Abstract:Focused on life, consumption, and leisure, communities have been regarded as the basic unit of energy use in a city owing to rapid urbanization, whose energy use density continues to increase. Moreover, community integrated energy systems (CIESs) in the rapid development stage have become embedded, small, and self-sufficient energy ecosystems within cities because of their environmental and economic benefits. CIESs face a competitive energy trading environment that comprises numerous entities and complicated relationships. This paper presents an extensive review of various issues related to CIES trading. First, the concepts, types, and resources of CIESs are described. Second, the trading patterns and strategies of CIESs are reviewed from the four perspectives of the trading objects: community-to-peer (C2P), peer-to-peer (P2P), community-to-community (C2C), and community-to-grid (C2G). Third, a tri-layer trading framework and the features of CIESs that participate in combined multienergy markets are proposed. Last, the key issues in CIES trading are summarized.

Abstract:The power system with high penetration of wind power is gradually formed, and it would be difficult to determine the optimal economic dispatch (ED) solution in such an environment with significant uncertainties. This paper proposes a multi-objective ED (MuOED) model, in which the expected generation cost (EGC), upside potential (USP), and downside risk (DSR) are simultaneously considered. The heterogeneous indices of upside potential and downside risk mean the potential economic gains and losses brought by high penetration of wind power, respectively. Then, the MuOED model is formulated as a tri-objective optimization problem, which is related to uncertain multi-criteria decision-making against uncertainties. Afterwards, the tri-objective optimization problem is solved by an extreme learning machine (ELM) assisted group search optimizer with multiple producers (GSOMP). Pareto solutions are obtained to reflect the trade-off among the expected generation cost, the upside potential, and the downside risk. And a fuzzy decision-making method is used to choose the final ED solution. Case studies based on the Midwestern US power system verify the effectiveness of the proposed MuOED model and the developed optimization algorithm.

Abstract:Economic dispatch problem (EDP) is a fundamental optimization problem in power system operation, which aims at minimizing the total generation cost. In fact, the power grid is becoming a cyber-physical power system (CPPS). Therefore, the quality of communication is a key point. In this paper, considering two important factors, i.e., time delays and channel noises, a fully distributed consensus based algorithm is proposed for solving EDP. The critical maximum allowable upper bounds of heterogeneous communication delays and self-delays are obtained. It should be pointed out that the proposed algorithm can be robust against the time-varying delays and channel noises considering generator constraints. In addition, even with time-varying delays and channel noises, the power balance of supply and demand is not broken during the optimization. Several simulation studies are presented to validate the correctness and superiority of the developed results.

Abstract:In northern China, thermal power units (TPUs) are important in improving the penetration level of renewable energy. In such areas, the potentials of coordinated dispatch of renewable energy sources (RESs) and TPUs can be better realized, if RESs and TPUs connected to the power grid at the same point of common coupling (PCC) are dispatched as a coupled system. Firstly, the definition of the coupled system is introduced, followed by an analysis on its characteristics. Secondly, based on the operation characteristics of deep peak regulation (DPR) of TPUs in the coupled system, the constraint of the ladder-type ramping rate applicable for day-ahead dispatch is proposed, and the corresponding flexible spinning reserve constraint is further established. Then, considering these constraints and peak regulation ancillary services, a day-ahead optimal dispatch model of the coupled system is established. Finally, the operational characteristics and advantages of the coupled system are analyzed in several case studies based on a real-world power grid in Liaoning province, China. The numerical results show that the coupled system can further improve the economic benefits of RESs and TPUs under the existing policies.

Abstract:With the growing interdependence between the electricity system and the natural gas system, the operation uncertainties in either subsystem, such as wind fluctuations or component failures, could have a magnified impact on the reliability of the whole system due to energy interactions. A joint reserve scheduling model considering the cross-sectorial impacts of operation uncertainties is essential but still insufficient to guarantee the reliable operation of the integrated electricity and natural gas system (IEGS). Therefore, this paper proposes a day-ahead security-constrained unit commitment (SCUC) model for the IEGS to schedule the operation and reserve simultaneously considering reliability requirements. Firstly, the multi-state models for generating units and gas wells are established. Based on the multi-state models, the expected unserved energy cost (EUEC) and the expected wind curtailment cost (EWC) criteria are proposed based on probabilistic methods considering wind fluctuation and random failures of components in IEGS. Furthermore, the EUEC and EWC criteria are incorporated into the day-ahead SCUC model, which is nonconvex and mathematically reformulated into a solvable mixed-integer second-order cone programming (MISOCP) problem. The proposed model is validated using an IEEE 30-bus system and Belgium 20-node natural gas system. Numerical results demonstrate that the proposed model can effectively schedule the energy reserve to guarantee the reliable operation of the IEGS considering the multiple uncertainties in different subsystems and the cross-sectorial failure propagation.

