This paper addresses the problem of management and coordination of energy resources in a typical microgrid, including smart buildings as flexible loads, energy storages and renewables. The overall goal is to provide a comprehensive and innovative framework to maximize the overall benefit, still accounting for possible requests to change the load profile coming from the grid and leaving every single building or user to balance between servicing those requests and satisfying his own comfort levels. The user involvement in the decision-making process is granted by a management and control solution exploiting an innovative distributed model predictive control approach with coordination. In addition, also a hierarchical structure is proposed, to integrate the distributed MPC user-side with the microgrid control, also implemented with an MPC technique. The proposed overall approach has been implemented and tested in several experiments in the laboratory facility for distributed energy systems (Smart RUE) at NTUA, Athens, Greece. Simulation analysis and results complement the testing, showing the accuracy and the potential of the method, also in the perspective of implementation. Index Terms-Energy Management, Distributed Control, Model Predictive Control, Microgrid(MPC), which is well suited to deal with a large amount of constraints of different types that have to be imposed in real time in microgrids. This technique has been exploited for example in [2,3,11,12] and [17][18][19][20][21][22][23]. In [2], [3] and [21] the flexible loads are modelled as a predefined range of possible load consumption and the microgrid controller can directly determine a load profile as long as it fulfils the range. This approach neglects the dynamic of load flexibility in the shiftable loads category (e.g., heating/cooling system, refrigerator, washing machine, etc.) since the size of the predefined range in one step of this load type can not be fixed in advance and it depends on the value of decision variable in the previous steps. On the other hand, in [17] and [23], the load side may reveal its model which later will be formulated in the optimization problem in a centralized controller.Concerning the control scheme, a centralized predictive scheme is considered in [2], [3], [12], [17] and [18]. However, it is well-known that this scheme presents issues of scalability, computational burden, failure of single unit, adaptability, etc. Recent works are putting more attention to the distributed MPC and hierarchical control schemes, such as [11], [19]. In particular, in [19], a two-layer control scheme based MPC operating at two different timescales has been studied. In the paper, some details on the markets (e.g., imbalance charge, difference in purchasing and selling tariffs) are neglected and the flexible load is not considered. On the other hand, the paper [11] employs a sequential distributed MPC on energy management problems in the microgrid; however, some details are missing, including the presence of RES (Renewable Energy Resource),...

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In this paper, a new approach to solve the magnetostatic inverse problem is proposed. The goal of the paper is to place magnetic sources and to specify their locations and intensities from the measurements of a desired magnetic field in the air. In this work, it is assumed that the magnetic sources are coils which their locations and ampere turns must be determined. By using gradient method, coils locations are specified by finding extremum of the desired measured magnetic field and with the Iterative Learning Control , coils ampere turns are determined. Selection of correction term in Iterative Learning Control is the most important part of the controller design which dramatically affects the convergence of the method. The most important merit of the proposed method is its simplicity for implementation. The simulation results of the method show the accuracy and effectiveness of the proposed technique.