One of fundamental and challenging issues in microgrids is to guarantee fairness of load sharing while realizing voltage regulation of distributed generations. In order to address this issue, a new multi-objective optimization problem with tunable weighting coefficients is first formulated for DC microgrids. Second, a new distributed control scheme, which only requires local communications among neighbors, is proposed to solve the optimization problem. It is theoretically proved that the distributed control scheme can exponentially achieve the global optimal outputs of voltages and currents at distributed generations. Compared with a centralized control scheme, the proposed distributed control scheme provides remarkable advantages in improving reliability and scalability of microgrids. Third, the distributed control scheme is extended to accommodate a constant communication delay. The effects of the communication delay on the stability of microgrids are explicitly characterized. Finally, the performance of the proposed control schemes are evaluated by a modified 6-bus microgrid with DC-powered trolleybus systems, in terms of their convergence, robustness to load variations, plug-and-play functionality, trade-off ability and effects of communication delays.

A state-of-charge (SOC) versus open-circuit-voltage (OCV) model developed for batteries should preferably be simple, especially for real-time SOC estimation. It should also be capable of representing different types of lithium-ion batteries (LIBs), regardless of temperature change and battery degradation. It must therefore be generic, robust and adaptive, in addition to being accurate. These challenges have now been addressed by proposing a generalized SOC-OCV model for representing a few most widely used LIBs. The model is developed from analyzing electrochemical processes of the LIBs, before arriving at the sum of a logarithmic, a linear and an exponential function with six parameters. Values for these parameters are determined by a nonlinear estimation algorithm, which progressively shows that only four parameters need to be updated in real time. The remaining two parameters can be kept constant, regardless of temperature change and aging. Fitting errors demonstrated with different types of LIBs have been found to be within 0.5%. The proposed model is thus accurate, and can be flexibly applied to different LIBs, as verified by hardware-in-the-loop simulation designed for real-time SOC estimation.

The platooning of automated vehicles has the potential to significantly benefit road traffic. This paper presents a distributed H _∞ control method for multi-vehicle systems with identical dynamic controllers and rigid formation geometry. After compensating for the powertrain nonlinearity, the node dynamics in a platoon is mathematically described by a multiplicative uncertainty model. The platoon control system is then decomposed into an uncertain part and a diagonal nominal system through linear transformation and eigenvalue decomposition of the information-exchange-topology matrix. Robust stability, string stability and distance tracking performance of the designed platoons are analyzed theoretically under the decoupled H _∞ framework. A comparative simulation with nonrobust controllers is used to demonstrate the effectiveness of this method.

This note develops a distributed algorithm to solve a convex optimization problem with coupled constraints. Both coupled equality and inequality constraints are considered, where functions in the equality constraints are affine and functions in the inequality constraints are convex. Different from primal-dual subgradient methods with vanishing decreasing stepsizes for nonsmooth optimizations, our algorithm focuses on smooth problems and uses a fixed stepsize to find the exact optimal solution. Convergence analysis is derived with rigorous proofs. Our result is also illustrated by simulations.

This paper is concerned with distributed energy management and control issues of both generators and loads. It aims to maximize the total social welfare that balances generation-side expanses, user-side payments, and transmission line costs. A distributed control strategy with continuous information exchange among neighbors is first proposed. It is shown that this distributed algorithm achieves the global optimal power outputs on generators and the optimal electricity usage on loads asymptotically. To reduce communication resource consumptions, the distributed optimization algorithm is further expanded to incorporate event-triggered communication and control mechanism. In this new algorithm, an event-triggering condition for each generator and each load is employed to determine when its related state information should be sampled and transmitted to its neighbors. Compared with the standard periodic sampling and communication schemes, this new distributed and event-triggered algorithm can significantly reduce communication data flows while achieving the nearly identical control performance to that under continuous data communications. The theoretical results of this paper are validated by using a simulation case study with distributed generators and multiple loads on an IEEE 9-bus system.