Key Takeaways

• In today's energy markets, maximizing revenue from energy resources requires an understanding of opportunity costs, rather than legacy production costs, as generators increasingly account for uncertainty in their price bidding behaviors. Legacy production cost models often fail to reflect the pricing dynamics associated with the energy transition.
• Weather and opportunity costs will increasingly drive price formation in energy markets, as weather-dependent renewable generation increases price uncertainty.
• Not accounting for underlying weather leads to decoupled price and generation simulations that lead to inflated capture rates and asset valuations.
• Appropriately accounting for new market dynamics in an energy forecast requires that all forecasting aspects be grounded in the context of a competitive market, and thus aligned to a world constrained by long-run equilibrium​.
• In a future with high renewable penetration, not intentionally modeling volatility will lead to overoptimized models that are inconsistent with actual market volatility and irrelevant for valuation of storage and other flexible assets.
• The Ascend Opportunity Cost Forecasting Framework (OCFF) was designed specifically for the energy transition, and reflects the market dynamics that come from the price-setting behaviors of renewables and storage.

New Analytic Lens Needed: Changing Energy Market Dynamics

During the energy transition, greater weather dependence in supply generates greater uncertainty in dispatch behavior. This leads to higher volatility and more curtailment than traditional production cost models would typically project. More renewables in the supply stack also lead to more weather-dependence in energy pricing, making it important to enforce appropriate correlations between renewable generation and prices. Consequently, pricing dynamics have fundamentally shifted: thermal generation is no longer always a price-setter. For example, negative prices occur frequently during daylight hours in regions with high solar penetrations, with prices rising significantly when the sun sets. Weather and opportunity costs have increasingly become the key drivers of price formation in many energy markets. Thus, there exists far more variability than what is being captured by production cost models.

Understanding value, then, requires understanding the relationship between production, price, and weather.  Energy storage, for example, creates value from price arbitrage: batteries charge when prices are low and discharge when prices are high. Models that fail to account for this price variability, and for its underlying drivers, undervalue storage, thus creating a biased and inconsistent forecast. An opportunity cost forecast like Ascend's OCFF addresses those biases and inconsistencies, and provides a more powerful, accurate, and defendable lens into the future.

Energy Forecasts Must Adhere to Long-Run Equilibrium

Appropriately capturing new market dynamics in an energy forecast requires that all forecasting aspects be grounded in the context of a competitive market and thus aligned to a world constrained by long-run equilibrium​, since supernormal returns would drive market entry and subnormal returns would deter entry. Legacy production cost forecasts often don't account for the fact that rational investments will converge markets toward an equilibrium between the locational cost of new entry (CONE) and locational project value, yielding outputs that are inconsistent with the model inputs. Premium locations cannot remain premium.  Consequently, forecasts must ensure that total projected revenues align to capital expenditures (CapEx), including forecasted changes in CapEx for each asset class over time.  

Weather Drives Energy Price Formation

Forecasts that reflect new market dynamics must ensure that prices become a function of net load (load – renewables) in which weather determines both supply and demand and renewable generation displaces higher cost thermal generation​. As renewable penetrations increase, energy prices decline. In this context, price extremes will be driven by renewable generation: the highest highs will occur when renewable production is lowest, and the lowest lows will occur when there is surplus renewable generation​.

Not accounting for the underlying weather leads to decoupled price and generation simulations with inflated capture rates that ignore price depression when renewable production is high. Mis-correlating independent price and generation timeseries can lead to overvaluation of renewable resources: average price and generation shapes are inaccurate proxies for generation-weighted prices. In reality, most renewable projects will see declining capture rates as renewable buildout grows​.

Permanent differences in climate also create permanent differences in renewable resource potential, population density, and cost of land. Thus, accurately accounting for long-term basis requires accounting for locational cost of production. Locational price patterns will be driven by variation in renewable resource quality and land cost​. Permanent differences in the cost of production lead to permanent price basis between locations​. New transmission build will only incentivize more project development in the lower-cost areas.

The Importance of Price Volatility in Energy Markets

Volatility is driven by opportunity cost and must be appropriately modeled for accurate resource valuation. Not intentionally modeling volatility will lead to overoptimized models that are inconsistent with the market and irrelevant for valuation of storage and other flexible assets. Forecasts, such as Ascend's OCFF, that are grounded in observed market behavior, reflect uncertainty and opportunity costs. This leads to price volatility that is typically much higher than what is predicted by traditional production cost models, but much more reflective of actual market dynamics​. Because storage and other flexible generation resources respond very differently to volatile prices than to flat prices, volatility must be both calibrated and forecasted appropriately​.

In considering near-term volatility, the real-time (RT) market generally has higher volatility than the day-ahead (DA) market. Real-time prices are more volatile than DA prices as forecast errors must be adapted to on short notice. The higher RT volatility can add 50% more revenue (or more) for batteries and other flexible resources. The long-term volatility evolution should be forecasted based on fundamental driving forces.

Accounting for Non-Economic Driving Forces on Energy Supply

Clean energy policy and corporate off-take demand are significant drivers for clean energy deployment​. Forecasts must account for these and other non-economic driving forces and should be based on the best expectations of future policy: ‘Status quo policy’ is certain to be incorrect​. Corporate off-take demand and environmental, social, and governance (ESG) factors will also drive clean energy deployment beyond what an economic model alone would predict. Not accounting for policy evolution and off-take demand will lead to under forecasting of renewable buildout, driving higher prices and lower volatility.

The Ascend Opportunity Cost Forecasting Framework

The Ascend OCFF overcomes model-limited choice by providing a powerful analytic lens that produces forecasts at hourly and sub-hourly levels. All Ascend forecasts align to market forwards in the near term, which reflect the consensus market expectation of all macro level assumptions, including greenhouse gas (GHG) and renewable portfolio standard (RPS) policy, economic growth, electrification, and technology costs. Ascend forecasts also adhere to long-run equilibrium, which ensures that resources earn normal returns. While Ascend forecasts enforce equilibrium on average and in the long run, regulatory and logistic barriers to entry create time lags between market signals and resource buildout. These barriers can lead to temporary disequilibrium periods. Geographic barriers, such as land costs, population density, bodies of water, mountains, interconnect boundaries, and variation in renewable resource potential, all lead to geographic variation in returns that can persist in the long run with limited mitigation potential. Finally, Ascend considers stakeholder demand and its influence on policy directions and procurement decisions, going beyond the unrealistic forecast scenario of only considering currently enacted policies. By focusing on these key policies, economic, and physical constraints that govern resource buildout and dispatch, Ascend forecasts focus on the most important drivers of uncertainty and risk in long-term planning and valuation.  

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Trusted in hundreds of projects and resource planning activities, supporting over $25 billion in project financing assessments, AscendMI™ (Ascend Market Intelligence) delivers proprietary power market forecasts that reflect the new market dynamics driving the energy transition. Contact us to learn more. 

Ascend Analytics is the leading provider of market intelligence and analytics solutions for the energy transition. The company’s offerings enable decision makers in power development and supply procurement to maximize the value of planning, operating, and managing risk for renewable, storage, and other assets. From real-time to 30-year horizons, their forecasts and insights are at the foundation of over $50 billion in project financing assessments. Ascend provides energy market stakeholders with the clarity and confidence to successfully navigate the rapidly shifting energy landscape.