This paper proposes a control scheme to involve the distributed Battery Energy Storage Systems (BESSs) in load frequency control (LFC) through BESS aggregators with sparse communication networks. In order to cope with the uncertainties associated with system operation, a two-layer Model Predictive Control (MPC) is developed so that more efficient control signals are provided to improve the response of BESSs to make larger contribution to the LFC. The outer layer in the proposed structure produces the command signal based on signals which are produced by the inner layer and signals which are provided from the actual system. The command signals are provided so as to achieve the least value of error in Area Control Error (ACE) with minimum control effort taking a variety of operational and physical constraints into consideration. Optimization procedures are also carried out to compute the optimal value of weighting coefficients contained in the objective functions. In addition, the delay caused by propagation channels in delivering control signals to BESSs is modeled, and evaluated. An intelligent fuzzy coordination control is then developed to coordinate the conventional power plants and BESS aggregators to avoid extra power injection/withdrawal by the conventional power plants in case of long delays.

This paper presents a frequency regulation approach applied to a renewable penetrated isolated power grid in the presence of a large number of Electric Vehicles (EVs). A disturbance observer is initially designed for a reduced-order model of the system to produce a supplementary frequency control signal for the aggregated EVs. The reduced order model is obtained by combining the variations in the load, wind, photovoltaic systems, and aggregated EVs in order to yield a lumped disturbance to be estimated by the proposed disturbance observer. A tube-based Model Predictive Control (MPC) is then proposed to provide efficient control signals for improving the response of the aggregated EVs. The control signals are produced so as to obtain the least value of frequency deviation error with a minimum control effort while taking a variety of operational and physical constraints into consideration. The proposed control strategy offers the capability to cope with the uncertainties caused by the external disturbances by coordinating the charging and discharging of aggregated EVs. The impact of the delay in communication links is also investigated by conducting a stability analysis to obtain the delay margin. Simulations are conducted on an isolated system to illustrate the effectiveness of the designed observer, and also to examine the advantage of the presented tube-based MPC over a conventional MPC, a fuzzy proportional-integral control, and a linear quadratic regulator control.

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.

In this paper, a robust Model Predictive Control (MPC) is proposed for the Gate-Controlled Series Capacitor (GCSC) to contribute to load frequency control. The proposed control model consists of a two-layer MPC in which a nominal MPC produces an initial control signal together with a prediction of system frequency response in a nominal system without uncertainty. An ancillary MPC then produces a control command for the actual system with uncertainty based on measurements and signals provided by the nominal system. The control signals are provided in such a way that the error in the frequency response of the actual system is minimized with respect to that of the nominal system. Optimization procedures are also conducted to attain optimal values of weighting coefficients associated with the inputs and outputs in the objective functions. The effectiveness of the proposed method to deal with uncertainties is compared with a conventional MPC, a scheme with proportional-integral controllers, and a system without GCSC. The method robustness is illustrated by case studies with load uncertainty and wind power fluctuations, and also uncertainty of parameters. The impact of the delay in communication links on the performance of the control scheme is also evaluated.

Due to the high price of fossil fuels, the increased carbon footprint in conventional generation units and the intermittent functionality of renewable units, alternative sources must contribute to the load frequency control (LFC) of the power system. To tackle the challenge, dealing with controllable loads, the ongoing study aims at efficient LFC in smart hybrid power systems. To achieve this goal, heat pumps (HPs) and electric vehicles (EVs) are selected as the most effective controllable loads to contribute to the LFC issue. In this regard, the EVs can be controlled in a bidirectional manner as known charging and discharging states under a smart structure. In addition, regarding the HPs, the power consumption is controllable. As the main task, this paper proposes a fractional order proportional integral differential (FOPID) controller for coordinated control of power consumption in HPs, the discharging state in EVs and automatic generation control (AGC). The parameters of the FOPID controllers are optimized simultaneously by the sine cosine algorithm (SCA), which is a new method for optimization problems. In the sequel, four scenarios, including step and random load changes, aggregated intermittent generated power from wind turbines, a random load change scenario and a sensitivity analysis scenario, are selected to demonstrate the efficiency of the proposed SCA-based FOPID controllers in a hybrid two-area power system.

The worldwide energy revolution has accelerated the utilization of demand-side manageable energy systems such as wind turbines, photovoltaic panels, electric vehicles, and energy storage systems in order to deal with the growing energy crisis and greenhouse emissions. The control system of renewable energy units and energy storage systems has a high effect on their performance and absolutely on the efficiency of the total power network. Classical controllers are based on integer-order differentiation and integration, while the fractional-order controller has tremendous potential to change the order for better modeling and controlling the system. This paper presents a comprehensive review of the energy system of renewable energy units and energy storage devices. Various papers are evaluated, and their methods and results are presented. Moreover, the mathematical fundamentals of the fractional-order method are mentioned, and the various studies are categorized based on different parameters. Various definitions for fractional-order calculus are also explained using their mathematical formula. Different studies and numerical evaluations present appropriate efficiency and accuracy of the fractional-order techniques for estimating, controlling, and improving the performance of energy systems in various operational conditions so that the average error of the fractional-order methods is considerably lower than other ones.