Reliable Operation Of Power System Research Articles

Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model

Wind energy is rapidly growing industry in Brazil. Wind speed forecasting is necessary in the planning, controlling, and monitoring for the reliable and efficient operation of the wind power systems. Thus, this study focuses on the impact of different physics parameterization in forecasting wind speed in two onshore wind farms using the Weather and Research Forecasting (WRF) model. The wind farms are located in Parazinho, in the northeast of Brazil, a region with high wind resource. Hindcasts are performed for a high (i.e., July 2017) and low (i.e., April 2017) wind speed regimes using different forecast lead-times (i.e., 24–48 h). The best performing setup consists of Thompson microphysics, Bougeault-Lacarrere PBL, Betts-Miller cumulus, New Goddard Longwave/Shortwave radiation, and Pleim-Xiu Land Surface schemes. Our findings also suggest that the model forecast setting with the TKE closure scheme, namely BouLac, performed better than that setting with first-order closure ACM2. The best mean monthly error (MAE) obtained is 1.1 m s−1 for wind and 12.6% for wind power.

하이브리드 측정데이터를 활용한 상태추정에 관한 연구

As the economy develops and power demand increases, the need for stable power system operation is also increasing. State estimation, one of the important functions of EMS(energy management system), estimates the state of the power system which is essential for reliable power system operation. State estimation has traditionally been performed using measurement data, obtained from SCADA(supervisory control acquisition system). PMU(phasor measurement unit), which appeared in the early 1990s, improved state estimation by providing more accurate synchronized data than SCADA measurement data. However, due to various problems, the PMU cannot be implemented at all buses in the power system, and for this problem, a study on a hybrid state estimation technique using both SCADA and PMU measurement data is required. This paper proposes a new technique for hybrid state estimation using SCADA and PMU measurement data. The proposed technique can improve the conventional state estimator and reduce the influence of bad data. Tests are done in the IEEE test system and the KEPCO power system.

DeepComp: Deep reinforcement learning based renewable energy error compensable forecasting

Recently, renewable energy is rapidly integrated into the power grid to prevent climate change, and accurate forecasting of renewable generation becomes critical for reliable power system operation. However, existing forecasting algorithms only focused on reducing forecasting errors without considering error compensability by using a large-scale battery. In this paper, we propose a novel strategy called error compensable forecasting. We switch the objective of forecasting from reducing errors to making errors compensable by leveraging a battery, which in turn reduces the dispatched error, the difference between forecasted value and dispatched value. The challenging part of the proposed objective lies in that the stored energy at current time is affected by the previous forecasting result. In this regard, we propose a deep reinforcement learning based error compensable forecasting framework, called DeepComp, having forecasting in the loop of control. This makes an action as a continuous forecasted value, which requires a continuous action space. We leverage proximal policy optimization, which is simple to implement with outstanding performance for continuous control. Extensive experiments with solar and wind power generations show that DeepComp outperforms the conventional forecasting methods by up to 90% and achieves accurate forecasting, e.g., 0.58–1.22% of the mean absolute percentage error.

HVDC Circuit Breakers: A Comprehensive Review

High voltage direct current (HVDC) systems are now well integrated into AC systems in many jurisdictions. The integration of renewable energy sources (RESs) is a major focus and the role of HVDC systems is expanding. However, the protection of HVDC systems against DC faults is a challenging issue that can have negative impacts on the reliable and safe operation of power systems. Practical solutions to protect HVDC grids against DC faults without a widespread power outage include: 1) using DC circuit breakers (CBs) to isolate the faulty DC-link, 2) using a proper converter topology to interrupt the DC fault current, and/or 3) using high-power DC transformers and DC hubs at strategic points within DC grids. The application of HVDC CBs is identified as the best approach that satisfies both DC grids and connected AC grids’ requirements. This article reports a comprehensive review of HVDC CBs technologies, including recent significant attempts in the development of modern HVDC CBs. The functional analysis of each technology is presented. Additionally, different technologies based on information obtained from literature are compared. Finally, recommendations for the improvement of CBs are presented.

A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering

Increasing levels of complexity, due to growing volumes of renewable generation with an associated influx of power electronics, are placing increased demands on the reliable operation of modern power systems. Consequently, phasor measurement units (PMUs) are being rapidly deployed in order to further enhance situational awareness for power system operators. This paper presents a novel data-driven event detection approach based on random matrix theory (RMT) and Kalman filtering. A dynamic Kalman filtering technique is proposed to condition PMU data. Both simulated and real PMU data from the transmission system of Great Britain (GB) are utilized in order to validate the proposed event detection approach and the results show that the proposed approach is much more robust with regard to event detection when applied in practical situations.

