Abstract:Exergy analysis is a unified approach to evaluating the quantity and quality of energy in integrated energy systems (IESs). However, exergy analysis models have been relatively underexplored for IES operation in energy transmission networks. This study addresses this gap by developing an input-benefit exergy model and an exergy loss calculation model for each link within heat-and-electricity-based IESs (HE-IESs), encompassing the transmission networks. Then, an exergy-based unified optimal operation model for HE-IESs is introduced by minimizing the total exergy loss of the system. In addition, the effect of load rate on the energy efficiency of energy conversion equipment is considered. By applying piecewise linearization to the non-convex terms in the objective function and the equality constraints, the proposed optimization model is accurately and efficiently analyzed as a mixed-integer second-order cone programming problem. The case study results demonstrate that the proposed model reduces equipment-related exergy loss, with approximately 3.2 times the heat network-related exergy loss. Moreover, the average deviation gaps of the overall system's exergy loss and equipment's output exergy power between the proposed and the existing models reach 12.94% and 27.83%, respectively. The maximum relative error of the solution results with the proposed linearization method remains below 1.2%, satisfying the requirements of practical application.

Abstract:To improve the resilience of distribution networks (DNs) in the event of extreme natural disasters such as typhoons and rainstorms, it is imperative to efficiently implement distribution service restoration (DSR) to restore loads as soon as possible. In previous studies, DSR has mainly adopted the distributed resource model with droop or PQ control. This inhibits the exploitation of the potential of distributed generators (DGs) in load restoration when the DN loses support from the upstream transmission network. Thus, this paper proposes a multi-resource collaborative service restoration (MRCSR) approach for DNs incorporating local soft open points, DGs, and tie switches. The MRCSR model is developed by integrating a decentralized hierarchical droop control (DHDC) strategy and incorporating the frequency and voltage features of the load demand. A two-stage iterative feedback optimization (TSIFO) algorithm is then developed to analyze the MRCSR model in an accurate and efficient manner. Finally, the proposed model and algorithm are tested on the modified IEEE 33-bus system and a practical distribution system of the Taiwan Power Company to verify their effectiveness and advantages over existing approaches.

Abstract:A subsequent commutation failure (SCF) can easily occur during the recovery process after a first commutation failure (1st CF). This paper analyzes the interaction mechanism of extinction angle, AC voltage, DC current and firing angle, and reveals that the complex coupling relationship during the dynamic process after the 1st CF has a significant effect on the SCF. The mathematical equations when considering different fault durations, fault severities and AC system strengths are then established. An AC fault voltage detection method based on reactive power and fault duration is also proposed to measure the fault severity, and an SCF inhibition control strategy (SCFICS) based on AC fault detection and reactive power control is subsequently proposed. This can not only inhibit the SCF, but also enhance the DC recovery speed effectively. Finally, based on the SCFICS, a simulation model is built, and the simulation results with different cases indicate that the SCFICS can effectively inhibit the SCF with good recovery performance, for three-phase-to-ground (TPG) and single-phase-to-ground faults, and with a fault inductance range of 0.01 H to 1 H.

Abstract:In this paper, a collaborative online algorithm is proposed to estimate the state of charge (SOC) and state of health (SOH) of lead-carbon batteries that participate in frequency regulation of a power system with a high proportion of renewable energy. The algorithm addresses the inaccurate estimation of energy storage battery states caused by continuous and alternating charging and discharging over a short period. Analysis of lead-carbon battery chemistry and materials reveals that the resistance of the diaphragm is the most influential factor in battery aging. In addition, the hysteresis characteristics of an energy storage battery vary significantly between the charging and discharging stages. A second-order RC equivalent circuit model is proposed that considers the contact and diaphragm resistances, and hysteresis characteristics. Based on this, models for constant current charging interaction, constant voltage charging interaction, and dynamic discharging interaction are developed. The adaptive forgetting factor recursive leassquare (AFF-RLS) method is used to identify the parameters of the interactive models. Then an interactive multiple model with the embedded unscented Kalmanfilter (UKF) is used to estimate the SOC of the energy storage battery. The membrane and contact resistances identified by the interactive multi-model (IMM) are used to estimate the SOH, and online collaborative optimization of the SOC and SOH is achieved. The error of the proposed SOC estimation method is experimentally verified to be within 2%, which is less than 5% of the standard value, and the error of SOH estimation is within 0.5%, demonstrating the high accuracy of the proposed method.

