Abstract:DC microgrids are gaining more attention with the increased penetration of various DC sources such as solar photovoltaic systems, fuel cells, batteries, etc., and DC loads. Due to the rapid integration of these components into the existing power system, the importance of DC microgrids has reached a salient point. Compared with conventional AC systems, DC systems are free from synchronization issues, reactive power control, frequency control, etc., and are more reliable and efficient. However, many challenges need to be addressed for utilizing DC power to its full potential. The absence of natural current zero is a significant issue in protecting DC systems. In addition, the stability of the DC microgrid, which relies on inertia, needs to be considered during system design. Moreover, power quality and communication issues are also significant challenges in DC microgrids. This paper presents a review of various value streams of DC microgrids including architectures, protection schemes, power quality, inertia, communication, and economic operation. In addition, comparisons between different microgrid configurations, the state-of-the-art projects of DC microgrid, and future trends are also set forth for further studies.

Abstract:The high penetration and uncertainty of distributed energies force the upgrade of volt-var control (VVC) to smooth the voltage and var fluctuations faster. Traditional mathematical or heuristic algorithms are increasingly incompetent for this task because of the slow online calculation speed. Deep reinforcement learning (DRL) has recently been recognized as an effective alternative as it transfers the computational pressure to the off-line training and the online calculation timescale reaches milliseconds. However, its slow offline training speed still limits its application to VVC. To overcome this issue, this paper proposes a simplified DRL method that simplifies and improves the training operations in DRL, avoiding invalid explorations and slow reward calculation speed. Given the problem that the DRL network parameters of original topology are not applicable to the other new topologies, side-tuning transfer learning (TL) is introduced to reduce the number of parameters needed to be updated in the TL process. Test results based on IEEE 30-bus and 118-bus systems prove the correctness and rapidity of the proposed method, as well as their strong applicability for large-scale control variables.

Abstract:In this paper, a new formulation for modeling the problem of stochastic security-constrained unit commitment along with optimal charging and discharging of large-scale electric vehicles, energy storage systems, and flexible loads with renewable energy resources is presented. The uncertainty of renewable energy resources is considered as a scenario-based model. In this paper, a multi-objective function which considers the reduction of operation cost, no-load and startup/shutdown costs, unserved load cost, load shifting, carbon emission, optimal charging and discharging of energy storage systems, and power curtailment of renewable energy resources is considered. The proposed formulation is a mixed-integer linear programming (MILP) model, of which the optimal global solution is guaranteed by commercial solvers. To validate the proposed formulation, several cases and networks are considered for analysis, and the results demonstrate the efficiency.

Abstract:The high-speed simulation of large-scale offshore wind farms (OWFs) preserving the internal machine information has become a huge challenge due to the large wind turbine (WT) count and microsecond-range time step. Hence, it is undoable to investigate the internal node information of the OWF in the electro-magnetic transient (EMT) programs. To fill this gap, this paper presents an equivalent modeling method for large-scale OWF, whose accuracy and efficiency are guaranteed by integrating the individual devices of permanent magnet synchronous generator (PMSG) based WT. The node-elimination algorithm is used while the internal machine information is recursively updated. Unlike the existing aggregation methods, the developed EMT model can reflect the characteristics of each WT under different wind speeds and WT parameters without modifying the codes. The access to each WT controller is preserved so that the time-varying dynamics of all the WTs could be simulated. Comparisons of the proposed model with the detailed model in PSCAD/EMTDC have shown very high precision and high efficiency. The proposed modeling procedures can be used as reference for other types of WTs once the structures and parameters are given.

Abstract:A hybrid drive wind turbine equipped with a speed regulating differential mechanism can generate electricity at the grid frequency by an electrically excited synchronous generator without requiring fully or partially rated converters. This mechanism has extensively been studied in recent years. To enhance the transient operation performance and low-voltage ride-through capacity of the proposed hybrid drive wind turbine, we aim to synthesize an advanced control scheme for the flexible regulation of synchronous generator excitation based on fractional-order sliding mode theory. Moreover, an extended state observer is constructed to cooperate with the designed controller and jointly compensate for parametric uncertainties and external disturbances. A dedicated simulation model of a 1.5 MW hybrid drive wind turbine is established and verified through an experimental platform. The results show satisfactory model performance with the maximum and average speed errors of 1.67% and 1.05%, respectively. Moreover, comparative case studies are carried out considering parametric uncertainties and different wind conditions and grid faults, by which the superiority of the proposed controller for improving system on-grid operation performance is verified.

