Harmonic mitigation and power quality improvement in utility grid with solar energy penetration using distribution static compensator

Abstract Distribution static compensator is based on power electronic devices technology which is utilized to supply rapid changes in active power as well as reactive power of utility grids. This is useful to achieve corrections in power factor, balancing of load, compensation of current and filtering of harmonics. Therefore, proposed work investigates the improvement of the power quality by utilizing the distribution static compensator, which is equipped by battery energy storage system and interfaced to distribution network with solar photo voltaic (PV) energy integration. In the present study, distribution static compensator is controlled using a control strategy based on the synchronous reference frame theory. Customised IEEE‐13 nodes test system incorporating solar PV generation and distribution static compensator, is utilized to perform the harmonic mitigation and power quality analysis. Disturbances of power quality and harmonics have been investigated due to abrupt changes in the insolation of solar radiation, outage of PV plant from grid and synchronization of PV plant to grid. MATLAB/Simulink environment is utilized to perform the study. Effectiveness of a developed approach is validated by comparing results of simulation with results extracted in real time using real time digital simulator. Results indicate that the developed method is more effective for harmonic mitigation and improving power quality of electrical power in distribution network integrated with solar PV generation. Performance of the approach is compared with the performance of methods reported in the literature to establish the suitability of the method for harmonics mitigation and power quality improvement in grid with solar energy

An Improved UFLS Scheme based on Estimated Minimum Frequency and Power Deficit

In the event of a power system disturbance, it is important that the decision to implement under frequency load shedding is based on both the minimum frequency and the magnitude of the disturbance. In this paper, we propose the use of higher order polynomial curve fitting to estimate the minimum frequency. If the prediction shows that the minimum frequency threshold will be violated, the magnitude of the total disturbance is estimated using the swing equation. In addition, the minimum amount of load that must be shed to restore the frequency just above the minimum value can also be directly calculated. Simulations are carried out for the considered Taiwan power system and the results prove the efficiency of the proposed technique

Beyond Environmental Kuznets Curve and Policy Implications to Promote Sustainable Development in Mediterranean

In acknowledgment of the devastating consequences of environmental deterioration, the Mediterranean members are committed to adopt the 2015 treaty action plans of the Paris Climate Agreement (COP21) as carbon dioxide emission (CO2) are on the rise in the Mediterranean region, which seems to be a serious challenge to our world's environment. To this end, our study examined the impact of Foreign Direct Investment (FDI) on environmental degradation for the Mediterranean members for the period between 1995 to 2016. However, variables such as, financial development, economic growth, renewable energy and fossil fuel were further examined by the use cross-sectional-Panel pooled Auto Regressive Distributed Lag methodology, Augmented Mean Group (AMG) and Dumitrescu and Hurlin panel causality test was used for causality analysis. The co-integration results from Westerlund (2007) shows a long-run equilibrium relationship between highlighted variables. The empirical result revealed a negative relation between FDI and CO2 indicating pollutant Hallo Hypothesis (PHH). Moreover, income and its square show an inverted U-Shaped curve indicating environmental Kuznets curve (EKC) hypothesis. Both financial development and renewable energy indicated an adverse association with CO2 emission whereas fossil fuel had a positive relationship with emissions. However, there was a feedback causality among income and carbon emission as well as financial development and carbon emission. Furthermore, we observe that FDI and carbon emission, renewable energy and carbon emission, as well as fossil fuel and carbon emission were found to have one-way causal relationship. Overall, the study suggests some policy prescriptions including the implementation of conservation initiatives and the establishment of clean energy regulation and strategies for the investigated bloc. © 2021 The Authors.The work of H. Haes Alhelou was supported in part by Science Foundation Ireland (SFI) under the SFI Strategic Partnership Programme Grant Number SFI/15/SPP/E3125 and additional funding provided by the UCD Energy Institute. The opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Science Foundation Ireland

Decarbonize Russia — A Best–Worst Method Approach for Assessing the Renewable Energy Potentials, Opportunities and Challenges

