Framework for Preventive Control of Power Systems to Defend Against Extreme Events

Enhancing power system resilience against extreme events is becoming increasingly critical. This paper discusses a unified framework for preventive control of power systems to enhance system resilience, which includes three parts: resilience assessment, resilience grading, and resilience enhancement. First, the resilience assessment contains facility-level and system-level resilience assessment. The concept of fragility curve is used in the facility-level resilience assessment. Various resilience indices are developed in system-level resilience assessment to roundly depict the impacts of extreme events on power systems and determine the system resilience. On this basis, the resilience is divided into different levels by resilience grading strategy, which can efficiently quantify the severity of the impact of extreme events and provide decision-making for the resilience enhancement strategies. Then, control strategies for enhancing power system resilience are also divided according to different resilience levels. A controlled islanding based preventive control is proposed to enhance system resilience, which aims to strengthen the first defensive line of power systems to deal with extreme events. Finally, taking the typhoon disaster in extreme events as an example, two tests carried out with two typhoons demonstrate the efficiency of the proposed method.

Distributed generator-based distribution system service restoration strategy and model-free control methods

The rapid growth of distributed generator(DG)capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration.Renewable DGs are of particular interest to utility companies,but the stochastic nature of intermittent renewable DGs could have a negative impact on the electric grid if they are not properly handled.In this study,we investigate distribution system service restoration using DGs as the primary power source,and we develop an effective approach to handle the uncertainty of renewable DGs under extreme conditions.The distribution system service restoration problem can be described as a mixed-integer second-order cone programming model by modifying the radial topology constraints and power flow equations.The uncertainty of renewable DGs will be modeled using a chance-constrained approach.Furthermore,the forecast errors and noises in real-time operation are solved using a novel model-free control algorithm that can automatically track the trajectory of real-time DG output.The proposed service restoration strategy and model-free control algorithm are validated using an IEEE 123-bus test system.

Chain Rule for Extracting Resilience Procurement Costs from Locational Marginal Prices

Power system resilience procurement costs in N-k contingencies have gained more prominence as number of extreme events continues to increase.A chain rule is presented in this paper for extracting resilience procurement costs from a fully decomposed locational marginal price(LMP)model.First,power transfer distribution factor(PTDF)matrices with AC power flow(i.e.,AC-PTDF)are determined.AC-PTDF and AC-LODF(line outage distribution factor)equations are derived for N-k contingencies and a fully decomposed LMP model is developed where generation and transmission security components are established for specific contingencies.Furthermore,resilience procurement costs can be measured at different buses for the proposed security components.Impact of N-k contingencies on resilience procurement costs at specific buses can be determined as proposed security components will gain more insight for resilience procurement in power systems.The modified IEEE 6-bus and 118-bus systems are adopted to verify effectiveness of the proposed resilience procurement method.

Resilience-oriented Valuation for Energy Storage Amidst Extreme Events

In power grids,the frequency is increasing of extreme accidents which have a low probability but high risk such as natural disasters and deliberate attacks.This has sparked discussions on the resilience of power grids.Energy-storage systems(ESSs)are critical for enhancing the resilience of power grids.ESSs,with their mechanism of flexible charging and discharging,adjust energy usage as needed during disasters,thereby mitigating the impact on the grid and enhancing security and resilience.This,in turn,ensures the power system’s stable operation.Currently,there is limited systematic research quantifying the economic value of energy storage in resilience scenarios.Therefore,a model and methodology were proposed to quantify the value of energy storage systems for enhancing grid resilience during extreme events.A two-stage stochastic optimization mathematical model was developed.The first stage involves pre-deployment based on day-ahead expectations,and the second stage involves simulating potential failure scenarios through real-time scheduling.Considering the temporal dimension,the energy storage systems with flexible regulation capabilities was used as emergency power sources to reduce occurrences of load-shedding.Here,a novel index was proposed that quantifies the resilience value of energy storage as the economic value of energy storage per unit of capacity,as reflected in the emergency dispatch model.This index helps determine the balance between the energy storage investment cost and resilience value.Finally,an IEEE-30 node transmission system was used to verify the feasibility and effectiveness of the proposed method.The findings revealed a significant improvement in the resilience value,with a 23.49%increase observed when energy storage systems were implemented compared to the scenario without energy storage systems.The optimal capacity configurations for the flywheel,lithium-ion batteries,and pumped hydro storage were 10 MW,11 MW,and 12 MW,respectively,highlight their potential to maximize value in experimental system.