Large-scale Grid Systems Research Articles

Machine Learning Accelerated Identification of Bifunctional Active Sites in Metal-Organic Framework Derived Metal Oxide Heterostructures for High-Performance Zinc-Air Batteries

The demand for electrochemical energy storage (ESS) systems and their market size has drastically increased in many applications ranging from portable devices through electric vehicles to large-scale grid systems over the past decades. Yet, a more advanced ESS is required due to the safety issues and limited energy density of currently commercialized lithium-ion batteries (LIBs) for wide and matured applications. As a solution to this challenge, zinc-air batteries (ZABs) have gained tremendous attention due to their high energy density, low cost, environmental friendliness, and much lower fire risk characteristics. ZABs utilize oxygen from the atmosphere at their cathode during discharge, where the atmospheric oxygen is reduced to hydroxide via an oxygen reduction reaction (ORR). On the other hand, hydroxide is released as oxygen via oxygen evolution reaction (OER) during charge. The fact that oxygen is drawn directly from the air enables high gravimetric and volumetric energy densities of ZABs compared to those of commercial LIBs. However, the sluggish kinetics of the four-electron transfer steps in ORR and the mass transport of oxygen in OER remain major challenges for the practical application of ZABs. Meanwhile, precious metal-based catalysts such as Pt/C and RuO2 are known to have good catalytic activity towards ORR and OER. However, the integration of different catalysts into the same cathode is very difficult. Additionally, the high cost and natural scarcity of novel metals limit their usage for practical applications.We synthesized a bifunctional CoO/Mn3O4 heterostructure (CMH) OER/ORR electrocatalyst which was derived from metal-organic framework (MOF) for excellent activities in ZABs. The CMH cathode exhibited high ORR and OER performances, as demonstrated by the higher ORR current density than that of the Pt/C catalyst as well as the low OER overpotential exceeding those of the RuO2 catalyst. Remarkably, atomic structures of the CMH were elucidated using global optimization of genetic algorithm integrated with the machine learning force field. By adopting first-principles electronic structure calculations, we further revealed the charge transfer and active sites for both reactions in the identified heterojunction. Furthermore, assembling CMH cathode and Zn metal anode into a ZAB full-cell configuration achieves a ~5.5-fold higher energy density of 879 Wh kg-1 exceeding those of commercial lithium-ion batteries (LIBs), a high-discharge outstanding power density that is 1.39 times higher than novel metal catalysts, and robust cycle stability with negligible voltage decay which is far superior to that of benchmark Pt/C+RuO2 electrocatalysts.

Surface-redox sodium-ion storage in anatase titanium oxide

Sodium-ion storage technologies are promising candidates for large-scale grid systems due to the abundance and low cost of sodium. However, compared to well-understood lithium-ion storage mechanisms, sodium-ion storage remains relatively unexplored. Herein, we systematically determine the sodium-ion storage properties of anatase titanium dioxide (TiO2(A)). During the initial sodiation process, a thin surface layer (~3 to 5 nm) of crystalline TiO2(A) becomes amorphous but still undergoes Ti4+/Ti3+ redox reactions. A model explaining the role of the amorphous layer and the dependence of the specific capacity on the size of TiO2(A) nanoparticles is proposed. Amorphous nanoparticles of ~10 nm seem to be optimum in terms of achieving high specific capacity, on the order of 200 mAh g−1, at high charge/discharge rates. Kinetic studies of TiO2(A) nanoparticles indicate that sodium-ion storage is due to a surface-redox mechanism that is not dependent on nanoparticle size in contrast to the lithiation of TiO2(A) which is a diffusion-limited intercalation process. The surface-redox properties of TiO2(A) result in excellent rate capability, cycling stability and low overpotentials. Moreover, tailoring the surface-redox mechanism enables thick electrodes of TiO2(A) to retain high rate properties, and represents a promising direction for high-power sodium-ion storage.

Dynamic Reduced-Order Observer-Based Detection of False Data Injection Attacks With Application to Smart Grid Systems

This article investigates the problem of attack detection of false data injection attacks for a class of large-scale smart grid systems in the context of cyber–physical systems. First, by exploiting the graph theory to decompose the considered system into multiple interconnected subsystems, a bank of dynamic reduced-order observers are delicately constructed to generate residual signals for the attack detection task. Then, a novel decentralized attack detection scheme is proposed based on the adaptive detection thresholds with prescribed performance. Compared with the existing results, the proposed detection scheme has less conservative thresholds and enhanced robustness against process disturbance and measurement noise, such that the detectability is improved. Finally, the effectiveness and availability of the proposed scheme are verified by two simulation examples and the experimental results from IEEE 30-bus system built in the OPAL-RT real-time simulator.