Abstract:Wind turbine blades have been constantly increasing since wind energy becomes a popular renewable energy source to generate electricity. Therefore, the wind sector requires a more efficient and representative characterization of vertical wind speed profiles to assess the potential for a wind power plant site. This paper proposes an alternative characterization of vertical wind speed profiles based on Ward’s agglomerative clustering algorithm, including both wind speed module and direction data. This approach gives a more accurate incoming wind speed variation around the rotor swept area, and subsequently, provides a more realistic and complete wind speed vector characterization for vertical profiles. Real wind data-base collected for 2018 in the Forschungsplattformen in Nord-und Ostsee (FINO) research platform is used to assess the methodology. A preliminary pre-processing stage is proposed to select the appropriated number of heights and remove missing or incomplete data. Finally, two locations and four heights are selected, and 561588 wind data are characterized. Results and discussion are also included in this paper. The methodology can be applied to other wind database and locations to characterize vertical wind speed profiles and identify the most likely wind data vector patterns.

Abstract:Regional photovoltaic (PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals (PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granule-based clustering (GC) and direct optimization programming (DOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction (NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples’ utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, DOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data.

Abstract:To reduce environmental pollution and improve the efficiency of cascaded energy utilization, regional integrated energy system (RIES) has received extensive attention. An accurate multi-energy load prediction is significant for RIES as it enables stakeholders to make effective decisions for carbon peaking and carbon neutrality goals. To this end, this paper proposes a multivariate two-stage adaptive-stacking prediction (M2ASP) framework. First, a preprocessing module based on ensemble learning is proposed. The input data are preprocessed to provide a reliable database for M2ASP, and highly correlated input variables of multi-energy load prediction are determined. Then, the load prediction results of four predictors are adaptively combined in the first stage of M2ASP to enhance generalization ability. Predictor hyper-parameters and intermediate data sets of M2ASP are trained with a metaheuristic method named collaborative atomic chaotic search (CACS) to achieve the adaptive staking of M2ASP. Finally, a prediction correction of the peak load consumption period is conducted in the second stage of M2ASP. The case studies indicate that the proposed framework has higher prediction accuracy, generalization ability, and stability than other benchmark prediction models.

Abstract:This paper develops a segmented real-time dispatch model for power-gas integrated systems (PGISs), where power-to-gas (P2G) devices and traditional automatic generation control units are cooperated to manage wind power uncertainty. To improve the economics of the real-time dispatch in regard to the current high operation cost of P2Gs, the wind power uncertainty set is divided into several segments, and a segmented linear decision rule is developed, which assigns adjustment tasks differently when wind power uncertainty falls into different segments. Thus, the P2G operation with high costs can be reduced in real-time adjustment. Besides, a novel segmented stochastic robust optimization is proposed to improve the efficiency and robustness of PGIS dispatch under wind power uncertainty, which minimizes the expected cost under the empirical wind power distribution and builds up the security constraints based on the robust optimization. The expected cost is formulated using a Nataf conversion-based multi-point estimate method, and the optimal number of estimate points is determined through sensitivity analysis. Furthermore, a difference-of-convex optimization with a partial relaxation rule is developed to solve the non-convex dispatch problem in a sequential optimization framework. Numerical simulations in two testing cases validate the effectiveness of the proposed model and solving method.

Abstract:The increasing penetration of renewable energy sources introduces higher requirements for the operation flexibility of transmission system (TS) and connected active distribution systems (DSs). This paper presents an efficient distributed framework for the TS and DSs to work cooperatively yet independently. In addition to conventional power interaction, upward and downward reserve capacities are exchanged to form the feasible access regions at the boundaries that apply to different system operation situations. A distributed robust energy and reserve dispatch approach is proposed under this framework. The approach utilizes the supply- and demand-side resources in different systems to handle various uncertainties and improve overall efficiency and reliability. In particular, integrated as aggregated virtual energy storage (AVES) devices, air-conditioning loads are incorporated into the optimal dispatch. In addition, a reserve model with charging/discharging-state elasticity is developed for AVESs to enhance system flexibility and provide additional reserve support. Different cases are compared to verify the effectiveness and superiority of the proposed approach.

