Optimal Coordination Strategy Research Articles

Distributed Neurodynamics-Based Backstepping Optimal Control for Robust Constrained Consensus of Underactuated Underwater Vehicles Fleet.

Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in 3-D space is a challenging but practical problem. To address this problem, this article develops a novel consensus-based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the spherical coordinate transform is introduced to tackle the nonholonomic constraint, and then a distributed optimal motion coordination strategy is developed. As a result, the optimal formation tracking of UUVs fleet can be achieved, and the constraints are fulfilled. To realize the generated optimal commands better and, meanwhile, deal with the underactuation, at the lower-level control loop a neurodynamics-based robust backstepping controller is designed, and in particular, the issue of "explosion of terms" appearing in conventional backstepping-based controllers is avoided and control activities are improved. The stability of the overall UUVs formation system is established to ensure that all the states of the UUVs are uniformly ultimately bounded in the presence of unknown disturbances. Finally, extensive simulation comparisons are made to illustrate the superiority and effectiveness of the derived optimal formation tracking protocol.

A multifunctional inverter power quality coordinated optimization strategy based on comprehensive evaluation

The large-scale integration of renewable energy sources and power electronic devices has led to increasingly dispersed and networked characteristics of power quality disturbances in distribution systems. Traditional control devices, limited by their fixed-point control, no longer meet the development needs of modern distribution systems. Considering the distribution and structural characteristics of the current new-type sources and loads, a multifunctional inverter power quality coordinated control strategy based on comprehensive evaluation is proposed. This strategy aims to achieve power quality coordinated control by utilizing optimal compensation capacity while the grid-connected inverter provides active power output. Firstly, the traditional ipiq power quality detection method is analyzed and improved to enable detection of harmonics, reactive power, and three-phase imbalance currents in single-phase systems or three-phase four-wire systems. Secondly, in the power quality assessment, a balanced algorithm is employed to obtain the comprehensive evaluation index of power quality, thereby improving the deficiencies of single-weight evaluation. Next, to ensure the grid-connected inverter achieves optimal power quality coordinated control with minimal compensation capacity, an optimization compensation function is established between compensation capacity and comprehensive power quality index. This function is optimized using non-dominated sorting genetic algorithm to enhance equipment utilization and system economy. Finally, the feasibility and effectiveness of the proposed method are validated through simulations, demonstrating the achievement of optimal capacity configuration for power quality coordinated control while ensuring active power output by the grid-connected inverter.

Optimal DGs coordination strategy for managing unbalanced and islanded distribution networks

Abstract Distributed generators (DGs) have the potential to act as alternative power sources for balancing and restoring service in distribution system breakdowns. Nevertheless, DGs variety of characteristics and uncertain behavior pose difficulties in optimizing their operation while restoring the system. In this paper, a hierarchical structure is introduced to manage the unbalanced and islanded distribution networks with the optimal coordination strategy of DGs. The proposed approach encompasses multiple control tiers that manage DGs coordination process into three specific levels, each accountable for distinct functions involving voltage and frequency regulation, power distribution within individual islands, and overseeing power exchange between these units for minimizing imbalance and maximizing the load restoration. This research delivers a comprehensive solution for optimally coordinating DGs in unbalanced and isolated networks during restoration, thus elevating system reliability and resilience. The proposed model for distributed control restoration is validated using a modified IEEE 123-bus test setup.

Optimal investment and coordination strategies for digital technology adoption in a dual-channel supply chain

Digitization has become a necessity. How upstream and downstream companies in the supply chain coordinate digital transformation is a challenge. To this end, this paper develops a dynamic differential game model that includes a single manufacturer and a single retailer, both of which can decide whether or not to invest in digital applications and thus classify them into Scenarios M and R. The result shows that, compared to the R model, in the M model, the manufacturer sets a higher wholesale price that will suppress the sales volume in the online channel, but its overall profit increase; adopting the M model may be more favorable to both parties, but for the retailer, there is a risk that the manufacturer may charge high wholesale prices. In contrast, for the manufacturer, the retailer has a free-riding behavior. Based on this, this paper proposes an improved model F, where the retailer shares a certain percentage of the investment cost of digital technology to incentivize the manufacturer to invest in digital technology and share information with the retailer. It shows that in this scenario, the supply chain participants realize a win–win situation and make the level of digital technology higher than in the previous two scenarios.

