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This paper presents a new optimization approach for solving the under-frequency load shedding (UFLS) problem in power systems. UFLS is a very important function in maintaining the power system within its safe operating limits.It is also the last resort in the event of frequency instability. This paper investigates the use of an adaptive neuro-fuzzy inference system (ANFIS)-based controller. The proposed ANFIS provides optimal response compared to conventional load shedding and other methods. The proposed approach is based on the simulation of the multi-zone load frequency control problem. Rate of change of frequency, voltage deviation and tie-line power transit are used as inputs to the ANFIS. The outputs in the first level are the optimal frequency threshold ( Fth ) values, and in the second level, they are the quantity of load shedding. Numerical results based on the IEEE 39 power system and the Algerian grid are used to demonstrate the effectiveness of the proposed approach in finding the optimal UFLS control compared with the conventional method as well as the results of the other intelligent method. The comparison showed that the proposed scheme gave the best response in all scenarios.

In contemporary power systems, the load shedding schemes are typically based on disconnecting a pre-specified amount of load after the frequency drops below a predetermined value. The actual conditions at the time of disturbance may largely differ from the assumptions, which can lead to non-optimal or ineffective operation of the load shedding scheme. For many years, increasing the effectiveness of the underfrequency load shedding (UFLS) schemes has been the subject of research around the world. Unfortunately, the proposed solutions often require costly technical resources and/or large amounts of real-time data monitoring. This paper puts forth an UFLS scheme characterized by increased effectiveness in the case of large disturbances and reduced disconnected power in the case of small and medium disturbances compared to the conventional load-shedding solutions. These advantages are achieved by replacing time-consuming consecutive load dropping with the simultaneous load dropping mechanism and by replacing ineffective fixed-frequency activation thresholds independent of the state of the system with implicit adaptive thresholds based on fuzzy logic computations. The proposed algorithm does not require complex and costly technical solutions. The performance of the proposed scheme was validated using multivariate computer simulations. Selected test results are included in this paper.

In viewing the power grid for large-scale new energy integration and power electrification of power grid equipment, the impact of power system faults is increased, and the ability of anti-disturbance is decreased, which makes the power system fault clearance more difficult. In this paper, a load shedding control strategy based on artificial intelligence is proposed, this action strategy of load shedding, which is selected by deep reinforcement learning, can support autonomous voltage control. First, the power system operation data is used as the basic data to construct the network training dataset, and then a novel reward function for voltage is established. This value function, which conforms to the power grid operation characteristics, will act as the reward value for deep reinforcement learning, and the Deep Deterministic Policy Gradient algorithm (DDPG) algorithm, with the continuous action strategy, will be adopted. Finally, the deep reinforcement learning network is continuously trained, and the load shedding strategy concerning the grid voltage control problem will be obtained in the power system emergency control situation, and this strategy action is input into the Pypower module for simulation verification, thereby realizing the joint drive of data and model. According to the numerical simulation analysis, it shows that this method can effectively determine the accurate action selection of load shedding, and improve the stable operational ability of the power system.

Conventional under-frequency and under-voltage load shedding schemes are known as effective approaches for load shedding purposes. Generally, these two schemes are deployed independently within which the combinatorial disturbances are overlooked. Contributing to this context, the ongoing study raises a high concern on developing a generalized wide-area adaptive load shedding scheme. In order to preserve the voltage and frequency stabilities, the established approach deploys the variations of voltage, frequency, active, and reactive power in all buses, simultaneously. More specifically, the reactive power variation on voltage stability is incorporated in the proposed load shedding index. This idea improves the performance of the proposed scheme, significantly. Infield phasor measurement units are establishing a wide-area basis, enabling the proposed approach in modern control centers. Extensive numerical studies are devised to assess the performance of the proposed approach encountering various disturbances. The obtained results are discussed in depth.

Electric power services are fundamental to prosperity and economic development. Disruptions in the electricity power service can range from minutes to days. Such events are common in many developing economies, where the power generation and delivery infrastructure is often insufficient to meet demand and operational challenges. Yet, despite the large impacts, poor data availability has meant that relatively little is known about the spatial and temporal patterns of electric power reliability. Here, we explore the expressions of electric power instability recorded in temporal profiles of satellite observed surface lighting collected by the Visible Infrared Imaging Radiometer Suite (VIIRS) low light imaging day/night band (DNB). The nightly temporal profiles span from 2012 through to mid-2020 and contain more than 3000 observations, each from a total of 16 test sites from Africa, Asia, and North America. We present our findings in terms of various novel indicators. The preprocessing steps included radiometric adjustments designed to reduce variance due to the view angle and lunar illumination differences. The residual variance after the radiometric adjustments suggests the presence of a previously unidentified source of variability in the DNB observations of surface lighting. We believe that the short dwell time of the DNB pixel collections results in the vast under-sampling of the alternating current lighting flicker cycles. We tested 12 separate indices and looked for evidence of power instability. The key characteristic of lights in cities with developing electric power services is that they are quite dim, typically 5 to 10 times dimmer for the same population level as in Organization for Economic Co-operation and Development (OECD) countries. In fact, the radiances for developing cities are just slightly above the detection limit, in the range of 1 to 10 nanowatts. The clearest indicator for power loss is the percent outage. Indicators for supply adequacy include the radiance per person and the percent of population with detectable lights. The best indicator for load-shedding is annual cycling, which was found in more than half of the grid cells in two Northern India cities. Cities with frequent upward or downward radiance spikes can have anomalously high levels of variance, skew, and kurtosis. A final observation is that, barring war or catastrophic events, the year-on-year changes in lighting are quite small. Most cities are either largely stable over time, or are gradually increasing in indices such as the mean, variance, and lift, indicating a trajectory that proceeds across multiple years.

