Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Shear behavior of coarse aggregates for dam construction under varied stress paths

Coarse aggregates are the major infrastructure materials of concrete-faced rock-fill dams and are consolidated to bear upper and lateral loads. With the increase of dam height, high confining pressure and complex stress states complicate the shear behavfor of coarse aggregates, and thus impede the high dam＇s proper construction, operation and maintenance. An experimental program was conducted to study the shear behavior of dam coarse aggregates using a large-scale triaxial shear apparatus. Through triaxial shear tests, the strain-stress behaviors of aggregates were observed under constant confining pressures： 300 kPa, 600 kPa 900 kPa and 1200 kPa. Shear strengths and aggregate breakage characteristics associated with high pressure shear processes are discussed. Stress path tests were conducted to observe and analyze coarse aggregate response under complex stress states. In triaxial shear tests, it was found that peak deviator stresses increase along with confining pressures, whereas the peak principal stress ratios decrease as confining pressures increase With increasing confining pressures, the dilation decreases and the contraction eventually prevails. Initial strength parameters （Poisson＇s ratio and tangent modulus） show a nonlinear relationship with confining pressures when the pressures are relatively low. Shear strength parameters decrease with increasing confining pressures. The failure envelope lines are convex curves, with clear curvature under low confining pressures. Under moderate confining pressures, dilation is offset by particle breakage. Under high confining pressures, dilation disappears.

Optimal one-bit perturbation in Boolean networks based on cascading aggregation

We investigate the problem of finding optimal one-bit perturbation that maximizes the size of the basin of attractions(BOAs)of desired attractors and minimizes the size of the BOAs of undesired attractors for large-scale Boolean networks by cascading aggregation.First,via the aggregation,a necessary and sufficient condition is given to ensure the invariance of desired attractors after one-bit perturbation.Second,an algorithm is proposed to identify whether the one-bit perturbation will cause the emergence of new attractors or not.Next,the change of the size of BOAs after one-bit perturbation is provided in an algorithm.Finally,the efficiency of the proposed method is verified by a T-cell receptor network.

Contingency Reserve Evaluation for Fast Frequency Response of Multiple Battery Energy Storage Systems in a Large-scale Power Grid

Recently,the fast frequency response(FFR)service by large-scale battery energy storage systems(BESSs)has been successfully proved to arrest the frequency excursion during an unexpected power outage.However,adequate frequency response relies on proper evaluation of the contingency reserve of BESSs.The BESS FFR reserve is commonly managed under fixed contracts,ignoring various response characteristics of different BESSs and their coexisting interactions.This paper proposes a new methodology based on dynamic grid response and various BESS response characteristics to optimise the FFR reserves and prevent the frequency from breaching the under-frequency load shedding(UFLS)thresholds.The superiority of the proposed method is demonstrated to manage three large-scale BESSs operating simultaneously in an Australian power grid under high renewable penetration scenarios.Further,the proposed method can identify remaining battery power and energy reserve to be safely utilised for other grid services(e.g.,energy arbitrage).The results can provide valuable insights for integrating FFR into conventional ancillary services and techno-effective management of multiple BESSs.

A New ANN Method of Aggregation and Stability Analtsis for Large－Scale System with Time Delay

This paper is the first to introduce the Artificial Neural Network(ANN) theory and techniques in the aggregation and stability analysis of large scale dynamic system with time delays.It presents a new intelligent method of the aggregation and stability analysis for large scale system with time delays. The method proposed in this paper can be used not only on linear constant large scale systems with time delays, but also on times-varying large scale systems with multi-group real time delays. Simulation results show the effectiveness of the method.

Aggregator Service for PV and Battery Energy Storage Systems of Residential Building

Distributed energy resources(DERs),including photovoltaic(PV)systems,small wind turbines,and energy storage systems(ESSs)are being increasingly installed in many residential units and the industry sector at large.DER installations in apartment buildings,however,pose a more complex issue particularly in the context of property ownership and the distribution of DR benefits.In this paper,a novel aggregator service is proposed to provide centralized management services for residents and DER asset owners in apartment buildings.The proposed service consists of a business model for billing and benefits distribution,and a model predictive control(MPC)control algorithm for managing and optimizing DER operations.Both physical and communication structures are proposed to ensure the implementation of such aggregator services for buildings.Three billing tariffs,i.e.,flat rate,time-of-use(TOU),and real time pricing(RTP)are compared by way of case studies.The results indicate that the proposed aggregator service is compatible with the business model.It is shown to offer good performance in load shifting,bill savings,and energy trading of DERs.Overall,the aggregator service is expected to provide benefits in reducing the pay back periods of the investment.

Frequency control of future power systems: reviewing and evaluating challenges and new control methods

Integration of more renewable energy resources introduces a challenge in frequency control of future power systems.This paper reviews and evaluates the possible challenges and the new control methods of frequency in future power systems.Different types of loads and distributed energy resources(DERs) are reviewed.A model representation of a population of the water heater devices for the demand side frequency response is considered.A model representation of a population of battery energy storage system(BESS)-based DERs such as smart electric vehicles(EVs) charging, large-scale BESSs, and residential and non-residential BESSs, are highlighted.The simplified Great Britain power system and the 14-machine South-East Australian power system were used to demonstrate the effectiveness of the new methods in controlling power system frequency following a disturbance.These new methods are effective in recovering the fallen frequency response and present a great potential in controlling the frequency in future power systems.

RiMOM-IM： A Novel Iterative Framework for Instance Matching

Instance matching, which aims at discovering the correspondences of instances between knowledge bases, is a fundamental issue for the ontological data sharing and integration in Semantic Web. Although considerable instance matching approaches have already been proposed, how to ensure both high accuracy and efficiency is still a big challenge when dealing with large-scale knowledge bases. This paper proposes an iterative framework, RiMOM-IM （RiMOM-Instance Matching）. The key idea behind this framework is to fully utilize the distinctive and available matching information to improve the efficiency and control the error propagation. We participated in the 2013 and 2014 competition of Ontology Alignment Evaluation Initiative （OAEI）, and our system was ranked the first. Furthermore, the experiments on previous OAEI datasets also show that our system performs the best.