Abstract:Laboratory testing of phasor measurement units (PMUs) guarantees their performance under laboratory conditions. However, many factors may cause PMU measurement problems in actual power systems, resulting in the malfunction of PMU-based applications. Therefore, field PMUs need to be tested and calibrated to ensure their performance and data quality. In this paper (Part I), a general framework for the field PMU test and calibration in different scenarios is proposed. This framework consists of a PMU calibrator and an analysis center, where the PMU calibrator provides the reference values for PMU error analysis. Two steps are implemented to ensure the calibrator accuracy for complex field signals: ① by analyzing the frequency-domain probability distribution of random noise, a Fourier-transform-based signal denoising method is proposed to improve the anti-interference capability of the PMU calibrator; and ② a general synchrophasor estimation method based on complex bandpass filters is presented for accurate synchrophasor estimations in multiple scenarios. Simulation and experimental test results demonstrate that the PMU calibrator has a higher accuracy than that of other calibrator algorithms and is suitable for field PMU test. The analysis center for evaluating the performance of field PMUs and the applications of the proposed field PMU test system are provided in detail in Part II of the next-step research.

Abstract:Doubly-fed induction generator (DFIG)-based wind farms (WFs) are interfaced with power electronic converters. Such interfaces are attributed to the low inertia generated in the WFs under high penetration and that becomes prevalent in a fault scenario. Therefore, transient stability enhancement along with frequency stability in DFIG-based WFs is a major concern in the present scenario. In this paper, a cooperative approach consisting of virtual inertia control (VIC) and a modified grid-side converter (GSC) approach for low voltage ride-through (LVRT) is proposed to achieve fault ride-through (FRT) capabilities as per the grid code requirements (GCRs) while providing frequency support to the grid through a synthetic inertia. The proposed approach provides LVRT and reactive power compensation in the system. The participation of the VIC in a rotor-side converter (RSC) provides frequency support to the DFIG-based WFs. The combined approach supports active power compensation and provides sufficient kinetic energy support to the system in a contingency scenario. Simulation studies are carried out in MATLAB/Simulink environment for symmetrical and unsymmetrical faults. The superiority of the proposed scheme is demonstrated through analysis of the performance of the scheme and that of a series resonance bridge-type fault current limiter (SR-BFCL).

Abstract:When a doubly-fed induction generator (DFIG) is connected to a weak grid, the coupling between the grid and the DFIG itself will increase, which will cause stability problems. It is difficult to maintain the tracking accuracy and robustness of the phase-locked loop (PLL) in the weak grid, and the risk of instability of the current-controlled DFIG (CC-DFIG) system will increase. In this paper, a new type of voltage-controlled DFIG (VC-DFIG) mode is adopted, which is a grid-forming structure that can independently support the voltage and frequency with a certain adaptability in the weak grid. A small-signal impedance model of the VC-DFIG system is also established. The impedance of DFIG inevitably generates coupling with the grid impedance in the weak grid, especially in parallel compensation grids, and results in resonance. On the basis of the VC-DFIG, impedance stability analysis is performed to study the influences of the control structure and short-circuit ratio. Then, a feedforward damping method is proposed to modify the impedance of the VC-DFIG system at resonance frequencies. The proposed fractional order damping is utilized, which can enhance the robustness and rapidity of resonance suppression under parameter fluctuations. Finally, the experimental results are presented to validate the effectiveness of the proposed control strategy.

