Future Power Systems Research Articles

Fault Location Study of Overhead Line–Cable Lines with Branches

A new fault location method based on the three-terminal travelling wave method is proposed for the fault location problem of multi-branch overhead line–cable transmission lines. Firstly, the process of fault travelling wave propagation in overhead transmission lines and the phenomenon of refraction are analysed, and an improved phase-mode transformation is introduced to decouple the electromagnetic coupling and perform fault phase selection. Secondly, the Pearson correlation coefficient is introduced to compare the similarity of the current travelling waveforms at different measurement points in order to implement fault segmentation. To solve the problems of the complexity of the fault travelling wave propagation process and the difficulty of identifying the travelling wavehead, the Hilbert–Huang transform is used to extract the fault signal characteristics, and the travelling wave arrival moment is accurately calculated by the sampling error correction method to determine the fault location. Finally, the accuracy and stability of the method are verified via a simulation test on the MATLAB/Simulink platform. The results show that the proposed positioning method combining the three-terminal travelling wave method with HHT and sampling error correction can locate the fault location more accurately, and it has good potential for application in the engineering field. It provides a new technical means for fault location in overhead transmission lines, which is expected to become one of the most important technologies in the future power system.

Fault diagnosis for wind turbines based on LSTM and feature optimization strategies

SummaryHigh penetration of renewable energy is the development trend of the future power system. As one of the clean energy sources, wind power generation has an increasing share in the energy market. However, due to the harsh working environment, the high fault rate and poor accessibility of the wind farms, resulting in the difficult maintenance process and high cost. This article proposes a fault diagnosis (FD) method based on long short‐term memory (LSTM) and feature optimization strategies for wind turbines (WTs), thus reducing the operation and maintenance costs of WTs. First, Pearson correlation coefficient analysis is performed on the collected data features to remove redundant features, and wavelet transform is adopted to remove the redundant data, so as to optimize the fault features and fault data. Then the selected features samples are used to train LSTM‐based FD model. Finally, the actual production data is adopted to verify the proposed method. The proposed method can effectively locate the faults, and provide data support for wind farms, thus improving the reliability, safety, and economic benefits of wind farms.

Assessing Hardware in the Loop Approaches for Wide-Area Monitoring Control and Protection Devices

Wide-area monitoring, protection, and control (WAMPAC) systems are expected to support the transmission system operators in maintaining stability and security of supply in the future power systems. The WAMPAC solutions heavily rely on deployment and use of phasor measurement units (PMU) and related communication technologies. Laboratory testing in a realistic environment utilizing hardware in the loop (HIL) approaches can be successfully used to evaluate WAMPAC functionalities algorithms and to accelerate proofs of concept. However, different implementations of the HIL testing can be adopted and their inherent characteristics affect both the accuracy of the results and the overall testing capabilities. The objective of this paper is to provide a comparative assessment of three HIL approaches for laboratory testing of WAMPAC systems and devices. These testing approaches have been implemented in a laboratory configuration and applied to characterize WAMPAC devices. Moreover, methodologies to quantify the error introduced by these testing approaches in static and dynamic conditions are presented and applied to this laboratory configuration.

Complementary potential of wind-solar-hydro power in Chinese provinces: Based on a high temporal resolution multi-objective optimization model

In the context of carbon neutrality, renewable energy, especially wind power, solar PV and hydropower, will become the most important power sources in the future low-carbon power system. Since wind power and solar PV are specifically intermittent and space-heterogeneity, an assessment of renewable energy potential considering the variability of wind power and solar PV with high temporal resolution in different regions will facilitate more accurate identification of the decarbonization pathway of power system. In this paper, the complementary output potential of wind-solar-hydro power every 15 min in 31 Chinese provinces is evaluated by developing a multi-objective optimization model based on Nondominated Sorting Genetic Algorithm II. The results show that the total annual complementary power generation potential in China is 17.57 PWh, of which the three components account for 47.8%, 25.3% and 26.9%, respectively. Inner Mongolia and Tibet are the major renewable energy source provinces with annual complementary power generation potential of 5330 TWh and 3907 TWh, accounting for 30.3% and 13.7% of the total power generation potential, respectively. The temporal potential of wind-solar-hydro power varies greatly, with daily potential is more volatile than monthly. Seasonal and spatial heterogeneity of the complemental renewable potential makes some provinces suffer power shortage during long-hours period especially in winter. Our findings will encourage a higher penetration of renewable energy, the promotion of multi-energy complementarity, and the development of inter-provincial power transmission and energy storage infrastructure in China's future power sector.