Dynamic energy scheduling and routing of a large fleet of electric vehicles using multi-agent reinforcement learning

As the world’s population and economy grow, demand for energy increases as well. Smart grids can be a cost-effective solution to overcome increases in energy demand and ensure power security. Current applications of smart grids involve a large numbers of agents (e.g., electric vehicles). Since each agent must interact with other agents when taking decisions (e.g., movement and scheduling), the computational complexity of smart grid systems increases exponentially with the number of agents. Computational tractability of planning is a significant barrier to implementation of large-scale smart grids of electric vehicles.Existing solution approaches such as mixed-integer programming and dynamic programming are not computationally efficient for high-dimensional problems. This paper proposes a reformulation of a Mixed-Integer Programming model into a Decentralized Markov Decision Process model and solves it using a Multi-Agent Reinforcement Learning algorithm to address the scalability issues of large-scale smart grid systems. The Decentralized Markov Decision Process model uses centralized training and distributed execution: agents are trained using a unique actor network for each agent and a shared critic network, and then agent execute actions independently from other agents to reduce computation time. The performance of the Multi-Agent Reinforcement Learning model is assessed under different configurations of customers and electric vehicles, and compared to the results from deep reinforcement learning and three heuristic algorithms. The simulation results demonstrate that the Multi-Agent Reinforcement Learning algorithm can reduce simulation time significantly compared to deep reinforcement learning, genetic algorithm, particle swarm optimization, and the artificial fish swarm algorithm. The superior performance of the proposed method indicates that it may be a realistic solution for large-scale implementation.

Design and Experimentation Guidelines for DER’s Emulation Testbed

Due to the increasing integration of distributed energy resources (DERs) into the national grid, research is needed to understand high DER penetration in relation to grid reliability and stability. Emulation-based research is a viable option to consider hardware intensive investigations. Indeed, DER emulation-based research has advantages over DER software-based simulation and real DER implementation research. Some of these advantages are more realistic performance and boundaries and considerably less expense, higher safety, and modularity. While the downside of DER emulation-based research is the complexity of modeling large scale grid systems. This paper provides a rationalization for the selection of an emulation-based DER laboratory and description of the DER laboratory's capabilities. Additionally, guidelines for running experiments are discussed and two approaches, power scaling using rescaling coefficients and system sizing using Multi-Port Thevenin Equivalence (MPTE), are proposed to emulate realistic complex interconnected DER grid connected systems.

Step Toward Eco-Friendly Green Source of Non-Aqueous Electrolytes for Redox Flow Battery

