Algorithmic + Fundamental Trading in Nodal Markets : How can AI layer contribute ?
EnAnalytica is a specialized consultancy focused on developing advanced optimization and analysis solutions for electricity markets, particularly those employing nodal pricing mechanisms like ERCOT or flow-based market coupling like Europe. Additionally, there is also some work performed on NEMDE in Australia.
Where does AI Really Fit in EnAnalytica Algorithmic Toolbox Palette:
Combinatorics/Learning (Third Pillar): This is the most obvious place. EnAnalytica in its algorithmic palette lists various machine learning techniques representing it as a 3rd pillar. This entire pillar is the AI layer. It's where models are trained, refined, and potentially used for prediction, classification, probabilistic reasoning and even interaction with LLM agents.
Integration with Power Market Modelling (Second Pillar): EnAnalytica knows really well that AI can significantly enhance the power market modelling acumen.
Demand Forecasting: Machine learning models can predict electricity demand more accurately by analyzing historical weather data, economic indicators, and real-time usage patterns.
Price Prediction: AI can be used to predict electricity prices in complex markets like nodal markets by learning from historical price data, bidding behavior, and grid conditions.
Optimization: AI algorithms can optimize bidding strategies for power producers or energy consumption for large industrial users.
In summary, AI is a crucial part of this architecture, particularly in the Combinatorics Learning pillar. However EnAnalytica is working on a much harder high-dimensional problem!
Will this suffice in trader's decision making process ? Simply, EnAnalytica considers that sequential approach will not suffice in cracking the market 'code'. Coupled OR + ML approach where these two domains operate iteratively is potentially the key.
Main question of interest =>
Can you use the AI layer, particularly the Generative AI component + Discriminative AI component, to predict initial generation based on the load of nodes, and to distribute renewable energy forecasts across individual renewable power plants ?
Let's break down how this would work:
Concept: Learning the Dispatch Logic
The core idea is to train a generative AI model to learn the relationship between:
Inputs:
Load at each bus (node): This is your primary input. You're providing the demand side of the equation.
Renewable energy forecasts (total): You might have an aggregate forecast for wind and solar generation for the entire region (e.g., from a weather forecasting service).
Network topology (optional but highly recommended): Providing the GNN with the grid structure (as described in previous responses) allows it to learn geographically-influenced dispatch patterns.
Generator characteristics (optional but very helpful): Including information like generator type (coal, gas, nuclear, wind, solar), capacity, location (which bus it's connected to), and potentially even cost curves (if you want the model to learn economic dispatch).
Previous dispatch (optional): Including the generation dispatch from the previous time step can help the model learn the temporal dependencies (ramp rates, unit commitment constraints).
Transmission limits (optional): Knowing the limits of the transmission lines.
Outputs:
Generation dispatch at each generator: The model will predict the active power (MW) output of each individual generator in the system. This is a much more granular output than just predicting total losses. It's predicting how the system will be balanced.
Renewable energy distribution: The model will allocate the total renewable forecast to individual wind and solar plants based on their location, capacity, and historical production patterns.
Why Generative Could Potentially be Suitable:
Complex Relationships: The relationship between load, renewable forecasts, and generator dispatch is highly complex. It depends on a multitude of factors:
Economic dispatch principles (merit order, cost curves).
Transmission constraints (congestion).
Generator operating constraints (ramp rates, minimum up/down times).
Reserve requirements (ensuring enough generation is available to handle unexpected changes in load or generation).
Historical patterns and operator experience.
Probabilistic Output: Generative models, particularly those like VAEs and GANs, can learn a probability distribution over possible dispatch scenarios. This is very useful because there often isn't a single "correct" dispatch for a given load; there might be multiple economically viable solutions. The generative model can capture this uncertainty.
Handling Variability: Renewable energy forecasts are inherently uncertain. A generative model can learn to produce dispatch patterns that are robust to this uncertainty. You can sample multiple dispatch scenarios from the model, given the same load forecast, to see a range of possible outcomes.
Data-Driven: The model learns the dispatch logic directly from historical data, rather than relying on hard-coded rules. This is important because grid operation is often influenced by complex, implicit rules and operator experience.
Specific Generative Model Choices:
Conditional Variational Autoencoder (CVAE): This is a strong candidate. You would condition the CVAE on the load at each bus, the total renewable forecast, and (optionally) the network topology and generator characteristics. The CVAE would then learn to generate a distribution of possible dispatch patterns.
Conditional Generative Adversarial Network (CGAN): Similar to a CVAE, but uses a different training mechanism (a generator network competing with a discriminator network). GANs can sometimes produce sharper, more realistic outputs, but can be harder to train.
Graph Neural Network (GNN) + Generative Component: A GNN is excellent for capturing the network topology. You could combine a GNN with a generative model (like a VAE or GAN) in several ways:
GNN as Encoder: Use the GNN to encode the network state (load, topology) into a latent representation. Then, use a CVAE or CGAN to generate the dispatch from this latent representation.
GNN with Generative Output Layer: Train a GNN where the output layer is designed to generate a probability distribution over possible dispatch values (e.g., using a mixture density network).
Normalizing Flows, and Autoregressive Models: Other options for generative models.
How it Fits into the Flowchart:
Input to the Generative AI block: The load at each bus from the model (after parsing and potentially data mapping) would be the primary input. Total renewable energy forecasts, generator characteristics, and potentially the previous dispatch would also be fed in.
Output from the Generative AI block: The predicted generation dispatch for each generator, and the allocation of the renewable forecast to each renewable plant.
Input to "Shift Factor Calculation" and "Optimization Layer": The output of the Generative AI (the predicted dispatch) would be used as the starting point for the optimization. The optimization layer would then fine-tune the dispatch to ensure it meets all constraints (line limits, generator limits, etc.) and minimizes costs. This is a huge advantage, as it gives the optimization a very good initial guess, making it much faster and more likely to find a good solution.
Advantages of this Approach:
Realistic Initial Dispatch: The generative model provides a much more realistic starting point for the optimization than simply assuming a uniform or arbitrary dispatch.
Faster Optimization: Because the initial dispatch is already close to a feasible and economical solution, the optimization layer converges much faster.
Handles Uncertainty: The generative model can capture the inherent uncertainty in renewable generation and load, leading to more robust solutions.
Learns Complex Relationships: The model can learn complex, implicit dispatch rules that might be difficult to code explicitly.
Please note that this is just a start...
EnAnalytica's ongoing validation efforts strongly suggest that the strategic integration of generative and discriminative AI with established power system optimization techniques holds significant promise for enhancing the efficiency, reliability, and cost-effectiveness of grid operations, particularly in systems with substantial renewable energy penetration. We will find out soon!
Comments