energy storage on the market.

Beyond its immediate applications in the energy sector, this dataset also has broader implications for understanding the US economy and society as a whole. Electricity is a critical input for businesses and households across the country, and changes in electricity prices and consumption can have far-reaching effects on economic growth, competitiveness, and quality of life. By providing such a rich and detailed portrait of the US electricity market, this dataset opens up new avenues for research and insights that can inform public policy, business strategy, and academic inquiry.

I hope you all enjoy using this dataset and find it useful! 🤗

UK Electricity decreased 23.24 GBP/MWh or 22.68% since the beginning of 2025, according to the latest spot benchmarks offered by sellers to buyers priced in megawatt hour (MWh). This dataset includes a chart with historical data for the United Kingdom Electricity Price.

Corresponding paper: O. Schmidt, S. Melchior, A. Hawkes, I. Staffell. Projecting the future levelized cost of electricity storage technologies. Joule (2018).Link to the paper: https://cell.com/joule/fulltext/S2542-4351(18)30583-XThis dataset compiles levelized cost of storage data in energy terms (LCOS, US$/MWh) and power terms, i.e. annuitized capacity cost (ACC, US$/kW-yr), for 9 electricity storage technologies from 2015 projected to 2050. One spreadsheet provides the data for 12 applications as well as the probability for each of the 9 technologies to exhibit lowest LCOS or ACC in a distinct application. Figures 1 and 2 and Supplementary Figures 3 and 4 of the respective publication are based on this data.The remaining files contain LCOS and ACC results for various annual full equivalent cycle and discharge duration combinations, regardless of actual application requirements. Electricity price is fixed at 50 US$/MWh. Figures 3 and 4 and Supplementary Figures 5 and 6 of the respective publication are based on this data.Please see the paper for a full analysis and discussion of the results.

Corresponding paper: O. Schmidt, A. Hawkes, A. Gambhir & I. Staffell. The future cost of electrial energy storage based on experience rates. Nat. Energy 2, 17110 (2017).Link to the paper: http://dx.doi.org/10.1038/nenergy.2017.110This dataset compiles cumulative capacity and product price data for electrical energy storage technologies, including the respective regression parameters to construct experience curves. Please see the paper for a full discussion on experience curves for electrical energy storage technologies and associated analyses on future cost, cumulative investment requirements and economic competitiveness of storage.The dataset also presents the underlying data for Figures 1 to 5 and Supplementary Figures 2 and 3 of the paper.

According to Cognitive Market Research, the global Electricity Generation market size was USD 2154.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 9.80% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 861.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.0% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 646.26 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 495.47 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.8% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 107.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.2% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 43.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.5% from 2024 to 2031.
Thermal Generation is the market leader in the Electricity Generation industry

Market Dynamics of Electricity Generation Market

Key Drivers for Electricity Generation Market

Rising need for cooling boosts the electricity generation market: The increased demand for cooling is projected to drive the electricity generating market in the future years. Cooling is the process of lowering the temperature of an object or environment, which is usually accomplished by transporting heat away from the intended location, typically utilizing air or a cooling medium. Power generation can be utilized to cool by running air conditioning (AC) and fans to keep indoor temperatures comfortable. For instance, According to the International Energy Agency, an autonomous intergovernmental body located in France, in July 2023, more than 90% of households in the United States and Japan had an air conditioner. Cooling accounts for around 10% of global electricity use. In warmer countries, this might result in a more than 50% increase in power demand during the summer months. As a result, increased demand for cooling is likely to drive expansion in the power generating industry.

Increasing applications of electricity in the transportation industry: The growing use of energy in the transportation industry is predicted to increase demand for electricity, hence pushing the power generation market. The electrification of railways in underdeveloped and developing countries, the establishment of public transportation networks such as rapid metro transit systems, and the growing use of electric vehicles in developed countries will all create significant market opportunities for power generation companies. For instance, in order to achieve net-zero carbon emissions, the Office of Rail and Road (ORR) predicts that 13,000 track kilometers - or roughly 450 km per year - of track in the UK will need to be electrified by 2050, with 179 km electrified between 2020 and 2021. According to the Edison Electric Institute (EEl), yearly electric car sales in the United States are estimated to exceed 1.2 million by 2025. Electric vehicles are projected to account for 9% of worldwide electricity demand by 2050.

