Smart Charging: A Comprehensive Review

Large scale adoption and public acceptance of Electric Vehicles (EVs) require availability of charging stations. Electrification of transport has been identified as the one of the significant factors that would increase the power demand. Management of charger load has become a matter of concern for the power system engineers. Uncoordinated charging can be detrimental to the smooth operation of the power grid. On the contrary, smart charging gives certain amount of control over the charging process with respect to the power grid. Hence, adaptivity of the charging process of EVs in smart charging assists to meet the needs of power system as well as EV users. A smart charger can adjust the charging power according to the power available from the grid, EV user needs, and also support the grid during emergency. Smart charging enables EVs to act as flexible grid resources thereby providing ancillary services to the grid in case of emergency. Further, EV users can gain significant financial benefits through smart timing of their charging against spot market prices. This work presents a comprehensive overview of smart charging thereby explaining its perception, impact, user acceptance, global status and pilot projects. Also, case studies highlighting the benefits of smart charging are presented. This detailed elucidation of smart charging will assist the researchers, and experts of power industry as well as transport to find research initiatives on smart charging at one platform thereby promoting adoption of smart charging.


I. INTRODUCTION
Transportation electrification is a viable alternative to deal with ever growing energy demand, air pollution, and global warming. Electrification of road transport has been identified as the one of the significant factors leading to increase in power demand. Uncoordinated charging is detrimental to power grid resulting in voltage instability, harmonic distortions, power losses, overloading, and degradation of grid reliability indices. For example, in ref [1] authors investigated how introducing EV charging load affects voltage stability, power losses, and reliability of the grid and found that fast chargers are detrimental to the smooth operation of 33 bus distribution network. In ref [2], the impact of fast EV chargers on the grid of a Latin American city is investigated. In ref [3], authors comprehensively reported how EV charging impacts grid operating parameters such as voltage stability, harmonics, power losses, reliability. In ref [4], the impact of EV charging induced harmonics on a real time demonstration of Los Angeles is reported. In ref [5], the impact of EV charging on distribution network reliability indices such as System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Customer Average Interruption Duration Index (CAIDI) etc is investigated quantitatively. In ref [6], authors solved the distribution network planning problem in presence of EV charging load thereby considering the security of the power grid. In ref [7], authors comprehensively reviewed the charging standards and the impact of EV charging on different power distribution networks. In ref [8], authors presented a pre-normative charging technology roadmap for heavy-duty vehicles with a focus on Europe. In ref [9], authors meticulously reviewed EV charging infrastructures and their impacts on power-quality of the utility grid. In ref [10], authors presented long-term electric vehicles outlook and their potential impact on electric grid. In ref [11], authors reported data for heuristic optimization of EV charging based on loading parameters. Smart charging is an effective means to manage grid loads from charging of EVs. Smart charger can adjust and manage the charging power according to the power available from the grid, EV user needs, and also support the grid during emergency [12]. Smart charging enables sufficient degree of control over the charging process. Adaptivity and control over the charging process of EVs in smart charging helps to meet the needs of power system as well as EV users. Further, smart charging assists EVs to become flexible grid resources and provide ancillary services to the grid in case of emergency. Also, the penetration of renewable energy is increasing and the electricity generation, transmission, distribution sector is experiencing a paradigm shift. The flexibility needs of the grid are increasing with increasing penetration of variable renewable energy. From the EV users perspective, smart charging can offer significant financial benefits through smart timing of their charging against spot market prices. This detailed review of smart charging will help the researchers, and experts of power industry as well as transport to find research efforts on smart charging at one platform and in turn will help in adoption of smart charging. The contributions of this work as compared to the review works reported in Table 1  • Case studies illustrating the effectiveness and benefits of smart charging The remainder of the paper is organized as follows. Section II gives a detailed overview of smart charging thereby providing a comparison of different smart charging strategies. Section III presents the impact of smart charging through elaborating some of the benefits of smart charging. Section IV presents an overview of charging demand prediction. Section V presents the smart charging pilot projects. Section VI presents smart pricing strategies. Section VII reports perceptions regarding smart charging whereas Section VIII discusses the role of artificial intelligence and blockchain in smart charging. Section IX deals with charging solutions for emergencies. Section X reports the case studies. Finally, Section XI concludes the work.