The efficient utilization of energy from non-exhaustive resources without adverse effects on the environment is a way forward for the sustainable development of society. The intermittent nature of renewable energy from non-exhaustive resources such as solar, wind, etc., requires a large-scale and advanced energy storage system to eliminate the impact of intermittency on the power grid. Redox flow battery (RFB) technology is one of the promising candidates for large-scale grid systems. Present commercial RFB technology is based on the redox reactions of electroactive metals, which are constrained by material abundance, cost, and environmental issues. These issues are prompting scientists to look into organic-based technologies as potential metal-free battery alternatives. Organic-based materials offer structural diversity of organic molecules to adjust the electrolyte materials' thermodynamics and redox characteristics, in addition to low cost and scalability. This opens the door to the development of green and sustainable energy storage devices.Despite development over the last decade, organic compound exploitation for RFB remains limited. Organics with limited ionic conductivity and poorly defined conducting channels have poor voltage efficiency and cyclability. In aqueous flow batteries, the cell potential is also limited by the electrolysis of water. The scientific community, on the other hand, is addressing this issue by introducing the non-aqueous RFB (NAqRFB).Quinones are the most redox-active moieties found in natural organic materials, and they play a critical role in biological electron transport. They are a class of molecules that are formed from aromatic compounds like benzene or naphthalene and have a fully conjugated cyclic diketone structure with a redox reaction based on the rearrangement of the conjugated double bond. 1,4-naphthoquinone (NQ) is investigated as a possible negolyte candidate. NQ as metal-free organic negolyte was coupled with metal acetylacetonate-based posolytes, M(acac), where M = Cr, V, Mn. Both types of redox species (metal & metal-free) have good solubility in the solvent - Dimethylacetamide (DMA) - and ionic conductivity was enhanced using the supporting electrolyte of TEABF4. Cyclic voltammetry (CV) analysis using glassy carbon working electrode (GCE) highlighted that NQ offers reversibility; the high value of negative redox potential -1.75 V vs. Ag/Ag+ enables higher cell potential and posses no irreversible side reactions.The electrochemical stability of the negolyte was further tested using the symmetrical cell having an electrolyte of 0.06 M concentration of NQ as redox species. The electrolyte flow from the reservoir, cathode, and anode in a series arrangement. The redox species are reduced at the cathode and then oxidized at the anode, which helps maintain the state-of-charge (SOC) at 50 %. The cell was operated for 50 cycles at the C-rate = 1C, i.e., 2.75 mA⋅cm-2 at the cutoff potentials of ±0.3 V. The energy efficiency of that symmetric cell was 99.7 %, and discharge capacity retention was 99.967%. These results are validated from the reported study by Yu Ding, 2016. This strengthen the motivation of using the NQ as metal-free organic negolyte for single NAqRFB-cell. The insertion of the mentioned metals (Cr, V, Mn) at the posolyte while keeping NQ ion at the negolyte ensured the generation of novel chemistries for NAqRFBs, in detail NQ||Cr, NQ||V, and NQ||Mn, with cell potential differences of 3.25 V, 2.2 V, and 2.45 V, respectively.Detailed characterization was performed using polarization, EIS, and charge-discharge cycling (CDC) – Table 1. Post-mortem CVs of the electrolyte using GCE highlight the stability of the electrolyte after intensive CDC. The RFB assembled with NQ||Cr offered the highest capacity and energy density. However, it was faced with lower energy efficiency. The post-mortem analyses on the posolyte signify that there was crossover through the membrane, which ended up with a contaminated electrolyte (mixture with negolyte chemistry) that affected the extended capacity of the cell. The RFB composed of NQ||V offers the 2nd highest capacity and energy density than others and is two times more than reported V||V NAqRFB. Reinforced membranes permit to decrease the permeability of the membrane toward smaller ions of NQ as compared to V2+ redox ions. Furthermore, the NQ||Mn battery demonstrated a stable performance up to 25 cycles having higher efficiencies and with a decent energy density of ca. 60 mWh.g-1 . NQ has high solubility of 1.5 M in DMA and future work at higher capacities is in progress for in-depth study covering other aspects. The study may ascertain the feasibility of NQ-based RFB to be the eco-friendly green alternate battery chemistry. Figure 1

Fast Power System Event Identification Using Enhanced LSTM Network With Renewable Energy Integration

Accurate and fast event identification in power systems is critical for taking timely controls to avoid instability. In this paper, a synchrophasor measurement-based fast and robust event identification method is proposed considering different penetration levels of renewable energy. A difference Teager-Kaiser energy operator (dTKEO)-based algorithm is first proposed to improve multiple-events detection accuracy. Then, feature extractions via the integrated additive angular margin (AAM) loss and the long short-term memory (LSTM) network are developed. This allows us to deal with intra-class similarity and inter-class variance of events when high penetration renewable energy occurs. With the extracted features, a multi-stage weighted summing (MSWS) loss-based criterion is developed for adaptive data window determination and fast event pre-classification. Finally, the re-identification model based on feature similarity is established to identify unknown events, a challenge for existing machine learning algorithms. Simulation results on the IEEE 39-bus, Kundur 2-area, and an actual large-scale power grid system are used to demonstrate the advantages of the proposed method over others.

Importance measure-based resilience analysis of a wind power generation system

Different from other forms of power generation, wind power generation has the characteristics of randomness, intermittentness, and volatility. Therefore, the wind power generation system (WPGS) is more prone to failures caused by external impacts during the operation. The reliability of the WPGS has a significant influence on the safety of large-scale power grid systems. Thus, how to accurately evaluate the reliability of the WPGS that is integrated into large-scale power grid systems has become a new challenge. Implementing effective resilience management in WPGSs can improve their ability to handle interruptions and increase the safety of the grid-connected system. This research proposes a new method of resilience management based on importance measure for the WPGS after multi-node failures due to natural disasters or man-made accidents. Firstly, both the performance of the WPGS before multi-node failures and the performance of the WPGS after multiple nodes failures are analyzed. Then, the performance loss and the performance recovery of the WPGS are evaluated. Finally, a new method for judging the loss importance measure of node, the recovery importance measure of node, and the resilience importance measure are proposed. The objective of this research is to analyze the restoration sequence of failed nodes in the WPGS with multi-node failures such that the system recover can be faster and more effective. The proposed method is proven to be effective by introducing the WPGS into the IEEE five-machine 14-node system.