Abstract:The variability of the output power of distributed renewable energy sources (DRESs) that originate from the fast-changing climatic conditions can negatively affect the grid stability. Therefore, grid operators have incorporated ramp-rate limitations (RRLs) for the injected DRES power in the grid codes. As the DRES penetration levels increase, the mitigation of high-power ramps is no longer considered as a system support function but rather an ancillary service (AS). Energy storage systems (ESSs) coordinated by RR control algorithms are often applied to mitigate these power fluctuations. However, no unified definition of active power ramps, which is essential to treat the RRL as AS, currently exists. This paper assesses the various definitions for ramp-rate RR and proposes RRL method control for a central battery ESS (BESS) in distribution systems (DSs). The ultimate objective is to restrain high-power ramps at the distribution transformer level so that RRL can be traded as AS to the upstream transmission system (TS). The proposed control is based on the direct control of the Δ P/Δ t, which means that the control parameters are directly correlated with the RR requirements included in the grid codes. In addition, a novel method for restoring the state of charge (SoC) within a specific range following a high ramp-up/down event is proposed. Finally, a parametric method for estimating the sizing of central BESSs (BESS sizing for short) is developed. The BESS sizing is determined by considering the RR requirements, the DRES units, and the load mix of the examined DS. The BESS sizing is directly related to the constant RR achieved using the proposed control. Finally, the proposed methodologies are validated through simulations in MATLAB/Simulink and laboratory tests in a commercially available BESS.

Abstract:This paper proposes an evolutionary game-theoretic model of massive distributed renewable energy deployment in order to shed light on the self-organization sustainable developments of renewable energies in distribution networks towards low-carbon targets. Since neighboring buses can interact in terms of energy exchanges, the return matrices of individual buses in the evolutionary game are established based on profiles of loads and renewable energy generation. More specifically, an evolutionary strategy is proposed based on the return matrices for individual buses to determine whether or not to deploy renewable energies in the next round of the game. Then, a dynamic model is derived for analyzing the renewable energy penetration rate in the distribution network throughout the multi-round evolutionary game. In theory, this model can reveal the self-organization process of renewable energy deployment in the distribution network. With this model, the distribution network operator would be aided in designing the incentives for buses deploying renewable energies toward a pre-defined low-carbon target. Numerical results on an actual 141-bus system and a synthetic 2000-bus system have demonstrated the validity and efficiency of the proposed model.

Abstract:The bipolar low-voltage DC (LVDC) distribution system has become a prospective solution to better integration of renewables and improvement of system efficiency and reliability. However, it also faces the challenge of power and voltage imbalance between two poles. To solve this problem, an interface converter with bipolar asymmetrical operating capabilities is applied in this paper. The steady-state models of the bipolar LVDC distribution system equipped with this interface converter in the grid-connected mode and off-grid mode are analyzed. A control scheme based on DC offset injection at the secondary side of the interface converter is proposed, enabling the bipolar LVDC distribution system to realize the unbalanced power transfer between two poles in the grid-connected mode and maintain the inherent- pole voltage balance in the off-grid mode. Furthermore, this paper also proposes a primary-side DC offset injection control scheme according to the analysis of the magnetic circuit model, which can eliminate the DC bias flux caused by the secondary-side DC offset. Thereby, the potential core magnetic saturation and overcurrent issues can be prevented, ensuring the safety of the interface converter and distribution system. Detailed simulations based on the proposed control scheme are conducted to validate the function of power and voltage balance under the operation conditions of different DC loads.

Abstract:The volatile and intermittent nature of distributed generators (DGs) in active distribution networks (ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units (D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming (SOCP) based robust state estimation (RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.