Abstract:The data for an energy management system (EMS) in an integrated energy system (IES) is obtained through state estimation. This is then the basis for optimal scheduling, protection and control. At present, the dynamic models of gas and heat networks are rarely considered in such state estimation, and the method lacks robustness. This paper develops dynamic state estimation models for gas and heat networks, and proposes a unified method for the electricity-gas-heat network, one which takes into account robustness while ensuring accuracy. First, the state transition equations in matrix form are formulated according to finite difference models considering the dynamic characteristics of the gas and heat networks. Then, combined with a quasi-steady state model of the electric power system, a unified state estimation method and multi-time-scale measurement strategy in the Kalman filter framework are proposed. In addition, the prediction accuracies of the electric power and gas systems are improved through adaptive adjustment. The kernel density estimation method is used to adjust the measurement weights and filter out bad data to ensure robust state estimation. Finally, simulation results show that the proposed method not only can improve the estimation accuracy by improving prediction accuracy, but also is robust to various types of bad data.

Abstract:The fault characteristics of microgrids are affected by the penetration of inverter-interfaced distributed generators (IIDGs). It makes conventional protection schemes no longer applicable. With different grid codes in different countries, IIDGs need to adopt different positive-sequence low-voltage ride-through (LVRT) control strategies during LVRT. Therefore, conventional protection schemes have to be modified according to the specific microgrid structure and the IIDGs' LVRT strategy. In order to adapt to different grid codes, a sequence component current-based fault control strategy and a coordinated microgrid fault detection method are proposed in this paper. The fault control strategy of IIDGs comprehensively considers the coordination between voltage support and fault characteristics generation, where the sequence currents are controlled separately. The positive-sequence current control strategy aims at supporting the microgrid voltage, whereas the negative-sequence current control strategy aims at generating or enhancing specific fault characteristics. Based on the proposed fault control strategy, the grid-feeding IIDGs can be equivalent to current sources and generate or enhance the negative-sequence fault characteristics in the equivalent additional networks of negative-sequence components. The fault feeder can then be accurately located by analyzing the phase relationship between the negative-sequence fault components of voltage and current phasors. A coordinated microgrid fault detection method based on the fault control strategy of IIDGs is proposed. The proposed fault control method makes the fault component protection principle applicable to all types of faults under any operational modes of microgrids. Finally, the correctness and effectiveness of the proposed coordinated fault control and protection strategy are verified in PSCAD/EMTDC.

Abstract:During the construction of an offshore wind farm (OWF), the capital cost of the collector cable system accounts for a large proportion of the total cost. Consequently, the optimal design of the collector system topology (CST) is one of the most crucial tasks in OWF planning. However, for a large-scale OWF, the optimal design of CST is a complex integer programming problem with high-dimension variables and various constraints. Therefore, it is difficult to acquire a high-quality optimal design scheme. To address this issue, this paper proposes a new grouping-based optimal design of CST for a large-scale OWF. First, all the wind turbines are divided into multiple groups according to their geographical locations and the maximum allowed connected wind turbines by each cable. This not only reduces the optimization dimension and difficulty, but also effectively satisfies the ‘no cross’ constraint by putting the geographically closed wind turbines into the same group. Secondly, the electrical topology among different wind turbines in each group is initially generated by an improved dynamic minimum spanning tree (DMST). The division groups of the OWF are then adjusted to further reduce the capital cost by improved simulated annealing. To verify the proposed technique, comparison case studies are carried out with five algorithms on two different OWF.

Abstract:In this paper, the combined k-out-of-n : G model and reliability block diagram model is used to analyze the reliability of a switched reluctance starter/generator system. First, the different operational modes of a switched reluctance motor starter/generator are analyzed, and the fault states of the system are briefly described. Then the fault criteria of the system in different operational states are put forward. Secondly, a reliability block diagram model is established to calculate the system-level reliability, and the k-out-of-n : G model is adopted to analyze the reliability of each part of the switched reluctance starter/generator system. To verify effectiveness, the first-order Markov model is also used to analyze the reliability of each part of the switched reluctance starter/generator system. Considering the computational complexity and accuracy of the system, the k-out-of-n : G model is more suitable for system component level reliability analysis. Finally, a 6/4 switched reluctance motor is used as the simulated and experimental platform motor. The final results verify the effectiveness of the reliability analysis model.

