Search

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.

The integration of renewable energy sources may result in more severe active power disturbances, and requires more load shedding. The battery energy storage system (BESS) characterized with high flexibility can handle such uncertain disturbances and reduce load shedding amount. This paper proposes a chance-constrained optimal sizing scheme of the BESS to coordinate with the synchronous generator and emergency load shedding (ELS) for frequency stability. First, considering the role of the synchronous generator, BESS, and ELS in frequency stability control, an extended system frequency response model is established to calculate the amount of load shedding, and the required capability of frequency regulation. Then, a multi-objective and chance-constrained mixed integer nonlinear programming (MINLP) model is built for the BESS sizing to minimize investment cost and maximize operation profits, which considers the uncertainties of disturbances. With the utilization of the linear weighted method, max-affine function-based piecewise linearization method and Bernstein approximation, the model is reformulated into a tractable form, which is iteratively solved by the multi-cut Benders decomposition. Case studies were carried out to verify the effectiveness of the proposed scheme, showing superior performance in reducing load shedding amount and improving frequency nadir. • A hierarchical optimization framework for the BESS sizing to coordinate with synchronous generators and ELS. • An estimation method for the required frequency regulation capability of the BESS and the amount of ELS. • A chance-constrained and multi-objective MINLP model for the BESS sizing with coordinated frequency control and uncertain disturbances. • Reformulate the chance-constrained and multi-objective model into a tractable MILP form. [ABSTRACT FROM AUTHOR]

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.

In the present days, power systems are operated favourably close to their stability limit. System stability can be safeguarded by employing an emergency control technique, known as load shedding; this technique is affected by shedding some loads. Such a technique implemented only when the voltage or frequency deterioration below a specific voltage or frequency threshold. In this work, an advanced emergency load shedding model is forwarded based on the voltage stability indicator aimed at enhancing voltage. This indicator facilitates online monitoring of voltage stability and predicts the voltage problem of the system with sufficient accuracy, fast and simple numerical calculation, also can work well in the steady-state as well as during the transient process. The proposed model and algorithm have been tested firstly on the IEEE 14 bus system as a standard system and then implemented on the Iraqi National Power Grid (INPG) as a particular reference system. The obtained results for the voltage profile has recovered by 11.1% for the standard system and 2.03% for the particular system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This work proposes a measurement-based voltage stability index, namely wide-area measurement-based voltage stability sensitivity based on L index. The sensitivities of L index with respect to nodal reactive power (Q) and real power (P) injections are first derived. The derived L - Q and L-P sensitivities analyze the impact of nodal injection to nodal voltage stability and can help determine the reactive power compensation and emergency load shedding amount for voltage stability control. To improve the computational efficiency, a simplified L index, L ′, along with its sensitivities with respect to nodal reactive and real power injection ( L ′- Q and L ′ -P sensitivities) are derived which makes the proposed approach suitable for the practical large-scale systems. Moreover, a control strategy for voltage stability is proposed based on the L - Q , L-P , L ′ -Q , and L ′ -P sensitivities. The proposed sensitivities and control strategy are tested on the New England 39 bus system and the IEEE 118 system. Test results on both systems verify the proposed sensitivities and the control strategy by demonstrating their accuracy and robustness in voltage stability assessment and control. In conclusion, the proposed measurement-based sensitivities can be applied to voltage stability assessment and control by using the wide-area measurements. [ABSTRACT FROM AUTHOR]

• A risk-averse deep learning method for real-time emergency load shedding. • Deep learning algorithm to enhance prediction accuracy on load shedding strategy. • A shedding amount inference model to speculate the stability after load shedding. • A risk-averse loss function to minimize the risk of load under-cutting events. • Experiments on multiple power systems to verify the practical values of the 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. [ABSTRACT FROM AUTHOR]