Congestion management in distribution system integrating plug-in electric vehicles: A review

Integration of PEVs in the DS

The climate change issues and environmental concerns, requirements of EVs, types and design of EVs, energy security and fossil fuel reserve concerns, and popularity of PEVs, and PHEVs around the world are discussed by Ehsani et al. (2005) and Larminie and Lowry (2003). The DS charging effect of PEVs in G2V has been broadly discussed in some articles. The different generations of progression of interaction between PEVs and the grid have been shown by Tuttle and Baldick (2012). The review of PHEV in the distribution network has been shown by Green et al. (2011). Giant car manufacturers have already started producing PEV from their units (Chevrolet, 2017; New Tesla Model S, 2017; Nissan LEAF Electric Car, 2017). The impact of charging of PEVs at different peak and off-peak hours has been shown and analyzed by Farnandez et al. (2011). The random charging of PEVs in an uncoordinated way increases the load on distribution transformers (DTs) and distribution lines. Therefore, coordinated charging of PHEVs can reduce losses and maximize the load factor of the grid (Clement-Nyns et al., 2010).

The impact on residential transformers due to the charging of PEVs has been shown and analyzed by Razeghi et al. (2014). According to Galus et al. (2010), the operation and planning of power systems by considering the integration of PHEVs have been shown. According to Shafiee et al. (2013), the characteristics of PHEVs have been accurately determined to know the PHEVs impact. Also, the impact of load growth due to the growing penetration of PHEVs has been discussed. According to Heydt (1983), the role of EVs in load management strategy has been discussed. The load factor improvement has been shown by shifting EV charging in the off-load period from the peak load period. The impact of PHEVs on the evaluation of adequacy and reliability using Monte Carlo simulation (MCS) in smart grid has been discussed by Hariri et al. (2021).

The EV charging and RES integration to the grid have been presented by Ferreira et al. (2011). Markov decision process model-based reinforcement learning has been used to make charging strategy of EVs to reduce voltage violation in DS (Ding et al., 2020a). The impact of smart charging on both transmission and distribution networks has been discussed by Crozier et al. (2020). The appropriate way of smart charging strategy may reduce the need for generation expansion in transmission level and may also flatten the load curve in DS. The expansion planning and operation of power systems due to the increase in EV growth have been discussed by Manriquez et al. (2020). The EV charging model considering optimal power flow, EV characteristics, satisfaction level, and cost of power grid using improved particle swarm optimization has been presented by Yang et al. (2014). In terms of fuel economy, PHEVs in the electric range of 10–40 miles perform better by reducing the consumption of gasoline. At the same time, PHEVs could reduce the CO₂ emission by 53%–58% (Weiller, 2011).

The algorithm for electric power scheduling and dispatch has been formulated for PEV fleet aggregators by considering real data on the travel patterns of vehicles (Wu et al., 2012). The estimation of distribution algorithm (EDA) has been used for the performance evaluation of PHEVs in the real world considering state-of-charge (SOC) maximization (Su and Chow, 2012). The charging plan control strategy based on different market mechanisms is utilized for the allocation of charging power to EVs. The charging scheme reduces overload on the grid with the help of grid constraints (Sundstrom and Binding, 2010, 2012). The late-night valley of the demand curve can be reduced by charging EVs at that time. This may result in reducing generation costs and improving the utilization of base-load power plants (Collins and Mader, 1983). The charging strategy in a coordinated way can improve system parameters which have been found from the literature survey (Das et al., 2025; Deilami et al., 2011).

The V2G impact is reviewed and presented by Bibak and Tekiner-Mogulkoc (2021), Zheng et al. (2019), and Yilmaz and Krein (2012). The V2G mode and its different concepts are discussed by Kempton and Tomic (2005a, 2005b). According to Kempton and Tomic (2005a), different EVs in the V2G mode are considered to supply power to the electricity markets. Different policy issues of battery, hybrid, and fuel cells for future electric grids are discussed by Letendre and Kempton (2002). The dual use of V2G for regulation and reduction in peak load brings significant profit to the users of PHEVs (White and Zhang, 2011). The V2G algorithm has been developed for EV aggregators to maximize their profits by shaving in peak load and reducing in charging cost of EVs (Ustun et al., 2018).

