This paper proposes a novel adaptive consensus algorithm (ACA) for distributed heat-electricity energy management (HEEM) of an islanded microgrid. In order to simultaneously satisfy the heat-electricity energy balance...
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This paper proposes a novel adaptive consensus algorithm (ACA) for distributed heat-electricity energy management (HEEM) of an islanded microgrid. In order to simultaneously satisfy the heat-electricity energy balance constraints, ACA is implemented with a switch between unified consensus and independent consensus according to the dynamic energy mismatches. The feasible operation region of a combined heat and power (CHP) unit is decomposed into eight searching sub-regions, thus its electricity and heat energy outputs can simultaneously match the incremental cost consensus requirement and the heat-electricity energy balance constraints. Case studies are thoroughly carried out to verify the performance of ACA for distributed HEEM of an islanded microgrid.
This paper addresses the distributed adaptive tracking control problem for a class of multi-agent systems under intermittent communication constraints, where the dynamics of each follower subsystem contain heterogeneo...
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This paper addresses the distributed adaptive tracking control problem for a class of multi-agent systems under intermittent communication constraints, where the dynamics of each follower subsystem contain heterogeneous mismatched affine uncertainties. Although some distributed adaptiveconsensus protocols were developed in previous work, several potential limitations of adaptiveconsensus controller design are difficult to overcome when the switching communication topology contains asymmetrical architecture and discontinuous failures. To overcome the main obstacles, the authors propose a novel adaptive coordination control algorithm such that the follower subsystems are enabled to track the states of a leader. By using the neighbourhood information, a distributed estimator like-protocol is constructed, under which, each subsystem can estimate the desire state information even under the intermittent connection constraint in directed switching topology. Then, in view of heterogeneous mismatched affine uncertainties, a decentralised adaptive controller with updating local parameters is presented to guarantee that each agent states track desire trajectories. Technically, by exploiting topology-dependent multiple Lyapunov functions approach, S-procedure technique and adaptive mechanism, the synchronisation conditions of the adaptive consensus algorithm are established to prove the synchronisation of heterogeneous agents even in the presence of intermittent communication failures. Finally, an example demonstrates the effectiveness for the proposed method.
The consensus problem of heterogeneous first-order multi-agent systems with diverse nominal velocities is investigated in this paper, and three adaptive consensus algorithms are constructed by introducing an adaptive ...
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The consensus problem of heterogeneous first-order multi-agent systems with diverse nominal velocities is investigated in this paper, and three adaptive consensus algorithms are constructed by introducing an adaptive variable into the usual consensusalgorithm. Under fixed interconnection topology, consensus criteria are obtained for the three algorithms. Moreover, delay-dependent consensus criteria are also presented for the three algorithms in synchronously coupled form under identical input delay and communication delay. Numerical examples are presented to illustrate the validity of our theoretical results.
A novel cyber-physical-social system (CPSS) with parallel learning is presented for distributed energy management (DEM) of a microgrid. CPSS is developed by extending the conventional cyber-physical system to the soci...
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A novel cyber-physical-social system (CPSS) with parallel learning is presented for distributed energy management (DEM) of a microgrid. CPSS is developed by extending the conventional cyber-physical system to the social space with human participation and interaction. Each energy supplier or each energy demander is regarded as a human in the social space, who is able to learn the knowledge, cooperate with others, and make a decision with various preference behaviors. The correlated equilibrium (CE) based general-sum game is employed for realizing the human interaction on the complex optimization subtask, while the novel adaptive consensus algorithm is used for achieving that on the simple optimization subtask with multi-energy balance constraints. A real-world system and multiple virtual artificial systems are introduced for parallel and interactive execution based on the small world network, thus a higher quality optimum of DEM can be rapidly emerged with a high probability. Case studies of a microgrid with 11 energy suppliers and 7 energy demanders demonstrate that the proposed technique can effectively achieve the human-computer collaboration and rapidly obtain a higher quality optimum of DEM compared with other centralized heuristic algorithms. (C) 2018 Elsevier Ltd. All rights reserved.
A novel cyber-physical-social system (CPSS) with parallel learning is presented for distributed energy management (DEM) of a microgrid. CPSS is developed by extending the conventional cyber-physical system to the soci...
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ISBN:
(纸本)9781538642924
A novel cyber-physical-social system (CPSS) with parallel learning is presented for distributed energy management (DEM) of a microgrid. CPSS is developed by extending the conventional cyber-physical system to the social space with human participation and interaction. Each energy supplier or each energy demander is regarded as a human in the social space, who is able to learn the knowledge, cooperate with others, and make a decision with various preference behaviors. The correlated equilibrium (CE) based general-sum game is employed for realizing the human interaction on the complex optimization subtask, while the novel adaptive consensus algorithm (ACA) is used for achieving that on the simple optimization subtask with multi-energy balance constraints. A real-world system and multiple virtual artificial systems are introduced for parallel and interactive execution based on the small world network, thus a higher quality optimum of DEM can be rapidly emerged with a high probability. Case studies of a microgrid demonstrate that the proposed technique can effectively achieve the human-computer collaboration and rapidly obtain a higher quality optimum of DEM.
This paper proposes a new structural novelty detection method in the case of vast measurement data having uncertainties. Considering the effects of measurement accuracy and environmental variations on measurement vari...
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This paper proposes a new structural novelty detection method in the case of vast measurement data having uncertainties. Considering the effects of measurement accuracy and environmental variations on measurement variance, precise analytical methods of adaptive confidence distance and measurement variance are presented on the basis of statistical theory, and thus an adaptiveconsensus data fusion algorithm has been firstly developed to deal with the large volume of data involving considerable uncertainties. The proposed adaptive fusion algorithm can adaptively choose sensors whose data will be subsequently fused. The algorithm is then incorporated with wavelet analysis for the purpose of structural novelty detection. Two numerical examples are carried out to validate the efficiency and adaptability of the proposed method. The obtained results have been compared with those from other existing methods, which demonstrate the high efficiency of the proposed method in data processing considering uncertainties and unsatisfied performance of some sensors, as well as its accuracy in structural novelty detection. The proposed method also shows some robustness to noise.
This paper proposes a new structural novelty detection method in the case of vast measurement data having uncertainties. Considering the effects of measurement accuracy and environmental variations on measurement vari...
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This paper proposes a new structural novelty detection method in the case of vast measurement data having uncertainties. Considering the effects of measurement accuracy and environmental variations on measurement variance, precise analytical methods of adaptive confidence distance and measurement variance are presented on the basis of statistical theory, and thus an adaptiveconsensus data fusion algorithm has been firstly developed to deal with the large volume of data involving considerable uncertainties. The proposed adaptive fusion algorithm can adaptively choose sensors whose data will be subsequently fused. The algorithm is then incorporated with wavelet analysis for the purpose of structural novelty detection. Two numerical examples are carried out to validate the efficiency and adaptability of the proposed method. The obtained results have been compared with those from other existing methods, which demonstrate the high efficiency of the proposed method in data processing considering uncertainties and unsatisfied performance of some sensors, as well as its accuracy in structural novelty detection. The proposed method also shows some robustness to noise.
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