Abstract:This paper proposes a hybrid control strategy of air-conditioning loads (ACLs) for participating in peak load reduction. The hybrid control strategy combines the temperature setpoint adjustment (TSA) control and on/off control together to make full use of response potentials of ACLs. The primary free transport model of ACLs has been established in literature at or near a fixed temperature setpoint. In this paper, a wide-range transport (WRT) model suitable for larger value of TSA is proposed. The WRT model can be constructed easily through the parameter of devices and indoor and outdoor temperature. To modulate the aggregate response characteristics of ACLs more friendly to the power grid, the safe protocol (SP) is adopted and integrated into the WRT model, which achieves a good unification of oscillation suppression and efficient modeling. Moreover, the hybrid control strategy is implemented based on the WRT model, and the model predictive control (MPC) controller is designed considering the tracking error and control switch cost. At last, the superiority of the hybrid control strategy is verified and the performance of ACLs for peak load reduction under this controller is simulated. The simulation results show that the hybrid control strategy could exploit the load reduction potential of ACLs fully than the TSA mode and track the reference signal more accurately.

Abstract:The incorporation of weather variables is crucial in developing an effective demand forecasting model because electricity demand is strongly influenced by weather conditions. The dependence of demand on weather conditions may change with time during a day. Therefore, the time stamped weather information is essential. In this paper, a multi-layer moving window approach is proposed to incorporate the significant weather variables, which are selected using Pearson and Spearman correlation techniques. The multi-layer moving window approach allows the layers to adjust their size to accommodate the weather variables based on their significance, which creates more flexibility and adaptability thereby improving the overall performance of the proposed approach. Furthermore, a recursive model is developed to forecast the demand in multi-step ahead. An electricity demand data for the state of New South Wales, Australia are acquired from the Australian Energy Market Operator and the associated results are reported in the paper. The results show that the proposed approach with dynamic incorporation of weather variables is promising for day-ahead and week-ahead load demand forecasting.

Abstract:Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems. This paper proposes a deep generative network based method to model time-series curves, e.g., power generation curves and load curves, of renewable energy sources and loads based on implicit maximum likelihood estimations (IMLEs), which can generate realistic scenarios with similar patterns as real ones. After training the model, any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of IMLEs. The proposed approach does not require any model assumptions or prior knowledge of the form in the likelihood function being made during the training process, which leads to stronger applicability than explicit density model based methods. The extensive experiments show that the IMLEs accurately capture the complex shapes, frequency-domain characteristics, probability distributions, and correlations of renewable energy sources and loads. Moreover, the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.

Abstract:Load frequency control (LFC) system may be destroyed by false data injection attacks (FDIAs) and consequently the security of the power system will be impacted. High-efficiency FDIA detection can reduce the damage and power loss to the power system. This paper defines various typical and hybrid FDIAs, and the influence of several FDIAs with different characteristics on the multi-area LFC system is analyzed. To detect various attacks, we introduce an improved data-driven method, which consists of fuzzy logic and neural networks. Fuzzy logic has the features of high applicability, robustness, and agility, which can make full use of samples. Further, we construct the LFC system on MATLAB/Simulink platform, and systematically simulate the experiments that FDIAs affect the LFC system by tampering with measurement data. Among them, considering the large-scale penetration of renewable energy with intermittency and volatility, we generate three simulation scenarios with or without renewable energy generation. Then, the performance for detecting FDIAs of the improved method is verified by simulation data samples.

Abstract:False data injection attack (FDIA) is a typical cyber-attack aiming at falsifying measurement data for state estimation (SE), which may incur catastrophic consequences on cyber-physical system operation. In this paper, we develop a deep learning based methodology for detection, localization, and data recovery of FDIA on power systems in a coherent and holistic manner. However, the multi-modal probability distributions of both measurements and state variables in SE due to ever-changing operating points and structural/topological changes pose great challenges in detecting and localizing FDIA. To address this challenge, we first propose an enhanced attack model to launch massive FDIA on limited access points. Second, we train an auto-encoder (AE) with a Bayesian change verification (BCV) classifier using N-1 contingencies to detect FDIA with unseen N-k operational topologies. Third, to avoid model collapse caused by multi-modal measurement distribution, an AE-based generative adversarial network (GAN) is derived to generate a diverse candidate set of normal measurement vectors with various operational topologies. Finally, we develop a pattern match algorithm to localize and recover the falsified measurements and state variables by comparing the falsified measurement vectors with the normal measurement vectors in the candidate set. Case studies with IEEE benchmark systems and a modified 415-bus China Southern Grid system are provided to validate the proposed methodology. It shows that the proposed methodology achieves an average 95% accuracy for detection, over 80% accuracy for localization of FDIA, and recovers the measurement and state variables close to their true values.

