Peak Demand Research Articles

Consumption patterns of fresh and frozen chicken: A Ghanaian food policy perspective

AbstractDespite the global increase in poultry consumption, there are different preferences for chicken products in different countries. In Ghana, two main categories dominate the market: live/fresh whole chickens and imported frozen chicken parts. This study leverages data from 13,201 households to explore Ghanaian demand dynamics for these products using the Quadratic Almost Ideal Demand System. Findings reveal stark differences between the two. Live/fresh whole chickens, considered luxury goods, display a complex interplay with other non‐chicken meat products, cultural festivities, and various sociodemographic factors. Meanwhile, frozen chicken parts, seen as necessities, exhibit different demand shifters and peak consumption periods. Importantly, these two categories emerge as distinct products, signaling to policymakers the need for differentiated strategies. Simulated policy scenarios further emphasize that reducing prices of live/fresh chickens significantly boosts domestic chicken demand more than policies raising prices of frozen parts. However, the broader implications on alternative protein sources differ between the two policy types. We urge policymakers to adopt an integrated approach, considering these nuances, to strengthen Ghana's domestic poultry sector without unintended market disruptions. [EconLit citations: Q11, Q13, Q17, Q18, D12].

Corrigendum: Optimal sizing of photovoltaic-battery system for peak demand reduction using statistical models

Corrigendum: Optimal sizing of photovoltaic-battery system for peak demand reduction using statistical models

Thermal performance study of PCM embedded radiant cooling ceiling assisted by infrared-transparent silicon wafers

Phase change materials embedded radiant chilled ceiling (PCM-RCC) systems are great for cutting peak power demand and boosting energy efficiency, but they might not work as well in really humid areas due to condensation issues. Infrared-transparent silicon wafer-assisted PCM-RCC (ISW-assisted PCM-RCC) is proposed to improve the cooling capacity in this study. A heat transfer model of ISW-assisted PCM-RCC is established and its reliability is verified. Effects of thermal conductivity, phase change temperature, and control strategy on the thermal performance of ISW-assisted PCM-RCC are discussed by using the model. The simulation results show that the thermal conductivity of PCM is recommended to be above 0.6 W/(m·K), and the temperature difference between the lower limit of the selected PCM phase transition temperature range and the chilled water temperature should not be less than 1℃. The maximum cooling power of the system is more than 80 W/m2, 46 % higher than the non-condensing cooling capacity of traditional RCC at 70 % relative humidity conditions. Interestingly, the prolonged cold energy storage strategy at night should be avoided which is not friendly to the energy efficiency of the system. In short, the combination with ISW is an effective means to increase the cooling capacity of the PCM-RCC.

Enabling EV Charging at varied locations using Smart Meters

Electric vehicles primarily run on stored electrical energy, which needs to be replenished at regular intervals of time. Commercially, EVs are charged at charging stations, however they can be charged anywhere whether it be at home, public charging stations or even at workplaces. The commercial charging stations are highly costly and require huge setup. The main aim of this paper is to enable charging at any commercial and non-commercial establishment and optimize the cost of charging and grid usage by using an IOT (Internet of Things) device for bill generation and grid usage analysis.The charge enabling process is based on a QR based activation of the device. The user scans the QR code and registers his details, this enables the charge cycle. The bill is generated based on the start time and the end time of the charge cycle and the energy consumed in that cycle and a text message is sent to the consumer. The effect of charging load is also analyzed and hence an appropriate action is taken to optimize the peak load or demand. Keywords: Internet of Things (IoT), Electric Vehicles (EVs), Smart Energymeter, QR-based activation

Ionblox’s SiOx-Based, High-Energy, High-Power Li-Ion Battery for Performance Electric Vehicles and Electric Aviation

Demands on land-based, high-performance electric vehicles (EVs) and electric aviation, by means of electric conventional takeoff and landing aircraft (eCTOLs) and electric vertical takeoff and landing aircraft (eVTOLs) demand more on a secondary cell chemistry than ever before. There are many secondary cells on the market that offer energy density, power density, fast-charge capability, or cycle life, but few, if any other manufacturing-ready cells, offer a combination of all four. IONBLOX has solved this by pairing it’s patented, Pre-lithiated SiOx anode with a high nickel, NMC cathode to produce cells exceeding 330 wh/kg. Herein we describe the performance of our 32 Ah cell, featuring high-energy density, high-power density, extreme fast charge capability, and good cycle life, within a package that is already in production.In particular, challenges for the eVTOL market will be addressed, which require peak power demand on the cell at low state-of-charge, without reducing the energy density. Methods for characterizing cells for such high-power applications will be further explored. DCIR pulse characterization and more customized testing like usable energy testing, which is important for identifying the applicable energy density for high-power use cases, will be demonstrated. Additionally, extreme fast charge capability is described on our large format cells with 5 min charge to >60% total cell capacity, over 1000 cycles. This shows such a cell is up to the expectations of a consumer familiar with the timing of a standard gasoline pump. Contribution from the information presented herein demonstrates the state of Si-based anode chemistry and can serve as useful guidelines to the academic community as future technologies are pursued for similar applications Figure 1

