Abstract:The rapid development of electric vehicles (EVs) has benefited from the fact that more and more countries or regions have begun to attach importance to clean energy and environmental protection. This paper focuses on the optimization of EV charging, which cannot be ignored in the rapid development of EVs. The increase in the penetration of EVs will generate new electrical loads during the charging process, which will bring new challenges to local power systems. Moreover, the uncoordinated charging of EVs may increase the peak-to-valley difference in the load, aggravate harmonic distortions, and affect auxiliary services. To stabilize the operations of power grids, many studies have been carried out to optimize EV charging. This paper reviews these studies from two aspects: EV charging forecasting and coordinated EV charging strategies. Comparative analyses are carried out to identify the advantages and disadvantages of different methods or models. At the end of this paper, recommendations are given to address the challenges of EV charging and associated charging strategies.

Abstract:With the rapid development of artificial intelligence (AI), it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI. To explore the interaction mechanism of power system dynamic simulations and AI, a general design for AI-oriented power system dynamic simulators is proposed, which consists of a high-performance simulator with neural network supportability and flexible external and internal application programming interfaces (APIs). With the support of APIs, simulation-assisted AI and AI-assisted simulation form a comprehensive interaction mechanism between power system dynamic simulations and AI. A prototype of this design is implemented and made public based on a highly efficient electromechanical simulator. Tests of this prototype are carried out in four scenarios including sample generation, AI-based stability prediction, data-driven dynamic component modeling, and AI-aided stability control, which prove the validity, flexibility, and efficiency of the design and implementation for AI-oriented power system dynamic simulators.

Abstract:This work presents a new approach to establishing the minimum requirements for anti-islanding protection of distributed energy resources (DERs) with focus on bulk power system stability. The proposed approach aims to avoid cascade disconnection of DERs during major disturbances in the transmission network and to compromise as little as possible the detection of real islanding situations. The proposed approach concentrates on the rate-of-change of frequency（RoCoF） protection function and it is based on the assessment of dynamic security regions with the incorporation of a new and straightforward approach to represent the disconnection of DERs when analyzing the bulk power system stability. Initially, the impact of disconnection of DERs on the Brazilian Interconnected Power System (BIPS) stability is analyzed, highlighting the importance of modeling such disconnection in electromechanical stability studies, even considering low penetration levels of DERs. Then, the proposed approach is applied to the BIPS, evidencing its benefits when specifying the minimum requirements of anti-islanding protection, without overestimating them.

Abstract:Considering a variety of sampled value (SV) attacks on busbar differential protection (BDP) which poses challenges to conventional learning algorithms, an algorithm to detect SV attacks based on the immune system of negative selection is developed in this paper. The healthy SV data of BDP are defined as self-data composed of spheres of the same size, whereas the SV attack data, i.e., the nonself data, are preserved in the nonself space covered by spherical detectors of different sizes. To avoid the confusion between busbar faults and SV attacks, a self-shape optimization algorithm is introduced, and the improved self-data are verified through a power-frequency fault-component-based differential protection criterion to avoid false negatives. Based on the difficulty of boundary coverage in traditional negative selection algorithms, a self-data-driven detector generation algorithm is proposed to enhance the detector coverage. A testbed of differential protection for a 110 kV double busbar system is then established. Typical SV attacks of BDP such as amplitude and current phase tampering, fault replays, and the disconnection of the secondary circuits of current transformers are considered, and the delays of differential relay operation caused by detection algorithms are investigated.

Abstract:The fault current level analysis is important for bipolar direct current (DC) grids, which determines the operation and protection requirements. The DC grid topology significantly impacts the current path and then the fault current level of the grid, which makes it possible to limit the fault current by optimizing the grid topology. However, the corresponding discussion in the literature is indigent. Aiming at this point, the impact of grid topology, i.e., the connecting scheme of converters, on the pole-to-ground fault current in bipolar DC grids, is investigated in this paper, and the ground-return-based and metallic-return-based grounding schemes are considered, respectively. Firstly, the decoupled equivalent model in frequency domain for fault current analysis is obtained. Then, the impacts of converters with different distances to the fault point on the fault current can be analyzed according to the high-frequency impedance characteristics. Based on the analysis results, a simplified fault current index (SFCI) is proposed to realize the fast evaluation of impact of grid topology on the fault current level. The SFCI is then applied to evaluate the relative fault current level. Finally, the simulation results validate the model, the analysis method, and the SFCI, which can effectively evaluate the relative fault current level in a direct and fast manner.

