Peak Demand Research Articles

Ensuring Sustainable Grid Stability through Effective EV Charging Management: A Time and Energy-Based Approach

The rise of electric vehicles (EVs) has significantly transformed transportation, offering environmental advantages by curbing greenhouse gas emissions and fossil fuel dependency. However, their increasing adoption poses challenges for power systems, especially distribution systems, due to the direct connection of EVs with them. It requires robust infrastructure development, smart grid integration, and effective charging solutions to mitigate issues like overloading and peak demand to ensure grid stability, reliability, and sustainability. To prevent local equipment overloading during peak load intervals, the management of EV charging demand is carried out in this study, considering both the time to deadline and the energy demand of EVs. Initially, EVs are prioritized based on these two factors (time and energy)—those with shorter deadlines and lower energy demands receive higher rankings. This prioritization aims to maximize the number of EVs with their energy demands met. Subsequently, energy allocation to EVs is determined by their rankings while adhering to the transformer’s capacity limits. The process begins with the highest-ranked EV and continues until the transformer nears its limit. To this end, an index is proposed to evaluate the performance of the proposed method in terms of unserved EVs during various peak load intervals. Comparative analysis against the earliest deadline first approach demonstrates the superior ability of the proposed method to fulfill the energy demand of a larger number of EVs. By ensuring sustainable energy management, the proposed method supports the widespread adoption of EVs and the transition to a cleaner, more sustainable transportation system. Comparative analysis shows that the proposed method fulfills the energy needs of up to 33% more EVs compared to the earliest deadline method, highlighting its superior performance in managing network loads.

Tolling scheme for traffic flow networks: modelling and control of positive systems

This article addresses modelling and control of traffic flow networks for congestion mitigation. We first model the traffic flow of an urban area and derive a state-space model composed of multiple compartments. Each compartment expresses the dynamics of the traffic demand that depends on drivers' decision-making on route choices influenced by tolling. Two practical properties are found in the proposed state space model: one is the positivity of the model, and the other is the conservation law in the number of vehicles. Based on the positivity of the model, we next propose the optimal tolling design aiming at the peak demand mitigation, which is expressed by nonlinear programming. We transform the nonlinear programming into a linear one, which is numerically tractable. Finally, we show that the proposed tolling design mitigates the peak demand in designated compartments in a numerical experiment and present a demonstration of the proposed control system.

Methods for Determining Losses and Parameters of Cylindrical-Rotor Medium-Power Synchronous Generators

This paper presents the analytical and numerical application of a method to determine the parameters and power losses in the core of two medium-power synchronous generators. These generators are used as emergency power sources powered by diesel engines, gas engines, and gas turbines. They cover peak electricity demand but can also be used in traction drives. This article presents a new numerical method for determining losses in the generator core based on the use of a time-stepping solution using the FEM method and calculating these losses using analytical formulas. In calculating the losses for the FEM method, approximations of the loss characteristics of the sheet were used with a wide range of induction values and frequencies. This method is specific to the solution used and was adapted from the authors’ previous work on losses in induction machines. A one-phase winding with alternating voltage was supplied to determine the basic parameters in the form of synchronous reactance. Also, an important novelty is the introduction of a new method of determining the saturation state of the magnetic circuit, which significantly affects the machine parameters. The obtained results were used in analytical calculations and implemented in a computer program that allows for the calculation of electromagnetic parameters, operating characteristics, and core losses, taking into account additional losses, total losses, and efficiency, as well as machine parameters in unsteady operating states and the current characteristics of a three-phase symmetrical short circuit at the machine terminals. The calculations obtained were verified experimentally by measurements of real machines.

A constrained price-based demand response framework employing utility functions in three-state Overlapping Generation and Gift and Bequest based model in distribution system

Demand response provides an opportunity for consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives. These programs are important as they have the potential to help electricity providers save money through reductions in peak demand and the ability to defer construction of new power plants and power delivery systems specifically, those reserved for use during peak times. For the successful application of DR in day-to-day life, DR models are necessary to be implemented. Many of the existing DR models primarily focus on the formulation of after-DR demand based on price elasticity. Though these models are devoid of basic humans’ micro-economic behavior, which is an essential part of a DR stakeholder. Considering these shortcomings of the existing DR literature, this paper envisages formulating DR models based on the foundation of basic humans’ manifestations of demand flexibility, willingness, load recovery, and altruistic behavior. Hence, this paper proposes two price-based DR models known as the three-state Overlapping Generation (OLG) model and the Gift and Bequest (G&B) based DR model. These models are based on customers’ microeconomic behaviors and are suitable for representing load recovery with minimal parameters. Both three-state OLG and G&B-based DR models are examined on IEEE 33-bus and 118-bus distribution systems and are compared with the existing price-elasticity model (PEM) and two-state OLG-based DR model.