Restraint Factor for the Electricity Generation Market

High initial capital investment for renewable projects: The high initial capital for renewable projects is indeed a limiting factor for the market growth of the electricity generation sector, as most such technologies, infrastructure, and installation depend on significant up-front funding. For instance, most renewable energy technologies are highly capital intensive-solar, and wind, in particular, scares investors away from taking action, especially if they are small or developing firms. There is thus an economic limitation that restricts competition and contributes toward slower development of cleaner energy solutions. Moreover, funding can be quite tricky and challenging-especially for a poor economic climate. The payback times attached to these investment options are long, leading to uncertainty and making stakeholders reluctant to commit. These financial constraints are, therefore, blighting the transition to renewable energy as well as, more broadly, the overall electricity generation market

Trends for the Electri...

Electricity prices in Germany are forecast to amount to ***** euros per megawatt-hour in November 2025. Electricity prices in the country have not yet recovered to pre-pandemic levels. Electricity price recovery German electricity prices began recovering back to pre-energy crisis levels in 2024, a period driven by a complex interplay of factors, including increased heating demand, reduced wind power generation, and water scarcity affecting hydropower production. Despite Germany's progress in renewable energy sources, with over ** percent of gross electricity generated from renewable sources in 2024, the country still relies heavily on fossil fuels. Coal and natural gas accounted for approximately ** percent of the energy mix, making Germany vulnerable to fluctuations in global fuel prices. Impact on consumers and future outlook The volatility in electricity prices has directly impacted German consumers. As of April 1, 2024, households with basic supplier contracts were paying around ** cents per kilowatt-hour, making it the most expensive option compared to other providers or special contracts. The breakdown of household electricity prices in 2023 showed that supply and margin, along with energy procurement, constituted the largest controllable components, amounting to **** and **** euro cents per kilowatt-hour, respectively. While prices have decreased since the 2022 peak, they remain higher than pre-crisis levels, underscoring the ongoing challenges in Germany's energy sector as it continues its transition towards renewable sources.

Green Power Capacity Futures Market Outlook

According to our latest research, the global Green Power Capacity Futures market size reached USD 15.2 billion in 2024, driven by increasing demand for renewable energy hedging instruments and the growing adoption of green power procurement strategies by utilities and corporates. The market is projected to grow at a robust CAGR of 18.7% from 2025 to 2033, reaching an estimated USD 82.3 billion by 2033. This significant growth is propelled by the ongoing global energy transition, policy mandates for carbon neutrality, and the maturation of financial instruments tailored for renewable energy capacity trading.

One of the most influential growth factors for the Green Power Capacity Futures market is the accelerating global shift toward decarbonization and clean energy integration. Governments across the world are implementing ambitious renewable energy targets, carbon pricing mechanisms, and supportive regulatory frameworks that incentivize both the production and consumption of green power. This policy momentum, combined with increasing investor and corporate focus on Environmental, Social, and Governance (ESG) criteria, is compelling energy market participants to seek sophisticated risk management tools such as green power futures. The ability of these instruments to provide price certainty, hedge against market volatility, and facilitate long-term power purchase agreements is making them indispensable in the evolving energy landscape.

Another key driver is the rapid technological advancement and cost reduction in renewable power generation, particularly in wind and solar segments. As the levelized cost of energy (LCOE) for renewables continues to decline, the share of variable and intermittent energy sources in the grid is rising, creating new challenges and opportunities in power market operations. Green Power Capacity Futures enable market participants to manage the inherent risks associated with these fluctuations, ensuring financial stability and operational predictability. Moreover, the increasing sophistication of trading platforms, including the integration of blockchain and AI-driven analytics, is enhancing transparency, liquidity, and efficiency in green power futures markets, thereby attracting a broader spectrum of participants from utilities to institutional investors.

The growing participation of corporate buyers and independent power producers (IPPs) is further fueling the expansion of the Green Power Capacity Futures market. Large industrial and commercial consumers are increasingly seeking to meet their sustainability commitments through direct procurement of renewable energy, often facilitated by futures contracts. These contracts allow buyers to lock in prices, hedge against future cost increases, and demonstrate compliance with renewable portfolio standards or voluntary green energy targets. The rise of green energy certificates and the convergence of physical and financial power markets are also contributing to the proliferation of customized futures products, tailored to the specific risk profiles and procurement strategies of diverse end-users.

From a regional perspective, Europe and North America continue to lead the Green Power Capacity Futures market in terms of trading volumes, regulatory maturity, and product innovation. However, the Asia Pacific region is emerging as a high-growth market, driven by rapid renewable energy capacity additions in China, India, and Southeast Asia. Regional exchanges are launching new green power futures products, and cross-border trading initiatives are gaining traction, reflecting the globalization of renewable energy finance. The interplay of regional policy frameworks, grid integration challenges, and market liberalization efforts will shape the competitive dynamics and growth trajectory of the global Green Power Capacity Futures market in the coming decade.