Online analysis and prediction of the inertia in power systems with renewable power generation based on a minimum variance harmonic finite impulse response filter

This paper deals with the inertia estimation of power systems in presence of significant contribution provided by renewable energy sources. The efforts to decommission conventional power plants and replace them with renewable resources could affect the robust and reliable operation of future power systems. The main issue related to the integration of renewable-based generation in the networks deals with the substitution of conventional synchronous generators connected to the grid and equipped by rotating masses, with converter-based generator which have not rotating masses, or they are decoupled by the grid due to the presence of interfacing power converters. Since the system inertia is the inherent ability of the online synchronous machines to oppose sudden changes in generation or load, the increasing share of renewable generators, such as wind and solar, the overall system inertia could decrease. This leading network operators to introduce new tools for monitoring the system inertia values, to contain them within admissible values, and to better prepare for different kinds of operational scenarios with undesired reduction of the system inertia. At this purpose, in this paper a tool for the analysis and prediction of inertia is proposed. The proposed method is based on a minimum variance harmonic finite impulse response and can consider the actual penetration of renewable energy sources. The proposed approach is tailored for online monitoring and exploits the real time measurements of relevant features of the network. The method is tested with actual data of the Italian Transmission Network and its effectiveness is studied by comparisons with some popular and well-established method of analysis and prediction.

Monitoring of ice formation of overhead power line wires

Reliable operation of electric power systems is impossible without the creation of modern monitoring devices that control all processes of production and distribution of electric energy. The authors of the article developed an intelligent system for monitoring ice formation and determining the wire sag boom using machine vision technology. The monitoring system under consideration is placed on a support in close proximity to the wire and consists of the following main elements: an electronic unit, an optical sensor, a solar panel and a GSM antenna. The developed monitoring system in close proximity to the thickness of the ice wall and indirectly, by the angle of deviation of the wire relative to the horizontal position, to determine the mass of ice deposits and the arrow of the wire sag.

Efficient and Privacy-Preserving Energy Trading on Blockchain Using Dual Binary Encoding for Inner Product Encryption.

The rapidly increasing expansion of distributed energy resources (DER), such as renewable energy systems and energy storage systems into the electric power system and the integration of advanced information and communication technologies enable DER owners to participate in the electricity market for grid services. For more efficient and reliable power system operation, the concept of peer-to-peer (P2P) energy trading has recently been proposed. The adoption of blockchain technology in P2P energy trading has been considered to be the most promising solution enabling secure smart contracts between prosumers and users. However, privacy concerns arise because the sensitive data and transaction records of the participants, i.e., the prosumers and the distribution system operator (DSO), become available to the blockchain nodes. Many efforts have been made to resolve this issue. A recent breakthrough in a P2P energy trading system on an Ethereum blockchain is that all bid values are encrypted using functional encryption and peer matching for trading is performed securely on these encrypted bids. Their protocol is based on a method that encodes integers to vectors and an algorithm that securely compares the ciphertexts of these vectors. However, the comparison method is not very efficient in terms of the range of possible bid values because the amount of computation grows linearly according to the size of this range. This paper addresses this challenge by proposing a new bid encoding algorithm called dual binary encoding, which dramatically reduces the amount of computation as it is only proportional to the square of the logarithm of the size of the encoding range. Moreover, we propose a practical mechanism for rebidding the remaining amount caused when the amounts from the two matching peers are not equal. Finally, the feasibility of the proposed method is evaluated by using a virtual energy trade testbed and a private Ethereum blockchain platform.

Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm

The optimal reactive power dispatch (ORPD) problem represents a fundamental concern in the efficient and reliable operation of power systems, based on the proper coordination of numerous devices. Therefore, the ORPD calculation is an elaborate nonlinear optimization problem that requires highly performing computational algorithms to identify the optimal solution. In this paper, the potential of metaheuristic methods is explored for solving complex optimization problems specific to power systems. In this regard, an improved salp swarm algorithm is proposed to solve the ORPD problem for the IEEE-14 and IEEE-30 bus systems, by approaching the reactive power planning as both a single- and a multi- objective problem and aiming at minimizing the real power losses and the bus voltage deviations. Multiple comparison studies are conducted based on the obtained results to assess the proposed approach performance with respect to other state-of-the-art techniques. In all cases, the results demonstrate the potential of the developed method and reflect its effectiveness in solving challenging problems.

