Electricity Demand Research Articles

Optimizing Smart Power Grid Stability Based on the Prediction of a Deep Learning Model

A smart grid is an electricity transmission system that uses digital technology to control getting and dispatching electricity from all generation sources to satisfy end users' fluctuating electricity demands. It achieves this through deploying technologies such as technology and smart grids, which are pivotal in increasing the power supply's efficiency, reliability, and sustainability to the public. Decentralized Smart Grid Control (DSGC) is a system where the control and decision-making functions are distributed to different grid points instead of in one central place. This paradigm is critical for the fault resistance and efficiency of the grid because it enables the local regions to carry on by themselves, manage electric power flows, respond to changes, and integrate many kinds of energy sources successfully. The grid frequency is monitored via the DSGC to ensure dynamic grid stability estimation. All parties, from users to energy producers, may take advantage of the price of power tied to grid frequency. The DSGC, a vital component of this research, gathered information about clients' consumption and used several assumptions to predict the behavior of the consumers. It establishes a method to assess against current supply circumstances and the resultant recommended pricing information. This research proposes a long short-term memory (LSTM) model to analyze data gathered regarding smart grid characteristics and predict grid stability. The results show a strong capacity for the LSTM model, achieving an accuracy of 96.73% with a loss of just 7.44%. The model also achieves a precision of 96.70%, recall of 98.18%, and F1-score of 97.43%.

Hybrid model for robust and accurate forecasting building electricity demand combining physical and data-driven methods

The high-precision prediction of building electrical demand is paramount significance for optimal control of building systems and the reduction of operational energy consumption. The pure physical simulation software struggles to depict buildings' energy usage behavior accurately; while purely data-driven methods lack the interpretability in real-world physical scenarios. To address the challenges existing in current models and algorithms, this paper proposes a hybrid model that combines physical mechanism simulation and machine learning to complement each other. Empirical Mode Decomposition (EMD) is utilized to decompose complex data into simple components in pre-processing (RMSE, MAE, and SMAPE all decreased by around 0.2–0.3). And K-means clustering algorithm is used to extract features of building electrical data and determine employee working states (R2 increased from 0.3 to 0.85). Shapley Additive explanations (SHAP) values and Pearson correlation coefficients are used for model interpretability analysis. Combining weather forecasts, the Monte Carlo method is employed to obtain prediction intervals. Furthermore, this study explores the use of transfer learning to save computational costs and reduce the amount of data needed for predicting, especially for high-rise buildings or floors with insufficient historical data. The hybrid model results indicate that the R2 can exceed 0.85, up to 0.9, while pure physical simulation can only achieve 0.3. MAE, MSE, and RMSE remain below 0.07, while SMAPE stays around 0.2. The R2 value of the transfer learning model can surpass 0.8 (MSE, MAE. MAE, MSE, and RMSE remain below 0.2, while SMAPE stays around 0.4). The model's residuals obey normal distribution, indicating the model has strong generalization capabilities.

Economic Load Dispatch on a 132 kV Line with Service Potential Transformer Substations: A Case Study of Juja-Rabai Line.

Power outages have created significant challenges for power system networks, particularly in developing countries where the electricity demand continues to rise without a corresponding increase in power generation or the expansion of transmission and distribution networks. In Kenya, while there is a well-established transmission line network, the distribution infrastructure remains inadequate for supplying electricity to end consumers. This paper examines the economic load dispatch (ELD) of power system networks utilizing Service Potential Transformer (SPT) substations to provide electricity to villages located near high voltage (HV) lines. The ELD analysis was conducted to identify the optimal economic power output from the Kipevu, Rabai, and Thika thermal power plants, addressing the demand for both conventional and non-conventional substations. A gradient method was employed to calculate the ELD for these three generating units, and the results were validated using the PowerWorld simulator. Findings indicated that the three generators supplied 20 MW, 37.5 MW, and 12.5 MW, respectively. The results obtained from the gradient method are consistent with those obtained from PowerWorld software. Additionally, this study projected an annual fuel cost savings of USD 17,695.20 when ELD was implemented, compared to a scenario of equal load distribution among generating units. Over a ten-year period, these savings would be sufficient to establish a conventional distribution substation to meet the power demands of villages located further away from high voltage lines.

