In order to enhance navigation safety and promote environmental protection, this paper takes the problem of energy management in a ship-integrated energy system into consideration. According to the characteristics of ...
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In order to enhance navigation safety and promote environmental protection, this paper takes the problem of energy management in a ship-integrated energy system into consideration. According to the characteristics of navigation, an intelligent ship energy management model, simultaneously considering the social and economic benefits, has been proposed. Meanwhile, this paper analyzes a distributed optimal scheduling problem which considers renewable generation devices and an energy storage system. Combined with an electricity-power system and thermal-power system, we propose an optimalscheduling scheme to accurately meet the actual load demand based on the pre-results analyzed by the ensemble learning short-term load forecasting algorithm. In addition, the related stability analysis is given. Further, a series of simulation results have been presented, which denote that the proposed load forecasting algorithm can accurately analyze the short-term load demand trend, and the proposed optimization algorithm can effectively coordinate economic and environmental protection.
In this paper, a distributed robust energy management scheme for multiple interconnected microgrids (MGs) is developed. It aims to optimize the total operational cost of the MGs through energy trading with neighboring...
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In this paper, a distributed robust energy management scheme for multiple interconnected microgrids (MGs) is developed. It aims to optimize the total operational cost of the MGs through energy trading with neighboring MGs and the main grid in the real-time energy market. Various uncertainties including renewable generation, load consumption, and buying/selling prices of the main grid are handled using an adjustable robust optimization technique. To keep consistent with the distributed nature of the multiple MGs, we propose a distributed adjustable robust optimalscheduling algorithm. Within the framework, each MG energy management system determines its own selling price and operation schedule via distributed communication of noncritical information with its neighboring MGs. Robust optimalscheduling and fair energy trading can be collectively achieved. A case study of a 4-MG system is conducted to validate the effectiveness of the proposed approach.
In the researching background of integrated energy system, a single electricity-gas-heating system (EGHS) can be regarded as an active producer. In order to solve the joint optimization of integrated energy systems un...
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In the researching background of integrated energy system, a single electricity-gas-heating system (EGHS) can be regarded as an active producer. In order to solve the joint optimization of integrated energy systems under the condition of incomplete information, this paper proposes a distributed optimal scheduling framework of EGHS. First, establish a coupling model of the interconnected EGHS, and perform strict second-order cone convexity of the complex natural gas flow model. Next, use the bus splitting method to realize the decoupling between different regions of the interconnected system, and employ the alternating direction multiplier method (ADMM) to solve the model. Then, construct two-region energy system (78-node grid & x002B;40-node gas grid & x002B;40-node heat grid) and three-region energy system (117-node grid & x002B;60 node gas grid & x002B;60-node heat grid) as simulation examples to verify the effectiveness of the distributed optimization framework. In the end, the algorithm solution process, the effectiveness of scheduling results, and the comparison of optimization results under different interconnection methods are analyzed in detail.
The ubiquitous power Internet of things (UPIoT) can realize the wide interconnection of all links of the power system. Based on this background, this paper proposes the price incentive agreement of distributed new ene...
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ISBN:
(纸本)9781728171951
The ubiquitous power Internet of things (UPIoT) can realize the wide interconnection of all links of the power system. Based on this background, this paper proposes the price incentive agreement of distributed new energy aggregators and establishes distributed dispatching architecture. Firstly, the information transmission matrix of distributed new energy aggregators with the property of double random matrix is established, and the distributed sub-gradient algorithm based on information transmission matrix is used to solve the distributed dispatching model of new energy aggregators. Finally, the feasibility and effectiveness of the optimization model and its solution method are analyzed through simulation examples, and the information interrupt, information error that may be encountered are also discussed.
With the high penetration of renewable energy, the addition of a large number of energy storage units, and flexible loads, the source-load-storage structure of active distribution networks is becoming increasingly com...
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With the high penetration of renewable energy, the addition of a large number of energy storage units, and flexible loads, the source-load-storage structure of active distribution networks is becoming increasingly complex, making optimization and scheduling more challenging. In response to issues as difficult global information acquisition, less consideration of flexible load and energy storage unit access, individual deception, and insufficient security in the optimization scheduling process of active distribution networks, this paper constructed a distribution network optimization scheduling model that includes sources, loads, and storage. It proposed a distributed optimization scheduling strategy for source-load-storage distribution networks, combined with alliance chains. This strategy is based on the FISCO BCOS consortium chain platform, with blockchain multi-agent nodes forming a consortium chain network. The consistency variables are the incremental cost of distributed power generation and the incremental benefits of flexible loads. distributedscheduling calculations were carried out using a consensus algorithm that includes leadership nodes. By combining the data storage mechanism and consensus algorithm advantages of the consortium chain, the centrality of leadership nodes is eliminated, achieving optimal power allocation in the distribution network at a minimum economic cost. The simulation results show that the distributed optimization scheduling strategy proposed in this paper can achieve optimal allocation of minimum cost in the distribution network and converge quickly in various scenarios such as non-flexible load fluctuations, leader node switching, node joining or leaving, and changes in power exchange instruction in the distribution network. It demonstrates good robustness and stability.
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