Optimization Operation Strategy for Shared Energy Storage and Regional Integrated Energy Systems Based on Multi-Level Game

Regional Integrated Energy Systems (RIESs) and Shared Energy Storage Systems (SESSs) have significant advantages in improving energy utilization efficiency. However, establishing a coordinated optimization strategy between RIESs and SESSs is an urgent problem to be solved. This paper constructs an operational framework for RIESs considering the participation of SESSs. It analyzes the game relationships between various entities based on the dual role of energy storage stations as both energy consumers and suppliers, and it establishes optimization models for each stakeholder. Finally, the improved Differential Evolution Algorithm (JADE) combined with the Gurobi solver is employed on the MATLAB 2021a platform to solve the cases, verifying that the proposed strategy can enhance the investment willingness of energy storage developers, balance the interests among the Integrated Energy Operator (IEO), Energy Storage Operator (ESO) and the user, and improve the overall economic efficiency of RIESs.

Vaccine supply chain decision and coordination with manufacturers competition under blockchain technology

With vaccine incidents occur frequently and the wide application of blockchain technology, the construction of blockchain-based vaccine supply chain (VSC) has become a feasible solution to effectively solve vaccine safety problems. However, there is a lack of research on the operation decision of blockchain-based VSC. Based on this, combined with the current situation of vaccine manufacturers competition in the vaccine market, we build a game model of blockchain-based VSC and explore its optimal decision and coordination strategies. The results show that the higher the vaccine substitutability, the higher the profit of each member. When the production cost ratio of the two vaccine manufacturers is low, they will be more willing to join the blockchain system. When the proportion of problematic vaccines is low, it is more beneficial for the VSC to adopt blockchain technology. The cost-sharing contract can neither effectively coordinate the blockchain-based VSC nor realize its Pareto optimality. The blockchain-based VSC coordination can be achieved by two vaccine manufacturers signing revenue-sharing contracts or two-part tariff contracts with the vaccination unit respectively.

Prosumer data center system construction and synergistic optimization of computing power, electricity and heat from a global perspective

In the context of achieving carbon neutrality, the imperative to save energy and reduce emissions in data centers (DCs) has become paramount. Current research predominantly concentrates on internal systems of DCs, lacking the perspective of treating DCs as prosumers and subsequent global optimization, resulting in limited results. In this paper, a novel prosumer DC integrated energy system is constructed and a globally coordinated optimization strategy for the synergy of computing power, electricity, and heat is proposed. This is achieved through meticulous adjustments to the battery charge–discharge processes on the supply side, computational workloads within the DC's internal systems, and the heating temperature for waste heat utilization on the load side. The optimization objectives are centered around minimizing the renewable energy waste and operation cost. Compared to the non-optimized system, the optimized system exhibits reductions of 11.39 % in renewable energy waste, 6.96 % in operation cost, 8.89 % in grid electricity consumption, and 4.18 % in total electricity consumption. Furthermore, the strategy effectively reduces renewable energy waste and operation cost at different occupancy rates by 8.02 %–12.21 % and 6.61 %–10.44 %, respectively. Under varying battery capacities, the system demonstrates reductions in renewable energy waste and operation cost by 9.42 %–26.57 % and 6.89 %–10.70 %, confirming the effectiveness of the proposed strategy across different occupancy rates and battery capacities. The research findings further highlight the potential of globally coordinated optimization in the synergy of computing power, electricity, and heat, providing valuable insights for the sustainable development of DCs.

Semantic-Preserving Surgical Video Retrieval With Phase and Behavior Coordinated Hashing.

Medical professionals rely on surgical video retrieval to discover relevant content within large numbers of videos for surgical education and knowledge transfer. However, the existing retrieval techniques often fail to obtain user-expected results since they ignore valuable semantics in surgical videos. The incorporation of rich semantics into video retrieval is challenging in terms of the hierarchical relationship modeling and coordination between coarse- and fine-grained semantics. To address these issues, this paper proposes a novel semantic-preserving surgical video retrieval (SPSVR) framework, which incorporates surgical phase and behavior semantics using a dual-level hashing module to capture their hierarchical relationship. This module preserves the semantics in binary hash codes by transforming the phase and behavior similarities into high- and low-level similarities in a shared Hamming space. The binary codes are optimized by performing a reconstruction task, a high-level similarity preservation task, and a low-level similarity preservation task, using a coordinated optimization strategy for efficient learning. A self-supervised learning scheme is adopted to capture behavior semantics from video clips so that the indexing of behaviors is unencumbered by fine-grained annotation and recognition. Experiments on four surgical video datasets for two different disciplines demonstrate the robust performance of the proposed framework. In addition, the results of the clinical validation experiments indicate the ability of the proposed method to retrieve the results expected by surgeons. The code can be found at https://github.com/trigger26/SPSVR.

