A model predictive control system for a hybrid battery-ultracapacitor power source is proposed and experimentally verified in this paper. The main advantage of the proposed system is that the battery current, the battery state of charge, and the ultracapacitor current and voltage are maintained within predefined limits during the operation. In addition, the controller allocates fast current changes to the ultracapacitor while the battery responds mainly to slow current changes which helps to increase the battery lifetime. The presented experimental results verify operation of the proposed system.

This paper presents an overview of the state of the art control strategies specifically designed to coordinate distributed energy storage systems in microgrids. Power networks are undergoing a transition from the traditional model of centralised generation towards a smart decentralised network of renewable sources and energy storage systems, organised into autonomous microgrids. Energy storage systems can provide a range of services, particularly when distributed throughout the power network. The introduction of distributed energy storage represents a fundamental change for power networks, increasing the network control problem dimensionality and adding long time-scale dynamics associated with the storage systems’ state of charge levels. Managing microgrids with many small distributed energy storage systems requires new scalable control strategies that are robust to power network and communication network disturbances. The paper reviews the range of services distributed energy storage systems can provide, and the control challenges they introduce. The focus of the paper is a presentation of the latest decentralised, centralised and distributed multi-agent control strategies designed to coordinate distributed microgrid energy storage systems. Finally, multi-agent control with agents satisfying Wooldridge’s definition of intelligence is proposed as a promising direction for future research.

Multilevel cascaded H-bridge converters are promising candidates for large-scale photovoltaic power plants. They allow direct connection to medium-voltage distribution networks without the presence of bulky line frequency power transformers. Owing to the stochastically variable nature of irradiance level, ambient temperature, and other factors, power levels in the three phases are expected to be unequal. The power imbalance condition creates unexpected problems with this topology, which was initially designed to operate under balanced power conditions. To deal with this issue, the paper proposes three novel zero-sequence injection methods as an expansion to the conventional zero-sequence injection method. Results obtained from simulations and a 430-V 8-kW three-phase seven-level cascaded H-bridge prototype are presented to verify the effectiveness and feasibility of the proposed methods.

This paper proposes the novel use of multi-agent sliding mode control for state of charge balancing between distributed DC microgrid battery energy storage systems. Unlike existing control strategies based on linear multi-agent consensus protocols, the proposed nonlinear state of charge balancing strategy (i) ensures the battery energy storage systems are either all charging or all discharging, thus eliminating circulating currents, increasing efficiency and reducing battery lifetime degradation, (ii) achieves faster state of charge balancing, (iii) avoids overloading the battery energy storage systems during periods of high load and (iv) provides plug and play capability. The proposed control strategy can be readily integrated with existing multi-agent controllers for secondary voltage regulation and accurate current sharing. The performance of the proposed control strategy was verified with an RTDS Technologies realtime digital simulator, using switching converter models and nonlinear lead-acid battery models.

This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear d-q reference frame voltage-current model and linearized power flow approximations. This allows the optimal power flows to be solved as a convex optimization problem, for which fast and robust solvers exist. The proposed method does not assume that real and reactive power flows are decoupled, allowing line losses, voltage constraints, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real-time MPC implementation.