Xinrui Liu

With high renewable energy source (RES) penetration, the response characteristics of different storage units and the effect of RES uncertainty on battery degradation are not fully considered in hybrid energy storage system (HESS) capacity configuration. To address this issue, this paper proposes a HESS capacity configuration framework that integrates two-stage power allocation based on frequency decomposition and entropy-based degradation cost quantification. First, a two-stage allocation strategy based on variational mode decomposition (VMD) and fuzzy logic control (FLC) is developed to coordinate the battery and flywheel power allocation. VMD decomposes the compensation demand into different frequency components, while FLC further adjusts the allocation according to battery state of charge (SOC) and flywheel operating state. Second, an entropy-based degradation cost model is developed to account for the battery-side residual fluctuation under RES uncertainty. The residual uncertainty factor derived from the conditional entropy of this residual component is incorporated into the equivalent throughput calculation. Finally, an improved stage-adaptive differential evolution algorithm (ISADE) is developed with adaptive mutation selection to efficiently solve the nonlinear capacity configuration problem. Case studies show that the proposed method improves HESS operation and configuration economics by considering RES uncertainty in battery degradation modeling.

High wind power penetration reduces the equivalent inertia of power systems. When the rotor kinetic energy of doubly fed induction generators (DFIGs) is utilized for primary frequency control, coordinating heterogeneous turbine states while mitigating secondary frequency drops (SFDs) caused by synchronous recovery and limiting frequency-regulation costs becomes challenging. To address these issues, this paper proposes an adaptive primary frequency control strategy for a grouped DFIG-based wind farm (WF) that considers frequency regulation cost and frequency disturbance severity. First, a grouped equivalent model is established using the Jensen wake model and K-means clustering. Second, the WF support demand is generated from the transient frequency deviation, rate of change of frequency (RoCoF), and predicted frequency nadir safety margin. The participating group combination is then selected online according to the available support capability and normalized marginal cost of each group, and the support power is allocated accordingly. Finally, a support power envelope, rotor speed recovery modulation, and ramp rate constraints are employed to coordinate support withdrawal and rotor speed recovery. Multi-scenario simulations are conducted on a two-area four-machine system containing a 60 MW DFIG-based WF. The results demonstrate that the proposed strategy improves the rotor speed safety margin and reduces the cumulative normalized frequency regulation cost without compromising frequency nadir performance.

In community microgrids, the community microgrid operator (CMO) coordinates power purchase and sale by scheduling distributed energy resources. However, renewable energy source (RES) uncertainty increases regulation pressure for the CMO in coordinating supply-demand balance and raises regulation costs. To address this, this paper proposes a bi-level coordinated economic dispatch framework integrating uncertainty compensation quantification and behavior-driven demand response (DR). First, an information entropy-based uncertainty compensation cost model is constructed to quantify the regulation equipment degradation costs resulting from RES uncertainty. This model uses the information intensity coefficient to quantify the additional regulation burden on the supply side and incorporates these costs into compensable expenses to offset the CMO’s regulation expenditures. Second, a behavior-driven DR method is developed by utilizing load recovery probabilities to achieve temporal coordination between demand-side actions and renewable energy fluctuations, thereby smoothing the load curve. Finally, an enhanced Kriging-based algorithm featuring variance-improved key region partitioning is devised to solve the nonlinear economic dispatch Stackelberg game, reducing computational time under nested constraints. Simulation results demonstrate that the proposed method enhances operational economy and mitigates the impact of RES uncertainty on the system.

To fully leverage the real-time regulatory potential of virtual power plants (VPP) that aggregate multiple types of flexible resources (FR), enhance VPP reserve capacity throughout the scheduling period, this paper investigates the time-decoupling aggregation flexibility (AF) characterization methods and online economic dispatch strategies. First, an offline time-coupling AF model for VPP is constructed, and the cost and AF of VPP are comprehensively considered, including the upper/lower boundaries of AF and the expected power. Then, to address the online solving challenges caused by time-coupling constraints, a Lyapunov optimization-based time-decoupling method for energy storage (ES)-type FR is proposed, transforming the offline problem into an online optimization problem and effectively mitigating the uncertainties of renewable energy output and electric vehicle (EV) charging/discharging behaviors. Finally, an online interaction framework between distribution system operator (DSO) and VPP is designed. Simulation results demonstrate that, compared to prediction-based online methods and traditional offline methods, the proposed method achieves more accurate AF. Additionally, the computation time of proposed online scheduling method is only 0.34s, significantly improving computational efficiency and fully meeting real-time requirements of online optimization.

In recent years, the penetration rate of electric vehicles (EVs) and renewable energy sources (RES) has surged, which poses new challenges for the safe operation of the urban smart grids. Therefore, we encourages the cooperative optimization operation of EV charging station (CS) and RES in VPP. Firstly, the operating characteristics of CS and RES are studied respectively, and RES was equipped with hydrogen energy storage (HES). The VPP distributed optimization model is established for the cooperation benefit between CS and RES. Then, in order to incorporate network constraints into the VPP distributed optimization model, voltage sensitivity factor is introduced to evaluate the impact of VPP participants on the safe operation of the system. The line transmission sensitivity factor and system loss sensitivity factor are introduced to evaluate the impact of VPP trading on system loss, and the system loss cost is allocated to CS and RES reasonably. Finally, an alternating direction method of multipliers (ADMM) algorithm considering system sensitivity (ADMM-SS) is proposed by integrating three factors into the ADMM algorithm. The simulation results show that the cost of the VPP is reduced by 18.3%, the line loss is reasonably distributed among the participants, and the node voltage constraint is guaranteed.

False data injection (FDI) attacks pose a great threat to the safe operation of power grid. By attacking nodes with weak defences, high transmission risks and high returns, attackers can cause more damage to the power grid with limited resources. Therefore, it is of great significance to evaluate these critical nodes for active defence of power grid. This paper presents an evaluation index and defence method of critical information node. Firstly, by establishing an FDI attack model, information system model and attack propagation model, quantitative analysis is carried out on basic indicators, such as attack return, attack success probability, transmission risk, transmission intensity and correlation, between attack return and transmission risk of information nodes under FDI attack scene. According to the attack detected and no attack detected scenes, the basic evaluation indexes were selected, respectively, to establish a comprehensive evaluation index. Finally, based on the comprehensive evaluation index, two defence methods are proposed to improve the power grid's ability to resist FDI attacks, respectively, applicable to detected attacks and undetected attacks. The effectiveness of the proposed evaluation index and defence method is verified by the simulation results of IEEE57 nodes system.