A fast and robust DOBC based frequency and voltage regulation scheme for future power systems with high renewable penetration

AbstractThis paper proposes a disturbance‐observer‐based control (DOBC) scheme for frequency and voltage regulation in modern power systems with high renewable energy sources (RES) penetration. The proposed approach acts as a feed‐forward control that improves the dynamic performance of the conventional proportional‐integral‐derivative (PID) controller. The proposed voltage and frequency control has been validated through hardware‐in‐loop (HIL) implementation on OPAL‐RT, and testing on laboratory‐scale experimental test setup. The robustness of the proposed control scheme has been validated through simulations under worst‐case and stochastic uncertainties to mitigate real‐time variability in RES output and load. Real‐time simulation results depict superior performance of the proposed control strategy in comparison to several well‐established techniques under practical operating conditions, in the presence of communication delay and white noise. To validate the proposed control on laboratory‐scale experimental setup, the digital twin of the physical plant transfer function has been designed. Results reveal that the proposed DOBC control scheme drastically improves the system performance without rendering much computational burden under practical operation scenarios.

Wide-Area Measurement-Based Two-Level Control Design to Tolerate Permanent Communication Failures

The operation of modern power systems must meet stability requirements to guarantee the supply of electrical energy. One of these requirements is to ensure that the low-frequency oscillation modes have high damping ratios to avoid angular instability and future power system blackouts. Advances in phasor measurement units (PMUs) have contributed to the development and improvement of wide-area damping controllers (WADCs) capable of increasing the damping rates of the oscillation modes of the system, especially the inter-area modes. Nevertheless, the operation of WADCs is vulnerable to communication failures and cyber-attacks, and if not properly designed the WADC can affect the stability of the entire system. This research proposes a procedure for designing a WADC robust to permanent communication failures using a linear quadratic regulator (LQR) and genetic algorithms. Case studies conducted on an IEEE 68-bus test power system show the effectiveness of the WADC designed by the proposed procedure even when communication failures are occurring in the system. The use of genetic algorithms improves the convergence and results of the LQR-based method.

Power availability of PV plus thermal batteries in real-world electric power grids

As variable renewable energy sources comprise a growing share of total electricity generation, energy storage technologies are becoming increasingly critical for balancing energy generation and demand.In this study, a real-world electricity system was modeled rather than modeling hypothetical future electric power systems where the existing electricity infrastructure are neglected. In addition, instead of modeling the general requirements of storage in terms of cost and performance, an existing thermal energy storage concept with estimated capital cost that are sufficiently low to enable large-scale deployment in the electric power system were modeled. The storage unit is coupled with a photovoltaic (PV) system and were modeled with different storage capacities, whereas each storage unit had various discharge capacities.The modeling was performed under a baseline case with no emission constraints and under hypothetical scenarios in which CO2 emissions were reduced. The results show that power availability increases with increasing storage size and vastly increases in the hypothetical CO2 reduction scenarios, as the storage unit is utilized differently. When CO2 emissions are reduced, the power system must be less dependent on fossil fuel technologies that currently serve the grid, and thus rely more on the power that is served from the PV + storage unit.The proposed approach can provide increased knowledge to power system planners regarding how adding PV + storage systems to existing grids can contribute to the efficient stepwise decarbonization of electric power systems.

