Master power forecasting to reduce customer risk and protect against price surges
As the energy landscape evolves, the complexity of managing the power grid has increased significantly. Renewable energy sources, such as wind and solar, are inherently variable and less predictable than traditional power generation, posing grid stability and reliability challenges. Additionally, as more renewable projects come online, grid congestion becomes a critical issue, leading to inefficiencies and potential bottlenecks in power distribution. The increasing electricity demand, driven by factors such as population growth, electrification of transportation and data center expansion, further complicates grid management and adds to the volatility of the power markets.
Examining the cumulative gap between peak day-ahead forecasts and real-time market prices highlights the growing challenges in predicting power markets. Rapid changes in supply and demand conditions have increased unpredictability, elevating risks for market participants.
The challenge in managing the grid is no more evident than in the work utility and asset managers perform daily. They must precisely forecast load and generation requirements to ensure they provide just the right amount of power—not too much, which risks financial losses, and not too little, which could lead to paying significantly more in power prices or risk blackouts for their customers. At the same time, they must remain agile in real-time markets, ready to shift strategies when unexpected events occur. Navigating this delicate balance requires modern tools and strategies to avoid pitfalls and optimize utility assets in an increasingly complex, and volatile grid environment.
This e-book provides strategies to help utilities master managing volatility on the grid. By refining load and generation forecast strategies and making precise real-time adjustments, utility managers can maintain balance amidst the volatile and complex energy landscape. These approaches ensure utilities can confidently navigate challenges, avoid pitfalls, and optimize their assets effectively.
Load forecasting is the process of predicting the amount of electrical power used by consumers over time. It is critical to ensure enough electricity is available to meet demand without overproducing or underproducing which can lead to inefficiencies and increased costs. Accurate load forecasting allows utilities to plan and manage their resources effectively, ensuring a stable and reliable power supply. Below are some common drivers of load and strategies to optimize load forecasts.
Weather conditions are a major driver of electricity demand because they directly influence how much power is used for heating, cooling and other weather-dependent activities. For instance, extremely hot or cold temperatures increase the need for air conditioning and heating, respectively, leading to higher electricity consumption. Forecasting the weather’s impact on the grid is a significant challenge. Fortunately, numerous solutions are available to help utilities accurately predict and manage load demands in their areas.
Modern load forecasting solutions employ sophisticated methodologies to predict electricity demand based on weather conditions. These include machine learning, data science and artificial intelligence (AI) techniques, or a hybrid of these approaches. Machine learning models and data science methods analyze historical data to identify patterns and trends, while AI-based solutions handle complex datasets and variables to provide nuanced predictions.
Some load forecasting solutions recognize that utilities have proprietary load data for their areas, which can enhance forecast accuracy. These solutions allow utilities to integrate their local historical and actual data into the forecast models. By combining this local data with a robust forecasting engine, utilities can achieve more accurate predictions tailored to the unique characteristics and consumption patterns of their specific regions.
.Another feature some load forecasting solutions have is a similar day analysis. Similar day analysis identifies historical days with weather conditions similar to the days that will be forecasted. By examining how the grid performed under comparable past conditions, utilities can gain insights into potential load and prepare accordingly. However, as the grid evolves, older historical data may become less relevant, especially if the grid has seen a significant change in growth or decline
An important aspect of load forecasting is evaluating past forecast performance. By doing so, utilities can understand the reliability and robustness of the forecasts and can refine their models for improved accuracy. Combining multiple forecasts can further enhance precision, as different forecasting tools have unique strengths and excel in varying situations.
Even for utilities operating in a small area, maintaining an awareness of the surrounding grid is crucial. Conditions affecting other parts of the grid can impact the local area, making it essential for utilities to anticipate and respond to changes in electricity demand influenced by broader grid conditions. Various power analyst publications, which offer daily or twice-daily macro-level views of the grid, provide valuable insights for this purpose.
Depending on the season and area, electricity demand follows daily patterns: during a typical workday, peaks occur in the morning as people start their day and in the evening as they return home and engage in leisure activities. Weekdays usually have higher demand due to business, industrial and school operations, while weekends see increased residential use as people are at home more. Understanding these daily and weekly load patterns is crucial for utilities to effectively manage and balance supply and demand, ensuring reliable power throughout the week.
To effectively manage electricity supply and demand throughout the day, utilities can rely on advanced forecasting tools that provide detailed, up-to-date load predictions. These tools offer hourly forecasts for up to fifteen days ahead, updated every hour. By receiving forecasts at this granularity, utilities can make more informed decisions and adjust their operations in the near term for their area.
Special events can cause temporary but significant fluctuations in electricity demand. Holidays often see a reduction in commercial and industrial electricity usage as businesses close or reduce operations, while residential demand might increase as people celebrate at home. Major sporting events or public celebrations can lead to spikes in electricity consumption as people gather in large numbers, either at home or in public venues, to watch the events. Utilities must anticipate these variations to manage the grid around these special events effectively, ensuring they can meet the increased demand without compromising reliability.
A similar day analysis is useful in anticipating how special events will affect load patterns. By examining historical data from similar past special events, utilities can gain insights into how these occasions have influenced electricity usage. For instance, during large events like the Super Bowl or Christmas, there may be notable spikes in electricity consumption in specific areas or times.
Demand-side management (DSM) encompasses a variety of strategies and policies aimed at reducing or shifting energy consumption to improve grid reliability, manage peak demand and promote efficient use of resources. Cities and utilities implement DSM measures to ensure a stable and balanced power supply, especially during periods of high demand or energy shortages. Key components of DSM include adjusting thermostat settings, rolling blackouts, load shifting and water conservation policies.