Hydropower Enhancing the Future of Variable Renewable Energy Integration: A Regional Analysis of Capacity Availability in Brazil

As the share of variable renewables in the power system generation mix increases, meeting capacity requirements becomes challenging. In this context, hydropower reservoirs can play a vital role in integrating renewable energy due to their storage potential, contributing to meeting power supply criteria. However, given that reservoirs serve multiple purposes, various constraints can limit their capacity potential. This article introduces an analytical methodology that is designed to evaluate the maximum available power of hydro plants in critical scenarios. By applying concepts related to hydropower production calculations for the peak power demand and metrics evaluating the compliance with supply criteria, this study distinguishes itself from region-specific investigations. It conducts a generalized analysis of power availability across all regions of Brazil, with a focus on identifying the reasons for the most significant power losses and their specific locations. The results of this analysis demonstrate the feasibility of enhancing the available power of reservoirs, effectively addressing demand fluctuations, and sustainably improving energy security. This is particularly crucial in countries that are heavily reliant on renewables, including hydropower, for a huge portion of their electricity. The findings underscore the feasibility of increasing the penetration of variable renewable generation by optimizing the operation of existing hydropower plants. This optimization not only enhances energy security but also contributes to a more resilient and sustainable future, benefiting policy makers, energy planners, and stakeholders in the field of hydropower with reservoirs.

Analysis of grid flexibility in 100% electrified urban energy community: A year-long empirical study

Achieving 'carbon neutrality' in the building sector involves high penetration of renewables, electrification of energy use, and improved flexible interactions with the grid to balance energy supply and demand. This study analyzed one year in an urban energy community powered entirely by electricity, using high levels of renewable sources. By facilitating energy sharing between residential and non-residential buildings, the community redirected surplus energy to meet demand, achieving 57.6% self-consumption, 38.4% self-sufficiency, and 126.3% energy independence rate. This achievement involved an annual electricity consumption of 107 MWh, sevenfold that of previous simulation-based studies, marking the first real-world demonstration. Using machine learning clustering with domain-based interpretation, the study unraveled complex interactions between building operations and the power grid, influenced by external factors (weather, occupant behavior), system design, and controls. Managing low-probability high-impact (LPHI) events of severe demand or production peaks, accounting for only 0.5% of the time and 1.8% of net energy annually, was crucial to grid stability and cost reduction. The study confirmed the limitations of increasing battery energy storage capacity in certain environments, highlighting the importance of demand-side management (DSM) with thermal systems. The DSM effectively handled LPHI events, reducing grid stress with an 18.3% decrease in annual electricity cost .

Understanding the Competition and Cooperation between Dockless Bike-Sharing and Metro Systems in View of Mobility

The advent of dockless bike-sharing (DBS) represents an effective solution to enhance public transportation usage. However, despite growing interest in integrating DBS with metro systems, comprehensive studies on their competitive and cooperative relationships remain limited. This study aims to analyze the spatial, temporal, and mobility characteristics of metro-related DBS to explore integration opportunities. Initially, three modes of interaction between DBS and metros are identified: strong competition, weak competition, and feeder relationships. Subsequently, based on these relationships, the analysis focuses on distance, spatio-temporal patterns, and the scope of DBS activities. Results from Beijing indicate that metro-associated DBS primarily serves as “last-mile” solutions without significant short-range competition with metro systems. Strongly competitive relationships, on the other hand, are interaction patterns due to the dense overlay of metro stations and inconvenient transfer facilities and are mainly used for non-commuting purposes. Furthermore, weakly competing and feeder DBS systems exhibit similar commuting patterns, highlighting bicycling as a viable alternative to walking within metro catchment areas and that metro catchment areas should be adapted to bicycling. Mobility communities, identified as tightly integrated cycling hubs, are proposed as strategic dispatch zones to manage peak demands and reduce operational strain on DBS fleets. These findings deepen our understanding of DBS and metro system interactions, offering insights to optimize public transport operations and enhance urban mobility solutions.

