Decentralized Model Predictive Control of Plug-in Electric Vehicles Charging based on the Alternating Direction Method of Multipliers

This paper presents a decentralized Model Predictive Control (MPC) for Plug-in Electric Vehicles (PEVs) charging, in presence of both network and drivers' requirements. The open loop optimal control problem at the basis of MPC is modeled as a consesus with regularization optimization problem and solved by means of the decentralized Alternating Direction Method of Multipliers (ADMM). Simulations performed on a realistic test case show the potential of the proposed control approach and allow to provide a preliminary evaluation of the compatibility between the required computational effort and the application in real time charging control system

A Review of Active Management for Distribution Networks: Current Status and Future Development Trends

Driven by smart distribution technologies, by the widespread use of distributed generation sources, and by the injection of new loads, such as electric vehicles, distribution networks are evolving from passive to active. The integration of distributed generation, including renewable distributed generation changes the power flow of a distribution network from unidirectional to bi-directional. The adoption of electric vehicles makes the management of distribution networks even more challenging. As such, an active network management has to be fulfilled by taking advantage of the emerging techniques of control, monitoring, protection, and communication to assist distribution network operators in an optimal manner. This article presents a short review of recent advancements and identifies emerging technologies and future development trends to support active management of distribution networks

Enhancing Resilience in Distribution Systems through Dynamic Reconfiguration

The resilience of power distribution systems is of paramount importance in ensuring consistent and reliable power supply. This review article delves into the recent advancements in enhancing the resilience of distribution systems through dynamic reconfiguration. The concept of islanding and dynamic reconfiguration for the resilience enhancement of active distribution systems is explored, highlighting the significance of these methods in modern power systems. Similarly, the role of proactive operational actions in elevating the resilience level of power distribution systems is emphasized. Through a comprehensive review of these studies, this article underscores the pivotal role of dynamic reconfiguration in bolstering the robustness of distribution systems against unforeseen disruptions. The findings suggest that integrating dynamic reconfiguration techniques can substantially mitigate the risks associated with power outages and system failures. This article serves as a testament to the ongoing research and innovations in the realm of power distribution, emphasizing the need for continuous advancements in dynamic reconfiguration methodologies

Stochastic optimal energy management system for RTG cranes network using genetic algorithm and ensemble forecasts

In low voltage networks, Energy Storage Systems (ESSs) play a significant role in increasing energy cost savings, peak reduction and energy efficiency whilst reinforcing the electrical network infrastructure. This paper presents a stochastic optimal management system based on a Genetic Algorithm (GA) for the control of an ESS equipped with a network of electrified Rubber Tyre Gantry (RTG) cranes. The stochastic management system aims to improve the reliability and economic performance, for given ESS parameters, of a network of cranes by taking into account the uncertainty in the RTGs electrical demand. A specific case study is presented using real operational data of the RTGs netwrok in the Port of Felixstowe, UK, and the results of the stochastic control system is compared to a standard set-point controller. In this paper, two forecast data sets with different levels of accuracy are used to investigate the impact of the crane demand forecast error in the proposed ESS control system. The results of the proposed control strategies indicate that the stochastic management system successfully increases the electric energy cost savings, the peak demand reductions and successfully outperforms a comparable set-point controller

Management Of Plug-In Electric Vehicles And Renewable Energy Sources In Active Distribution Networks

Near 160 million customers in the U.S.A. are served via distribution networks (DNs). The increasing penetration level of renewable energy sources (RES) and plug-in electric vehicles (PEVs), the implementation of smart distribution technologies such as advanced metering/monitoring infrastructure, and the adoption of smart appliances, have changed distribution networks from passive to active. The next-generation of DNs should be efficient and optimized system-wide, highly reliable and robust, and capable of effectively managing highly-penetrated PEVs, RES and other controllable loads. To meet new challenges, the next-generation DNs need active distribution management (ADM). In this thesis, we study the management of PEVs and RES in active DNs. First, we propose a novel discrete-event modeling method to model PEVs and other loads in distribution networks. In addition, a new optimization algorithm to integrate as many PEVs as possible in DNs without causing voltage issues, including the violation of voltage security ranges and voltage stability, is studied. To further explore the active management of PEVs in the DNs, we develop a universal demonstration platform, consisting of software packages and hardware remote terminal units. The demonstration platform is designed with the capabilities of measurement, monitoring, control, automation, and communications. Furthermore, we have studied the reactive power management in microgrids, a special platform to integrate distributed generations and energy storage in DNs. To solve possible voltage security issues in a microgrid with high penetration of single-phase induction machines under the condition of fault-induced islanding, a voltage-sensitivity-based reactive power management algorithm is proposed