The operation of EVs in V2G mode reduces both the intermittency of RES and the cost of energy. Also, the charging–discharging cycle of EV batteries has been kept minimum to reduce the degradation of batteries (Mehrjerdi and Rakhshani, 2019). The earning of income by trading electricity due to EV integration in V2G to vehicles, markets, and building modes of operations has been discussed by Gough et al. (2017). The power storage in EVs could be utilized to get benefits like system reliability improvement, and lower cost by considering it as intermittent renewable energy sources (Kempton and Letendre, 1997). The integration of PEVs with the grid has been made by using a distributed resource allocation method for smoothening load profiles and minimizing shifting of load in a fair way of charging PEVs (Tiwari et al., 2020). The V2G frequency regulation services for control strategies of charging are discussed by Han et al. (2010).

Penetration of renewables creates stability and intermittency issues in the power grid (Farooq et al., 2022b; Hussain et al., 2020). While there are different solutions are proposed in the literature (Latif et al., 2021; Ranjan et al., 2021), it is also possible to use the integration of PEVs and distributed generators as a demand response in the system. The alternating direction method of multipliers (ADMM) has been utilized to flatten the demand response and reducing the electricity bill (Tan et al., 2014). The fleet of EVs in the V2G mode for voltage and frequency regulation has been discussed by Amamra and Marco (2019). The integration of vehicles in V2G mode as DERs has further been discussed in some articles. The integration of vehicles in V2G mode as DERs act as controllable loads to flatten system load during off-peak hours and as power sources to provide energy services at other times of the day (Guille and Gross, 2009). The concept of V2G and vehicle-to-home (V2H) for PEVs has been considered by Turker and Bacha (2018). The V2G algorithm named optimal logical control (V2G-OLC) has been utilized to solve the optimization problem (Turker and Bacha, 2018). The energy imbalances of grid-connected microgrids can be managed by designing a feasible and cost-effective energy management strategy using PEVs in V2G mode (Kumar Nunna et al., 2018). The PHEVs in V2G mode can improve system reliability and grid efficiency (Clement-Nyns et al., 2011). Uncoordinated charging of PHEVs results in significant voltage deviation which can later on improved by using PHEVs in V2G mode in a coordinated charging scenario (Clement-Nyns et al., 2011). The achievable power capacity (APC) from V2G services has been calculated in a probabilistic way to contract it further with the grid to maximize the profit of PEV owners (Han et al., 2011). The scheduling of PHEVs in deterministic and probabilistic ways has been considered by Rahman et al. (2020). The load profile also changes significantly when PHEVs in V2G and G2V modes of operation (Rahman et al., 2020). The improved vehicle-for-grid (iV4G) compensates the issues related to power quality such as distortion of current harmonic, and reactive power, and improves power factor as well (Monteiro et al., 2019). The EVs have a significant potential for V2G services. The grid can benefit from EVs in demand-side management (Dallinger et al., 2011).

Renewable energy-powered parking lot

The emergence of solar power-based charging and parking lots reduces the fossil fuel-dependent transportation sector. This transition of the transportation sector helps to reduce GHG emissions. A detailed review of solar power-dependent parking lots and their advantages is addressed by Nunes et al. (2016). The two-layered parking lot recharge scheduling with revenue maximization for EV charging using vehicular mobility or parking pattern has been proposed by Kuran et al. (2015). The charging of PHEVs by PV array in a short-term period is feasible which may reduce burdens like loading and upgradation of costly equipment of the distribution grid (ElNozahy and Salama, 2014). According to Wu et al. (2020), Mohamed et al. (2014), and Jiang and Zhen (2019), a real-time algorithm has been developed to combine EV parking lots with renewable energy sources.

Solar power has been the front-runner of renewable energy resources (Abdolrasol et al., 2022). Consequently, the roof-top solar PV panel integrated with the parking lot can enhance grid capacity which has been found by Chukwu and Mahajan (2014). This integrated system improves grid capability by relieving loads. The grid-connected roof-top mounted PV system-based charging station for EVs has been considered for the energy management system (Turan et al., 2019). The presence of solar power in the charging station reduces both continuous and peak load demand during the charging of EVs. The peak hour load due to the charging of PHEVs can be reduced by integrating PV systems with smart charging stations and grids (Goli and Shireen, 2014). The system consists of a PV system, DC/DC boost converter, DC/DC buck converter, and DC/DC bi-directional converter. The charging of PHEVs in office buildings integrated with PV systems and combined heat and power units has been shown by Van Roy et al. (2014). A large number of EVs can be charged at office buildings with less number of charging spots. The priority-based PHEV charging is gaining popularity in the grid-integrated solar-powered parking lot. The bidirectional converters such as AC–DC and DC–DC allow PHEVs in both V2G and V2V modes (Ma and Mohammed, 2014).

