The strong nonlinearity, time-varying structure, and complex operating conditions in interconnected power systems with the wind farm deteriorate the control performance of widearea damping control (WADC) designed by conventional modelbased methods. In this paper, an improved model-free adaptive control (MFAC), which employs the pseudo-partial derivative (PPD) to linearize the nonlinear power system dynamically, considering WADC requirements and system disturbances is proposed to damp out the inter-area oscillation for wind farm. In addition, to compensate for communication delays in a wide-area measurement system (WAMS), an adaptive delay compensator (ADC) is employed to reimbursed both constant and variable delays. The proposed controller depends only on online I/O data, which avoids the complicated system modeling process and can adapt to variations of system operating conditions and delay scenarios. Moreover, compared to existing MFAC damping approaches, the proposed approach provides additional advantages for wind farm on managing initial values and the stability region of PPD. The communication delays are compensated, which is not considered in other MFAC-based methods. Finally, case studies with various uncertainties and disturbances are conducted to validate the proposed control strategy.

Carbon trading has emerged as an effective way to promote the renewable generation and sustainable energy development. Since carbon emissions are closely coupled to energy system, it is a challenge to design a market mechanism for joint energy and carbon trading to achieve better strategies. In this study, the energy management problem with a specific focus on joint trading in multi-microgrid system is investigated by utilizing a multi-agent deep reinforcement learning approach. Initially, a joint energy and carbon trading market is established and the dispatch optimization problem is formulated as a Markov decision process without modeling uncertainties accurately. This mechanism enables direct one-to-one energy transactions among all areas, avoiding the market clearing in traditional multi-party local energy trading markets. To enhance the learning efficiency and maintain agent privacy, an enhanced multi-agent proximal policy optimization (MAPPO) algorithm that incorporates a parameter sharing mechanism is introduced. Moreover, the recurrent neural networks (RNN) structure is leveraged to perform feature encoding for individual agents, which improves the overall feature extraction capability. Through comprehensive experiments involving various algorithms, the proposed approach reduce operating costs 14.86 $\%$ and carbon emissions 19.04 $\%$ compared with traditional MAPPO, which validates the effectiveness and performance benefits.

Load frequency control (LFC) of modern power systems depends on open communication networks to transmit measurement/control signals, which may make the LFC scheme vulnerable to cyberattacks. Therefore, it is necessary to ensure the resilience of LFC systems against cyberattacks so as to detect the attack and maintain the stable operation of the system during the attack. This article aims to investigate the attack detection and mitigation scheme of LFC systems against false data injection attacks. A model-based unknown input observer (UIO) is designed to simultaneously estimate the system state and attack signal based on load prediction, where the attack estimation is utilized to achieve real-time attack detection. Moreover, we present a sufficient and necessary condition for the existence of the UIO and a design method of the UIO based on linear matrix inequalities. Based on the attack estimation, a resilient LFC scheme is proposed to mitigate the attack impact and restore the frequency and tie-line power within an accepted range during the attack. Finally, case studies are undertaken by the IEEE 9- and 39-bus test systems to show the effectiveness of the proposed scheme.

Incorporating subsynchronous damping controllers (SSDCs) into the control loops of doubly-fed induction generators (DFIGs) is one of the most cost-effective methods to suppress subsynchronous oscillation (SSO). However, it is difficult to achieve satisfactory control performance of SSDCs due to the time-varying structure and operating conditions of wind integrated power systems. In this paper, an improved model-free adaptive control (MFAC) method is developed for DFIGs to overcome the limitations of conventional methods for SSO mitigation. First, the structure of the MFAC-based SSDC (M-SSDC) for DFIGs is designed. Then, an improved MFAC predictive control algorithm is proposed to achieve SSO mitigation. Moreover, the stability of the closed-loop system with the proposed M-SSDC is analyzed, which provides some guidelines for determining controller parameters. Additionally, the input-output signal pair of M-SSDC is selected by using geometric measure for the optimum damping performance. Case studies under various operating conditions of the test power system validate the effectiveness of the proposed M-SSDC as well as its superior damping performance over conventional approaches.

Grid-forming converters (GFC) are promising technology for improving the grid inertia and frequency stability via voltage source converters (VSC). However, GFCs could make the ac-side oscillations more complicated since it mimics the dynamics of synchronous generators (SG), and the ac-side oscillations could spread to the dc systems. This paper investigates the mechanism of the interactions between GFCs and ac grids, and proposes an enhanced GFC to improve the damping performance on oscillations. By separating the system model into GFC-related transfer functions (TF) and the ac grid-related TFs, the interaction and stability margin can be investigated by the phase difference of these TFs at the cross-gain frequency (CGF). A lead compensator is designed to reshape the phase characteristic of GFC-related TF for enhancing the damping performance of the GFC. The effectiveness of the enhanced GFC is tested according to the simulations on DIgSILENT/PowerFactory.

Low-inertia power grids could suffer from large frequency excursions under even small power disturbances. When a voltage source converter-based high voltage direct current (VSC-HVDC) system is used to integrate a low-inertia grid into a main grid, an ancillary frequency support service can be provided to the low-inertia grid by the VSC-HVDC system. This paper proposes a perturbation observer-based fast frequency support controller (POFFS) of VSC-HVDC systems to improve the frequency stability of low-inertia power grids. According to the feedback linearization and the high gain observer technique, the perturbation observer of the inverter station is designed to estimate the comprehensive impact of multiple perturbations, including the power disturbances in low-inertia grids, the uncertainty of grid inertia, and unknown nonlinear dynamics. The estimate of the perturbations are further compensated by the feedback control loop to achieve robust frequency regulation. Compared with the conventional frequency controller, the proposed POFFS can provide better frequency support to low-inertia power grids, without requiring an accurate system model and parameters. Two test systems are used to verify the effectiveness of the proposed POFFS.