The intermittency of renewable energy sources makes the use of energy storage systems (ESSs) indispensable in modern power grids for supply-demand balancing and reliability enhancement. Besides pumped-storage hydroelectric power stations, energy storage deployment worldwide is still quite low. However, the status quo might rapidly change as the energy storage technologies are growing and facilitating market regulations are being ratified. Battery energy storage systems (BESSs), Li-ion batteries in particular, possess attractive properties and are taking over other types of storage technologies. Thus, in this article, we review and evaluate the current state of the art in managing grid-connected Li-ion BESSs and their participation in electricity markets. The review mainly includes battery modeling, the architecture of battery management systems (BMSs), the incorporation of BESSs for electricity market services, global utility-scale battery storage facilities, and challenges in implementing and managing grid-connected BESSs.

The rapid development of microgrid (MG) has made it ready to connect multiple MGs for providing regional power supply. The energy management of a multi-microgrid (MMG) system is the key to the reliable, economic operation of the interconnected power system. The MMG system is rich in distributed generation sources. The intermittence and volatility of renewable energy sources (RESs) such as wind and solar cannot be ignored in a large-scale hybrid power system. Therefore, the energy management of a MMG system is a complex problem that coordinates distributed generation units within individual MGs, the power exchange among MGs, and the power trading between the grid and MGs. System of systems (SoS) has been identified as an effective way of forming a MMG system and coordinating the participating MGs for energy management. This paper proposes a hierarchical decentralized SoS architecture for the energy management of a MMG system and, accordingly, formulates it as a bi-level optimization problem. Energy management at the level of individual MGs is modeled as a multiple-stage robust optimization to handle the issue of RES uncertainty. Energy management at the level of MMG system further addresses the problem of spatially unbalanced demand and generation in the area. The proposed MMG energy management is further verified through a case study.

As the penetration level of distributed photovoltaic (PV) systems keeps increasing in distribution networks, overvoltage due to reverse power flow is an urgent issue to be addressed. This paper proposes a voltage regulation method by utilizing the voltage control capability of PV inverters. A novel network partition approach based on a community detection algorithm is presented to realize zonal voltage control in a shorter control response time using the minimum amount of reactive power compensation and active power curtailment. An improved modularity index that considers local reactive power balance is introduced to partition a distribution network into several clusters/communities with PVs based on the node voltage sensitivity analysis. An optimal reactive and active power control strategy is proposed for voltage control in each cluster. The voltage management of the overall system can be achieved by controlling each cluster separately. The proposed approach is applied to the voltage control of a practical 10 kV, 37-node feeder. Case studies on the real distribution network and a modified IEEE 123-node system are carried out to verify the feasibility and effectiveness of the proposed method.

Uncertainties from multiple generation resources and loads have introduced tremendous challenges to the optimal dispatch of microgrids. This paper presents a novel two-stage min-max-min robust optimal dispatch model for a representative islanded AC/DC hybrid microgrid that faces uncertainties in renewable energy generation and customer loads. The first stage of the model determines the startup/shutdown state of the diesel engine generator and the operating state of the bi-directional converter of the microgrid. Then, the second stage optimizes the power dispatch of individual units in the microgrid. A new linearized equipment cost model is developed, counting for the degradation of energy storage. The use of this linear model helps maintain the linearity of objective function without compromising the solution accuracy. The column-and-constraint generation algorithm is implemented to efficiently obtain a robust dispatching plan for the microgrid, which minimizes the daily operating cost in the worst-case scenario. A case study and sensitivity analyses further demonstrate the rationale and the unique capability of the proposed model for planning the operation of AC/DC hybrid microgrids.

Accurate estimation of the state of charge (SOC) of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li)-ion batteries. First, an enhanced battery model is proposed to include the impacts due to different discharge rates and temperatures. An adaptive joint estimation of the battery SOC and battery internal resistance is then presented to enhance system robustness with battery aging. The SOC estimation algorithm has been developed and verified through experiments on different types of Li-ion batteries. The results indicate that the proposed method provides an accurate SOC estimation and is computationally efficient, making it suitable for embedded system implementation.