Enhanced Multi-Objective Power Flow Optimization for IEEE-118 Bus Test System using Advanced Grey Wolf Optimization Algorithm

This research article thoroughly examines the implementation of an enhanced Grey Wolf Optimization (GWO) method for Multi-Objective Power Flow Optimization (MO-PFO) in the IEEE-118 bus test system. Given the growing complexity and uncertainty in modern power systems, power flow optimization presents a crucial challenge. The suggested enhanced GWO algorithm improves the performance of the traditional GWO for MO-PFO issues while addressing its inherent flaws.
 The goal of this study is to obtain optimal solutions concurrently for multiple objectives, including the reduction of power losses, voltage variations, and the enhancement of system stability. In the Related Works section, I review the research on power flow optimization in the literature, which also showcases the improvements achieved by applying various optimization techniques and discusses the benefits and drawbacks of traditional optimization methods and their effectiveness in dealing with MO-PFO issues.
 The improved GWO algorithm, designed specifically for MO-PFO, is described in detail in the Methodology section. I explain the adjustments made to the regular GWO algorithm and introduce a cutting-edge method for handling multiple objectives. In the IEEE-118 bus test system, I present the mathematical model of the MO-PFO problem and develop fitness functions for each objective.
 Furthermore, I describe the implementation of the enhanced GWO algorithm, including parameter settings and termination criteria. The proposed algorithm demonstrates its capability to effectively handle the multi-objective nature of power flow optimization, thereby paving the way for future power system operations that are more dependable and sustainable

Enhancing System Reliability and Transient Voltage Stability through Optimized Power Sources and Network Planning

Renewable energy is an important means of addressing climate change and achieving carbon peaking and carbon neutrality goals. However, the uncertainty and randomness of renewable energy also have a certain impact on the flexibility, reliability, and transient voltage stability of the power system. These effects also pose great challenges to power system planning. In order to address the impact of renewable energy on power system planning, this paper proposes a two-layer optimization model for power sources and network planning which takes into account both reliability and transient voltage stability requirements. The upper-layer grid planning problem is formulated with consideration of the system reliability index, and the transient stability requirements and construction and operation costs are included in the lower-layer problem to determine a construction scheme for power generation and energy storage units. To solve the complex nonlinear problem efficiently, a two-layer iterative algorithm utilizing the adaptive particle swarm optimization (PSO) technique is proposed. The effectiveness of the proposed method is demonstrated via its application to the IEEE 33 test system. The results show that the proposed optimization approach effectively addresses the power system transmission and generation planning problem while improving the efficiency and reliability of the system’s operation. The findings can guide the design and implementation of future power system planning and operation strategies.

Influence of bioenergy and transmission expansion on electrical energy storage requirements in a gradually decarbonized European power system

With the ongoing energy transition in Europe, the share of variable renewable energies (VRE) in the power system increases steadily and different flexibility options gain importance. This work investigates the deployment of supply side flexibility options in the future European power system by energy system modelling focusing on classic power storage technologies, hydrogen storage, generation by biomass and grid expansion. While investigating different flexibility availability scenarios, for the first time the competition of long-term flexibility options is investigated across a broad decarbonization spectrum. In all scenarios, an increasing need for additional flexibility options is found for moderate decarbonization levels with VRE shares higher than 50%. From there on, the deployment of power storages grows nonlinearly, and eventually features a remarkable leap when approaching a zero-emission system, which is mainly driven by hydrogen storage investments. This also comes along with a leap in the total system costs. When the expansion of biomass capacities is available, biomass partly takes over the function of hydrogen as a long-term flexibility option. When enabling grid expansion, additional transmission capacities lead to a stronger integration of wind generation, while (especially short-term) storage requirements for higher decarbonization levels decrease. Both the additional investments into biomass generation and transmission grid result in up to 4% lower total system costs, yet, without completely displacing any other flexibility option. We conclude that the interaction of multiply flexibility options will lead to a cost minimal power system.