Establishing consensus recommendations for long-term osteoporosis care for patients who have attended an Australian fracture liaison service: a Delphi study

SummaryCoordinating healthcare activities between fracture liaison services (FLS) and primary care is challenging. Using a Delphi technique, we developed 34 consensus statements to support improved care coordination across this healthcare transition.PurposeEvidence supporting an optimal coordination strategy between fracture liaison services (FLS) and primary care is lacking. This study aimed to develop consensus statements to support consistency and benchmarking of clinical practice to improve coordination of care for patients transitioning from FLS to primary care following an osteoporotic fracture.MethodsA Delphi technique was used to develop consensus among a panel of experts, including FLS clinicians (medical and non-medical), general practitioners (GPs), and consumers.ResultsResults of a preparatory questionnaire (n = 33) informed the development of 34 statements for review by expert panellists over two Delphi rounds (n = 25 and n = 19, respectively). The majority of participants were from New South Wales (82%), employed as FLS clinicians (78.8%) and working in metropolitan centres (60.6%). Consensus was achieved for 24/34 statements in round one and 8/10 statements in round two. All statements concerning patient education, communication, and the GP-patient relationship achieved consensus. Expert opinions diverged in some areas of clinician roles and responsibilities and long-term monitoring and management recommendations.ConclusionWe found clear consensus among experts in many key areas of FLS integration with primary care. While experts agreed that primary care is the most appropriate setting for long-term osteoporosis care, overall confidence in primary care systems to achieve this was low. The role of (and responsibility for) adherence monitoring in a resource-limited setting remains to be defined.

Optimal coordination in logistics warehousing with sensing and communication limits: A distributed differential game approach

AbstractOptimal coordination is essential for multi‐automated guided vehicle (AGV) systems, particularly in logistic transportation cases, where the system task completion time needs to be minimized, with the guarantee of safe operation. This is because an optimal coordination strategy (OCS), if achieved, can significantly improve the transportation system's efficiency. In this paper, to deal with the dynamic interaction process among AGVs, and sensing and communication range limits, we formulate the optimal coordination problem into a distributed differential game (DDG) framework, where individual AGVs only use information communicated from nearby AGVs to design their optimal operation trajectories. This helps to significantly reduce the computational and communication requirements for the multi‐AGV logistic transportation systems. Targeting operation safety and working efficiency requirements, we incorporate collision avoidance and trajectory optimization objectives into the proposed framework. It is shown that local OCS, obtained by solving the DDG problem for each AGV, will converge to the global Nash equilibrium, which represents the most efficient operating condition for the entire logistic transportation system. Finally, the efficacy of the proposed method is demonstrated, based on simulations and experiments, benchmarked with existing logistic warehousing planning and differential game methods. Compared with conventional methods, the proposed framework successfully helps reduce the task completion time by up to 16%.

Optimal coordination of directional overcurrent relays in complex networks using the Elite marine predators algorithm

The integration of renewable energy sources in the distribution network (DN) has had a significant influence by reducing power loss and enhancing network dependability. Aside from that, the protection system has met coordination issues as a result of bidirectional power flow and variations in fault levels. Therefore, an optimal coordination strategy is required to deal with relays coordination problem. The coordination problem of the directional overcurrent relays (DOCRs) is a restricted optimization issue that involves determining appropriate time dial settings (TDS) and plug setting (PS) to reduce relays operating time. Currently, a various nontraditional optimization strategies have been presented to overcome this challenge. In this paper, a modified version of the marine predators algorithm (MPA) referred to as Elite marine predator (EMPA) is developed for the optimal coordination of DOCRs. Therefore, the EMPA method is used to find out the optimal settings for the DOCRs problem. The suggested algorithm's performance is evaluated using standard test systems, including 3-bus, 8-bus, 9-bus, and 15-bus. The findings are compared with the traditional MPA and with other recent optimization methods presented in the literature to prove the efficiency and superiority of the proposed EMPA in reducing relay operation time for optimal DOCRs coordination.

Fault Recovery Strategy for Power–Communication Coupled Distribution Network Considering Uncertainty

In the face of multiple failures caused by extreme disasters, the power and communication sides of the distribution network are interdependent in the fault recovery process. To improve the post-disaster recovery efficiency of the distribution network, this paper proposes a coordinated optimization strategy for distribution network reconfiguration and repair, which integrates the power and communication aspects. First, the recovery process is divided into islanding–reconfiguration and dynamic emergency repair. The coupling relationship between power and communication is considered; that is, power failure may cause communication nodes to lose power, and communication failure may affect the effective operation of remote control devices. Based on this, the fault recovery process is optimized with the objective of maximizing load transfer and direct recovery while introducing a stochastic model predictive control method to handle the uncertainty of distributed power generation by rolling optimization of typical scenarios. Finally, the effectiveness of the proposed strategy is verified using an improved IEEE33-node distribution network system. The simulation results show that the proposed method can recover power to the maximum extent and reduce loss while ensuring the safe and stable operation of the distribution system.