II. OVERVIEW OF SMART CHARGING
Uncoordinated charging can be detrimental to the smooth operation of the power grid as shown in Table 2. Smart charging gives certain amount of control over the charging process. Adaptivity of the charging process of EVs in smart charging assists to meet the needs of power system as well as EV users. Different types of smart charging strategies are elaborated in Table 3. As depicted in Table 3, there are a variety of smart charging strategies such as ON/OFF control, V1G, V2G, V2B etc. It has been observed that the maturity and acceptance of ON/OFF control, V1G is high and V2G, and V2B is medium.

III. IMPACT OF SMART CHARGING
The impact of smart charging on power grid is elaborated in this section. As depicted in Table 3, smart charging  TABLE 1. Existing reviews on smart charging. TABLE 2. EV usage scenarios in different countries and possible impact of uncoordinated charging and coordinated smart charging [1], [2], [3], [4].
strategies have several benefits such as peak shaving, congestion management, frequency regulation, voltage profile improvement etc. The research initiatives quantifying the impact of smart charging are reported in Table 4. In [28], a fuzzy logic controller was proposed to control smart charging and it was observed that by smart charging, maximum power and transformer overloading was reduced by 20% as compared to uncontrolled charging. Also, by applying a smart charging strategy, reduction of cable maximum loading by more than 10% as compared to uncontrolled charging was achieved. In [29], a convex optimization model for smart charging was proposed that reduced networks requiring intervention from 28% to 9 %. In [30], a water filling based smart charging strategy was proposed that reduced the monthly demand charges by 20 to 35% for 30% EV penetration. In [31], an optimal charging strategy integrated with utility demand response program was proposed that reduced the transformer ageing by 80%. In [32], centralized and decentralized smart charging strategies were compared. It was found that decentralized approaches provide the same CO 2 emissions benefits and within 2% of the NO x emissions benefits achieved with centralized approaches, but only if the frequency of communication between vehicles and the electric grid is sufficiently high (less than 60 min). In [33], it was reported that with 100 % EV penetration scenario in Norway, Denmark, Germany and Sweden and V2G, 7% reduction in peak load can be achieved. In [34], it was reported that with 1 million EVs in Guangzhou, China and smart charging 43% to 50% reduction in peak load can be achieved. In [35], it was observed that with smart charging strategies, reliability indices such as LOLE and AENS improved considerably.

IV. CHARGING DEMAND PREDICTION
The shift towards EVs will increase the load demand of the power grid as the EVs need to be charged after travelling certain distance depending on their driving range. Thus, accurate prediction of the charging load is essential in order to save the power grid from becoming overloaded. This section puts forward a systematic review of charging demand prediction of EVs. In recent years, accurate prediction and forecasting  of EV charging load has received a lot of research focus. Table 5 presents a systematic review of research works on this arena. In [36], a wavelet decomposition-based approach was used charging demand prediction of central road that is an urban area of Sri Lanka. In [37], a Markov chain and graph theory-based approach was used for predicting the charging demand of private EVs operating in Seoul, South Korea. In [38], authors have used different machine learning techniques such as Gradient Boosting, Support Vector Machine (SVM) for charging demand prediction of Nebraska, USA. Further, the performance of Gradient Boosting and SVM on charging demand prediction was compared based on Mean square error (MSE) and Root Mean square error (RMSE). In [39], a probabilistic approach based on normal distribution was used for charging demand prediction of Copenhagen, Denmark. In [40], a two-layer data driven approach and neural network was used for predicting the charging demand of Guangzhou, China. In [41], the authors have used Monte VOLUME 10, 2022 TABLE 6. Globally executed pilot projects on smart charging [21], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59]. 134694 VOLUME 10, 2022 TABLE 6. (Continued.) Globally executed pilot projects on smart charging [21], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59].
Carlo method for predicting the charging demand of taxis, buses and official EVs of Shenzhen, China. In [42], authors have used Monte Carlo technique for simulating the EV arrival rate and Hidden Markov Model (HMM) for predicting the charging demand of standard IEEE 53 bus network considering dumb as well smart charging scenario. In [43], the authors proposed a queuing theory and Monte Carlo based model for charging demand prediction. The model was validated on 33 node road network. In [44], authors have proposed an Adaptive Neuro Fuzzy Inference System (ANFIS) and Teaching Learning Based Optimization (TLBO) model for predicting State of Charge (SoC) of private EVs. The proposed model was validated on experimental datasets from the Prognostics Center of Excellence at NASA. In [45], a Support Vector Machine (SVM) and Wolf Pack Algorithm (WPA) based approach is used for short term load forecasting of e bus charging stations. In [46], the authors proposed a hybrid fuzzy inference and Monte Carlo based approach for charging demand prediction of private EVs. In [47], the authors have proposed a Q learning based model for charging demand prediction of EVs considering different scenarios such as uncoordinated charging, coordinated charging, and VOLUME 10, 2022  [66], [67], [68], [69]. smart charging. In [48], the authors have developed a Markov model-based tool named bCharge for charging demand prediction and scheduling of e buses. The model was validated for the real time bus e fleet of Schenzen, China. In [49] , the authors have modelled an ideal city having e taxis for public transport and proposed a Monte Carlo based approach for charging demand prediction of the taxis.

