Abstract:The oscillations in a power system can be categorized into free oscillations and forced oscillations. Many algorithms have been developed to estimate the modes of free oscillations in a power system. Recently, forced oscillations have caught many researchers’ attentions. Techniques are proposed to detect forced oscillations and locate their sources. In addition, forced oscillations may have a negative impact on the estimation of mode and mode-shape if they are not properly accounted for. To improve the power system reliability and dynamic properties, it is important to first distinguish forced oscillations from free oscillations and then locate the sources of forced oscillations in a timely manner. The negative impact of forced oscillation can be mitigated when they are detected and located. This paper provides an overview of the analysis technique of forced oscillations in power systems. In addition, some future opportunities are discussed in forced oscillation studies.

Abstract:It is a common practice to simulate some historical or test systems to validate the ef?ciency of new methods or concepts. However, there are only a small number of existing power system test cases, and validation and evaluation results, obtained using such a limited number of test cases, may not be deemed suf?cient or convincing. In order to provide more available test cases, a new random graph generation algorithm, named ‘‘dualstage constructed random graph’’ algorithm, is proposed to effectively model the power grid topology. The algorithm generates a spanning tree to guarantee the connectivity of random graphs and is capable of controlling the number of lines precisely. No matter how much the average degree is, whether sparse or not, random graphs can be quickly formed to satisfy the requirements. An approach is developed to generate random graphs with prescribed numbers of connected components, in order to simulate the power grid topology under fault conditions. Our experimental study on several realistic power grid topologies proves that the proposed algorithm can quickly generate a large number of random graphs with the topology characteristics of real-world power grid.

Abstract:The effective identi?cations of important nodes in power grid are foundations of differentiated management and stable operation of power grid. However, the current studies on this ?eld are not thorough enough. This paper applies the model based on co-citation (MBCC)hypertext induced topic selection (HITS) algorithm used in web page ranking to power grid and proposes an index to determine node importance of power grid from the perspectives of in?ow and out?ow power. MBCC-HITS algorithm is modi?ed considering load, power source, and line ?ow comprehensively for the differences between Internet and power grid in this paper. Then a method for evaluating node importance is proposed using the modi?ed algorithm. Lastly, the rationality and validity of the proposed method are veri?ed by comparisons with other methods in case studies of IEEE 14-bus and 118-bus systems.

Abstract:In this paper, a data-driven stochastic subspace identi?cation (SSI-DATA) technique is proposed as an advanced stochastic system identi?cation (SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate and robust extraction of the modes’ parameters (frequency, damping and mode shape), SSI has already been veri?ed as an effective identi?cation algorithm for output-only modal analysis. The new feature of the proposed SSI-DATA applied to inter-area oscillation modal identi?cation lies in its ability to select the eigenvalue automatically. The effectiveness of the proposed scheme has been fully studied and veri?ed, ?rst using transient stability data generated from the IEEE 16-generator 5-area test system, and then using recorded data from an actual event using a Chinese wide-area measurement system (WAMS) in 2004. The results from the simulated and recorded measurements have validated the reliability and applicability of the SSI-DATA technique in power system low frequency oscillation analysis.

Abstract:Transmission congestion management became a grievous issue with the increase of competitiveness in the power systems. Competitiveness arises due to restructuring of the utilities along with the penetration of auxiliary services. The present study depicts a multi objective technique for achieving the optimal capacities of distributed generators (DG) such as solar, wind and biomass in order to relieve congestion in the transmission lines. Objectives like transmission congestion, real power loss, voltages and investment costs are considered to improve the technical and economical performances of the network. Multi objective particle swarm optimization algorithm is utilized to achieve the optimal sizes of unity power factor DG units. The insisted methodology is practiced on IEEE-30 and IEEE-118 bus systems to check the practical feasibility. The results of the proposed approach are compared with the genetic algorithm for both single and multi-objective cases. Results revealed that the intimated method can aid independent system operator to remove the burden from lines in the contingency conditions in an optimal manner along with the improvement in voltages and a reduction in real power losses of the network.

