The significant penetration of renewable energy resources and ever-rising load demand has gradually aggravated the safe operation of the interconnected power system. A generalized extended state observer and fractional-order theory based integral sliding mode control strategy is introduced in the interconnected power system to minimize the frequency deviation by controlling the governor of the generation unit. The generalized extended state observer is designed to estimate the disturbance from system uncertainty, load demand, and wind power fluctuation to improve the robustness of the interconnected power system. The fractional-order integral sliding mode control relieves the chattering of frequency deviation and tie-line power deviation. Besides, the superconducting magnetic energy storage and the wind model, as the ancillary frequency regulation devices, are implemented in the interconnected power system. The novel sliding mode controller based on generalized extended state observer is designed for the interconnected power system with the addition of the superconducting magnetic energy storage and the wind model. Stability proof and simulation comparison with the integer-order sliding mode control and other three control schemes are carried out as well.

The universal use of open communication networks in interconnected power systems has changed the constant delay in the formerly dedicated channels into time-varying delay. The delay will deteriorate the dynamic performance of the load frequency control and lead to system frequency instability. This paper presents a fractional-order sliding mode controller for interconnected power systems with time-varying delay and performs stability analysis based on the Lyapunov direct method. Firstly, fractional-order integral sliding mode control is used to improve the stability of interconnected power systems containing time-varying delay. This expands the delay margin by taking advantage of the scalability of the fractional-order to the sliding mode surface. Then, the Riemann-Liouville fractional-order Lyapunov functional is introduced to stability analysis. This reduced the conservatism of the stability analysis by adjusting the upper limit of the functional integral. Lastly, combining Lyapunov's theory and integral inequality to obtain the stability conditions of the time-varying delay power system described by linear matrix inequality, and bring the stability margin of the system. The simulation results show that the designed controller can guarantee that the maximum delay time of the system is less than a predefined upper limit. The proposed control strategy obtains a larger stability margin than the other four scenarios. Additional simulation study was also conducted to verify its robustness to load variations and wind fluctuations.

With the massive penetration of renewable energy, traditional reinforcement learning algorithms suffer from slow convergence and area control error (ACE) in interconnected power systems. This paper proposes data-driven load frequency control (LFC) based on multi-agent reinforcement learning with attention mechanism in interconnected power systems. It can be divided into two phases; in the centralized training, the agents are trained by an experience replay mechanism; in the decentralized execution, the trained agent automatically regulates the generation power to control the load frequency by real-time access to the grid data in the area. The agent can selectively focus on specific information in the environment by introducing a criticism network with an attention mechanism. The attention mechanism can reduce the training time for reinforcement learning while improving control performance under disturbance. A novel reward function based on a cooperation mechanism is used to score the performance of agent, which can guide the reinforcement learning algorithm to reduce the ACE of each area simultaneously. The proposed method is validated by the IEEE three-area interconnected power system, and it is concluded that the method can reduce the ACE caused by load and renewable power disturbances, and greatly reduce the training time of the algorithm.

Amorphous lithium lanthanum titanate (LLTO) thin films were successfully prepared by a sol-gel method with an all-alkoxide based route. The thin film resistance was determined from the complex spectra by fitting experimental data to the equivalent circuit. The ionic conductivities of amorphous LLTO thin films were 4.5 × 10⁻⁶, 6.9 × 10⁻⁶, 1.3 × 10⁻⁵, and 3.8 × 10⁻⁵ S/cm at 30°C, 50°C, 70°C, and 90°C, respectively. The activation energy of lithium ion conduction in the thin film was evaluated to be 0.36 eV in the temperature range of 30–90°C. The structure of amorphous LLTO was predicted by the ab-initio molecular dynamics (AIMD) simulations and analyzed by partial pair distribution functions, coordination numbers and Voronoi tessellation. It is observed that the local environment of Ti in amorphous LLTO is quite similar to that in the crystal state but the atomic packing is much less dense. The ionic diffusivities were derived from the mean square displacement curves and the conductivities were evaluated from the Nernst-Einstein's relation, showing good agreement with experimental data. The good ionic conductivity of amorphous LLTO is attributed to its open and disordered structure with large excessive volumes.

This paper proposes a multi-level operation framework for an active distribution network (ADN) with multiple network-constrained virtual power plants (VPPs). We consider the market clearing process, ADN and VPPs' profits, peer-to-peer (P2P) energy trading among and between the ADN and VPPs, and carbon trading to be functioning cooperatively under the energy and reactive power auxiliary service markets. Through this approach we have developed a multi-layer framework for the multi-VPP system by formulating bidding plans for the superior market operator and comprehensively issuing active/reactive incentive prices to the VPPs. In order to overcome the tractability of the solution, we transform the original multi-layer model into an equivalent single-level mixed integer linear programming (MILP) problem with Karush Kuhn Tucker (KKT) optimality conditions and a strong duality theorem. Through case studies based on the IEEE 33-bus test system we verify that the proposed method can effectively enhance system voltage security and will result in greater economic benefits to system participants.