The problem of the parking lot with an EV charging facility has been solved by the game theoretical approach-based method. The factors like SOC, transformer capacity, and price of electricity affect the objective utility function (Zhang and Li, 2016). The behaviors of the PEV parking lot in the market can be done by a two-stage optimization model which also shows PLs profit (Neyestani et al., 2015). The impact of PV-integrated workplace charging facilities on the economics and emission reduction has been presented by Tulpule et al. (2013). The strategy helps to reduce the overall load from the system and increase the penetration level of RES in the transportation and power sectors. The problem of voltage magnitude can be overcome by integrating EV charging stations with energy storage systems (Marra et al., 2013). The profit maximization during the V2G mode of PHEVs and EVs integrated in parking lots while accommodating a large number of vehicles has been shown by Hutson et al. (2008).

The popularity of PV-based charging facilities has grown due to the reduction in PV panels' price and also GHG emissions. The details of PV-based charging and its future aspects and challenges are shown by Bhatti et al. (2016). The charging scheduling of EVs in solar-powered charging lots with a target to maximize profit has been discussed by Zhang and Cai (2018). The profit of parking lot operators has been done by fuzzy-based optimization techniques with an aim to satisfy EV owners' requirements (Faddel et al., 2017). Also, the uncertainties associated with EV are also considered by Faddel et al. (2017). According to Awad et al. (2017), the owner of PEV-parking lots maximizes their profit by supplying the demand for PEVs. The techno-economic evaluation and reduction in emissions have also been shown by Shrivastava et al. (2019).

Charging coordination of PEVs

The charging coordination of PEVs is gaining popularity due to the use of PEVs in both way of charging and discharging. The detailed review of charging–discharging coordination of EVs and PEVs is discussed by Solanke et al. (2020) and Garcia-Villalobos et al. (2014). The coordination of PEV charging–discharging in real-time scenario is discussed by Shaaban et al. (2014) and Hajforoosh et al. (2015). Fuzzy-based PEV charging strategy has been formulated to reduce both cost and losses while maintaining system security, as shown by Masoum et al. (2015). Centralized PEV charging coordination with decentralized PEV discharge has been presented by Jabalameli et al. (2019). The communication requirements for such extended connectivity are discussed by Ustun and Hussain (2019).

The coordination strategy has been made to reduce loading on the transformer and improve the voltage profile. The online coordinated strategy of PEVs using online aggregated particle swarm optimization has been formulated to reduce grid overloading by optimizing battery charge rate (Hajforoosh et al., 2016). Meta-heuristic algorithms are used to formulate PEV charging coordination considering DG units in the system, as shown by Arias et al. (2017). The coordination of EVs considering the V2G strategy has been formulated using different heuristic-based algorithms to reduce cost, and loss and increase EVs profit (Sufyan et al., 2020). Each PEV updates the charging schedule as per the electricity price signal (Ma et al., 2015). The introduction of a phase switcher along with the charger of PEVs helps to decide the proper phase for PEVs connection or disconnection (Kikhavani et al., 2020). The multi-stage coordinated scheduling of EVs in an active distribution network has been shown by Zhu et al. (2018).

The uncoordinated PEV charging leads to a rise in peak-hour demand in the distribution network. The dynamic charging coordination strategy of the PEV fleet in a two-stage process has been discussed by Yi et al. (2020). The decentralized charging coordination strategy for PEVs using the mean-field game approach for exchanging information among the agents has been presented by Tajeddini and Kebriaei (2019). An apartment-level EV charging coordination has been established to reduce peak charging load due to EV penetration and also to reduce the apartment-level charging cost of EVs (Jang et al., 2020). The charging coordination of plug-in electric buses (PEBs) in fast charging stations to optimize the economic benefits has been shown by Chen et al. (2018).

