DC Nanogrids for Integration of Demand Response and Electric Vehicle Charging Infrastructures: Appraisal, Optimal Scheduling and Analysis

Abstract

With the development of electronic infrastructures and communication technologies and protocols, electric grids have evolved towards the concept of Smart Grids, which enable the communication of the different agents involved in their operation, thus notably increasing their efficiency. In this context, microgrids and nanogrids have emerged as invaluable frameworks for optimal integration of renewable sources, electric mobility, energy storage facilities and demand response programs. This paper discusses a DC isolated nanogrid layout for the integration of renewable generators, battery energy storage, demand response activities and electric vehicle charging infrastructures. Moreover, a stochastic optimal scheduling tool is developed for the studied nanogrid, suitable for operators integrated into local service entities along with the energy retailer. A stochastic model is developed for fast charging stations in particular. A case study serves to validate the developed tool and analyze the economical and operational implications of demand response programs and charging infrastructures. Results evidence the importance of demand response initiatives in the economic profit of the retailer.

Optimization Model of Electric Vehicles Charging and Discharging Strategy Considering the Safe Operation of Distribution Network

“…This paper discusses a DC isolated nanogrid layout for the integration of renewable generators, battery energy storage, demand response activities, and electric vehicle charging infrastructures. A DC isolated nanogrid layout for the integration of renewable generators, battery energy storage, demand response activities, and electric vehicle charging infrastructures was discussed by Habeeb et al [17]. Ortiz et al [18] presented a strategy based on a mixedinteger linear programing (MILP) model to improve the resilience in electric distribution systems (EDSs).…”

Online Charging Strategy for Electric Vehicle Clusters Based on Multi-Agent Reinforcement Learning and Long–Short Memory Networks

“…In Equation (7), Sni is the rated power of the charging post, Cos∅2 is the power factor of the secondary, and P(i, t) is the output power.…”

“…However, such nonlinear programming and dynamic programming are too computationally large and too slow in the improvement in relative progress to satisfy only the charging scheduling in a specific environment. The literature [7] has developed a stochastic optimized dispatch model that combines energy retailers with local operators with a high degree of flexibility and has been tested in several scenarios with uncertain parameters, but with the popularity of the V2G model, the instability of grid-connected tariffs should be taken into account and should be combined with the V2G model to further reduce charging costs by selling excess electricity to local operators. In recent years, with the increasing maturity of annual machine learning techniques, we find that RL in the EV cluster charging strategy has started to become popular, and the literature [8] seeks the best charging strategy by controlling the charging of EVs throughout the charging station through a batch reinforcement learning approach, where the value network takes a fully connected neural network for fitting; conversely, the ability to handle high-dimensional data is poor when the smart body state parameters increase, and the charging characteristics of individual EVs are retained in the process of binning the total demand and the approximation error is introduced in the discretization process, which affects the learning speed of the whole algorithm.…”

An Adaptive Energy Optimization Method of Hybrid Battery-Supercapacitor Storage System for Uncertain Demand

“…A strong pulse load can cause large power fluctuations and impact the DC bus voltage and energy-storage devices; HESS can give full play to the characteristics of SC power density and battery energy density, to reduce the adverse effects of a fluctuation load [5,6]. The relevant literature shows that the SC can compensate the output current of the battery, relieve the pressure of the high-output battery current, reduce the battery terminal voltage drop and internal losses, and improve battery characteristics and extend its life [7][8][9]. In Ref.…”