Day-ahead optimal scheduling considering thermal and electrical energy management in smart homes with photovoltaic–thermal systems

The environmental impact on the energy sector has become a significant concern, necessitating the implementation of Home Energy Management Systems (HEMS) to enhance the energy efficiency of buildings, reduce costs and greenhouse gas emissions, and ensure user comfort. This paper presents a novel approach to provide optimal day-ahead energy management plans in smart homes with Photovoltaic/Thermal (PVT) systems, aiming to achieve a balance between energy cost and user comfort. This multi-objective problem employs the Non-dominated Sorting Genetic Algorithm III as the optimization algorithm and the Nonlinear Auto-regressive with External Input to forecast the day-ahead meteorological variables, which serve as inputs to predict the PVT electrical and heat production in the thermal resistance model. The HEMS benefits from the time-of-use tariff due to the flexibility provided by the energy storage from a battery bank and a boiler. Furthermore, it performs a load scheduling for 10 controllable loads based on three feature parameters to characterize occupant behavior. A study case analysis revealed a cost reduction of approximately 66% in the solution close to the knee of the Pareto curve (S3 solution). The environmental impact on the energy sector has become a The PVT heat production was sufficient to meet the thermal demand of the showers. The proposed hybrid battery management model effectively eliminated the export of electricity to the grid, reducing consumption during peak periods and the maximum peak demand.

Optimal strategy for transition into nearly zero energy residential buildings: A case study

This study suggests a customized design for turning an existing residential structure into a nearly zero energy building, along with a peak-power limiting strategy. The proposed work focuses on curtailing peak power demand of residential consumers through shifting the appliances connection time. The load scheduling is modeled as an optimization problem and solved using whale optimization algorithm and fuzzy logic. This is followed by reviewing the required size of the rooftop solar photovoltaic system for various types of residential consumers in a community using self-consumption and self-sufficiency-based indices. The proposed optimization algorithm effectively reduces the peak power drawn by 27 % and the peak-to-average ratio by 30.69 % on average in the residential community. Further, the rooftop PV size assessed substantiates that the energy drawn from the grid is nearly zero, attaining energy independence.

Integration of Steam Recovered from Molten Salts in a Solar Integrated Combined Cycle

In the current context of the energy transition, Integrated Solar Combined Cycle (ISCC) power plants are an alternative that are able to reduce carbon emissions from combined cycle (CC) power plants. In addition, the coupling to an energy storage system based on molten salts benefits hybridization, allowing the energy surplus to be to stored to cover peaks in energy demand. Because it is a recent technology, the determination of the optimal injection points for the solar-generated steam into the combined cycle is a critical issue. In this work, a thermodynamic model of a hybrid natural gas and solar thermal CC power plant has been developed using Thermoflex to analyze the integration effects in terms of efficiency and power. For all the steam injection candidate positions, the effects of ‘power boosting’ and ‘fuel saving’ operation modes have been simulated, considering operation conditions that are compatible with the useful range of molten salts. The results show that injection of steam at the high-pressure line before the steam turbine increases the cycle’s gross efficiency with respect to the reference case, estimating a reduction of carbon emissions of 6696 kg/h in the ‘fuel saving’ mode and an increase in gross power of 14.4 MW in the ‘power boosting’ mode. Hence, adapting current combined cycles for hybridization with solar power is a viable solution in the transition period towards more sustainable energy sources.

