IET Renewable Power Generation最新文献

In the classical differential-algebraic equations (DAEs) framework for the traditional power system stability analysis, synchronous generators are depicted by differential equations and network by algebraic equations under the quasi-steady-state assumption. Differently, in the power-electronic-dominated power system (PEDPS), the dynamics of transmission lines of network for fully differential equations should be considered, due to the rapid response of converters' controls, for example, the alternating current controls. This poses a great challenge for the cognition, modeling, and analysis of the PEDPS. In this article, a nonlinear DAE model framework for the PEDPS is established with differential equations for the source nodes and algebraic equations for the dynamical electrical network, by generalizing the application scenarios of Kron reduction. The internal and terminal voltages of source nodes of converters are chosen as ports of nodes and network. Namely, the internal and terminal voltages of source nodes work as their output and input, respectively, whereas they work as the input and output of the algebraic network, respectively. The impact of dynamical network becomes clear, namely, it serves as a (linear) voltage divider and generates the terminal voltage based on the internal voltage of the sources simultaneously. By keeping only useful independent state variables, all differential equations for the transmission lines can be transferred to algebraic equations. With this simple model, the roles of both nodes and network become apparent, and it enhances the understanding of the PEDPS dynamics. On the other hand, broad simulations are conducted and compared to verify the proposed DAE framework for the PEDPS. As all independent variables have been kept in the model, it is found that they show the same computational accuracy, but better efficiency in computational time, compared to the electromagnetic-transient simulation results.

The mechanism of oscillations that occur in the system connected to renewable energy sources is complex, generally including negative damping oscillation, limit induced oscillation etc. Combining with the dynamic characteristics of the low-dimensional system after limit working, this paper analyses the mechanism of a type of limit induced oscillation that occurs when the output of the DC voltage outer loop continuously touches the limit for single-machine infinite-bus system of permanent magnet synchronous generator with positive damping under a large disturbance. Specific study as follows: firstly, a non-smooth state space model of single-machine infinite-bus system of permanent magnet synchronous generator with nonlinear state limit (clarified as a Filippov system) is established. Secondly, the small disturbance characteristics of the system are analysed when the limit does not work. Thirdly, the piecewise dynamic description of the system is analysed with the limit working, and the reason that the final contraction of the system to an eight-dimensional manifold is explained based on mathematical derivation and physical characteristics. Finally, the characteristics of equilibrium point of the system in a low-dimensional manifold are analysed when the limit continuously working, revealing that the existence of a pair of conjugate unstable eigenvalues in the low-dimensional system is the reason of system oscillation.

Optimal reactive power dispatch (ORPD) is taken as a vital problem related to electric power networks for economic and control operations. Nowadays, thermal generators are no longer utilized and renewable resources (RERs) have been integrated owing to their marvellous benefits. The integration of RERs into power networks is considered as a strenuous imposition due to their uncertainties. The objective is to determine the placement of four wind and four PV units into large-scale 118-bus network to reduce expected power losses. The normal, lognormal, and Weibull distributions are utilized to model system uncertainties, while Monte-Carlo simulation and reduction-based approaches are utilized to generate the novel set of optimal scenarios. To avoid stagnation problems in skilled optimization algorithm (SOA), three strategies such as fitness-distance balance selection, mutation, and gorilla troops-based approaches are utilized to improve overall strength of SOA. Effectiveness of ESOA is proved via statistical and non-parametric analysis using benchmark functions, the results are further compared with other optimization techniques. The proposed ESOA is also used to resolve the deterministic and stochastic ORPD frameworks to reduce power losses and expected power losses. By incorporation of RERs into the stochastic ORPD framework can saved the expected power losses around 24.01%.

As the electrical system becomes more and more decentralized, new control algorithms are necessary to manage the intermittent and non-deterministic production of non-programmable renewable sources, as well as the consumption of new loads like electric vehicles and heat pumps. Traditionally, electrical networks are controlled centrally, which provides full controllability of the system but introduces issues on scalability and complexity. This paper proposes a distributed multilayer control scheme based on model predictive control (MPC) applied to different portions of an electrical grid, optimizing power exchanges for balancing services. The first layer comprises local decentralized MPC controllers managing their areas, while the high-level distributed supervisor layer coordinates the exchange of flexibility between the network areas by acting on AC/DC converters. The overall distributed control architecture is applied and experimentally validated through the distributed energy resources test facility of RSE, showing enhanced performances in terms of prompt control action and compensation of the power disturbances.

An essential component of guaranteeing the stability and safety of electricity distribution networks is islanding detection. In this work, a novel method for islanding detection which combined both phasor measurement units (PMU) and artificial neural network (ANN) is proposed. Using PMU measurements, the technique extracts features including phasor voltage, voltage frequency, and voltage rate of change of frequency (ROCOF), which later are fed into an ANN classifier. Using a huge dataset of more than a hundred thousand observations of both islanding and non-islanding scenarios, testing was done on 24 distinct types of inverters in compliance with PN-EN 62116 protocol criteria. The tests were carried out using Regenerative Grid Simulator Chroma 61815-powered system which was connected in parallel to adjusting RLC load; the tested inverters were linked to a Photovoltaic Panels Simulator, the National Instruments cRIO-9024 measuring equipment was used to carry out the measurements, MATLAB and LabVIEW were used for analyzing the data and results. With a testing accuracy of 99.05% and a training accuracy of 99.34%, the results demonstrate a high degree of accuracy. This work offers a practical solution for problems that occurred due to islanding phenomenon in power networks which can enhance the system dependability and security.

