Electric Vehicle Charging Coordination Research Articles

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework

Burdens of increasing penetration of electric vehicles (EVs) on distribution systems have attracted wide attention to the research of EV charging coordination. Nevertheless, existing coordination lacks the joint consideration of the temporal-spatial flexibility of EVs, which could reduce load fluctuations when meeting the EV charging demands. Moreover, a thorough investigation is required for the coordination of the benefits to the distribution system operator (DSO), EV aggregator (EVA), and EVs. In this paper, a novel EV charging scheme considering the temporal-spatial features of EVs is proposed, achieving the coordination of DSO, EVA, and EVs in two steps. First, a mixed-integer second-order cone programming (MISOCP)-based EV charging schedule is developed for DSO. Second, the temporal-spatial features of EVs are exploited by EVA to track the charging schedule. Specifically, a dynamic charging price mechanism based on active power margin is proposed for public charging facilities and a specific load regulation is designed for private charging facilities. On this basis, the modified adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed, including adaptive flight parameter adjustment and termination mechanisms. Case studies demonstrate the proposed strategy can attenuate load variance and raise EVA revenue. Further, the impact analysis of penalty price and the price elasticity of electricity demand can provide references for stable distribution network operation, higher EVA revenue, and charging cost reduction.

On the Utilization of Blockchain and Smart Contracts in Charging Coordination of Roadway-Powered Electric Vehicles

On the Utilization of Blockchain and Smart Contracts in Charging Coordination of Roadway-Powered Electric Vehicles

Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation.

Machine learning, powered by cloud servers, has found application in medical diagnosis, enhancing the capabilities of smart healthcare services. Research literature demonstrates that the support vector machine (SVM) consistently demonstrates remarkable accuracy in medical diagnosis. Nonetheless, safeguarding patients' health data privacy and preserving the intellectual property of diagnosis models is of paramount importance. This concern arises from the common practice of outsourcing these models to third-party cloud servers that may not be entirely trustworthy. Few studies in the literature have delved into addressing these issues within SVM-based diagnosis systems. These studies, however, typically demand substantial communication and computational resources and may fail to conceal classification results and protect model intellectual property. This paper aims to tackle these limitations within a multi-class SVM medical diagnosis system. To achieve this, we have introduced modifications to an inner product encryption cryptosystem and incorporated it into our medical diagnosis framework. Notably, our cryptosystem proves to be more efficient than the Paillier and multi-party computation cryptography methods employed in previous research. Although we focus on a medical application in this paper, our approach can also be used for other applications that need the evaluation of machine learning models in a privacy-preserving way such as electricity theft detection in the smart grid, electric vehicle charging coordination, and vehicular social networks. To assess the performance and security of our approach, we conducted comprehensive analyses and experiments. Our findings demonstrate that our proposed method successfully fulfills our security and privacy objectives while maintaining high classification accuracy and minimizing communication and computational overhead.

Sustainable electric vehicle charging coordination: Balancing CO2 emission reduction and peak power demand shaving

Although electric vehicles (EVs) eliminate CO2 emissions on the road, the upstream emissions from electricity generation still affect the emissions benefit brought by vehicle electrification. Coordinating EV charging to match the low marginal emission factors (MEFs) of electricity generation could reduce emissions, while aggregating the charging events may create a higher power demand undermining the grid stabilization goal. To address the trade-off between emission reduction and peak power demand shaving, this study developed a bi-objective optimization model for quantifying the relationship between minimizing CO2 emissions and peak power demand. Using one-year EV charging data from a workplace parking lot with 50 charging piles in California, USA, the results show a non-linear relationship between the two objectives throughout the year. Compared to uncoordinated charging, coordinated charging can reduce CO2 emissions by 18% annually while also reducing peak power demand by 33%. However, further reducing emissions by around 1% requires a significant increase in peak power demand by 84%, which is 23% higher than the peak power demand without coordination. These findings provide quantitative insights for policymakers and other stakeholders to balance emissions reduction and grid stabilization goals toward sustainable EV charging coordination.