Utility companies can analyze historical data to understand the impact of DSM measures. By comparing load profiles from before and after DSM implementation, they can identify trends and changes in energy consumption. This analysis helps utilities gauge the impact of DSM initiatives throughout the day, such as thermostat adjustments or water conservation policies, and adjust their forecasting models to predict future demand more accurately.
There are also demand response programs that are used to help curb electricity use. A demand response program is a strategy used by utilities to manage and balance electricity demand during peak periods or when the grid is under stress. By incentivizing customers to reduce or shift their electricity usage during high-demand times, these programs help stabilize the grid, avoid blackouts and reduce the need for additional power generation.
Having a tool that provides an alert when a demand response program is implemented is crucial for utilities because it enables them to react quickly to sudden changes in demand. These timely alerts allow utilities to adjust their generation and distribution strategies, optimizing grid performance and maintaining a stable supply.
Industry experts are another source in providing valuable insights on the impact of DSM initiatives on load. These experts, with their extensive experience in energy management and past DSM episodes, offer critical perspectives on the potential outcomes of DSM measures. Incorporating expert analysis into forecasting enhances utilities’ ability to predict and manage load during DSM periods.
Accurately forecasting renewable generation is essential for effective supply and demand management, given its close ties to weather conditions. While many solutions provide a broad view, they often lack the granularity needed for specific grid areas, leading to inefficiencies. Analyzing renewable data at regional and unit levels offers superior generation forecasting, especially with bottom-up approaches that start from the sub-farm level. This method adapts to the evolving grid, easily incorporates new renewable farms and can be input into power-flow models for enhanced accuracy. In contrast, top-down approaches, which adjust macro-level forecasts to actual production, struggle to account for economic curtailment and outages and are harder to integrate into power-flow models.
Furthermore, solutions that provide multiple weather models and confidence intervals allow users to compare where models align or diverge, helping quantify uncertainty and predict local impacts on power supply more accurately.
The addition of new power generation projects to the grid can significantly alter the generation landscape. Utilities must carefully manage the development and integration of new projects and understand how these new projects will impact grid stability.
Publications that monitor and release new power generation projects offer timeline updates that track the progress and anticipate online dates of these developments. These insights are invaluable for utilities as they prepare for new power added to the grid. By staying informed about projects coming online in their area, utilities can prepare and plan for the additional generation capacity, ensuring a plan is in place when the new power comes online.
New solar and wind projects present unique challenges because their impact on the grid is weather-dependent. Estimating the generation capacity of these renewable sources as they are added to the grid is complex. However, understanding the impact of new renewable projects allows utilities to plan and optimize their strategies, as they must manage a variable generation source influenced by weather conditions.
In the dynamic landscape of power management, real-time asset optimization is crucial for utility companies to maintain efficient and reliable operations. As power grids become larger and increasingly complex with the integration of diverse energy sources and varying demand patterns, utilities must be adept at making swift adjustments when unexpected events affect the grid.
Generation and transmission outages and failures represent some of the most common unexpected significant events that can impact the grid’s stability. When a power plant unexpectedly shuts down or a generator fails, for example, due to a severe weather event, it can lead to substantial shortfalls in energy supply. Similarly, offline transmission and distribution outages can create bottlenecks, disrupt service and compromise other parts of the grid. These unforeseen disruptions necessitate immediate and effective responses to maintain grid stability and ensure continuous power delivery.
Monitoring the grid and quickly detecting when a generator goes offline or when there is a transmission outage provides a crucial advantage in adjusting operations to minimize impacts on asset management. Proper management of these events is essential to avoid escalating operational costs and long-term reliability issues.
When a generation or transmission outage occurs, it is crucial to quickly detect its grid impact. Real-time congestion information and insights into its causes help utilities assess the impact, allowing them to adjust asset positions and avoid significant losses.
If a generation or transmission outage persists, it is crucial to forecast this new reality for the coming days, allowing the utility to navigate asset operations through the outage period.
During peak load situations, the electricity demand can exceed available supply, leading to strain on the grid. Alerts can notify utilities of approaching peak load conditions, allowing them to take preemptive measures such as adjusting generation schedules, activating demand response programs, or procuring additional power from the market to meet the increased demand.
The integration of renewable energy sources adds complexity to power grid management, as their generation depends heavily on weather conditions. Fluctuations in wind and solar output can cause rapid changes in available power, challenging utilities to maintain grid balance and stability. Additionally, severe weather conditions such as high winds, winter storms, or smoke can disrupt renewable energy generation. Monitoring renewable output and receiving alerts for weather conditions that may impact generation at ISO, regional or local levels enables utilities to adapt more effectively to this variability.
With the rise of variable renewable energy sources like wind and solar, utilities have a difficult job managing their assets. Grid congestion, driven by the influx of new projects, can threaten balance, while increasing electricity demand from population growth, transportation electrification and data center expansion adds further volatility.
In this e-book, we explored strategies on how to optimize power asset operations by refining load and generation forecasting methods and making real-time adjustments. By understanding and implementing these approaches, utilities can effectively manage the complexities of the grid and optimize their assets for reliable and efficient power distribution.
This e-book is brought to you by Enverus, the world’s largest energy-focused software company, this e-book provides insights from a trusted leader in power and renewables. With 25+ years of expertise in the power market space, more than 6,000 businesses, including 1,000+ in electric power markets, rely on our solutions daily, with 7,500+ users using Enverus to develop projects, manage the grid, trade power and facilitate asset transactions.
Our 1,700-strong team includes more than 300 power and renewables experts, including industry veterans and PhDs, ensuring our data and intelligence address the evolving challenges in today’s power industry.
Enverus customers, on average, achieved 20% more trading profits and traded 4x more CRR megawatts than non-Enverus customers. With a 25-year track record of forecasting load more accurately than the ISOs, we’re uniquely positioned to support your needs across the entire power market.
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