Emergency Load Shedding Research Articles

Real-time outage management in active distribution networks using reinforcement learning over graphs

Self-healing smart grids are characterized by fast-acting, intelligent control mechanisms that minimize power disruptions during outages. The corrective actions adopted during outages in power distribution networks include reconfiguration through switching control and emergency load shedding. The conventional decision-making models for outage mitigation are, however, not suitable for smart grids due to their slow response and computational inefficiency. Here, we present a graph reinforcement learning model for outage management in the distribution network to enhance its resilience. The distinctive characteristic of our approach is that it explicitly accounts for the underlying network topology and its variations with switching control, while also capturing the complex interdependencies between state variables (along nodes and edges) by modeling the task as a graph learning problem. Our model learns the optimal control policy for power restoration using a Capsule-based graph neural network. We validate our model on three test networks, namely the 13, 34, and 123-bus modified IEEE networks where it is shown to achieve near-optimal, real-time performance. The resilience improvement of our model in terms of loss of energy is 607.45 kWs and 596.52 kWs for 13 and 34 buses, respectively. Our model also demonstrates generalizability across a broad range of outage scenarios.

Emergency load shedding to maintain line flows in presence of WTDFIG using modified crow search algorithm

Emergency load shedding to maintain line flows in presence of WTDFIG using modified crow search algorithm

Precise emergency load shedding approach for distributed network considering response time requirements

Emergency load shedding (ELS) is a vital measure for power systems to manage extreme events, ensuring the safety, stability, and economic operation of the grid. The integration of distributed energy resources and controllable devices in modern power systems has bolstered grid flexibility. Consequently, developing precise load shedding strategies to balance economic and security goals has emerged as a prominent subject in power system optimization. However, existing methods exhibit inadequacies, including overlooking practical operability, privacy concerns, and a lack of adaptability to response time requirements. To address these gaps, this paper introduces a precise ELS approach for distributed networks with a focus on response time needs. Contributions encompass designing load shedding processes for various response times, integrating demand response, and partitioning networks for optimized load shedding. Through validation using standard test cases, the proposed approach effectively utilizes response time and demand-side resources for precise ELS control in distribution networks. It accommodates different scenarios, offering a robust solution for rapid and accurate load shedding during emergencies.

Spatial–temporal QoS assessment of the EV charging network considering power outages

The rapid expansion of electric vehicle charging networks (EVCNs) makes them critical infrastructures bridging power and traffic systems. The EVCN could be vulnerable during power outages at fast charging stations (FCSs), which are induced by planned maintenance or emergency load shedding. This paper proposes an approach to assess the impact of power outages on the Quality-of-service of the EVCN. The Markov decision process is utilized to model the spatial–temporal randomness of EV movement in a graph-based EVCN. The decision of charging by EV drivers is estimated by a fuzzy logic inference system. The spatial–temporal EV charging load at FCSs is formulated by a queuing-based non-linear optimization problem. Yen’s algorithm is adopted to simulate the EV redistribution phenomenon of searching adjacent healthy FCSs in response to the power outage. Quality-of-service (QoS) indices are derived to assess the potential congestions in the adjacent healthy FCSs. The case studies demonstrate that power outages may cause congestion at peripheral FCSs, exacerbating the QoS of the EVCN. Partial charging may alleviate the QoS deterioration in the event of FCS outages.

Emergency load shedding strategy for high renewable energy penetrated power systems based on deep reinforcement learning

Traditional event-driven emergency load shedding determines the quantitative strategy by simulation of a specific set of expected faults, which requires high model accuracy and operation mode matching. However, due to the model complexity of renewable power generators and fluctuating power generation, traditional event-driven load shedding strategy faces the risk of mismatching in high renewable energy penetrated power systems. To address these challenges, this paper proposes an emergency load shedding method based on data-driven strategies and deep reinforcement learning (RL). Firstly, the reason for the possible mismatch of the event-driven load shedding strategy in the renewable power system is analyzed, and a typical mismatch scenario is constructed. Then, the emergency load shedding strategy is transformed into a Markov Decision Process (MDP), and the decision process’s action space, state space, and reward function are designed. On this basis, an emergency control strategy platform based on the Gym framework is established for application of deep reinforcement learning in the power system emergency control strategy. In order to enhance the adaptability and efficiency of the RL intelligence agent to multi-fault scenarios, the Proximal Policy Optimization (PPO) is adopted to optimize the constructed MDP. Finally, the proposed reinforcement learning-based emergency load shedding strategy is trained and verified through a modified IEEE 39-bus system. The results show that the proposed strategy can effectively make correct strategies to restore system frequency in the event-driven load shedding mismatch scenario, and have good adaptability for different faults and operation scenarios.