The centralized charging coordination strategy of PEVs for analyzing computational performance along with a reduction in generation cost, load variance, and carbon emission has been presented by Shang et al. (2020). The charging coordination of PEVs using a load-guided strategy for smart buildings has been presented by Yoon and Hwang (2019). The load-guided charging coordination strategy of PEVs helps to reduce the electricity cost of smart buildings. The smart charging–discharging scheduling of EVs for common parking lots of multiple home-based smart households has been presented by Mehrabi and Kim (2018). The coordinated strategy helps the EV owners to maximize their profits and ancillary services to the grid. The charging schedule of EVs has been formulated a day ahead considering electricity price and next day trip schedule of EVs (D’hulst et al., 2015). The approach of EV charging coordination helps to maintain minimum voltage magnitude and reduce the overload of the transformer (Cardona et al., 2018). According to He et al. (2012), global-based and local-based coordinated EV charging reduces charging costs with close performance with each other. A summary of recent published papers on charging coordination of EVs is given in Table 1.

OPEN IN VIEWER

Table 1.

A summary of charging coordination of electric vehicles.

Authors and year	Objectives	Methodology
Modarresi et al. (2024)	To coordinate the charging power of electric vehicles for various microgrids while taking power loss, service charge, and reliability cost into account, a game theory-based approach is presented.	A General Algebraic Modeling Solver (GAMS) environment is used.
Yan et al. (2021)	Power allocation and coordination of EVs in charging stations are considered.	Game theory and Nash equilibrium strategies are used.
Lee et al. (2025)	A large-scale EV charging coordination that improves cost effectiveness, cost fairness, and target SOC level satisfaction based on three-level TOU rates is proposed.	A novel selective extra charging algorithm (SECA) and low-price pursuit algorithm are used.
Ma et al. (2018)	Charging coordination of the class of PEVs is formulated in a distribution network with capacity-constrained feeder lines.	A gradient projection-based decentralized method is proposed.
Ding et al. (2020b)	A marginal price-based coordination mechanism for EV charging demand management schemes to optimize the operating decisions for both EVCSs and EV aggregators.	A mixed integer linear optimization (MILP) model is used.
Qian et al. (2020)	A price-based management strategy of coupled public distribution network (PDN) and urban transportation network (UTN) by adjusting charging service fees.	Deep reinforcement learning based on gradient-based and gradient-free training algorithms are implemented.
Da Silva et al. (2020)	A multiagent multi-objective reinforcement learning architecture that permits EV charging while attempting to prevent transformer overloads and minimize energy expenses.	MultiAgent Selfish Collaborative architecture (MASCO), a Multiagent Multiobjective Reinforcement Learning has been used.
Sufyan et al. (2020)	The study investigated EV charging coordination with V2G technology to optimize EV owners’ profits while lowering system costs.	The problem is formed as an optimization problem and the firefly algorithm is used to solve the problem.
Yuan et al. (2023)	The research formulates a novel low-carbon EV charging coordination strategy as a bi-level optimization in a connected transportation and power network.	The bi-level problem is solved through a modified particle swarm optimization.
Li and Ma (2022)	A novel temporal-spatial EV charging coordination approach jointly considering EV arrival time and supplied charging power from the charging station is proposed.	A Game theory-based receding horizon optimization is formulated and solved.
Kang et al. (2023)	A bi-objective optimization model is developed to address the trade-off between peak power demand shaving and CO2 emission minimization.	The problem is solved using the ɛ-constraint method.
Wang et al. (2019)	Coordinated planning of EV charging stations and the coupled traffic-electric networks.	Coordinated planning model formed as MILP problem.
Jang et al. (2020)	An apartment-level EV charging coordination scheme is proposed with the goal of peak load reduction and charging payment minimization.	The coordination scheme operates based on the on-off charging control.
Infante and Ma (2020)	A layered decomposition model is proposed to consider decisions made by the main stakeholders in coordinating power transfers and assessing charging facility ratios.	The problem is formulated as an MILP problem and a branch and bound method is employed.
Yu et al. (2024)	To solve the current problem of difficult-to-charge, this study presents a high-efficiency and cost-effective shared fast-charging scheme for old residential communities.	A queuing charging mechanism and an integrated and multi-stage power electronic device called a smart interlinking unit (SIU) are introduced.
Yu et al. (2022)	This work proposes a pseudo-hierarchical management architecture based on the smart charging station for a small, affordable, and simple-to-implement energy management system for nanogrids.	The proposed architecture incorporates the upper-level central controller with an EV charging point, which facilitates the V2G operation and nanogrid energy management.
Yu et al. (2023)	The article suggests an inexpensive and simple hybrid AC/DC microgrid rebuilding plan for low-voltage distribution systems in older residential neighborhoods.	The smart interlinking unit (SIU) is the key element of the introduced architecture, which features multiple conversion stages, AC/DC interfaces, and flexible control capability.