Computation of Invariant Measures and Stationary Expectations for Markov Chains with Block-Band Transition Matrix

This paper deals with the computation of invariant measures and stationary expectations for discrete-time Markov chains governed by a block-structured one-step transition probability matrix. The method generalizes in some respect Neuts’ matrix-geometric approach to vector-state Markov chains. The method reveals a strong relationship between Markov chains and matrix continued fractions which can provide valuable information for mastering the growing complexity of real-world applications of large-scale grid systems and multidimensional level-dependent Markov models. The results obtained are extended to continuous-time Markov chains.

The eco-city and its core environmental dimension of sustainability: green energy technologies\xa0and their integration with data-driven smart solutions

Ecological urbanism is seen today as one of the keys towards unlocking the quest for a low-carbon or fossil fuel–free society. Global and local policies promote and advocate the eco–city as the most environmentally sound model of sustainable urbanism. It is argued that the eco–city strategies and solutions are expected to deliver positive outcomes in terms of minimal demand on energy resources and thus minimal environmental impacts. Moreover, it has recently been suggested that the eco-city needs to embrace and leverage what advanced ICT has to offer, particularly with regard to sustainable energy systems, so as to improve and advance its contribution to the goals of environmental sustainability. This paper examines how the eco–city especially its core environmental dimension is practiced and justified in urban planning and development with respect to sustainable energy systems and their integration with data-driven smart technologies at the district level. To illuminate this urban phenomenon accordingly, a descriptive case study is adopted as a qualitative research methodology where the empirical basis is formed by urban planning and development documents combined with secondary data and scientific literature. To provide a theoretical foundation and produce a rationale for this study, this paper first provides a state–of–the–art review of the field of ecological urbanism in terms of its foundations, models, strategies, research issues, as well as data–driven smart technological trends. This study shows that the Eco-city District of Stockholm Royal Seaport uses green energy and data-driven smart technologies as the key strategies and solutions for achieving the environmental objectives of sustainable development in terms of lowering energy consumption and mitigating pollution. This entails conserving and decreasing the demand for energy through renewable resources (i.e., sun, wind, and water), bio–fuelled Combined Heat Power system, large-scale smart grid system, energy management, sustainable waste management, and passive solar houses. This research enhances the scholarly community’s current understanding of the emerging phenomenon of the smart eco-city with respect to the synergic potential of the integration of its sustainable energy strategies with data-driven technology solutions for advancing environmental sustainability.

Application of a Smart Grid Interoperability Testing Methodology in a Real-Time Hardware-In-The-Loop Testing Environment

Interoperability testing is widely recognized as a key to achieve seamless interoperability of smart grid applications, given the complex nature of modern power systems. In this work, the interoperability testing methodology proposed by the European Commission Joint Research Centre is applied to a specific use case in the context of smart grids. The selected use case examines a flexibility activation mechanism in a power grid system and includes DSO SCADA, Remote Terminal Unit and flexibility source, interacting to support a voltage regulation service. The adopted test bed consists of a real-time power grid simulator, a communication network emulator and use case actors’ models in a hardware-in-the-loop setup. The breakdown of the interoperability testing problem is accomplished by mapping the use case to the SGAM layers, specifying the Basic Application Profiles together with the Basic Application Interoperability Profiles (BAIOPs) and defining the design of experiments to carry out during the laboratory testing. Furthermore, the concepts of inter- and intra-BAIOP testing are formalized to reflect complementary interests of smart grid stakeholders. Experimental results prove the applicability of the methodology for testing the interoperability of large-scale and complex smart grid systems and reveal interesting features and possible pitfalls which should be considered when investigating the parameters responsible for the disruption of a system interoperability.