Abstract:With the increasing penetration of photovoltaics in distribution networks, the adaptability of distribution network under uncertainties needs to be considered in the planning of distribution systems. In this paper, the interval arithmetic and affine arithmetic are applied to deal with uncertainties, and an affine arithmetic based bi-level multi-objective joint planning model is built, which can obtain the planning schemes with low constraint-violation risk, high reliability and strong adaptability. On this basis, a bi-level multi-objective solution methodology using affine arithmetic based non-dominated sorting genetic algorithm II is proposed, and the planning schemes that simultaneously meet economy and adaptability goals under uncertainties can be obtained. To further eliminate bad solutions and improve the solution qualities, an affine arithmetic based dominance relation weakening criterion and a deviation distance based modification method are proposed. A 24-bus test system and a 10 kV distribution system of China are used for case studies. Different uncertainty levels are compared, and a sensitivity analysis of key parameters is conducted to explore their impacts on the final planning schemes. The simulation results verify the advantages of the proposed affine arithmetic based planning method.

Abstract:This paper proposes a voltage stability constrained optimal power flow (VSC-OPF) for an unbalanced distribution system with distributed generators (DGs) based on semidefinite programming (SDP). The AC optimal power flow (ACOPF) for unbalanced distribution systems is formulated as a chordal relaxation-based SDP model. The minimal singular value (MSV) of the power flow Jacobian matrix is adopted to indicate the voltage stability margin. The Jacobian matrix can be explicitly expressed by ACOPF state variables. The nonlinear constraint on the Jacobian MSV is then replaced with its maximal convex subset using linear matrix inequality (LMI), which can be incorporated in the SDP-based ACOPF formulation. A penalty technique is leveraged to improve the exactness of the SDP relaxation. Case studies performed on several IEEE test systems validate the effectiveness of the proposed method.

Abstract:The accuracy of distribution system state estimation (DDSE) is reduced when phasor measurement unit (PMU) measurements contain outliers because of cyber attacks or global positioning system spoofing attacks. Therefore, to enhance the robustness of DDSE to measurement outliers, approximate the target distribution of Metropolis-Hastings (MH) sampling, and judge the prediction of the long short-term memory (LSTM) network, this paper proposes an outlier reconstruction based state estimation method using the equivalent model of the LSTM network and MH sampling (E-LM model), motivated by the characteristics of the chronological correlations of PMU measurements. First, the target distribution of outlier reconstruction is derived using a kernel density estimation function. Subsequently, the reasons and advantages of the E-LM model are explained and analyzed from a mathematical point of view. The proposed LSTM-based MH sampling can approximate the target distribution of MH sampling to decrease the number of the futile iterations. Moreover, the proposed MH-based forecasting of the LSTM can judge each LSTM prediction, which is independent of its true value. Finally, simulations are conducted to evaluate the performance of the E-LM model by integrating the LSTM network and the MH sampling into the outlier reconstruction based DDSE.

Abstract:This paper investigates network partition and edge server placement problem to exploit the benefit of edge computing for distributed state estimation. A constrained many-objective optimization problem is formulated to minimize the cost of edge server deployment, operation, and maintenance, avoid the difference in the partition sizes, reduce the level of coupling between connected partitions, and maximize the inner cohesion of each partition. Capacities of edge server are constrained against underload and overload. To efficiently solve the problem, an improved non-dominated sorting genetic algorithm III (NSGA-III) is developed, with a specifically designed directed mutation operator based on topological characteristics of the partitions to accelerate convergence. Case study validates that the proposed formulations effectively characterize the practical concerns and reveal their trade-offs, and the improved algorithm outperforms existing representative ones for large-scale networks in converging to a near-optimal solution. The optimized result contributes significantly to real-time distributed state estimation.

