In this paper, a modified frequency control model is proposed, where the demand response (DR) control loop is added to the traditional load frequency control (LFC) model. One of the main obstacles for using DR in the frequency regulation is communication delay. To overcome this issue, an adaptive delay compensator (ADC) is used. In this regard, a weighted combination of several vertex compensators, whose weights are updated according to the measured delay, is employed. Moreover, to overcome the impact of disturbances and uncertainties in power system, an active disturbance rejection control (ADRC) is utilized as the load frequency controller. This robust controller employs an extended state observer to estimate the disturbances and uncertainties and uses a feedback controller to compensate them. The confined iterative rational Krylov algorithm (CIRKA) is employed to reduce the order of the detailed 39-bus test system model meticulously and facilitate the controller process design. Therefore, a DR control loop, ADC, and ADRC are employed in the LFC to regulate the frequency of the power system in a more efficient way. The simulation results confirm the effectiveness of the frequency control in presence of communication delay and model uncertainties.

The increasing integration of renewable energy sources (RESs) into power systems has introduced new complexities due to the inherent variability and uncertainty of these energy sources. In addition to the uncertainty in RES generation, the demand-side load of power systems is also subject to fluctuations, further complicating system operations. Addressing these challenges requires effective modeling and assessment techniques to quantify and mitigate the risks associated with system uncertainties. This paper evaluates the impact of various uncertainty modeling techniques on power system reliability with wind farm integration. Furthermore, this paper reviews the state of the art of the various uncertainty and risk modeling techniques in power systems. Through a detailed case study, the performance of these techniques in modeling uncertainties of wind speeds is analyzed. Based on the results, the integration of wind turbines improves the system’s overall reliability when there is a reduction in conventional power plants (CPPs)’ generation, which are dispatchable energy sources providing a stable and flexible supply. However, the generation of wind farms is associated with uncertainty. The results show Monte Carlo simulation combined with the K-Means method is consistently a more accurate uncertainty model for wind speeds, closely aligning with real-case scenarios, compared to other methods such as Markov Chain Monte Carlo (MCMC), robust optimization (RO), and information-gap decision theory (IGDT).

This article introduces a method employing demand response (DR) and wind turbine (WT) to decrease the first and second frequency dips in power systems with the massive proliferation of wind resources. Inverter air conditioners (IACs) are utilized to participate in the DR program. The buildings' thermal model and the IACs' electrical model are integrated to characterize a relation between power system frequency and IACs regulation power. An adaptive delay controller is presented to capture the command communication delays from the DR controller to IACs. In addition, a controller is suggested to adopt wind power plants in system frequency regulation. This controller rises the wind plant output power for some seconds and returns it, following frequency recovery, to the initial power. The recovery period causes the second frequency dip, in response to which, a method is introduced to identify the moment of the second dip and alleviate it via DR.

In this paper, security constrained reserve management (SCRM) problem is formulated. In this trend, security constrained economic dispatch as well as operating reserve in both normal and ${N-1}$ contingency condition are considered. To address the computational complexity associated with the SCRM problem, the distributed-SCRM (D-SCRM) is proposed. In the suggested method, the power system is decomposed to several subsystems called zone. These zones are interconnected via some tie-lines. By defining two virtual generation units at the middle of each tie-line, all neighbor zones become isolated. This causes the main SCRM problem to be converted to some subproblems. To solve these subproblems, some initial values are first dedicated to the virtual generation units. Next, the independent SCRM subproblems are solved simultaneously using the genetic algorithm (GA). The objective function is then calculated for the main problem and the virtual values are updated using the GA again. This process continues until the global optimum is obtained. IEEE 24-bus, 39-bus, and 118-bus test systems are considered to evaluate the proposed algorithm. Results confirm the effectiveness of proposed algorithm.