Abstract:The real-time transient stability assessment (TSA) and emergency control are effective measures to suppress accident expansion, prevent system instability, and avoid large-scale power outages in the event of power system failure. However, real-time assessment is extremely demanding on computing speed, and the traditional method is not competent. In this paper, an improved deep belief network (DBN) is proposed for the fast assessment of transient stability, which considers the structural characteristics of power system in the construction of loss function. Deep learning has been effective in many fields, but usually is considered as a black-box model. From the perspective of machine learning interpretation, this paper proposes a local linear interpreter (LLI) model, and tries to give a reasonable interpretation of the relationship between the system features and the assessment result, and illustrates the conversion process from the input feature space to the high-dimension representation space. The proposed method is tested on an IEEE new England test system and demonstrated on a regional power system in China. The result demonstrates that the proposed method has rapidity, high accuracy and good interpretability in transient stability assessment.

Abstract:With the integration of large-scale renewable energy and various new types of loads, the stochastic fluctuation characteristic of the power has seriously affected the secure and stable operation of interconnected power grids. In addition to active power fluctuations of AC tie-lines that have been studied in the past, the problem of dynamic fluctuation of bus voltage is also increasingly prominent. Firstly, the typical power fluctuations of electric railway traction load, smelting load, and wind power generation in modern power systems are presented and the accompanying voltage fluctuations are analyzed. Secondly, the dynamic fluctuation mechanism and distribution characteristic of the system voltage under special forced oscillation and generalized forced oscillation are studied. It is found that when the periodic disturbance induces a special forced oscillation, the voltage fluctuation has the same mode information compared to the power angle fluctuation. And it also has obvious peak characteristics near the natural frequency. When the stochastic disturbance induces generalized forced oscillation, the voltage fluctuations can be decomposed into proportional and resonant components according to its frequency domain characteristic. Finally, the above conclusions are verified by the simulation results of an IEEE 4-generator 2-area system.

Abstract:Direct current (DC) power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources (DERs) and the construction of advanced urban power grids. In this study, a typical topology analysis is performed for an advanced urban medium-voltage DC (MVDC) distribution network with DERs, including wind, photovoltaic, and electrical energy storage elements. Then, a multi-time scale optimal power flow (OPF) strategy is proposed for the MVDC network in different operation modes, including utility grid-connected and off-grid operation modes. In the utility grid-connected operation mode, the day-ahead optimization objective minimizes both the DER power curtailment and the network power loss. In addition, in the off-grid operation mode, the day-ahead optimization objective prioritizes the satisfaction of loads, and the DER power curtailment and the network power loss are minimized. A dynamic weighting method is employed to transform the multi-objective optimization problem into a quadratically constrained quadratic programming (QCQP) problem, which is solvable via standard methods. During intraday scheduling, the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery, and then seeks to minimize the DER power curtailment and the network power loss. Model predictive control (MPC) is used to correct deviations according to the results of ultra short-term load forecasting. Furthermore, an improved particle swarm optimization (PSO) algorithm is applied for global intraday optimization, which effectively increases the convergence rate to obtain solutions. MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient.

Abstract:Phasor measurement units (PMUs) are fundamental tools in the applications of modern power systems, where synchronized phasor estimations are needed. The accuracy and dynamic performance requirements for phasor, frequency, and rate of change of frequency (ROCOF) estimations are established in the IEEE Std. C37.118.1-2011 along with the IEEE Std. C37.118.1a-2014, where two PMU performances are suggested: P class filters for applications requiring fast response and M class filters for applications requiring high rejection to aliased signals. In this paper, a methodology to design new phasor estimators that satisfy the P class and M class requirements in PMUs is presented. The proposed methodology is based on finite impulse response filters, brick-wall filters, and complex filter design concepts, where frequency range, time performance, harmonic rejection and out-of-band interference requirements are considered in its design. A comparative analysis using the reference model given by the IEEE Std. C37.118.1 is presented. The results show the effectiveness of the phasor estimators under steady-state and dynamic conditions according to the PMU standard, making them suitable tools for applications in power systems.