Distribution system CM

Congestion control in DS management using various methods has been documented in some articles. The distribution congestion price (DCP)-based congestion management considering household demand responses has been reported by Liu et al. (2014). Direct load control refers to the controlling of household appliances, whereas, indirect load control motivates consumers to shift their consumption as per price change signal (Haque et al., 2019). The two-level approach for distribution network CM using multi-type DER has been presented by Luo et al. (2020).

The dynamic tariff (DT)-based distribution network congestion control has been presented by Shen et al. (2019) and Huang et al. (2019a, 2019b). Distribution locational marginal pricing-based EV charging has been demonstrated by Li et al. (2014). According to Zhou et al. (2022), a continuous double auction (CDA) mechanism-based peer-to-peer energy trading sequence is suggested to address network congestion brought on by P2P energy trading. A game theoretic approach for solving network congestion can be found by Ul Haq et al. (2022). A real-time congestion management methodology for medium voltage/low voltage (MV/LV) transformers can be found by Haque et al. (2017).

In another way, the CM in the distribution network has been solved by the locational price method, named as dynamic subsidy (DS) method (Huang and Wu, 2018a). The distributed locational marginal pricing (DLMP) concept in the distribution network has been considered for CM (Bai et al., 2018; Huang et al., 2015). The gossip algorithm-based CM by applying decentralized demand management in the distribution grid without local coordination has been formulated by Koukoula and Hatziargyriou (2016). The price-based approaches like nodal and local price mechanisms have been combined together for CM with the objective of maximizing social benefits by maintaining network constraints in the distribution network (Jafarian et al., 2020).

The market-based CM using DT and daily power-based network tariff signals has been constructed by considering flexible and controllable DERs like EVs and HPs (Ghazvini et al., 2019). In DS, a day-ahead two-level optimization model for CM considering DG power uncertainties has been presented by Ni et al. (2017). The effectiveness and integration of variable RES and flexible electrical load demands, such as EV charging, on congestion and its management have been studied by Verzijlbergh et al. (2014). The real-time transactive CM in the DS and flexible demand swapping in the swap market have been introduced by Shen et al. (2020). The transactive way mechanisms allow consumers to consider their willingness to provide flexibility in demand response. According to Huang and Wu (2018b), the CM has been solved by swapping the demand for charging of EV and consumption of EVs. The CM in two-level approaches for DS integrated with flexible building has been presented by Hanif et al. (2017).

A distributed generation in conjunction with commercial cryogenic energy storage (CES) and EVs with the goal of managing congestion while accounting for DLMP is proposed by Noori et al. (2024). The congestion control in a three-level hierarchical and distributed way in the distribution network has been presented by Kulmala et al. (2017). The CM of the distribution grid considering EV fleets charging while reducing charging cost and degradation of battery has been shown by Hosseini et al. (2020). EVs can act as spinning reserves or frequency regulation. Integration of EVs parking lot and capacitor bank in DS helps for CM and reactive power support has been formulated by Sachan and Amini (2020). At the distribution level, DSOs are managing congestion in an effective way (Hadush and Meeus, 2018; Prajapati et al., 2021). According to Hu et al. (2021), a price-based paradigm for incorporating PVs and EVs for high penetration prosumers into the distribution grid is proposed.

Role of PEVs in distribution system CM

The dual-mode operation of PEVs in G2V and V2G makes them quite important in the transportation sector due to environmental benefits and ancillary service providers. The emergence of PEVs plays a key role as an ancillary service in DS due to its capability to provide power back to the system while in V2G mode of operation. The PEV's V2G mode helps to lower the demand for peak load since PEV charging in G2V mode acts as an additional electrical load along with existing electrical load demand in the distribution network. When PEVs are charged in G2V mode, the DS experiences congestion as a result of the increased electrical load. The PEVs or EVs in V2G mode of operation can effectively manage DS congestion (Andersen et al., 2012; Asrari et al., 2020; Hu et al., 2014, 2015; Huang et al., 2019a; Lv et al., 2021; Mehta et al., 2016, 2018; Prakash et al., 2022; Quddus et al., 2019; Zhao et al., 2019). The CM by different approaches based on PEVs charging coordination in DS has been presented by Mehta et al. (2016, 2018). Further, CM in a 38-bus radial distribution network considering machine learning-based PEVs SOC prediction with different case studies have been shown by Deb et al. (2020a, 2020b, 2021a, 2021b).