V. SMART CHARGING PILOT PROJECTS
Pilot projects driven by academia as well as industry across the world are exploring various aspects of smart charging. The list of major pilot projects on smart charging are listed in Table 6. The pilot projects reported in Table 6 investigated different aspects of smart charging such as frequency response achieved by smart charging, smart pricing, field trials, potential of EV batteries as medium of storage, economics of smart charging.

VI. SYSTEM COST AND SMART PRICING
With increasing penetration of renewable energy and EVs, power system investments, system cost and requirements for sector integration are emerging. EVs have already early been identified as a potential new flexibility element in the system [60]. Studies on sector coupling within the European energy system have shown high benefits of the flexibility from EVs especially through balancing of solar and also wind power production [61]. The same conclusion of the synergic co-existence of high penetration of EV's and expanding solar power in power system expansion in the Chilean power system was made in [62]. Smart pricing is a sort of cost-effective alternative where pricing signals are sent to consumers regarding the net cost of generating and delivering electricity [63]. An overview of smart pricing strategies is presented in Table 7. It was observed that with two period time of use tariff, a Nissan Leaf EV user will save approximately 167 Euros per year by night charging rate [64].
In a similar type of study on plug-in hybrid vehicles with small batteries concluded that most of the end user benefits of smart EVs come from smart timing of charging although benefits are also accrued from provision of reserves and lower power plant portfolio cost. The owner benefits of smart charging of EVs were in this study estimated at 227 e/vehicle/ year [65].

VII. PERCEPTIONS REGARDING SMART CHARGING
The adoption and promotion of smart charging scheme depends on different stakeholders. The views of the different stakeholders on different aspects of smart charging are systematically captured in this section. Analyzing the charging behavior of EV drivers is an important aspect for adoption of smart charging. A number of research initiatives have made attempts to analyze the charging behavior of EVs as reported in Table 8. The viewpoints of different stakeholders on Vehicle Grid Integration (V1G and V2G) in Indian context is captured in [50] as shown in Table 9.

VIII. ROLE OF ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN IN SMART CHARGING
The recent popularity of Internet of Things (IoT) has paved the path of smart and connected charging infrastructure. Also, Artificial Intelligence (AI) and blockchain technology have played an impressive role in streamlining smart and secure charging infrastructure. AI and blockchain can be utilized to deal with key issues related to charging infrastructure such as security in the charging stations, charging scheduling in the charging stations. Scheduling of EV charging is a complex task involving conflicting objective functions. There has to be a tradeoff between EV drivers' convenience, and security of the power grid. The EV charging service has multiple protocols imposing additional vulnerabilities to the system. For example, Open Charge Point Protocol (OCPP) is found prone to Man-In-The-Middle Attacks (MITM) [81]. Further, having multiple entry points to the system increases the chance that all the entities and protocols within the charging system may be compromised [82].
Blockchain networks can be utilized for enhancing the security within the charging infrastructure. Blockchain has the capacity to enable safe and secure trading of energy and allow homeowners to trade their energy and make their EVCS open to public. Different companies have taken initiatives to build Blockchain-enabled EV charging networks within P2P framework of energy trading. For example, Oxygen Initiative has extended already existing EV charging protocols (ISO-15118) and proposed a Blockchain network that enables either the utilities or any EVCS to offer pricing and grid conditions for EVs [81]. Also, a company named Charg offers an Uber-like service, through the Ethereum network, for energy trading by allowing anyone to lease their EVCSs to EV drivers [82]. AI can be effectively used for predicting the charging demand and driver behavior that in turn will assist in planning and operation of smart charging infrastructure.