Abstract:The penetration of multi-carrier energy systems in distribution system gains more and more concerns. In this paper, a bi-level transactive energy trading framework is proposed to improve the energy scheduling and operation efficiency for multi-carrier energy systems which are modeled as energy hubs (EHs). In the upper level, each EH in the distribution system not only makes energy scheduling decisions considering supplies and demands of local energy, but also trades energy with each other to further maximize their social welfare. The associated trading payment among EHs is made in a fair manner by applying Nash bargaining theory. We solve the bargaining problem by decomposing it into two subproblems: operation cost minimization problem and payment bargaining problem. Then, based on the trading decision, the nodal equivalent loads of EHs are sent to the distribution system operator (DSO) without publishing trading details. By applying the second-order cone programming (SOCP), DSO reconfigures the network to reduce the transmission loss of the system in the lower level. The network reconfiguration and the trading behavior of EHs interact and iterate until the convergence. Numerical studies on modified IEEE 33-bus distribution system demonstrate the effectiveness of the proposed framework.

Abstract:Owing to the uncertainty and volatility of wind energy, forecasted wind power scenarios with proper spatio-temporal correlations are needed in various decision-making problems involving power systems. In this study, forecasted scenarios are generated from an estimated multi-variate distribution of multiple regional wind farms. According to the theory of copulas, marginal distributions and the dependence structure of multi-variate distribution are modeled through the proposed distance-weighted kernel density estimation method and the regular vine (R-vine) copula, respectively. Owing to the flexibility of decomposing correlations of high dimensions into different types of pair-copulas, the R-vine copula provides more accurate results in describing the complicated dependence of wind power. In the case of 26 wind farms located in East China, high-quality forecasted scenarios as well as the corresponding probabilistic forecasting and point forecasting results are obtained using the proposed method, and the results are evaluated using a comprehensive verification framework.

Abstract:The integration of network reconfiguration and distributed generation (DG) can enhance the performances of overall networks. Thus, proper sizing and siting of DG need to be determined, otherwise it will cause degradation in system performance. However, determining proper sizing and siting of DG together with network reconfiguration is a complex problem due to huge solution search space. This search space mostly contains non-radial network configurations. Eliminating these non-radial combinations during optimization process increases computational overhead and may end up at local optimal solution. To reduce the searching complexity, this paper considers the discretized network reconfiguration via dataset approach. Water cycle algorithm (WCA) is used to obtain the near optimal solution of network reconfiguration, and sizing and sitting of DG. In addition, the power factor of DG is also optimized to reduce the power loss. The proposed method is tested on an IEEE 33-bus network and an IEEE 69-bus network considering different scenarios to show the effectiveness of simultaneous approach considering variable power factor. The results show that the discretization of reconfiguration search space avoids that WCA to get trapped in local optima. The proposed method outperforms other technique such as harmony search algorithm (HSA), fireworks algorithm (FWA), Cuckoo search algorithm (CSA) and uniform voltage distribution based constructive algorithm (UVDA) and improves the solution quality of IEEE 33-bus network and 69-bus network by 29.20% and 27.88%, respectively.

Abstract:With the proliferation of renewable energy and electric vehicles (EVs), there have been increasing uncertainties in power systems. Identifying the influencing random variables will reduce the effort in uncertainty modeling and improve the controllability of power systems. In this paper, a density-based global sensitivity analysis (GSA) method is proposed to evaluate the influence of uncertainties on islanded microgrids (IMGs). Firstly, the maximum IMG loadability evaluation model is established to assess the distance from the current operation point to the critical operation point. Secondly, the Borgonovo method, which is a density-based GSA method, is used to evaluate the influence of input variables on IMG loadability. Thirdly, to improve GSA efficiency, a modified Kriging model is used to obtain a surrogate model of IMG loadability, and Borgonovo indices are calculated based on the surrogate model. Finally, the proposed method is tested on a 38-bus IMG system. Simulation results are compared with those considering other methods to validate the effectiveness of the proposed method. Energy storage systems are considered to diminish the influence of critical uncertainties on IMG operation.

Abstract:This paper develops a stochastic framework for the energy management of a microgrid to minimize the energy cost from the grid. It considers the uncertainties in solar photovoltaic (PV) generation, load demand, and electricity price. Furthermore, the opportunity of flexible load demand, i.e., the effect of demand response (DR), on the test system is studied. The uncertainties are modeled by using Monte Carlo simulations and the generated scenarios are reduced to improve the computational tractability. In general, microgrid scheduling is implemented by using substation (source node) price as a reference, but that reference price is not the same at all nodes. Therefore, this paper develops the nodal price based energy management in a microgrid to improve the scheduling accuracy. The stochastic energy management framework is formulated as a mixed integer non-linear programming (MINLP). Four case studies are simulated for a modified 15-node radial distribution network integrated with solar PV and battery energy storage system (BESS) to validate the effectiveness of the energy management framework for a microgrid with nodal pricing.