Is single-phase residential EV fast-charging a good idea? Evidence from optimal charging plans based on realistic data

One way to encourage the usage of electric vehicles (EVs) is to reduce the connection time by using higher-powered charging points at home. Nonetheless, a massive adoption of this technology could increase the network power requirements, stressing the grid beyond the technical limits. Therefore, it is important to assess how much the aggregate power will increase compared to the slow-charging process and how to minimize it. Hence, an EV charging coordination model is developed to compare the impact on the system demand of residential slow and fast-charging plans by minimizing the maximum aggregate demand. Here, “slow” and “fast” correspond to the minimum and the maximum charging speeds for 1-phase, mode 3 of the IEC 61851-1. To create an accurate model, realistic UK load profiles, real EV energy requirements, and real travel statistics of car drivers in the UK were included. Additionally, the stochasticity of customers was incorporated by solving the optimization for thousands of different groups of households and EVs. The results indicate that due to the greater flexibility of fast-charging events, if no coordination is considered, the aggregate effects increase by 15% compared to the slow-charging case. However, if the process is coordinated, nearly the same maximum aggregate demand is found regardless of whether the charging is fast or slow.

Achieving reliable coordination of residential plug-in electric vehicle charging: A pilot study

Achieving reliable coordination of residential plug-in electric vehicle charging: A pilot study

Hierarchical Optimization for User-Satisfaction-Driven Electric Vehicles Charging Coordination in Integrated MV/LV Networks

The growing uptake of electric vehicles (EVs) will likely require management schemes to enable their connection into distribution systems. While most of the existing approaches are developed from the operator’s perspective considering EV aggregated demands at the medium-voltage (MV) level, individual users’ comfort and the particularities associated with low-voltage (LV) networks need to be considered to holistically assess the EV effects in an integrated MV/LV network. This article proposes a two-level hierarchical optimization framework for the EV charging coordination (EVCC) that maximizes users’ satisfaction, while avoiding operational grid issues in the whole distribution system. The framework is tailored for unbalanced distribution systems with high penetration of EVs and introduces a novel index to measure charging priority-based EV user satisfaction. To reduce the computational burden, the EVCC problem is disaggregated into an upper level for MV network operation, and a lower level for LV network and individual EV scheduling, using mixed-integer linear programming models. This framework is later embedded in a dynamic scheduling approach that copes with unexpected EV arrivals. Benefits (increased overall user satisfaction and reduced strain over distribution assets) are demonstrated via case studies in a 459-node three-phase network in which solutions were achieved under a 60-s threshold.

Low Carbon Electric Vehicle Charging Coordination in Coupled Transportation and Power Networks

As a part of the global decarbonization agenda, the electrification of the transport sector involving the large-scale integration of electric vehicles (EV) constitutes one of the key initiatives. However, the upstream generation carbon emissions associated with EV charging demand are still accountable which should not be overlooked. In this context, efficient coordination of EV flows and their charging demand in the coupled transportation and power networks promise significant decarbonization potential. Nevertheless, such potential can only be realized under adequate incentive mechanisms. In this article, a novel low carbon EV charging coordination approach in coupled transportation and power network is proposed and formulated as a bi-level optimization. In the upper level, an AC optimal power flow problem is formulated and solved to determine the optimal operation for power system. Then the carbon emission flow tracing is performed to compute rational locational-differentiated price signals. Given price-based incentives, the lower level employs traffic user equilibrium to describe the distribution of EV path flow and charging demands taking into account the uncertain traffic condition, and the aggregate charging demands are fed back to the upper level. The bi-level problem is solved iteratively through a modified particle swarm algorithm with enhanced convergence properties. Case studies demonstrate the effectiveness of the proposed coordination method in effectively mitigating the global carbon emission of the coupled networks.