Effect of electricity policy uncertainty and carbon emission prices on electricity demand in China based on mixed-frequency data models

This paper first constructs the electricity policy uncertainty (EPU) in China with textual methods and analyses the effect of the EPU and carbon emission prices (CEPs) on the total electricity demand. This paper also forecasts the demand for electricity in China with three mixed-frequency data models. The results show that the EPU index efficiently captures the uncertainty of China's electricity policy. The effects of EPU and CEPs on electricity demand are significant, and incorporating them into the forecasting model will improve the accuracy and timeliness. Moreover, compared with the ARMA model and LSTM neural networks, mixed-frequency data models perform better in electricity demand forecasting.

Theoretical study on demand-side management to reduce imbalance between electricity supply and demand

The gap between electricity supply and demand, which is the electricity imbalance, can have negative economic impacts on retail electricity suppliers. This paper presents a methodology for designing demand response (DR) programs while mitigating the negative impacts. The DR programs require the retailers to set target electricity consumption and prices of economic incentives for customers who cooperate with the DR programs. First, the target electricity consumption is determined by solving a problem of social welfare maximization in which the objective function is the sum of economic surplus of the retailers and their customers and impacts of electricity imbalance. The optimal electricity prices or the optimal rebate levels are then calculated under the principle that rational customers act to maximize their economic surplus. Through numerical simulations, the authors confirmed that the proposed methodology mitigated the impacts of electricity imbalance. Meanwhile, results of the numerical simulations showed that in practical situations, there were cases where the retailers unable to reduce the electricity imbalance profitably. Therefore, the authors additionally discuss ways to assess the electricity imbalance, which motivates the retailers to reduce the imbalance, in such cases.

Optimizing electric load forecasting with support vector regression/LSTM optimized by flexible Gorilla troops algorithm and neural networks a case study

This research work focuses on addressing the challenges of electric load forecasting through the combination of Support Vector Regression and Long Short-Term Memory (SVR/LSTM) methodology. The model has been modified by a flexible version of the Gorilla Troops optimization algorithm. The objective of this study is to enhance the precision and effectiveness of load forecasting models by integrating the adaptive functionalities of the Gorilla Troops algorithm within the SVR/LSTM framework. To assess the efficacy of the proposed methodology, a comprehensive series of experiments and evaluations have been undertaken, utilizing authentic data obtained from 200 residential properties located in Texas, United States of America. The dataset comprises historical records of electricity consumption, meteorological data, and other pertinent variables that exert an impact on energy demand. The presence of this general dataset enhances the dependability and inclusiveness of the empirical findings. The proposed methodology was evaluated against various contemporary load forecasting techniques that are widely employed in the industry. The results of a comprehensive evaluation and performance analysis indicate that the modified SVR/LSTM model exhibits superior performance compared to the existing methods in terms of accuracy and robustness. The comparison results unequivocally demonstrate the superiority of the proposed method in accurately forecasting electric load demand.

Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System Under Deep Climate Uncertainty

AbstractClimate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate‐related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

A techno-economic feasibility analysis of solutions to cover the thermal and electrical demands of anaerobic digesters

The use of biogas from anaerobic digestion (AD) has been switching in recent years from electricity to biomethane (BM) production. This choice leads to a higher energy efficiency compared to the use of biogas for combined heat and power (CHP) production, but results in a higher electricity demand for the upgrading and in the necessity of a heat supply for the digesters. This study analyses possible technical solutions, such as heat pumps or wood chip boilers to cover the thermal needs of the digester and photovoltaic panels (PV) to supply electricity. Three feedstocks were considered with high (organic waste), low (livestock manure), and intermediate (sewage sludge) biochemical methane potential (BMP), resulting in different electricity and heating needs. Results show that heat pumps (HP) are an economically viable solution for medium/low BMP feedstocks, for which relative payback times are in the range 1.5–5.4 years; in these cases, a reduction of 47–83 % of greenhouse gases (GHG) emissions and of 59–68 % of non-renewable primary energy is also achieved. For high BMP feedstocks, economic benefits are possible only with low electricity cost (below 130€/MWhel), and a reduction of GHG emissions is possible only using low-carbon electrical energy (below 202 kgCO2eq/kWh).

