Load Shedding Scheme Research Articles

The use of dynamic programming and golden section search for the optimal load‐shedding strategy of HEMS participating in demand response program

This paper presents the optimal load-shedding strategy of home energy management systems (HEMSs). The HEMS uses the dynamic programming (DP) method to plan the day ahead, schedule, and connects to smart plugs to perform load-shedding. HEMS solves the shedding schedule of smart plugs considering the prediction of consumption of each smart plug and the consumer's demand response (DR) setting. In addition, the maximum DR capacity was determined by using golden section search (GSS). Some DR experiments were conducted by the proposed method in laboratory and these comprised three different DR durations. The results show that the estimated DR capacities and shedding schedules were feasible. Thus, it helps both the DR participants and aggregator to seize the optimum performance and benefits.

Unintentional Islanding Transition Control Strategy for Three-/Single-Phase Multimicrogrids Based on Artificial Emotional Reinforcement Learning

Distribution network failures can cause unintentional islanding of three-/single-phase multimicrogrids (MMGs). The transient impulse that occurs during the unintentional islanding period can affect the stability of the voltage and frequency in an MMG. To address this problem, this article proposes an unintentional islanding transition control strategy based on artificial emotional reinforcement learning (AERL) for a three-/single-phase MMG. First, to solve the three-phase unbalance problem that occurs during the unintentional islanding period, a three-phase combination method based on a merge sort is proposed to realize the combination optimization of the single-phase source-load-storage. Second, a load-shedding strategy based on AERL is proposed to deal with the tie-line power shortage caused by the distribution network failures. This strategy can quickly eliminate power shortages and ensure the uninterrupted power supply of critical loads. Finally, the performance of the proposed transition strategy is verified in a three-/single-phase MMG model based on a modified IEEE 37-bus system and a modified IEEE 118-bus system. During the unintentional islanding period, the proposed transition strategy reduces the frequency recovery time by 21.74%, 14.29%, and 10%, the voltage recovery time by 16.22%, 13.89%, and 8.82% compared with the mixed-integer second-order cone programming method (MISOCPM), the implicit enumeration method (IEM), and the compound storage-regulating and load-shedding method (CSLM) in the modified IEEE 37-bus system, respectively; the proposed transition strategy reduces the frequency recovery time by 25.64%, 21.62%, and 12.12%, the voltage recovery time by 19.15%, 15.56%, and 7.32% compared with the MISOCPM, IEM, and CSLM in the modified IEEE 118-bus system, respectively. The test results show that the proposed transition control strategy realizes the seamless transition of a three-/single-phase MMG and ensures the uninterrupted power supply of critical loads.

Reduction Strategy for Electrical Loads Based on Demand Priority in Power System

Background: Most non-developing and under developing countries strive hard to tackle the situation of power crisis and to combat the imbalance between the power generation and load demand, especially in the case of increasing of the population. In this situation, the load shedding scheme has been extremely implemented as a fast solution for unbalance conditions. Thus, load shedding is crucial to investigate supply-demand balancing in order to protect the network from collapsing and to sustain stability as possible; however its implementation is mostly undesirable. Objective: prioritize the loads according to their importance and apply reduction strategy in the demands while the supplied power to the important loads such as health care and security installation are kept intact without any interruption as possible. Methods: The conventional methods of load shedding lead to over or under shedding and this may lead to many problems with the network. Under the scheme, these methods disconnect the load or the entire feeder without considering their priorities and may not perform as anticipated. In this work, we propose a logarithmic reduction method to reduce the load according to the priority and day life criticality. The method for shedding the load base on Reduction Matrix and which in turn depend on the priority demands. Results: The higher priority demands are fed with a reliable power source by the real time monitoring of the network accompanied with power reducing for the lower priority demands. Conclusion: We test a real data sample provided by the Iraqi national grid control center in Baghdad. Our simulation results prove effectiveness and practicality of the applied method paving the way for possible applications in power systems.

A New Adaptive Underfrequency Load Shedding Scheme to Improve Frequency Stability in Electric Power System

This paper proposes a new adaptive underfrequency load shedding scheme (UFLS) to avoid frequency instability in electrical power system during abnormal wide area disturbances. The developed scheme is based on online monitoring of the distance relay zone 3 decisions of some tie lines using (WAMS) and SCADA/EMS systems, to check rapidly and reliably the uncontrolled islanding conditions and, permit an automatic load shedding action to maintain frequency stability of power system. Simulation results on 400 kV Turkish transmission systems demonstrated the effectiveness of the proposed scheme compared to current frequency load shedding schemes which they cannot consider all possible circumstances because of their limited access to the power network data. Simulation results clearly indicate that large disturbances in power systems can be avoided and their propagation can also be stopped using the proposed scheme.