This study advances the efficiency of a recompression power generation cycle using supercritical carbon dioxide, leveraging solar energy as a sustainable alternative to fossil fuels. It is the first to uniquely address the performance of air-cooled solar recompression cycles by evaluating both the heat and cooling source. Traditional water and cooling towers are replaced with air and fans to conserve water and enhance cooling efficiency. The study employs advanced exergy analysis to identify optimization strategies and reduce exergy destruction. The baseline system “System A” identifies the precooler and main compressor as key areas for improvement. Two novel systems are proposed: “System B,” which integrates an intercooler and a secondary compressor to significantly cut exergy losses, and “System C,” which uses a single-effect absorption refrigeration cycle to further reduce exergy destruction. The results show an increase in energy efficiency and exergy efficiency from 23.24% in System A to 25.72% in System B and 24.28% in System C. Advanced exergy analysis reveals that, although the central receiver and high-temperature recuperator are major sources of exergy destruction, the heliostat and low-temperature recuperator are crucial for system optimization. This study's unique approach combines comprehensive exergy analysis with innovative system proposals based on the results.

Among the various methods proposed for modeling solar panels, those based on equivalent circuits have received significant attention. In these approaches, determining unknown parameters varies depending on the modeling objective. To model voltage–current characteristics, circuit analysis methods are employed to extract these unknown parameters. However, this modeling method relies on data provided by the solar panel manufacturer, leading to increased modeling error over time as coefficients change. In this article, a method independent of the manufacturer's data for modeling solar panels is presented. This method enables accurate modeling of pre-installed solar power plants. By utilizing genetic programming on a single day's worth of data from a solar panel, the proposed method can establish relationships with a high degree of fit for the open-circuit voltage, maximum power point, and short-circuit current based on weather conditions. Through these relationships, the voltage–current characteristics can be modeled with greater precision compared to traditional circuit analysis methods, and without the need for data from the solar panel manufacturer. Finally, for further evaluation, a 3 kW solar power plant is modeled, which demonstrates the effectiveness of the proposed method.

The dependency of photovoltaic (PV) systems-based generation systems on constantly varying temperatures and incident sunlight affect the non-linear behaviour of PV panels. Therefore, tracking the maximum power output of the PV panels for efficient utilization becomes a challenge, resulting in power losses and the creation of intense heating spots in the shaded areas of the PV modules when the PV modules receive varying degrees of insolation, that is, under partial shading conditions (PSCs). The inclusion of bypass diodes in parallel to each PV module mitigates this problem to an extent but leads to the formation of several peaks in the P-V characteristics. As a result, maximum power point tracking (MPPT) to deliver maximum power at the load becomes a limitation for conventional optimization algorithms as they are normally based on hill climbing algorithm and get stuck at local maxima of the P-V curve. Therefore, metaheuristic algorithms are used thereby eliminating the possibility of getting trapped at the local optima. However, particle swarm optimization (PSO) suffers from delayed convergence, more iterations to reach the optimal point, and random parameter selection. Hence, this study employs an improved version of PSO called Phasor-PSO (P-PSO) in an MPPT controller. The proposed algorithm is parameter-less which results in reduced computational complexity and thus provides quick decision in achieving the maximum power point (MPP). The hardware-in-loop real-time analysis demonstrates the supremacy of P-PSO over PSO in faster convergence, higher efficiency, and reduced power losses during tracking under various PSCs. The P-PSO based MPPT method will find application in grid connected and stand-alone solar PV system with better efficiency of power transfer.

Accurate and reliable forecasting of wind power is essential for the stable integration of wind energy into the electrical grid. However, the chaotic nature of wind power presents a significant challenge in utilizing data for effective short-term forecasting, such as 60-min predictions. This article introduces a hybrid data-driven framework that employs an ensemble deep learning model to provide highly precise short-term wind power predictions. The framework leverages a data-driven approach to identify the intrinsic components of wind power data, including high-frequency and low-frequency components. A convolutional layer-based feature fusion network is then established to properly extract important information from irrelevant wind energy features. Subsequently, an ensemble of long short-term memory (LSTM) networks is developed to forecast wind power using the fused features, thereby mitigating the disadvantage of a single prediction model. The numerical experiment is carried out based on two different real-life datasets. The results demonstrate the effectiveness of the proposed method in forecasting short-term wind power compared to five benchmarks.

To promote peer-to-peer trading in a distribution system with the franchise owned by the concerned distribution company respected, a so-called “source-grid-load-storage” (SGLS) integrated project is promoted in China. Given this background, this paper proposes a multi-stage joint optimization model to optimize the participating strategy for SGLS-IPs in electricity energy and ancillary service markets. A multi-energy flow park model is presented with electricity, gas, and heat included. A two-stage model for optimal participating strategy of SGLS-IPs in electricity and ancillary service markets is then presented. Through scenario analysis of a sample system, the attained revenue of an SGLS-IP in different markets is evaluated. Additionally, a bilevel Stackelberg game model is introduced for internal electric vehicle clusters (EVCs) within an SGLS-IP, with the upper level representing an SGLS-IP, and the lower level representing EV users engaging in the game. The effectiveness of the model is validated through case studies. Simulation results demonstrate that reasonable market participation and pricing strategies contribute to the efficient allocation of resources within a SGLS-IP and a win-win outcome for both the SGLS-IP entity and EV users.