Abstract:In this paper, we present a time-domain dynamic state estimation for unbalanced three-phase power systems. The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the network, i.e., the dynamics of the electrical lines. This enables our approach to release the assumption of the network being in quasi-steady state. Initially, based on the line dynamics, we derive a graph-based dynamic system model. To handle the large number of interacting variables, we propose a port-Hamiltonian modeling approach. Based on the port-Hamiltonian model, we then follow an observer-based approach to develop a dynamic estimator. The estimator uses synchronized sampled value measurements to calculate asymptotic convergent estimates for the unknown bus voltages and currents. The design and implementation of the estimator are illustrated through the IEEE 33-bus system. Numerical simulations verify the estimator to produce asymptotic exact estimates, which are able to detect harmonic distortion and sub-second transients as arising from converter-based resources.

Abstract:In a smart grid, state estimation (SE) is a very important component of energy management system. Its main functions include system SE and detection of cyber anomalies. Recently, it has been shown that conventional SE techniques are vulnerable to false data injection (FDI) attack, which is a sophisticated new class of attacks on data integrity in smart grid. The main contribution of this paper is to propose a new FDI attack detection technique using a new data-driven SE model, which is different from the traditional weighted least square based SE model. This SE model has a number of unique advantages compared with traditional SE models. First, the prediction technique can better maintain the inherent temporal correlations among consecutive measurement vectors. Second, the proposed SE model can learn the actual power system states. Finally, this paper shows that this SE model can be effectively used to detect FDI attacks that otherwise remain stealthy to traditional SE-based bad data detectors. The proposed FDI attack detection technique is evaluated on a number of standard bus systems. The performance of state prediction and the accuracy of FDI attack detection are benchmarked against the state-of-the-art techniques. Experimental results show that the proposed FDI attack detection technique has a higher detection rate compared with the existing techniques while reducing the false alarms significantly.

Abstract:Micro-phasor measurement units (μPMUs) with a micro-second resolution and milli-degree accuracy capability are expected to play an important role in improving the state estimation accuracy in the distribution network with increasing penetration of distributed generations. Therefore, this paper investigates the problem of how to place a limited number of μPMUs to improve the state estimation accuracy. Combined with pseudo-measurements and supervisory control and data acquisition (SCADA) measurements, an optimal μPMU placement model is proposed based on a two-step state estimation method. The E-optimal experimental criterion is utilized to measure the state estimation accuracy. The nonlinear optimization problem is transformed into a mixed-integer semidefinite programming (MISDP) problem, whose optimal solution can be obtained by using the improved Benders decomposition method. Simulations on several systems are carried out to evaluate the effective performance of the proposed model.

Abstract:The distribution of measurement noise is usually assumed to be Gaussian in the optimal phasor measurement unit (PMU) placement (OPP) problem. However, this is not always accurate in practice. This paper proposes a new OPP method for smart grids in which the effects of conventional measurements, limited channels of PMUs, zero-injection buses (ZIBs), single PMU loss contingency, state estimation error (SEE), and the maximum SEE variance (MSEEV) are considered. The SEE and MSEEV are both obtained using a robust t-distribution maximum likelihood estimator (MLE) because t-distribution is more flexible for modeling both Gaussian and non-Gaussian noises. The A- and G-optimal experimental criteria are utilized to form the SEE and MSEEV constraints. This allows the optimization problem to be converted into a linear objective function subject to linear matrix inequality observability constraints. The performance of the proposed OPP method is verified by the simulations of the IEEE 14-bus, 30-bus, and 118-bus systems as well as the 211-bus practical distribution system in China.