Hierarchically coordinated optimization of power distribution systems with soft open points and electric vehicles

The integration of large-scale electric vehicles (EVs) into power distribution systems (PDSs) causes low voltage and line congestion problems. EV charger converters can improve the voltage profile by compensating the reactive power, whereas soft open points (SOPs) can transfer active power between branches and compensate reactive power, which can help balance the branch power. Therefore, this study proposes a coordinated day-ahead (DA) and real-time (RT) optimization model for PDSs with SOPs and high penetration of EVs. In addition, directly controlling large-scale EVs is difficult for the distribution systems operator. Thus, hierarchical management of EVs is performed by using an EV aggregator (EVA) model. First, on the basis of the probability distribution of the state of EVs of each EVA, a DA optimization model with the minimum operation cost is established. A dynamic SOC boundary model is proposed to optimize active and reactive power of EVAs and EVs in RT stage. The original nonlinear problems of the DA and RT models are linearized by convex relaxation and linearization, which significantly improves the solving efficiency. Finally, the case studies demonstrate that SOPs can promote the integration of EVs, and the hierarchical coordinated optimization strategy not only can reduce the operation cost of the PDS, but also control active and reactive power of EVs in real time.

Hybrid Tripping Characteristic-Based Protection Coordination Scheme for Photovoltaic Power Systems

Due to the high penetration of renewable energy sources into the electrical power network, overcurrent relays coordination with highly sensitive and selective protection systems are now two of the most important power protection concerns. In this research, an optimal coordination strategy utilising a new hybrid tripping scheme based on current–voltage characteristics has been devised for overcurrent relays in a power network coupled to a photovoltaic system. This research develops and proves a new optimal coordination scheme based on two optimisation methods, the vibrating particles system and particle swarm optimisation algorithms, in consideration of the impact of renewable sources on fault characteristics. The new optimal coordination approach aims to improve the sensitivity and dependability of the protection system by reducing the tripping time of the overcurrent relays by employing a new hybrid tripping scheme. A specific case study, Conseil International des Grands Réseaux Electriques (CIGRE) distribution network connected to two photovoltaic systems is constructed and presented utilising Industrial software (namely ETAP), and the outcomes of the proposed optimal coordination scheme are compared with standard and recent characteristics from the literature. The hybrid tripping scheme and optimisation techniques are evaluated using different fault and power network model scenarios. The results show that the optimal hybrid tripping scheme provided successfully decreases the overall operating time of the overcurrent relays and increases the sensitivity of the relay during all fault scenarios. The reduction in overall time for the proposed hybrid tripping scheme was 35% compared to the literature for the scenario of a power grid with and without photovoltaic systems.

A steady-state optimal coordination strategy for DERs systems with guaranteed probabilistic performance

We consider the problem of coordinating multiple Distributed Energy Resources (DERs) so as to supply energy to the grid while minimizing its variability around a reference profile that must also be optimized. We focus on the case when each DER is equipped with solar panels and a battery storage device, and jointly design the disturbance compensation strategies for charging and discharging the batteries on a one-day time horizon. To this purpose, we linearly parameterize the strategies and search for a solution minimizing the fluctuations of the energy exchange with the grid in steady-state, with a bound on their extent that holds in probability given the stochastic nature of the solar energy. Interestingly, the probability measure of the resulting chance-constrained optimization problem depends on the parameters of the disturbance compensation strategies, which makes the application of the scenario approach not standard. The proposed scenario-based solution is feasible for the original steady-state chance-constrained optimization problem and proves effective in numerical simulations.

Multi-layer coordinated optimization of integrated energy system with electric vehicles based on feedback correction

The integrated energy system with electric vehicles can realize multi-energy coordination and complementarity, and effectively promote the realization of low-carbon environmental protection goals. However, the temporary change of vehicle travel plan will have an adverse impact on the system. Therefore, a multi-layer coordinated optimization strategy of electric-thermal-hydrogen integrated energy system including vehicle to grid (V2G) load feedback correction is proposed. The strategy is based on the coordination of three-level optimization. The electric vehicle charging and discharging management layer comprehensively considers the variance of load curve and the dissatisfaction of vehicle owners, and the charging and discharging plan is obtained through multi-objective improved sparrow search algorithm, which is transferred to the model predictive control rolling optimization layer. In the rolling optimization process, according to the actual situation, selectively enter the V2G load feedback correction layer to update V2G load, so as to eliminate the impact of temporary changes in electric vehicle travel plans. Simulation results show that the total operating cost with feedback correction is 4.19% lower than that without feedback correction and tracking situation of tie-line planned value is improved, which verifies the proposed strategy.