The concept of a Renewable Energy Hedge Contract is becoming increasingly important in the context of green power futures. These contracts are designed to provide financial stability and predictability for both producers and consumers of renewable energy by locking in prices for future power delivery. As renewable energy sources like wind and solar become more prevalent, their inherent variability poses challenges for grid operators and market p

Germany Electricity decreased 17.60 EUR/MWh or 15.21% since the beginning of 2025, according to the latest spot benchmarks offered by sellers to buyers priced in megawatt hour (MWh). This dataset includes a chart with historical data for Germany Electricity Price.

The system marginal price refers to the electricity market price (KRW/kWh) for the amount of electricity applied by trading hour, and you can search for the system marginal price information by hour, divided into the mainland and Jeju regions. ㅇ Note 1: The trading time 0:00 of the API indicates the period starting immediately after 0:00 and ending at 01:00. ㅇ Note 2: The API will be deleted in the future, and we recommend using the Korea Power Exchange_System Marginal Price and Demand Forecast (for one-day-ahead power generation plan) API. ㅇ Updated to OPENAPI User Guide v1.5 on 2024.11.29

Predicting The Energy Market's Day Ahead Prices

Analyses of Energy Systems and Prices

By [source]

About this dataset

This dataset offers an in-depth look at the dynamic European energy markets, with hourly updates on the power prices associated with each system. By offering a comprehensive view of electricity markets across Europe, this data can empower both academics and those in industry to draw implications from correlations between different energy systems, analyze how prices fluctuate across markets, and better understand the complex dynamics of these European energy systems. This comprehensive dataset provides invaluable insights into economic trends in this region and the future outlook for energy pricing

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How to use the dataset

This dataset provides an excellent analysis of Europe’s energy systems and power prices on an hourly basis. It can be used in many ways to examine the electricity market of Europe, including correlations between different energy systems, implications for prices in specific markets, and much more.

Here is a guide for how to use this dataset: - First inspect the columns provided in this dataset; they include date/time information (fecha, hora), system (sistema), flag (bandera), price (precio), currency type (tipo_moneda), source of data(origen_dato) and date of last update(fecha_actualizacion). - Understand what each column represents as some columns may be more important than others depending on your particular analysis. For example, when examining energy system correlations you may want to focus primarily on the ‘system’ column while if price fluctuations are your focus you may want to pay most attention to the ‘price’ column. - Gather the data from all desired columns that you need for your analysis into a single table or format for better organization and readability. This will make it easier to visualize trends or patterns that you find interesting.
- Utilize tools such as Microsoft Excel functions or programming languages such as Python/R to create representations like line graphs which reveal correlations over time or region-specific market power price differences etc.
- Finally present your findings in written form such as a report or share visualized results like infographics!

Research Ideas

• Analyzing correlations between energy systems in Europe, price behavior and its implications across different markets.
• Analyzing historical trends in pricing behavior to predict future prices for energy markets in Europe.
• Recommending differentiated approaches for infrastructure investments that mitigate risk and optimize cost benefit analysis among utilities and businesses across Europe's electricity markets

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: da_market_data.csv | Column name | Description | |:------------------------|:--------------------------------------------------------------------------------------------------------------------------------| | fecha | Date of the power prices in DD/MM/YYYY format. (Date) | | hora | Hour that corresponds with each set of power prices listed by minute. (Time) | | sistema | Numeric code for system identifier for each set of reported price points for a specific hour across EU countries. (Numeric) | | bandera | Indicator of whether or not electricity is green (Y) or non-green/conventional electricity (N). (Boolean) | | precio | Cost per Megawatt Hour expressed in Euro €/MWh currency format. (Currency) | | tipo_moneda | Euros represented as Euros € EUROSCURSUSD ($ EURS = US Dollars $ USD) as well as other available foreign currencies. (Currency) | | origen_dato | D...

This data supplements the "Electrification Futures Study: End-Use Electric Technology Cost and Performance Projections through 2050" report. The data included here consist of the cost and performance estimates for electric end-use technologies developed for the three sensitivity cases in the Electrification Futures Study: Slow Advancement, Moderate Advancement, and Rapid Advancement.

The objective of the project was to provide econometric analysis and theory for modelling energy and soft commodity prices. This necessitated data analysis and modelling together with theoretical econometrics, dealing with the specific stylised facts of commodity prices. In order to analyse energy and soft commodity prices, the determination of spot energy prices in regulated markets was first considered, from the point of view of the regulator. Direct data analysis of futures commodity prices was then undertaken, resulting in the collection of an extensive dataset of most traded futures commodity prices at a daily frequency, covering 16 different commodities over a 10-year period.