Abstract:To optimize the placement of soft open points (SOPs) in active distribution networks (ADNs), many aspects should be considered, including the adjustment of transmission power, integration of distributed generations (DGs), coordination with conventional control methods, and maintenance of economic costs. To address this multi-objective planning problem, this study proposes a multi-stage coordinated robust optimization model for the SOP allocation in ADNs with photovoltaic (PV). First, two robust technical indices based on a robustness index are proposed to evaluate the operation conditions and robust optimality of the solutions. Second, the proposed coordinated allocation model aims to optimize the total cost, robust voltage offset index, robust utilization index, and voltage collapse proximity index. Third, the optimization methods of the multi- and single-objective models are coordinated to solve the proposed multi-stage problem. Finally, the proposed model is implemented on an IEEE 33-node distribution system to verify its effectiveness. Numerical results show that the proposed index can better reveal voltage offset conditions as well as the SOP utilization, and the proposed model outperforms conventional ones in terms of robustness of placement plans and total cost.

Abstract:The large-scale deployment of electric vehicles (EVs) poses critical challenges to the secure and economic operation of power distribution networks (PDNs). Therefore, a method for evaluating the hosting capacity that enables a PDN to determine the EV chargeable area (EVCA) to satisfy the charging demand and ensure the secure operation is proposed in this paper. Specifically, the distribution system operator (DSO) serves as a public entity to manage the integration of EVs by determining the presence of the charging load in the EVCA. Hence, an EVCA optimization model is formulated on the basis of the coupling effect of the charging nodes to determine the range of the available charging power. In this model, nonlinear power flow equations and operational constraints are considered to maintain the solvability of the power flow of the PDN. Subsequently, a novel multipoint approximation technique is proposed to quickly search for the boundary points of the EVCA. In addition, the impact of the demand response (DR) mechanism on the hosting capacity is explored. The results show that the presence of the DR significantly enlarged the EVCA during peak hours, thus revealing the suitability of the DR mechanism as an important supplement to accommodate the EV charging load. The examined case studies demonstrate the effectiveness of the proposed model and show that the unmanaged allocation of the charging load impedes secure operation. Finally, the proposed method provides a reference for the allocation of the EV charging load and a reduction in the risk of line overloading.

Abstract:With the popularity of smart meters and the growing availability of high-resolution load data, the research on the dynamics of electricity consumption at finely resolved timescales has become increasingly popular. Many existing algorithms underperform when clustering load profiles contain a large number of feature points. In addition, it is difficult to accurately describe the similarity of profile shapes when load sequences have large fluctuations, leading to inaccurate clustering results. To this end, this paper proposes a high-resolution load profile clustering approach based on dynamic largest triangle three buckets (LTTBs) and multiscale dynamic time warping under limited warping path length (LDTW). Dynamic LTTB is a novel dimensionality reduction algorithm based on LTTB. New sequences are constructed by dynamically dividing the intervals of significant feature points. The extraction of fluctuation characteristics is optimized. New curves with more concentrated features will be applied to the subsequent clustering. The proposed multiscale LDTW is used to generate a similarity matrix for spectral clustering, providing a more comprehensive and flexible matching method to characterize the similarity of load profiles. Thus, the clustering effect of a high-resolution load profile is improved. The proposed approach has been applied to multiple datasets. Experiment results demonstrate that the proposed approach significantly improves the Davies-Bouldin indicator (DBI) and validity index (VI). Therefore, better similarity and accuracy can be achieved using high-resolution load profile clustering.

Abstract:This paper addresses a distributed real-time optimal power flow (RTOPF) strategy for DC microgrids. In this paper, we focus on the scenarios where local information sharing is conducted in stochastic communication networks subject to random failures. Most existing real-time optimal power flow (OPF) algorithms for the DC microgrid require all controllers to work in concert with a fixed network topology to maintain a zero gap between estimated global constraint violations. Thus, the high reliability of communication is required to ensure their convergence. To address this issue, the proposed RTOPF strategy tolerates stochastic communication failures and can seek the optimum with a constant step size considering the operation limitations of the microgrid. These aspects make the strategy suitable for real-time optimization, particularly when the communication is not reliable. In addition, this strategy does not require information from non-dispatchable devices, thereby reducing the number of sensors and controllers in the system. The convergence of the proposed strategy and the optimal equilibrium points are theoretically proven. Finally, simulations of a 30-bus DC microgrid are performed to validate the effectiveness of the proposed designs.

