A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid

Xu, Chentao; He, Xing

doi:10.1109/jas.2021.1004048

Cited by 30 publications

(6 citation statements)

References 26 publications

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“…Figure 4C shows for the model 2(c), and the algorithm (3) can make an agreement and converge closely to the optimal solution of problem (15) while the comparison algorithm cannot make an agreement in Figure 5C.…”

Section: Ta B L Ementioning

confidence: 89%

“…13 A distributed algorithm based on the Euler-Lagrange system was designed to deal with the reward function, which was privately for each node. 14 Moreover, a fully distributed algorithm was used for solving grid optimization problem with three types of load, 15 and series of distributed algorithms were proposed to solve the stochastic big-data problems with expected convergence rate. 16 For distributed online optimization problems, a distributed algorithm was designed based on the conditional gradient without any central coordinator.…”

Section: Introductionmentioning

confidence: 99%

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A trust‐based resilient consensus algorithm for distributed optimization considering node and edge attacks

Liu

2022

Intl J Robust & Nonlinear

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Summary Distributed optimization algorithms have various advantages and wide applications, while the distributed nature also makes them flimsy in the face of external attacks. In this paper two Byzantine attack models are designed, in which the edge attack model is seldom considered before. A resilient distributed consensus algorithm based on the trusted nodes and edges is proposed to defend against the two attack models. The agreement and optimality of the proposed consensus algorithm are guaranteed with theoretical analysis. The simulation results and comparison experiments verify the proposed algorithm to be more effective than the compared one. In the perspective of attackers, which attack model is more recommended is also analysed based on the experiments.

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Section: Ta B L Ementioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

A trust‐based resilient consensus algorithm for distributed optimization considering node and edge attacks

Liu

2022

Intl J Robust & Nonlinear

Self Cite

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“…Examples include the stochastic biobjective disassembly sequence planning (DSP) problem, which involves maximizing disassembly profit and minimizing energy consumption subject to a chance constraint 1 ; the multiobjective resource-constrained disassembly optimization problem, which involves optimizing disassembly sequences in industrial products in order to improve recovery efficiency and reduce environmental impact 2 ; the problem of deploying energyharvesting directional sensor networks for optimal target coverage, which involves optimizing the communication route selection and energy usage in a wireless network 3 ; the joint slot scheduling and power allocation problem in clustered Underwater Acoustic Sensor Networks (UASNs), which involves scheduling the use of communication resources to optimize the energy usage of the network 4 ; and the fully distributed microgrid system model, which involves optimizing the charging and discharging states of electric vehicles to maximize benefits. 5 In the same sense, as energy availability on a satellite is directly related to its capacity to perform different tasks simultaneously or accommodate higher consumption payloads, there is a general interest in maximizing its generated energy and efficiently distributing the harvested energy. The satellite missions with efficient energy harvesting are attractive in terms of costs, mainly when achieved without new extra complex, and expensive hardware.…”

Section: Introductionmentioning

confidence: 99%

On‐board energy scheduling optimization algorithm for nanosatellites

Martinez

Seman

Filho

et al. 2023

Circuit Theory & Apps

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SummaryThis work proposes a real‐time and online energy scheduling optimization algorithm for nanosatellites operating with a direct energy transfer (DET) architecture. The goal of the algorithm is to optimize energy utilization by maximizing energy harvesting, meeting task deadlines and priorities, and ensuring a minimum quality of service (QoS) for the system. The algorithm is composed of a modified perturb and observe algorithm to reach the best energy efficiency point of the solar panels, a task prioritization algorithm to guarantee their times and minimum QoS, and a backpack optimization problem to maximize the energy efficiency of the satellite. To demonstrate the effectiveness of the proposed algorithm, simulations of a nanosatellite operating in a given orbit, attitude, and thermal parameters were conducted and compared to other task scheduling strategies. The results showed that the proposed energy scheduling algorithm had the best performance in terms of energy balance and average power consumption, with an energy balance up to 16% higher compared to the other tested strategies. This demonstrates the ability of the proposed algorithm to optimize power consumption and energy utilization, as well as efficiently schedule tasks to maximize energy harvesting. The proposed energy scheduling optimization algorithm has the potential to be a useful tool for the design and optimization of future satellite missions and could potentially guide the design of electrical power systems for new CubeSat missions operating with a DET architecture. The algorithm's ability to optimize energy utilization and ensure a minimum QoS for the system could improve the overall efficiency and performance of the satellite.

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“…Optimization plays a vital role during DSM and EM process. Xu and He have proposed a neurodynamic algorithm by combining neural networks and differential algorithm for energy scheduling considering both load and EVs 17 . Game theory approach combined with stochastic dynamic programming has been used by Rathor and Saxena 18 for energy scheduling in LVMG.…”

Section: Introductionmentioning

confidence: 99%

“…Xu and He have proposed a neurodynamic algorithm by combining neural networks and differential algorithm for energy scheduling considering both load and EVs. 17 Game theory approach combined with stochastic dynamic programming has been used by Rathor and Saxena 18 for energy scheduling in LVMG. Wang et al 19 have also used stochastic optimization technique to analyze the interaction between main grid and multiple MGs.…”

Section: Introductionmentioning

confidence: 99%

Intelligent electric vehicles charging coupled demand response of isolated microgrid

Niharika

Mukherjee

2022

Energy Storage

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In recent times, the shrinkage of fossil fuel reserves and increase in greenhouse emissions have compelled to switch to suitable eco‐friendly solutions. Electric vehicles (EVs), being one such solution, have been the center of attraction for the industries as well as the researchers. This paper deals with the inclusion of plug‐in EVs (PEVs) in isolated microgrid (I‐MG). An intelligent demand‐side management approach has been proposed for co‐ordination of the power generation from distributed generators, battery energy storage systems (BESSs), and other forms of distributed energy resources (DERs) to fulfill the daily load demand as well as energy demand of EVs. Medium voltage I‐MG integrated with DERs, BESSs and PEVs has been used for the study and the analysis has been done to justify the need for smart charging of PEVs in an I‐MG. The proposed technique combines smart charging and demand response which simplifies the consumer’s concerns and provides a cost‐effective solution.

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A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid

Cited by 30 publications

References 26 publications

A trust‐based resilient consensus algorithm for distributed optimization considering node and edge attacks

A trust‐based resilient consensus algorithm for distributed optimization considering node and edge attacks

On‐board energy scheduling optimization algorithm for nanosatellites

Intelligent electric vehicles charging coupled demand response of isolated microgrid

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