Evaluation and development of the clean water distribution network in Pancang Village, North Sebatik District, Nunukan Regency

Background: Rapid population growth and the large number of residential developments go hand in hand with the increasing need for clean water, especially drinking water. Basically, a city's development plan is closely related to the availability of clean water in the area, because clean water is a basic human need that is very important to fulfill. However, several consumers in certain villages which are part of the PDAM North Sebatik branch pipe network service area do not receive a clean water supply. With this research, it is hoped that it can provide alternatives/solutions in solving clean water problems, especially for the Pancang Village area, North Sebatik District. Methods: The data needed in this study is data on the population of Sei Pancang Village, North Sebatik District for the last 5 years, data on PDAM customers for the last 5 years, data on clean water needs, data on clean water sources and reservoir volume, as well as map data on the clean water distribution network. The evaluation technique used in this study uses the method with the largest correlation calculation, namely the arithmetic method. The clean water distribution network map will be evaluated using epanet software. Findings: The results of this research are that the population of Sei Pancang Village, North Sebatik District in 2022 will be 5250 people and projections for the number of residents and customers for the next 10 years show that the population will increase to 7018 people. Conclusion: Water use in the development plan year, namely 2023, in the residential area of Sei Pancang Village, North Sebatik District, includes, among other things, an average daily need of 9,045 lt/s, a maximum daily need of 10,402 lt/s, and a peak hour need of 13,568 lt/s. Meanwhile, in 2032 water demand will increase, with an average daily demand of 11,697 lt/second, a maximum daily demand of 13,451 lt/second, and a peak hour demand of 20,177 lt/second. Novelty/Originality of this article: This study develops a predictive model integrating demographic, climate, and infrastructure data to project water demand and optimize distribution. This model can be applied across regions to improve water management efficiency.

Quantifying peak heat demand in neighbourhoods: A UBEM approach and its implications for residential heating electrification in the UK—A case study of Newcastle upon Tyne

Developing Urban Building Energy Models (UBEM) to manage heat decarbonisation at the neighbourhood level is both crucial and challenging. Our study addresses key research challenges: quantifying peak heat energy demand and developing reliable and replicable urban energy demand microsimulations. We applied our UBEM approach to a case study area of 228 houses served by the Ridgeway New Low Voltage (LV) electrical substation in Newcastle upon Tyne, to estimate the difference between existing peak heat and electricity demand at this scale. Results show that peak heat demand is 5 to 14 times greater than peak electricity demand, significantly exceeding estimates from national studies. Furthermore, our work follows a comprehensive validation framework, comparing our simulation results against all relevant and available UK datasets. This comparison demonstrates that our model is an overall good fit at the declared Level of Detail. However, our paper identifies significant challenges, particularly in validation, that need to be addressed to improve the reliability and usability of future UBEMs. Finally, we reflect on all our findings and make policy recommendations, as we believe addressing the issues raised in this paper is vital for enabling area-based planning of residential heating electrification, particularly in the UK.

Enhanced hybrid energy storage system combining battery and supercapacitor to extend nanosatellite lifespan

The power limitations of nanosatellites, particularly during peak demand periods such as data transmission and maneuvering, hinder their operational capabilities. Traditional Electrical Power Subsystems relying solely on batteries face challenges in delivering the necessary power density and maintaining battery life, especially during eclipses. This study proposes an innovative Hybrid Energy Storage System for a 3U nanosatellite, integrating high-energy-density batteries with high-power-density supercapacitors, using an active parallel hybrid topology with two bidirectional converters and an optimal power management strategy. Using MATLAB and Simulink models, the study optimizes the Hybrid Energy Storage System by focusing on minimizing the capacity rate and depth of discharge to extend battery life. Simulation results show a 53.42% reduction in depth of discharge compared to a battery-only system, indicating a significant extension of battery life. Additionally, the proposed system allows for a minimal 2.08% increase in overall mass while maintaining enhanced performance. This research fills a critical gap in the literature by exploring active Hybrid Energy Storage System topologies for spacecraft applications, beyond the traditional passive and semi-active configurations. The innovative power management strategy ensures efficient energy utilization, significantly enhancing the reliability and efficiency of nanosatellite missions. The findings offer a practical solution to improve mission performance and extend the operational lifespan of nanosatellites, paving the way for more robust and capable small satellite deployments.

The Effect of Smart Grid Technology on Energy Consumption Management in Canada

Purpose: The aim of the study was to evaluate the effect of smart grid technology on energy consumption management in Canada. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Smart grid technology has significantly enhanced energy consumption management in Canada. By integrating advanced metering infrastructure and real-time data analytics, smart grids optimize energy distribution, reduce peak demand, and enhance grid reliability. This technology enables better demand-response programs and promotes renewable energy integration, leading to more efficient energy use and reduced environmental impact. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & the theory of planned behavior (TPB) may be used to anchor future studies on the effect of smart grid technology on energy consumption management in Canada. Smart grid technology enables more efficient energy usage by providing consumers with real-time data and insights into their consumption patterns. The vast amounts of data generated by smart grids necessitate robust policies to protect consumer privacy and ensure data security. Policymakers should establish clear regulations governing the collection, storage, and use of smart grid data.