Sky-Scanning for Energy: Unveiling Rural Electricity Consumption Patterns through Satellite Imagery’s Convolutional Features

The pursuit of the Sustainable Development Goals has highlighted rural electricity consumption patterns, necessitating innovative analytical approaches. This paper introduces a novel method for predicting rural electricity consumption by leveraging deep convolutional features extracted from satellite imagery. The study employs a pretrained remote sensing interpretation model for feature extraction, streamlining the training process and enhancing the prediction efficiency. A random forest model is then used for electricity consumption prediction, while the SHapley Additive exPlanations (SHAP) model assesses the feature importance. To explain the human geography implications of feature maps, this research develops a feature visualization method grounded in expert knowledge. By selecting feature maps with higher interpretability, the “black-box” model based on remote sensing images is further analyzed and reveals the geographical features that affect electricity consumption. The methodology is applied to villages in Xinxing County, Guangdong Province, China, achieving high prediction accuracy with a correlation coefficient of 0.797. The study reveals a significant positive correlations between the characteristics and spatial distribution of houses and roads in the rural built environment and electricity demand. Conversely, natural landscape elements, such as farmland and forests, exhibit significant negative correlations with electricity demand predictions. These findings offer new insights into rural electricity consumption patterns and provide theoretical support for electricity planning and decision making in line with the Sustainable Development Goals.

A decentralized energy management scheme for a DC microgrid with correlated uncertainties and integrated demand response

Secure operation of a DC microgrid with high penetration of renewables and electric vehicle load is challenging. This paper proposes a decentralized energy management scheme for a grid-connected renewable integrated community DC microgrid considering the water-energy nexus. Uncertainties of renewable generation, plug-in electric vehicle load, electricity demand, electricity price in the day-ahead wholesale market, ambient temperature, and water demand are modelled using a probabilistic approach. Correlation between the input random variables is modelled using Copula theory. Flexibilities on the consumer side across multiple entities (temperature-dependent loads, electric vehicles, and water supply system storage) are coordinated to form an “integrated demand response entity”, which is further coordinated with the DC microgrid operator side flexibility (electrochemical storage) to support system operation. A distributed dynamic pricing scheme is used to implement the integrated demand response program. The objectives of the energy management scheme are to minimize the DC microgrid operator’s operating cost and consumers’ electricity cost. The decentralized algorithm is solved by the “alternating direction method of multipliers”. Simulation studies on a six-bus DC microgrid test system demonstrate that the proposed strategy reduces the operating cost of the DC microgrid operator by ∼19.28% and the electricity cost of the consumers by ∼13.82%.

Addressing the reliability challenge: Subsidy policies for promoting renewable electricity consumption

The intermittency and volatility of renewable electricity pose challenges to supply reliability, which is not conducive to renewable energy consumption. To ensure a reliable electricity supply, more governments implement subsidy policies to promote the adoption of innovative technologies by renewable energy producers to enhance supply reliability. We compare two types of subsidies provided by a government: investment subsidy (IS) policy, which is implemented in the deployment stage to directly reduce improvement costs, and operational subsidy (OS) policy, which is implemented in the operational stage to increase the renewable energy producer’s marginal returns. First, we show that without government intervention, customers’ low green consciousness or higher improvement costs may prevent the renewable energy producer from enhancing supply reliability. Second, through a comprehensive comparison, we find that both subsidy policies can incentivize the renewable energy producer to improve supply reliability when customers are more green-conscious, and the improvement cost is high. However, the OS policy and the IS policy operate on different mechanisms: the IS policy can directly alleviate the improvement cost burden on the renewable energy producer, while the OS policy serves a dual role of increasing the renewable energy producer’s marginal operational profit and expanding the market demand for renewable electricity. When customers’ green consciousness is low, the government can only choose whether or not to implement the OS policy. Finally, we highlight that the implementation of the IS policy by the government may not be more beneficial to both the renewable energy producer and customers compared to the OS policy. This result informs regulators that energy security should be considered when designing subsidy policies and should not be limited to promoting the interests of participants.

Flexible operation of nuclear hybrid energy systems for load following and water desalination

Nuclear hybrid energy systems (NHES) have the potential to provide dependable and emission-free electricity to the grid while also increasing the flexibility and reliability of the electrical grid. Molten salt reactor (MSR) technology can provide consistent, carbon-free electricity while also increasing efficiency, security, and sustainability and reducing nuclear waste. This study investigates the integration of Molten Salt Reactors (MSR) and conventional Pressurized Water Reactors (PWR) with desalination technologies: Direct Contact Membrane Distillation (DCMD), Multi-Stage Flash Distillation (MSFD), and Reverse Osmosis (RO). Dynamic first-principles models were developed and tested using real grid data from the New York Independent System Operator. The results demonstrate that nuclear power is capable of flexibly responding to changing grid demand while simultaneously producing clean water, particularly during periods of low electricity demand. The MSR-RO system was found to be the most efficient in electricity generation and water production, and all hybrid systems reduced CO2 emissions by 356,000 to 682,000 tons annually. Economic analysis reveals that nuclear desalination technologies are cost-competitive with conventional systems, especially when paired with RO. These findings confirm the technical feasibility and environmental benefits of nuclear hybrid systems for sustainable electricity and water production.

