Abstract:The deregulation of the power industry requires avoiding market power abuse to maintain the market competitiveness. To this end, a sequence of assessment measurements or mitigation mechanisms is required. Meanwhile, the increasing renewable energy resources (RESs) and flexible demand response resources (DRSs) are changing the behaviors of market participants and creating new cases of market power abuse. Such new circumstances bring the new evaluation and control methods of market power to the forefront. This paper provides a comprehensive review of market power in the reshaping of power systems due to the increasing RES and the development of DRS. The market power at the supply side, demand side, and in the multi-energy system is categorized and reviewed. In addition, the applications of market power supervision measures in the US, the Nordics, UK, and China are summarized. Furthermore, the unsolved issues, possible key technologies, and potential research topics on market power are discussed.

Abstract:Accurate transient stability assessment (TSA) and effective preventive control are important for the stable operation of power systems. With the superiorities in precision and efficiency, data-driven methods are widely used in TSA nowadays. Data-driven TSA model can be adopted in the stability constraints of preventive control optimization, but existing methods are mostly iteration-based ones, which may result in low efficiency, sometimes even non-convergence. In this paper, an analytical representation method of data-driven transient stability constraint is proposed based on a non-parametric regression model built for TSA. Key feature extraction and dominant sample selection are proposed to reduce the scale of the TSA model, and bi-level linearization is applied to further modify it. Optimal preventive control model is then formulated as a mixed-integer linear program (MILP) problem with the linearized analytical data-driven transient stability constraint, which can be solved without iterations. An overall procedure of data-driven TSA and preventive control is finally developed. Case studies show that the proposed method has high accuracy in TSA and can achieve effective preventive control of power system with high efficiency.

Abstract:Modern power systems are experiencing larger fluctuations and more uncertainties caused by increased penetration of renewable energy sources (RESs) and power electronics equipment. Therefore, fast and accurate corrective control actions in real time are needed to ensure the system security and economics. This paper presents a novel method to derive real-time alternating current (AC) optimal power flow (OPF) solutions considering the uncertainties including varying renewable energy and topology changes by using state-of-the-art deep reinforcement learning (DRL) algorithm, which can effectively assist grid operators in making rapid and effective real-time decisions. The presented DRL-based approach first adopts a supervised-learning method from deep learning to generate good initial weights for neural networks, and then the proximal policy optimization (PPO) algorithm is applied to train and test the artificial intelligence (AI) agents for stable and robust performance. An ancillary classifier is designed to identify the feasibility of the AC OPF problem. Case studies conducted on the Illinois 200-bus system with wind generation variation and N - 1 topology changes validate the effectiveness of the proposed method and demonstrate its great potential in promoting sustainable energy integration into the power system.

Abstract:The formulations and approximations of the branch flow model for general (radial and mesh) power networks (General-BranchFlow) are given in this paper. Using different sets of the power flow equations, six formats of the exact General-BranchFlow model are listed. The six formats are mathematically equivalent with each other. Linear approximation and second-order cone programming (SOCP) are then used to derive the six formats of the convex General-BranchFlow model. The branch ampacity constraints considering the shunt conductance and capacitance of the transmission line Π -model are derived. The key foundation of deriving the ampacity constraints is the correct interpretation of the physical meaning of the transmission line Π -model. An exact linear expression of the ampacity constraints of the power loss variable is derived. The applications of the General-BranchFlow model in deriving twelve formats of the exact optimal power flow (OPF) model and twelve formats of the approximate OPF model are formulated and analyzed. Using the Julia programming language, the extensive numerical investigations of all formats of the OPF models show the accuracy and computational efficiency of the General-BranchFlow model. A penalty function based approximation gap reduction method is finally proposed and numerically validated to improve the AC-feasibility of the approximate General-BranchFlow model.