Decarbonization through Active Participation of the Demand Side in Relatively Isolated Power Systems

In the context of power system decarbonization, the demand-side strategy for increasing the share of renewable energy is studied for two constrained energy systems. This strategy, which is currently widely suggested in policies on the energy transition, would impact consumer behavior. Despite the importance of studying the latter, the focus here is on decisions regarding the type, location, and timeframe of implementing the related measures. As such, solutions must be assessed in terms of cost and feasibility, technological learning, and by considering geographical and environmental constraints. Based on techno-economic optimization, in this paper we analyze the evolution of the power system and elaborate plausible long-term trajectories in the energy systems of two European islands. The case studies, Procida in Italy and Hinnøya in Norway, are both electrically connected to the mainland by submarine cables and present issues in their power systems, which are here understood as relatively isolated power systems. Renewable energy integration is encouraged by legislative measures in Italy. Although not modeled here, they serve as a backbone for the assumptions of increasing these investments. For Procida, rooftop photovoltaics (PV) coupled with energy storage are integrated in the residential, public, and tertiary sectors. A price-based strategy is also applied reflecting the Italian electricity tariff structure. At a certain price difference between peak and off-peak, the electricity supply mix changes, favoring storage technologies and hence decreasing imports by up to 10% during peak times in the year 2050. In Norway, renewable energy resources are abundant. The analysis for Hinnøya showcases possible cross-sectoral flexibilities through electrification, leading to decarbonization. By fine-tuning electric vehicle charging tactics and leveraging Norway’s electricity pricing model, excess electricity demand peaks can be averted. The conclusions of this double-prospective study provide a comparative analysis that presents the lessons learnt and makes replicability recommendations for other territories.

Match-to-Match Variation on High-Intensity Demands in a Portuguese Professional Football Team.

The aim of this study was to analyze the match-to-match variation in high-intensity demands from one Portuguese professional football team according to playing positions. Twenty-three male outfield professional football players were observed during eighteen matches of the Portuguese Second League. Time-motion data were collected using Global Positioning System (GPS) technology. Match running performance was analyzed based on the following three playing positions: defenders (DF), midfielders (MF), and forwards (FW). Repeated measures ANOVA was utilized to compare match running performance within each position role, and seasonal running variation. Practical differences were assessed using the smallest worthwhile change (SWC), coefficient of variation (CV), and twice the coefficient of variation (2CV). Significant differences were found among playing positions in total distance covered (F = 15.45, p < 0.001, η2 = 0.33), average speed (F = 12.79, p < 0.001, η2 = 0.29), high-speed running (F = 16.93, p < 0.001, η2 = 0.36), sprinting (F = 13.49, p < 0.001, η2 = 0.31), accelerations (F = 4.69, p = 0.001, η2 = 0.132), and decelerations (F = 12.21, p < 0.001, η2 = 0.284). The match-to-match running performance encompassed TD (6.59%), AvS (8.67%), HSRr (37.83%), SPR (34.82%), ACC (26.92%), and DEC (27.85%). CV values for total distance covered ranged from 4.87-6.82%, with forwards and midfielders exhibiting the greatest and smallest variation, respectively. Midfielders demonstrated the highest match-to-match variation for all other analyzed variables (8.12-69.17%). All playing positions showed significant variation in high-demanding variables (26.94-37.83%). This study presents the initial analysis of match-to-match variation in high-intensity demands within a Portuguese professional football team. Thus, the position's specificity and context can provide a helpful strategy for evaluating match-to-match running performance, and for recommending individualized training exercises based on the peak and high-intensity demands for each player's role within the game.

Do Elite Basketball Players Maintain Peak External Demands throughout the Entire Game?