On the Different Fair Allocations of Economic Benefits for Energy Communities

Energy Communities (ECs) are aggregations of users that cooperate to achieve economic benefits by sharing energy instead of operating individually in the so-called “disagreement” case. As there is no unique notion of fairness for the cost/profit allocation of ECs, this paper aims to identify an allocation method that allows for an appropriate weighting of both the interests of an EC as a whole and those of all its members. The novelty is in comparing different optimization approaches and cooperative allocation criteria, satisfying different notions of fairness, to assess which one may be best suited for an EC. Thus, a cooperative model is used to optimize the operation of an EC that includes two consumers and two solar PV prosumers. The model is solved by the “Social Welfare” approach to maximizing the total “incremental” economic benefit (i.e., cost saving and/or profit increase) and by the “Nash Bargaining” approach to simultaneously maximize the total and individual incremental economic benefits, with respect to the “disagreement” case. Since the “Social Welfare” approach could lead to an unbalanced benefit distribution, the Shapley value and Nucleolus criteria are applied to re-distribute the total incremental economic benefit, leading to higher annual cost savings for consumers with lower electricity demand. Compared to “Social Welfare” without re-distribution, the Nash Bargaining distributes 39–49% and 9–17% higher annual cost savings to consumers with lower demand and to prosumers promoting the energy sharing within the EC, respectively. However, total annual cost savings drop by a maximum of 5.5%, which is the “Price of Fairness”.

A novel energy management of public charging stations using attention-based deep learning model

Electricity grids are complex systems that must balance the supply and demand of electricity in real-time. However, with the increasing adoption of electric vehicles (EVs), managing the grid’s stability has become more challenging. EV charging can cause spikes in electricity demand, leading to peak demand periods that strain the power grid’s infrastructure. With the help of load forecasting, this effect on the grid can be mitigated by predicting the charging demand of electric vehicles in advance. This will help utilities adjust their energy supply in real-time, ensuring enough energy is available to meet demand, and preventing overloads or under utilization of the grid. Moreover, the EV charging demand is influenced by a wide range of factors, including charging station locations, weather, and time of day. Therefore, advanced deep learning models are required to learn these complex relationships and identify patterns in EV charging demand, enabling utilities to make more informed decisions. In this research, an attention-based deep learning approach is proposed for more accurate prediction of EV load demand. This novel approach integrates attention mechanisms with traditional deep learning models like LSTM and GRU, allowing the model to dynamically weight the importance of different features and focus on the most relevant information. The outcomes are compared to conventional deep learning and machine learning algorithms. To test the efficacy of the proposed framework, an actual ACN dataset for public EV charging stations is utilized.

Improved TLBO algorithm for optimal energy management in a hybrid microgrid with support vector machine-based forecasting of uncertain parameters

The worldwide need for electrical energy is increasing, and integrating renewable energy sources (RES) into the power grid will enhance the efficient use of clean energy to fulfill the growing demand for energy. However, the uncertain nature of power from the RES like solar and wind, utility price, and load demand, necessitates accurate forecasting of the uncertain parameters (UP) to improve the reliability of the hybrid microgrid. In this work, optimal energy management (EM) of a hybrid AC-DC microgrid (HMG) is proposed which comprises of two phases, forecasting and scheduling. In the former phase, the uncertainties like day-ahead utility price, electrical demand, and power from the RES are forecasted using the support vector machine (SVM) algorithm and the results are compared with the artificial neural network (ANN) algorithm. In the second phase, the improved Teaching and Learning-Based Optimization (ITLBO) algorithm isused to reduce the generation costs over a 24-h period in a hybrid microgrid. The forecasted uncertain parameters are used as input in the second phase. Power trading occurs between the utility grid and the hybrid microgrid based on load demand and bidding costs, aiming to minimize generation costs. The proposed framework's viability and performance are assessed using IEEE standard test systems. The generating cost, as well as the optimal power dispatch of the HMG, is obtained using the ITLBO algorithm, and the results are compared with different meta-heuristic techniques such as the teaching and learning-based algorithm (TLBO), Ant Lion Optimization algorithm (ALO) and the artificial bee colony algorithm (ABC). The results obtained demonstrate the superiority of the SVM algorithm in forecasting and the ITLBO algorithm over other methods in minimizing operating costs.