Analysis of Waste Energy Utilization in Ballast Load at Suka Sama Micro Hydro Power Plant

The electrical power generated by a Micro Hydropower Plant (PLTMH) should be fully utilized without any wastage. The PLTMH Suka Sama, equipped with an Electronic Load Controller (ELC), diverts unused electrical power to a ballast load. This action is necessary to maintain system frequency stability in the event of excess power during a drop in demand. The utilization of energy from PLTMH Suka Sama can be improved by using the electrical power diverted to the ballast load to process sugarcane juice into palm sugar using a vacuum evaporator. The potential energy wasted to the ballast load will be calculated based on the comparison data of the power used by the PLTMH and the power wasted during peak and low demand periods over 9 days. The study results show that the power diverted to the ballast load ranges from 2,000 W to over 3,000 W for 4-7 hours per day. This research indicates that the wasted electrical energy can be utilized to produce palm sugar using a vacuum evaporator with a capacity of 2,000 W for 4-5 hours. The wasted energy, if utilized, can produce 2.5 kg to 3.5 kg of palm sugar.

Control of dynamic ride-hailing networks with a mixed fleet of autonomous vehicles and for-hire human drivers

This study examines a ride-hailing platform that employs a mixed fleet of autonomous vehicles (AVs) and human drivers to offer on-demand ride-hailing services across a network over a finite planning horizon. In the mixed fleet, AVs have a fixed fleet size and are centrally managed by the platform, while human drivers can dynamically enter and exit from the ride-hailing market based on the prospective earning opportunities at various time periods. We introduce a game-theoretic model that encapsulates the strategic behavior of human drivers, passengers, and the ride-hailing platform. In this model, passengers choose between ride-hailing and alternative transportation options by considering fares and waiting times. Human drivers schedule their working hours in response to potential earnings and strategically relocate to maximize passenger pickups, while the platform adjusts pricing and AV repositioning strategies to maximize its profit. The solution is characterized as a Nash equilibrium within a two-player game, with one player representing the ride-hailing platform, and the other as a virtual representative of the collective human drivers. To address the non-convex nature of the game, we employ a convex relaxation technique to ascertain a near-optimal solution framed as an ϵ-Nash equilibrium, with ϵ accurately characterized. The proposed model and solution algorithm are validated in a case study for Manhattan, New York City. The simulation results underscore that the distribution of human drivers is closely aligned with the immediate, minute-to-minute fluctuations in passenger demand, enabling them to effectively meet the surge during peak demand windows. Conversely, the deployment patterns of AVs are more attuned to the extended, daily cycles of passenger movements throughout different city zones, from daytime to nighttime. We further compare the mixed fleet model with scenarios where the platform exclusively utilizes human drivers or AVs, respectively. The comparison show that the mixed fleet model excels in balancing the supply and demand over space and time, thereby leading to shorter wait times and reduced travel cost for ride-hailing passengers, as well as improved profit for the ride-hailing platform.

