Plug-in Vehicles Research Articles

Electric Vehicle Fast Charging: A Congestion-Dependent Stochastic Model Predictive Control under Uncertain Reference

This paper presents a control strategy aimed at efficiently operating a service area equipped with stations for plug-in electric vehicles’ fast charging, renewable energy sources, and an electric energy storage unit. The control requirements here considered are in line with the perspective of a service area operator, who aims at avoiding peaks in the power flow at the point of connection with the distribution grid, while providing the charging service in the minimum time. Key aspects of the work include the management of uncertainty in the charging power demand and generation, the design of congestion and state-dependent weights for the cost function, and the comparison of control performances in two different hardware configurations of the plant, namely BUS and UPS connection schemes. All of the above leads to the design of a stochastic model predictive controller aimed at tracking an uncertain power reference, under the effect of an uncertain disturbance affecting the output and the state of the plant in the BUS and UPS schemes respectively. Simulation results show the relevance of the proposed control strategy, according to an incremental validation plan focused on the tracking of selected references, the mitigation of congestion, the stability of storage operation over time, and the mitigation of the effect of uncertainty.

Securing Smart Grid Enabled Home Area Networks with Retro-Reflective Visible Light Communication.

Smart appliances' run schedule and electric vehicles charging can be managed over a smart grid enabled home area network (HAN) to reduce electricity demand at critical times and add more plug-in electric vehicles to the grid, which eventually lower customers' energy bills and reduce greenhouse gas emissions. Short range radio-based wireless communication technologies commonly adopted in a HAN are vulnerable to cyber attacks due to their wide interception range. In this work, a low-cost solution is proposed for securing the low-volume data exchange of sensitive tasks (e.g., key management and mutual authentication). Our approach utilizes the emerging concept of retro-reflector based visible light communication (Retro-VLC), where smart appliances, IoT sensors and other electric devices perform the sensitive data exchange with the HAN gateway via the secure Retro-VLC channel. To conduct the feasibility study, a multi-pixel Retro-VLC link is prototyped to enable quadrature amplitude modulation. The bit error rate of Retro-VLC is studied analytically, numerically and experimentally. A heterogeneous Retro-VLC + WLAN connection is implemented by socket programming. In addition, the working range, sniffing range, and key exchange latency are measured. The results validate the applicability of the Retro-VLC based solution.

Well-to-Wheels for Light-Duty Vehicle Powertrains by Segments in Isolated Systems

The transportation sector has the highest energy demand worldwide and bears the primary responsibility for CO2 emissions. Electromobility has emerged as the most feasible way to alleviate this problem. However, its potential depends heavily on the development of renewable energies. Island regions raise additional barriers to electromobility due to their heavy dependence on fossil fuels. This article addresses this challenge by presenting a comprehensive well-to-wheel framework to assess the levels of efficiency and CO2 emissions of electromobility options such as battery and plug-in electric vehicles (BEVs and PHEVs). The results were compared with those for internal combustion engine vehicles (ICEVs). The framework proposed takes account of various factors including the extraction, refining, and transport of oil, different segments of land vehicles, and electricity system operations. The framework is demonstrated with a case study of the Spanish Canary archipelago. The results show that BEVs improve efficiency and CO2 emissions by around 30% compared to ICEVs on islands where the share of renewable energies is higher than 21%. In contrast, limited renewable generation may lead to BEVs polluting up to 15% more than ICEVs. PHEVs should be considered as a suitable alternative if the share of renewable generation is higher than 35%.

An optimal solution to unit commitment problem of realistic integrated power system involving wind and electric vehicles using chaotic slime mould optimizer

Plug-in electric vehicles (PEVs) could be integrated into power networks to meet rising demand as well as provide mobile storage to help the electric grid operate more efficiently. The most efficient charging and discharging of PEVs are required for the effective utilization of this potential. PEVs with poor charging management may see a spike in peak demand, resulting in increased generation. To take advantage of off-peak charging benefits and avoid load shedding, PEVs charging and discharging must be intelligently scheduled. This paper offers a solution to optimal generation scheduling and the impact of vehicle to grid (V2G) operation in the presence of wind as a renewable energy source using the chaotic slime mould algorithm (CSMA). Further, the effectiveness of the proposed simulation results for a 10-unit system incorporating V2G operation has been compared with other well-known optimization techniques such as harmony search algorithm (HAS), chemical reaction optimization(CRO), genetic algorithm and artificial neural network(GA-ANN), particle swarm optimization (PSO), and cuckoo search (CS). The comparative analysis of the results reveals a significant cost savings in power generation.

