• Energy Research

Normal form (NF) is an effective tool to quantitatively analyze nonlinear modal interaction, which is believed to contribute to the complex nonlinear dynamics in power systems. However, in conventional NF analysis, the solution under resonance condition cannot be expressed in a closed form. Hence the NF analysis of power system higher order modal interaction was limited to nonresonant cases, or directly neglecting the resonant terms. In this paper, the NF solutions under modal resonance are derived, by means of the polynomial vector space decomposition. The obtained solution is in a simple closed form, including both the nonresonant part and the resonant part. Numerical simulations are performed to verify the effectiveness of the proposed approach. The results show that, under modal resonant conditions, neglecting the resonant terms may cause significant errors to the obtained numerical solutions, whereas including modal resonant terms in NF may increase the accuracy of the analysis. Finally, the approach proposed in this paper is compared with the modal series method, which, unlike NF method, is not limited by resonant condition. The results show that the proposed method in this paper has better performance in both accuracy and computing time.

With the development of the wide-area power systems, time delays, including inherent and malicious network attacks time delays, are introduced into control signal communication channel, which jeopardize power system operation, control and protection. To eliminate the threats, state estimation was used to monitor the power system operating state. However, traditional state estimation methods are difficult to carry out due to the time delays have randomness. Therefore, a dynamic state estimation (DSE) method for power system based on delay and its stochastic characteristics was proposed in this paper. First, dynamic equations of important components, such as generators, exciters and power system stabilizers (PSSs), are introduced to describe complex power systems. In order to facilitate the use of Kalman filter (KF) methods, the power system state equations are discretized. Then, the correlation between stochastic delays and power system operating state indicated by neural network (NN). On the basis of the correlation, a KF method combined with NN has been proposed to adapt to the power system with stochastic delays. Finally, to verify the effectiveness of the proposed method, simulation experiments has been carried out on an actual power system. The simulation results show that the proposed method performs well in monitoring generators’ dynamic performance when the power system was disturbed by stochastic delays.

Abstract Current constraints require limiting the power transmitted from wind farms to power grids. Hence, wind turbine systems should be capable of regulating and limiting the generation capacity for a wide range of operating wind speeds. In addition, wind power plants must be endowed with active power and frequency control capabilities. In a single wind farm, wind turbines can be installed at different heights or altitudes, consequently exposing them to variable environmental conditions that can cause variations among their surrounding wind speeds. Thus, we propose a de-loading control strategy that integrates over-speeding and pitch control of wind turbines operating at variable wind speeds. This strategy allows not only storing power to satisfy the constraint demand of the power grid, but also contributing to primary frequency control. In fact, the proposed control strategy can adjust the static frequency difference coefficient of wind turbines, which is based on the proportion of variable-speed wind turbines operating under high wind speeds. Moreover, the proposed control strategy, which supports frequency control of the power grid under power constraints, suitably supports the frequency regulation of wind turbines that work at different wind speeds. Simulation results suggest that the proposed control strategy meets the power grid constraints, enhances the performance of primary frequency control, alleviates the frequency control pressure from thermal power plants, and appropriately generates curtailed wind power under variable wind speeds.