Abstract:Smart networks such as active distribution network (ADN) and microgrid (MG) play an important role in power system operation. The design and implementation of appropriate protection systems for MG and ADN must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small graphic processing unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE 13-node test feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

Abstract:The aging of lines has a strong impact on the economy and safety of the distribution network. This paper proposes a novel approach to conduct line aging assessment in the distribution network based on topology verification and parameter estimation. In topology verification, the set of alternative topologies is firstly generated based on the switching lines. The best-matched topology is determined by comparing the difference between the actual measurement data and calculated voltage magnitude curves among the alternative topologies. Then, a novel parameter estimation approach is proposed to estimate the actual line parameters based on the measured active power, reactive power, and voltage magnitude data. It includes two stages, i.e., the fixed-step aging parameter (FSAP) iteration, and specialized Newton-Raphson (SNR) iteration. The theoretical line parameters of the best-matched topology are taken as a warm start of FSAP, and the fitted result of FSAP is further renewed by the SNR. Based on the deviation between the renewed and theoretical line parameters, the aging severity risk level of each line is finally quantified through the risk assessment technology. Numerous experiments on the modified IEEE 33-bus and 123-bus systems demonstrate that the proposed approach can effectively conduct line aging assessment in the distribution network.

Abstract:With the wide integration of various distributed communication and control techniques, the cyber-physical microgrids face critical challenges raised by the emerging cyberattacks. This paper proposes a three-stage defensive framework for distributed microgrids against denial of service (DoS) and false data injection (FDI) attacks, including resilient control， communication network reconfiguration, and switching of local control. The resilient control in the first stage is capable of tackling simultaneous DoS and FDI attacks when the connectivity of communication network could be maintained under cyberattacks. The communication network reconfiguration method in the second stage and the subsequent switching of local control in the third stage based on the software-defined network (SDN) layer aim to cope with the network partitions caused by cyberattacks. The proposed defensive framework could effectively mitigate the impacts of a wide range of simultaneous DoS and FDI attacks in microgrids without requiring the specific assumptions of attacks and prompt detections, which would not incorporate additional cyberattack risks. Extensive case studies using a 13-bus microgrid system are conducted to validate the effectiveness of the proposed three-stage defensive framework against the simultaneous DoS and FDI attacks.

Abstract:The renewable microgrid (RMG) is a critical way to organize and utilize new energy. Its control and protection strategies during the fault are the core technologies to ensure the safe operation and stability of the system. The traditional protection principles are unsuitable for RMGs due to the flexibility of RMG operation, the complexity of RMG topology, and the variety of fault control strategies of inverter-interfaced distributed generators (IIDGs). The traditional fault component protection principle is affected by the low voltage ride-through (LVRT) control strategy and will fail in some scenarios. In order to make the fault component protection principle available in every scenario, a current-based fault control strategy is proposed. Specific fault characteristics are generated by the grid-feeding IIDGs during the fault so they can be equivalent to the open circuits, and the fault models in additional network can be simplified. By analyzing the fault characteristics, an RMG protection strategy based on the current-based fault control of IIDGs is presented. The fault directions of feeders can be distinguished and the fault feeder can be located accurately in both grid-connected and islanded RMGs. Then, the grid-feeding IIDGs can transit to LVRT mode smoothly. Thus, IIDGs are considered comprehensively in terms of coordinating with fault control and fault characteristic generation. Finally, the experimental results of the hardware platform prove the effectiveness of the proposed current-based fault control strategy, and the simulation results based on PSCAD/EMTDC verify the correctness of the protection strategy.

Abstract:Power sharing can improve the benefit of the multi-microgrid (MMG) system. However, the information disclosure may appear during the sharing process, which would bring privacy risk to a local microgrid. Actually, the risk and coordination cost are different in different sharing modes. Therefore, this paper develops a decision-making method to decide the most suitable one of three mostly used sharing modes (i.e., cooperative game with complete information, cooperative game with incomplete information, and noncooperative game). Firstly, power sharing paradigms and coordination mechanisms in the three modes are formulated in detail. Particularly, different economic operation models of MMG system are included to analyze the economic benefit from different sharing modes. Based on the different disclosed information, the risk cost is evaluated by using the simplified fuzzy analytic hierarchy process (FAHP). And the coordination cost for different sharing modes is expressed in different functions. In addition, a hierarchical evaluation system including three decision-making factors (e.g., economics, risk, and coordination) is set up. Meanwhile, a combination weighting method (e.g., the simplified FAHP combined with the anti-entropy weight method) is applied to obtain the weight of each factor for comprehensive evaluation. Finally, the optimal sharing solution of MMG system is decided by comparing and analyzing the difference among the three sharing modes. Numerical results validate that the proposed method can provide a reference to deciding a suitable sharing mode.