Abstract:With the increase in the penetration rate of renewable energy, the planning and operation of power systems will face huge challenges. To ensure the sufficient utilization of renewable energy, the reasonable arrangement for the long-term power generation plan has become more crucial. Security-constrained unit commitment (SCUC) is a critical technical means to optimize the long-term power generation plan. However, the plentiful power sources and the complex grid structure in large-scale power systems will bring great difficulties to long-term SCUC. In this paper, we propose a fast calculation method for long-term SCUC of large-scale power systems with renewable energy. First, a method for unit status reduction based on temporal decomposition is proposed, which will reduce plenty of binary variables and intertemporal constraints in SCUC. Then, an efficient redundant constraint identification (RCI) method is developed to reduce the number of network constraints. Furthermore, a joint accelerated calculation framework for status reduction and RCI is formed, which can reduce the complexity of long-term SCUC while ensuring a high-precision feasible solution. In case studies, numerical results based on two test systems ROTS2017 and NREL-118 are analyzed, which verify the effectiveness and scalability of the proposed calculation method.

Abstract:Accelerating the development of renewable energy and reducing CO ₂ emissions have become a general consensus and concerted action of all countries in the world. The electric power industry, especially thermal power industry, is the main source for fossil energy consumption and CO ₂ emissions. Since solvent-based post-combustion carbon capture technology would bring massive extra energy consumption, the application of solar-assisted carbon capture technology has attracted extensive attention. Due to the important role of coal-fired combined heat and power plants for serving residential and industrial heating districts, in this paper, the low-carbon operation benefits of combined heat and power integrated plants based on solar-assisted carbon capture (CHPIP-SACC) are fully evaluated in heat and power integrated energy system with a high proportion of wind power. Based on the selected integration scheme, a linear operation model of CHPIP-SACC is developed considering energy flow characteristics and thermal coupling interaction of its internal modules. From the perspective of system-level operation optimization, the day-ahead economic dispatch problem based on a mix-integer linear programming model is presented to evaluate the low-carbon benefits of CHPIP-SACC during annual operation simulation. The numerical simulations on a modified IEEE 39-bus system demonstrate the effectiveness of CHPIP-SACC for reducing CO ₂ emissions as well as increasing the downward flexibility. The impact of different solar field areas and unit prices of coal on the low-carbon operation benefits of CHPIP-SACC is studied in the section of sensitivity analysis.

Abstract:To address the problems of wind power abandonment and the stoppage of electricity transmission caused by a short circuit in a power line of a doubly-fed induction generator (DFIG) based wind farm, this paper proposes an intelligent location method for a single-phase grounding fault based on a multiple random forests (multi-RF) algorithm. First, the simulation model is built, and the fundamental amplitudes of the zero-sequence currents are extracted by a fast Fourier transform (FFT) to construct the feature set. Then, the random forest classification algorithm is applied to establish the fault section locator. The model is resampled on the basis of the bootstrap method to generate multiple sample subsets, which are used to establish multiple classification and regression tree (CART) classifiers. The CART classifiers use the mean decrease in the node impurity as the feature importance， which is used to mine the relationship between features and fault sections. Subsequently, a fault section is identified by voting on the test results for each classifier. Finally, a multi-RF regression fault locator is built to output the predicted fault distance. Experimental results with PSCAD/EMTDC software show that the proposed method can overcome the shortcomings of a single RF and has the advantage of locating a short hybrid overhead/cable line with multiple branches. Compared with support vector machines (SVMs) and previously reported methods, the proposed method can meet the location accuracy and efficiency requirements of a DFIG-based wind farm better.

Abstract:The high penetration of variable renewable energies requires the flexibility from both the generation and demand sides. This raises the necessity of modeling stochastic and flexible energy resources in power system operation. However, some distributed energy resources have both stochasticity and flexibility, e.g., prosumers with distributed photovoltaics and energy storage, and plug-in electric vehicles with stochastic charging behavior and demand response capability. Such partly controllable participants pose challenges to modeling the aggregate behavior of large numbers of entities in power system operation. This paper proposes a new perspective on the aggregate modeling of such energy resources in power system operation. Specifically, a unified controllability-uncontrollability-decomposed model for various energy resources is established by modeling the controllable and uncontrollable parts of energy resources separately. Such decomposition enables the straightforward aggregate modeling of massive energy resources with different controllabilities by integrating their controllable components with linking constraints and uncontrollable components with dependent discrete convolution. Furthermore, a two-stage stochastic unit commitment model based on the proposed model for power system operation is established. The proposed model is tested using a three-bus system and real Qinghai provincial power grid of China. The result shows that this model is able to characterize at high accuracy the aggregate behavior of massive energy resources with different levels of controllability so that their flexibility can be fully explored.