A Systematic Review of Expert Systems for Improving Energy Efficiency in the Manufacturing Industry

Against the backdrop of the European Union’s commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements, thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art ES approaches aimed at improving energy efficiency in industry with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, this review reveals research gaps, pointing toward promising topics for future research.

Research on the Cable-to-Terminal Connection Recognition Based on the YOLOv8-Pose Estimation Model

Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system’s diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects.

DESENVOLVIMENTO DE PROTÓTIPO UTILIZANDO MICROCONTROLADORES PARA CONTROLE DE EFICIÊNIA ENERGÉTICA EM RESIDÊNCIAS UNIFAMILIARES

The search for solutions that aim to improve the way we use electrical energy in our daily lives is of great importance when it comes to sustainability. Energy efficiency plays a fundamental role in the development of sustainable technologies. In a period of high demand for electricity and concerns about the environment, energy efficiency has become increasingly important, both in industrial and domestic applications. In this scenario, microcontrollers play a fundamental role in optimizing and controlling electrical energy. Among its various applications is monitoring and managing energy consumption in real time, making intelligent decisions to minimize waste. These systems, when integrated with sensors and other devices, allow the development of solutions that not only save energy, but also make environments smarter and more sustainable. Observing this context, this study aims to develop a device capable of providing accurate information about the electrical energy consumption of equipment. The developed prototype aims to provide detailed information about the energy consumption of monitored equipment, contributing to better energy management. The developed energy meter prototype demonstrated the potential to contribute significantly to energy efficiency, by providing accurate and real-time data, thus resulting in a more intelligent use of the energy consumed, as, with knowledge of real-time data, it is possible to monitor and work on energy optimization.

Environmental and Social Life Cycle Assessment of Data Centre Heat Recovery Technologies Combined with Fuel Cells for Energy Generation

The energy sector is essential in the transition to a more sustainable future, and renewable energies will play a key role in achieving this. It is also a sector in which the circular economy presents an opportunity for the utilisation of other resources and residual energy flows. This study examines the environmental and social performance of innovative energy technologies (which contribute to the circularity of resources) implemented in a demonstrator site in Luleå (Sweden). The demo-site collected excess heat from a data centre to cogenerate energy, combining the waste heat with fuel cells that use biogas derived from waste, meeting part of its electrical demand and supplying thermal energy to an existing district heating network. Following a cradle-to-gate approach, an environmental and a social life cycle assessment were developed to compare two scenarios: a baseline scenario reflecting current energy supply methods and the WEDISTRICT scenario, which considers the application of different renewable and circular technologies. The findings indicate that transitioning to renewable energy sources significantly reduces environmental impacts in seven of the eight assessed impact categories. Specifically, the study showed a 48% reduction in climate change impact per kWh generated. Additionally, the WEDISTRICT scenario, accounting for avoided burdens, prevented 0.21 kg CO2 eq per kWh auto-consumed. From the social perspective, the WEDISTRICT scenario demonstrated improvement in employment conditions within the worker and local community categories, product satisfaction within the society category, and fair competition within the value chain category. Projects like WEDISTRICT demonstrate the circularity options of the energy sector, the utilisation of resources and residual energy flows, and that these lead to environmental and social improvements throughout the entire life cycle, not just during the operation phase.

Flexibility justice: Exploring the relationship between electrical vehicle charging behaviors, demand flexibility and psychological factors

The adoption of electric vehicles (EVs) is transforming the landscape of energy consumption. While the technical and economic dimensions of EV adoption are increasingly well understood, the aspect of justice in demand flexibility remains underexplored. This study examines the complex relationship between flexibility in EV charging behaviors and the influence of socio-psychological and justice factors. We explore a range of demographic and social-psychological variables including charging anxiety, environmental concerns, perceived cost-saving perception, perceived privacy, and trust in utility providers. Our results reveal that these variables positively influence the changes in charging habits, including time-shifting and load-reduction. This study also uncovers disparities in charging behavior adjustments across various demographics groups. For instance, White respondents are more likely to charge their EVs during off-peak hours than their non-White counterparts and homeowners show a greater intention to reduce EV charging load during peak hours compared to renters. Additionally, high-income individuals exhibit a stronger willingness to shift charging times to off-peak, with White respondents within the high-income group being the most likely to reduce the amount of charging load during peak hours. Conversely, low-income White respondents are less inclined to make such adjustments. These disparities are likely tied to socioeconomic status, as more vulnerable groups often face greater constraints in adjusting their schedules. Therefore, it is imperative that policies prioritize flexibility justice by addressing the specific needs and behaviors of vulnerable groups, aiming to mitigate the additional burdens resulting from their limited flexibility.