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With the increasing penetration of distributed energy in the distribution network, it is urgent to study how to ensure the stable and reliable operation of the power grid under fault conditions. To solve this problem, this study proposes a method for partitioning distribution network islands and reconstructing faults considering the Conditional Value at Risk (CVaR). This method aims to enhance the resilience of the distribution network and the recovery capability of critical loads. Initially, a partitioning model for distribution network islands based on depth-first and breadth-first search algorithms was constructed. Building upon this partitioning, a fault reconstruction method for distribution networks that considers CVaR was developed. This method utilizes CVaR theory to transform costs and quantifies the risk that the uncertainty of distributed energy resources poses to distribution network reconstruction strategies. Finally, the effectiveness of the proposed method is demonstrated using an improved IEEE 33-node system, generating typical fault scenarios. [ABSTRACT FROM AUTHOR]

This paper introduces a post-disaster load restoration approach for the distribution grid, utilizing network reconfiguration (NR) and dispatching of repair crews (RCs) to significantly enhance grid resilience. We propose an RC-NR coordinated model that leverages diverse flexible resources within the active distribution network (ADN), aimed at not only enhancing the grid's resilience level but also efficiently mending the fault lines. The model introduces fault repairing and sequential NR coupled constraints to devise an optimal resilience strategy within temporal domain cooperation, focusing on minimizing repair and penalty costs associated with the restoration process. To tackle the challenge of computational complexity, the nonlinear model is reformulated into a mixed-integer second-order cone programming model. The efficacy of the approach is validated through case studies on an IEEE 33- bus system, in which simulation results demonstrate a considerable improvement in grid resilience, achieving optimal load recovery with reduced restoration time and costs. The proposed approach outperforms traditional methods with optimal repair sequence and RC scheduling, aligned with NR efforts, and contributes to an improved system resilience level. [ABSTRACT FROM AUTHOR]

With the increasing penetration of distributed energy in the distribution network, it is urgent to study how to ensure the stable and reliable operation of the power grid under fault conditions. To solve this problem, this study proposes a method for partitioning distribution network islands and reconstructing faults considering the Conditional Value at Risk (CVaR). This method aims to enhance the resilience of the distribution network and the recovery capability of critical loads. Initially, a partitioning model for distribution network islands based on depth-first and breadth-first search algorithms was constructed. Building upon this partitioning, a fault reconstruction method for distribution networks that considers CVaR was developed. This method utilizes CVaR theory to transform costs and quantifies the risk that the uncertainty of distributed energy resources poses to distribution network reconstruction strategies. Finally, the effectiveness of the proposed method is demonstrated using an improved IEEE 33-node system, generating typical fault scenarios.

This paper introduces a post-disaster load restoration approach for the distribution grid, utilizing network reconfiguration (NR) and dispatching of repair crews (RCs) to significantly enhance grid resilience. We propose an RC–NR coordinated model that leverages diverse flexible resources within the active distribution network (ADN), aimed at not only enhancing the grid’s resilience level but also efficiently mending the fault lines. The model introduces fault repairing and sequential NR coupled constraints to devise an optimal resilience strategy within temporal domain cooperation, focusing on minimizing repair and penalty costs associated with the restoration process. To tackle the challenge of computational complexity, the nonlinear model is reformulated into a mixed-integer second-order cone programming model. The efficacy of the approach is validated through case studies on an IEEE 33-bus system, in which simulation results demonstrate a considerable improvement in grid resilience, achieving optimal load recovery with reduced restoration time and costs. The proposed approach outperforms traditional methods with optimal repair sequence and RC scheduling, aligned with NR efforts, and contributes to an improved system resilience level.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most of the previous researches on fault recovery focused on the distribution network and the recovery goal was only a single load recovery amount, without considering the resource cost and energy consumption environmental protection cost, etc. A multi-objective load recovery strategy based on multi side coordination and optimization was proposed. This method not only realized the network reconfiguration, priority recovery of higher level loads, but also realized the optimal system operation cost. After the system failed, it was recovered in two stages: first, the minimum spanning tree(MST) was used to reconstruct the system structure to realize the reconfiguration of the system network and form the final area of the recoverable load. Then, considering the purchase cost of resources at the source side, the compensation cost of load shedding at the user side and the environmental protection cost of carbon emissions, an optimization objective function was constructed, and the improved particle swarm optimization (PSO) algorithm was used to solve it to obtain the final recovery scheme. The effectiveness of the load restoration strategy was verified by the simulation of 33 bus distribution network, 14 bus gas network and 6 bus heat network test systems. [ABSTRACT FROM AUTHOR]

