Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods

Abstract

Microgrids create conditions for efficient use of integrated energy systems containing renewable energy sources. One of the major challenges in the control and operation of microgrids is managing the fluctuating renewable energy generation, as well as sudden load changes that can affect system frequency and voltage stability. To solve the above problems, hierarchical control techniques have received wide attention. At present, although some progress has been made in hierarchical control systems using classical control, machine learning-based approaches have shown promising features and performance in the control and operation management of microgrids. This paper reviews not only the application of classical control in hierarchical control systems in the last five years of references, but also the application of machine learning techniques. The survey also provides a comprehensive description of the use of different machine learning algorithms at different control levels, with a comparative analysis for their control methods, advantages and disadvantages, and implementation methods from multiple perspectives. The paper also presents the structure of primary and secondary control applications utilizing machine learning technology. In conclusion, it is highlighted that machine learning in microgrid hierarchical control can enhance control accuracy and address system optimization concerns. However, challenges, such as computational intensity, the need for stability analysis, and experimental validation, remain to be addressed.

Software Requirement Risk Prediction Using Enhanced Fuzzy Induction Models

“…This research employed well-established evaluation metrics, including accuracy, fmeasure, the area under the curve (AUC), and Matthew's correlation coefficient (MCC), to assess and contrast the predictive capabilities of the various investigated models. The selection of these performance indicators was based on their frequent utilization in prior studies in the assessment of rule-based and ML-based software risk prediction models [43][44][45]. Furthermore, these metrics are reported to be dependable collectively, as they consider all areas of the confusion matrix produced for each developed model [46,47].…”

Analytical Overvoltage and Power-Sharing Control Method for Photovoltaic-Based Low-Voltage Islanded Microgrid

“…The load demand should also be shared proportionally among the DGs to limit voltage and frequency variation at different buses. To this end, hierarchical control schemes, including primary, secondary, and tertiary, have been presented [5]. Fig.…”

Development of Control Techniques for AC Microgrids: A Critical Assessment

“…Equations ( 9) and ( 10) are binary restrictions that indicate the state of charge (SoC) of the battery as well as the interaction of the MG with the main. In Equation (11), the SoC is modeled, and the final restrictions are for the SoC and the max power of the battery and external grid, respectively.…”

“…The primary control coordinates the internal control of the DGs to keep the MG stable, i.e., it stabilizes f and V but in values different from the nominal ones, using fast and efficient strategies. Centralized and decentralized strategies are implemented at this level, with droop control being used the most for stability and power sharing between all DGs [11]. Droop control provides effective load contribution without the need for communication links.…”