Abstract:Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power systems. By considering the wind uncertainty and both binary and continuous decisions of quick-start generation units within the intraday dispatch, we develop a Wasserstein-metric-based distributionally robust optimization model for the day-ahead network-constrained unit commitment (NCUC) problem with mixed-integer recourse. We propose two feasible frameworks for solving the optimization problem. One approximates the continuous support of random wind power with a finite number of events, and the other leverages the extremal distributions instead. Both solution frameworks rely on the classic nested column-and-constraint generation (C&CG) method. It is shown that due to the sparsity of L 1 -norm Wasserstein metric, the continuous support of wind power generation could be represented by a discrete one with a small number of events, and the rendered extremal distributions are sparse as well. With this reduction, the distributionally robust NCUC model with complicated mixed-integer recourse problems can be efficiently handled by both solution frameworks. Numerical studies are carried out, demonstrating that the model considering quick-start generation units ensures unit commitment (UC) schedules to be more robust and cost-effective, and the distributionally robust optimization method captures the wind uncertainty well in terms of out-of-sample tests.

Abstract:The dynamic pricing environment offers flexibility to the consumers to reschedule their switching appliances. Though the dynamic pricing environment results in several benefits to the utilities and consumers, it also poses some challenges. The crowding among residential customers is one of such challenges. The scheduling of loads at low-cost intervals causes crowding among residential customers, which leads to a fall in voltage of the distribution system below its prescribed limits. In order to prevent crowding phenomena, this paper proposes a priority-based demand response program for local energy communities. In the program, past contributions made by residential houses and demand are considered as essential parameters while calculating the priority factor. The non-linear programming (NLP) model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding phenomena. Since the NLP model does not guarantee global optima due to its non-convex nature, a second-order cone programming model is proposed, which captures power flow characteristics and guarantees global optimum. The proposed formulation is solved using General Algebraic Modeling System (GAMS) software and is tested on a 12.66 kV IEEE 33-bus distribution system, which demonstrates its applicability and efficacy.

Abstract:Among hybrid energy storage systems (HESSs), battery-ultracapacitor systems in active topology use DC/DC power converters for their operations. HESSs are part of the solutions designed to improve the operation of power systems in different applications. In the residential microgrid applications, a multilevel control system is required to manage the available energy and interactions among the microgrid components. For this purpose, a rule-based power management system is designed, whose operation is validated in the simulation, and the performances of different controllers are compared to select the best strategy for the DC/DC converters. The average current control with internal model control and real-time frequency decoupling is proposed as the most suitable controller according to the contemplated performance parameters, allowing voltage regulation values close to 1%. The results are validated using real-time hardware-in-the-loop (HIL). These systems can be easily adjusted for other applications such as electric vehicles.

Abstract:In the electricity market environment, electricity price forecasting plays an essential role in the decision-making process of a power generation company, especially in developing the optimal bidding strategy for maximizing revenues. Hence, it is necessary for a power generation company to develop an accurate electricity price forecasting algorithm. Given this background, this paper proposes a two-step day-ahead electricity price forecasting algorithm based on the weighted K-nearest neighborhood (WKNN) method and the Gaussian process regression (GPR) approach. In the first step, several predictors, i.e., operation indicators, are presented and the WKNN method is employed to detect the day-ahead price spike based on these indicators. In the second step, the outputs of the first step are regarded as a new predictor, and it is utilized together with the operation indicators to accurately forecast the electricity price based on the GPR approach. The proposed algorithm is verified by actual market data in Pennsylvania-New Jersey-Maryland Interconnection (PJM), and comparisons between this algorithm and existing ones are also made to demonstrate the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can attain accurate price forecasting results even with several price spikes in historical electricity price data.

Abstract:—This paper presents a control strategy for residential battery energy storage systems, which is aware of volatile electricity markets and uncertain daily cycling loads. The economic benefits of energy trading for prosumers are achieved through a novel modification of a conventional model predictive control (MPC). The proposed control strategy guarantees an optimal global solution for the applied control action. A new cost function is introduced to model the effects of volatility on customer benefits more effectively. Specifically, the newly presented cost function models a probabilistic relation between the power exchanged with the grid, the net load, and the electricity market. The probabilistic calculation of the cost function shows the dependence on the mathematical expectation of market price and net load. Computational techniques for calculating this value are presented. The proposed strategy differs from the stochastic and robust MPC in that the cost is calculated across the market price and net load variations rather than across model constraints and parameter variations.