Consideration of workload intensity and peak demands across different periods of basketball games contributes to understanding the external physical requirements of elite basketball players. Therefore, the aim of this study was to investigate the average intensity and peak demands encountered by players throughout game quarters. PlayerLoad per minute and PlayerLoad at three different time samples (30 s, 1 min, and 3 min) were used as workload metrics. A total of 14 professional elite male basketball players were monitored during 30 official games to investigate this. A linear mixed model and Cohen's d were employed to identify significant differences and quantify the effect sizes among game quarters. The results showed a significant, moderate effect in PlayerLoad per minute between Q1 vs. Q4, and a small effect between Q2 and Q3 vs. Q4. Furthermore, a small to moderate decline was observed in external peak values for PlayerLoad across game quarters. Specifically,, a significant decrease was found for the 3 min time window between Q1 and other quarters. The findings from the present study suggest that professional basketball players tend to experience fatigue or reduced physical output as the game progresses.

A Novel Function of a Research Process Based on a Power Internet of Things Architecture Intended for Smart Grid Demand Schemes

The global energy sector is currently undergoing a significant transformation to address sustainability, energy efficiency, and grid resilience. Smart grids, leveraging advanced technologies like the power internet of things (PIoT), play a crucial role in this transformation. This research focuses on enhancing the efficiency, reliability, and sustainability of electricity distribution through IoT technologies. It envisions a system where interconnected devices, sensors, and data analytics optimize energy consumption, monitor grid conditions, and manage demand response scenarios. Central to this effort is the integration of PIoT into the smart grid infrastructure, particularly in implementing dynamic pricing strategies for demand response. Leveraging power line communication (PLC) techniques, this innovative approach facilitates real-time communication between grid components and consumers. The results demonstrate improved grid stability through dynamic load management, effectively responding to demand fluctuations, and minimizing disruptions. The deployment of dynamic pricing methods using PLC-driven schemes empowers customers by offering access to real-time energy use data. This access incentivizes energy-efficient behavior. leading to a 30% increase in the adoption of energy-saving techniques among consumers. A utility company pilot study claimed a 12% drop in peak demand after adopting time-of-use charges, with an accuracy rate of 98.87% in total.

A modular multiport Landsman converter-driven hybrid EV charging station with adaptive power management system

This article presents the implementation of a hybrid charging station that utilizes renewable energy sources to power its operations. The core of this system is a modular multiport Landsman converter, which serves as the interface between diverse renewable sources, including photovoltaic panels, wind turbines and fuel cells. A sophisticated multi-agent-based energy management system is employed to address the intermittency of renewable energy supply throughout the day. Additionally, a fuzzy logic controller (FLC) facilitates the seamless charging of electric vehicles within the system. The FLC detects the state of charge (SoC) of the vehicle's battery and the battery present in the station, enabling efficient EV charging. The multi-agent energy management system with load shedding controller (MAEMLS) and FLC continuously monitor the power availability, power demand, charging time, and charging duration of the charging station. This approach reduces the charging rate for EV owners during peak demand periods. The proposed system is modelled and tested using MATLAB/SIMULINK software, and a laboratory prototype has been constructed. The results demonstrate that the anticipated converter is highly suitable for integrating hybrid energy sources and charging electric vehicles (EVs). Notably, this converter exhibits an efficiency ranging from 85 %-95.05 %, based on the availability of input DC sources.

Dynamic scheduling of electricity demand for decentralized EV charging systems

The rapid growth of electric vehicles (EVs) has brought forth new challenges to the power grid. Further, the simultaneous charging of EVs could lead to peak demand, potentially causing overloading, voltage swings, and other grid-related problems. To address these issues and lower the high energy costs faced by EV owners and grid operators, EV charging must be optimized. The study proposes an innovative strategy that utilizes decentralized charging systems to lessen the impact of EVs on the grid. A decentralized EV scheduling strategy offers scalability. Thus, making it suitable for a large EV population and it remains resilient to the dynamic arrivals of the EVs. The approach aims to balance the load on the grid and improve the effectiveness of charging operations. To achieve this, a convex optimization problem has been developed to effectively regulate the charging procedure, taking into account the distinct attributes of each EV. The mechanism operates by dividing time into several intervals. Each electric vehicle in the system autonomously adjusts its charging rate during the assigned time slots, with the goal of minimizing individual charging expenses. Moreover, the system demonstrates flexibility in deciding when to charge and discharge, allowing prioritization based on individual EV battery levels and power grid conditions. As a result, the cost analysis was conducted using the number of EVs and the average group size. A comparison of computational times between centralized and decentralized systems was undertaken to demonstrate the efficacy of the system.

Renewable, Flexible, and Storage Capacities: Friends or Foes?

