A tri-level planning strategy for integrated power and transport systems incorporating EV elastic charging behaviors

Modeling the impact of vehicle-to-grid discharge technology on transport and power systems

Optimal configuration of an integrated power and transport system

In order to reduce greenhouse gas emission and fossil fuel dependence, Electric Vehicle (EV) has drawn increasing attention due to its zero emission and high efficiency. However, new problems such as range anxiety, long charging duration and high charging power may threaten the safe and efficient operation of both traffic and power systems. This paper proposes a probabilistic approach to model the nodal EV load at fast charging stations in integrated power and transport systems. Following the introduction of the spatial-temporal model of moving EV loads, we extended the model by taking fast charging station into consideration. Fuzzy logic inference system is applied to simulate the charging decision of EV drivers at fast charging station. Due to increasing EV loads in power system, the potential traffic congestion in fast charging stations is modeled and evaluated by queuing theory with spatial-temporal varying arrival and service rates. The time-varying nodal EV loads are obtained by the number of operating fast chargers at each node of the power system. System studies demonstrate that the combination of AC normal and DC charging may share the EV charging demand and alleviate the impact to power system due to fast charging with high power.

Battery prices and capacity sensitivity: Electric drive vehicles

Electrification of substantial percentages of individual transportation through Plug-In Hybrid Electric Vehicles (PHEVs) will lead to an integration of power and transport systems. This, in turn, will impose an additional demand on today's power system, potentially stressing hazardous for some pieces of equipment. Smart management schemes, investigated in this paper, can alleviate possible congestion issues in power systems by intelligently distributing available energy among connected PHEVs, modelled as agents with individual parameters and goals. The management scheme is integrated in aggregation entities, clustering PHEVs in various urban areas. Network impacts resulting from the smart management scheme will be studied based on power flow calculations. The analysis will give implications for PHEV integration schemes as well as tentative ideas of possible repercussions on hybrid power systems and how to counteract.

Road transport and power system scenarios for Northern Europe in 2030

Decarbonization is driving power systems toward more decentralized, self-governing models. While these technologies improve efficiency, planning, operations, and reduce the carbon footprint, they also introduce new challenges. In modern grids, particularly with the integration of power electronic devices and high penetration of Renewable Energy Sources (RES) and Inverter-Based Resources (IBRs), traditional reliability concepts may no longer ensure adequate performance due to systemic restructuring. This shift necessitates new or significantly modified reliability indices to capture the characteristics of the evolving power system. Ensuring converter reliability is essential for effective planning, which requires precise, component-to-system-level modeling, as different converters impact system performance indicators. However, the existing literature in this field faces a significant limitation, as most studies focus on a singular perspective. Some examine reliability at the device-level, others at the component-level, while broader reviews in power systems often emphasize system-level analysis. In this paper, we aim to bridge these gaps by comprehensively reviewing the interconnections between these levels and analyzing the mutual influence of power converter and system reliability. A key point to highlight is that, with the rapid evolution of modern power grids, decision-makers must adopt a multi-level approach that incorporates insights from all levels to enable more accurate and realistic planning and operational strategies. Our ultimate goal is to provide an in-depth investigation of studies addressing the unique challenges posed by modern power grids. Finally, we will highlight the gaps in the literature and suggest directions for future research.

Electric vehicles (EVs) are pivotal in the progression toward sustainable transportation. However, a growing body of recent studies has challenged the often-assumed notion that electric vehicles are often considered to be net-zero emissions. These studies suggest that electric vehicles could potentially generate substantial emissions due to their reliance on fossil fuels to generate electricity, especially when receiving fast charging services. This phenomenon has been mitigated with the use of renewable energy sources in the power system. However, due to the intermittent nature of renewable energy sources, additional requirements have arisen. To address this situation, this paper presents an interdisciplinary study of collaborative fast-charging stations (FCS) and distributed renewable energy planning based on the interactions between traffic and power networks in order to increase the adoption rate of electric vehicles and reduce emissions caused by traffic and power systems. The locations and sizing of fast-charging stations, distributed photovoltaic generation, and renewable energy power supply for electric vehicles are determined in the proposed strategy through multi-objective integer programming. The proposed planning strategy considers heterogeneous electric vehicle driving range constraints, the operation security of the power distribution system, the reliability and expansion of the current power distribution, as well as different types of air pollutant classifications resulting from the charging behaviors. To guarantee the feasibility and accuracy of the planning results, an interactive algorithm is applied to solve the multi-objective integer model. Several cases are conducted to validate the proposed approach. The results demonstrate that the proposed planning strategy has good performance in the planning of fast-charging stations and distributed renewable energy integration.