Abstract:The source and load uncertainties arising from increased applications of renewable energy sources such as wind and photovoltaic energy in the power system have had adverse effects on optimal planning and dispatching. Models for generating typical renewable energy and load scenarios are constructed to reduce such effects and improve the applicability of a planning and optimal dispatching model of power systems with a high proportion of renewable energy. The traditional clustering-based model for representing such scenarios cannot handle high-dimensional time-series data and consequently the feature-related information obtained cannot fully reflect the characteristics of the data. Thus, a deep convolutional embedded clustering model based on multi-head self-attention is proposed. First, a variational mode decomposition model is optimized to reduce the influence of noise-related signals on the feature extraction. The deep features are then extracted from the data using an improved convolutional autoencoder, and the appropriate number of clusters is determined using the elbow method. Following this, the network parameters are optimized based on the sum of losses during reconstruction and clustering. Subsequently, typical scenarios are then generated based on the optimized network model. Finally, the proposed method is evaluated based on data visualization and evaluation metrics. It is shown that the quality of features and the accuracy of clustering can be effectively improved by the proposed scenario generation method.

Abstract:Unified power quality conditioner (UPQC) is a power electronics device consisting of a series active filter (Se-AF) and a shunt active filter (Sh-AF) connected in parallel through a DC-link circuit to overcome power quality problems, i.e., voltage sag, voltage swell, and non-linear (NL) load. The weakness of the UPQC is that it cannot function normally if the Se-AF and/or Sh-AF fail, and the devices are unable to transfer active power to the load in the event of an interrupt voltage at the source. This paper proposes a novel configuration of a dual UPQC supplied by a dual photovoltaic (PV), hereinafter referred to as 2UPQC-2PV, to improve the power quality performance of a single-phase 220 V/50 Hz distribution system. The 2UPQC-2PV configuration is proposed to anticipate the possible failure of both inverters in one of the UPQC circuits. The PV array replaces the DC-link capacitor to maintain its voltage connected to the DC-link of the UPQC constant while at the same time supplying active power to the load during an interruption voltage. The dual-fuzzy Sugeno (dual-FS) method is used to overcome the weakness of the dual-proportional-integral (dual-PI) control in determining the optimum parameters of proportional and integral constants. There are three disturbances simulated in each-of 2UPQC-2PV and 1UPQC-1PV using dual-FS and dual-PI, i.e., Case 1 (S-Sag-NL), Case 2 (S-Swell-NL), and Case 3 (S-Inter-NL). Each UPQC-PV combination using FS control is compared with PI control resulting in a total of six cases. The 2UPQC-2PV configuration with dual-PI and dual-FS controls, in the three fault cases, is able to produce higher voltage changes than the 1UPQC-1PV configuration. For the 2UPQC-2PV configuration in the three fault cases, Dual-FS control is able to produce lower THDs of load voltage and source current, than the dual-PI control, while meetings the limits of IEEE 519 Standard. In Case 3, the configuration of 2UPQC-2PV using the dual-FS method, is capable of delivering the active power of the load close to that in Case 1.

Abstract:In a DC/AC microgrid system, the issues of DC bus voltage regulation and power sharing have been the subject of a significant amount of research. Integration of renewable energy into the grid involves multiple converters and these are vulnerable to perturbations caused by transient events. To enhance the flexibility and controllability of the grid connected converter (GCC), this paper proposes a common DC bus voltage maintenance and power sharing control strategy of a GCC for a DC/AC microgrid. A maximum power point tracking algorithm is employed to enhance the power delivered by the wind turbine and photovoltaic module. The proposed control strategy consists of primary and secondary aspects. In the primary layer control, the DC bus voltage is regulated by the GCC. In the secondary layer, the DC bus voltage is maintained by the energy storage device. This ensures reliable power for local loads during grid failures, while power injection to the grid is controlled by an energy management algorithm followed by reference generation of inductor current in the GCC. The proposed control strategy operates in different modes of DC voltage regulation, power injection to the grid and a hybrid operating mode. It provides wide flexible control and ensures the reliable operation of the microgrid. The proposed and conventional techniques are compared for a 15.8 kW DC/AC microgrid system using the MATLAB/Simulink environment. The simulation results demonstrate the transient behaviour of the system in different operating conditions. The proposed control technique is twice as fast in its transient response and produces less oscillation than the conventional system.