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.
- 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.
32 countries. Others available on demand.
CIS - Russia
The proven methodological foundation of Power Price Projections is our 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, but also by taking into account 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 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.
- 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.
- 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.
EnerBase describes a world in which existing policies are tendentially continued and trends recently observed are pursued. The lack of support for GHG emission mitigation affects entire energy systems over a long period, with increasing energy demand and limited fuel diversification. This scenario leads to a temperature rise above 3°C.
EnerBlue is based on the successful achievement of current NDC’s (Nationally Determined Contributions) emission targets for 2030, as well as a continuation of consistent efforts post 2030. Sustained growth in emerging countries is a powerful driver of global energy demand, but policies play a key role in controlling the pace of growth. This scenario leads to a global temperature rise between 2°C and 2.5°C.
EnerGreen explores the implications of more stringent climate policies, with countries fulfilling or overachieving their NDC commitments and then regularly revising their emissions goals. These changes lead to significant improvements in energy efficiency and a strong deployment of renewables. In this cleaner trajectory, global temperature increase is limited to below 2 °C.
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Recognised, comprehensive simulation model for worldwide energy supply, demand and prices.