Abstract:As the share of wind power in power systems continues to increase, the limited predictability of wind power generation brings serious potential risks to power system reliability. Previous research works have generally described the uncertainty of wind power forecast errors (WPFEs) based on normal distribution or other standard distribution models, which only characterize the aleatory uncertainty. In fact, epistemic uncertainty in WPFE modeling due to limited data and knowledge should also be addressed. This paper proposes a multi-source information fusion method (MSIFM) to quantify WPFEs when considering both aleatory and epistemic uncertainties. An extended focal element (EFE) selection method based on the adequacy of historical data is developed to consider the characteristics of WPFEs. Two supplementary expert information sources are modeled to improve the accuracy in the case of insufficient historical data. An operation reliability evaluation technique is also developed considering the proposed WPFE model. Finally, a double-layer Monte Carlo simulation method is introduced to generate a time-series output of the wind power. The effectiveness and accuracy of the proposed MSIFM are demonstrated through simulation results.

Abstract:The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective short-term wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e., a wind power prediction model based on multi-class autoregressive moving average (ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method; the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy, but also the parameter estimation efficiency.

Abstract:To facilitate the large-scale integration of distributed wind generation (DWG), the uncertainty of DWG outputs needs to be quantified, and the maximum DWG hosting capacity (DWGHC) of distribution systems must be assessed. However, the structure of the high-dimensional nonlinear dependencies and the abnormal marginal distributions observed in geographically dispersed DWG outputs lead to the increase of the complexity of the uncertainty analysis. To address this issue, this paper proposes a novel assessment model for DWGHC that considers the spatial correlations between distributed generation (DG) outputs. In our method, an advanced dependence modeling approach called vine copula is applied to capture the high-dimensional correlation between geographically dispersed DWG outputs and generate a sufficient number of correlated scenarios. To avoid an overly conservative hosting capacity in some extreme scenarios, a novel chance-constrained assessment model for DWGHC is developed to determine the optimal sizes and locations of DWG for a given DWG curtailment probability. To handle the computational challenges associated with large-scale scenarios, a bilinear variant of Benders decomposition (BD) is employed to solve the chance-constrained problem. The effectiveness of the proposed method is demonstrated using a typical 38-bus distribution system in eastern China.

Abstract:As the global energy transforms to renewable-based power system, the wind power generation has experienced a rapid increase. Due to the loss of synchronous machines and its frequency control mechanisms, the gradual evolution leads to critical challenges in maintaining the frequency stability. Under post-fault condition, the wind power generation has a slow recovery due to the fault ride-through (FRT) control strategy and may cause a larger frequency deviation due to the power imbalance between the supply and demand. Then, the impacts of the frequency deviations would further cause inaccuracy and instability in the control system for wind power generation. Considering the long parking time of electric vehicles (EVs), the demand-side response is provided to support the power grid via load-to-grid technology. Thus, a power-balancing coordinated control strategy of the wind power and the demand-side response is developed. It can significantly mitigate the power imbalance, thereby resulting in the enhanced frequency stability. Finally, the simulation results are provided to validate the power-balancing coordinated control strategy.

Abstract:Accurate short-term load forecasting is essential to modern power systems and smart grids. The utility can better implement demand-side management and operate power system stably with a reliable load forecasting system. The load demand contains a variety of different load components, and different loads operate with different frequencies. The conventional load forecasting methods, e.g., linear regression (LR), auto-regressive integrated moving average (ARIMA), deep neural network, ignore the frequency domain and can only use time-domain load demand as inputs. To make full use of both time-domain and frequency-domain features of the load demand, a load forecasting method based on hybrid empirical wavelet transform (EWT) and deep neural network is proposed in this paper. The proposed method first filters noises via wavelet-based denoising technique, and then decomposes the original load demand into several sub-layers to show the frequency features while the time-domain information is preserved as well. Then, a bidirectional long short-term memory (LSTM) method is trained for each sub-layer independently. In order to better tune the hyperparameters, a Bayesian hyperparameter optimization (BHO) algorithm is adopted in this paper. Three case studies are designed to evaluate the performance of the proposed method. From the results, it is found that the proposed method improves the prediction accuracy compared with other load forecasting method.