An underfrequency load shedding scheme for high dependability and security tolerant to induction motors dynamics✰

The load shedding scheme is a last resort to prevent power system collapse when the available generation is less than the load demand. The traditional load shedding schemes monitors the frequency and voltage by underfrequency and undervoltage relays and provides load cutoff until the frequency returns to a stable point of operation. The reduction of rotating inertia demands faster load shedding to meet the stability requirements. In some electrical power systems in Brazil, it was found that, in the event of opening a transmission line upstream of the load shedding substation, some under frequency relay may operate improperly. This paper proposes a new method to avoid the unwanted operation of load shedding schemes when the utility substation (source) is out of service. The proposed method monitors negative sequence voltage, frequency and its rate of change, enabling faster load shedding (high dependability) without unwanted (high security) operation when compared with traditional methods. Events were classified by linear discriminant analysis and tested in the IEEE 9 bus test system. The tests consisted of several disturbances such as loss of generation and line outages at different levels of feeder loading, reactive compensation and distributed generation. The proposed method reached a 100% rate of correct classification.

Voltage Sag Mitigation in DER-Connected Distribution Networks by STATCOM

This article proposes a voltage regulation scheme for distributed energy resource-connected distribution networks based on static synchronous compensator to compensate for both short-term and long-term voltage sags. The proposed voltage regulation scheme provides a fast and precise control on the nodal voltage, with no need to load shedding, capacitor switching, transformers, or power management schemes. The principles supporting the proposed voltage regulation scheme are discussed and the effectiveness of the proposed control is demonstrated through time-domain simulation of a two-unit test distribution network in the PSCAD/EMTDC software environment.

A practical under-voltage load shedding strategy for regional power grid considering multiple operating modes

Nowadays, under-voltage load shedding (UVLS) schemes in China’s regional power grids are often drafted according to one certain operating mode, and then been chosen by experienced technicians based on comparison between simulation results on various operating modes. This process seriously depends on the skill and experience of the technician, and cannot guarantee the safety under some circumstances. Considering the above issues, this paper proposes a configuration method of UVLS applying to distributed control systems, and an automatic scheme formulating process. Specifically, aiming at developing a strategy suitable for multiple operating modes, Analytic Hierarchy Process (AHP) is introduced to obtain the fuzzy comprehensive sensitivities of the buses. The transient voltage stability of a regional power grid is investigated and the UVLS strategy is studied, which indicates the validity and practicality of the proposed configuration method.

Emergency Control Strategy for Line Overload Considering Power Source and Load Fluctuation

Uncertain factors such as the integration of renewable energy into the grid and load fluctuations have brought more risks to the safe dispatch of the grid. The random power flow is used to calculate the impact of distributed power sources and load fluctuations on the grid, and to quantify the probability distribution of line power, which can avoid the drawbacks of deterministic power flow that cannot fully reflect the line overload risk. Combining the line overload level with the traditional load shedding method can effectively prevent the system from cascading failures caused by the risk of line overload, improve the optimistic calculation results caused by the load shedding strategy in emergency situations that cannot consider source load fluctuations, and make the grid control more safe and reliable.

Underfrequency Load Shedding Using Locally Estimated RoCoF of the Center of Inertia

The conventional Under Frequency Load Shedding (UFLS) scheme could result in unacceptably low frequency nadirs or overshedding in power systems with volatile inertia. This paper proposes a novel UFLS scheme for modern power systems whose inertia may vary in a wide range due to high penetration of renewable energy sources (RESs). The proposed scheme estimates the rate of change of frequency (RoCoF) of the center of inertia (CoI), and consequently, the loss of generation (LoG) size, using local frequency measurements only. An innovative inflection point detector technique is presented to remove the effect of local frequency oscillations. This enables fast and accurate LoG size calculation, thereby more effective load shedding. The proposed UFLS scheme also accounts for the effect of the inertia change resulting from LoG events. The performance of the proposed scheme is validated by conducting extensive dynamic simulations on the IEEE 39-bus test system using Real Time Digital Simulator (RTDS). Simulation results confirm that the proposed UFLS scheme outperforms the conventional UFLS scheme in terms of both arresting frequency deviations and the amount of load shed.

