Integration of Event-Driven Modeling and Stochastic Optimization Within Control Frameworks of Regional Energy Systems

Abstract

A unified software-analytical suite is proposed. It implements a closed-loop control cycle for regional energy systems. The implementation combines event-driven modeling with two-stage stochastic optimization. The suite includes adaptive web parsers. The parsers extract and semantically verify telemetry data regardless of changes in web page structures and anti-bot mechanisms. The system employs the discrete-event simulator SimPy. That simulator reproduces equipment failures, load fluctuations and external disturbances. An analytical subsystem processes textual event logs. It applies TF-IDF and cosine similarity. It simulates quantum annealing to determine automatically the optimal cluster count. It evaluates cluster stability. A Pyomo-based optimization module solves a two-stage optimization program. Scenario generation employs Monte Carlo and Latin-Hypercube sampling. Subtasks distribute across computing resources in parallel. The system provides scalable configuration. It ensures high availability and fault tolerance. The solution offers extensible visualization. It supports flexible parameterization and API integration. Testing on real operational data for a regional energy system confirmed adaptability of the suite. It also demonstrated capacity to scale when the number of nodes and the volume of events increases. Future work will integrate machine learning algorithms for predictive analytics. The plan includes extending the model to multistage problems with distributed ledger technologies.