Abstract:With the high integration of power electronic technologies in microgrids, the reliability assessment considering power electronic devices has become a hot topic. However, so far no research has considered the impact of the operation failure probability of power electronic equipment on the overall reliability of the microgrid. This paper aims to construct a holistic operation failure rate model of power electronic systems based on the overall reliability assessment of islanded microgrid with high penetration of renewable energy sources (RESs). In addition, to improve the reliability of islanded microgrid, the conventional battery energy storage system (BESS) is replaced by the hybrid energy storage system (HESS). Based on the proposed model, the operation failure models for the power electronic modules in microgrid are built and tested, and then the sensitivity analysis is performed for exploring the influence of various factors on the reliability of the microgrid.

Abstract:Smart parking lots are smart places capable of supporting both parking and charging services for electric vehicles (EVs). In order to manage EV charging, the parking lot local controller (PLLC) requires data exchange with EV charging stations (EVCSs) through communication infrastructures. However, data losses and communication delays are unavoidable and may significantly degrade the system performance. This work aims to investigate the underlying communication networks for remote monitoring of EVCSs in a smart campus parking lot. The communication network consists of two subnetworks: parking area network (PAN) and campus area network (CAN). PAN covers communication among EVs, charging stations and PLLC, while CAN enables dedicated communication between PLLCs and a global controller of the university. As one of the major obstacles in EV system is the lack of unified communication architecture to integrate EVCS in the power grid, we develope communication models for the in-vehicle system and EVCSs based on logical node concept of IEC 61850 standard. Furthermore, we implement network models for EVCSs using OPNET modeler. Different communication technologies and configurations are considered in modeling and simulations, and end-to-end delay is evaluated and discussed.

Abstract:The emerging multi-energy system has brought new challenges and opportunities to energy business worldwide. To address the issues in multi-energy trading, this paper proposes an electricity, heating, and cooling trading model for the interaction between the multi-energy service provider (MESP) and multi-energy consumer (MEC) by using bi-level programming. In the upper level, the model instructs the MESP to make decisions on the optimal energy purchasing scheme and energy economic dispatch. In the lower level, optimal consuming patterns of different energies with the given retail prices are modeled for the MEC. Specifically, a novel multi-energy demand response (DR) program that employs energy conversion devices is proposed. Numerical results show that MEC can reduce its consumption cost via the multi-energy DR. Meanwhile, MESP benefits greatly from the flexibilities of energy conversion. This research can provide theoretical support for the future development of multi-energy trading.

Abstract:Wind power production is uncertain. The imbalance between committed and delivered energy in pool markets leads to the increase of system costs, which must be incurred by defaulting producers, thereby decreasing their revenues. To avoid this situation, wind producers can submit their bids together with hydro resources. Then the mismatches between the predicted and supplied wind power can be used by hydro producers, turbining or pumping such differences when convenient. This study formulates the problem of hydro-wind production optimization in operation contexts of pool market. The problem is solved for a simple three-reservoir cascade case to discuss optimization results. The results show a depreciation in optimal revenues from hydro power when wind forecasting is uncertain. The depreciation is caused by an asymmetry in optimal revenues from positive and negative wind power mismatches. The problem of neutralizing the effect of forecasting uncertainty is subsequently formulated and solved for the three-reservoir case. The results are discussed to conclude the impacts of uncertainty on joint bidding in pool market contexts.

Abstract:The voltage characteristics of a half-wavelength transmission (HWLT) system lack mechanism explanation and systematic summary. In this paper, both the steady-state and fault voltage characteristics are analyzed and explained based on the wave process method. An analysis model is established for HWLT lines and wave propagation equations with different terminal conditions are deduced. For steady-state conditions, voltage profiles along HWLT lines are depicted and partial overvoltage occurs when there is an overload or low-power factor. In addition, fault voltage characteristics are analyzed under load-rejection or short-circuit conditions. The series resonance overvoltage will happen when short-circuit faults occur at certain positions and its peak value is influenced by grid strength or fault type. Furthermore, a refined transmission line model is presented to consider non-ideal factors and simulation results demonstrate that geomorphic conditions and electromagnetic environments influence line voltage characteristics to a certain extent.

