In an era of geopolitical tensions, volatile fuel prices, rapid renewable integration, and the impact of changing policies/regulations, energy markets are more uncertain than ever. For planners, investors, operators and traders, it is important to be wary of unusual conditions and prepare for the unexpected. Both technology and knowledge play a role as organizations attempt to navigate the speed at which the market operates and evolves. The ability to predict how scenarios will develop to enable decision-making that ensures optimal future operations, is needed more than ever. Predictive analytics can help with impact analyzes through simulation modeling and projections in the areas of transmission and asset planning, investments, load and generation, risk management and trading.
Transmission Planning: Strengthening Grid Reliability
The energy transition drives the shift from a central architecture, where vertically integrated utilities generate, supply, and distribute energy from centralized power stations to their customers, to decentralized energy system where this generation is replaced by distributed renewable energy sources, battery storage and more localized generation at homes and businesses. This pushes the grid to its limits. Grid operators, regulators and policy makers need to conduct future power system studies to understand emerging issues on the power system, as it moves to uncharted operating conditions, and quantifying gaps in known system requirements for immediate emerging operating conditions.
Transmission Planners are challenged to evaluate different transmission projects and finding the most optimal site and size, reduction in system cost and congestion and present the cost benefit metrics of each project.
A power generation and transmission modeling system provides a range of planning capabilities, including zonal and nodal locational marginal price (LMP) forecasting, renewable siting and curtailment analysis, financial transmission right (FTR) valuation, environmental analysis, asset valuation, interconnector socio-economic welfare analysis, and transmission congestion analysis. These capabilities allow market participants to make data-driven decisions, optimize capital allocation, and enhance overall grid efficiency.
Resource Capacity Planning: Meeting needs of Electrification
According to the IEA, global electricity demand is expected to increase at an average rate of around 4% through 2027 driven by growing use for industry, air conditioning, electrification and data centers. Most of the additional demand over the next three years will come from emerging and developing economies, which account for 85% of the demand growth.
Power supply planners deal with complexity in meeting their load and contract obligations in the future while considering technology improvements, aging assets and self-build versus buy options. Long term resource planning and reliability requirements can be achieved with capacity expansion solutions enabling planners to run scenarios and efficiently assess and develop strategies including evaluation of resource acquisition, emissions compliance and renewable generation targets, all while trying to minimize the incremental system cost over the desired time horizon. This includes long-term, 20- to 30-year resources investment plans, factoring in technology type, fuel, size, location and timing of capital projects required to meet reliability requirements. The solution uses either mixed integer programming (MIP) or linear programming (LP) algorithms for the optimal solution to solve for the desired time period with the existing system as well as alternatives for future expansion plans
While more Distributed Energy Resources (DER) are permitted in the wholesale markets, and can help with price volatility, many markets are only beginning to understand how these DERs fit in the wider energy system and their impact on the grid. Also here, capacity expansion software solutions offer the ability to have a detailed analysis of the impact of these individual proposed and/or permitted DERs from a variety of perspectives (i.e. Utility, Participant, Societal, Rate Payer impact, etc.).
Market Simulation: Understanding Complex Interactions
Market simulations provide a sandbox to test different market conditions by modeling factors such as regulatory changes, fuel price fluctuations, and demand shifts. Accurate modeling of renewable and traditional energy assets is crucial for transmission operators, planners, renewable asset developers, marketers, and traders. Studies and simulations allow them to determine the most optimal site and size for transmission projects, optimizing capital allocation and operational planning.
For traders, these simulations can help in stress-testing portfolios by providing valuable information on the dynamics of the marketplace by determining the effects of transmission congestion, generator availability, bidding behavior, and load growth on market prices, thereby reducing exposure to unforeseen price shocks. Asset Modelling software performs a security constrained unit commitment and economic dispatch, recognizing both generation and transmission impacts at the nodal and zonal level. Additionally, modeling helps project future asset performance under different market and regulatory conditions, enabling a quantitative comparison of various outcomes. For example, this ensures traders can value complex PPA structures for optimal revenue and risk distribution.
Long term fundamental forecast: Bank/investor-compliant market revenue projections
When making investment decisions in energy markets, utilities, financiers, developers, and other energy market investors, need unbiased reports with third-party energy price data to develop an actionable business case reports that includes a fundamental base forecast of market clearing prices for 25-year study period. This analysis, such as Power Reference Case from Hitachi Energy, produces integrated, internally consistent forecasts of hourly, monthly, and annual wholesale electricity prices, annual capacity prices, annual emission allowance prices and renewable energy credit prices. A needed, experts with detailed knowledge of market development provide a unique combination of software, data, and advisory services to enhance model accuracy and decision-making.
AI-Powered Forecasting: Harnessing Data for Competitive Advantage
Artificial intelligence is transforming energy trading by delivering accurate demand and price forecasts. Machine learning algorithms analyze vast datasets—weather patterns, market trends, historical trading behavior—to generate real-time insights. Outdated forecasting models and systems often struggle to manage the unpredictability of renewable energy. These models fail to consolidate the vast amounts of data needed to make accurate predictions, which is essential for forecasting.
Load, production, and price forecasting are essential in a world where energy supply is becoming increasingly decentralized and weather dependent. AI-powered forecasting provides advanced capabilities, using historical data and machine learning algorithms to predict demand, production, and price fluctuations. The result is forecasting precision that significantly enhances the ability of energy traders and portfolio managers to anticipate market movements and adjust their strategies accordingly.
What-If Scenarios: Preparing for Every Possibility
Energy markets are shaped by a multitude of variables, from regulatory shifts to unexpected outages. What-If scenario modeling empowers traders to evaluate the impact of different contingencies. By stress-testing their portfolios against various disruptions, traders can develop hedging strategies that minimize risk exposure and capitalize on market opportunities. Advanced analytics with what-if scenarios can assess market risks, monitor positions, and provide risk metrics to support decision-making.
In trading, algorithms are used to simulate or optimize operations. Next-generation Energy Trading and Risk Management (ETRM) systems support the ability to create What-if scenarios, allowing to view exposures under different scenarios, for example the impact of price and volume changes on portfolios and P&L. Mechanisms can be set up in support of market strategies based on set levels, averages, deltas, or targets for volume and price.
A ‘live’ ETRM system facilitates such operations by reporting position changes and receiving market information in real-time and having direct interfaces with the various Trading Exchanges. In this way, the What-if scenario allows a business to respond to alternative situations more quickly and effectively because they have developed strategies to rely upon.
Prediction is very difficult, especially if it’s about the future
The need to transition to a low-carbon economy has added a new and challenging dimension to planning and trading. Ambitious environmental targets are impacting the wholesale and retail markets as they adjust to this transition towards an electrified, zero-carbon economy. As the energy space continues to evolve, predictability will be the key to success. By integrating advanced planning tools, leveraging AI-driven insights, and preparing for market contingencies, transmission operators, planners, renewable asset developers, marketers, and traders can turn uncertainty into opportunity. Next generation tools are required to consolidate the vast amounts of data needed to make accurate predictions. What-If your solutions have more past than future?