Strategy optimization of emergency frequency control based on new load with time delay characteristics

With the development of modern communication technology and the large number of new controllable loads connected to the power grid, the new controllable loads with flexible regulation characteristics can participate in the emergency frequency stability control. However, the communication state differences and spatial distribution characteristics of controllable load will affect the actual effect of frequency control. In this paper, an emergency frequency control method based on deep reinforcement learning is proposed considering the response time of controllable load shedding. The proposed method evaluates response ability for emergency control of controlled loads through load response time, controllable load amount and controllable load buses. Then, the controllable load with smaller response time is cut out preferentially to ensure rapid control, and the Markov Decision Process (MDP) is used to model the emergency frequency control problem. Finally, Rainbow algorithm of Deep Reinforcement Learning (DRL) is used to optimize the emergency frequency stability control strategy involving controllable load resources. The formation of emergency load shedding instruction is directly driven by high-dimensional operation state data after power grid failure, so that, the aim of minimizing the economic cost is achieved under the constraint of system stability. The effectiveness of the proposed method is verified in the IEEE 39-bus system.

Resilience assessment for natural gas systems and electrical power systems without complete integration

Considering the limited information sharing between natural gas systems (NGS) and electrical power systems (EPS), this paper proposes a distributed resilience assessment method for NGS and EPS. First, a coordinated restoration framework is presented for incompletely integrated energy systems, and the collaborative benefit redistribution mechanism is proposed to motivate the participation of the cooperative restoration. Then fragility and recovery models for NGS and EPS components are developed, involving the impact of wind and rainfall during typhoons. An emergency load shedding model considering the conditional value at risk under typhoons is built, where the dynamic constraints of gas storage in NGS's pipelines is considered, and a linear approximation method is adopted to make the solution feasible. The modified alternating direction method of multipliers algorithm is used to solve the problem, and herein the self-adaptive penalty factor is leveraged to boost the efficiency. Based on the valley-shaped resilience model, the concept of skewness and kurtosis are introduced to give risk-adverse metrics more systematically to the impact of extreme events. The proposed model and algorithm are validated by case studies. Except for the performance exploration, the weakness and enhancement suggestions are also concluded from the case analysis.

Cooperative overload control strategy of power grid-transformer considering dynamic security margin of transformer in emergencies

With the integration of large-scale renewable energy resources and growth of electricity demand, the peak-to-valley load difference increases constantly. The overload risk of transformer in emergencies is enlarging, which urgently requires cooperative control of power grid-transformer to avoid catastrophic accidents. Thus, a cooperative control strategy of power grid-transformer considering the dynamic security margin of the transformer in emergencies is proposed in this paper. First, the safety domain model of the transformer is constructed based on the temperature rising model as well as insulation deterioration pattern, and the dynamic security margin of the transformer is evaluated accordingly. Then, targeted counter measures are selected according to the safety status of the transformer, and different control schemes such as emergency overload, topology adjustment, and load shedding are generated by solving the optimization model. Finally, the effectiveness of the proposed strategy is validated in various scenarios. The results show that when the security margin of transformer is sufficient, the strategy can fully exploit its overload capability to support grid regulation and avoid premature load shedding; when the security margin is insufficient, load shedding is coordinated to ensure the security of transformer. The proposed strategy can realize the synergistic control and mutual support between power grid safety and equipment safety.

Robustly Coordinated Generation Dispatch and Load Shedding for Power Systems Against Transient Instability Under Uncertain Wind Power

Transient stability of a power system can be significantly affected by wind power generators due to their stochastic power output and complex dynamic characteristics. This paper proposes a robust optimization approach for coordinating generation dispatch and emergency load shedding against transient instability under uncertain wind power output. The problem is modelled as a two-stage robust optimization (TSRO) model considering transient stability constraints, where the first-stage is to optimize the generation dispatch (preventive control) before a contingency and the second-stage decision is the emergency load shedding (emergency control) after the contingency occurrence under the worst case of wind power variation. To solve this TSRO problem, this paper also proposes a solution algorithm which integrates transient stability assessment and transient stability constraint construction in a column and constraint generation framework. The proposed method is validated on the New-England 39-bus system and the Nordic32 system, which shows high computational efficiency and stability robustness against uncertain wind power.