Scenario discovery SAMBA data files:

1) The folder SAMBA_324_datafiles.zip contains all 324 data files for the OSeMOSYS run. Each of these files has a code on top referring to the combination that it represents. The key to the levers is in the Excel file "Metafile". There the naming convention of technologies as well as corresponding combination for scenario are also available. 2) The Access database Scenario_discovery_database.mbd contans results from the 324 runs. The key to the scenarios are in the Excel file "Metafile" tab "Scenario_key". 3) The file OSeMOSYS_SAMBA_161130.txt is the version OSeMOSYS that was used to run all scenarios. 4) The PRIM analysis is available on the GitHub repository: https://github.com/NMoksnes/Scenario_discovery

The average wholesale electricity price in September 2025 in the United Kingdom is forecast to amount to*******British pounds per megawatt-hour, a decrease from the previous month. A record high was reached in August 2022 when day-ahead baseload contracts averaged ***** British pounds per megawatt-hour. Electricity price stabilization in Europe Electricity prices increased in 2024 compared to the previous year, when prices stabilized after the energy supply shortage. Price spikes were driven by the growing wholesale prices of natural gas and coal worldwide, which are among the main sources of power in the region.

… and in the United Kingdom? The United Kingdom was one of the countries with the highest electricity prices worldwide during the energy crisis. Since then, prices have been stabilizing, almost to pre-energy crisis levels. The use of nuclear, wind, and bioenergy for electricity generation has been increasing recently. The fuel types are an alternative to fossil fuels and are part of the country's power generation plans going into the future.

This dataset compiles cumulative capacity and product price data for electrical energy storage technologies, including the respective regression parameters to construct experience curves. The update includes new data from up to 2017. Update to paper: O. Schmidt, A. Hawkes, A. Gambhir & I. Staffell. The future cost of electrical energy storage based on experience rates. Nat. Energy 2, 17110 (2017).Link to the original paper with original dataset: http://dx.doi.org/10.1038/nenergy.2017.110

The European electricity system is currently facing a major transformation, with renewable energy (RE) technologies being expected to constitute an important part of the future generation mix. In light of the recent debate on the European Union’s (EU) energy and climate policy until 2030, this thesis contributes both to the academic and public discourse about RE targets and infrastructure needs, and to the methodological advancement of power system models. In particular, I focus on two major aspects: (i) the efficient representation of the RE’s temporal variability in large-scale power system models, and (ii) the explicit consideration of uncertainty in analyzing investment strategies for the future European power system. In the first part of this thesis, I present the long-term investment model for the European electricity system LIMES-EU. The model constitutes the methodological basis of the thesis; it facilitates the analysis of technically feasible and economically viable investment pathways for individual countries and for Europe on aggregate. LIMES-EU simultaneously optimizes investment and dispatch decisions for generation, storage and transmission technologies in an intertemporal way from 2010 to 2050. Despite the model’s long-term focus until 2050, it effectively accounts for the short-term variability of electricity demand and infeed from wind and solar power plants. The fluctuations are reflected by modeling the operation of technologies for a set of representative days. These days are selected with a novel and computational efficient approach that is suitable for input data with a large number of different fluctuating time series (i.e. multiple different RE technologies and/or regions). With the approach that has been developed for this thesis it is possible to reflect the characteristic fluctuations of the input data already with a small number of model days. To enable its applicability for other models, it is based on an established clustering algorithm and transparently documented. The second part of the thesis provides an in-depth analysis of cost-efficient future investment strategies for the European power system in order to reach the EU’s long-term decarbonization targets until 2050. The analysis includes an explicit consideration of uncertainty and comprises both aggregate European and national results. Thereby, the work adds important aspects to the European Commission’s official impact assessment on the 2030 policy framework as this impact assessment completely disregards the existence of uncertainties and provides only few results on national level. A major focus of the analysis is on the cost-efficient RE expansion until 2030. Their optimal share in the 2030 generation mix varies considerably across the studied scenarios that account for various uncertainties about future techno-economic developments, for example with regard to fuel prices and investment costs. The national results show a strong difference in optimal RE deployment across countries, which is caused by the unequal distribution of RE sources. A cost-optimal RE expansion would result in large international transmission needs and would make some countries importing a large share of their electricity demand from foreign power plants. In addition to determining cost-efficient investment pathways for different future scenarios, the thesis provides an analysis of investment strategies that help to increase the robustness of the power system, i.e. result in a system that performs reasonably well for a large variety of possible futures. The performance of different systems under short-term shocks is tested in a total of more than 40,000 model runs. The analysis shows, that despite the benefits of a further integration of the European electricity system, strategies promoting the capability of countries to produce at least 95% of their electricity demand domestically significantly help to increase the robustness of the European power system.