• Energy Research

The integration of renewable resources is quickly growing in the Nordic power system (NPS), and it has led to increasing challenges for the operation and control of the NPS. Nordic countries require that the first-generation power plants have a more flexible operation regime to overcomes power imbalances coming from fluctuations of the demand and supply. This paper assesses optimal frequency support of variable-speed hydropower plants installed in Telemark and Vestfold, Norway, considering future scenarios of NPS. The total kinetic energy of the NPS is expected to be significantly reduced in the future. This paper looks into the implementation of hydropower units with a variable-speed operation regime and battery energy storage systems (BESS), equipped with fast-active power controller (FAPC) technology, to provide fast frequency response after a system frequency disturbance. The frequency support was formulated as an optimization process; therefore, the parameter of the FAPC was optimally calculated for future scenarios of low inertia in NPS. Three main futures scenarios were developed for technology penetration in the Vestfold and Telemark area in Norway. The simulation results showed that the integration variable-speed hydropower units and BESS technologies improved the frequency response even in low-kinetic energy scenarios.

This paper presents different forms of Fast Active Power Injection (FAPI) control schemes for the analysis and development of different mitigation measures to address the frequency stability problem due to the growth of the penetration level of the Power Electronic Interfaced Generation (PEIG) in sustainable interconnected energy systems. Among the studied FAPI control schemes, two different approaches in the form of a derivative-based control and a virtual synchronous power (VSP) based control for wind turbine applications are also proposed. All schemes are attached to the PEIG represented by a generic model of wind turbines type 4. The derivative-based FAPI control is applied as an extension of the droop based control scheme, which is dependent on the measurement of the network frequency. By contrast, the proposed VSP-based FAPI is fed by the measurement of the active power deviation. Additionally, unlike existing approaches for virtual synchronous machines, which are characterized by high-order transfer functions, the proposed VSP-based FAPI is defined by a second-order transfer function, which can contribute to fast mitigation of the system primary frequency deviations during containment period. The Great Britain (GB) test system, for the Gone-Green planning scenario for the year 2030 (GG2030), is used to evaluate the effects of the proposed FAPI controllers on the dynamics of the system frequency within the frequency containment period. Thanks to proposed FAPI controllers, it is possible to reach up to 70% for the share of wind power generation without violating the threshold limits for frequency stability. For verification purposes, a full-scale wind turbine facilitated with each FAPI controller is tested in EMT real-time simulation environment. This paper presents different forms of Fast Active Power Injection (FAPI) control schemes for the analysis and development of different mitigation measures to address the frequency stability problem due to the growth of the penetration level of the Power Electronic Interfaced Generation (PEIG) in sustainable interconnected energy systems.

Abstract This paper presents a wind-resource atlas of Venezuela based on wind observations recorded from on-site meteorological stations. Meteorological datasets of 32 weather stations located over northern Venezuela are used in the development of the maps of three main regions in Venezuela: West, Central and East. Hourly observations of wind speed and direction at each anemometer mast, recorded during the period 2005–2007, have been analysed in order to define the statistical description of the wind resource in the studied area. This processed data along with information on elevation and roughness length is used to model the horizontal and vertical extrapolation of wind data and the estimation of the wind resource. An implementation of Mass-conservation Wind-Flow Model in OpenWind software is used to calculate the wind resource at each anemometer mast. A distance-squared interpolation method is proposed as the post-processing procedure and blending technique to create each map upon which a Venezuelan wind atlas is then built. Simulation results include two main wind-resource atlases obtained at 80 m height above ground: (i) a traditional map of mean wind speed for each direction; and (ii) a map of power density. Results show that the best wind-energy resources are located in the northern coastal area of Venezuela.

Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Auto-regressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time-series data in order to predict the frequency features such as Nadir. Simulated scenarios are based on the gradual inertia reduction by including non-synchronous generation into the Nordic 32 test system, whereas the PMU collected data is taken from different locations in the Nordic Power System (NPS). Several horizon-windows are experimented in order to observe an adequate margin of prediction. Scenarios considering noisy signals are also evaluated in order to provide a robustness index of predictability. Results show the proper performance of the method and the adequate level of prediction based on the Root Mean Squared Error (RMSE) index.

This paper presents an application of the Mean-Variance Mapping Optimization (MVMO) algorithm to the identification of the parameters of Gaussian Mixture Model (GMM) representing variability of power system loads. The advantage of this approach is that different types of load distributions can be fairly represented as a convex combination of several normal distributions with respective means and standard deviation. The problem of obtaining various mixture components (weight, mean, and standard deviation) is formulated as a problem of identification and MVMO is used to provide an efficient solution in this paper. The performance of the proposed approach is demonstrated using two tests. Results indicate the MVMO approach is efficient to represented load models.

This paper presents a comprehensive analysis of the effect of the converter synchronizing methods on the contribution that Battery Energy Storage Systems (BESSs) can provide for the support of the inertial response of a power system. Solutions based on phase-locked loop (PLL) synchronization and virtual synchronous machine (VSM) synchronization without PLL are described and then compared by using time-domain simulations for an isolated microgrid (MG) case study. The simulation results showed that inertial response can be provided both with and without the use of a PLL. However, the behavior in the first moments of the inertia response differed. For the PLL-based solutions, the transient response was dominated by the low-level current controllers, which imposed fast under-damped oscillations, while the VSM systems presented a slower response resulting in a higher amount of energy exchanged and therefore a greater contribution to the support of the system inertial response. Moreover, it was demonstrated that PLL-based solutions with and without derivative components presented similar behavior, which significantly simplified the implementation of the PLL-based inertia emulation solutions. Finally, results showed that the contribution of the BESS using VSM solutions was limited by the effect of the VSM-emulated inertia parameters on the system stability, which reduced the emulated inertia margin compared to the PLL-based solutions.

The method proposed in the procedure of the first step of the spectral clustering controlled islanding (SCCI) is actually equivalent to the application of slow coherency. The slow coherency method is very useful for ofiline analysis. However, the following two questions must be answered before slow coherency can be applied to identify suitable generator groups: 1) have the generators lost synchronism, or will they, i.e., is the separation of generator groups necessary? 2) How many generator groups should be formed? This means that there are distinct drawbacks when applying slow coherency online; however, the method can still be adapted to this purpose to a certain extent [1]. We think a better way is using an online algorithm to replace the slow coherency method [2], [3]. The drawbacks and limitations of the first step of the SCCI have been discussed in Section III-A.

The reactive power control mechanisms at the smart inverters will affect the voltage profile, active power losses and the cost of reactive power procurement in a different way. Therefore, this paper presents an assessment of the cost–benefit relationship obtained by enabling nine different reactive power control mechanisms at the smart inverters. The first eight reactive power control mechanisms are available in the literature and include the IEEE 1547−2018 standard requirements. The ninth control mechanism is an optimum reactive power control proposed in this paper. It is formulated to minimise the active power losses of the network and ensure the bus voltages and the reactive power of the smart inverter are within their allowable limits. The Vestfold and Telemark distribution network was implemented in DIgSILENT PowerFactory and used to evaluate the reactive power control mechanisms. The reactive power prices were taken from the default payment rate document of the National Grid. Simulation results demonstrate that the optimal reactive power control mechanism provides the best cost–benefit for the daily steady-state operation of the network.