Abstract:With the increasing penetration of local renewable energy and flexible demand, the system demand is more unpredictable and causes network overloading, resulting in costly system investment. Although the energy storage (ES) helps reduce the system peak power flow, the incentive for ES operation is not sufficient to reflect its value on the system investment deferral resulting from its operation. This paper designs a dynamic pricing signal for ES based on the truncated strategy under robust operation corresponding to the network charge reduction. Firstly, the operation strategy is designed for ES to reduce the total network investment cost considering the uncertainties of flexible load and renewable energy. These nodal uncertainties are converted into branch power flow uncertainties by the cumulant and Gram-Charlier expansion strategy. Then, a time of use (ToU) pricing scheme is designed to guide the ES operation reflecting its impact on network investment based on the long-run investment cost (LRIC) pricing scheme. The proposed ToU LRIC method allocates the investment costs averagely to network users over the potential curtailment periods, which connects the ES operation with network investment. The curtailment amount and the distribution of power flow are assessed by the truncated strategy considering the impact of uncertainties. As demonstrated in a Grid Supply Point (GSP) distribution network in the UK, the network charges at the peak time reduce more than 20% with ES operation. The proposed method is cost-reflective and ensures the fairness and efficiency of the pricing signal for ES.

Abstract:Hydrogen is being considered as an important option to contribute to energy system decarbonization. However, currently its production from renewables is expensive compared with the methods that utilize fossil fuels. This paper proposes a comprehensive optimization-based techno-economic assessment of a hybrid renewable electricity-hydrogen virtual power plant (VPP) that boosts its business case by co-optimizing across multiple markets and contractual services to maximize its profits and eventually deliver hydrogen at a lower net cost. Additionally, multiple possible investment options are considered. Case studies of VPP placement in a renewable-rich, congested area of the Australian network and based on real market data and relevant sensitivities show that multi-market participation can significantly boost the business case for cleaner hydrogen. This highlights the importance of value stacking for driving down the cost of cleaner hydrogen. Due to the participation in multiple markets, all VPP configurations considered are found to be economically viable for a hydrogen price of 3 AUD$/kg (2.25 USD$/kg), which has been identified as a threshold value for Australia to export hydrogen at a competitive price. Additionally, if the high price volatility that has been seen in gas prices in 2022 (and by extension electricity prices) continues, the flexibility of hybrid VPPs will further improve their business cases.

Abstract:Increasing intermittent renewable energy sources (RESs) intensifies the imbalance between demand and generation, entailing the diversification of the deployment of electrical energy storage systems (ESSs). A large-scale biogas plant (LBP) installed with heating devices and biogas energy storage (BES) usually exhibits a storage-like characteristic of accommodating an increasing penetration level of RES in rural areas, which is addressed in this paper. By utilizing the temperature-sensitive characteristic of anaerobic digestion that enables the LBP to exhibit a storage-like characteristic, this paper proposes a bi-level energy trading model incorporating LBP and demand response aggregator (DRA) simultaneously. In this model, social welfare is maximized at the upper level while the profit of DRA is maximized at the lower level. Compared with cases only with DRA, the results show that the proposed model with the LBP improves the on-site accommodation capacity of photovoltaic (PV) generation up to 6.3%, 18.1%, and 18.9% at 30%, 40%, and 50% PV penetration levels, respectively, with a better economic performance. This nonlinear bi-level problem is finally recast by a single-level mathematical program with equilibrium constraints (MPEC) using Karush-Kuhn-Tucker (KKT) conditions and solved by the Cplex solver. The effectiveness of the proposed model is validated using a 33-bus test system and a sensitivity analysis is provided for analyzing what parameter influences the accommodation capacity most.

Abstract:The installed capacity of renewable energy generation has continued to grow rapidly in recent years along with the global energy transition towards a 100% renewable-based power system. At the same time, the grid-connected large-scale renewable energy brings significant challenges to the safe and stable operation of the power system due to the loss of synchronous machines. Therefore, self-synchronous wind turbines have attracted wide attention from both academia and industry. However, the understanding of the physical operation mechanisms of self-synchronous wind turbines is not clear. In particular, the transient characteristics and dynamic processes of wind turbines are fuzzy in the presence of grid disturbances. Furthermore, it is difficult to design an adaptive fault ride-through (FRT) control strategy. Thus, a dual-mode switching FRT control strategy for self-synchronous wind turbines is developed. Two FRT control strategies are used. In one strategy, the amplitude and phase of the internal potential are directly calculated according to the voltage drop when a minor grid fault occurs. The other dual-mode switching control strategy in the presence of a deep grid fault includes three parts: vector control during the grid fault, fault recovery vector control, and self-synchronous control. The proposed control strategy can significantly mitigate transient overvoltage, overcurrent, and multifrequency oscillation, thereby resulting in enhanced transient stability. Finally, simulation results are provided to validate the proposed control strategy.