Detection and Location of Bias Load Injection Attack in Smart Grid via Robust Adaptive Observer

This article studies the detection and location of the bias load injection attack (BLIA) in smart grid. As one of typical false data injection attacks (FDIAs), the BLIA aims at destroying the vulnerable generator load. In particularly, the BLIA can bypass the traditional bad data detection techniques, by compromising the measurable sensor-data estimation. Because of this reason, the emergency of BLIA brings enormous threat to the security of smart grid. To address this problem, a detection and location framework against BLIA consisting of three steps is proposed. In the first step, we propose a topology structure-based subregion division algorithm to reduce the complexity of attack detection in the large-scale grid system. In the second step, taking the stealthy characteristics of the BLIA into account, a robust adaptive observer-based detection algorithm is proposed. Through of capabilities of observers, we can estimate the physical dynamics accurately. To detect the BLIA quickly and avoid missed alarm, we compute the adaptive threshold as a substitute for the precomputed threshold, by taking the model linearization error and external disturbance into account. In the third step, we propose a logical judgment matrix to address the sensor attack undetectability problem under structure vulnerability, based on the combinations of all observable sensors. Finally, the effectiveness of the proposed detection and location framework is illustrated, by using detailed case studies on the IEEE 55-bus smart grid system.

Affordable Energy for Humanity: A Global Movement to Support Universal Clean Energy Access

Bold actions are necessary to unlock the potential for economic empowerment by eradicating energy poverty (UN Sustainable Development Goal 7) by 2030. This will require a sustained commitment to significant levels of new investments. Delivering on the promise of universal energy access and improved life quality has eluded policy-makers and governments over the past seven decades. Affordability of energy services for every global citizen, spanning vastly diverse regions and local contexts, requires the development and massive diffusion of technologies that offer “point-of-use” options combined with new business models. Social innovations and flexible governance approaches will also need to be integrated with technological advances. The scope and scale of developmental change span large-scale grid systems to decentralized distributed resources at community levels to the households. We recommend a global network of “energy access innovation centers” dedicated to providing a dynamic “extension service” that bolsters the entire supply chain of talent and expertise, design and operational requirements of system deployment and capacity to embed low-cost, high-performance next-generation technological solutions in the field. To meet the needs of those at the base of the economic and social pyramid, the dual challenges of economic development and transition to a low-carbon energy future make clean energy access the quintessential challenge of the 21st century.

Distributed detection and isolation of false data injection attacks in smart grids via nonlinear unknown input observers

In this paper, a distributed detection and isolation scheme against False Data Injection attacks FDIAs in smart grids is studied. Taking the stealthy characteristics of FDIAs into account, we propose a nonlinear unknown input observer UIO-based distributed detection method. Through the capabilities of designed UIO to deal with the effects of the interconnected relations among the grid subareas and external disturbance, we can obtain the accurate estimation of internally physical state. To detect the FDIAs more quickly and avoid missed detection, an adaptive threshold is computed to replace the traditional precomputed threshold. A distributed isolation scheme against the FDIAs is proposed with two steps, by considering the exchanged information of adjacent grid subareas. In the first-step, each local control center of subareas is to isolate the possible actuator attack set via the proposed local attack signature judgment logic matrix. In the second-step, the possible subarea attack set is isolated by the established global attack signature judgment logic matrix. The distributed isolation logic decision against the FDIAs relays on the combination of isolation results in the first-step and second-step. Finally, the performance of the proposed distributed detection method on the IEEE 28-bus smart grid system is evaluated. And the effectiveness of the proposed distributed detection and isolation scheme on large-scale IEEE 128-bus smart grid system is illustrated.

Least cost combinations of solar power, wind power, and energy storage system for powering large-scale grid

A number of valid possible arrangements of renewable energy sources (wind turbines, solar photovoltaics) with energy storage systems (electrochemical storage, fuel cell, battery) for the large-scale electric grid system (26 GW) are explored in this paper. There are two core motivations for choosing such arrangements. First, the same type of renewable resources located at a single site have high fluctuation, so we model various type of renewable resources located at various locations to have lesser fluctuations accompanied with relatively compact energy storage systems. The second incentive is to find the true minimum cost combination, excluding subsidies and relaxation in taxes for governments. This study includes the whole year (hourly, 8760 h in total) electric load and related weather elements. It is found that the least cost combinations require excessive generation capacity, diverse renewable generation resources and energy storage techniques, which would meet the load requirements and have less carbon emissions.