Abstract:Electric load forecasting is an efficient tool for system planning, and consequently, building sustainable power systems. However, achieving desirable performance is difficult owing to the irregular, nonstationary, nonlinear, and noisy nature of the observed data. Therefore, a new attention-based encoder-decoder model is proposed, called empirical mode decomposition-attention-long short-term memory (EMD-Att-LSTM). EMD is a data-driven technique used for the decomposition of complex series into subsequent simpler series. It explores the inherent properties of data to obtain the components such as trend and seasonality. Neural network architecture driven by deep learning uses the idea of a fine-grained attention mechanism, that is, considering the hidden state instead of the hidden state vectors, which can help reflect the significance and contributions of each hidden state dimension. In addition, it is useful for locating and concentrating the relevant temporary data, leading to a distinctly interpretable network. To evaluate the proposed model, we use the repository dataset of Australian energy market operator (AEMO). The proposed architecture provides superior empirical results compared with other advanced models. It is explored using the indices of root mean square error (RMSE) and mean absolute percentage error (MAPE).

Abstract:The increasing integration of photovoltaic generators (PVGs) and the uneven economic development in different regions may cause the unbalanced spatial-temporal distribution of load demands in an urban distribution network (UDN). This may lead to undesired consequences, including PVG curtailment, load shedding, and equipment inefficiency, etc. Global dynamic reconfiguration provides a promising method to solve those challenges. However, the power flow transfer capabilities for different kinds of switches are diverse, and the willingness of distribution system operators (DSOs) to select them is also different. In this paper, we formulate a multi-objective dynamic reconfiguration optimization model suitable for multi-level switching modes to minimize the operation cost, load imbalance, and the PVG curtailment. The multi-level switching includes feeder-level switching, transformer-level switching, and substation-level switching. A novel load balancing index is devised to quantify the global load balancing degree at different levels. Then, a stochastic programming model based on selected scenarios is established to address the uncertainties of PVGs and loads. Afterward, the fuzzy c-means (FCMs) clustering is applied to divide the time periods of reconfiguration. Furthermore, the modified binary particle swarm optimization (BPSO) and Cplex solver are combined to solve the proposed mixed-integer second-order cone programming (MISOCP) model. Numerical results based on the 148-node and 297-node systems are obtained to validate the effectiveness of the proposed method.

Abstract:Aiming at the high-dimensional uncertainties of restoration process, an optimization model for distribution system restoration control is proposed considering expected restoration benefits, expected restoration costs, and security risks of the overall restoration scheme. In the proposed model, the effect of security control on restoration process is actively analyzed considering the security control costs of preventive, emergency, and correction controls. A two-layer decision support framework for distribution system restoration decision support system (DRDSS) is also designed. The upper layer of the proposed framework generates the pre-adjustment schemes of operation mode for energized power grid by load transfer and selects the optimal pre-adjustment scheme and the corresponding partitioning scheme based on the partition adjustment results of each pre-adjustment scheme. In addition, it optimizes the spatial-temporal decision-making of the inter-partition connectivity. For each partition, the lower layer of the proposed framework pre-selects the units and loads to be restored according to the pre-evaluated restoration income, generates the table of alternative restoration scheme for coping with uncertain events through simulation and deduction, and evaluates the risk and benefit of each scheme. For the uncertain events in the actual restoration process, the current restoration scheme can be adaptively switched to a sub-optimal scheme or re-optimized if necessary. Meanwhile, the proposed framework provides an information interaction interface for collaborative restoration with the related transmission system. A 123-node test system is built to evaluate the effectiveness and adaptability of the proposed model and framework.