Optimal Energy Scheduling of Microgrid With Electric Vehicles Based on Electricity Market Price

Abstract Due to rising global energy demand and mounting environmental concerns associated with the widespread use of fossil fuels in conventional power plants, it is imperative that viable and cleaner energy sources are used. Here, virtually pollution-free renewable energy sources have replaced traditional fossil fuels as the go-to option for meeting the rising energy demand. This research article utilizes a new formulation for minimizing the total cost of a microgrid through a short-term operational strategy. Microgrids and demand-side management can improve the distribution network’s efficiency and reliability. To achieve this goal, this paper explores how to best schedule the uncertain operation of a microgrid including both renewable energy resources like wind turbines and photovoltaics, as well as dispatchable resources like fuel cells, microturbines, and electrical storage devices connected to charging stations for electric vehicles. Considering the unpredictability of wind power and solar power outputs, besides the behavior of plug-in electric vehicle owners in terms of plugging into the grid to inject or receive power, a stochastic programming-based framework is introduced for the operation of microgrids running in the grid-integrated mode. In this study, an innovative and effective optimization algorithm is employed, which is the modified manta ray foraging optimization algorithm, as a high-efficiency method for maximizing the microgrid efficiency. After applying the proposed method to a standard microgrid, the simulation results show how effective it is compared with other approaches.

Optimal location of fast charging stations alongside pricing design from distribution company viewpoint considering renewable energy resource: A modified capacitated flow-capturing location model

Investigating the optimal location of fast charging stations (FCSs) followed by optimizing the electricity sale prices simultaneously for plug-in electric vehicles (PEVs) are considered as critical challenges in a power grid. This paper proposes the improved flow-capturing location model (FCLM) with mix-integer linear programming (MILP), in which the location of FCSs, alongside the electricity sale price to PEVs, is optimized so that the profit of the distribution company (DISCO) is maximized. Also, it considers renewable energy resources (RESs) to support FCSs. DISCO is assumed to be in charge of the cost of stations, the expansion network, RESs, and the electricity purchase from the network. The goal is to determine with how much price DISCO should sell the electricity to PEVs for the return on investment (ROI) rate to be positive at a specified time. Moreover, the PEV owners’ non-utilization function is considered in such a way that the electricity sale price to PEVs in stations has a direct relationship with the PEV owners’ non-utilization. In addition, the growth of PEVs, electric load, and electricity purchase from the network are considered. The proposed model has been implemented on a 25-node urban traffic network fed by a 33-bus distribution network. The results demonstrate that the investor provides electricity for PEVs with less price in the proposed model because the investment costs of FCSs are reduced by 34.4%. Also, by raising the PEVs’ charging demand, the electricity sale price in the station is reduced. In this condition, the utilization of PEV owners is maximized. Moreover, considering RESs reduces voltage drop and electricity sales price to PEVs.

A chance constraint microgrid energy management with phase balancing using electric vehicle demand aggregation

ABSTRACT Microgrids are sustainable distribution systems which integrate plug-in electric vehicles (EVs) and renewable energy sources to minimize grid dependency. The uncertainty in the forecast data of renewables and erratic consumption of energy by consumers aggravates voltage stability in a three-phase microgrid. Voltage instability and ineluctable uncertainty in the system inevitably make energy management of resources infeasible. To address the challenges of voltage stability and uncertainty in microgrid energy management, a novel two-stage energy management framework using stochastic chance constraint model predictive control (MPC) is proposed to optimize operational costs with reduced reserve costs. In the primary stage, an optimal day-ahead resource dispatching strategy is modeled using robust chance-constrained to handle the uncertainty of the forecasted data. Wasserstein metric ambiguity set is developed against uncertainty risk operations for low conservativeness and tractable constraints. The secondary stage control, with a shorter timescale, regulates the deviations in the primary stage state parameters using the MPC technique. Also, a fuzzy rule-based unbalance voltage control for EV parking lots (EVPL) is proposed in the secondary stage by modifying each phase’s power using phase switches. The proposed energy management framework adopts a mixed-stage optimization structure, where the concerned problem is progressively optimized over diverse time scales. The results of the proposed strategy show that lowering the confidence interval and increasing sampled data reduces operating costs by 7.57% and 5.34%, while penalty costs are reduced by 51.9% and 74.9%, respectively. It is also observed that this strategy minimizes the voltage unbalance on a modified IEEE 123 bus system and validates with benchmark approaches.