Problem definition: More than 99% of the new power generation capacity to be installed in the United States from 2023 to 2050 will be powered by wind, solar, and natural gas. Additionally, large-scale battery systems are planned to support power systems. It is paramount for policymakers and electric utilities to deepen the understanding of the operational and investment relations among renewable, flexible (natural gas-powered), and storage capacities. In this paper, we optimize both the joint operations and investment mix of these three types of resources, examining whether they act as investment substitutes or complements. Methodology/results: Using stochastic control theory, we identify and prove the structure of the optimal storage control policy, from which we determine various pairs of charging and discharging operations. We find that whether storage complements or substitutes other resources hinges on the operational pairs involved and whether executing these pairs is constrained by charging or discharging. Through extensive numerical analysis using data from a Florida utility, government agencies, and industry reports, we demonstrate how storage operations drive the investment relations among renewable, flexible, and storage capacities. Managerial implications: Storage and renewables substitute each other in meeting peak demand; storage complements renewables by storing surplus renewable output; renewables complement storage by compressing peak periods, facilitating peak shaving and displacement of flexible capacity. These substitution and complementary effects often coexist, and the dominant effect can alternate as costs change. A thorough understanding of these relations at both operational and investment levels empowers decision makers to optimize energy infrastructure investments and operations, thereby unlocking their full potential. Funding: This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute. This research was also supported by Kelley School of Business, Indiana University, and Haslam College of Business, University of Tennessee. O. Q. Wu thanks Grant Thornton for their generous support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0068 .

Ensuring energy security: situating the Bay of Bengal in cooperative architecture narrative

ABSTRACT In today’s world, the concept of security is incomplete without the concept of energy security. Unless a country has access to uninterrupted flow of energy resources, its massive stockpile of weapons and ammunition is unlikely to keep it safe. Therefore, contemporary security studies include concerns about energy security as one of the key components of non-traditional security issues. Ensuring free access to energy for all – is a top priority in the BIMSTEC region. Trans-border cooperation can help diversify energy sources, reduce average supply costs, and meet peak demands as the gap between supply and demand grows in BIMSTEC countries. The paper examines the current energy scenario in BIMSTEC, evaluates the scope of cross-border energy trade in the region. Energy security and maritime security are intertwined with each other in today’s world. The paper also addresses the broader geopolitical, geostrategic, and geoeconomic aspects of the Bay of Bengal, illustrating how maritime security is integral to ensuring energy security in the region.

Generating synthetic building electrical load profiles using machine learning based on the CRISP-ML(Q) framework

Abstract This study presents a machine learning application for generating synthetic building electrical load profiles. The implementation followed the Cross Industry Standard Process for the development of Machine Learning Applications with Quality assurance methodology, or CRISP-ML(Q) framework, to ensure a systematic machine learning development process. The model training performance was evaluated using the mean absolute error (MAE), root mean squared error (RSME), and coefficient of determination (R2) which were observed to be 0.0739, 0.1119, and 0.5728, respectively. These metrics remained consistent during the model testing phase, suggesting robust model performance. During the initial simulation experiment, the MAE and RMSE of the generated synthetic load profile were found to be 0.014 and 0.016, respectively, underscoring high model accuracy. Additional evaluation experiments showed that the developed machine learning application can generate realistic building load profiles using high-level parameters such as building type, average daily load, and peak demand. This study can aid in the development of demand-side management strategies and building energy management systems by providing realistic building electrical load profiles especially when real-world data is limited. For future work, researchers can consider integrating additional model features, refining data processing methods, and developing an agile version of the CRISP-ML(Q) framework.

System Optimization and Operating Strategy of Single-Stage Air Source Heat Pump with Thermal Storage to Reduce Wind Power Curtailment

Wind power generation has increased in China to achieve the target of decreasing CO2 emissions by 2050, but there are high levels of wind curtailment due to the mismatch between electricity supply and demand. This paper proposes a single-stage air source heat pump coupled with thermal storage for building heating purposes. The main objective is to find the proper system designs and operating strategy, which can help to avoid peak demand periods while obtaining minimized running costs and reduced wind energy curtailment. Dynamic simulations were performed using TRNSYS to investigate its use in a typical office building based on an actual electricity tariff, wind power, and meteorological data. The proper system designs, including the tank size and thermal storage temperature, were determined to maximize the system’s performance. It was found that a proper combination of the two parameters exists for a specific application. Further, results showed that the use of auxiliary electric heating is necessary for single-stage air source heat pumps to participate in a wind curtailment reduction. The operating strategy of the system was also studied. Results indicate that by implementing a proper operating strategy, non-renewable power consumption can be reduced by 11% for the studied building, with a total wind power utilization of 3348 kWh during the heating season while still satisfying the heating demands of users. These findings can contribute to the green and low-carbon development of the building industry and further enhance the grid’s accommodation capacity for renewable energy sources.