Coordinated fuzzy logic‐based virtual inertia controller and frequency relay scheme for reliable operation of low‐inertia power system

Coordination between protection and control devices is crucial for maintaining continuous operation of power systems. Existing strategies suffer from improper coordination of control and protection devices. Moreover, high penetration levels of renewable energy sources result in lowering the overall power system inertia. Therefore, this paper presents a robust fuzzy-logic control (FLC) method for superconducting magnetic energy storage (SMES) in low inertia power systems. The new proposed FLC enables robust and wide operating range for SMES compared to the widely employed controllers. The proposed FLC method and load frequency control are coordinated to emulate virtual inertia. In addition, a cooperate coordination between frequency relay and proposed controller is preserved to maintain reliable operation of low inertia power systems. To prove the effectiveness of proposed coordination strategy, it has been tested with considering different load and renewable energy sources (RESs) disturbances with varying inertia level of the selected case study. The results demonstrate that the proposed FLC method can achieve robust SMES operation as virtual inertia controller (VIC) at wide operating range. Moreover, cooperative operation of VIC and frequency protection is preserved using the proposed coordination strategy. The power system availability, frequency regulation, and dynamic stability are improved using the proposed method.

Demand response integrated day-ahead energy management strategy for remote off-grid hybrid renewable energy systems

With the anticipated future price reductions in renewable technologies, hybrid renewable energy systems have emerged as a feasible retrofit to the primarily diesel-based remote off-grid power systems. However, in the presence of many supply/storage systems along with high penetration of intermittent renewable energy, energy management becomes a decisive step to achieve a stable and reliable power system operation. The intended benefits can be further maximized when the energy management framework is unified with demand response strategies. This paper presents a novel demand response integrated day-ahead energy management framework subjecting remote off-grid power systems. Several measures are taken to enhance the consumer acceptance and practical implementation of the demand response platform. Responsiveness of the customers to price-based incentives is estimated using a probabilistic fuzzy inference system to accurately model the stochastic human nature. Results are demonstrated for an isolated remote community in Northern Canada for both summer and winter seasons. The results confirm the applicability of the proposed method in achieving the intended objectives of day-ahead energy management.

Voltage and frequency control in conventional and PV integrated power systems by a particle swarm optimized Ziegler\u2013Nichols based PID controller

Variations of load demands, expansion of power system by interconnections among different areas and integration of renewable energy sources bring new challenges for stable, reliable and uninterrupted operations of power systems. In this paper, a control technique is proposed to control and optimize the performances of the three models having importance in the present and future energy systems. These are the output variations of an automatic voltage regulation (AVR) system, frequency variations in a load frequency control (LFC) system of a thermal power plant and frequency variations of a PV integrated thermal power plant. The proposed controller is a particle swarm optimized Ziegler–Nichols (ZN) method based proportional-integral-derivative (PID) controller. A particle swarm optimization (PSO) method suffers from the unavailability of prior knowledge of initial values of parameters. Whereas, the classical ZN method leaves the scope for performance improvements of a system. A rejuvenation to the classical ZN method is proposed by integrating PSO. The combined effect optimizes the voltage and the frequency performances, while ensuring system stability. Additionally, different objective functions inspired from energy industry requirements are considered to demonstrate performance improvements of the systems (e.g. maximum overshoot, steady-state error, settling time). The robustness of the proposed method is demonstrated by considering parametric uncertainty in the system. The proposed method is compared with performances of different controllers (e.g. PI, fuzzy PI, fuzzy PID), different iterative soft computing methods (e.g. pattern search, artificial bee colony, different variants of PSO) and classical optimization method (e.g. linear matrix inequality) considering different objective functions and different load disturbances for the aforementioned models. It is also observed that better performances are obtained using a significantly less number of iterations.

Increasing Turkey's power system flexibility for grid integration of 50% renewable energy share

Secure and reliable operation of power systems with high wind and solar shares requires system flexibility. In this paper, an hourly-based market and grid simulation model is developed to assess security and reliability of a power system with high wind and solar energy share. The model is applied to Turkey as an emerging G20 country that aims to supply its rapidly growing electricity demand from local renewables and lignite as well as nuclear energy. The most ambitious scenario that covers the 2016–2026 period assumes half of all electricity demand is supplied from renewables (30% wind and solar and 20% other resources). This is achieved by ensuring system flexibility through system-friendly location of wind and solar capacity, energy storage, flexible thermal generators, and demand response. Without system flexibility, 3% of renewable power is curtailed and redispatch share required for system security and reliability doubles from current levels. Moreover, additional transmission grid investments are needed. Improving system flexibility ensures secure and reliable operation but increases system costs by 1%–5% with each flexibility option providing different scale benefits, indicating the need for system-wide planning. As gas-based generation declines below 10%, accounting for energy security benefits will be important. On the other hand, coal supply remains around 25% depending on nuclear energy development. At this crossroad, Turkey needs to make its choices to transition to a secure, clean and affordable energy system. The study addresses quantitatively how the flexibility options contribute to such a transition, providing learnings for countries with similar conditions.