Bi-level deep reinforcement learning for PEV decision-making guidance by coordinating transportation-electrification coupled systems

The random charging and dynamic traveling behaviors of massive plug-in electric vehicles (PEVs) pose challenges to the efficient and safe operation of transportation-electrification coupled systems (TECSs). To realize real-time scheduling of urban PEV fleet charging demand, this paper proposes a PEV decision-making guidance (PEVDG) strategy based on the bi-level deep reinforcement learning, achieving the reduction of user charging costs while ensuring the stable operation of distribution networks (DNs). For the discrete time-series characteristics and the heterogeneity of decision actions, the FEVDG problem is duly decoupled into a bi-level finite Markov decision process, in which the upper-lower layers are used respectively for charging station (CS) recommendation and path navigation. Specifically, the upper-layer agent realizes the mapping relationship between the environment state and the optimal CS by perceiving the PEV charging requirements, CS equipment resources and DN operation conditions. And the action decision output of the upper-layer is embedded into the state space of the lower-layer agent. Meanwhile, the lower-level agent determines the optimal road segment for path navigation by capturing the real-time PEV state and the transportation network information. Further, two elaborate reward mechanisms are developed to motivate and penalize the decision-making learning of the dual agents. Then two extension mechanisms (i.e., dynamic adjustment of learning rates and adaptive selection of neural network units) are embedded into the Rainbow algorithm based on the DQN architecture, constructing a modified Rainbow algorithm as the solution to the concerned bi-level decision-making problem. The average rewards for the upper-lower levels are ¥ -90.64 and ¥ 13.24 respectively. The average equilibrium degree of the charging service and average charging cost are 0.96 and ¥ 42.45, respectively. Case studies are conducted within a practical urban zone with the TECS. Extensive experimental results show that the proposed methodology improves the generalization and learning ability of dual agents, and facilitates the collaborative operation of traffic and electrical networks.

Impact of plug-in electric vehicles on grid integration with distributed energy resources: A review

The rise of electric vehicles (EVs) has a massive impact on the electricity grid due to the electrification of vehicles in the transportation sector. As a result, various techniques are needed to minimize the effects of charging on the grid. One of these techniques is having intelligent coordination between the various components of the EV charging network. This ensures that the network has enough electricity to support the charging needs of the vehicles. This article provides an overview of the many aspects of the EV industry and its charging infrastructure. It also provides a step-by-step approach for implementing the Vehicle to Grid (V2G) deployment, the utilization of recordings from the data by the EV battery through Artificial Intelligence and the cost-benefit analysis from effective utilization of the V2G method. The paper also explores the various aspects of the EV market and the role of aggregators and consumers. Finally, it assesses the possibility of expansion of the EV charging and grid integration system and outlines its challenges and solutions.

Finding an energy efficient path for plug-in electric vehicles with speed optimization and travel time restrictions

Transportation is one of the main factors when global total energy consumption is considered and is a significant contributor to emissions of harmful gases including carbon dioxide (CO2). Due to their lower tailpipe CO2 emissions compared to the vehicles with internal combustion engines, electric vehicles provide an opportunity to reduce environmental impacts of transportation. In this direction, a problem for plug-in electric vehicles (PEVs) is studied where the aim is to find an energy efficient path. Given an origin–destination pair over a directed network, this problem involves determining a path joining origin and destination, the speed of the PEV on each road segment, i.e., arc, along the path, the charging stations the PEV will stop by, and how much to recharge at each stop so as to minimize the total amount energy consumption. There are speed limits on each road segment, and PEV has to arrive at the destination on or before a given total time limit. For this problem, firstly, a mixed-integer second order cone programming formulation (MISOCP) is proposed. Secondly, to be able to solve larger size instances, a matheuristic is developed. Lastly, an iterated local search (ILS) algorithm is designed for this problem. Solution quality and computation times of the heuristics and the exact algorithm are compared on different instances. Differently from the literature, the speed values of the PEV on the arcs are considered as continuous decision variables in all proposed solution approaches. Moreover, consideration of the speed limits which can be legal limits or limits imposed by congestion makes our problem more realistic. The analysis of the results of the computational experiments gives the user an insight to select the proper solution approach based on the instance settings. MISOCP formulation becomes inadequate for larger instances. On the other hand, the heuristic solution approaches can solve such instances within reasonable computational times and therefore they have the potential to be integrated in some software to dynamically find energy efficient paths.

