The transition to decentralized energy distribution, where any node can function as a consumer and/or producer, presents challenges in the design and testing of control algorithms, particularly in maintaining production. The existing energy scheduling model, assuming uniformity, struggles to capture the unique dynamics and constraints of individual production units. This paper introduces KEMASS, a method and system for generating a Multi-Agent System using Ontologies and Knowledge Graphs to tailor optimization algorithms for power plants. Implemented in a specific energy production valley, KEMASS closely simulates the actual system, optimizing energy schedule while considering local constraints. Although not yet a complete Digital Twin for Energy Scheduling Support, KEMASS, with its dynamic Knowledge Graphs and Ontologies, is more adaptable to evolving into one compared to other systems. The use of Knowledge Representation technologies makes it suitable for various applications.