This paper proposes a two-stage planning method of distributed generation based on coordinated recovery of load partition to improve the resilience of the power grid in extreme weather. The method includes a scenario generation model and an optimization model. In the first stage, a scenario generation model is established, including the distributed generation output and line failure models, to obtain the power output and line status in different scenarios with different weather. Then, an optimal subnetwork screening robust optimization model is built to screen the optimal subnetworks for the deployment of distributed generation in each scenario. In the second stage, a node location optimization model is developed to obtain the optimal node locations for deploying distributed generation within the subnetwork, aiming at maximizing the recovery efficiency of critical loads. Case studies based on a modified IEEE 30-bus system are used to demonstrate the effectiveness of the proposed method. The findings show that the recovered load and recovery efficiency of the power system can be significantly improved.

The shortage of fossil fuels has led to increasing attention on new energy technologies, such as wind power and photovoltaic energy technologies, and the volatility of new energy has become the biggest obstacle for its participation in the process of power-system restoration. This paper presents an optimal control method of the parallel closing angle of the loop network in the process of power system recovery considering the uncertainty of wind-power output. Firstly, based on solving the uncertainty of wind-power output, a probability-prediction model of wind-power output based on the quantile regression of long-term and short-term memory networks (LSTMs) is established. Based on predicting the future wind-power output interval, the probability density function of the output at each time point in the future is obtained by a kernel density estimation. Secondly, by adjusting the output of conventional units and restoring the feeder load, a multiobjective optimal-control model is established to minimize the output change of conventional units and restore the most important feeder load. Based on considering the output probability of new energy, the optimal control of the phase angle difference at both ends of the line to be paralleled is realized, and the multiobjective optimization algorithm is used to solve the established model. Finally, the effectiveness of the proposed method and model is verified by IEEE 39-bus system simulation. [ABSTRACT FROM AUTHOR]

As the final stage of power system restoration, the critical task of load restoration is to restore the remaining load as quickly as possible. With the continuous increase of the temperature-controlled load and the proportion of electric vehicle load in the urban power grid, the complexity of the load side in the restoration process gradually increases. Therefore, based on the existing grid environment, this paper considers the sudden increase in load recovery caused by cold load pick-up and the auxiliary effect of electric vehicle discharge on load recovery during the load recovery process. From the perspective of economy, safety, and speed, this paper establishes a multi-objective function that includes the amount of load, improved weighted power flow entropy, and the number of recovered lines. The multi-objective evolutionary algorithm based on decomposition is used to optimize the constructed multi-objective load recovery model. Through the IEEE30 node system, it is verified that the method proposed in this paper can effectively establish a fast and safe load recovery plan that meets the actual grid environment.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The shortage of fossil fuels has led to increasing attention on new energy technologies, such as wind power and photovoltaic energy technologies, and the volatility of new energy has become the biggest obstacle for its participation in the process of power-system restoration. This paper presents an optimal control method of the parallel closing angle of the loop network in the process of power system recovery considering the uncertainty of wind-power output. Firstly, based on solving the uncertainty of wind-power output, a probability-prediction model of wind-power output based on the quantile regression of long-term and short-term memory networks (LSTMs) is established. Based on predicting the future wind-power output interval, the probability density function of the output at each time point in the future is obtained by a kernel density estimation. Secondly, by adjusting the output of conventional units and restoring the feeder load, a multiobjective optimal-control model is established to minimize the output change of conventional units and restore the most important feeder load. Based on considering the output probability of new energy, the optimal control of the phase angle difference at both ends of the line to be paralleled is realized, and the multiobjective optimization algorithm is used to solve the established model. Finally, the effectiveness of the proposed method and model is verified by IEEE 39-bus system simulation.

Residential air conditionings (RACs) can be seen as one of the important flexible demand resources controlled to provide balance services for the power system due to their spatial thermal storage feature. However, such control on RACs will bring out the negative impact on the comfort level of residents inevitably, affecting their proactivity in demand response (DR). So, a novel two-stage control model of large-scale RACs is proposed to participate in DR considering different comfort sensitivity of various residents. The comfort sensitivity index is developed in terms of daily operation time and daily power consumption per area of RACs, classified by K-means clustering. Besides, the DR potential of RACs is established with the adjusted temperature setpoint, where the temperature setpoint is determined by the comfort sensitivity index. Moreover, a two-stage control model of large-scale RACs is proposed comprised of load response and recovery, where the corresponding control strategies at each stage are proposed. Simulations of the proposed method have been performed on residential 20,000 RACs to validate its effectiveness in Changzhou, China. The outcome reveals that the strategies could maximize the DR potential of RACs and ensure the stable recovery of RACs under the premise of meeting the comfort demand of differentiated population.