Distributed charging management of multi‐class electric vehicles with different charging priorities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166242/1/gtd2bf02710.pd

A robust vehicle to grid aggregation framework for electric vehicles charging cost minimization and for smart grid regulation

In this paper, we propose an optimal hierarchical bi-directional aggregation algorithm for the electric vehicles (EVs) integration in the smart grid (SG) using Vehicle to Grid (V2G) technology through a network of Charging Stations (CSs). The proposed model forecasts the power demand and performs Day-ahead (DA) load scheduling in the SG by optimizing EVs charging/discharging tasks. This method uses EVs and CSs as the voltage and frequency stabilizing tools in the SG. Before penetrating EVs in the V2G mode, this algorithm determines the on arrival EVs State of Charge (SOC) at CS, obtains projected park/departure time information from EV owners, evaluates their battery degradation cost prior to charging. After obtaining all necessary data, it either uses EV in the V2G mode to regulates the SG or charge it according to the owner request but, it ensure desired SOC on departure. The robustness of the proposed algorithm has been tested by using IEEE-32 Bus-Bars based power distribution in which EVs are integrated through five CSs. Two intense case studies have been carried out for the appropriate performance validation of the proposed algorithm. Simulations are performed using electricity pricing data from PJM and to test the EVs behaviour 3 types of EVs having different specifications are penetrated. Simulation results have proved that the proposed model is capable of integrating EVs in the voltage and frequency stabilization and it also simultaneously minimizes approximately $1500 in term of charging cost for EVs contributing in the V2G mode each day. Particularly, during peak hours this algorithm provides effective grid stabilization services.info:eu-repo/semantics/publishedVersio

Smart management strategies of utility-scale energy storage systems in power networks

Power systems are presently experiencing a period of rapid change driven by various interrelated issues, e.g., integration of renewables, demand management, power congestion, power quality requirements, and frequency regulation. Although the deployment of Energy Storage Systems (ESSs) has been shown to provide effective solutions to many of these issues, misplacement or non-optimal sizing of these systems can adversely affect network performance. This present research has revealed some novel working strategies for optimal allocation and sizing of utility-scale ESSs to address some important issues of power networks at both distribution and transmission levels. The optimization strategies employed for ESS placement and sizing successfully improved the following aspects of power systems: performance and power quality of the distribution networks investigated, the frequency response of the transmission networks studied, and facilitation of the integration of renewable generation (wind and solar). This present research provides effective solutions to some real power industry problems including minimizationof voltage deviation, power losses, peak demand, flickering, and frequency deviation as well as rate of change of frequency (ROCOF). Detailed simulation results suggest that ESS allocation using both uniform and non-uniform ESS sizing approaches is useful for improving distribution network performance as well as power quality. Regarding performance parameters, voltage profile improvement, real and reactive power losses, and line loading are considered, while voltage deviation and flickers are taken into account as power quality parameters. Further, the study shows that the PQ injection-based ESS placement strategy performs better than the P injection-based approach (in relation to performance improvement), providing more reactive power compensations. The simulation results also demonstrate that obtaining the power size of a battery ESS (MVA) is a sensible approach for frequency support. Hence, an appropriate sizing of grid-scale ESSs including tuning of parameters Kp and Tip (active part of the PQ controller) assist in improving the frequency response by providing necessary active power. Overall, the proposed ESS allocation and sizing approaches can underpin a transition plan from the current power grid to a future one