Search

DC microgrid systems have been increasingly employed in recent years to address the need for reducing fossil fuel use in electricity generation. Distributed generations (DGs), primarily DC sources, play a crucial role in efficient microgrid energy management. Energy storage systems (ESSs), though vital for enhancing microgrid stability and reliability, currently lack cost-effectiveness. Each ESS technology serves a specific purpose, suggesting that hybridizing these technologies can improve microgrid stability and reliability while extending the ESS’s lifespan. This paper proposes an optimization of the capacity and cost of a hybrid ESS, comprising a battery and a supercapacitor, in a standalone DC microgrid. This optimization is achieved by calculating the cut-off frequency of a low-pass filter (LPF). The supercapacitor supplies the high fluctuation component of renewable power generation and load demand, while the battery caters to the low fluctuation component. To minimize the designed objective function, including the total net present value (NPV) and replacement cost of the hybrid ESS, a meta-heuristic strategy called the Whale optimization algorithm (WOA) is employed within a MATLAB environment. The optimization takes into account real PV power, wind turbine power and load demand. The results show that reducing power fluctuation for the battery can lower the cost of the hybrid ESS. Compared to a battery-only microgrid system with an $NPV_{total}$ of $\$ $ 6,153,059, the hybrid ESS has an $NPV_{total}$ of $\$ $ 5,413,846. Thus, the hybrid ESS can reduce the total cost of the entire project by 12.01% compared to the system with only a battery. Consequently, the hybrid ESS’s total system life-cycle cost is lower than that of a system using only a battery.

In standalone microgrids, the Battery Energy Storage System (BESS) is a popular energy storage technology. Because of renewable energy generation sources such as PV and Wind Turbine (WT), the output power of a microgrid varies greatly, which can reduce the BESS lifetime. Because the BESS has a limited lifespan and is the most expensive component in a microgrid, frequent replacement significantly increases a project’s operating costs. This paper proposes a capacity optimization method as well as a cost analysis that takes the BESS lifetime into account. The weighted Wh throughput method is used in this paper to estimate the BESS lifetime. Furthermore, the well-known Particle Swarm Optimization (PSO) algorithm is employed to maximize battery capacity while minimizing the total net present value. According to simulation results, the optimal adjusting factor of 1.761 yields the lowest total net present value of US$200,653. The optimal capacity of the BESS can significantly reduce the net present value of total operation costs throughout the project by extending its lifetime. When applied to larger power systems, the proposed strategy can further reduce total costs.

Unbalanced active powers can affect power quality and system reliability due to high penetration and uneven allocation of single-phase photovoltaic (PV) rooftop systems and load demands in a three-phase four-wire microgrid. This paper proposes a distributed control strategy to alleviate the unbalanced active powers using distributed single-phase battery storage systems. In order to balance the unbalanced active powers at the point of common coupling (PCC) in a distributed manner, the agents (households’ single-phase battery storage systems) must have information on the active powers and phases. Inspired by supervised learning, a clustering approach was developed to use labels in order to match the three-phase active powers at the PCC with the agents’ phases. This enables the agent to select the correct active power data from the three-phase active powers. Then, a distributed power balancing control strategy is applied by all agents to compensate the unbalanced active powers. Each agent calculates the average grid power based on information received from its neighbours so that all agents can then cooperatively operate in either charging or discharging modes to achieve the compensation. As an advantage, the proposed distributed control strategy offers the battery owners flexibility to participate in the strategy. Case studies comparing performance of local, centralized, and the proposed distributed strategy on a modified IEEE-13-bus test system with real household PV powers and load demands are provided.

The penetration and integration of renewable energy sources into modern power systems has been increasing over recent years. This can lead to frequency excursion and low inertia due to renewable energy sources’ intermittency and absence of rotational synchronous machines. Battery energy storage systems can play a crucial role in providing the frequency compensation because of their high ramp rate and fast response. In this paper, a decentralized frequency control system composed of three parts is proposed. The first part provides adaptive frequency droop control with its droop coefficient a function of the real-time state of charge of battery. The second part provides a fully decentralized frequency restoration. In the third part, a virtual inertia emulation improves the microgrid resilience. The presented results demonstrate that the proposed control system improves the microgrid resilience and mitigates the frequency deviation when compared with conventional ω -P droop control and existing control systems. The proposed control system is verified on Real-Time Digital Simulator (RTDS), with accurate microgrid model, nonlinear battery models and detailed switching models of power electronic converters.