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In this paper, a new distributed multi-agent framework based on the three layers' fog computing architecture is developed for real-time microgrid economic dispatch and monitoring. To this end, the changes of load at any time will be tracked by the proposed technique, considering unit sudden exits and entries. Moreover, to make the system more realistic, different renewable energies, including photovoltaics (PVs), wind turbines (WTs), fuel cells (FCs), and microturbines (MT) are considered in the proposed technique. To overcome the complexity of the problem, by using advantages of fog computing, a new fast consensus-based optimization algorithm is used, which is modified based on the fuzzy adaptive leader technique. Finally, the proposed technique is simulated and tested on microgrids with 6 and 14 buses, respectively. Simulation results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate. • We developed a three-layer framework based on fog computing for MG. • We developed a modified distributed consensus-based technique to achieve the optimal dispatch solutions. • We simulated and analyzed the proposed model on two IEEE standard test system with two different scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a robust control method is used to intelligently regulate the DC bus voltage deviation for an islanded microgrid (MG) within the scenario of energy Internet (EI). In order to cope with the uncertain and nonlinear nature of the power exchange in EI, a new mathematical model is formulated to present the dynamics of the islanded MG, where the state equation is expressed by a class of nonlinear stochastic differential equations (SDEs) with parameter uncertainties appearing in system coefficients and the controllers. Specifically, the nonlinearities appear in both drift and diffusion parts of such SDEs. Our aim is to design a controller such that the effects of the modelling uncertainty and the external disturbance to the DC bus voltage of the off-grid MG are minimized. Mathematically, this can be regarded as problems of solving the non-fragile robust stochastic stabilization and non-fragile robust H ∞ control, both of which can be solved via the linear matrix inequality (LMI) approach sufficiently. We emphasize such new control system with its solution can also be applied into fields of industry, economics, etc. Based on real world data, numerical results are illustrated, and simulations show that our target is achieved. [ABSTRACT FROM AUTHOR]

Distributed Constraint Optimization (DCOP)-based approaches, as the distributed version of constraint optimization, provide a framework for coordinated decision making by a team of agents. In this paper, an agent-based DCOP model is developed to allocate water and reclaimed wastewater to demands considering the conflicting interests of involved stakeholders. One of the well-known DCOP algorithms, ADOPT1, is modified to incorporate an agent responsible for monitoring and conserving water resources. This new algorithm considers the social characteristics of agents and a new form of interaction between agents. For the first time in the literature, a real-world water and reclaimed wastewater allocation problem is formulated as a DCOP and solved using the Modified ADOPT (MADOPT) algorithm. To evaluate the MADOPT algorithm, it is applied to a water and reclaimed wastewater allocation problem in Tehran, Iran. The results illustrate the applicability and efficiency of the proposed methodology in dealing with large-scale multi-agent water resources systems. It is also shown that agents' selfishness and social relationships could affect their water use policies. • A multi-agent model is presented for urban water and treated wastewater management. • A modified version of Asynchronous Distributed Constraint Optimization is proposed. • The algorithm considers a monitoring agent which exists in environmental systems. • The algorithm is applied to a large-scale real-world environmental system. • The results illustrate the applicability and efficiency of the proposed methodology. [ABSTRACT FROM AUTHOR]

To solve the increasingly serious air pollution problem, regional air pollution collaborative governance as an effective way is introduced in this paper. Firstly, this study constructs a game model of regional air pollution collaborative governance, and three classic cost sharing methods are used to allocate the cost of air pollution governance; Then the emission reduction cost of regional cooperative governance is obtained; Finally, the study makes an empirical analysis on the cost sharing of SO 2 emission reduction by four local governments of the Yangtze River Delta region in 2017. The results show that: (i) The emission reduction cost of regional cooperative governance is better than that of individual governance, and in 2017 the cost of SO 2 cooperative governance in Yangtze River Delta region is reduced by about 1.8% compared with the individual governance; (ii) The Shapley value method is utilized to allocate the benefits of the cooperative SO 2 governance of four provinces in the Yangtze River Delta region, which indicates that all local governments can obtain certain profits from this cooperation and the cooperative alliance is stable; (iii) Collaborative governance of regional air pollution can meet the requirements of both collective rationality and individual rationality at the same time. • A regional air pollution collaborative governance game model was constructed. • The emission reduction cost of regional cooperative governance was obtained. • Three classic cost sharing methods were used to allocate the air pollution governance cost. • The cost sharing results of SO 2 emission reduction by four provincial governments was calculated. [ABSTRACT FROM AUTHOR]