Abstract:The construction of advanced metering infrastructure and the rapid evolution of artificial intelligence bring opportunities to quickly searching for the optimal dispatching strategy for reactive power optimization. This can be realized by mining existing prior knowledge and massive data without explicitly constructing physical models. Therefore, a novel data-driven approach is proposed for reactive power optimization of distribution networks using capsule networks (CapsNet). The convolutional layers with strong feature extraction ability are used to project the power loads to the feature space to realize the automatic extraction of key features. Furthermore, the complex relationship between input features and dispatching strategies is captured accurately by capsule layers. The back propagation algorithm is utilized to complete the training process of the CapsNet. Case studies show that the accuracy and robustness of the CapsNet are better than those of popular baselines (e.g., convolutional neural network, multi-layer perceptron, and case-based reasoning). Besides, the computing time is much lower than that of traditional heuristic methods such as genetic algorithm, which can meet the real-time demand of reactive power optimization in distribution networks.

Abstract:Low-voltage (LV) distribution networks are unbalanced and present loads with nonlinear behavior, which introduce harmonics in the networks. The predictable increase in photovoltaic microgeneration (PV µG) accentuates this unbalanced characteristic, as well as poses new technical problems, namely voltage rise and reverse power flow. To accurately account for the distributed PV and loads in the LV network, unbalanced three-phase power flow algorithms should be utilized, where different approaches may be used to represent lines with various degrees of accuracy. The more accurate algorithm considers the electromagnetic coupling between the line conductors, whereas the simpler algorithm represents each conductor of the line as a single-phase line with pure resistive behavior. This paper aims to analyze the influence of the line model on the load flow in a highly unbalanced LV network with a high penetration of PV production, and considers the impact of the harmonics produced by nonlinear loads. Based on the results obtained, it is possible to identify the most suitable model to be used, depending on the study to be performed. Different scenarios of PV generation and loads are addressed in this paper.

Abstract:This paper presents the development of improved synergetic current control for the injected current of an inverter in the grid-connected microgrid and the distributed generation system (DGS). This paper introduces new formulas of the macro-variable functions for integral synergetic control (SC) and integral fast terminal SC, which both have an integral term to guarantee zero steady-state error. The proposed integral SC and integral fast terminal SC achieve a seamless performance such as the fast convergence, minimal overshoot, zero steady-state error, and chattering-free operation. To demonstrate the meritorious performance of the proposed scheme for injected current control, it is compared with the performance of a proportional-integral (PI) controller and advanced exponential sliding mode control (SMC). Finally, the practicality of the proposed scheme is justified by experimental results obtained through rapid control prototyping (RCP) using the dSPACE-SCALEXIO platform.

Abstract:A distributed secondary control (DSC) strategy that combines Q-learning and pinning control is originally proposed to achieve a fully optimal DSC for droop-controlled microgrids (MGs). It takes advantages of cross-fusion of the two algorithms to realize the high efficiency and self-adaptive control in MGs. It has the following advantages. Firstly, it adopts the advantages of reinforcement learning in autonomous learning control and intelligent decision-making, driving the action value of pinning control for feedback adaptive correction. Secondly, only a small part of points selected as pinned points needs to be controlled and pre-learned, hence the actual control problem is transformed into a synchronous tracking problem and the installation number of controllers is further reduced. Thirdly, the pinning matrix can be modified to adapt to plug-and-play operation under the distributed control architecture. Finally, the effectiveness and versatility of the proposed strategy are demonstrated with a typical droop-controlled MG model.

Abstract:With the rapid increase of distributed photovoltaic (PV) power integrating into the distribution network (DN), the critical issues such as PV power curtailment and low equipment utilization rate have been caused by PV power fluctuations. DN has less controllable equipment to manage the PV power fluctuation. To smooth the power fluctuations and further improve the utilization of PV, the regulation ability from the demand-side needs to be excavated. This study presents a continuous control method of the feeder load power in a DN based on the voltage regulation to respond to the rapid fluctuation of the PV power output. PV power fluctuations will be directly reflected in the point of common coupling (PCC), and the power fluctuation rate of PCCs is an important standard of PV curtailment. Thus, a demand-side management strategy based on model predictive control (MPC) to mitigate the PCC power fluctuation is proposed. In pre-scheduling, the intraday optimization model is established to solve the reference power of PCC. In real-time control, the pre-scheduling results and MPC are used for the rolling optimization to control the feeder load demand. Finally, the data from the field measurements in Guangzhou, China are used to verify the effectiveness of the proposed strategy in smoothing fluctuations of the distributed PV power.