Russia is known to be a country with enormous energy resources both renewables and non-renewables. Much of the country's effort towards energy generation has been on the development of the non-renewables over the years. This study examined the opportunities and challenges in Russia's Renewable energy (RE) sector. By coupling both interviews and literature reviews, a total of 8 main opportunities and 7 key challenges were identified and discussed. The Best–Worst-Method was used to assign weights to the various factors using inputs of 30 experienced experts in Russia's RE sector. According to the obtained results, the most significant opportunity that the country would have to take advantage of is the opportunity to export RE outside the shores of the country, it recorded 27.7 percent. This is followed by the country's target for the RE sector which scored 18%, hydrogen production and need to meet local energy requirements followed with 12% each. The greatest challenge which also serve as a hindrance to the development of RE in the country is the low attention given to clean technologies from government, it recorded a weight of 31.4%. This is followed by unequal playing field, and strict local content requirements which recorded 17.9% and 13.5%, respectively. The study ended with some strategic recommendations to authorities for the development of the sector. © 2021 The Authors

Special issue on advances and technologies in high voltage power systems operation, control, protection, and security

The electrical demands in several countries around the world are increasing due to the huge energy requirements of prosperous economies and the human activities of modern life [...

Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

With the expansion of the use of frequency response analysis (FRA) as a reliable tool for fault detection in transformers, more capabilities of this method are discovered every day. So that today the number of transformer faults that can be identified by FRA method has also increased. One of the most critical steps in fault detection with FRA is to distinguish faults and classify them in different classes. In this paper, well-known intelligent classifiers (probabilistic neural network, decision tree, support vector machine, and k-nearest neighbors) are used to classify transformer faults. For this purpose, the necessary measurements are performed on the model transformers under the healthy condition and under different fault conditions (axial displacement, radial deformation, disc space variation, short-circuits, and core deformation). Then, by dividing the frequency ranges of the measured transfer functions of the transformer, a new feature based on numerical and statistical indices for training and validation of classifiers is proposed. After completing the training process, the performance of the classifiers is evaluated and compared by applying the data obtained from real transformers

An optimal probabilistic spinning reserve quantification scheme considering frequency dynamic response in smart power environment

These frequency responses in tandem with spinning reserve are a prominent problem in the future smart grid environment due to the increase of non-dispatchable energy sources. However, this new environment allows incorporating new technologies, that is, distributed energy resource (DER), energy storage systems (ESSs), and shiftable loads, to prevent emergency situations and blackouts. This paper proposes a novel, energy reserve operational scheduling method for future smart grid frequency enhancement considering wind generation, ESSs, thermal generating units, shiftable loads, and power system frequency response characteristics. The proposed method divides the reserve into two different parts, that is, active power reserve, which is responsible for smoothing the frequency fluctuations due to load variations and the second part is essential to control the frequency after large disturbances. The spinning reserve required for safe operation of the power system is determined based on a reliability criterion, that is, total expected energy not supplied (TEENS). The proposed method aims to maintain the frequency response in a stable mode by incorporating active power reserve from both generation and demand-sides. IEEE 30-bus and RTS-96 systems are utilized to verify the proposed reserve scheduling methods using GAMS programming environment. The obtained results verify the superiority and usefulness of the proposed method over others

A Novel Unknown Input Observer-Based Optimal Load Frequency Control for Smart Power Systems Considering EV and DR Participation

A new decentralized load frequency control (LFC) scheme based on the concept of dynamic state estimation is proposed in this paper. The suggested novel, scheme makes use of an efficient deterministic state estimator, known as unknown input observe (UIO) utilized in each area-control for online observing of the dynamic states of the system. To achieve a decentralized approach, the demand and transferred power among areas are considered as unknown inputs to the power system operators. Then, the optimal control theory is adopted to control the frequency deviation in each area which is designed based on the observed dynamic states. An interconnected power system with four-areas is used to verify the applicability of the proposed scheme, while the robustness, accuracy, and superiority of the proposed method is shown by several comparisons with other methods recently published in literature