Performance Indices for Assessing the Power Quality of Islanded Operation of Distributed Generators

Currently, most utilities require the immediate disconnection of distributed generators in case of unintentional islanding, due to safety and technical reasons. However, there is a trend for allowing the islanded operation of distributed resources under specific conditions, so that the reliability of the energy supply to the consumers can be improved. In this case, the power quality inside the energized island must be assured by the distributed generators’ operators, which means that the transient and steady-state voltage and frequency of the isolated systems must be within acceptable limits. In this context, in this paper we proposed four indices for assessing the technical performance of the islanded operation of distributed generators. The indices are based on power quality issues, and they were proposed by considering the unplanned islanding of a synchronous distributed generator, its transition from the grid-connected mode to the islanded mode and the steady-state operation after a successful islanding. Results have shown the indices application to different operating scenarios, including a load shedding scheme, and they proved to be an interesting tool to technically evaluate the islanded operation of distributed generators, under a power quality perspective.

Duck curve leveling in renewable energy integrated grids using internet of relays

Power grids are undergoing deregulation, privatization, and decentralization worldwide. The smart grid allows the integration of clean power renewable energy technologies to the utility grid through mini, micro, and nano grids. Conventional centralized power grids used to rely on fossil fuel-fired power plants which are being replaced worldwide with solar, wind, small hydro, geothermal, biomass, wave, and alternative energy technologies. Installed solar and wind power generation capacity has surpassed 1300 GW in 2020. Solar and wind powers rise during the day and fall during the evening. Consumer demand increases steadily during the evening and peaks after sunset when solar generation becomes zero and wind power falls substantially. Decentralized smart grid faces duck curve limitation on the integration of renewable energy technologies as centralized utilities used to face the peak-hours issue. Smart grid operators have no effective solution of load peaking after sundown except keeping fossil fuel-fired plants on standby to ride through the duck curve. We present a solution to the duck curve problem by integrating smart load shedding devices in micro and nano grids to adjust their demand frugally during peak hours to support the national grid. Information and communication (ICT) technologies that enabled the internet of things (IoT) have been attempted to facilitate smart load shedding in nano grids. ICT enabled voltage transformers (VT) and current transformers (CT) supply signals to heuristic rules based multifunction smart relaying system. Smart meters, instrument transformers, and status monitoring field devices generate a massive amount of data. The research designed an IoT and LabVIEW based software platform to carry out demand-side management. Under frequency (UF) and under-voltage (UV) relaying functions were used to conduct load shedding in nano grids. This nanoscale demand-side management experiment paves the way to ride through utility-scale duck curve setback. This PT/CT data based economic solution performs at nanoscale same as ABB PML 630 load shedding controller. This UF and UV relaying based multifunction relay starts shedding loads when frequency falls below <1% and voltage below 10% of rated values. Our load shedding scheme has successfully functioned on nanoscale 5 kW system. The future study section proposes to integrate big data technologies in-home energy management system (HEMS) through extract, transform and load (ETL) pipeline to import data from PT/CT and circuit breakers (CB) integrated smart meter to transform it according to requirement by loading data into Hadoop “big data” processing platforms for utility-scale load management with help of day-ahead load and weather-dependent renewable power generation forecasting techniques.

Optimal Reduction of electrical Loads Based on Priorities Developed Using a Genetic Algorithm

Most developing countries are now working to combat imbalances between power generation and load demand. In such situations, load shedding schemes have been implemented frequently as rapid solutions for unbalanced conditions to protect networks from collapsing and to sustain stability. The conventional methods of load shedding disconnect loads without considering their priorities. In this work, a logarithmic reduction method is thus proposed to reduce loads according to the priority and criticality. A Reduction Matrix is introduced as a tuning factor scaled to the size of the network, and the paper thus presents an optimisation tool based on Genetic Algorithms, developed in MATLAB, that can be applied to minimise the error between the amount of load to be reduced and the actual load reduced in electric power systems. The proposed algorithm was tested on a practical data system sample as provided by the Iraqi Ministry of Electricity from the control center of the Iraqi national grid in Baghdad, and was shown to offer optimal load reduction with reduce error, which is necessary to eliminate the impact of load reduction in electrical networks on critical loads when total demand cannot be supplied. The simulation results thus support the effectiveness and practicality of the applied method, paving the way for its possible application in power systems.