Use of a low-cost phase change material emulsion in de-centralized thermal energy storage for district heating network enlargement

A detailed heat transfer model of a storage tank was developed to assess the performance of a district heating system with de-centralized storage solutions. The performance of two cases, with water and a low-cost phase change material emulsion as storage medium was analysed for 40 residential buildings in Zaragoza (Spain). The chosen configuration is a typical cylindrical tank with internal coils through which the district heating water flows for the charge and discharge. This decentralized thermal energy storage unit reduced peak demand and mass flow in the network, allowing additional buildings to connect to a saturated grid. Four configurations were examined: (1) A 180 m3 water tank reduced district heating power demand by 36.6 %. (2) The same 180 m3 tank using the emulsion required fewer coils, lowering costs, stabilizing TES temperature, and increasing water supply temperature, thus extending system lifespan. (3) A 130 m³ tank with the emulsion offered operational characteristics similar to the water tank, with economic and structural benefits from reduced size. (4) An 180 m³ optimised tank with the emulsion achieved a 38.8 % reduction in district heating power demand. These configurations demonstrate the efficiency and cost-effectiveness of using phase change material emulsion in thermal energy storage systems.

MASS RAPID TRANSIT (MRT) DEMAND AND CAPACITY ASSESSMENT FOR RAIL TRANSPORT IN MALAYSIA

Assessing the quality of MRT services unveils a generally satisfactory experience among respondents, with comfort emerging as a particularly noteworthy aspect. Satisfaction levels for MRT service quality range from 3.63 to 4.12, indicating a favorable perception overall. Notably, comfort stands out with the highest satisfaction rating, surpassing the "Satisfied" threshold, while accessibility receives the lowest rating. In essence, the majority of MRT passengers’ express contentment with the service, spanning operation, maintenance, and performance. Additionally, Bukit Bintang station emerges as a focal point during peak hours due to its proximity to diverse amenities. However, the prevalence of respondents underscores the necessity for broader demographic representation in future studies. Anticipated increases in MRT ridership necessitate proactive measures to accommodate the growing demand, with projected passenger numbers expected to rise significantly. Peak demand for the MRT SBK line is estimated at 115,498.543 passengers per day, reflecting a notable escalation of 281.82% across various purposes. Yet, reliance on responses may introduce biases in trip generation models and subsequent travel demand forecasting, emphasizing the importance of diversifying the respondent pool for more accurate predictions. Overall, the evaluation underscores the satisfactory nature of MRT services, particularly in terms of passenger comfort.

Non-intrusive demand response management strategy to mitigate the impacts of residential electric vehicle charging on distribution systems

The proliferation of electric vehicles (EVs) is expected to significantly increase electricity demand. However, if left uncontrolled, the aggregated demands from EVs have the potential to affect the power balance and quality of distribution systems adversely. Given this complex framework, this work presents a non-intrusive demand response management (DRM) approach to mitigate the impacts of residential EV charging on distribution systems. After analyzing a database comprising 157,250 operations, 74,316 charging patterns described by 17 attributes are obtained. These patterns are used for sampling EV fleets distributed in the Institute of Electrical and Electronics Engineers (IEEE) 33-bus test system. From scenarios with different levels of EV penetration, critical regions in the demand curve are identified, while actions for demand response (DR) are taken. Despite the increase in total energy consumption, larger EV fleets have greater potential for participation in DR. At a penetration level of 5%, it is not possible to shift excess energy consumption during the peak period, even with the participation of about 47% of the fleet, which corresponds to all users involved in EV charging during the peak period. At a penetration level of 50%, the energy goal can be achieved with the participation of about 15% of the fleet. In this latter scenario, the proposed management strategy can achieve a peak demand reduction of 9–11%.