Coordination of Plug-In Electric Vehicle Charging in a Stochastic Framework: A Decentralized Tax/Incentive-Based Mechanism to Reach Global Optimality

We address the problem of charging plug-in electric vehicles (PEVs) in a decentralized way and under stochastic dynamics affecting the real-time electricity tariff. The model is formulated as a Nash equilibrium seeking problem, where players wish to minimize the costs for charging their own PEVs. For finite PEVs populations, the Nash equilibrium does not correspond to the social optimum, i.e., to a control strategy minimizing the total electricity costs at the aggregate level. We accordingly introduce some taxes/incentives on the price of electricity for charging PEVs and show that it is possible to tune them so that (a) the social optimum is reached as a Nash equilibrium, (b) in correspondence with this equilibrium, players do not pay any net total tax, nor receive any net total incentive.

Optimal coordination of electric vehicle charging and photovoltaic power curtailment in unbalanced low voltage networks: An experimental case

AbstractThis study introduces a quadratic programming‐based optimisation method to coordinate electric vehicle (EV) charging and photovoltaic (PV) curtailment in unbalanced low voltage (LV) networks. The proposed model is defined as a convex model that guarantees the optimal global solution of the problem avoiding the complexity of non‐linear models and surpassing the limitations of local solutions derived from meta‐heuristics algorithms reported in the literature. The coordination is carried out through a centralised controller installed at the header of the LV feeder. The objective of the proposed strategy is to minimise the power curtailment of all PV systems and maximise the power delivered to all EVs by optimising at every time step a suitable setpoint for the PV units and the charging rate of each EV connected without surpassing network constraints. A new energy‐boundary model is also proposed to meet the energy requirements of all EVs, which is based on a recurrent function that depends on the arrival‐and‐desired energy states of the vehicle to compute its charging trajectory optimally. The effectiveness of the proposed coordination strategy was successfully proven through three scenarios in a laboratory environment, making use of two commercial EVs and a PV inverter in a Power Hardware‐in‐the‐Loop setup.

Metaheuristic-based isolated microgrid sizing and uncertainty quantification considering EVs as shiftable loads

Isolated microgrids are increasingly recognised as an effective platform for the optimal coordination of integrated distributed energy resources – including renewable energy generation and storage technologies – for remote, island, and peripheral communities. However, they require advanced off-grid microgrid capacity planning optimisation models to achieve the globally optimum sizing results. In this context, while considerable attention has been devoted to a range of off-grid microgrid sizing methods, leveraging the potential of metaheuristic optimisation algorithms is less well-explored. Furthermore, there is a general lack of electrified transportation interventions considered during long-term grid-independent microgrid planning phases. In response, this paper introduces a novel metaheuristic-based strategic off-grid microgrid capacity planning optimisation model that is applicable to associated integrated energy and e-mobility resource planning problems. To test the effectiveness of the proposed model, three independent microgrid development projects have been considered for three communities residing on Aotea–Great Barrier Island in Aotearoa–New Zealand. The sites of interest have different demand profiles and renewable energy potentials – with consequent changes in the technologies considered in the associate candidate pools. Moreover, to generate key insights into the impact of the forecast uncertainty of different variables on the costing and configuration of the associated microgrids, a number of scenario analyses have been performed. In addition, the numeric simulation results and the associated capital budgeting analyses have demonstrated the economic viability of the project proposals formulated based on the proposed off-grid microgrid planning method, especially with controlling the charging of electric vehicles. Particularly, the potential of the artificial hummingbird algorithm in reducing the total discounted costs of off-grid microgrids by at least ∼6% has been shown. Also, the importance of electric vehicle charging coordination in reducing the off-grid microgrid costs (by at least ∼9%) has been substantiated.