Aggregated short-term load forecasting for heterogeneous buildings using machine learning with peak estimation

System operations and planning are crucial aspects of power system management. They aim to maintain the equilibrium of electricity supply and demand while ensuring reliable and secure power system operation. Consumers have to pay more for electricity during periods of high peak demand in various sectors. If consumers have knowledge about expected peak load ahead of time, such extra charges could potentially be avoided. Accurate energy demand forecasting, and therefore expected peak load information, not only will help to provide a reliable supply of electricity, but also can be useful in reducing the cost of electricity at the consumer level. In this paper, we develop a comparative study for aggregated short-term load forecasting using different data strategies and compare two prediction levels: predicting the aggregated load using a district-level data set, and performing predictions on a lower level and then aggregating them at the district level. After finding the best forecasting model and strategy, these accurate predictions will help to predict the percentage of peak over a certain subscribed power in the entire district. The results showed that the mean absolute percentage error is between 1.67% and 4.80% depending on the machine learning algorithm and the prediction horizon used.

Gradient Ascent Optimization for Fault Detection in Electrical Power Systems based on Wavelet Transformation

Background: This paper presents electrical power system comprises many complex and interrelating elements that are susceptible to the disturbance or electrical fault. The faults in electrical power system transmission line (TL) are detected and classified. But, the existing techniques like Artificial Neural Network (ANN) failed to improve the Fault Detection (FD) performance during transmission and distribution. In order to reduce the Power Loss Rate (PLR), Daubechies Wavelet Transform based Gradient Ascent Deep Neural Learning (DWT-GADNL) Technique is introduced for FDin electrical power sub-station. DWT-GADNL Technique comprises three step, normalization, feature extraction and FD through optimization. Initially sample power TL signal is taken. After that in first step, min-max normalization process is carried out to estimate the various rated values of transmission lines. Then in second step, Daubechies Wavelet Transform (DWT) is employed for decomposition of normalized TL signal to different components for feature extraction with higher accuracy. Finally in third step, Gradient Ascent Deep Neural Learning is an optimization process for detecting the local maximum (i.e., fault) from the extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. DWT-GADNL Technique is measured with PLR, Feature Extraction Accuracy (FEA), and Fault Detection Time (FDT). The simulation result shows that DWT-GADNL Technique is able to improve the performance of FEA and reduces FDT and PLR during the transmission and distribution when compared to state-ofthe- art works. Materials and Methods: An electric power system incorporates production, broadcast and distribution of electric energy. To send the electric power to massive load centers, transmission lines are exploited. The fast growth of electric power systems results in huge number of lines in operation and total length. TL are susceptible to faults in case of lightning, short circuits, mis-operation, human errors, overload, etc. Faults resulted in tiny to long power outages for customers. To protect the reliable power system operations, Fault identification, isolation and localization are imperative. The voltage lessened to minimal value, when fault occurs on TL. FD is an essential problem in power system engineering to minimize the PLR. DWT-GADNL Technique is introduced for FD in TL during transmission and distribution. Results: Power Loss due to the fault occurrence during the transmission and distribution is a common problem in electrical power system. To lessen the PLR, the fault is detected in earlier stage. From the sample transmission line, the features are extracted and the values are calculated. When the observed value is lesser than the actual value, the fault is detected through performing the gradient ascent optimization process in transmission line. In this optimization process, the local maxima are identified to reduce the PLR. At different time instances, PLR gets changed. At instance 3, the PLR of proposed DWTGADNL framework is 12% where the PLR of Fuzzy Logic Based Algorithm and Fault Diagnosis Framework are 27% and 19% respectively. Through comparing all the ten instances, PLR is reduced in GWMD-DE technique by 59% and 40% compared to existing respectively. Conclusion: DWT-GADNL Technique is introduced for FD during transmission and distribution with minimal PLR. Sample power TL signal is taken and min-max normalization process performs the various rated values estimation of transmission lines. DWT decomposes normalized TL signal to different components for feature extraction with higher accuracy. Gradient Ascent Deep Neural Learning detects the local maximum from extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. The performance of DWT-GADNL technique is tested with the metrics such as PLR, FEA and FDT. With the simulations conducted for all techniques, the proposed DWT-GADNL technique presents better performance on FD during transmission and distribution as evaluated to state-of-the-art works. From simulations results, the DWT-GADNL technique lessens PLR by 50% and enhances FEA by 9% than the existing methods.