Abstract Unlike steam and gas cycles, the Kalina cycle system can utilize low-grade heat to produce electricity with water-ammonia solution and other mixed working fluids with similar thermal properties. Concentrated photovoltaic thermal systems have proven to be a technology that can be used to maximize solar energy conversion and utilization. In this study, the integration of Kalina cycle with a concentrated photovoltaic thermal system for multigeneration and hydrogen production is investigated. The purpose of this research is to develop a system that can generate more electricity from a solar photovoltaic thermal/Kalina system hybridization while multigeneration and producing hydrogen. With this aim, two different system configurations are modeled and presented in this study to compare the performance of a concentrated photovoltaic thermal integrated multigeneration system with and without a Kalina system. The modeled systems will generate hot water, hydrogen, hot air, electricity, and cooling effect with photovoltaic cells, a Kalina cycle, a hot water tank, a proton exchange membrane electrolyzer, a single effect absorption system, and a hot air tank. The environmental benefit of two multigeneration systems modeled in terms of carbon emission reduction and fossil fuel savings is also studied. The energy and exergy efficiencies of the heliostat used in concentrating solar radiation onto the photovoltaic thermal system are 90% and 89.5% respectively, while the hydrogen production from the two multigeneration system configurations is 10.6 L/s. The concentrated photovoltaic thermal system has a 74% energy efficiency and 45.75% exergy efficiency, while the hot air production chamber has an 85% and 62.3% energy and exergy efficiencies, respectively. Results from this study showed that the overall energy efficiency of the multigeneration system increases from 68.73% to 70.08% with the integration of the Kalina system. Also, an additional 417 kW of electricity is produced with the integration of the Kalina system and this justifies the importance of the configuration. The production of hot air at the condensing stage of the photovoltaic thermal/Kalina hybrid system is integral to the overall performance of the system.

Smart substation, in which various types of information are being exchanged on a communication network, is regarded as a typical cyber-physical system (CPS). The network-induced time delay, inherently stochastic, makes the performance of power system operation and protection unpredictable. This paper attempts to study the stochastic properties of end-to-end network-induced time delay in a time-critical smart substation CPS environment. The components in a smart substation CPS, including data flow, communication network, and intelligent electronic device (IED), are modeled. The data flow on the network is categorized into two different stochastic types according to their source: Poisson and Pareto data flow. The numerical simulation of the hybrid data sources on substation network is implemented to evaluate the stochastic property, by combining Monte Carlo method and queueing theory. The obtained distribution results are identified by using chi-square and Kolmogorov-Smirnov tests. It is identified that Normal distribution can best characterize the delay in smart substation, meanwhile, the proposed model is verified with the experiment. A typical 220kV smart substation is studied under various scenarios.

Due to the geographic features and fruitful water resources, the distributed generation of Small Hydropower Generator Stack (SHGS) are vigorously developed in southwest China. As the more concerns about security and stability of power grids with the large amount of SHGS feed into the grid, the accurate models are critical to analyze those potential impacts. With the development of online monitoring technologies in smart grid, PMU based dynamic equivalent modeling for SHGS drawn many attentions. However, field applications reveal that traditional SHGS equivalent model may not be robustness enough to adapt the different disturbances. To overcome this deficiency, method to improve the robustness of SHGS dynamic equivalent modeling is proposed. In the paper, a coherency identification based multi-machine modeling method is introduced to enhance the accuracy of equivalent model. Sensitivity and correlation analysis based key parameters selection scheme is implemented to select the critical parameters, meanwhile avoiding the multiple solutions problem. Moreover, multi-objective optimization algorithm is designed for key parameters identification of the equivalent model. Additionally, hybrid dynamic simulation has adopted into the equivalent process to improve the computational efficiency. The effectiveness of the proposed method has been validated with an actual case from China Southwest grids.

This paper presents a new computational attractive interval power flow (PF) approach with correlated uncertain power injections while maintaining high estimation accuracy. The key idea is to integrate the ellipsoid convex model with the evidence theory. This allows us to effectively account for uncertain power injection correlations and eliminate irrelevant focal elements, yielding significantly improved computational efficiency. Extensive comparisons with other approaches demonstrate its effectiveness under various conditions.

Abstract Wind power has already brought lots of benefits for energy market and environment. To promote the utilization of wind energy further and achieve the ambitious goals of applying sustainable energy, the improvement of the performance of wind speed forecasting will play a significant role. The whole system will benefit from an accurate wind forecasting system with high resolution. This paper focused on short-term wind speed forecasting and proposed a method providing the wind speed forecasting with short-term horizon and high resolution. In this paper, a novel method based on the wavelet decomposition, the phase space reconstruction and the self-organizing map (SOM) artificial neural network was proposed. Study case was provided to demonstrate the performance of the proposed forecasting method. Comparative results from several classic forecasting methods were provided as well. Based on the outcomes of study cases and comparison results, it can be concluded that the proposed method performed well in short term forecasting horizon.