A sharing economy model for a sustainable community energy storage considering end-user comfort

A promising solution for the current and future power system challenges like the increasing penetration of demand-side distributed energy resources and the economies of scale of individual energy storage systems could be the concept of the smart Community Energy Storage (CES). This paper focuses on developing an energy cooperation framework between photovoltaic prosumers and CES. In the proposed structure, an appropriate environment is provided in the energy community to consider several smart homes that together form an energy community. They can store and generate energy by some central devices, including combined heat and power unit, thermal storage system, and electrical energy storage. Furthermore, each end-user individually owns a rooftop PV system and a heating, ventilation, and air conditioning system where the uncertainty of the PV generation unit is modeled by a scenario-based approach. The main question of this study is what benefits an energy community provides to the set of end-users. The goal of the energy community manager of the smart CES as the objective function of the scheduling problem is to minimize energy cost and end-user discomfort level, where ε-constraint method is used to solve the multi-objective CES scheduling problem. The main contribution of this paper is introducing the sharing economy concept and considering the role of occupants' thermal comfort, the impacts of time importance and end-user thermal sensitivity, and the impact of thermal comfort in the CES. The proposed model is structured as a mixed integer linear programming problem that is implemented in the GAMS software. Results show that by using CES, the energy cost decreases about 28.48% while the end-user discomfort level significantly improves.

Strategies for Continuous Balancing in Future Power Systems with High Wind and Solar Shares

The use of wind power has grown strongly in recent years and is expected to continue to increase in the coming decades. Solar power is also expected to increase significantly. In a power system, a continuous balance is maintained between total production and demand. This balancing is currently mainly managed with conventional power plants, but with larger amounts of wind and solar power, other sources will also be needed. Interesting possibilities include continuous control of wind and solar power, battery storage, electric vehicles, hydrogen production, and other demand resources with flexibility potential. The aim of this article is to describe and compare the different challenges and future possibilities in six systems concerning how to keep a continuous balance in the future with significantly larger amounts of variable renewable power production. A realistic understanding of how these systems plan to handle continuous balancing is central to effectively develop a carbon-dioxide-free electricity system of the future. The systems included in the overview are the Nordic synchronous area, the island of Ireland, the Iberian Peninsula, Texas (ERCOT), the central European system, and Great Britain.

Virtual Inertia Control of Isolated Microgrids Using an NN-Based VFOPID Controller

Reduction in system inertia and maintaining the frequency at the nominal value is a staple of today's and future power systems since their operation, stability, and resiliency are degraded by frequency oscillation and cascading failures. Consequently, designing a stable, scalable, and robust virtual inertia control system is highly relevant to skillfully diminishing the deviations during major contingencies. Therefore, considering the potential problems in predesigned nonflexible control systems with offline tuning techniques, we propose a variable fractional-order PID controller for virtual inertia control applications, which is tuned online using a modified neural network-based algorithm. The new proposed tuner algorithm is trained using a deep reinforcement learning strategy with a simplified deep deterministic policy gradient, which considers microgrid uncertainties. Compared with existing methods, all the tuning knobs of the discrete type and fully tunable variable FOPID controller (for both gain and order) can be captured based on the proposed hybrid algorithm, which inherits features from both classical and advanced techniques. To demonstrate the effectiveness of the training of the proposed controller, a comparative analysis with the standard FOPID and PID controllers is given under three different scenarios with a smooth (dis)connection of renewable energy sources and loads.