High dependability, efficiency and low carbon emissions are just a few of the many potential environmental advantages of distributed generation (DG), which is used widely. Accurate islanding detection and quick DG disconnection were crucial to avoid safety concerns and equipment damage brought on by the island mode actions of DGs. Several researchers concentrate on island detection and load scheduling separately. The proposed work focused on islanding detection and load shedding during an island condition. A sophisticated, intelligent mode detection controller detected the system circumstance, and an intelligent shedding controlled the optimal load shedding. The proposed model used a standard IEEE 30-bus system with voltage and current parameters sensed by sensors and given inputs to the intelligent mode detection controller. If the island condition was predicted to move on load scheduling or normal condition was predicted, electricity continued to the utility grid and fulfilled the consumer's required power. The load shedding working process includes system design, data collection and the creation of efficient load scheduling. Maintain steady voltage stability margin throughout that time to complete that priority-based shedding depending on the power generation and its accompanying load restriction. The proposed method was tested for two operating modes, namely detection of islanding modes and load shedding. The proposed approach provides better results in both modes and maintains voltage stability across the entire time period. The proposed method offered a better accuracy of 99% for islanding detection mode when the results were contrasted with those from several other existing methods to validate the performance. As a result, it can be demonstrated that the suggested approach offers better islanding detection performance as well as load shedding with a constant voltage stability margin.

Under-frequency load shedding (UFLS) prevents a power grid from a blackout when a severe contingency occurs. UFLS schemes can be classified into two categories—event-based and response-driven. A response-driven scheme utilizes 81L relays with pre-determined settings while an event-based scheme develops a pre-specified look-up table. In this work, an event-based UFLS scheme is presented for use in an offshore standalone power grid with renewables to avoid cascading outages due to low frequency protection of wind power generators and photovoltaic arrays. Possible “N-1” and “N-2” forced outages for peak and off-peak load scenarios in summer and winter are investigated. For each forced outage event, the total shed load is minimized and the frequency nadir is maximized using particle swarm optimization (PSO). In order to reduce the computation time, initialization and parallel computing are implemented using MATLAB/Simulink because all forced outage events and all particles in PSO are mutually independent. A standalone 38-bus power grid with two wind turbines of 2 × 2 MW and photovoltaics of 7.563 MW was studied. For each event, the proposed method generally obtains a result with a smaller shed load and a smaller overshoot frequency than the utility and existing methods. These simulation results verify that the proposed method is practically applicable in a standalone power system with penetration of renewables.

DC microgrid is a leading technology that enables the integration of distributed generation (DG) units and avoids extreme complexity within the power system. One of the main challenges associated with islanded microgrids is the limited primary resources and variation of DGs' output power. For this reason, in some cases, the microgrid may face an imbalance in the amount of power generation and consumption, which, if it exceeds the standard limit, would lead to severe voltage collapse and power system failure. In fact, when demand surpasses the available power generation, in cases where the imbalance is not too extreme, it results in a decrease in voltage; in more severe instances, it can cause a breakdown in the system's stability. In such scenarios, the emergency control and protective unit of the microgrid becomes active and initiates a process of disconnecting non-essential loads, known as load shedding process. Hence, a load shedding strategy is necessary for such circumstances. In this paper, by sampling the bus voltage to which the sensitive loads are connected and calculating the voltage curve fitting, the emergency control system can estimate the maximum voltage drop caused by the imbalance and optimally shed the non-critical loads. Three different scenarios are defined. The simulation results verify the accurate performance of the proposed load shedding method.

Event-driven load shedding is an effective countermeasure against voltage collapse in power systems. Conventionally, its optimisation relies on sensitivity-based linear methods, which, however, could suffer from unrealistic assumptions and sub-optimality. In this study, an alternative approach based on parallel-differential evolution (P-DE) is proposed for efficiently and globally optimising the event-driven load shedding against voltage collapse. Working in a parallel structure, the approach consists of candidate buses selection, voltage stability assessment (VSA) and DE optimisation. Compared with conventional methods, it fully considers the non-linearity of the problem and is able to effectively escape from local optima and not limited to system modelling and unrealistic assumptions. Besides, any type of objective functions and VSA techniques can be used. The proposed approach has been tested on the IEEE 118-bus test system considering two cases for preventive control and corrective control, respectively, and compared with the two existing methods. Simulation results have verified its effectiveness and superiority over the compared methods.

With the increasing expansion of power systems, there is a growing trend towards active distribution networks for decentralized power generation and energy management. However, the instability of distributed renewable energy introduces complexity to power system operation. The active symmetry and balance of power systems are becoming increasingly important. This paper focuses on the characteristics of distributed resources and under-frequency load shedding, and a coordinated operation and control strategy based on the rapid adjustment of energy storage power is proposed. The characteristics of various controllable resources are analyzed to explore the rapid response capabilities of energy storage. The energy storage types are categorized based on the support time, and the final decision is achieved with power allocation and adjustment control of the energy storage system. Additionally, a comprehensive control strategy for under-frequency load shedding and hierarchical systems is provided for scenarios with insufficient active support. The feasibility of the proposed model and methods is verified via a multi-energy system case.