Abstract:In this paper, a wind energy conversion system (WECS) is presented for the electrification of rural areas with wind energy availability. A three-phase AC-DC converter based on a bridgeless Cuk converter is used for power extraction from the permanent magnet synchronous generator (PMSG). The bridgeless topology enables the elimination of the front-end diode bridge rectifier (DBR). Moreover, the converter has fewer components, simple control, and high efficiency, making it suitable for a small-scale WECS. A squirrel cage induction motor (SCIM) is used to emulate a MOD-2 wind turbine to implement the PMSG-based WECS. A direct-drive eight-pole PMSG is used in this study; thus, a low-input-voltage system is designed. The converter is designed to operate in the discontinuous inductor current mode (DICM) for inherent power factor correction (PFC) and the maximum power point tracking (MPPT) is achieved through the tip-speed ratio (TSR) following. The performance of the developed system is analyzed through simulation, and a 500 W hardware prototype is developed and tested in different wind speed conditions.

Abstract:This paper proposes a fault ride-through hybrid controller (FRTHC) for modular multi-level converter based high-voltage direct current (MMC-HVDC) transmission systems. The FRTHC comprises four loops of cascading switching control units (SCUs). Each SCU switches between a bang-bang funnel controller (BBFC) and proportional-integral (PI) control loop according to a state-dependent switching law. The BBFC can utilize the full control capability of each control loop using three-value control signals with the maximum available magnitude. A state-dependent switching law is designed for each SCU to guarantee its structural stability. Simulation studies are conducted to verify the superior fault ride-through capability of the MMC-HVDC transmission system controlled by FRTHC, in comparison to that controlled by a vector controller (VC) and a VC with DC voltage droop control (VDRC).

Abstract:Non-isolated DC/DC converter based on modular multilevel converter （MMC） technology is expected to play an important role in future DC transmission grids. This paper presents a phasor analytical model for this new family of converters which is suitable for a range of studies like DC grid power flow or DC/DC parametric design. The 30^th-order phasor model is derived in 3 coordinate frames: zero sequence (DC), fundamental frequency (dq), and double frequency (d2q2). The second-harmonic current suppression control is included as an option. Additionally, an estimation of the required control signals is presented, and a closed-loop model is developed which facilitates direct calculation of all variables and fast parametric studies. The accuracy of the proposed models is verified against a detailed PSCAD model for a wide range of parameters. The studies illustrate the importance of the second-harmonic components on the model accuracy. Finally, the impact of the converter parameters on the performance is studied, and a basic eigenvalue stability analysis is given.

Abstract:The AC electric arc furnace (EAF) is becoming a core apparatus of the modern steel industry. Nevertheless, it used to be a major threat of power quality in the traditional power supply system. In this paper, a flexible power supply system of the AC EAF is proposed, which is expected to completely alter its inherent cognition of impact load in the power grid. The basics of the power supply for EAF are first reviewed and the novel techniques to enhance the operation flexibility of EAF are introduced. The power circuit and the control structure are then presented, followed by the detailed strategies of various operations fully considering the features of EAF. A large disturbance stability criterion based on the mixed potential theory is also established for the practical application. Both electromagnetic transient simulations using PSCAD and benefit analyses verify the feasibility of the proposed system.

Abstract:This paper presents a parameter estimation technique for the hot-spot thermal model of power transformers. The proposed technique is based on the unscented formulation of the Kalman filter, jointly considering the state variables and parameters of the dynamic thermal model. A two-stage estimation technique that takes advantage of different loading conditions is developed, in order to increase the number of parameters which can be identified. Simulation results are presented, which show that the observable parameters are estimated with an error of less than 3%. The parameter estimation procedure is mainly intended for factory testing, allowing the manufacturer to enhance the thermal model of power transformers and, therefore, its customers to increase the lifetime of these assets. The proposed technique could be additionally considered in field applications if the necessary temperature measurements are available.

