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.
There has always been this conundrum => Can a roundtrip between the market simulation modelling tool and power system tool where data from a copper plate or DC model is passed to the power system tool ensure convergence?
If the question is rephrased, it would imply that if the power system simulation is a learnable process from a machine learning perspective, the answer would be positive, and convergence would be ensured.
Introduction
Simulation Modelling Tool (Copper Plate/DC Model): This tool likely operates at a very detailed level, possibly focusing on the market infrastructure itself.
This is a sophisticated software used for simulating electricity markets (e.g. PLEXOS, PROMOD, ANTARES). It typically handles:
Economic Dispatch and Unit Commitment: Optimizing generation dispatch based on costs, constraints, and demand.
Locational Marginal Pricing (LMP): Calculating prices at different nodes in the network, reflecting congestion and losses.
Transmission Constraints: Modeling limits on power flow through transmission lines and interfaces.
Market Clearing and Settlement: Simulating the processes of market operation and financial settlement.
Long-term Planning and Investment: Assessing generation and transmission expansion scenarios.
Power System Tool (AC Power Flow Tool): This is a more traditional tool like PSSE, PowerWorld, ETAP, etc., designed for:
System-level AC power flow analysis: Focusing on the interconnected AC network, transmission lines, generators, loads, and overall grid behavior.
Simplified models: Using per-unit systems, aggregated loads, and simplified representations of components.
Steady-state or quasi-steady-state analysis: Primarily concerned with power flow solutions and network stability under various operating conditions.
The Roundtrip Challenge: The "roundtrip" is where data from the detailed "copper plate/DC model" is passed to the system-level "power system tool."
The core question is:
Will this data exchange ensure convergence in the power system tool when it attempts to solve power flow?
Why Convergence Can Be Problematic:
Several factors contribute to why this roundtrip data exchange might not guarantee convergence and can even hinder it:
Differing Model Granularity and Assumptions
Data Compatibility and Representation
Data Format and Units
Data Abstraction Level
Non-Linearities and Iterative Processes
Power Flow is Non-Linear: Power flow equations are inherently non-linear. Convergence in iterative solvers (like Newton-Raphson) relies on finding a consistent solution that satisfies all equations within a tolerance.
Inconsistent Boundary Conditions: Data from the copper plate model might impose boundary conditions or constraints on the system-level model that are inconsistent or lead to ill-conditioned power flow equations. This can make convergence difficult or impossible.
Purpose and Time Scale Mismatch
Rephrasing the Question: Learnability and Convergence
"If the power flow is a learnable process from a machine learning perspective, the answer would be positive, convergence would be ensured."
Implications of Learnability for Convergence:
ML Bypasses Iterative Solvers: If machine learning can effectively "learn" the power flow process, it potentially offers a way to circumvent the traditional iterative numerical solvers (like Newton-Raphson) that are prone to convergence issues. ML aims to directly predict the power flow solution based on input data, without explicitly iterating.
Implicit Convergence through Learning: In ML, "convergence" shifts from numerical iteration to the accuracy of the learned model. A well-trained ML model that accurately predicts power flow for a wide range of inputs implies that it has implicitly learned the underlying relationships and constraints that ensure a "converged" and physically plausible solution.
Data-Driven Consistency: If the ML model is trained on data that inherently respects the physical laws and constraints of power systems (e.g., data generated by accurate power flow solvers, or even high-fidelity simulations), then the ML model will learn to produce outputs that are also physically consistent and likely to represent a "converged" state.
Therefore, in the context of ML, the answer to rephrased question is potentially YES, but with important caveats:
If power flow is truly learnable by ML to a high degree of accuracy and generalizability: Then an ML model could indeed provide power flow solutions that are inherently "converged" in the sense that they represent physically consistent steady-state operating points.
However, "ensured convergence" is not guaranteed even with ML:
Data Quality and Representation are Still Crucial: The ML model is only as good as the data it is trained on. If the training data is flawed, inconsistent, or doesn't adequately represent the system's behavior, the ML model might learn to predict inaccurate or non-physical solutions, which, in a sense, are not "converged."
Generalization Limits: An ML model trained on a limited set of scenarios or a specific system might not generalize well to significantly different conditions or systems, potentially leading to inaccurate or non-converged predictions outside its training domain.
Implicit Assumptions Learned: While ML bypasses explicit equation solving, it implicitly learns patterns from the training data. If the training data itself reflects scenarios where convergence is problematic (e.g., very stressed systems), the ML model might also struggle to produce reliable solutions in similar situations, effectively mirroring the convergence challenges of traditional methods, albeit in a different form (accuracy of prediction).
Potential ML-Based Solutions for the Roundtrip Conundrum:
Instead of just blindly feeding copper plate data into a power system tool, ML could offer more sophisticated approaches to bridge this gap:
Learn Data Transformations: ML models could be trained to learn the mapping from the detailed copper plate/DC model data to the appropriate inputs for the power system tool. This could involve feature extraction, dimensionality reduction, and learning non-linear transformations to make the data compatible and physically meaningful for the system-level tool.
Hybrid Approach - ML for Initialization or Refinement:
ML for Initial Guess: Use an ML model to predict a good initial guess for the power flow solution in the system-level tool, based on the copper plate data. A better initial guess can significantly improve the convergence speed and robustness of iterative solvers.
ML for Model Refinement: Use ML to learn how to refine the simplified models within the power system tool based on insights from the more detailed copper plate model. This could involve adjusting parameters, adding non-linearities, or creating surrogate models.
Direct ML-Based Power Flow with Integrated Detailed Models: More ambitiously, research could explore developing ML architectures that can directly incorporate features and data from both system-level and detailed market models simultaneously. This would require carefully designed ML models and training strategies to handle the multi-scale nature of the problem.
Summary:
Market to power flow conundrum is very valid and reflects a real-world challenge in power system simulation. While simply passing raw data from a market model to a system-level tool is sometimes unlikely to guarantee convergence (REACTIVE POWER EQUATIONS) and might even worsen it, the perspective of "learnability" through machine learning offers a more nuanced and potentially powerful way to address this problem.
ML's ability to learn complex mappings and bypass or complement iterative solvers provides pathways to create more integrated, data-driven simulation workflows that can potentially overcome the convergence limitations and inconsistencies inherent in traditional multi-tool approaches. However, the success of ML-based solutions will critically depend on data quality, model design, and careful validation to ensure accuracy and generalizability.
So, we just say learn the mapping or data transformation, as power flow simulation is after all a deterministic or learnable process.
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