Optimization of microgrid operations using renewable energy sources

Microgrids, comprising localized energy systems capable of operating independently or in conjunction with the main grid, are increasingly being recognized as vital components of modern energy infrastructure. The integration of renewable energy sources (RES) into microgrids offers significant potential for enhancing operational efficiency, sustainability, and resilience. This paper presents an overview of recent advancements and methodologies for optimizing microgrid operations utilizing renewable energy sources. The optimization of microgrid operations involves the strategic coordination and management of diverse energy resources, including solar photovoltaic (PV) systems, wind turbines, and energy storage systems (ESS). Key objectives include minimizing operational costs, reducing greenhouse gas emissions, ensuring reliable power supply, and maintaining system stability. Advanced optimization techniques, such as model predictive control (MPC), mixed-integer linear programming (MILP), and heuristic algorithms, play a crucial role in achieving these objectives by enabling the dynamic adjustment of energy generation and distribution in response to real-time conditions. A critical aspect of microgrid optimization is the accurate forecasting of renewable energy generation and load demand. Machine learning (ML) and artificial intelligence (AI) algorithms have been effectively employed to enhance prediction accuracy, thereby improving decision-making processes. Furthermore, the integration of ESS, such as batteries and flywheels, helps to address the intermittency of RES, providing a buffer that can store excess energy during periods of high generation and release it during peak demand. The implementation of demand response (DR) strategies within microgrids further contributes to optimization efforts. By incentivizing consumers to adjust their energy usage patterns in response to price signals or grid needs, DR programs help to balance supply and demand, reduce peak loads, and enhance overall grid reliability. Case studies and field implementations demonstrate the practical benefits of optimized microgrid operations. For instance, microgrids incorporating high shares of RES have been shown to achieve significant cost savings, improved energy security, and reduced environmental impacts. These successes underline the importance of ongoing research and development in optimization techniques and the need for supportive policy frameworks to facilitate the broader adoption of microgrids. In conclusion, optimizing microgrid operations using renewable energy sources presents a promising pathway toward a more sustainable and resilient energy future. Continued advancements in optimization algorithms, predictive analytics, and integrated system design are essential for unlocking the full potential of microgrids, ensuring they can effectively meet the evolving energy demands and environmental challenges of the 21st century. Keywords: Optimization, Microgrid, Operations, Renewable Energy, Energy Sources.

Stochastic Optimal Strategies and Management of Electric Vehicles and Microgrids

This study combines the Nash–Cournot competition model and the stochastic optimization model to examine the impact of electric vehicle (EV) quantity fluctuations on microgrid operations, aiming to optimize energy usage in a competitive electricity market. Integrating distributed energy resources and bidirectional charging, microgrids offer a novel approach for energy optimization, aiding in renewable energy generation, peak demand management, and emission reduction. Empirical evidence highlights benefits in Taiwan’s electricity market and net-zero emissions target by 2050, with a case study demonstrating enhanced local renewable energy generation due to EVs and microgrid integration. As the number of EVs increases, electricity sales from microgrids decline, but electricity purchases remain stable. The degree of electricity liberalization also influences the supply and demand dynamics of the electricity market. Microgrids selling electricity only to the main grid increases total power consumption by 65.55 million MWh, reducing the market share of the state-owned utility (Taipower). Conversely, allowing retailers to purchase from microgrids increases total consumption by 30.87 million MWh with a slight market share decrease for Taipower. This study contributes to providing an adaptable and flexible general model for future studies to modify and expand based on different scenarios and variables to shape energy and environmental policies.

Modeling a game to shift peak water demands: WaterTime leaderboard

Residential water demands vary with a diurnal pattern, and peak hour demands lead to inefficiencies in the operation and management of urban water distribution systems. Peak hourly demands generate significant costs due to the energy requirements of producing and pumping large volumes of potable water. High peak demands also require large investments in infrastructure expansion to support urban growth and economic development. This research develops an agent-based model to assess a demand-side management strategy that shifts high hourly demands. This research envisions a gamification system that rewards players through a leaderboard for shifting water end uses for laundry, irrigation, and bathing from on-peak to off-peak periods. Gamification of demand shifting behaviors is enabled through advanced metering infrastructure, which measures hourly or sub-hourly demands at the account level, and can be analyzed to assess water use behaviors. This research develops an Agent-based Modeling approach based on the Opinion Dynamics theory to calculate the changing opinion of household agents toward shifting water demands, based on utility broadcasts, observations of neighbor water use, social media, and participation in a leaderboard. The model is applied for eight months of hourly demand data collected through smart meters at a participating utility. Results show that a high level of participation in the leaderboard increases the adoption rate of customers shifting the time of specific end uses and may account for up to a 37 % reduction of the peak volume. The diurnal curve is flattened as consumers shift water use from on-peak to off-peak hours. The Agent-Based Model - Opinion Dynamics approach can assist water utilities in designing and evaluating gamification approaches that mitigate peak demands to support water infrastructure management.