Coordinated energy management for commercial prosumers integrated with distributed stationary storages and EV fleets

The integration of intermittent renewable energy and stochastic electric vehicle (EV) loads in buildings and power systems poses new challenges such as increased peak demands and grid instability. Regarding multi-directional interactions among heterogeneous loads, generation, storages, and power grid, the synergy between prosumers, stationary storages and electromobility is crucial for future energy systems. In this study, coordinated energy management was investigated for a neighborhood involving commercial buildings with distributed renewables and stationary batteries, and plug-in EVs. Compared to the collaborative control of EV and stationary storages at single prosumers, the energy sharing system with centralized control of mobile and stationary storages achieved superior techno-economic performance. Case studies demonstrated that the renewables-storage sharing with centralized storage scheduling decreased annual energy costs for both buildings and EV owners by over 15%, and the cycle aging cost of stationary storage by 25%. While the performance under day-ahead scheduling of EV and stationary storages depends more on the prediction accuracy, the real-time renewables-storage sharing strategy integrated with pre-scheduled EV charging provides an effective alternative solution for the stakeholders. The study explores potential coordinated approaches for communities with low-carbon buildings, flexible storages, and intelligent infrastructure to maximize the operational value and improve the grid stability.

A cyber attack detection strategy for plug-in electric vehicles during charging based on CEEMDAN and Broad Learning System

Cyber attacks targeting the charging process of plug-in electric vehicles (PEVs) have become more frequent with the popularity of PEVs, posing a significant threat to the battery life of PEVs and the stability of the power distribution network system. To mitigate this threat, a cyber attack detection strategy based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Broad Learning System (BLS) is proposed in this paper. The strategy exploits the coupling between PEVs and the power distribution network system during the charging process to achieve the detection of cyber attacks. CEEMDAN is implemented to extract features of system signals, and BLS uses the features extracted by CEEMDAN for feature mapping and enhancement to capture the presence of cyber attacks. Simulation analysis demonstrates that the proposed strategy can achieve more than 85% detection accuracy, with CEEMDAN bringing about 7% improvement in accuracy.

Computational intelligence based PEVs aggregator scheduling with support for photovoltaic power penetrated distribution grid under snow conditions

• A snow loss-aware deep learning-based short-term PV yield predictor is developed. • Stochastic day-ahead scheduling of an independent PEVs aggregator is investigated. • A novel local balancing service offered by the PEVs aggregator to DSO is proposed. • The service benefits PEV owners through providing extra energy demand of the grid. This paper addresses the issue of optimal day-ahead scheduling of a plug-in electric vehicles (PEVs) aggregator that participates in the electricity market and offers an out-of-market balancing service to the local renewable power penetrated distribution system in a snow-prone area. The proposed balancing service provides a reliable source of flexibility for the extra real-time energy demand of the distribution system operator (DSO) which originates from the difference between its day-ahead bids and the actual demand. The problem is investigated on a snowy day when the DSO's day-ahead decisions encounter more uncertainty due to the considerable effect of snow loss on the DSO's photovoltaic plant performance. The aggregator's scheduling is formulated as two-stage stochastic programming which minimizes the PEVs’ charging cost. Monte Carlo simulation and K-means clustering are implemented to generate scenarios of driving patterns and real-time energy market prices, respectively. Offering the balancing service requires day-ahead predictions of the photovoltaic power and the grid load demand which are modeled using long short-term memory networks. The problem is formulated as mixed-integer linear programming. The results show that the proposed scheduling approach reduces the PEVs’ charging cost by 53% and guarantees the grid normal operation. Moreover, the balancing service can reduce the expected PEVs’ charging cost and the DSO's real-time cost by 12% and 14%, respectively. © 2017 Elsevier Inc. All rights reserved.

