Abstract:Active distribution networks utilize advanced sensors, communication, and control technologies to achieve flexible and intelligent power distribution management. Reliable state estimation (SE) is crucial for distribution management systems to monitor these networks. Historically, the scarcity of measurement resources has hindered the application of SE technology in distribution networks. Establishing a dependable pseudo-measurement model for active distribution networks can significantly enhance the feasibility of SE. This paper proposes a pseudo-measurement model that aligns with the actual operating status of the distribution network, considering the uncertainty in output from distributed generations (DGs) such as wind turbines and photovoltaics. Firstly, it analyzes and models the uncertainty of high-penetration DG output, establishing a reliable output model that incorporates the physical characteristics of wind and photovoltaic output. Secondly, it proposes a pseudo-measurement modeling method based on support vector machine (SVM), where the kernel function of the SVM is weighted according to the information entropy of fluctuations in historical operating data. This weighting ensures that the established pseudo-measurement model better reflects the actual operating status of the active distribution network. Finally, a mathematical model for optimizing pseudo-measurement selection is developed, with the minimum state estimation error as the objective function and the observability of the active distribution network system as the constraint. Case studies demonstrate the accuracy and effectiveness of this approach.

Abstract:Because of their advantages of high energy and power density, low self-discharge rate, and long lifespan, lithium-ion batteries (LIBs) have been widely used in many applications such as electric vehicles, energy storage systems, smart grids, etc. However, lithium-ion battery systems (LIBSs) frequently malfunction because of complex working conditions, harsh operating environment, battery inconsistency, and inherent defects in battery cells. Thus, safety of LIBSs has become a prominent problem and has attracted wide attention. Therefore, efficient and accurate fault diagnosis for LIBs is very important. This paper provides a comprehensive review of the latest research progress in fault diagnosis for LIBs. First, the types of battery faults are comprehensively introduced and the characteristics of each fault are analyzed. Then, the fault diagnosis methods are systematically elaborated, including model-based, data processing-based, machine learning-based and knowledge-based methods. The latest research is discussed and existing issues and challenges are presented, while future developments are also prospected. The aim is to promote further researches into efficient and advanced fault diagnosis methods for more reliable and safer LIBs.

Abstract:This paper studies the rolling security-constrained unit commitment (RSCUC) problem with AC power flow and uncertainties. For this NP-hard problem, it is modeled as a Markov decision process, which is then solved by a transfer-based approximate dynamic programming (TADP) algorithm proposed in this paper. Different from traditional approximate dynamic programming (ADP) algorithms, TADP can obtain the commitment states of most units in advance through a decision transfer technique, thus reducing the action space of TADP significantly. Moreover, compared with traditional ADP algorithms, which require to determine the commitment state of each unit, TADP only needs determine the unit with the smallest on-state probability among all on-state units, thus further reducing the action space. The proposed algorithm can also prevent the iterative update of value functions and the reliance on rolling forecast information, which makes more sense in the rolling decision-making process of RSCUC. Finally, numerical simulations are carried out on a modified IEEE 39-bus system and a real 2778-bus system to demonstrate the effectiveness of the proposed algorithm.

Abstract:Renewable energy dominated future power grids require enhanced system flexibility, in particular, activating the participation from various distributed energy resources (DERs). A coordinated two-stage flexibility trading framework for distribution system with microgrids (MGs) is proposed in this paper. At day-ahead stage, a peer to peer (P2P) trading mechanism and the associate leasing strategy of shared energy storage system are performed to solve the power variations caused by the wide spread integration of renewable resources, where asymmetric Nash bargaining is used to realize the fair revenue allocation according to the contribution of each MG in P2P trading. At intra-day stage, given the power imbalances from unexpected uncertainties, MGs exploit the adjustability of the DERs in responding to rapid flexibility requirements issued by distribution system operator. In particular, the average consensus based decentralized Newton method with super linear convergence is utilized to meet the requirements of flexibility while maintaining the information security. The feasibility, effectiveness and equity of the proposed trading strategies are verified through various simulation studies.

Abstract:As conventional synchronous generators are replaced by large-scale converter-interfaced renewable-energy sources (RESs), the electric power grid encounters the challenge of low rotational inertia. Consequently, system frequency deviation is exacerbated and system instability may occur when the frequency deviates beyond the acceptable range. To mitigate this effect, this study proposes a virtual inertia control (VIC) strategy based on a fractional-order derivative and controller parameter-tuning method. The tuning method uses the stability boundary locus and provides a stability criterion for identifying the stability region in the parameter space. The controller parameters are then optimized within the identified stability region to suppress frequency deviation and enhance system robustness. The proposed controller and tuning method is applied to a battery energy-storage system (BESS) in a low-inertia power system with the integration of RESs. Time-domain simulations are carried out to verify the stability region and compare the performance of the optimized proposed controller to that of the traditional integral-order controller. The simulation results show that the stability-analysis method is effective and that the fractional-order VIC, tuned with the proposed method, outperforms the traditional method in both frequency-regulation performance and parametric robustness.