Abstract:Distributed generation units (DGUs) bring some problems to the existing protection system, such as those associated with protection blinding and sympathetic tripping. It is known that fault current limiters (FCLs) help minimize the negative impact of DGUs on the protection system. In this paper, a control-based FCL is proposed, i.e., the FCL is integrated into the DGU control law. To this end, a predictive control strategy with fault current limitation is suggested. In this way, a DGU is controlled, not only for power exchange with the power grid but also to limit its fault current contribution. The proposal is posed as a constrained optimization problem allowing taking into account the current limit explicitly in the design process as a closed-loop solution. A linear approximation is proposed to cope with the inherent nonlinear constraints. The proposal does not require incorporating extra equipment or mechanisms in the control loop, making the design process simple. To evaluate the proposed control-based FCL, both protection blinding and sympathetic tripping scenarios are considered. The control confines the DGU currents within the constraints quickly, avoiding large transient peaks. Therefore, the impact on the protection system is reduced without the necessity that the DGU goes out of service.

Abstract:This paper proposes a branch-independence-based reliability assessment approach for transmission systems. The approach consists of branch decoupling and state-space partition techniques. By integrating an impact-increment-based reliability index calculation model and the proposed branch decoupling technique, a proportion of sampled contingency states no longer need to be analyzed using the time-consuming optimal power flow (OPF) algorithm. In this way, the technique speeds up the calculation of reliability indices. Since first-order contingency states have a high probability of being sampled, we propose a state-space partition technique to replace first-order contingency state simulation with first-order contingency state enumeration. Consequently, the calculation of reliability indices is further accelerated by avoiding a large amount of repetitive OPF analyses during simulation process without affecting reliability index accuracy. The validity and applicability of our approach are verified using the IEEE 118-bus and IEEE 145-bus systems. Numerical results indicate that the proposed approach can improve computational efficiency without decreasing accuracy.

Abstract:With various components and complex topologies, the applications of high-voltage direct current (HVDC) links bring new challenges to the interconnected power systems in the aspect of frequency security, which further influence their reliability performances. Consequently, this paper presents an approach to evaluate the impacts of the HVDC link outage on the reliability of interconnected power system considering the frequency regulation process during system contingencies. Firstly, a multi-state model of an HVDC link with different available loading rates (ALRs) is established based on its reliability network. Then, dynamic frequency response models of the interconnected power system are presented and integrated with a novel frequency regulation scheme enabled by the HVDC link. The proposed scheme exploits the temporary overload capability of normal converters to compensate for the imbalanced power during system contingencies. Moreover, it offers frequency support that enables the frequency regulation reserves of the sending-end and receiving-end power systems to be mutually available. Several indices are established to measure the system reliability based on the given models in terms of abnormal frequency duration, frequency deviation, and energy losses of the frequency regulation process during system contingencies. Finally, a modified two-area reliability test system (RTS) with an HVDC link is adopted to verify the proposed approach.

Abstract:The National Electricity Market (NEM) in Australia was suspended during June 15-23, 2022, with a primary attribution to the lack of available generation capacity. This incident is noteworthy because it was the first market suspension in NEM’s history and took place in a major energy exporting country. In this letter, we review the outline and impacts of the incident. From the perspectives of market regulation, electricity supply, and electricity demand, we identify three underlying causes of the market suspension and offer four recommendations for the market mechanism evolution to ensure power supply security.

Abstract:When applying the widely-used Newton-Raphson (N-R) method to the integrated solution of the hybrid power-water flow problem of the power-water integrated energy system (PW-IES), it is found that there are numerical differences among Jacobian elements, which may yield an ill-conditioned Jacobian matrix and even cause convergence problem. Therefore, this letter proposes to per-unitize the water distribution network (WDN) to reduce the numerical differences and thus to improve the Jacobian matrix conditioning and the integrated N-R solution to the hybrid power-water flow problem.

Abstract:Determining security/stability boundaries is a common and critical means of preventing cascading failures induced by voltage-related issues, which represents one of the major challenges in bulk power systems. However, traditional approaches suffer from conservative issues and heavy computational burdens. To address these challenges, the concept of an autonomous-synergic voltage security region (AS-VSR) and the corresponding dynamic constraint coefficient pruning (DCCP) computation method, which fully consider the volt/var characteristics of bulk power systems, are proposed in this letter. Both linearized and nonlinearized robust optimization problems are introduced to obtain accurate results. The computational accuracy, time cost, and advantages of autonomous-synergic control are observed in the simulation results.