Legal Support of Digitalization in the Wholesale Electricity and Capacity Market

The main objective of the electric power industry is to provide for uninterrupted and reliable operation of power systems in order to meet the demand for electric power. The introduction of digital technologies in the wholesale electricity and capacity market is aimed at improving the efficiency, safety and continuity of its operation. The author highlights the features of legal support of digitalization in the wholesale electricity and capacity market, and proposes the possibility of expansion of the experimental legal regime to the wholesale electricity and capacity market. Information security issues are considered taking into account the fact that market participants may suffer not only financial and reputational losses from the implementation of security threats, but also damage to the life and health of people, the environment and the market infrastructure. The author concludes that it is necessary to develop means of legal protection of interests of owners and users of electronic software systems, as well as legal regulation of automated actions carried out in the wholesale electricity and capacity market. The research was carried out with the financial support of the RFBR within scientific project No. 20-311-90034.

Правовое обеспечение цифровизации на оптовом рынке электрической энергии и мощности

The main objective of the electric power industry is to provide for uninterrupted and reliable operation of power systems in order to meet the demand for electric power. The introduction of digital technologies in the wholesale electricity and capacity market is aimed at improving the efficiency, safety and continuity of its operation. The author highlights the features of legal support of digitalization in the wholesale electricity and capacity market, and proposes the possibility of expansion of the experimental legal regime to the wholesale electricity and capacity market. Information security issues are considered taking into account the fact that market participants may suffer not only financial and reputational losses from the implementation of security threats, but also damage to the life and health of people, the environment and the market infrastructure. The author concludes that it is necessary to develop means of legal protection of interests of owners and users of electronic software systems, as well as legal regulation of automated actions carried out in the wholesale electricity and capacity market. The research was carried out with the financial support of the RFBR within scientific project No. 20-311-90034.

Reclosing of Distribution Systems for Wildfire Prevention

The risk of catastrophic wildfire has challenged our societies and impacted various industries, with imminent danger to people's lives, financial burden, and irreparable damages to the environment, among other consequences. While progressive wildfires can impact power system operation and customer reliability and resilience, the relationship between power systems and wildfires is mutual: power systems can also contribute to wildfires. Wildfire risk of power systems is related to, in particular, fault-induced ignition events that can quickly turn into wildfires. While some electric utilities are working on approaches to mitigate such risk, the need for further research, development and demonstration of novel methodologies still exists. This paper identifies, analyzes, and compares the practices that can be further employed by utilities to manage the risk and mitigate the impact of power-system generated wildfires.

Faulty Line Identification Method Based on Bayesian Optimization for Distribution Network

Selective ground-fault protection is greatly valued for the safe and reliable operation of power systems. With the wide adoption of fault indicator in distribution network, the amount of available fault data increases dramatically. The in-depth investigation of fault recording data helps improve the accuracy of faulty line identification. To perform fault data analysis with higher efficiency, a single-phase-to-ground fault identification model based on the k-Nearest Neighbor (kNN) classification algorithm is proposed in the paper. In this model, the eigenvectors consist of wavelet energy ratio, wavelet coefficients variance and wavelet power obtained by the decomposition of transient components. Furthermore, through the theoretical analysis and experimental comparison of three parameter adjustment algorithms, Bayesian Optimization algorithm is selected to find the optimal parameters of fault identification model, and realize the adaptive adjustment of model parameters. Finally, the validity and feasibility of the model are verified by the experimental data, and the accuracy and efficiency of fault identification are improved by using Bayesian Optimization algorithm.

Auxiliary service strategy model of power plant participating in peak load regulation

With the large-scale grid connection of renewable energy, it is necessary to ensure that the peak shaving demand of the system due to the volatility of renewable energy such as wind power can be met. On the premise of ensuring benefits, how to further tap the peak shaving potential of thermal power units and ensure the safe and reliable operation of power system is the primary problem faced by thermal power plants at this stage. According to the mechanism of peak load regulation auxiliary service in Northeast China, this paper puts forward the strategy model of participating in peak load regulation auxiliary service of thermal power plants, so as to provide basis for further tapping the peak load regulation potential of thermal power units and improving the actual consumption of renewable energy such as wind power by power grid.