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Distributed integrated multi-energy systems (DIMSs) can be regarded as virtual power plants to provide additional flexibility to the power system. This paper proposes a robust active dynamic aggregation model for the DIMSs to describe the maximum feasible region. The aggregation model includes the power constraints, energy constraints, and ramping constraints to aggregate different types of resources in the DIMSs. The proposed generator-like and storage-like model does not depend on the ancillary service market and can be directly incorporated into the economic dispatch model of the power system. A novel algorithm based on the column-and-constraint generation algorithm and convex-concave procedure is proposed to solve the two-stage robust optimization problem, which is more efficient than the KKT-based algorithms. Finally, a case study of an actual DIMS is developed to demonstrate the effectiveness of the proposed model. Index Terms-Aggregation, distributed integrated energy system (DIMS), robust optimization, virtual power plant. NOMENCLATURE A. Sets and Parameters ϒ Ω ε CCP ε η EBi η ci η dci Set of economic dispatch periods Uncertainty set Convergence tolerance of the convex-concave procedure (CCP) Convergence tolerance of upper power value UB Efficiency of electric boiler i Charging and discharging efficiency of battery i c pmin t

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Distributed storage systems are becoming more and more popular with the rapidly increasing demand for large-scale data storage. To increase the capacity and I/O performance of a distributed storage system, scaling it up is a common method. Regenerating Codes are a class of distributed storage codes that offer good reliability through encoding and provide good bandwidth cost on failed nodes repairing. This paper studies the scaling problem of E-MSR codes based distributed storage systems with fixed number of redundancy codes. We generate the encoding matrices of an storage system carefully from the encoding matrices of an storage system to minimize the changes of encoded blocks when scaling. Therefore the system can be scaled up with relatively low bandwidth cost and computation cost.

Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot be applied to the complex motion situation. To balance the bicycle robot with time‐varying forward velocity, only with the steering actuator, the multiple objective optimal balance control issue is studied here. A fuzzy state‐space model under different forward velocities is firstly offered based on the non‐linear Euler–Lagrange model. Based on this, a closed‐loop equation under a fuzzy controller is verified. To regulate the feedback gain of the fuzzy controller, a modified particle swarm optimization (MPSO) algorithm with two stages is proposed. In the MPSO's second stage, a novel objective fitness function, consisting of multiple objectives and combining the conventional Hurwitz stability analysis criterium, is designed. Procedures for the MPSO dynamic programming approach are presented. By two examples, the efficiency of the MPSO algorithm, for time‐varying and time‐constant velocity situations, and faster capacity for iteration convergence, are examined.