Application of Time-Frequency Analysis with Selection of Suitable Mother Wavelets for Transient States of HVDC Transmission Lines

Recent developments in power semiconductor devices have made High Voltage Direct Current (HVDC) transmission systems the most crucial technology for future power systems. Today several HVDC projects have been commissioned around the world. The primary concern for such an advanced HVDC grid is to follow standard grid codes to maintain the safety and stability of the electrical equipment during transient and dynamic circumstances. Although the HVDC system has been recognized as the most prominent technology to transmit electric power, some issues must be addressed, such as selective and quick detection of faults. For selective fault detection, it is essential to look into the several fault detection strategies that fortify the HVDC system with time-frequency signal processing techniques. This research uses a time frequency-based method to identify the different HVDC transmission line faults. The most suitable mother wavelet for various transient states is selected by analyzing fault voltage and current signals based on variance, standard deviation, and range parameters. An experimental setup of a thyristor-based HVDC transmission system has been developed in the MATLAB/Simulink environment, and multiple fault scenarios have been analyzed. The simulation outcomes reveal that the most effective mother wavelet for detecting DC line, symmetrical and unsymmetrical faults occurs at the 6th level of fault signal decomposition. By examining current and voltage signals, our work has determined the best mother wavelet for various faults. Using more than one mother wavelet to detect faults is more reliable than using a single mother wavelet to detect all faults. Additionally, it has been noted that each fault's voltage and current signals have two distinct mother wavelets.

AdapSafe: Adaptive and Safe-Certified Deep Reinforcement Learning-Based Frequency Control for Carbon-Neutral Power Systems

With the increasing penetration of inverter-based renewable energy resources, deep reinforcement learning (DRL) has been proposed as one of the most promising solutions to realize real-time and autonomous control for future carbon-neutral power systems. In particular, DRL-based frequency control approaches have been extensively investigated to overcome the limitations of model-based approaches, such as the computational cost and scalability for large-scale systems. Nevertheless, the real-world implementation of DRLbased frequency control methods is facing the following fundamental challenges: 1) safety guarantee during the learning and decision-making processes; 2) adaptability against the dynamic system operating conditions. To this end, this is the first work that proposes an Adaptive and Safe-Certified DRL (AdapSafe) algorithm for frequency control to simultaneously address the aforementioned challenges. In particular, a novel self-tuning control barrier function is designed to actively compensate the unsafe frequency control strategies under variational safety constraints and thus achieve guaranteed safety. Furthermore, the concept of meta-reinforcement learning is integrated to significantly enhance its adaptiveness in non-stationary power system environments without sacrificing the safety cost. Experiments are conducted based on GB 2030 power system, and the results demonstrate that the proposed AdapSafe exhibits superior performance in terms of its guaranteed safety in both training and test phases, as well as its considerable adaptability against the dynamics changes of system parameters.

A review of system topologies, key operation and control technologies for offshore wind power transmission based on HVDC

AbstractOffshore wind farm (OWF) is considered as a perfect zero‐carbon energy source for the future power system. However, the growing offshore distance and water depth of OWF make the OWF HVDC transmission technique a more promising solution than HVAC due to higher cost‐efficiency and reliability. In this paper, the current situation of OWF‐HVDC projects is introduced at first. Then, novel converter topologies with the higher power density and cost‐efficiency are presented, including the hybrid modular multilevel converter (MMC), alternative arm converter (AAC), and diode rectifier (DR). Next, several OWF HVDC transmission system topologies are introduced, including terminal‐hybrid, station‐hybrid and all‐DC delivered system. Furthermore, the key technologies for OWF HVDC operation and control are summarized, including grid‐forming control strategy for offshore wind turbines, stability analysis method, corresponding stability enhancement measures and frequency support control strategies. Additionally, the fault ride‐through and protection strategies for different fault locations have been presented. Finally, the main conclusions and prospects for OWF HVDC are summarized.

