Server Side Chat: Forecasting and optimization in the energy sector

Forecasting and optimization in energy management

Martin's talk focused on how energy companies like Frank Energy use machine learning and optimization algorithms to maximize profit, enhance sustainability, and help customers reduce their energy bills. Here's a summary of the key points discussed:

The energy market is complex, with numerous actors involved, from high voltage network operators to energy providers. Companies must forecast their energy needs and production accurately to avoid costly imbalances. The day-ahead market prices are determined based on forecasts, while real-time adjustments are made through intraday trading to account for unexpected changes in supply and demand.

Machine learning for day-ahead forecasting

Accurate day-ahead forecasting is crucial to minimizing imbalance costs. Martin explained how machine learning models are trained using historical data on factors such as temperature, solar radiation, and consumption patterns. These models predict energy consumption and production, helping companies like Frank Energy to plan their energy procurement and distribution better.

Intraday trading

Intraday trading allows companies to adjust their positions based on updated forecasts and market conditions. This helps in mitigating the risk of imbalances, which can be extremely costly. By buying or selling energy closer to the actual delivery time, companies can more accurately match their supply and demand, avoiding high imbalance penalties.

Strategic energy management

Beyond forecasting and trading, energy companies can leverage consumer assets such as electric vehicles (EVs) and home batteries to further optimize energy use. Martin discussed several initiatives at Frank Energy:

1. Virtual Power Plant (VPP): This project involves steering EV charging times to take advantage of low energy prices while also using imbalance prices to maximize savings.

2. Trading Home Batteries: By optimizing when home batteries charge and discharge based on market prices and imbalance signals, consumers can significantly reduce their energy costs.

3. Smart Homes: Integrating various smart devices to optimize energy consumption and production in a holistic manner, considering factors like solar power generation and real-time pricing.

Optimization algorithms

Two main types of optimization algorithms were highlighted:

1. Genetic Algorithms: These are used for their flexibility in handling complex optimization problems, though they can be computationally intensive.

2. Linear Programming: This method is widely used for its efficiency in solving problems with clear constraints and objectives, such as minimizing energy costs or balancing grid interactions.

Join us in building a strong community

Our Service Side Chats are more than just presentations; they are an opportunity to connect with like-minded professionals, share experiences, and learn from each other. We look forward to seeing you at our next meetup on August 22, where Daniel Bojczuk will discuss engaging developers in infrastructure as code using AWS and Azure.

With the Service Side Chat community, it is our goal is to create a vibrant community where professionals can share knowledge, discuss innovative ideas, and collaborate on projects related to server-side technologies. We encourage anyone to become a part of our community on Meetup.

Stay tuned for more insightful discussions and be a part of our growing community.