Real-time emergency load shedding for power system transient stability control: A risk-averse deep learning method

Emergency load shedding is an effective and frequently used emergency control action for power system transient stability. Solving the full optimization models for load shedding is computational burdensome and thus slow react to the intense system variations from the increasing renewable energy sources and the more active demand-side behavior. Other sensitivity-based methods impair the control accuracy and may not guarantee global optimality. Artificial intelligence methods, as the data-driven approaches, have recently been well-recognized for its real-time decision-making capability to tackle the system variations. The existing artificial intelligence methods for emergency load shedding are based on shallow learning algorithms and can lead to both load under-cutting and over-cutting events. However, when the loads are under-cut, the power system will be exposed to a high risk of post-control instability that can propagate into cascading events, which incurs significantly higher cost than an over-cutting event. Being aware of such unbalanced control costs, this paper proposes a risk-averse deep learning method for real-time emergency load shedding, which trains deep neural network towards the reluctance to load under-cutting events, so as to avoid the huge control cost incurred by control failure. The case studies on two renewable power systems demonstrate that, compared to the state-of-the-art methods, the proposed risk-averse method can significantly improve the control success rate with negligible increase in prediction error, ending up with lower overall control cost. The results verify the enhanced control performance and the practical values of the proposed method.

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.

Autonomous Operation of Microgrid and Minimization of Fault in Case of Failure in High-Voltage Lines

The manual reaction approach to faults is exhibited in conventional grid. Manual operations are slow in many cases and resulting in big fault and power cut. Turkey has experienced it by living on March 31, 2015. The switching to smart grids is inevitable in order to minimize human errors and avoid big failures. It is a solution to turn the appropriate zones back to the island mode, especially in case of emergency load shedding due to the basic frequency. However, large power fluctuations occur in the microgrids when switching to the island mode or connecting to the grid. Therefore, In Matlab / Simulink, a microgrid is designed that can operate in island mode in accordance with the smart grid structure to minimize the damage of symmetrical and asymmetrical of high voltage lines on loads, grid and its components in this study. Also the effects of Superconducting Fault Current Limiter (SFCL) have been studied to limit power fluctuations in the microgrid when switching to island mode and exit island mode. In addition, autonomous maneuver management has been carried out on the designed high voltage line to prevent faults resulting in long term power cut.

Risk-Based Stochastic Scheduling of Resilient Microgrids Considering Demand Response Programs

In this article, a risk-constrained stochastic framework is presented for joint energy and reserve scheduling of a resilient microgrid considering demand side management. The optimization problem is formulated to schedule the system operation in both normal and islanding modes by addressing the prevailing uncertainties of islanding duration as well as prediction errors of loads, renewable power generation, and electricity price. In a normal operation mode, where the grid-connection is available, the energy and reserve of local resources and energy trading with the main grid is scheduled to maximize the operator's profit considering feasible islanding. In a resilient operating mode, which is triggered by a disturbance in the main grid, the local resources should be scheduled to supply loads with the lowest emergency load shedding. To balance the economy and security requirements under uncertainties, the optimal scheduling is done properly through a security-constrained power flow method by considering system's objectives and constraints. Moreover, to properly handle the uncertainties of the problem, conditional value-at-risk metric is incorporated with the optimization model to control the risk of profit variability. The proposed scheme is implemented on a test microgrid and various case studies are presented to verify its effectiveness in normal and resiliency operating conditions.

Emergency Load Shedding for Voltage Stability Enhancement: With Particular Reference to the Iraqi National Power Grid

Emergency Load Shedding for Voltage Stability Enhancement: With Particular Reference to the Iraqi National Power Grid

Emergency control strategy for renewable power systems considering utility-scale energy storage transient

Integration of renewable energy generators has greatly altered both static and dynamic characteristics of the system. Combined with the uncertainties it introduced, the risk of a system being transient instable is significantly alleviated. This paper proposes a multi-objective coordinated post-contingency control method. It aims to increase post-contingency system security with emergence control (EC) while minimizing the total control cost. Two ECs are adopted in this paper: energy storage systems (ESSs) and emergency load shedding (ELS). ESSs are immediately connected to the network after contingency occurrence to provide both active and reactive power support. ELS will be triggered when the support from ESSs is insufficient to stabilize the system to prevent further deterioration of system security. Performance of the proposed method was evaluated on a modified New England 39-bus benchmark system. The results indicate that the proposed method can find solutions to stabilize the system against credible contingencies and optimally balance between system stability and economy.

