Battery Swapping Station

State-of-the-art research to solve the grid congestion due to EVs is focused on smart charging and using (centralized, de-centralized, vehicle-to-grid) stationery energy storage as a buffer between times of peak and off-peak demand. On the other hand, the charging of EVs introduces new challenges and opportunities. This can prove to be beneficial for the EV aggregator as well as to consumers, regarding the economy. Also, EV as distributed storage makes the grid more steady, secure, and resilient by regulating frequency and the spinning reserve as backup power. However, the charging time and range anxiety lead to peak challenges for the use of EVs. In this chapter battery swapping station (BSS) as solution to the EV charging station is discussed.

Some Properties for Adjustable Entropy Function Method and Its Application to Power Systems Dynamic Economic Dispatch with Wind Integrated

With the continuous development of the world economy, the development and utilization of environmentally friendly and renewable energy have become the trend in many countries. In this paper, we study the dynamic economic dispatch with wind integrated. Firstly, we take advantage of the positive and negative spinning reserve to deal with wind power output prediction errors in order to establish a dynamic economic dispatch model of wind integrated. The existence of a min function makes the dynamic economic dispatch model nondifferentiable, which results in the inability to directly use the traditional mathematical methods based on gradient information to solve the model. Inspired by the aggregate function, we can easily transform the nondifferentiable model into a smooth model when parameter p tends to infinity. However, the aggregate function will cause data overflow when p tends to infinity. Then, for solving this problem, we take advantage of the adjustable entropy function method to replace of aggregate function method. In addition, we further discuss the adjustable entropy function method and point out that the solution generated by the adjustable entropy function method can effectively approximate the solution of the original problem without parameter p tending to infinity. Finally, simulation experiments are given, and the simulation results prove the effectiveness and correctness of the adjustable entropy function method.

Role of Lithium-Ion Batteries in Increasing the Operational Safety of Offshore Service Vessels

For ensuring the operational safety of offshore service vessels (OSVs) during critical operations, marine classification agencies recommend operating redundant diesel generators (DGs) as a spinning reserve for dynamic positioning (DP) systems. In view of the reduced fuel efficiency and increased emissions of DG sets when operated at low loads, lithium-ion (Li-ion) batteries are preferred as a green choice to serve the class-recommended DP power backup for 12 min. Based on the on-demand reliability analysis and Li-ion battery failure models, it is identified that an OSV with a 1.6-MW‐capacity DP system requires 538 kWh of Li-ion battery power to reliably replace a spinning DG set in serving power backup for 12 min. Based on the IEC61508/11 health, safety, and environment framework, the methodology to identify the OSV risk under various operational conditions and the battery system minimum maintenance interval requirements to meet various safety integrity levels are described.

OPTIMUM UNIT COMMITMENT SOLUTION OF A POWER SYSTEM BASED ON SALP SWARM ALGORITHM

This paper presents an algorithm to solve the unit commitment problem in a power system. The proposed algorithm employs the Salp swarm algorithm technique to search the optimum unit schedule for a particular daily demand pattern and specific time horizon. Different constraints are taken into consideration, transition cost (start-up and shut down )cost, mean-up time, mean-down time, spinning reserve, and power balance. The proposed algorithm is applied to 10-units and 26-unit. The obtained results are compared with other methods. It reveals the robustness of the proposed algorithm in terms of minimizing overall running costs.