Abstract:A bulk power system is conventionally characterized by a complex structure with a large number of components. Each component generally has a different contribution to the transmission congestion (TC) of a system. Thus, a TC sharing method that can be used to evaluate the contribution of each component to the system TC and recognize the weak parts from the perspective of TC should be built. This paper presents a transmission congestion tracing (TCT) principle based on the failed component sharing principle and proportional sharing principle and a TCT model using the Monte Carlo simulation method. Case studies on the IEEE Reliability Test System indicate that the proposed method is effective and feasible.

Abstract:Forecasting error amending is a universal solution to improve short-term wind power forecasting accuracy no matter what speci?c forecasting algorithms are applied. The error correction model should be presented considering not only the nonlinear and non-stationary characteristics of forecasting errors but also the ?eld application adaptability problems. The kernel recursive least-squares (KRLS) model is introduced to meet the requirements of online error correction. An iterative error modi?cation approach is designed in this paper to yield the potential bene?ts of statistical models, including a set of error forecasting models. The teleconnection in forecasting errors from aggregated wind farms serves as the physical background to choose the hybrid regression variables. A case study based on ?eld data is found to validate the properties of the proposed approach. The results show that our approach could effectively extend the modifying horizon of statistical models and has a better performance than the traditional linear method for amending short-term forecasts.

Abstract:Flexibility in plug-in electric vehicle (PEV) charging can reduce the ancillary cost effects of wind variability and uncertainty on electric power systems. In this paper, we study these benefits of PEV charging, demonstrating that controlled PEV charging can reduce costs associated with wind uncertainty and variability. Interestingly, we show that the system does not require complete control of PEV-charging loads to mitigate the negative cost impacts of wind variability and uncertainty. Rather, PEV owners giving the system a two-hour window of flexibility in which to recharge their vehicles provides much of the benefits that giving full charging control does.

Abstract:Wind power in China has experienced fast development in recent years. However, areas rich in wind power resources are often far away from loads centers, which leads to weak connection between wind turbines and power grid. When a grid fault occurs, new transient characteristics in weak grid integrated with doubly-fed induction generators (DFIGs) may present, such as voltage phase angle jump. Current control strategies for wind turbine with strong grid connection are hard to be adapted under weak gird connection. This paper explores the transient characteristics of DFIGs under voltage phase angle jump through analyzing the operation and control characteristics of DFIGs connected into weak grid when the voltage phase angle jumps. Fault ride through (FRT) control strategy of DFIGs based on adaptive phase-locked loop is proposed to adapt weak grid condition. The reference frame of the proposed strategy will be changed in real-time to track the operation condition of DFIGs according to the terminal voltage, and different phase tracking method is adopted during the grid fault. Field data analysis and time domain simulation are carried out. The results show that voltage phase angle jumps when a grid fault occurs, which weakens the FRT capability of DFIGs, and the proposed FRT control strategy can optimize transient characteristics of DFIGs, and improve the FRT capability of DFIGs.

Abstract:This paper proposes a new multi-areaframework for unbalanced active distribution network (ADN) state estimation. Firstly, an innovative three-phase distributed generator (DG) model is presented to take the asymmetric characteristics of DG three-phase outputs into consideration. Then a feasible method to set pseudo -measurements for unmonitored DGs is introduced. The states of DGs, together with the states of alternating current (AC) buses in ADNs, were estimated by using the weighted least squares (WLS) method. After that, the ADN was divided into several independent subareas. Based on the augmented Lagrangian method, this work proposes a fully distributed three-phase state estimator for the multi-area ADN. Finally, from the simulation results on the modi?ed IEEE 123-bus system, the effectiveness and applicability of the proposed methodology have been investigated and discussed.

