Due to the requirements of industrial drive system for high output power, uniform torque distribution, and accurate synchronous movement, the synchronous control methods of multimotor system have drawn increasing attention. This article refers to a large number of research on synchronous control methods and summarizes and compares different control structures and algorithms. Based on the evaluation criteria of tacking error, synchronization error, and adjustment time, the advantages and disadvantages of three representative control algorithms, including proportional integral derivative (PID) control, model predictive control, and sliding mode control, are studied through experiments. Finally, the development trend of multimotor synchronous control is summarized and prospected.

In DC distribution networks, the uncertainties of renewable energies and loads cause frequent voltage fluctuations. In this paper, a voltage regulation strategy based on the combined coordination of centralized optimal control (COC) and adaptive droop control (ADC) is proposed for droop-controlled DC distribution networks. In the COC strategy, the operation reference power of voltage source converters (VSCs) is optimized using the bus injection power measurements to improve the voltage profile and balance the power loading rates of different VSCs. The VSC capacity margin constraint is established to ensure that the capacity margin of each VSC is sufficient to cope with the unexpected fluctuation of loads and renewable energies. Then, an ADC strategy is adopted to handle voltage fluctuation between adjacent two COCs. In the ADC strategy, the operation reference power of the VSCs is further adjusted to recover the voltages. The voltage regulation is achieved based on the relationship among the VSC voltages and the minimum/maximum voltages of the whole network, which does not depend on the information of each nodal voltage and thus real-time network communication is avoided. The simulation results show that the proposed voltage regulation strategies can effectively improve the voltage profile under various uncertain environments.

Hybrid AC/DC distribution networks with the high-efficiency consumption and high-proportion access of new energy have become a crucial trend for future modern distribution networks. High penetration of renewable energy brings various controllable resources along with uncertainties to hybrid AC/DC distribution networks, making conventional single objective optimization fail to meet optimal operation requirements of flexibility and reliability. In this paper, considering uncertainties of distribution generations and loads, a multi-objective robust optimization model based on various controllable devices is proposed. To increase resource utilization, the proposed method comprehensively and properly models the full variety of possible control means (i.e., flexible distribution switch, voltage source converter, energy storages, et al). Utilizing the abundant control means, a multi-objective optimization which minimizes network losses, voltage deviations and operation cost simultaneously is modeled. Then, based on the multi-objective model, a two-stage robust optimization based on second order cone method and column constraint generation algorithm is proposed to achieve the solution, which can deal with the fluctuation of loads and renewable energy quickly and effectively. Outdoing traditional single objective robust optimization, our multi-objective robust optimization based on various controllable resources can decease the network losses, increase the profits and improve voltage profiles significantly, which obviously improves the safety, flexibility and economy simultaneously. Finally, compared with existing robust optimization, the proposed method is tested in numerous case studies to verify its effectiveness and advantages in a modified IEEE 33-node system.

Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large.

A coupled dynamic-static decentralized robust state estimation (RSE) methodology is proposed for integrated electricity-gas systems (IEGSs). Firstly, a discrete time state space model is established for natural gas systems considering pipeline dynamics and spatiotemporal boundary conditions. Then a coupled dynamic-static RSE framework is constructed, which performs local Huber M-based static RSE for power systems with fast dynamics, and local Huber M Kalman filter (KF)-based dynamic RSE for natural gas systems with slow dynamics. A constrained iteratively reweighted least squares algorithm is proposed to uniformly solve the equality constrained RSE model and the batch-form robust linear regression model in the robust KF. Based on local prior estimates, estimation error covariance matrices and coupling constraints, an upper-level SE model is constructed to eliminate boundary mismatch and obtain posterior globally consistent estimates. The upper-level SE is converted into a non-convex distributed formulation through network partitioning, which is then solved using an improved ALADIN algorithm in a fully decentralized manner. A multi-time-scale SE scheme is also proposed to handle the measurement deficiency issue caused by different measurement refreshing rates. Comparison with existing decentralized SE methods on a typical IEGS verifies the effectiveness of the proposed methodology on estimation accuracy, robustness and convergence performance.

In this article, a Trust-Tech source-point method is proposed to systematically compute multiple local optimal solutions (LOSs) for continuous unconstrained nonlinear optimization problems. This proposed method consists of four stages. Stage I finds one LOS (in which existing effective optimizers can be applied), stage II is the stage of escaping an LOS while stage III is the stage for entering the stability region (SR) of another stable equilibrium point (SEP) (i.e., another LOS). Stage IV computes other SEPs (i.e., LOSs) in corresponding SRs. A theoretical foundation for both stages II and III is developed, and these theoretical results are quite general on their own. The proposed method is numerically evaluated to compute multiple LOSs. For instance, a total of 5085 LOSs have been computed by the proposed Trust-Tech source point method on a 50-D test function. In addition, the proposed method can find the global optimal solutions of several test functions with 50 dimensions and 100 dimensions.