Exploring and field-demonstrating geofence-based occupancy-centric control in residential buildings

Several studies have highlighted the thermal comfort challenges associated with occupancy-centric control (OCC) when occupants first arrive, despite OCC demonstrating energy savings potential in buildings. To address this challenge, a geofence-based OCC (Geo-OCC), where the virtual boundary of the occupant’s physical location is infused into the smart thermostat, is explored to pre-condition buildings, aiming to alleviate thermal discomfort. This research adopted an apples-to-apples field testing approach to quantify how Geo-OCC impacts the thermal comfort and energy consumption of the heating, ventilation, and air-conditioning (HVAC) system in two 1,200 ft2 lab homes located in Texas, USA. These two homes were designed and built as identically as they could be. Two side-by-side experimental scenarios were designed to compare different control strategies through integrations with smart thermostats. In scenario 1, Geo-OCC was implemented in the east home, while a baseline case without OCC (Non-OCC) was executed in the west home. In scenario 2, Geo-OCC was employed in the east home, and a traditional OCC based on occupancy presence sensors (Sensed-OCC) was implemented in the west home, demonstrating Geo-OCC’s ability to alleviate thermal discomfort. The field-testing results of scenario 1 suggested that Geo-OCC achieved a 46.41 % reduction in heating energy consumption compared to Non-OCC over the testing duration of 25 days, resulting in a maximum unmet degree (UD) of 3.83 °C in a 5-minute sampling frequency, in contrast to a maximum UD of 0.83 °C in Non-OCC mode. The results from the field testing in scenario 2 indicated that Geo-OCC reduced the maximum UD to 1.94 °C in a 5-minute sampling frequency, compared to 2.28 °C in Sensed-OCC, without an increase in heating energy consumption. The results of the scenario 1 experiment suggested the OCC decreased the HVAC energy consumption while having a slightly adverse effect on thermal comfort during occupied periods, especially upon the first arrival of occupants. The field testing of scenario 2 exhibited the capability of Geo-OCC to enhance thermal comfort with minor effects on the HVAC energy consumption. In addition, Geo-OCC displayed the potential to reduce the peak demand increase caused by traditional Sensed-OCC.

Multi-Agent Reinforcement Learning Optimization Framework for On-Grid Electric Vehicle Charging from Base Transceiver Stations Using Renewable Energy and Storage Systems

Rapid growth in a number of developing nations’ mobile telecommunications sectors presents network operators with difficulties such as poor service quality and congestion, mostly because these locations lack a dependable and reasonably priced electrical source. In order to provide a sustainable and reasonably priced energy alternative for the developing world, this study provides a detailed examination of the core ideas behind renewable energy technology (RET). A multi-agent-based small-scaled smart base transceiver station (BTS) site reinforcement strategy is presented to manage energy resources by boosting resilience so to supply power to essential loads in peak demand periods by leveraging demand-side management (DSM). Diverse energy sources are combined to create interconnected BTS sites, which enable energy sharing to balance fluctuations by establishing a market that promotes economical energy. A MATLAB simulation model was developed to assess the effectiveness of the proposed system by using real load data and fast electric vehicle charging loads from five different base transceiver stations (BTSs) located throughout Pakistan’s southern area. In this proposed study, the base transceiver station (BTS) sites can share their energy through a multi-agent-based system. From the results, it is observed that, after optimization, the base transceiver station (BTS) sites trade their energy with the grid at rate of 0.08 USD/kWh and with other sites at a rate of 0.04 USD/kWh. Therefore, grid dependency is decreased by 44.3% and carbon emissions are reduced by 71.4% after the optimization of the base transceiver station (BTS) sites.

Dynamic Charging Optimization Algorithm for Electric Vehicles to Mitigate Grid Power Peaks

The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new challenges for grid operators and EV owners equally. The uncoordinated nature of electric vehicle charging may lead to the emergence of new peak loads. Grid operators typically plan for peak demand periods and deploy resources accordingly to ensure grid stability. Uncoordinated EV charging can introduce unpredictability and variability into peak load patterns, making it more challenging for operators to manage peak loads effectively. This paper examines the implications of uncoordinated EV charging on the electric grid to address this challenge and proposes a novel dynamic optimization algorithm tailored to manage EV charging schedules efficiently, mitigating grid power peaks while ensuring user satisfaction and vehicle charging requirements. The proposed “Proof of Need” (PoN) charging algorithm aims to schedule the charging of EVs based on collected data such as the state of charge (SoC) of the EV’s battery, the charger power, the number of connected vehicles per household, the end-user’s preferences, and the local distribution substation’s capacity. The PoN algorithm calculates a priority index for each EV and coordinates the charging of all connected EVs at all times in a way that does not exceed the maximum allocated power capacity. The algorithm was tested under different scenarios, and the results offer a comparison of the charging power demand between an uncoordinated EV charging baseline scenario and the proposed coordinated charging model, proving the efficiency of our proposed algorithm, thus reducing the charging demand by 40.8% with no impact on the overall total charging time.