Under Frequency Protection Enhancement of an Islanded Active Distribution Network Using a Virtual Inertia-Controlled-Battery Energy Storage System

When an islanding condition caused by an unintentional single-line to ground fault occurs in an active distribution network with distributed generation, the frequency stability and protection issues remain challenging. Therefore, this paper presents the under frequency protection enhancement of the active distribution network using a virtual inertia-controlled-battery energy storage system to improve the frequency stability under the islanding condition caused by unintentional faults. The virtual inertia control is designed based on the direct and quadrature axis-controlled battery energy storage system to generate the virtual inertia power, compensating the system’s inertia to enhance the stability margin. The proposed method is verified by the simulation results that reveal the frequency stability performance and the under-frequency load shedding enhancement of the study active distribution network in Thailand. The study is divided into two cases: the normal control parameters and the parameter uncertainty scenarios, compared with a power-frequency droop control. The simulation results demonstrate that the proposed virtual inertia control can effectively improve the frequency and transient stabilities in the islanding condition, diminishing the number of loads disconnected by the proposed under-frequency load shedding scheme.

Deep Feedback Learning Based Predictive Control for Power System Undervoltage Load Shedding

In practical power systems, it is still very challenging to figure out a cost-effective undervoltage load shedding (UVLS) scheme that can reliably and adaptively react to the short-term voltage stability (SVS) problem. Faced with this challenge, this paper develops an intelligent data-driven predictive UVLS scheme for online SVS enhancement. Inspired by valuable ideas in model predictive control and supplementary excitation control in power systems, a novel deep feedback learning machine (DFLM) is designed to precisely predict future voltage violations after UVLS execution. With the help of the DFLM, the UVLS scheme is aware of potential effects of various candidate UVLS actions. Owing to this desirable nature, it can adaptively respond to diverse SVS conditions and optimize UVLS decisions in a non-iterative way. Further, two well-designed strategies, i.e., stepwise constraint relaxation and incremental DFLM adaptation, are introduced to enhance the scheme's reliability and adaptability during online application. Numerical test results on the Nordic test system and the realistic North GZ Power Grid in China showcase the excellent performances of the proposed scheme.

Emergency Load Shedding Strategy for Microgrids Based on Dueling Deep Q-Learning

The rapid drop of frequency under the disturbance is a major threat to the safe and stable operation of a microgrid (MG) system. Emergency load shedding is the main measure to prevent continuous frequency drop and power outage. The existing load shedding strategies have poor adaptability to deal with the problem of MG load shedding under different disturbance situations, and it is difficult to ensure the safe and stable operation of an MG in different operating environments. To address this problem, this paper proposes a data-driven load shedding strategy. First, considering the importance of the load and the frequency recovery time of the system, a load shedding contribution indicator is designed that takes into account the load frequency adjustment effect and the load shedding priority. This contribution indicator is introduced as a load shedding criterion into the reward value function of dueling deep Q learning. Second, considering the suddenness and uncertainty of emergency load shedding, a MG emergency load shedding strategy (ELSS) based on dueling deep Q-learning is proposed. On this basis, the dueling deep Q learning algorithm is used to obtain the load shedding decision with the maximum cumulative reward. Finally, taking the MG load shedding cases in two different scenarios as examples, a simulation study is carried out on a modified IEEE-25 bus MG. The simulation results show that, compared with the model-driven implicit enumeration strategy (IES), the proposed ELSS has superiority in maintaining stable power supply for important loads and reducing load shedding decision-making time and frequency fluctuations.

An enhanced fuzzy logic based strategy for non-disruptive load shedding and the predictive energy management system

Electrical energy capacity mismatch and immense power shortages due to energy deficit in the power system keep on increasing because of inadequate power capacity, insufficient investment, demand growth, and the rise in living standards. To maintain a balanced relationship between production and consumption, several load shedding schemes have been implemented for load management in the last few years, but their inconsistency poses a challenge. This research work will apply the fuzzy logic algorithm (FLA) and optimise the existing conventional power systems with and without dispersed generators (DGs). In critical circumstances, the algorithm will find node sets or desired locations where limits are violated and system operators may request the utility or industrial customer to shed a required amount of load by operating distributed generators to maintain its system integrity. The performance and suitability of the proposed scheme are demonstrated and proved by testing on a standard 33-bus radial distribution system in MATLAB Simulink having an objective function of minimising its total load curtailed and reducing power system losses by improving its system stability.