Probabilistic operational costs assessment of combined PV–PEV connections in LV distribution networks

Decarbonization stimulates the increased penetration of photovoltaic (PV) systems and plug-in electric vehicles (PEV) in low-voltage (LV) distribution networks. However, the individual or combined PV and PEV massive connections may incur significant economic costs for distribution utilities due to technical issues caused by these connections. This paper proposes a probabilistic approach for operational costs assessment of combined PV–PEV penetration in LV distribution networks catering to the uncertainties associated with PV, PEV and households. In addition, to identify the relationships between the technical issues with each other and the increased combined PV and PEV penetration levels, a correlation analysis is developed to help utility engineers to estimate the operational costs of combined PV and PEV connections. The proposed methodology is applied to a real Brazilian LV distribution network showing that the costs of real power losses decrease in reduced penetration levels of combined PV–PEV. However, they increase under high combined penetration levels representing financial losses to utilities. Also, operational costs related to steady-state voltage level compliance indicators increase with PV connection, and the combined PV–PEV connections have beneficial results, reducing these costs. Further, the proposed methodology can be used by utility engineers responsible for studies of LV distribution network reinforcement actions. • Proposed methodology converts technical parameters into operational costs. • Operational costs in utility’s point-of-view due to combined PV–PEV connection. • Proposed approach considering stochastic uncertainties of PV, PEV and households. • Identifies high-risk consumer units, allowing LV network reinforcements. • Proposed methodology uses technical correlation analysis and Monte Carlo Simulation.

Joint Mobility and Vehicle-to-Grid Coordination in Rebalancing Shared Mobility-on-Demand Systems

Vehicle-to-Grid (V2G) technology enables plug-in electric vehicles (PEVs) to act as controllable loads and distributed energy resources for power systems. However, these resources, under the context of Mobility-on-Demand (MoD) market, have yet to be fully exploited for the grid services. This paper investigates how providing energy service from the shared PEVs affects both the power and transportation systems. We consider a shared MoD platform where PEVs can choose to provide either the V2G service or the traveling service when they are rebalanced to future high demand areas. In this market, the power and transportation system operators determine appropriate rewards to attract the PEV drivers. On the other hand, these drivers choose between energy and mobility services to maximize their aggregate benefits. In particular, we analytically model drivers’ preferences for discharging and traveling services through carefully selected utility functions. Moreover, a distributed algorithm is developed to manage the interactions between PEVs and the grid operation. We show that PEV drivers can be encouraged to achieve a desired energy and mobility pattern by optimizing their individual utility. Numerical results demonstrate that integrating energy service into the ride-sourcing market can benefit both PEV drivers and the power system.

Active power management of grid-connected PV-PEV using a Hybrid GRFO-ITSA technique

In this manuscript proposed a hybrid Garra Rufa Fish Optimization (GRFO) and Improved Tunicate Swarm Algorithm (ITSA) for improving the power quality of the integrated Photovoltaic (PV) and Plug-in Electric Vehicle (PEV) in Smart Grid (SG) system. The GRFO-ITSA approach is hybrid wrapper of GRFO and ITSA. Commonly it is named as GRFO-ITSA approach. The grid-connected PV-PEV, active power management is performed by the proposed approach. The proposed GRFO approach is used to determine the individual harmonic components and to reduce the recompense currents applied to PVs via PEV converters. The load flow control is performed by ITSA approach, which controls the power among the PVs, and PEVs. Additionally, it satisfies the power demand, and voltage variation. The proposed approach is also to analyze the mutual properties of PVs as well as PEVs on the feeder and transmitting loads, voltage outlines, harmonic alterations of an urban electric power distribution system. Also, the performance of the GRFO-ITSA is implemented on MATLAB site as well as associated with several existing approaches. The GRFO-ITSA have improved the power quality and compensate the harmonics and reactive power of the system. The optimal outcome is obtained by GRFO-ITSA with less computation time.

A Robust Renewable Energy Source-oriented Strategy for Smart Charging of Plug-in Electric Vehicles Considering Diverse Uncertainty Resources

Nowadays, the notion of plug-in electric vehicle (PEV) as a valuable tool of energy management has been extensively employed in smart distribution grids. The main advantage of clean energy as well as elastic behaviour of operation in both electrical load/generation modes can sufficiently justify the utilization of such emerging technology. Moreover, the specific capability of renewable energy sources (RESs) in terms of contribution in PEV smart charging/discharging scheme would cause to remarkable techno-economic benefits in smart grids. However, the load demand, RES generation and also the electrical energy price encounter with uncertainty in practice required to be properly handled. Hence, a non-deterministic optimization model based on information gap decision theory (IGDT) is proposed in this paper to specify a robust PEV smart charging pattern. To solve the multi-objective proposed IGDT-based PEV smart charging (IGDT-PSC) model, the multi-objective version of particle swarm optimization (MOPSO) is utilized to define a set of Pareto optimal solutions. Furthermore, the final solution among the Pareto solutions is selected by means of a linear fuzzy satisfaction rule. The simulation results for a test smart microgrid comprising a PEV, a set of RES units and a load demand verify the effectiveness of the proposed IGDT-PSC model.