Abstract:This study investigates a hybrid hierarchical multi-agent system for distributed cooperative voltage control in active distribution networks. The hybrid hierarchical multi-agent system adopts on-load tap-changing (OLTC) agents for the distribution transformers and feeder control section (FCS) agents for the distributed generators (DGs). The objective is to minimize the voltage deviations over the network. The FCS agents also have the objective of minimizing reductions in DG power output. A least squares method is used for curve ?tting to achieve the two objectives. The OLTC agent receives voltage information from the FCS agents to evaluate the state of the voltage in each feeder and the distribution network and cooperates with the FCS agents to control the voltage of the network. The FCS agents exchange the ?tted curve parameters and basic information on the DGs with other agents to achieve the objectives. The effectiveness of the proposed distributed cooperative voltage control scheme is veri?ed through simulations. Depending on the network voltages obtained by the OLTC agent, different operations are executed to prevent voltage limit violations and to minimize the voltage deviations and reductions in the DG power outputs.

Abstract:To deal with uncertainties of renewable energy, demand and price signals in real-time microgrid operation, this paper proposes a model predictive control strategy for microgrid economic dispatch, where hourly schedule is constantly optimized according to the current system state and latest forecast information. Moreover, implicit network topology of the microgrid and corresponding power ?ow constraints are considered, which leads to a mixed integer nonlinear optimal power ?ow problem. Given the nonconvexity feature of the original problem, the technique of conic programming is applied to ef?ciently crack the nut. Simulation results from a reconstructed IEEE-33 bus system and comparisons with the routine day-ahead microgrid schedule suf?ciently substantiate the effectiveness of the proposed MPC strategy and the conic programming method.

Abstract:This paper examines the impact of power transmission network topology change on locational marginal price (LMP) in real-time power markets. We consider the case where the false status of circuit breakers (CBs) that bypass topology error processing can generate an incorrect power system network topology, subsequently distorting the results of the state estimation and economic dispatch. The main goal of this paper is to assess the economic impact of this miscon?gured network topology on realtime LMP in an entire power system with network congestion. To this end, we start with our prior result, a simple and analytical congestion price equation, which can be applied to any single line congestion scenario. This equation can be extended to better understand the degree to which the LMP at any bus changes due to any line status error. Furthermore, it enables a rigorous analysis of the relationship between the change in LMP at any bus with respect to any line error and various physical/economical grid conditions such as the bidding prices for marginal generators and the locations of the congested/erroneous lines. Numerical examples on the impact analysis of this topology error are illustrated in IEEE 14-bus and 118-bus systems.

Abstract:With the development of electricity market mechanism and advanced metering infrastructure (AMI), demand response has become an important alternative solution to improving power system reliability and efficiency. In this paper, the agent-based modelling and simulation method is applied to explore the impact of symmetric market mechanism and demand response on electricity market. The models of market participants are established according to their behaviors. Consumers’ response characteristics under time-of-use (TOU) mechanism are also taken into account. The level of clearing price and market power are analyzed and compared under symmetric and asymmetric market mechanisms. The results indicate that the symmetric mechanism could effectively lower market prices and avoid monopoly. Besides, TOU could apparently flatten the overall demand curve by enabling customers to adjust their load profiles, which also helps to reduce the price.

Abstract:This paper presents a multi-objective Pareto optimal method for allocation of fault current limiters based on an immune algorithm, which takes into account two objectives of the cost and fault current mitigation effect. A sensitivity factor calculation method based on the rate of fault current mitigation is proposed to reduce the search space and improve the ef?ciency of the algorithm. In this approach, the objective functions related to the cost and fault current mitigation effect are established. A modi?ed inversion operator based on equal cost is proposed to converge to global optimal solutions more effectively. The proposed algorithm is tested on the IEEE 39-bus system, and obtains the Pareto optimal solutions, from which the user can select the most suitable solutions according to the preferences and relative importance of the objective functions. Simulation results are used to verify the proposed method.