Load Shedding and Restoration for Intentional Island with Renewable Distributed Generation

Due to the high penetration of renewable distributed generation (RDG), many issues have become conspicuous during the intentional island operation such as the power mismatch of load shedding during the transition process and the power imbalance during the restoration process. In this paper, a phase measurement unit (PMU) based online load shedding strategy and a conservation voltage reduction (CVR) based multi-period restoration strategy are proposed for the intentional island with RDG. The proposed load shedding strategy, which is driven by the blackout event, consists of the load shedding optimization and correction table. Before the occurrence of the large-scale blackout, the load shedding optimization is solved periodically to obtain the optimal load shedding plan, which meets the dynamic and steady constraints. When the blackout occurs, the correction table updated in real time based on the PMU data is used to modify the load shedding plan to eliminate the power mismatch caused by the fluctuation of RDG. After the system transits to the intentional island seamlessly, multi-period restoration plans are generated to optimize the restoration performance while maintaining power balance until the main grid is repaired. Besides, CVR technology is implemented to restore more loads by regulating load demand. The proposed load shedding optimization and restoration optimization are linearized to mixed-integer quadratic constraint programming (MIQCP) models. The effectiveness of the proposed strategies is verified with the modified IEEE 33-node system on the real-time digital simulation (RTDS) platform.

A Frequency and Voltage Stability-Based Load Shedding Technique for Low Inertia Power Systems

The aim of this research work is to develop a load shedding methodology to improve the frequency response of low inertia grids by attaining satisfactory voltage stability. In recent times, wind energy integration has considerably increased in many power grids. Consequently, conventional synchronous machines are being replaced from dispatch. Unlike traditional synchronous machines, variable speed wind turbine generators usually do not take part in frequency regulation without supplementary control mechanism. During substantial wind penetration, a power system may have a small number of online synchronous machines. As a result, synchronous inertia and governor responsive reserve significantly reduce. Under such situation, a system has to rely on load shedding as a last line of defense to rescue the system frequency following a large contingency. However, the conventional Under-Frequency Load Shedding (UFLS) strategy may lead to larger frequency deviation and higher amount of load cut in certain cases. A new load shedding methodology is presented in this paper to overcome this challenge. Unlike conventional UFLS technique, higher proportion of load shedding is applied to relatively weaker buses in terms of voltage stability in the proposed mechanism. Based on reactive power margin, which is an index to specify voltage stability, a general expression to quantify load shedding is derived. Also, the adaptability of the proposed strategy to various load levels is ensured. Later on, performances of the developed strategy are explored in a low inertia wind dominated test network. Simulations are executed considering various penetration levels of wind power and for two severe contingencies - loss of 550 MW interconnection and loss of 650 MW interconnection. Investigations reveal that the proposed load shedding methodology ensures satisfactory frequency response in all simulation cases. Also, the developed technique yields less frequency deviation and load cut compared to the conventional UFLS mechanism. Therefore, the reported load shedding scheme is found to be more competent to concurrently maintain frequency and voltage stabilities in renewable dominated power systems.

Power-saving smart energy grid meter for blackout management

ABSTRACT Power generation and transmission effectiveness of energy systems have always been impacted around the globe. An imbalanced load of different devices on whole grids have factually conceived disastrous events across history. Traditionally, to resolve imbalances, blackouts are conducted to stabilize the supply to the remainder of the grid network. This paper presents the development of a remote energy measurement system using Arduino and sensors (C.T. & P.T.), to measure and protect user-end components from potential failure. Arduino records individual sensor data and transmits the relative data through Wi-Fi via a local internet connection to the base station. The unit also displays all related data such as energy consumption (Power), current, and voltage on an integrated display panel. This paper also proposes an improved energy meter design, capable of monitoring load prioritized electrical circuits for load management to avoid complete blackouts. The model is simulated, and energy meter hardware is developed, assessing the load shedding scheme using the proposed prioritization of load circuits.

A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control

Fast and accurate prediction and control of power system dynamic frequency after disturbance is essential to enhance power system stability. Machine learning methods have great potential in harnessing data for online application with accurate predictions. This paper proposes a two-stage novel transfer and deep learning-based method to predict power system dynamic frequency after disturbance and provide optimal event-based load shedding strategy to maintain system frequency. The proposed deep learning model combines convolutional neural network (CNN) and long short-term memory (LSTM) network to harness both spatial and temporal measurements in the input data, through a four-dimensional (4-D) tensor input construction process including, 1) capture system network topology information and critical measurements from different time intervals; 2) compute a multi-dimensional electric distance matrix and reduce to a 2-D plane which can describe the system nodal distribution; 3) construct 3-D tensors based on state variables at different sample times; and 4) integrate into 4-D tensor inputs. Moreover, a transfer learning process is employed to overcome the challenge of insufficient data and operating condition changes in real power systems for new prediction tasks. Simulation results in IEEE 118-bus system verify that the CNN-LSTM method not only greatly improves the timeliness of online frequency control, but also presents good accuracy and effectiveness. Test cases in the New England 39-bus system and the South Carolina 500-bus system validate that the transfer learning process can provide accurate results even with insufficient training data.