Estimation of demand diversity and daily demand profile for off-grid electrification in developing countries

The potential for small self-contained grid systems to provide electricity for currently unserved regions of the developing world is widely recognised. However planning and managing the electrical demand that will be supported, so that a mini-grid system is not overloaded and its available resource is used as fully as possible, is actually more difficult than for a large scale grid system. This paper discusses the mathematical reasons why this is the case, and describes a practical software tool for mini-grid demand estimation and planning that is complementary to the widely used HOMER software. This software tool is made available for download on an open source basis. Finally a conclusion is offered that mini-grid systems should aim to serve at least 50 households so that demand variability is more manageable and economies of scale can be realised.

Cyclic ultracapacitor for fast-charging and scalable energy storage system

ESSs (Energy storage systems) for large-scale grid systems and next generation secondary battery systems require an ideal device that satisfies diverse properties such as a high energy density, high power density, low cost, and safe and reliable performance. In this study, we present a CUCap (cyclic ultracapacitor), which is comprised of two reservoirs and one flat flow capacitor cell with a cyclic continuous flow mode and independently tunable power rating and energy capacity. CUCap provides fast-charging and high capacity technology with a simple and practical design for high density and large-scale energy storage systems. The best performance appeared in slurry ratio (electrode to electrolyte) 1 to 7 with the total reservoir volume of 150 mL and the flow rate 300 ml/min, resulting in volumetric energy density, specific capacitance, and discharge time of 7.7 Wh L−1, 14.2 F ml−1, 100 min, respectively. Moreover, the slurry electrode of the CUCap cell had a maximum current density around 260 mA cm−2 which could possibly result in a fast-charging CUCap system.

ATLAS grid workload on NDGF resources: Analysis, modeling, and workload generation

Evaluating new ideas for job scheduling or data transfer algorithms in large-scale grid systems is known to be notoriously challenging. Existing grid simulators expect to receive a realistic workload as an input. Such input is difficult to obtain in the absence of an in-depth study of representative grid workloads.In this work, we analyze the workload of the ATLAS experiment at CERN at the LHC, processed on the resources of Nordic Data Grid Facility. ATLAS is one of the biggest grid technology users, with extreme demands for CPU power, data volume and bandwidth. The analysis is based on the data sample with ∼1.6 million jobs, 3029 TB of data transfer, and 873 years of processor time. Our additional contributions are (a) scalable workload models that can be used to generate a synthetic workload for a given number of jobs, (b) an open-source workload generator software integrated with existing grid simulators, and (c) suggestions for grid system designers based on the insights of our analysis.

Co-scheduling Deadline-Sensitive Applications in Large-scale Grid Systems

In large-scale grid systems, plenty of applications are constrained by soft or hard deadline requirement. However, it is difficult to guarantee the deadline requirements of these applications because of the dynamical nature of distributed systems. In this paper, a novel approach is proposed to evaluate the deadline-guarantee of co-allocation schemes that obtained from conventional co-allocation policies. Based on this approach, a hybrid-policy co-allocation model is also proposed to address the issue of deadline-constrained resource co-allocation in grid environments. The proposed model integrates multiple co-allocation policies to generate different co-allocation schemes, and selects the one with optimal deadline-guarantee for grid applications. By this way, the hybrid-policy model combines the merits of different policies, and overcomes the shortcomings of those policies. Extensive simulations are conducted to verify the effectiveness and the performance of the proposed model in terms of deadline-miss rate. Experimental results show that it can provide coallocation scheme with enhanced deadline-guarantee as well as lower deadline-miss rate.

Review on Grid Resource Discovery: Models and Strategies

As grid technique is growing rapidly in recent years, large-scale grid systems usually have the capability of providing flexible, secure, coordinated resource-sharing and problem solving among dynamic virtual organizations. Grid resource discovery (GRD) is one of the crucial issues in the whole system and the discovery models and strategies have a vital influence on the performance of grid systems. In this paper, the definition and principles of GRD are explained first. Then, several existing GRD models, namely centralized, hierarchical, Peer-to-Peer (P2P)-based, group-clustering, and semantic-based models, are introduced in detail from both system architecture and resource matching pattern aspects. In particular, the strategies to increase the searching performance in these models are presented. Moreover, trust mechanism to ensure secure communications in P2P-based GRD models is emphasized. Besides, the comparative analysis among the GRD models is made. Finally, several promising research directions of GRD are provided.