Large-scale wind and solar power integration are likely to cause a short-term mismatch between generation and load demand because of the intermittent nature of the renewables. System frequency is therefore challenged. In recent years, it has been proposed that a part of the residential load can be controlled for frequency regulation with little impact on customer comfort. This paper proposes a thermostatic load control strategy for primary and secondary frequency regulation, in particular, using heating, ventilation, and air-conditioning units and electric water heaters. First, daily demand profile modeling indicates that these two loads are complementary in the daytime and can provide a relatively stable frequency reserve. Second, the progressive load recovery is specifically considered in the control scheme. The random switching and cycle recovery method is proposed for mitigating power rebound after switching the air conditioners on again. The proposed control strategy can organize a large population of thermostatic loads for the provision of a frequency reserve. Consequently, the requirement of a spinning reserve is reduced. Finally, the proposed control strategy is verified by the dynamic simulation of IEEE RTS 24-bus system. [ABSTRACT FROM AUTHOR]

Critical peak pricing (CPP) is a demand response program that can be used to maximize profits for a load serving entity in a deregulated market environment. Like other such programs, however, CPP is not free from the payback phenomenon: a rise in consumption after a critical event. This payback has a negative effect on profits and thus must be appropriately considered when designing a CPP scheme. However, few studies have examined CPP scheme design considering payback. This study thus characterizes payback using three parameters (duration, amount, and pattern) and examines payback effects on the optimal schedule of critical events and on the optimal peak rate for two specific payback patterns. This analysis is verified through numerical simulations. The results demonstrate the need to properly consider payback parameters when designing a profit-maximizing CPP scheme.

A time-domain technique for estimating dynamic loads acting on a structure from structural response measured experimentally at a finite number of optimally placed sensors on the structure is presented. The technique relies on an existing solution method based on dynamic programming, which consists of a backward (inverse) time sweeping phase followed by a forward time sweeping phase. The dynamic programming method of load identification, similar to all other inverse methods, suffers from ill-conditioning. Small variations (noise) in response measurements can cause large errors in load estimates. The condition of the inverse problem, and hence the quality of load estimates, depends on the locations of sensors on the structure. There can be a large number of locations on a structure where sensors can potentially be mounted. A D-optimal design algorithm is used to arrive at optimal sensor locations such that the condition of the inverse problem is improved and precise load estimates are obtained. Another major limitation of the dynamic programming technique is that the computation time increases dramatically as the model size increases. To deal with this shortcoming, a technique based on Craig–Bampton model reduction is also proposed in this paper. Numerical results illustrate the effectiveness of the proposed technique in accurately recovering the loads imposed on discrete as well as continuous systems. [ABSTRACT FROM AUTHOR]

The large-scale integration of wind generation in power systems increases the need for reserve procurement in order to accommodate its highly uncertain nature, a fact that may overshadow its environmental and economic benefits. For this reason, the design of reserve procurement mechanisms should be reconsidered in order to embed resources that are capable of providing reserve services in an economically optimal way. In this study, a joint energy and reserve day-ahead market structure based on two-stage stochastic programming is presented. The developed model incorporates explicitly the participation of demand side resources in the provision of load following reserves. Since a load that incurs a demand reduction may need to recover this energy in other periods, different types of load recovery requirements are modeled. Furthermore, in order to evaluate the risk associated with the decisions of the system operator and to assess the effect of procuring and compensating load reductions, the Conditional Value-at-Risk metric is employed. In order to solve the resulting multi-objective optimization problem, a new approach based on an improved variant of the epsilon-constraint method is adopted. This study demonstrates that the proposed approach to risk management presents conceptual advantages over the commonly used weighted sum method. [ABSTRACT FROM AUTHOR]

Integrating wind and solar energy resources poses intermittency to power systems, which faces independent system operators with new technical and economic challenges. This study proposes a novel model to integrate the uncertainties of wind power on the supply side and roof‐top solar photovoltaic (PV) on the demand side. To cope with their uncertainties, a demand response (DR) aggregator is proposed, which is enabled to participate in reserve markets. To this end, a new DR model is developed considering both customers' options to reduce and increase load through the DR aggregator. As such, besides improving the existing DR models (load shifting and curtailment), two DR programmes, i.e. load growth and load recovery, are mathematically modelled. Numerical studies indicate the effectiveness of the proposed model to reduce the total operation cost of the system and facilitate the integration of wind power and roof‐top PV. [ABSTRACT FROM AUTHOR]