Abstract:This paper proposes a tri-level defense planning model to defend a power system against a coordinated cyber-physical attack (CCPA). The defense plan considers not only the standalone physical attack or the cyber attack, but also coordinated attacks. The defense strategy adopts coordinated generation and transmission expansion planning to defend against the attacks. In the process of modeling, the upper-level plan represents the perspective of the planner, aiming to minimize the critical load shedding of the planning system after the attack. The load resources available to planners are extended to flexible loads and critical loads. The middle-level plan is from the viewpoint of the attacker, and aims at generating an optimal CCPA scheme in the light of the planning strategy determined by the upper-level plan to maximize the load shedding caused by the attack. The optimal operational behavior of the operator is described by the lower-level plan, which minimizes the load shedding by defending against the CCPA. The tri-level model is analyzed by the column and constraint generation algorithm, which decomposes the defense model into a master problem and subproblem. Case studies on a modified IEEE RTS-79 system are performed to demonstrate the economic efficiency of the proposed model.

Abstract:For conventional power systems, the forced outage of components is the major cause of load shedding. Unreliability tracing is utilized to allocate the total system load-shedding risk among individual components in accordance with their different contributions. Therefore, critical components are identified and pertinent measures can be taken to improve system reliability. The integration of wind power introduces additional risk factors into power systems, causing previous unreliability tracing methods to become inapplicable. In this paper, a novel unreliability tracing method is proposed that considers both aleatory and epistemic uncertainties in wind power output and their impacts on power system load-shedding risk. First, modelling methods for wind power output considering aleatory and epistemic uncertainties and component outages are proposed. Then, a variance-based index is proposed to measure the contributions of individual risk factors to the system load-shedding risk. Finally, a novel unreliability tracing framework is developed to identify the critical factors that affect power system reliability. Case studies verify the ability of the proposed method to accurately allocate load-shedding risk to individual risk factors, thus providing decision support for reliability enhancement.

Abstract:Because of the rapid growth of new energy and the accompanying considerable uncertainty in the power market, the demand for flexibility in a power system has risen sharply. In the meantime, the market structure of auxiliary services has changed, resulting in market participants (MPs) benefiting less than expected from providing flexible services. To encourage MPs to provide flexibility, this study proposes a dynamic design framework for an auxiliary service compensation mechanism. To evaluate the proposed framework, a case study is conducted, examining a peak-shaving service in Liaoning province in northeast China. First, the operational status and limitations of the typical product, the peak-shaving service, in China's flexibility auxiliary services market are analyzed. Then, taking into consideration the time value of the flexible products provided by the MPs, a dynamic mechanism for hierarchical compensation of flexibility auxiliary service costs is proposed, and a mathematical model aimed at optimizing the MPs' comprehensive income is constructed. The results show that, compared with the existing traditional mechanism, the proposed method can effectively guarantee fair remuneration for the flexibility provider, while easing the tense supply-demand relationship in the flexibility market.

Abstract:A robust optimal framework is designed herein to mitigate the oscillatory dynamics in a doubly fed induction generator (DFIG) even in the presence of network disturbances and input variation. To address uncertain dynamics, herein, a novel transformation formula is developed for a wind energy conversion system. An unscented Kalman filter is applied to estimate the unmeasured internal states of the wind energy conversion system using terminal measurements. The detailed convergence and stability analyses of the presented framework are investigated to validate its effectiveness. Additionally, comparative modal analyses are carried out to demonstrate the improvement in the damping of critical low-frequency oscillatory modes using the presented framework. The simulation results demonstrate satisfactory performance under various operating scenarios, such as increasing and decreasing wind speed and varying the terminal voltage. The comparative performance is demonstrated to validate the effectiveness of the presented framework over that of the state-of-the-art frameworks.

Abstract:Marine renewable energy, combining wave energy converters (WECs) and floating wind turbines (FWTs) into hybrid wave-wind energy converters (HWWECs), garners significant global interest. HWWECs offer potential cost reductions, increased power generation, and enhanced system stability. The absorption power of high wind energy sites is primarily influenced by the complex hydrodynamic interactions among floating bodies, which are closely related to the location and wind-wave environment of high wind energy sites. To delve into the positive interactions among HWWECs, this paper proposes a HWWEC array optimization strategy based on the artificial ecosystem optimization-manta ray foraging optimization coordinated optimizer (EMCO). In EMCO, the decomposition operator of artificial ecosystem optimization (AEO) and the flipping-dipper foraging operator of manta ray foraging optimization coordinated (MRFO) cooperate dynamically to effectively balance local exploitation and global exploration. To validate the effectiveness of EMCO, experiments were conducted in scenarios with 3, 5, 8, and 20 HWWECs, and compared with five typical algorithms. Experimental results demonstrate the existence of multiple optimal solutions for HWWEC arrays. EMCO achieves maximum total absorption power and exhibits good stability. Notably, EMCO enhances the q -factor values of HWWECs across four scales: 1.0478, 1.0586, 1.0612, and 0.9965, respectively.

Abstract:To enhance multi-energy complementarity and foster a low carbon economy of energy resources, this paper proposes an innovative low-carbon operation optimization method for electric-thermal-gas regional integrated energy systems. To bolster the low-carbon operation capabilities of such systems, a coordinated operation framework is presented that integrates carbon capture devices, power to gas equipment, combined heat and power units, and a multi-energy storage system. To address the challenge of high-dimensional constraint imbalance in the optimization process, a novel low-carbon operation optimization method is then proposed. The new method is based on an adaptive single-objective continuous optimization spiking neural P system, specifically designed for this purpose. Furthermore, simulation models of four typical schemes are established and employed to test and analyze the economy and carbon environmental pollution degree of the proposed system model, as well as the performance of the operation optimization method. Finally, simulation results show that the proposed method not only considers the economic viability of the target integrated energy system, but also significantly improves the wind power utilization and carbon reduction capabilities.