Abstract:Residential heating, ventilation and air conditioning (HVAC) provides important demand response resources for the new power system with high proportion of renewable energy. Residential HAVC scheduling strategies that adapt to real-time electricity price signals formulated by demand response program and ambient temperature can significantly reduce electricity costs while ensuring occupants ’ comfort. However, since the pricing process and weather conditions are affected by many factors, conventional model-based method is difficult to meet the scheduling requirements in complex environments. To solve this problem, we propose an adaptive optimal scheduling strategy for residential HVAC based on deep reinforcement learning (DRL) method. The scheduling problem can be regarded as a Markov decision process (MDP). The proposed method can adaptively learn the state transition probability to make economical decision under the tolerance violations. Specifically, the residential thermal parameters obtained by the least-squares parameter estimation (LSPE) can provide a basis for the state transition probability of MDP. Daily simulations are verified under the electricity prices and temperature data sets, and numerous experimental results demonstrate the effectiveness of the proposed method.

Abstract:The multi-directional flow of energy in a multi-microgrid (MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.

Abstract:Due to the uncertain fluctuations of renewable energy and load power, the state variables such as bus voltages and pipeline mass flows in the combined cooling, heating, and power campus microgrid (CCHP-CMG) may exceed the secure operation limits. In this paper, an optimal energy flow (OEF) model for a CCHP-CMG using parameterized probability boxes (p-boxes) is proposed to describe the higher-order uncertainty of renewables and loads. In the model, chance constraints are used to describe the secure operation limits of the state variable p-boxes, and variance constraints are introduced to reduce their random fluctuation ranges. To solve this model, the chance and variance constraints are transformed into the constraints of interval cumulants (ICs) of state variables based on the p-efficient point theory and interval Cornish-Fisher expansion. With the relationship between the ICs of state variables and node power, and using the affine interval arithmetic method, the original optimization model is finally transformed into a deterministic nonlinear programming model. It can be solved by the CONOPT solver in GAMS software to obtain the optimal operation point of a CCHP-CMG that satisfies the secure operation requirements considering the higher-order uncertainty of renewables and loads. Case study on a CCHP-CMG demonstrates the correctness and effectiveness of the proposed OEF model.

Abstract:The scheduled electric vehicle (EV) charging flexibility has great potential in supporting the operation of power systems, yet achieving such benefits is challenged by the uncertain and user-dependent nature of EV charging behavior. Existing research primarily focuses on modeling the uncertain EV arrival and battery status yet rarely discusses the uncertainty in EV departure. In this paper, we investigate the EV charging scheduling strategy to support load flattening at the distribution level of the utility grid under uncertain EV departures. A holistic methodology is proposed to formulate the unexpected trip uncertainty and mitigate its negative impacts. To ensure computational efficiency when large EV fleets are involved, a distributed solution framework is developed based on the alternating direction method of multipliers (ADMM) algorithm. The numerical results reveal that unexpected trips can severely damage user convenience in terms of EV energy content. It is further confirmed that by applying the proposed methodology, the resultant critical and sub-critical user convenience losses due to scheduled charging are reduced significantly by 83.5% and 70.5%, respectively, whereas the load flattening performance is merely sacrificed by 17%.

Abstract:A new privacy-preserving algorithm based on the Paillier cryptosystem including a new cooperative control strategy is proposed in this paper, which can resist the false data injection (FDI) attack based on the finite-time control theory and the data encryption strategy. Compared with the existing algorithms, the proposed privacy-preserving algorithm avoids the direct transmission of the ciphertext of frequency data in communication links while avoiding complex iterations and communications. It builds a secure data transmission environment that can ensure data security in the AC microgrid cyber-physical system (CPS). This algorithm provides effective protection for AC microgrid CPS in different cases of FDI attacks. At the same time, it can completely eliminate the adverse effects caused by the FDI attack. Finally, the effectiveness, security, and advantages of this algorithm are verified in the improved IEEE 34-node test microgrid system with six distributed generators (DGs) in different cases of FDI attacks.