Abstract:Voltage source converters have become the main enabler for the integration of distributed energy resources in microgrids. In the case of islanded operation, these devices normally set the amplitude and frequency of the network voltage by means of a cascade controller composed of an outer voltage control loop and an inner current control loop. Several strategies to compute the gains of both control loops have been proposed in the literature in order to obtain a fast and decoupled response of the voltages at the point of common coupling. This paper proposes an alternative and simple methodology based on the introduction of a virtual conductance in the classic cascade control. This strategy allows to design each control loop independently, obtaining a closed-loop response of a first-order system. In this way, the gains of each control loop are easily derived from the parameters of the LC coupling filter and the desired closed-loop time constants. Furthermore, a state observer is included in the controller to estimate the inductor current of the LC filter in order to reduce the number of required measurements. A laboratory testbed is used to validate and compare the proposed controller. The experimental results demonstrate the effectiveness of the proposal both in steady-state and transient regimes.

Abstract:An energy storage station (ESS) usually includes multiple battery systems under parallel operation. In each battery system, a power conversion system (PCS) is used to connect the power system with the battery pack. When allocating the ESS power to multi-parallel PCSs in situations with fluctuating operation, the existing power control methods for parallel PCSs have difficulty in achieving the optimal efficiency during a long-term time period. In addition, existing Q-learning algorithms for adaptive power allocation suffer from the curse of dimensionality. To overcome these challenges, an adaptive power control method based on the double-layer Q-learning algorithm for n parallel PCSs of the ESS is proposed in this paper. First, a selection method for the power allocation coefficient is developed to avoid repeated actions. Then, the outer action space is divided into n + 1 power allocation modes according to the power allocation characteristics of the optimal operation efficiency. The inner layer uses an actor neural network to determine the optimal action strategy of power allocations in the non-steady state. Compared with existing power control methods, the proposed method achieves better performance for both static and dynamic operation efficiency optimization. The proposed method optimizes the overall operation efficiency of PCSs effectively under the fluctuating power outputs of the ESS.

Abstract:The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system. Mobile energy storage systems (MESSs) have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ability. However, when urban traffic flows rise, the congested traffic environment will prolong the transit time of MESS, which will ultimately affect the operation state of the power networks and the economic benefits of MESS. This paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks, considering road congestion and the operation constraints of the power networks. The upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator (DNO) in order to maximize the total revenue of the system. With fuzzy time windows and fuzzy road congestion indexes, the lower-level model optimizes the route for the transit problem of MESS. Therefore, road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation scheme. Both the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed model. The simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road congestion. Besides, the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.

Abstract:Battery energy storage (BES) systems can effectively meet the diversified needs of power system dispatching and assist in renewable energy integration. The reliability of energy storage is essential to ensure the operational safety of the power grid. However, BES systems are composed of battery cells. This suggests that BES performance depends not only on the configuration but also on the operating state over different lifetime durations. The lack of safety and reliability is the main bottleneck preventing widespread applications of BES systems. Therefore, a reliability assessment algorithm and a weak-link analytical method for BES systems are proposed while considering battery lifetime degradation. Firstly, a novel lithium-ion battery model is proposed to identify the degradation rate of solid electrolyte interphase film formation and capacity plummeting. The impacts of different operating conditions are considered in stress factor models. Then, a reliability assessment algorithm for a BES system is introduced based on a universal generating function. An innovative weak-link analytical method based on the reliability importance index is proposed that combines the evaluation results of state-oriented and state-change-oriented indexes through an entropy weight method. The model, algorithm, indexes, and the usefulness are demonstrated in case studies based on aging test data and actual bus operating data. The results demonstrate the effects of the battery status and working conditions on BES reliability. Weak-link analysis is also used to assist BES systems in avoiding short-board batteries to achieve long lifetimes and efficient operation.

Abstract:The current increase in the number of electric vehicles in Germany requires an adequately developed charging infrastructure. Large numbers of public and semi-public charging stations are necessary to ensure sufficient coverage of charging options. In order to make the installation worthwhile for the mostly private operators as well as public ones, a sufficient utilization is decisive. This paper gives an overview of the differences in the utilization across the public charging infrastructure in Germany. To this end, a dataset on the utilization of 21164 public and semi-public charging stations in Germany is evaluated. The installation and operating costs of various charging stations are modeled and economically evaluated in combination with the utilization data. It is shown that in 2019-2020, the average utilization in Germany was rather low, albeit with striking regional differences. We consider future scenarios allowing the regional development forecasting of economic viability. It is demonstrated that a growth in electric mobility of 20%-30% per year leads to a large number of economically feasible charging parks in urban agglomeration areas.