Abstract:The use of unmanned aerial vehicles (UAVs) in the collection of data from wireless devices, sensor nodes, and the Internet of Things (IoT) devices has recently received significant attention. In this paper, we investigate the data collection process from a set of smart meters in advanced metering infrastructure (AMI) enabled by UAVs. The objective is to minimize the total annual cost of the electric utility by jointly optimizing the number of UAVs, their power source sizing, the charging locations as well as the data collection trip planning. This is achieved while considering the energy budgets of batteries of UAVs and the required amount of collected data. The problem is formulated as a mixed-integer nonlinear programming (MINLP), which is decoupled into two sub-problems where a candidate UAV and a number of buildings are first grouped into trips via genetic algorithms (GAs), and then the optimum trip path is found using a traveling salesman problem (TSP) branch and bound algorithm. Simulation results show that the battery capacity or the number of UAVs increases as the coverage area or the density increases.

Abstract:More customers are tending to install batteries with photovoltaic (PV), so they can better control their electricity bills. In this context, customers may be tempted to go off-grid at a substantial up-front cost, leading electricity companies into a death spiral, thereby raising electricity price further on those remaining on grid. Neighborhood energy markets can promote the sharing of locally generated renewable energy and encourage prosumers to stay on grid with financial incentives. A novel neighborhood energy trading (NET) mechanism is developed using the topology of existing radial distribution network to encourage sustainable energy sharing in neighborhood and encourage prosumers to stay on grid. This mechanism considers loss, congestion management, and voltage regulation, and it is scalable with low computation and communication overhead. An IEEE test system is used to validate the NET mechanism. The simulation shows that the price and flow results are obtained with fast computation speed (within 10 iterations) and with loss reflected, flow limit reinforced, and voltage regulated. This study proves that the economic demand-supply-based pricing mechanism can be applied effectively in distribution networks to help encourage more renewable energy sharing in sustainable neighborhood and avoid energy network death spiral.

Abstract:In response to the imbalance between power generation and demand, demand response (DR) projects are vigorously promoted. However, customers’ DR behaviors are still difficult to be simulated accurately and objectively. To tackle this challenge, we propose a new DR behavioral learning method based on a generative adversary network to learn customers’ DR habits. The proposed method is also extended to maximize the economic revenues of generated DR policies on the premise of obeying customers’ DR habits, which is hard to be realized simultaneously by existing model-based methods and traditional learning-based methods. To further consider customers’ time-varying DR patterns and trace the changes dynamically, we define customers’ DR participation positivity as an indicator of their DR pattern and propose a condition regulation approach improving the natural generative adversary framework to generate DR policies conforming to customers’ current DR patterns. The proposed method is applied to hourly electricity price optimization to reduce the fluctuation of system aggregate loads. An online parameter updating method is also utilized to train the proposed behavioral learning model in continuous DR simulations during electricity price optimization. Finally, numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.

Abstract:Relieving network congestions is a critical goal for the safe and flexible operation of modern power systems, especially in the presence of intermittent renewables or distributed generation. This paper deals with the real-time coordinated operation of distributed static series compensators (DSSCs) to remove network congestions by suitable modifications of the branch reactance. Several objective functions are considered and discussed to minimize the number of the devices involved in the control actions, the total losses or the total reactive power exchanged, leading to a non-convex mixed-integer non-linear programming problem. Then, a heuristic methodology combining the solution of a regular NLP with k-means clustering algorithm is proposed to get rid of the binary variables, in an attempt to reduce the computational cost. The proposed coordinated operation strategy of the DSSCs is tested on several benchmark systems, providing feasible and sufficiently optimal solutions in a reasonable time frame for practical systems.

