At last, an alternative choice is available in future power price data. Our Power Price Projections service is a subscription-based service offering insights and time series on the long-term evolution of the day-ahead wholesale prices of electricity. This service leverages our Enerdata Power Model based on the PyPSA open-source library and on Enerdata’s renowned energy-climate scenarios. It is backed by our extensive databases (sectoral electricity demand and power plant level data) and energy markets knowledge. Power Prices Projections is the ultimate strategic tool for energy investors and developers to estimate their returns on investments, on the short-, mid- and long-term.
of european energy markets
of the wholesale prices
annual projections
Country analysis agenda
Hourly day ahead price - EnerBase Scenario - Spain
Sample hourly day ahead price - EnerBase Scenario
- Unlike traditional optimisation models, benefit from the big picture thanks to endogenous modelling of electricity demand and power generation/capacities developments
- In-house globally recognised model covering all technologies, including renewables, and the whole energy systems.
- Globally recognised energy expertise.
- Science-based modelling
- Three contrasting long-term capacity scenarios featuring various climate and price possibilities, to explore realistic and probable future pathways for each country. Custom scenarios can be used on-demand.
Dataset in Excel format:
- Most up-to-date demand as well as power generation inputs
- Day-ahead wholesale prices
- Average hourly prices up to 2050 with a 5-years time-step, yearly updated
- Mid-term annual prices up to 2030 with a 1-year time-step, quarterly updated
- Renewable capture prices
- Min & max monthly average prices - depending on climate scenarios
- Yearly commodity prices
PDF report:
- Documentation on the methodology, hypotheses, projects, regulatory contexts, and scenarios.
Geographical Covered
- European power markets
- On-demand: any other country with a zonal market (i.e. with an open electricity market in which the spot electricity price is the same in a given zone)
Methodology
Our Power Price Projections are built from two proprietary models:
- Our POLES model: A robust, multi-country power projection model that is used by numerous energy companies, utilities, investors and developers worldwide. It models at country level (among other indicators), yearly installed capacities and power demand by sector.
- Our PyPSA-Enerdata model: build from the open-source library PyPSA with Enerdata’s own data and post-treatments. This model uses POLES's output to model an hourly dispatch of electricity prices at bidding zone and Power Plant Level.
Main differences between our approach and “Pure Optimisation” Power Models:
- Using POLES modelling approach for capacity and demand, avoids the ‘winner-takes-all’ effect often observed in pure optimisation models. Due to the consideration of historical capacity and production mixes, along with the introduction of non-economic competition parameters, POLES allocates electricity generation technologies on the basis of LCOEs and variable costs, but also by considering non-economical parameters (policies, mix diversification, and more).
- The POLES model considers technology classes with their technical, economic and environmental parameters, with a year-by-year, recursive approach presenting more suited to depicting real energy systems with their imperfections and barriers: Where optimisation models often use a perfect foresight approach – allowing economic agents to dispose of all information over the whole time horizon – POLES implements an iterative process, accounting for long-term capacity needs and ensuring a user-defined reserve security is reached on top of peak demand.
- The other clear added-value of POLES is that sectoral energy demand is endogenous and can be modelled/refined by the user, who will find logical retroactions between supply and demand of electricity. Energy system optimisation models, in contrast, generally use energy demand as an exogenous input parameter – once again reflecting either a fixed long-term assumption or a perfect long-term foresight for agents of the energy system.
The PyPSA-ENERDATA model has two clear additional advantages compared to existing Power Models:
- The electricity dispatch is refined at power plant level based on the data gathered on our Power Plant Tracker.
- The behaviour of economic agent is modelled to reflect for non-rational bidding strategies and take into account non-linear constraints (e.g., ramp-up time, minimum loads, yearly emissions requirements…)
Energy-Climate Scenarios
EnerBase
EnerBase describes a continuation of existing policies and trends. This scenario leads to a temperature rise above 3°C.
Realistic Scenario
Enerdata’s central scenario challenges focuses on short-term trends by assessing the probability of success for announced short-term power generation projects and examinating trends in demand electrification. For the mid to long-term, the scenario considers the estimated probability of each country meeting its NDCs.
EnerBlue
EnerBlue is based on an achievement of new NDC’s (Nationally Determined Contributions) submitted up to end of 2023. This scenario leads to a global temperature rise between 2°C and 2.5°C.