Benefits and barriers concerning demand response stakeholder value chain: A systematic literature review

Demand Response (DR) is acknowledged to play a relevant role in future electrical power systems. To achieve decarbonization, increasing shares of renewable electricity will induce changes in market design and system operation. New opportunities and challenges will emerge for all market participants. However, regulatory and technology bottlenecks combined with conflicts of interest among actors may deaccelerate DR implementation.This research performed a systematic literature review on five different databases to identify DR benefits and barriers attributable to different actors. Both benefits and barriers were found for all the stakeholders. Consumer bill savings and financial incentives were the most common benefits attributable to DR, on the contrary, consumer lack of interest and knowledge, privacy protection and cyber security were the most common referred barriers. In the second stage, both benefits and barriers were respectively divided into four main categories: Society-wide; Technology-wide; Financial and Market-wide and Environmental-wide benefits and Technology-wide, Economic and Market-wide context barriers. The classification of individual benefits and barriers demonstrated that some categories, such as technology-wide, are relevant both for benefits and barriers. DR implementation complexity and the number of stakeholders involved require DR policies to have certain specific characteristics to give them directionality towards DR successful implementation.

A Data-Driven Analysis Method for the Trajectory of Power Carbon Emission in the Urban Area.

"Industry 4.0" aims to build a highly versatile, individualized digital production model for goods and services. The carbon emission (CE) issue needs to be addressed by changing from centralized control to decentralized and enhanced control. Based on a solid CE monitoring, reporting, and verification system, it is necessary to study future power system CE dynamics simulation technology. In this article, a data-driven approach is proposed to analyzing the trajectory of urban electricity CEs based on empirical mode decomposition, which suggests combining macro-energy thinking and big data thinking by removing the barriers among power systems and related technological, economic, and environmental domains. Based on multisource heterogeneous mass data acquisition, effective secondary data can be extracted through the integration of statistical analysis, causal analysis, and behavior analysis, which can help construct a simulation environment supporting the dynamic interaction among mathematical models, multi-agents, and human participants.

Trade-offs between aggregated and turbine-level representations of hydropower in optimization models

To model a future power system with high shares of variable renewables, it is essential to capture the flexibility of dispatchable technologies such as hydropower. However, the representation of hydropower is often oversimplified in energy system investment models, such that the flexibility of hydropower is significantly exaggerated. This suggests the need for improved representations of hydropower that capture physical river dynamics but are computationally efficient to maintain the tractability of large models. Here, we develop a series of hydropower optimization models for a single river with various levels of techno-physical detail to evaluate options for hydropower representations in energy system investment models. All models operate hourly over a full year with perfect foresight. We explore trade-offs between accuracy and computational time involved in including features such as the river network, head-dependent power production, and discharge-dependent turbine efficiencies. We find that the level of detail significantly affects the optimal production and confirm that a simplistic hydropower representation similar to those often used in investment models significantly overestimates the flexibility of hydropower. The most detailed nonconvex model includes a full river network, head-dependency, and turbine efficiencies and is solved in just one hour on a modern desktop computer. Furthermore, we linearize this detailed model, thereby reducing computation time to one minute while featuring production dynamics substantially more similar to the full nonconvex model than a naive linear network model. These contributions pave the way for improving hydropower representations in investment models to avoid overestimating the flexibility that hydropower may provide.

Analysis of overvoltage reactive power margin for future power systems with renewable energy generation

As modern power systems continue to adopt wide usage of Renewable Energy (RE) sources, the reliance on conventional generators is slowly reducing. Countries are adopting new energy policies to speed up the transition of future power systems to a fully renewable and clean system, and this is centered around the increase of RE penetration to reduce carbon emissions. However, it is expected that this transition will be met with stability issues due to the intermittent nature of REs and reduction of conventional generators. In recognition of these expected complications for the future systems, an analysis method is proposed in this paper to identify critical buses in terms of voltage stability for power systems with high RE penetration level. A new method that utilizes both modified continuation power flow and Q-V analysis to determine acceptable reactive power margins for each bus considering the intermittency of RE sources is presented in this study. This work is focused on the future Korea Power Systems with 16.05 GW of renewable energy. Considering different RE levels and loading conditions, several scenarios are studied to identify the voltage stability in the different Korea regions with a high penetration of RE. The resulting reactive power margins can be used to prevent overvoltage through the identification of weak and vulnerable buses for the future power systems.