Energy Research & Data

Power Price Projections

A 360° approach to long-term wholesale power price data

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At last, an alternative choice is available in future power price data. Our Power Price Projections service gives you annual wholesale price projections backed by the energy modelling expertise of Enerdata and its globally recognised POLES model. The ultimate strategic tool for energy investors and developers to estimate their long-term returns on investments.

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Why Subscribe?

  • Unlike traditional optimisation models, see the big picture thanks to endogenous modelling of electricity demand and power generation/capacities developments.
  • Model covering all technologies, including renewables.
  • Avoid "winner-takes-it-all" effect often observed in pure optimisation models. 
  • Three long-term scenarios for each country to explore possible future pathways.
  • The data you need, without unnecessary features you don’t, means more value for your money compared with other services on the market.
  • Independent perspective: Enerdata is not linked to any governmental bodies or energy companies.

 

Key Features

 

  • Annual projections (in US$/MWh), from three detailed scenarios through 2050.
  • Projections based on latest available data.
  • Up-to-date historical data.
  • 32 countries covered.
  • Output in both table and graph format, available as an Excel export.
  • Fully updated annually.

 

 

Countries Covered

32 countries. Others available on demand.

 

Country List

EUROPE

Austria
Belgium
Czech Republic
Denmark
France
Germany
Hungary
Italy
Netherlands
Norway
Poland
Portugal
Romania
Slovakia
Spain
Sweden
Switzerland
Turkey
United Kingdom

CIS

Russia

AMERICAS

Argentina
Brazil
Canada
Mexico
United States

ASIA-PACIFIC

Australia
China
India
Indonesia
Japan
South Korea

AFRICA

South Africa

Methodology

 

The proven methodological foundation of Power Price Projections is Enerdata’s proprietary POLES model: A robust, multi-country power projection model that is used by numerous energy companies, utilities, investors and developers worldwide.

Power Price Projections data utilises historical spot prices, which are indexed to the POLES model’s wholesale price projections going forward.

 

Main Differences Between POLES and “Pure Optimisation” Power Models

The first advantage of the POLES modelling approach for capacity and production planning is that it 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, with the possibility of more easily adjusting competition between these.

The POLES model considers technology classes with their technical, economic and environmental parameters, with a year-by-year, recursive approach presenting two main advantages compared with optimisation models:

  • The POLES approach is 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 so-called ‘bang-bang’ or ‘penny-switching’ effects cannot be found in the POLES approach, where overall, the user has more control over modelling and parametrisation. (These effects, often observed in pure optimisation models, refer to achieving a completely different solution based on a negligible change to one or several input parameters.)

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.

 

Overview of POLES Power Module

  • Two problems addressed: capacity planning and dispatch
  • 20+ technologies detailed, including CAPEX, variable cost, fuel cost, carbon taxes, subsidies, lifetime, load factor, efficiency and more.

 

Capacity Planning

  • Based on LCOE + constraints (potential and backup for RES, acceptance of nuclear, etc.)
  • Competition on seven duration loads (from 8760h/year to 730h/year)
  • Market share of each technology is distributed, thanks to a multinomial logit distribution function.
  • Recursive approach: Capacities to be installed at year y+1 are computed at year y.

 

Dispatch

  • Based on variable costs, including subsidies and taxes.
  • Market share of each technology in the merit order is distributed, thanks to a multinomial logit distribution function,
  • Must run and fatal technologies’ power generation computed separately.
  • Dispatch at year y depends on capacity installed at year y.
  • Year y is split into two typical days and 12 two-hour slices each.

 

EnerFuture Scenarios

EnerBase

EnerBase

Describes a continuation of past trends and a world with a lack of support for GHG emission mitigation.

The EnerBase emission trajectory leads to a temperature increase between 5°C and 6°C.

 

EnerBlue

EnerBlue

Based on the successful achievement of the 2030 NDC targets, which enable control of energy demand growth and CO2 emissions through 2030.

The EnerBlue emission trajectory leads to a temperature increase between 3°C and 4°C.

 

EnerGreen

EnerGreen

Stringent climate policies and ambitious climate change mitigation trajectories lead to significantly improved energy efficiency and strong deployment of renewables.

The EnerGreen scenario explores a world in which temperature increase is limited to 2°C.

 

 More details on Enerdata scenarios

 

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