The problem formulation of CM in DS can be expressed as follows (Deb et al., 2020a):

min ( a − b )

(1)

a = ∑ t ∈ T Δ t ∑ i ∈ V 1 μ c ( P i G 2 V _ Mode − ( P sol × N sol ) )

(2)

b = ∑ t ∈ T Δ t ∑ i ∈ V 2 μ d P i V 2 G _ Mode

(3)

P sol ( t ) = P r s × f loss × G h ( t ) G s

(4)

The goal of the objective function is to reduce the overall cost of operations as given in (1). Equations (2) and (3) display the expenses incurred during the G2V and V2G modes of operation. The total cost is described by the first term in equation (1) and is related to PEV energy in G2V mode minus the total energy provided by SPCPL as given in equation (2). PEV energy cost in V2G mode is described by the second term in (1) and is given by equation (3). Equation (4) displays the SPCPL total power production. A schematic diagram showing a renewable energy parking lot integrated with the grid is shown in Figure 2.

OPEN IN VIEWER

Figure 2.

Renewable energy-powered parking lot (Deb et al., 2020b).

Constraints

The maximum power transfer can be exchangeable during V2G and G2V by the following equations:

P ( t ) battery V 2 G _ Mode = f ( SOC ) = ( SOC current − SOC min ) × B capacity R discharging

(5)

P ( t ) battery G 2 V _ Mode = f ( SOC ) = ( SOC max − SOC current ) × B capacity R charging

(6)

The G2V/V2G maximum transferable power is shown in the following equations:

P i max _ G 2 V = min { P battery G 2 V _ Mode , P owner }

(7)

P i max _ V 2 G = min { P battery V 2 G _ Mode , P owner }

(8)

The limit on PEV charging can be done by using a variable quantity λ. The PEV's proper coordination strategy can be ensured by quantity λ which helps to decrease distribution line congestion.

∑ i ∈ V 1 P i G 2 V _ Mode − ∑ i ∈ V 2 P i V 2 G _ Mode = λ × ∑ i ∈ V P i Max _ G 2 V

(9)

λ = V / V 1 × k , where k = 0 − 1.0

P line ≺ P line max

(10)

SOC min ≺ SOC ≺ SOC max

(11)

The results of CM in 38 bus radial DS integrated with solar-powered parking lot (SPPL) are shown in Figure 2 considering 350 kW SPPL and 800 PEVs. Figure 3 considers 220 kW SPPL and 500 PEVs. The machine learning (XGBoost algorithm) based PEVs SOC has been predicted initially. Based on the predictions, the available, PEVs for G2V and V2G modes are decided. The participation of PEVs along with SPCPL in 38-bus radial distribution networks has been coordinated for the CM in the DS. Figures 4 and 5 demonstrate the effective CM during coordinated operation of PEVs G2V and V2G modes considering both 800 and 500 PEVs in the DS. It can be observed that congestion can be eliminated successfully during coordinated operations, whereas congestion is present during uncoordinated operations.

OPEN IN VIEWER

Figure 3.

IEEE 38-bus radial distribution network (Chakraborty et al., 2022).

OPEN IN VIEWER

Figure 4.

Congestion management with 800 PEVs in coordinated mode considering 350 kW SPPL.

OPEN IN VIEWER

Figure 5.

Congestion management with 500 PEVs in coordinated mode considering 220 kW SPPL.

Thus, as the research analysis has shown in the literature survey, PEVs and EVs are essential to a coordinated strategy to reduce congestion in DSs. A summary of the last few years research papers on CM in DS integrated with PEVs is shown in Table 2.

OPEN IN VIEWER

Table 2.

A brief summary of congestion management in the distribution system.