IX. SMART EMERGENCY EV-TO-EV PORTABLE BATTERY CHARGER
Charging EVs on the road during emergency is still a challenge. To overcome this challenge in recent years, researchers have proposed an innovative EV-to-EV Portable Battery Charger (EPBC) [83]. This smart charger has the capability to charge another EV by examining the SoC and other battery specifications in a reliable manner. The charger can also control the output voltage and injected current to the EV at the same time. The charger uses a non-linear integral backstepping control to regulate the output voltage of the battery charger. The proposed smart charger can share up to 15% of the stored energy while taking into consideration the state of charge (SoC), capacity, and important technical specifications of the EV's battery. By using a bidirectional dual active bridge (DAB) dc-dc converter, the proposed EPBC can FIGURE 1. Test system [73], [74].
regulate the output voltage and the injected current to the EV simultaneously.

X. CASE STUDIES A. CASE STUDY 1
A case study to illustrate the benefits of smart charging is illustrated in this section. The test system is as shown in Fig.1. The test system resembles the distribution network of a highway in Guwahati, India. In ref [72], [73] the planning of charging infrastructure for this network was performed considering cost, VRP index, accessibility index, and waiting time as objective functions. Six planning schemes were obtained after solving the multi-objective optimization [72], [73]. In this work, we tried to compare the impact of unmanaged charging with smart charging schemes (Coordinated charging and V2G) on VRP index as shown in Fig.2. The advantages of coordinated charging and V2G over uncoordinated charging is prominent from the simulation results.

B. CASE STUDY 2
A case study assessing the cost effectiveness of smart charging is elaborated in this section. ERDF in France compared the cost of smart charging with uncoordinated charging for 1 million EVs traversing globally for charging at multi VOLUME 10, 2022 FIGURE 2. VRP index value for three scenarios in case of distribution network of Guwahati [73], [75].
dwelling buildings and public charging stations [76]. The cost for charging at multi-dwelling buildings for the two scenarios of smart charging and dumb charging is as shown in Fig. 3. And the cost for charging at public charging stations for the two scenarios of smart charging and dumb charging is as shown in Fig. 4. The cost effectiveness of smart charging as compared to dumb charging is clearly revealed from Fig.3, and Fig.4.

C. CASE STUDY 3
Nordpool Day-ahead Market (DAM) hourly price data [77] were used to analyze potential end user cost benefits from smart charging. Day-ahead hourly spot prices from 2019, 2020 and 2021 were downloaded and analyzed for four areas in the Nordic countries: Finland, SE3 from Sweden, Oslo area from Norway, and DK1 from Denmark. To illustrate the DAM hourly spot price dependency on the time of charging, three different six-hour periods were isolated from the hourly data for each year: mid-day charging during office hours (9am -3 pm), afternoon-evening charging (4pm -10 pm), and night hours (0am -6 am) and compared with 0-24 h 'dumb' or 'random' charging involving no informed or forced decision-making on when to charge. The hourly DAM spot prices were averaged for the said time slots and plotted for each month of the yeas as well as the all-year average for yeas 2020 and 2021 and for the four market areas mentioned. The results are shown in Fig. 5 -12. Additionally, the graphs in the figures show by dotted lines the relative DAM hourly price difference (%) for each of the three 6-hour periods as compared to 'dumb' charging 0-24h. This illustrates the relative savings potential for the smart end user who can choose the timing for charging.
As the data shows, 2020 was a more stable year for the market whereas towards the end of 2021 both price and its volatility increased; this trend is still continuing in 2022. Generally, Finland and SE3 show more intra-day variations in hourly spot price, whereas Oslo and DK1 are somewhat more stable. The end-user potential for cost savings through smart timed charging emerges during the night hours 0am-6am. In the analyzed dataset, this potential is largest in Finland: up to −50% year average night charging vs 24h average in 2020, and −40% in 2021). The same year average numbers, night hours vs 24h, are for SE3 −44% (2020) and −38% (2021), for   Oslo region −11% (2020) and −13% for 2021, and for DK1 −30% (2020) and −22% (2021). While the annual averages already show significant financial savings potential through smart charging, it should be noted that the momentary savings potential is still higher than the annual averages. Also, year 2019 was analyzed, the results for all areas show similar trends but a more stable spot price variation during the 24h of the day, and therefore slightly less smart charging potential end user gain.
Summarizing, all four Nordic market areas show potential gain for the EV user from timing charging to night hours and using the DAM spot tariff. The potential gain is significant and naturally dependent on the EV user's driving patterns. The largest potential gain for the analyzed years is in Finland and the smallest in Norway. Volatility of the spot market has increased during recent months, but the trend remains for December 2021 with peaking prices. Secondly, charging from the spot market during the mid-day hours (9am-3pm) is more   expensive than the 24h average. This is true especially in Finland and SE3, and to a lesser extent in Oslo region and DK1. This indirectly implies an improved business case for (local) PV production to support mid-day charging.
The monetary and business impact and opportunities from smart charging and pricing through use of the dynamic DAM depend on the use case and ownership or business model related to the charger. For EV owners with private chargers behind own metering the potential financial benefits can be directly cashed in through choice of tariff and smart   charging. For public chargers, the DAM offers the possibility for the charging point operators (CPO) and e-mobility service providers (EMSP) to offer dynamic pricing models utilizing smart charging, in addition to fixed prices.