Abstract:Designing an optimal tracking controller and system observer is a nonlinear problem in the variable-speed wind energy conversion system (WECS). In this paper, an adjusted feedforward and feedback optimal controller with extended Kalman filter (EKF) is introduced to estimate and control the permanent magnet synchronous generator (PMSG) for the maximum power point tracking (MPPT) with several disturbances of the wind speed. An augmented model of wind turbine and PMSG is expanded, and then the parameters of the optimal controller and estimator are obtained. The dynamic stability of the closed-loop system with feedback-feedforward controller (FFC), EKF and speed controller are analyzed. To compare the dynamic performance of EKF and FFC with the conventional controllers, numerical results are demonstrated with the disturbances of the wind speed and faults in power grid.

Abstract:In a permanent magnet synchronous generator(PMSG) system, conversion systems are major points of failure that create expensive and time-consuming problems. Fault detection is usually used to achieve a steady system. This paper presents a full analysis of a PMSG system for wind turbines (WT) and proposes a fault detection method using correlation features. The proposed method is motivated by the balance among the three-phase currents both before and after an open-circuit fault occurs in a converter of the PMSG system. It is unnecessary to analyze the output waveforms of a converter during fault detection. In this study, two correlation features of stator currents, the mean and covariation, are extracted to train an artificial neural network (ANN), thereby enhancing the performance of the proposed method under different wind speed conditions. Moreover, additional sensors and the collection of a massive amount of data are not required. Model simulations of an ideal inverter and a PMSG system are conducted using PSCAD software. The simulation results show that the proposed method can detect the locations of faulty switches with a diagnostic rate greater than 99.4% for the ideal inverter, and the PMSG drives settings at different wind speeds.

Abstract:This paper investigates the ability of correcting the power factor at the point of common coupling (PCC) of the source side using dynamic voltage restorer (DVR). By applying the phase angle control (PAC) method, the DVR compensating voltage will be injected with a specific phase angle and magnitude in series with the transmission line, which leads to a power factor angle shift of the resultant load voltage. As a result, the source voltage is always in phase with the source current under different load conditions, which means that the power factor correction is achieved at the PCC of the source side. A laboratorial prototype of the DVR is utilized to verify the proposed control algorithm. The experimental results validate that an approximate unity power factor can be maintained at the source side.

Abstract:With the rapid deployment of the advanced metering infrastructure (AMI) and distribution automation (DA), self-healing has become a key factor to enhance the resilience of distribution networks. Following a permanent fault occurrence, the distribution network operator (DNO) implements the self-healing scheme to locate and isolate the fault and to restore power supply to out-of-service portions. As an essential component of self-healing, service restoration has attracted considerable attention. This paper mainly reviews the service restoration approaches of distribution networks, which requires communication systems. The service restoration approaches can be classified as centralized, distributed, and hierarchical approaches according to the communication architecture. In these approaches, different techniques are used to obtain service restoration solutions, including heuristic rules, expert systems, meta-heuristic algorithms, graph theory, mathematical programming, and multi-agent systems. Moreover, future research areas of service restoration for distribution networks are discussed.

Abstract:With the gradual upgradation of global energy consumption and the associated development of multi-energy sources, the pace of unified energy planning and design has been accelerated and the concept of multi-energy system (MES) has been formed. The industrial structure of industrial park (IP) consists of production and marketing of multi-energy sources, which makes IP become an ideal application scenario for MES. The coupling between multi-sources raises the complexity level of IP, which requires the demand side analysis in IP as it enables customers to actively participate in energy planning and development. This paper presents the concept and operation strategies of integrated demand response (IDR), and its model classification is analyzed in detail. Optimization model and IDR with varying time period are studied in IP to determine their impacts on the system. A detailed survey of different techniques in both operation strategies and model classification is presented and the classification is based on pros and cons. Finally, key issues and outlooks are discussed.

Abstract:This letter investigates how to identify the marginal bottleneck, which is defined as the constraint most likely to be violated with the increasing wind generation uncertainty of power system in real-time dispatch. The presented method takes the correlation of wind power prediction error (WPPE) into account, leading to an ellipsoidal formulation of wind power generation region (WGR). Based on constructed WGR, the identification procedure is formulated as a max-max-min problem, which is solved by the algorithm based on iteration linear program with the proposed method to select appropriate initial points of WPPE. Finally, two cases are tested, demonstrating the efficacy and efficiency of the procedure to identify marginal bottleneck.