Abstract:Line-commutated converter based high-voltage direct-current (LCC-HVDC) transmission systems are prone to subsequent commutation failure (SCF), which consequently leads to the forced blocking of HVDC links, affecting the operation of the power system. An accurate commutation failure (CF) identification is fairly vital to the prevention of SCF. However, the existing CF identification methods cause CF misjudge or detection lag, which can limit the effect of SCF mitigation strategy. In addition, earlier approaches to suppress SCF do not clarify the key factor that determines the evolution of extinction angle during system recovery and neglect the influence. Hence, this paper firstly analyzes the normal commutation process and CF feature based on the evolution topology of converter valve conduction in detail. Secondly, the energy in the leakage inductance of converter transformer is presented to characterize the commutation state of the valves. Then a CF identification method is proposed utilizing the leakage inductance energy. Thirdly, taking the key variable which is crucial to the tendency of extinction angle during the recovery process into account, a fault current limiting strategy for SCF mitigation is put forward. Compared with the original methods, the proposed methods have a better performance in CF identification and mitigation in terms of detection accuracy and mitigation effect. Finally, case study on PSCAD/EMTDC validates the proposed methods.

Abstract:Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism and handle limited types of power flow models. In this letter, we propose a deep active learning framework for solvability prediction in power systems. Compared with passive learning where the training is performed after all instances are labeled, active learning selects most informative instances to be labeled and therefore significantly reduces the size of the labeled dataset for training. In the active learning framework, the acquisition functions, which correspond to different sampling strategies, are defined in terms of the on-the-fly posterior probability from the classifier. First, the IEEE 39-bus system is employed to validate the proposed framework, where a two-dimensional case is illustrated to visualize the effectiveness of the sampling method followed by the high-dimensional numerical experiments. Then, the Northeast Power Coordinating Council (NPCC) 140-bus system is used to validate the performance on large-scale power systems.

Abstract:This letter extends the complex-variable perturbed Gauss-Newton method to estimate the state of unbalanced power systems by exploiting the Fortescue transformation. It proposes a novel and efficient graph-based way to deal with singularities due to zero-sequence network parts bounded with delta transformer windings and isolated from the ground. The estimator can handle both phasor and complex power measurements. Compared with the standard complex-variable unbalanced state estimator, it achieves better numerical stability and a speed-up of around three times using a sequential implementation and five times using parallel execution.

Abstract:The capability of shifting the electricity generation or consumption to proper time of the day, also defined as energy shift (ES), is the key factor to ensure the power balance， especially under high penetration of variable renewable energy (VRE). However, the ES is not characterized and traded as an independent product in current market mechanisms. In this letter, the marginal utility of an ES is assessed and leveraged to characterize the effective ES, while a novel market scheme is proposed considering the trading of both ES and energy level (EL). The proposed scheme can well integrate ES producers such as virtual power plants that cannot be rewarded sufficiently to actively participate in the current market because they are principally labeled as EL consumers. Finally, the novel concept and mechanism are illustrated by a numerical study and verified to outperform the existing price schemes on integrating the ES resources and VRE.

Abstract:The controllers of wind parks that are connected to weak grids can induce unstable oscillations near the fundamental frequency. Such phenomenon can be studied with equivalent impedances of the wind generators, which depend on their operational setpoints at the fundamental frequency. The feasibility of such setpoint, i.e., solution to the power flow, does not depend on the control parameters. However, oscillations from unfeasible setpoints and control instabilities both occur at weak grids and near the fundamental frequency. This letter presents an exploratory study to map the conditions where both phenomena occur, using sensitivity studies comprising multiple setpoints and control parameter tunings. The results are visually presented as the regions which inform the configurations leading to unfeasible setpoints, and unstable control interaction scenarios. Amongst the results, it can be observed that the trajectory of the eigenvalues in the Nyquist plots towards an unfeasible setpoint approaches a fundamental frequency instability.