Electric Vehicles Charging Algorithm with Peak Power Minimization, EVs Charging Power Minimization, Ability to Respond to DR Signals and V2G Functionality

The number of electric vehicles (EV) on the roads, as well as the share of EVs in use, will inevitably increase in coming decades. This creates a number of problems. A large EV fleet is a significant additional load in the power system that is impossible to accurately predict. Another related problem is the limited distribution network capacity, which is not ready for the additional load from the widespread EV infrastructure. There is a need for an EV charging coordination algorithm capable of fulfilling the charging EV needs, while using as low demanded power as possible and using the lowest power values in each EV charging profile. We propose an EV coordinating algorithm that is capable of ensuring that all connected EVs in the considered parking lot will be charged at the user-defined departure time. The algorithm also controls the charging/discharging power of every connected EV in such a way that the parking lot as a whole will use minimal possible peak power while minimizing the charging power of every EV. The proposed algorithm is also capable of responding to demand response (DR) signals. The paper also includes the results of simulation with a statistical summary of the proposed algorithm effectiveness.

Probabilistic feasibility guarantees for solution sets to uncertain variational inequalities

We develop a data-driven approach to the computation of a-posteriori feasibility certificates for sets of solutions of variational inequalities affected by uncertainty. Specifically, we focus on variational inequalities with a deterministic mapping and an uncertain feasible set, and represent uncertainty by means of scenarios. Building upon recent advances in the scenario approach literature, we quantify the robustness properties of the entire set of solutions of a variational inequality, with feasibility set constructed using the scenario approach, against a new unseen realization of the uncertainty. Our results extend existing ones that typically impose that the solution set is a singleton and require certain non-degeneracy properties: hence, we thereby offer probabilistic feasibility guarantees for any feasible solution of the underlying variational inequality. We show that assessing the violation probability of an entire set of solutions requires enumeration of the support constraints that “shape” this set, and also propose a procedure to enumerate the support constraints that does not require a description of the solution set. We illustrate our results through numerical simulations on a robust game involving an electric vehicle charging coordination problem.

Review of Communication Technologies for Electric Vehicle Charging Management and Coordination

Recently, electric vehicles (EVs) have been introduced as an alternative method of transportation to help mitigate environmental issues, such as carbon emissions and fuel consumption, caused by conventional transportation systems. The implementation of effective EV charging systems is essential to motivate mass adoption of EVs. Accordingly, fast and reliable communications between the charging systems and the EVs are vital for efficient management of the charging process. Different radio access technologies (RATs) are discussed in the literature to enable communication between the highly mobile EVs and the charging subsystems, to collect and exchange information such as state of charge (SoC), users’ locations, and charging decisions between the different network entities. This information can be used to coordinate charging plans and select the optimal routes for moving vehicles. This paper presents a survey of existing literature on vehicular communications for EV charging coordination and management. The communication requirements and feasible communication technologies for vehicular communication are first discussed in details. A review of the physical layer security strategies is then presented and the role of the different RATs in EV charging coordination and management is described and studied.

Local Stackelberg Equilibrium Seeking in Generalized Aggregative Games

We propose a two-layer, semi-decentralized algorithm to compute a local solution to the Stackelberg equilibrium problem in aggregative games with coupling constraints. Specifically, we focus on a single-leader, multiple-follower problem, and after equivalently recasting the Stackelberg game as a mathematical program with complementarity constraints (MPCC), we iteratively convexify a regularized version of the MPCC as inner problem, whose solution generates a sequence of feasible descent directions for the original MPCC. Thus, by pursuing a descent direction at every outer iteration, we establish convergence to a local Stackelberg equilibrium. Finally, the proposed algorithm is tested on a numerical case study involving a hierarchical instance of the charging coordination of Plug-in Electric Vehicles (PEVs).

A Two-stage Autonomous EV Charging Coordination Method Enabled by Blockchain

Increasing electric vehicle (EV) penetration in distribution networks necessitate EV charging coordination. This paper proposes a two-stage EV charging coordination mechanism that frees the distribution system operator (DSO) from extra burdens of EV charging coordination. The first stage ensures that the total charging demand meets facility constraints, and the second stage ensures fair charging welfare allocation while maximizing the total charging welfare via Nash-bargaining trading. A decentralized algorithm based on the alternating direction method of multipliers (ADMM) is proposed to protect individual privacy. The proposed mechanism is implemented on the blockchain to enable trustworthy EV charging coordination in case a third-party coordinator is absent. Simulation results demonstrate the effectiveness and efficiency of the proposed approach.