Abstract:During state perception of a power system, fragments of harmonic data are inevitably lost owing to the loss of synchronization signals, transmission delays, instrument failures, or other factors. A harmonic data recovery method is proposed based on multivariate norm matrix in this paper. The proposed method involves dynamic time warping for correlation analysis of harmonic data, normalized cuts for correlation clustering of power-quality monitoring devices, and adaptive alternating direction method of multipliers for multivariable norm joint optimization. Compared with existing data recovery methods, our proposed method maintains excellent recovery accuracy without requiring prior information or optimization of the power-quality monitoring device. Simulation results on the IEEE 39-bus and IEEE 118-bus test systems demonstrate the low computational complexity of the proposed method and its robustness against noise. In addition, the application of the proposed method to field data from a real-world system provides consistent results with those obtained from simulations.

Abstract:The recent in-depth development of hybrid high-voltage direct current (HVDC) transmission systems poses looming adaptability challenges to protection. The various and disparate direct current (DC) transmission topologies can profoundly affect the operating characteristics of DC transmission networks, which result in the lack of performance of conventional DC protection schemes in such topologies. This significantly limits the application of hybrid HVDC technologies. This paper proposes a single-end protection scheme based on the transient power waveshape for fast and sensitive detection and classification of different types of DC faults in hybrid HVDC transmission lines. The fault characteristics and their causes under different fault conditions are analyzed in detail with a pre-introduced linearized transient model of a hybrid HVDC transmission system, demonstrating that the formation of the fluctuation characteristics of local measurements is mainly determined by the buffering and absorption effects of lumped-parameter reactors on transient traveling-wave (TW) energy. Simulation results verify the sensitivity, rapidity, reliability, and anti-interference ability of the proposed scheme when applied to hybrid HVDC transmission lines. Furthermore, it is confirmed that the proposed scheme is adaptable to symmetric voltage-sourced converter (VSC) and conventional line-commutated converter (LCC) based HVDC transmission lines.

Abstract:Wind-farm-side modular multilevel converters (WFMMCs) used in modular multilevel converter based high-voltage direct current (MMC-HVDC) transmission systems must be able to control the AC grid voltage in offshore wind farms. Different AC voltage control strategies can significantly affect the dynamic characteristics of WFMMCs. However, existing studies have not provided a general methodology of controller parameter design, and few comparative studies have been conducted on control performance under varying operating conditions as well as the effects of different AC voltage control modes (AVCMs) on the stability of MMC-HVDCs with offshore wind farms. This paper provides a controller parameter design method for AVCMs, which is tested in various operating scenarios. Sequence impedance models of offshore wind farms and WFMMCs under different AVCMs are then developed. The effects of AVCMs on the small-signal stability of the interconnected system are then analyzed and compared using the impedance-based method. Finally, case studies are conducted on a practical MMC-HVDC system with offshore wind farms to verify the theoretical analysis.

Abstract:Wind power converter (WPC) is a key part of a wind power unit which delivers electric energy to power grid. Because of a large number of semiconductors, WPC has a high failure rate. This paper proposes a method to accurately evaluate the reliability of WPC, which is crucial for the design and maintenance of wind turbines. Firstly, the index of effective temperature (ET) is presented to quantify the effects of temperature and humidity on the semiconductor operation. A novel method is proposed to evaluate the lifetime and calculate the aging failure rates of the semiconductors considering the fluctuations of ET. Secondly, the failure mode and effect analysis (FMEA) of WPC is investigated based on the topology and control scheme. The conventional two-state reliability model of the WPC is extended to the multi-state reliability model where the partial working state under the fault-tolerant control scheme is allowed. Finally, a reliability evaluation framework is established to calculate the parameters of the WPC reliability model considering the variable failure rates and repair activities of semiconductors. Case studies are designed to verfify the proposed method using a practical wind turbine.

Abstract:The increasing penetration of the renewable energy sources brings new challenges to the frequency security of power systems. In order to guarantee the system frequency security, frequency constraints are incorporated into unit commitment (UC) models. Due to the non-convex form of the frequency nadir constraint which makes the frequency constrained UC (FCUC) intractable, this letter proposes a revised support vector machine (SVM) based system parameter separating plane method to convexify it. Based on this data-driven convexification method, we obtain a tractable FCUC model which is formulated as a mixed-integer quadratic programming (MIQP) problem. Case studies indicate that the proposed method can obtain less conservative solution than the existing methods with higher efficiency.