A Dynamic Optimal Scheduling Strategy for Multi-Charging Scenarios of Plug-in-Electric Vehicles Over a Smart Grid

Green transportation has become our top priority due to the depletion of the earth’s natural resources and rising pollutant emission levels. Plug-in electric vehicles (PEVs) are seen as a solution to the problem because they are more cost and environment friendly. Due to rapid industrialization and government incentives for zero-emission transportation, a significant challenge is also constituted in power grids by the self-interested nature of PEVs, with the asymmetry of information between the charging power demand and supply sides. In this paper, we propose an optimal strategy in industrial energy management system, based on evolutionary computing, to characterize different charging situations. The proposed approach considers stochastic, off-peak, peak, and electric power research institute charging scenarios for attaining the vehicle-to-grid capacity in terms of optimal cost and demand. An extensive scheduling of charging cases is studied in order to avoid power outages or scenarios in which there is a significant supply-demand mismatch. Furthermore, the proposed scheme model also reduces the greenhouse gases emission from generation side to build a sustainable generation infrastructure, which maximizes the utility of fuel-based energy production in the presence of certain nonlinear constraints. The simulation analysis demonstrates that PEVs can be charged and discharged in a systematic manner. The participation of transferable load through the proposed methodology can significantly reduce the economic costs, pollutant impacts, efficiency, and security of power grid operation.

Hierarchical High-Resolution Load Forecasting for Electric Vehicle Charging: A Deep Learning Approach

The mass roll-out of electric vehicles substantially contributes to reducing fossil fuel consumption and environmental emissions. Meanwhile, the large fluctuation, strong uncertainty, and multiple coupled impact factors of plug-in electric vehicle charging significantly challenge the existing load forecasting approaches. Unlike the conventional load, the fluctuation of plug-in electric vehicle charging is particularly significant at the very-short-term minute-level time scale given the large charging power and stochastic human behaviors. In this article, a novel historical-data-based high-resolution plug-in electric vehicle load forecasting model structure is established, accommodating the various factors that affect the charging load. Moreover, an enhanced attention-based long short-term memory deep learning approach is proposed for solving the intractable forecasting problem, where a feature upscaling and downscaling algorithm is proposed for the hierarchical high-resolution data processing. Numerical results on real-world data of a charging station in Shenzhen show that the proposed model structure and algorithm demonstrate well performance in short-term and very-short-term hierarchical high-resolution plug-in electric vehicles charging load forecasting problems.

Techniques for compensation of unbalanced conditions in LV distribution networks with integrated renewable generation: An overview

An increasing number of single-phase loads and renewable energy resources (RESs), such as single-phase rooftop PV units, are unevenly distributed in low voltage (LV) distribution networks. This exacerbates unbalanced conditions in the network which in turn adversely affects the power quality, stability and reliability. This paper presents a comprehensive review of the state-of-the-art techniques for alleviation of the unbalanced conditions in both three-wire and four-wire LV three-phase distribution networks. Technical issues arising from the unbalanced conditions are highlighted. Several definitions of the unbalance indicators and standards that are widely used for unbalance analysis are also given. Furthermore, recent unbalance compensation techniques based on three devices; namely, power electronic converters, electric vehicles (EVs) and energy storage systems (ESSs) are critically reviewed. It is found that the power electronic converters with suitable control strategies and management of EV chargers and plug-in electric vehicles (PEVs) have been intensively studied so far. However, based on the literature survey, there is currently no work on applying cutting-edge methods, such as the distributed cooperative control strategy and multi-agent deep reinforcement learning (MADRL), to utilize distributed single-phase ESSs to mitigate the unbalanced conditions. Therefore, key challenges, possible unbalance mitigation techniques and future research directions are recommended and discussed. • Techniques for alleviating unbalanced conditions in LV systems are reviewed. • Three devices with recent techniques for compensation are presented. • Indicators and standards for analysis of unbalanced conditions are provided. • 1-phase ESSs is recommended with new strategies for future research directions.