Optimal coordination strategy to form and dissolve truck platoons on a highway stretch

Optimal coordination strategy to form and dissolve truck platoons on a highway stretch

Dynamic Carbon Reduction and Marketing Strategies with Consumers’ Environmental Awareness under Cap-and-Trade Regulation

This paper targets a supply chain system consisting of a manufacturer and a retailer and studies the optimal dynamic carbon reduction, marketing, and coordination strategies under the cap-and-trade regulation with the presence of consumers’ environmental awareness. To this end, we first depict the dynamics of carbon emission in the supply chain and reveal the relationship between carbon accumulation and operational decisions. Then, we set up a differential game model under the cap-and-trade regulation and derive the optimal profits and decisions in decentralized, centralized, and cost-sharing scenarios, respectively. In the end, we also use the two-subsidy and revenue-sharing mechanism to coordinate the supply chain. After analyses and comparisons of the results, we can obtain some interesting results. The carbon trading price positively affects the manufacturer’s carbon reduction effort in all scenarios but exerts no influence on the retailer’s marketing effort in the decentralized and cost-sharing scenario. Only in the centralized scenario will the carbon trading price affect the retailer’s marketing decision and the retailer should decrease the marketing effort when the carbon trading price increases. Hence, the marketing effort in the centralized scenario may not always be the highest among these scenarios. We also find that full cooperation may not always help to reduce the carbon emission of the system, but the cost-sharing strategy is certain to reduce the carbon emission. Meanwhile, numerical analysis proves that when the revenue sharing rate is within a certain range, the two-subsidy and revenue-sharing mechanism will be able to perfectly coordinate the supply chain.

Flexible Interconnected Distribution Network with Embedded DC System and Its Dynamic Reconfiguration

Flexible interconnection transformation of a distribution network using all-control power electronics technology will help to overcome the technical bottleneck of distributed generation (DG) penetration and high-quality power supply in the traditional distribution network. However, the flexible interconnection form that uses back-to-back power electronic converters to replace tie switches has disadvantages such as high cost and large footprint, so its mass promotion and application are bound to be limited. Therefore, a new topology of flexible interconnected distribution network with embedded DC system (FDN+EDC) with a better economy is proposed. Firstly, the topology of FDN+EDC is introduced to improve the controllability level of the distribution network under high percentage DG penetration, and is compared with the conventional flexible interconnection method. Secondly, in order to realize the coordinated optimal economic operation of source, network and load in the FDN+EDC, a dynamic reconfiguration model of switches status and continuous adjustment of flexible interconnection device (FID) are proposed. Then, to achieve a fast and globally optimal solution of the model, a hierarchical coordinated optimization strategy is designed based on an improved particle swarm algorithm for the optimization of discrete and continuous variables. Finally, the validity of the algorithm is verified, and an 88-node FDN+EDC is constructed to verify the advantages of the proposed embedded DC flexible interconnection topology and realize the joint optimal dispatch of network-source-load.

Distributed Hierarchical Coordination of Networked Charging Stations Based on Peer-to-Peer Trading and EV Charging Flexibility Quantification

Power systems are facing the challenges of integrating massive electric vehicles (EVs) and lacking operational flexibility. An optimal and efficient coordination strategy is needed to coordinate the operations among EV charging stations (CSs) and the distribution system operator (DSO) and to make full use of the EVs’ charging flexibility for improving the operational flexibility of power systems. To this end, this paper proposes a CSs-DSO distributed coordination strategy. The proposed strategy integrates a supply-curve-based quantification method to quantify the EVs’ flexibility contribution and a Nash bargaining game-theory-based peer-to-peer (P2P) transactions model for maximizing the profits of entities participating in the coordination. To tackle the CSs-DSO coordination problem that has a bi-level-parallel coupling structure, an analytical target cascading (ATC) and alternating direction method of multipliers (ADMM) jointly-based distributed solution approach is proposed. First, the CSs-DSO bi-level model is decoupled by ATC with tie-line power being the only exchanged information; then, the model is further decomposed for each CS by ADMM for protecting the CSs’ privacy and autonomy. The effect of coordination and the computational efficiency of the proposed strategy is verified by numerical simulations on a modified IEEE 33-bus distribution system connecting multiple CSs.