Authors and year	Objectives	Methodology
Liu et al. (2014)	To mitigate potential distribution system congestion, a market mechanism based on distribution congestion pricing (DCP) is presented.	The locational marginal pricing model has been used for problem-solving.
Haque et al. (2019)	A comprehensive strategy for managing traffic congestion that combines direct load control with market-based demand response is proposed.	A sensitivity-based curtailment scheme is incorporated.
Luo et al. (2020)	For an active distribution network (ADN) connected to multiple types of distributed energy resources (DERs) and microgrids, a two-stage hierarchical control system is suggested.	An analytical target cascading (ATC) method has been used.
Shen et al. (2019)	Managing day-ahead congestion in distribution networks with high penetration of distributed energy resources (DERs).	A relaxed reconfiguration-based dynamic tariff (DT) optimization model is used.
Li et al. (2014)	An integrated distribution locational marginal pricing (DLMP) approach is presented to reduce grid congestion caused by electric vehicles (EVs).	DC optimal power flow (DCOPF) is used for LMP calculation.
Zhou et al. (2022)	A continuous double auction (CDA) mechanism-based P2P energy trading sequence is suggested to address network congestion brought on by P2P energy trading.	The problem is formed as an optimization problem and is solved in MATLAB.
Haq et al. (2022)	A game-theoretic approach is proposed for solving network congestion in the smart grid.	A General Algebraic Modeling Solver (GAMS) environment is used.
Haque et al. (2017)	A real-time congestion management methodology for MV/LV transformers has been considered.	Mixed integer nonlinear programming (MILP) is used.
Bai et al. (2018)	A day-ahead market clearing model for a smart distribution system including microgrids (MGs), local aggregators (LAs), and distributed generation (DGs) is proposed.	Mixed integer second-order cone programming (MISOCP) modeling is used.
Huang et al. (2015)	A distribution locational marginal pricing (DLMP) is proposed for congestion alleviation in distribution networks with high penetration of flexible demands,	Quadratic programming has been used for problem-solving.
Koukoula and Hatziargyriou (2016)	A novel decentralized method of congestion management in radial distribution feeders has been considered.	A gossip algorithm has been used for problem-solving.
Jafarian et al. (2020)	A price-based strategy is considered to maximize social welfare while managing congestion in the distribution system using renewable generation.	Solved by Lagrangian together with KKT conditions.
Ghazvini et al. (2019)	A market-based mechanism utilizing centralized coordinated household energy management systems (HEMs) is to reduce congestion in distribution networks.	The optimization problem is formulated as the MILP problem.
Ni et al. (2017)	A DR-based congestion control approach for smart distribution systems has been adopted.	Bi-level linear programming solved with commercial solver CPLEX.
Noori et al. (2024)	A distributed generation in conjunction with commercial cryogenic energy storage (CES) and electric vehicles (EVs) with the goal of managing congestion while accounting for distribution locational marginal pricing (DLMP) is proposed.	Mixed-integer nonlinear programming problem is used.
Prajapati et al. (2021)	For an integrated transmission and distribution system under stress, a multi-objective optimization-based congestion strategy is proposed.	A General Algebraic Modeling Solver (GAMS) environment is used.
Hu et al. (2021)	A price-based paradigm for incorporating PVs and EVs or high-penetration prosumers into the distribution grid is considered.	Iterative distribution locational marginal price (iDLMP) is used.
Ma et al. (2018)	The impact of plug-in electric vehicle charging on capacity-constrained feeder lines has been considered.	A gradient projection-based decentralized method is used.
Asrari et al. (2020)	The day-ahead market architecture for smart distribution network congestion management is proposed.	Optimization problem and solved in GAMS.
Hu et al. (2014)	A coordination strategy among DSO, fleet operators (FOs), and EV owners is conceptualized for congestion management and energy system balance.	Linear programming has been used for problem-solving.
Hu et al. (2015)	To control congestion in the distribution system while integrating electric vehicles, this article uses multi-agent technology.	Convex optimization problems have been used for problem-solving.
Lv et al. (2021)	A framework for bi-level integrated demand response (IDR) is suggested to reduce congestion in coupled networks.	An efficient iterative algorithm for MPCC is developed based on a relaxation scheme.
Huang et al. (2019b)	A dynamic power tariff (DPT) based congestion management in distribution networks with high penetration of electric vehicles and heat pumps has been considered.	An iterative congestion management method based on DPT has been considered.
Zhao et al. (2019)	A low-voltage active distribution network congestion control technique is suggested.	A semidefinite programming approach using the symmetrical component approach is considered.
Hu et al. (2016)	Distribution grid congestion management considering coordination of DSO, fleet operator, and EV owners has been done.	Market-based and price-based control techniques are integrated into EV management.
Deb et al. (2018)	A grid-to-vehicle (G2V) and vehicle-to-grid (V2G) charging coordination strategy for congestion management in distribution systems considering the high penetration of EVs.	Power flow sensitivity factor and optimization algorithms have been used for problem-solving.