The challenges presented by market fluctuations and environmental events such as lightning are addressed in this paper by integrating fuzzy logic with Markov models. The challenges presented by market fluctuations and environmental events such as lightning are addressed in this paper by integrating fuzzy logic with Markov models. This integration is critically needed because market uncertainties (e.g., prices) often follow probabilistic patterns, while lightning impacts involve imprecise, linguistic assessments. A unified Fuzzy-Markov framework is therefore essential to holistically manage these hybrid uncertainties and enhance decision-making in smart grid self-scheduling. The objective of this research is to create a dependable framework for improving the predictability and stability of smart grid systems under unforeseen circumstances. The proposed Fuzzy-Markov approach facilitates the proactive decision-making process and the effective forecasting of future market conditions by categorizing complex numerical data into fuzzy states and analyzing the transition probabilities between these states. One of the most significant contributions is the successful classification of financial metrics, including price, revenue, and sales, into qualitative fuzzy states. The Markov transition matrix's construction and analysis provide critical insights into state transitions, with the model attaining an accuracy of 56.13%. Although this accuracy is moderate, it illustrates the model's effectiveness in predicting future conditions, superseding random conjecture and establishing a strong foundation for strategic planning. The research also emphasizes significant findings through rolling statistics, which are crucial for risk management. The novelty of this work is its distinctive integration of fuzzy logic and Markov models to address both market and environmental uncertainties in smart grids.
Benistan et al. (Tue,) studied this question.