Boosting the Usage of Green Energy for EV Charging in Smart Buildings Managed by an Aggregator Through a Novel Renewable Usage Index

The growing trend of electric vehicles (EVs) and building integrated photovoltaics (BIPVs) is a promising means to reduce related climate change issues. EV loads can be managed via an aggregator to maximize the usage of green energy produced by photovoltaic units (PV) through smart charging strategies that exploit controllable EV demand connected to BIPV. Previous works have focused on the EV charging coordination in a smart BIPV, although without an optimization that encourages EV charging with the energy produced by the PV units. This paper proposes an aggregation strategy that maximizes a green energy index (GEI) for the smart charging coordination of EVs, which takes advantage of periods with high PV availability to charge the EV batteries; moreover, a post-processing stage for the GEI provides EV owners with information about the percentage of charged energy, period by period, that comes from PV generation. The results for a case study with 510 EVs integrated with 17 smart BIPVs show that the strategy effectively optimizes the usage of the energy produced by the PV units to charge the EVs, contributes to reduce non-renewable energy consumption of the building sector, and satisfies the EV owners’ energy requirements for transportation.

A Fuzzy Energy Management Strategy for the Coordination of Electric Vehicle Charging in Low Voltage Distribution Grids

Electric vehicles (EVs) have become widespread during the last decade because of the distinct advantages they offer compared to the conventional ones. However, the increased penetration of EVs in the global transportation market has led increased electricity demands, which is expected to affect the operation of energy distribution systems. In the present paper, a demonstration about the effects of uncontrolled EVs charging in a case study low voltage (LV) network is demonstrated and a fuzzy energy management strategy for the coordination of EV charging in LV networks is presented, by including the distance of the EVs from the transformers in the fuzzy management systems for the first time. The Institute of Electrical and Electronics Engineers (IEEE) European Test Feeder is used as a case study low voltage distribution grid. In particular, the developed system configuration takes into consideration the architecture of the grid, the ampacities of the lines and the voltages at the system’s buses. Moreover, electric vehicles are considered as agent-based models, which are characterized by the model of each EV, the state-of-charge of their batteries and the charging power. In particular, an investigation into the effects of uncontrolled charging is performed, in which two approaches are examined. The first approach investigates the maximum number of chargeable EVs in the case study network and how it is influenced by the grid’s household loads. The second approach examines the number of network undervoltages and lines ampacity violations in a set of simulation scenarios. The results of the first approach show that the distance of the EVs from the networks substation affects the maximum number of chargeable EVs in a significant manner. Based on the observed results of the two approaches, a fuzzy management system is designed for the coordination of EV changing, which takes into account the distance from the EV charging points to the feeder substation, the state-of-charge of the EVs’ batteries and the EVs’ charging delay time.

Apartment-level electric vehicle charging coordination: peak load reduction and charging payment minimization

In future apartment complexes, high penetration of electric vehicles (EVs) may cause to significantly increase electricity load at certain times, and the demand for uncoordinated EV charging power may significantly affect the stability of the apartment-level power grid. In order to efficiently accommodate the significant charging demand from a large number of EVs in apartment complexes, we propose an apartment-level EV charging coordination scheme to not only reduce the peak EV charging load but also minimize the apartment-level EV charging payment, and then we validate it through various case studies. To be specific, we compare the performance of the proposed EV charging coordination scheme with that of a typical as-soon-as-possible (ASAP) charging scheme and a random charging scheme in terms of peak-to-average power ratio (PAPR), relative charging duration (RCD), and total charging payment. It is observed that the proposed coordination scheme manages the apartment-level power grid more stable, and EV owners in this apartment-level power grid can obtain economic benefits by participating in the EV charging coordination.