D. CASE STUDY 4
The Electric Buses (EBs) impact on the power grid were studied. In this analysis, two scenarios were investigated. Scenario 1 includes standard charging, and Scenario 2 includes VOLUME 10, 2022 FIGURE 12. Nordpool day-ahead hourly spot prices per month in 2021 for Denmark (DK1). Averaged hourly prices for 0-24h as well as isolated average prices for mid-day charging (9am-3pm), evening charging (4pm-10pm) and night charging (0am-6am). The dashed lines show the difference (%) of the three timed slots vs the 24h average. smart charging. The EBs were charged with depot charging (CCS2) of max power of 128kW and opportunity charging based on pantograph of max power of 320kW. The depot happens when the EBs parked for several hours, usually at night. Pantograph charging usually happens when the EBs are stopped for short time, 5-7 min to collect the passenger or a bit longer according to their working schedule [78], [79].
EV integration has risen considerably over the past few years. Generally, EVs' impact on the grid depends on the grid infrastructure. In some grids, a 20% EV penetration has no impact on the DSO networks. On the other hand, some grids tolerate no more than 10% standard (uncoordinated) load charging, which could reach 40% in the case of smart charging. In reality, it appears that every DSO grid is a special case requiring an autonomous study to explore the issues and limits of EV charging load [80].
In this work, the single-phase subsystem was modeled and simulated in pandapower library (Python software) to study various factors that influence the charging infrastructure on the system capacity and the ability to host the EVs' loads.
In order to show the impact of conventional normal charging and fast charging on the DSO grid, a small spot (terminal EBs stop) that includes both CCS2 and pantograph solutions was modeled and simulated. This spot area had 20 plug-in charging solutions and 7 pantograph charging stations with a distance between them of less than 350 m.
The standard charging is where EBs plug-in to the charger and start charging with the maximum chargers' supplied power until fully charged (100% SoC) without taking into account the impact on the grid infrastructure. In the case of a high number of EBs connected to be charged simultaneously, there would be a high impact on the grid in terms of the DSO transformer load profile, the voltages on the bus bars, and line rating. Furthermore, the energy price is not taken into account, which increase the charging cost since the energy price in peak loads is higher than the price in off-peak hours.
Smart charging of EBs is done based on load shaving and charging cost optimization. The peak shaving mainly depends on minimizing the charging power in order to minimize its impact on the DSO grid. The cost optimization is based on  Day-ahead Market (DAM) energy price. The aim of smart charging is to flatten the load profile. This practice offers direct and indirect benefits to the DSO utilities in generation of costs, line and transformer loss reduction, and voltage support. The cost optimization aims to optimize the energy cost based DAM.
In this section, real data were used to simulate smart and standard charging with 100% EB integration in the PKM depot charging station. The standard and smart charging load profiles are shown in Fig. 13. Based on both scenarios, in the case of standard charging, there are valleys and peaks in the load profile. This results in no impact on the grid during a period of time and huge impact during a different period. To minimize the impact on the grid, the smart charging could be adopted, where the EBs connect to the grid to be charged and take into account the other factors that have an impact on the grid. Fig.13. shows how the charging load profile could be coordinated to lessen the impact on the grid by flattening the load profile instead of having some peaks and valleys. The corresponding impact of standard and smart charging on the busbars' voltages are shown in Fig.14. As can be seen in Fig.14. the standard charging has notorious impact on the busbars' voltage, which could be increased as more EBs' integrated unlike the smart charging, which support the voltages on the busbars [79].
The charging cost optimization based on DAM was simulated according to CCS2 in depot charging (see Fig.15). The standard charging starts charging with max allowed power i.e. 120kW without taking into account the energy price during the charging time. On the other hand, the smart charging takes the DAM energy price into account to optimize the EBs' charging cost, where the supplied power by the charger is start with low level i.e. 25kW when the energy price is high, then the supplied power increase gradually as the DAM decrease. This results in a cost saving of no less than 10% for full charge EBs' with battery capacity of 220kWh. The consumer naturally also has to evaluate and make the choice between a DAM spot tariff and fixed rate electricity tariffs available.

