The power market is experiencing a renaissance driven by surging demand coupled with a broader landscape of fuels, generation assets and technology innovation. As a result, reliable price forecasts have become a much more complex exercise. Gone are the days when you could reliably forecast power market load demand and pricing using forward curves generated from an internally developed or third-party model. Additionally, you can no longer depend on third-party forecast tables that simply start with a current snapshot of the market and extrapolate data 10 years into the future by estimating a few percentage points of annual growth. Today’s power market forecasts demand a model that encompasses all the physical generation and transmission assets in your designated service area with quarterly forecast updates that reflect any changes in infrastructure, regulatory policy or supply/demand that could impact asset valuation, profitability or risk.
Mixed Models Create Mixed-Up Results
Despite some regulatory easing on carbon emissions, GHG targets are still important to stakeholders from both an economic and corporate responsibility perspective. Yet some stakeholders still forecast carbon pricing using separate models that may not mirror the data points of their supply, demand and infrastructure model. This mixing of apples and oranges can produce questionable forecasts of GHG emissions that affect the accuracy of carbon cost calculations and the profitability of Power Purchase Agreements (PPAs).
PowerIntel is unique in its use of a physical model for each energy market to conduct both expansion planning and production forecasts, including both Locational Marginal Prices (LMP) and Locational Marginal Emission Rates (LMERs). PowerIntel’s expansion planning modelling projects the generation make up of each market over the time frame of concern. Using the results from the expansion planning forecast, a yearly production cost simulation is conducted to identify the economic and environmental impact of the alternative scenarios.
Maintaining Consistency Between Economic and Environmental Results.
Unlike projections that are derived from forecast algorithms, PowerIntel’s forecasts include the nonlinear physical interdependency between input variables and market results. This way, physical interrelationships are maintained throughout the forecast period, providing consistency between economic and environmental results. As an example, PowerIntel is currently helping to support one of the world’s largest social media companies owning numerous data centers. The company wants to achieve net zero GHG emissions through Power Purchase Agreements. PowerIntel will help the company to more accurately forecast the value of the carbon they are curtailing through the purchase of these contracts.
Forecasts Curated by the Thought Leaders in LMP and LMER Formulas
PowerIntel’s market forecasts are curated by our recognized industry experts. These economists and engineers were the thought leaders and drivers behind the use of Locational Marginal Pricing and Locational Marginal Emission Rates as accurate measures of the financial and environmental impact of both proposed projects and Power Purchase Agreements (PPAs). They have extensive experience conducting bespoke impact analysis and recognized the need to encapsulate their methodologies into a data service that can be used by stakeholders to easily identify the economic and environmental impact of alternative forecast scenarios. PowerIntel is the data service that was born as a result of their efforts.