Application of Dual Artificial Neural Networks for Emergency Load Shedding Control

This paper proposes a new model in emergency control of load shedding based on the combination of dual Artificial Neural Network to implement the load shedding, restore the power system frequency and prevent the power system blackout. The first Artificial Neural Network (ANN1) quickly recognizes the state with or without load shedding when a short-circuit occurs in the electrical system. The second Artificial Neural Network (ANN2) identifies and controls the selection of load shedding strategies. These load shedding strategies include pre-designed rules which is built on the AHP algorithm to calculate the importance factor of the load units and select the priority of the load shedding. In case the ANN1 results in a load shedding, the load shedding control strategy is immediately implemented. Therefore, the decision making time is much shorter than the under frequency load shedding method. The effectiveness of the proposed method is tested on the IEEE 39-bus system which proves the effectiveness of this method.

Study on Emergency Load Shedding of Hybrid AC/DC Receiving-End Power Grid with Stochastic, Static Characteristics-Dependent Load Model

When large-capacity HVDC (high voltage direct current) transmission line blocking occurs in a hybrid AC/DC (alternating current/direct current) power grid, the receiving-end system will encounter a huge power imbalance, which will lead to a frequency drop and redistribution of the power flow, and which may further lead to the overload of other transmission lines, cascading failures and a large-scale blackout. To resolve these problems, an emergency load-shedding strategy for the DC receiving-end system is proposed from the perspective of a quasi-steady state. The proposed method can accurately calculate the actual total power imbalance by modeling more detailed stochastic loads with static frequency/voltage characteristics and involving the inertia effect of the generator during the response delay period, which can effectively reduce the amount of load curtailment. In addition, several factors affecting the power imbalance estimation in stochastic scenarios and their mechanisms are analyzed in detail, and the key aspects relevant to the DC blocking fault analysis are identified as well. Finally, the influence of different load-shedding strategies on the receiving-end system security after a DC blocking fault is compared with the security indices, including those that are relevant to the frequency/load change proposed herein, and a uniform load-shedding coefficient is obtained via the proposed method, even for different power imbalances under a stochastic context, which makes the load-shedding strategy more practical.

A MILP-Based Restoration Technique for Multi-Microgrid Distribution Systems

The main focus of the work presented in this paper is on the outage management of interconnected microgrids during islanded operation after being disconnected from the utility main supply. The proposed two-stage load restoration technique is formulated as a Mixed Integer Linear Programming (MILP) optimization problem with the sole objective of optimally restoring the maximum number of disconnected loads. The proposed technique is applied to a distribution system comprising of several microgrids, i.e., Multi-Microgrid (MMG) distribution system. In this proposed technique, the power transactions between individual microgrids are managed through (1) determining the schedule of local energy resources, and (2) by obtaining the control signals pertaining to local flexible loads. Flexible load control signals are prioritized through two incorporated Demand Response (DR) programs, namely emergency load shedding and preemptive load shifting. To quantify the restoration technique performance, a new index—denoted as restoration technique success index (SI)—is proposed. The effectiveness of the proposed restoration technique is verified through different test case scenarios and the obtained results are discussed.

Evaluation of reliability in risk‐constrained scheduling of autonomous microgrids with demand response and renewable resources

Uncertainties in renewable energy resources and electricity demand have introduced new challenges to energy and reserve scheduling of microgrids, particularly in autonomous mode. In this study, a risk-constrained stochastic framework is presented to maximise the expected profit of a microgrid operator under uncertainties of renewable resources, demand load and electricity price. In the proposed model, the trade-off between maximising the operator's expected profit and the risk of getting low profits in undesired scenarios is modelled by using the conditional value-at-risk (CVaR) method. The influence of consumers’ participation in demand response (DR) programs and their emergency load shedding for different values of lost load (VOLL) are then investigated on the expected profit of the operator, CVaR, expected energy not served and scheduled reserves of the microgrid. Moreover, the impacts of different VOLL and risk aversion parameters are illustrated on the system reliability. Extensive simulation results are also presented to illustrate the impact of risk aversion on system security issues with and without DR. Numerical results demonstrate the advantages of customers’ participation in the DR program on the expected profit of the microgrid operator and the reliability indices.

Study on Emergency Load Shedding Based on Frequency and Voltage Stability

Under frequency load shedding is widely used under emergency situations in the power system. It significantly ensures frequency stability of power system by dropping excessive loads at certain places. In order to improve the traditional UFLS schemes, the rate of frequency change is taken into consideration when calculating the quantity of active power imbalance in the power system. The measure factors of frequency change rate are analyzed and a new function is created to calculate the active power imbalance based on frequency and voltage. Moreover, a new method which utilizes frequency and voltage sensitivity to determine the location and corresponding quantity of load shedding is proposed. Voltage stability should be maintained at the time of recovering system frequency stability. The simulation results based on IEEE39 buses system show that the new self-adaptive scheme of load shedding is more reasonable and superior.