XI. CONCLUSION
Adoption of EVs call for development of sustainable and accessible charging infrastructure. Management of charger load has become a matter of concern for the power system engineers. Uncoordinated charging can be detrimental to the smooth operation of the power grid. Smart charging gives certain amount of control over the charging process. Adaptivity of the charging process of EVs in smart charging assists to meet the needs of power system as well as EV users. Further, smart charging enables EVs to act as flexible grid resources thereby providing ancillary services to the grid in case of emergency. Flexibility from smart charging can provide benefits to power and transmission system investments and system cost, as well as cost benefits to the end users through optimized timing of charging. This work presents a comprehensive overview of smart charging thereby explaining its perception, impact, user acceptance, global status and pilot projects. An investigation highlighting the benefits of smart charging in case of EVs is presented. In smart charging, the EBs load profiles are distributed almost evenly. In reality, this could be done by shifting the charging of the EVs in a way that takes into consideration the transformer and line load profiles, and could also be done by decreasing the charging power, which prolongs the charging time while taking into consideration the user comfort and expectation, including the charging period, departure time and targeted SoC. In this study, smart charging was adopted by minimizing the charging of the EVs that were parked for a longer time and giving charging priority to other EVs parked less time. The aim of smart charging is to flatten the load profile. This practice offers direct and indirect benefits to the DSO utilities in generation of costs, line and transformer loss reduction, and voltage support. The cost optimization was also simulated based on DAM, which aims to optimize the energy cost. In terms of potential cost savings for end users who can time their EV charging from mid-day, evening or day-round random average to the night hours, can potentially save several tens of % through use of hourly spot prices and optimized timing for charging.
This detailed elucidation of smart charging will assist the researchers, and experts of power industry as well as transport to find research initiatives on smart charging at one platform thereby promoting adoption of smart charging. After the Ph.D. degree, she joined the Alliance for an Energy Efficient Economy, New Delhi, as a Research Associate and worked on a project related to vehicle grid integration in Indian aspect. She is currently an EUTOPIA SIF Fellow associated with the Power and Control Laboratory, School of Engineering, University of Warwick. Earlier, she was associated with the Smart eFleet Group of VTT Technical Research Centre as an ERCIM Fellow. She has published nearly 30 research articles and co-edited a book on smart charging. Her research interests include different aspects of power and energy such as e-mobility, charging infrastructure planning, artificial intelligence applications in power and energy, microgrid planning, distribution network planning, local energy systems with cogeneration, vehicle grid integration, optimization, metaheuristics algorithms, tool development for charging station placement, machine learning applications, power system reliability, intelligent transport, autonomous vehicle, quantum computing applications, and solar-based charging infrastructure for electric vehicles (EVs). She serves as the reviewer for several peer-reviewed journals of Elsevier, IEEE, and Springer. She was a recipient of several prestigious fellowships and awards such as ERCIM Fellowship, EUTOPIA Fellowship, and Anandaram Baruah award. She is a member of the IEEE Power and Energy Society.