EV Scheduling Framework for Peak Demand Management in LV Residential Networks

Rafique, Sohaib; Nizami, Mohammad Sohrab Hasan; Irshad, Usama Bin; Hossain, M. J.; Mukhopadhyay, Subhas Chandra

doi:10.1109/jsyst.2021.3068004

Cited by 19 publications

(6 citation statements)

References 28 publications

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“…An IEEE 15-bus distribution system for load management was discussed in the paper [207] as a case study. For decades, numerous parametric models have been chosen for predictive demand load modelling, such as K-means, artificial neural network (ANN), and Bayesian approaches, and they bring significant results [209][210][211][212][213][214][215][216]. The smart charging strategy is very efficient and it enhances the reserve capacity of power in electric grids to maintain load levelling.…”

Section: Load Forecasting and Maximum Demand Modelmentioning

confidence: 99%

Energy Anxiety in Decentralized Electricity Markets: A Critical Review on EV Models

Gopinathan

Shanmugam

2022

Energies

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The automobile sector is a promising avenue for enhancing energy security, economic opportunity, and air quality in India. Before penetrating a large number of electric vehicles (EV) into the power grid, a thorough investigation and assessment of significant parameters are required, as additional nonlinear and EV loads are linked to the decentralized market. Many automobile companies have already invested in electric vehicle research; hence, a detailed analysis on range anxiety and grid connectivity concerns are the important factors affecting the future of the electric vehicle industry. In this paper, the initial review is about the decentralized market in India and sustainable aspects of electric mobility based on the Indian context, as it is a developing nation with an enormous resource and scope for EV markets. With recent literature from the last three years, the substantial constraints observed in benefits and challenges are reviewed. The financial stability aspects and the incentives to overcome the barriers to EV adoption are briefly discussed. From the review, it has come to the limelight that infrastructure availability, technology, load demand, and consumer behaviour are all major obstacles in the electric vehicle ecosystem. For the overall design and study of the vehicle to grid (V2G) infrastructure, this paper also provides insight into the representation of electric vehicles in different energy-efficient models and their categorization while connecting to the grid. The methodology adopted for energy-efficient models includes lifecycle emissions, economy, smart charging, real-time optimization, aggregated EV resource modelling, and a support vector machine (SVM)-based method. This paper gives a positive impact on EV fleet integration and electric mobility in general, as it critically reviews the influential parameters and challenges. This classification depends on crucial parameters that are at the frontline of EV grid integration research. This review is a solution to enhance grid stability in regard to new EV models. With the advanced electric motors development and renewed battery technology models, longer-distance automobiles are now available on the market. This paper investigates the constraints of EV grid integration and analyzes different EV models to ease the grid stability for a decentralized market.

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Section: Load Forecasting and Maximum Demand Modelmentioning

confidence: 99%

Energy Anxiety in Decentralized Electricity Markets: A Critical Review on EV Models

Gopinathan

Shanmugam

2022

Energies

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“…Scheduling strategies have also been used to mitigate the load overcharging of the grid. These methods, mostly known as load shifting strategies, search to displace the peak demand to the periods where the load demand is lower using specific loads, i.e., space heating systems, water heaters and electric vehicles [30]; however, as the EV penetration is in augmentation, and combined with other loads, a rebound effect has been identified in literature, that can be seen as a new potential menace to overcharge the capacity of the grid during the periods identified as off-peak periods [31,32]. Further, these scheduling strategies require a representative amount of information (customer behaviour, outdoor temperature, etc.)…”

Section: Ev Charging Challenges and Opportunitiesmentioning

confidence: 99%

EV Overnight Charging Strategy in Residential Sector: Case of Winter Season in Quebec

2021

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Electric Vehicle (EV) technologies offer a leading-edge solution for clean transportation and have evolved substantially in recent years. The growing market and policies of governments predict EV massive penetration shortly; however, their large deployment faces some resistances such as the high prices compared to Internal Combustion Engine (ICE) cars, the required infrastructure, the liability for novelty and standardisation. During winter periods of cold countries, since the use of heating systems increases, the peak power may produce stress to the grid. This fact, combined with EVs high penetration, during charging periods inside of high consumption hours might overload the network, becoming a threat to its stability. This article presents a framework to evaluate load shifting strategies to reschedule the EV charging to lower grid load periods. The undesirable “rebound” effect of load shifting strategies is confirmed, leading us to our EV local overnight charging strategy (EV-ONCS). Our strategy combines the forecast of residential demand using probabilistic distribution from historical consumption, prediction of the EV expected availability to charge and the charging strategy itself. EV-ONCS avoids demand rebound of classic methods and allows a peak-to-average ratio reduction demonstrating the relief for the grid with very low implementation cost.

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“…In the spot market, the intraday market will gradually play a more important role and balance the transaction volume in the market in real time. will increase, and at the same time, the volatility of the electricity spot market clearing price will increase [18][19].…”

Section: Key Issues Of Constructionmentioning

confidence: 99%

Structural Changes, Efficiency Improvements, and Energy Demand Forecasting Based on Empirical Analysis of Decomposition Models

2021

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Forecasting of energy demand plays a vital role in the built environment. The main purpose of this paper is to study the structural changes and efficiency improvements based on the empirical analysis of the decomposition model, and to analyze the related application research of energy demand forecasting. This paper mainly analyzes the impact of new energy participation on the electricity spot market, the forecast of new energy generation power and the forecast of electricity price in the electricity spot market. Experiments show that the first year to achieve the 18% emission reduction target is very likely. Under the baseline scenario, CI falls to 66.7% in the tenth year, implying that the reduction target of up to 65% is barely attainable. It can be concluded that if the industrial and energy structure in the tenth year remains unchanged compared to the first year, and the sectoral energy consumption follows the growth trend of the previous years, the CI can be reduced, but only slightly above the 65% setting Target.

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EV Scheduling Framework for Peak Demand Management in LV Residential Networks

Cited by 19 publications

References 28 publications

Energy Anxiety in Decentralized Electricity Markets: A Critical Review on EV Models

Energy Anxiety in Decentralized Electricity Markets: A Critical Review on EV Models

EV Overnight Charging Strategy in Residential Sector: Case of Winter Season in Quebec

Structural Changes, Efficiency Improvements, and Energy Demand Forecasting Based on Empirical Analysis of Decomposition Models

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