This paper presents a time domain technique for estimating dynamic loads acting on a structure from acceleration time response measured experimentally at a finite number of optimally placed accelerometers on the structure. The technique utilizes model reduction to obtain precise load estimates. The structure essentially acts as its own load transducer. The approach is based on the standard equilibrium equations of motion in modal coordinates. The modal parameters of a system – natural frequencies, mode shapes and damping factors – can be estimated experimentally from measured data, analytically for simple problems, or using the finite element method. For measurement of the acceleration response, there can be a large number of locations on the structure where the accelerometers can be mounted, and the precision with which the applied loads are estimated from measured acceleration response may be strongly influenced by the locations selected for accelerometer placements. A solution approach, based on the construction of D-optimal designs, is presented to determine the number and optimum locations of accelerometers that will provide the most precise load estimates. An improvement in the algorithm, based on reduced modal matrix, is further proposed to reconstruct the input forces accurately. Numerical examples that help understand the main characteristics of the proposed approach are also presented. The numerical results illustrate the effectiveness of the proposed technique in accurately recovering the loads imposed on discrete as well as continuous systems. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a time domain technique for estimating dynamic loads acting on a structure from strain time response measured at a finite number of optimally placed strain gages on the structure. The technique utilizes model reduction to obtain precise load estimates. The structure essentially acts as its own load transducer. The approach is based on the fact that the strain response of an elastic vibrating system can be expressed as a linear superposition of its strain modes. Since the strain modes as well as the normal displacement modes are intrinsic dynamic characteristics of a system, the dynamic loads exciting a structure are estimated by measuring induced strain fields. The accuracy of estimated loads is dependent on the placement of gages on the instrumented structure and the number of retained strain modes from strain modal analysis. A solution procedure based on the construction of a D-optimal design is implemented to determine the optimum locations and orientations of strain gages that will provide the most precise load estimates. A novel approach is proposed which makes use of model reduction technique, resulting in significant accuracy in the dynamic load estimation. Validation of the proposed approach through numerical example problems is also presented which reveals the effectiveness and robustness of the technique even in the presence of errors (noise) in strain measurements. [Copyright &y& Elsevier]

This paper presents a modeling approach for estimating time varying loads acting on a component from experimental strain measurements. The strain response of an elastic vibrating system is written as a linear superposition of strain modes. Since the strain modes, as well as the normal displacement modes, are intrinsic dynamic characteristics of a component, the dynamic loads exciting a component are estimated by measuring induced strain fields. The accuracy of the estimated loads depends on a number of factors, such as the placement locations and orientations of the gauges on the instrumented structure, as well as the number of retained modes from strain modal analysis. A solution procedure based on the construction of D-optimal designs is implemented to determine the optimum locations and orientations of strain gauges such that the variance in load estimates is minimized. A numerical as well as an experimental validation of the proposed approach through two example problems is also presented. [ABSTRACT FROM AUTHOR]

In this paper we develop a novel mathematical model to describe the localized economic impact of load recovery by electricity consumers following demand response (DR) event participation. In this model—termed “securitization”—cost and benefit associated with an amount of load recovery change exponentially with time-varying stochastic discount rates. These volatile rates, which can be estimated using the principle of Brownian motion, are further examined via case studies on the Roy Billinton test system. A formal application of the proposed recovery model to the crucial task of global DR scheduling is then studied. The study indicates that it is possible to optimize a DR schedule to cater for uncertainty in the subsequent load recovery. [ABSTRACT FROM PUBLISHER]

Lignin is a low-cost, natural polymer with abundant polar sites on its backbone that can be utilized for physical cross-linking of polymers. Here, we use lignin for additional cross-linking of hydrophilic polyether-based polyurethane (HPU) hydrogels, aiming to improve their mechanical properties and processability. Without reducing the swelling, simple addition of 2.5 wt % lignin increases the fracture energy and Young's modulus of HPU hydrogels from, respectively, 1540 ± 40 to 2050 ± 50 J m -2 and 1.29 ± 0.06 to 2.62 ± 0.84 MPa. Lignin also increases the lap shear adhesiveness of hydrogels and induces an immediate load recovery of 95%. We further confirm that hydrogen bonding is the dominant toughening mechanism and elucidate the toughening mechanism by applying the Lake-Thomas and a recently developed sequential debonding theory. We show that unlike the Lake-Thomas theory, the latter model is able to capture the impact of lignin on toughening of hydrogels. Moreover, the lignin-loaded HPU hydrogels are easily processable by various techniques, such as fiber spinning, casting, and 3D printing and are biocompatible with primary human dermal fibroblasts.