This paper proposes an effective dc voltage and power-sharing control structure for multiterminal dc (MTDC) grids based on an optimal power flow (OPF) procedure and voltage-droop control implemented in the different hierarchical layers. In the proposed approach, an OPF algorithm is executed at the secondary control level of the MTDC grid to find the optimal reference values for the dc voltages and active power of the voltage-regulating converters. Then, at the primary control level, the voltage-droop characteristics of the voltage-regulating converters are tuned based upon the OPF results. In this control structure, the optimally-tuned voltage-droop controllers lead to the optimal operation of the MTDC grid. In case of variation in load or generation of the grid, a new stable operating point is achieved based on the voltage-droop characteristics. Then by executing a new OPF, the voltage-droop characteristics are retuned for optimal operation of the MTDC grid after the load or generation variations. This paper also considers the integration of frequency support loop in the proposed control framework in case of connection of weak ac grids. The simulations performed on a study case inspired by the CIGRE B4 dc grid test system demonstrate the efficient grid performance under the proposed control strategy.Index Terms-CIGRE B4 dc grid test system, multiterminal dc (MTDC) grids, optimal power flow (OPF), voltage-droop control. 2168-6777

This paper examines distribution systems with a high integration of distributed energy resources (DERs) and addresses the design of local control methods for real-time voltage regulation. Particularly, the paper focuses on proportional control strategies where the active and reactive output-powers of DERs are adjusted in response to (and proportionally to) local changes in voltage levels. The design of the voltage-active power and voltage-reactive power characteristics leverages suitable linear approximations of the AC power-flow equations and is network-cognizant; that is, the coefficients of the controllers embed information on the location of the DERs and forecasted non-controllable loads/injections and, consequently, on the effect of DER power adjustments on the overall voltage profile. A robust approach is pursued to cope with uncertainty in the forecasted non-controllable loads/power injections. Stability of the proposed local controllers is analytically assessed and numerically corroborated .

Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.

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