Vehicle-to-grid aggregator to support power grid and reduce electric vehicle charging cost

This paper presents an optimised bidirectional Vehicle-to-Grid (V2G) operation, based on a fleet of Electric Vehicles (EVs) connected to a distributed power system, through a network of charging stations. The system is able to perform day-ahead scheduling of EV charging/discharging to reduce EV ownership charging cost through participating in frequency and voltage regulation services. The proposed system is able to respond to real-time EV usage data and identify the required changes that must be made to the day-ahead energy prediction, further optimising the use of EVs to support both voltage and frequency regulation. An optimisation strategy is established for V2G scheduling, addressing the initial battery State Of Charge (SOC), EV plug-in time, regulation prices, desired EV departure time, battery degradation cost and vehicle charging requirements. The effectiveness of the proposed system is demonstrated using a standardized IEEE 33-node distribution network integrating five EV charging stations. Two case studies have been undertaken to verify the contribution of this advanced energy supervision approach. Comprehensive simulation results clearly show an opportunity to provide frequency and voltage support while concurrently reducing EV charging costs, through the integration of V2G technology, especially during on-peak periods when the need for active and reactive power is high

Optimal scheduling of smart microgrids considering electric vehicle battery swapping stations

Smart microgrids belong to a set of networks that operate independently. These networks have technologies such as electric vehicle battery swapping stations that aim to economic welfare with own resources of smart microgrids. These resources should support other services, for example, the supply of energy at peak hours. This study addresses the formulation of a decision matrix based on operating conditions of electric vehicles and examines economically viable alternatives for a battery swapping station. The decision matrix is implemented to manage the swapping, charging, and discharging of electric vehicles. Furthermore, this study integrates a smart microgrid model to assess the operational strategies of the aggregator, which can act like a prosumer by managing both electric vehicle battery swapping stations and energy storage systems. The smart microgrid model proposed includes elements used for demand response and generators with renewable energies. This model investigates the effect of the wholesale, local and electric-vehicle markets. Additionally, the model includes uncertainty issues related to the planning for the infrastructure of the electric vehicle battery swapping station, variability of electricity prices, weather conditions, and load forecasting. This article also analyzes how both the user and the providers maximize their economic benefits with the hybrid optimization algorithm called variable neighborhood search - differential evolutionary particle swarm optimization. The strategy to organize the infrastructure of these charging stations reaches a reduction of 72% in the overall cost. This reduction percentage is obtained calculating the random solution with respect to the suboptimal solution

Distributed Multi-agent Optimization and Control with Applications in Smart Grid

With recent advancements in network technologies like 5G and Internet of Things (IoT), the size and complexity of networked interconnected agents have increased rapidly. Although centralized schemes have simpler algorithm design, in practicality, it creates high computational complexity and requires high bandwidth for centralized data pooling. In this dissertation, for distributed optimization of networked multi-agent architecture, the Alternating Direction Method of Multipliers (ADMM) is investigated. In particular, a new adaptive-gain ADMM algorithm is derived in closed form and under the standard convex property to greatly speed up the convergence of ADMM-based distributed optimization. Using the Lyapunov direct approach, the proposed solution embeds control gains into a weighted network matrix among the agents uses and those weights as adaptive penalty gains in the augmented Lagrangian. For applications in a smart grid where system parameters are greatly affected by intermittent distributed energy resources like Electric Vehicles (EV) and Photo-voltaic (PV) panels, it is necessary to implement the algorithm in real-time since the accuracy of the optimal solution heavily relies on sampling time of the discrete-time iterative methods. Thus, the algorithm is further extended to the continuous domain for real-time applications and the convergence is proved also through Lyapunov direct approach. The algorithm is implemented on a distribution grid with high EV penetration where each agent exchanges relevant information among the neighboring nodes through the communication network, optimizes a combined convex objective of EV welfare and voltage regulation with power equations as constraints. The algorithm falls short when the dynamic equations like EVs state of charge are taken into account. Thus, the algorithm is further developed to incorporate dynamic constraints and the convergence along with control law is developed using Lyapunov direct approach. An alternative approach for convergence using passivity-short properties is also shown. Simulation results are included to demonstrate the effectiveness of proposed schemes