Abstract:The development of DC custom power protection devices is still in infancy that confines the sensitive loads integrated into medium-voltage (MV) and low-voltage (LV) DC networks. Considering the DC doubly-fed induction generator (DC-DFIG) based wind energy conversion system (WECS), this paper proposes a dual active bridge (DAB) based DC unified power quality conditioner (DC-UPQC) with the integration of superconducting magnetic energy storage (SMES) to maintain the terminal voltage of DC-DFIG and regulate the current flow. The principle of the proposed DC-UPQC has three parts, i.e., parallel-side DAB (PDAB), series-side DAB (SDAB), and SMES, used for the voltage compensation, current and power regulation, and energy storage, respectively. The circuit principle of the PDAB and SDAB and the modeling of SMES are analyzed in this paper. A DC dual control strategy is also proposed to deal with the DC voltage oscillation generated by the AC-side asymmetrical fault. A case study of DC-DFIG interfaced with DC power grid is carried out, integrated with the proposed SMES-based DC-UPQC to verify the high-power applications of the proposed structure. Finally, an experiment is implemented, and the results demonstrate the correctness of the theoretical analysis and the feasibility of the proposed structure.

Abstract:Symmetrical monopolar configuration is the prevailing scheme configuration for modular multilevel converter based high-voltage direct current (MMC-HVDC) links, in which severe DC overvoltage or overcurrent can be caused by the DC faults. To deal with the possible asymmetry in the DC faults and the coupling effects of the DC lines, this paper analyzes the DC fault characteristics based on the phase-mode transformation. First, the DC grid is decomposed into the common-mode and the differential-mode networks. The equivalent models of the MMCs and DC lines in the two networks are derived, respectively. Then, based on the state matrices, a unified numerical calculation method for the fault voltages and currents at the DC side is proposed. Compared with the time-domain simulations performed on PSCAD/EMTDC, the accuracy of the proposed method is validated. Last, the system parameter analysis shows that the grounding parameters play an important role in reducing the severity of the pole-to-ground faults, whereas the coupling effects of the DC lines should be considered when calculating the DC fault currents associated with the pole-to-pole faults.

Abstract:The commutation failure (CF) mitigation effectiveness is normally restricted by the delay of extinction angle (EA) measurement or the errors of existing prediction methods for EA or firing angle (FA). For this purpose, this paper proposes a CF mitigation method based on the imaginary commutation process. For each sample point, an imaginary commutation process is constructed to simulate the actual commutation process. Then, the imaginary EA is calculated by comparing the imaginary supply voltage-time area and the imaginary demand voltage-time area, which can update the imaginary EA earlier than the measured EA. In addition, the proposed method considers the impacts of commutation voltage variation, DC current variation, and phase angle shift of commutation voltage on the commutation process, which can ensure a more accurate EA calculation. Moreover, the DC current prediction is proposed to improve the CF mitigation performance under the single-phase AC faults. Finally, the simulation results based on CIGRE model prove that the proposed method has a good performance in CF mitigation.

Abstract:To achieve the efficient application of impedance analysis in the stability assessment and enhancement of multi-terminal DC distribution systems, this paper proposes the DC-side reduced-order impedance models with power control and AC voltage control, respectively, by taking the load converter station as the object. By using the DC-side current as the feedforward state, the active compensator applied to the load converter station with two control modes is also derived as well as the corresponding reduced-order impedance models. Combined with the reduced-order impedance models, a method based on damping factor sensitivity is further proposed to design the parameters of the derived active compensators. The verification results in the frequency domain and time domain demonstrate the accuracy of the reduced-order impedance and the effectiveness of the proposed compensator parameter design method.

Abstract:This study proposes a statistical measure and a stochastic optimization model for generating risk-seeking wind power offering strategies in electricity markets. Inspired by the value at risk (VaR) to quantify risks in the worst-case scenarios of a profit distribution, a statistical measure is proposed to quantify potential high profits in the best-case scenarios of a profit distribution， which is referred to as value at best (VaB) in the best-case scenarios. Then, a stochastic optimization model based on VaB is developed for a risk-seeking wind power producer, which is formulated as a mixed-integer linear programming problem. By adjusting the parameters in the proposed model, the wind power producer can flexibly manage the potential high profits in the best-case scenarios from the probabilistic perspective. Finally, the proposed statistical measure and risk-seeking stochastic optimization model are verified through case studies.