The transition to net zero cities is a pivotal challenge in the face of climate change. This research introduces a novel methodology termed "Net Zero Corridors," which emphasizes a bottom-up approach to decarbonize urban power and transport systems. By focusing on urban corridors, this method integrates distributed power systems with urban transport, offering a localized solution to the broader challenge of urban decarbonization. Drawing from urban fabric theory and urban metabolism data, the study provides insights into the application of various renewable technologies in diverse urban settings, particularly in automobile-dominated cities like Perth, Australia. The net zero city agenda is gathering momentum but faces the issues of transition using either top-down large-scale technologies or bottom-up local-scale technologies that make the most out of the small-scale niches that have been created around renewables. This paper seeks to show how a bottom-up process can be used to start a more effective local scale approach using net zero corridors that can enable more net zero precincts with distributed power systems and at the same time integrate and decarbonize transport systems. Data on Perth are collected and processed to show the economic viability of such net zero projects though they are not yet linked to good transit systems. The net zero corridor concept is demonstrated and shows how to enable a series of net zero precincts that create large steps in removing fossil fuels. These corridor precincts can spread into surrounding suburbs through expanding the local microgrids and their local governance embracing more and more of the city. The net zero corridor concept can be used to transition to net zero cities using bottom-up approaches that link the transformation of power systems and the transformation of transport systems.

Assessing the resilience of multi microgrid based widespread power systems against natural disasters using Monte Carlo Simulation

We are witnessing a huge growth of clean energy technologies during the last 15 years, spearheaded by government policies. Can this growth be sustained through challenges of economic crisis, the complications of achieving higher penetration of variable renewables, and high socioeconomic costs of slowly winding down fossil fuel sector? This paper will argue that the clean technologies are now technically and economically viable with much lower level of support and that new financial mechanisms built on grid parity and hourly markets will enable the continuation of the transition process. Policies should now be directed towards decreasing fossil fuel subsidies and other barriers to renewables. Also, the technologies needed to enable the increase of penetration of variable renewables, such as flexible combined and Rankine cycle power plants, and smart energy systems based on demand side management, including through the integration of power, heat, water, and transport systems, are now at various levels of readiness. The integration will slowly enable the transition from a power system in which supply is following demand to a power system in which demand is following variable supply. The main issue will be the growing opposition from fossil fuels sectors, which are starting to be hurt by the new technologies.

In the attempt to tackle the issue of climate change, governments across the world have agreed to set global carbon reduction targets. [...]

According to the Environmental Protection Agency, the power and transport systems contribute over 50% of greenhouse gas emissions in the United States. The combination of renewable energy resources and electrified transportation play a crucial role in helping countries meet their carbon neutrality goals. Coordinated planning of the power and transportation systems is necessary to ensure reliable, efficient, and economic operation of both systems. This paper surveys applications of large-scale transportation electrification, vehicle-to-grid charging, prosumer behavior, sector coupling, route optimization, and charging demand and estimation. The co-simulation frameworks available for coupling the power and transport systems to further study these applications are also surveyed. Most of the existing literature utilizes stochastic transportation models or ad-hoc co-simulation approaches which do not promote scalability, extensibility, and interoperability. It is proposed to extend the Hierarchical Engine for Large-Scale Infrastructure Co-simulation to communicate with an agent based transportation simulator for examining these interconnected applications.

The increasing penetration of electric vehicles (EVs) presents both challenges and opportunities for integrated transportation and power systems. This paper addresses the pricing issues of distribution networks and charging stations (CSs) simultaneously, proposing a bilevel noncooperative pricing methodology that considers traffic flow, power flow, and renewable energy integration. Key stakeholders—including distribution networks, CSs, and EVs—are thoroughly analyzed, with EV charging behavior modeled through a combination of charging probability, pricing, detour distance, and charging level. The upper‐level model focuses on optimal economic scheduling and calculates locational marginal prices using a power flow trace method. Meanwhile, the lower‐level model represents CS price adjustments as a noncooperative game, solved via a greedy algorithm. To validate this pricing methodology, an integrated traffic and power distribution network testbed based on the Dublin area was established. Results demonstrate that the proposed dynamic price of the game (DPG) significantly enhances the EV charging market environment compared to traditional time‐of‐use tariffs or flat rates. Notably, the DPG improves the profitability and service ratio of CSs located near wind farms, with daily profits for these stations increasing by an average of 17.55% and 17.03% compared to the other pricing mechanisms. Furthermore, the average daily utilization rate of these CSs rose by 7.08% and 6.42%. In terms of promoting renewable energy use and alleviating traffic congestion, the DPG also outperforms the other pricing strategies by effectively adjusting charging prices to influence EV drivers’ charging behavior. This dynamic pricing strategy is poised to be widely applicable in future integrated transportation and power systems with high levels of renewable energy penetration.

Ahstract-Transforming to a low-carbon future requires massive efforts from both transport and power systems. Electric vehicles (EVs) can reduce CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emission in road transport through eco-routing while providing carbon intensity service for power systems via vehicle-to-grid (V2G) scheduling. This paper studies the coordinated effect of routing and scheduling problems of EVs via a novel model-free multi-agent reinforcement learning (MARL) method. In this context, EVs do not reply on any knowledge of the simulated environment and are capable of handling the system with various uncertainties and dynamics during the learning process, which can lead to timely decision making and better privacy protection. Extensive case studies based on a virtual 7-node 10-edge transportation network are developed to demonstrate the effectiveness of the proposed MARL method on reducing carbon emissions in the transportation system and providing carbon intensity service in the power system.