Energy Management Plan Research Articles

Methods of reducing fuel consumption by seagoing vessels in order to reduce emissions of harmful substances into the atmosphere

The aim of the paper is to draw attention to the emissions of toxic compounds into the atmosphere problem by seagoing ships. Annex VI of the MARPOL Convention, which came into force in 2005, concern the prevention of air pollution from ships, forced shipowners to use solutions aimed at limiting the emission of these harmful substances into the atmosphere. One of the instruments enabling the implementation of these guidelines is the design energy efficiency index EEDI, introduced obligatorily for designed and newly built ships. In turn from 2013, the regulations also oblige shipowners to maintain an effective energy management plan during ship's operation, SEEMP (Ship Energy Efficiency Management Plan). The paper presents factors that influence the reduction of fuel consumption by ships, leading to a reduction in emissions of harmful substances into the atmosphere and thus achieving the required EEDI and other indicators. These are factors related to the ship's hull, the main propulsion engine and the propeller-rudder system. The influence of each of these factors on the level of fuel consumption by the ship was indicated. The work highlights the methods of reducing fuel consumption by seagoing ships aimed at reducing emissions of toxic compounds into the atmosphere, which are currently the most popular and effective, and relate mainly to the hull and main propulsion engines.

SHORT INTRODUCTION ON PRESENT DESIGN AND TRENDS REGARDING

This paper covers some aspects regarding present technology used in large crude oil carriers design and operation, as well as requirements regarding energy management on board. Obviously, due to complexity of such subject, there cannot be an exhaustive presentation. It is intended as a mere starting point for further discussion and research regarding possibilities to improve vessel’s general energy efficiency, including ways of using non-conventional and renewable energy, with due regard to specific trading routes involving large oil tankers. Some practical figures achieved typically on a Very Large Crude Oil Carriers engaged in international voyages are presented. A brief explanation on Energy Efficiency Index (EEI) and Carbon Conversion Factors (CF), as accepted by IMO, is also included. Work may be continued with further theoretical research in energy efficiency improvement on board such vessels. The article is structured in five parts: Introduction, Main Particulars of Large Tankers, Oil Tankers Outfitting, Energy Management Plan and Conclusion.

Artificial Intelligence, Smart Applications and Sustainable Consumption: A Theoretical Overview

Sustainable consumption means consuming natural resources consciously, considering future generations. In today's technological age, artificial intelligence and smart applications are used to achieve sustainability goals. In this context, this article examines the impact of artificial intelligence (AI) and smart applications on promoting sustainable consumption behavior. Providing a comprehensive theoretical framework, this article explores how AI technologies support informed decision-making, maximize resource management, and deliver positive environmental impact across a variety of industries. Through a variety of examples, from energy management plans to environmentally friendly retail platforms, the effects of artificial intelligence and smart applications on sustainable consumption are highlighted. This article includes examples of smart applications used to promote sustainable consumption around the world and in Türkiye. Natural challenges that need to be overcome, such as algorithmic biases, data privacy issues and the digital divide, are also mentioned. The article offers recommendations for Türkiye, highlighting the importance of financing digital infrastructure, data privacy laws, digital literacy initiatives and innovation ecosystems, with the aim of emphasizing the importance of artificial intelligence (AI) and smart applications for sustainable consumption.

Exploring a Self-Sufficiency Approach within a Sustainable Integrated Pisciculture Farming System

The pandemic crisis has created significant challenges for small farms, leading to increased energy costs, higher prices for feed and nutrients, unreliable supplies of chemical fertilizers, and disruptions in product sales markets. These factors have collectively compromised the operational viability and economic sustainability of small-scale agricultural enterprises. To address these challenges, this paper explores the concept of a self-sufficient farming system, focusing on locally producing most of the resources needed to sustain operations and reduce dependence on external sources. A self-sufficient integrated pisciculture farming system is proposed and evaluated, promoting an autonomous circular model that prioritizes environmental sustainability. This system incorporates the integration of local livestock into fish diets, production of renewable energy sources, and efficient water and sludge management to reduce reliance on external resources. The detailed methodology used to evaluate sustainability indicators objectively demonstrates that the proposed system can be self-sustainable and autonomous; however, it requires considerable initial investments that can be recovered within at least six years. Optimizing the energy management plan can reduce daily power consumption by up to 25%. However, local conditions may challenge the efficiency of photovoltaic–hybrid energy production, requiring slight oversizing of the system. The research indicated that rearing carp with cereal-based feed mixtures produces growth results comparable to those achieved with commercially purchased feed. The indicators of resource efficiency, reliability, flexibility, productivity, environmental impact, and social impact were met as expected. The weakest indicator was the technology’s potential for scalability, due to its strong dependence on various regional factors.

Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability

As electric vehicles (EVs) are becoming more common and the need for sustainable energy practices is growing, better management of EV charging station loads is a necessity. The simple act of folding renewable power from solar or wind in an EV charging system presents a huge opportunity to make them even greener as well as improve grid resiliency. This paper proposes an innovative EV charging station energy consumption forecasting approach by incorporating integrated renewable energy data. The optimization is achieved through the application of SARLDNet, which enhances predictive accuracy and reduces forecast errors, thereby allowing for more efficient energy allocation and load management in EV charging stations. The technique leverages comprehensive solar and wind energy statistics alongside detailed EV charging station utilization data collected over 3.5 years from various locations across California. To ensure data integrity, missing data were meticulously addressed, and data quality was enhanced. The Boruta approach was employed for feature selection, identifying critical predictors, and improving the dataset through feature engineering to elucidate energy consumption trends. Empirical mode decomposition (EMD) signal decomposition extracts intrinsic mode functions, revealing temporal patterns and significantly boosting forecasting accuracy. This study introduces a novel stem-auxiliary-reduction-LSTM-dense network (SARLDNet) architecture tailored for robust regression analysis. This architecture combines regularization, dense output layers, LSTM-based temporal context learning, dimensionality reduction, and early feature extraction to mitigate overfitting. The performance of SARLDNet is benchmarked against established models including LSTM, XGBoost, and ARIMA, demonstrating superior accuracy with a mean absolute percentage error (MAPE) of 7.2%, Root Mean Square Error (RMSE) of 22.3 kWh, and R2 Score of 0.87. This validation of SARLDNet’s potential for real-world applications, with its enhanced predictive accuracy and reduced error rates across various EV charging stations, is a reason for optimism in the field of renewable energy and EV infrastructure planning. This study also emphasizes the role of cloud infrastructure in enabling real-time forecasting and decision support. By facilitating scalable and efficient data processing, the insights generated support informed energy management and infrastructure planning decisions under dynamic conditions, empowering the audience to adopt sustainable energy practices.

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.

Optimizing power system efficiency and costs in smart buildings with renewable resources

The increasing industrial development and energy demand have necessitated the maximization of available energy use and the deployment of renewable resources. Effective energy management, optimal modeling, and efficient planning are essential to transform the power system into a high-efficiency, optimal model. This study focuses on the initial step of modeling smart buildings (SBs) equipped with non-responsive devices and renewable photovoltaic sources. A comprehensive energy management (EM) plan is formulated for these buildings, incorporating the KNX protocol for solar energy system management. Batteries are integrated into the building model to store energy during periods of low consumption and serve as generators during peak load conditions, with the primary goal of minimizing power system losses and related costs. To address the complexity of this model, whale optimization algorithm (WOA) is employed for optimization. Optimal candidate buses are selected for interconnected building management based on a suggested sensitivity analysis to minimize losses. Cost performance is then assessed, considering energy production and sales. The findings indicate that substantial control of operating costs can be achieved through strategic management of battery charging and discharging, as well as the utilization of photovoltaic units. The proposed model is evaluated across various scenarios using a test system comprising 30 modified system, demonstrating its effectiveness in enhancing energy efficiency and management.

Optimization of Distributed Energy Resources Operation in Green Buildings Environment.

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant's comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant's comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA-GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler.

Internet of Things-Based Robust Green Smart Grid

Renewable energy sources play a critical role in all governments’ and organizations’ energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the generation of green smart grids. Smart grids are critical for modernizing electricity distribution by using new communication technologies that improve power system efficiency, reliability, and sustainability. Smart grids assist in balancing supply and demand by allowing for real-time monitoring and administration, as well as accommodating renewable energy sources and reducing outages. However, their execution presents considerable problems. High upfront expenditures and the need for substantial and reliable infrastructure changes present challenges. Despite these challenges, shifting to green smart grids is critical for a resilient and adaptable energy future that can fulfill changing consumer demands and environmental aims. To this end, this work considers developing a reliable Internet of Things (IoT)-based green smart grid. The proposed green grid integrates traditional grids with solar energy and provides a control unit between the generation and consumption parts of the grid. The work deploys intelligent IoT units to control energy demands and manage energy consumption effectively. The proposed framework deploys the paradigm of distributed edge computing in four levels to provide efficient data offloading and power management. The developed green grid outperformed traditional grids in terms of its reliability and energy efficiency. The proposed green grid reduces energy consumption over the distribution area by an average of 24.3% compared to traditional grids.

Electrical Load Forecasting using Improved Feature Selection and Hybrid Deep Learning Algorithms for Power System Situational Awareness

Accurate electrical load prediction is imperative for effective energy management, grid stability, and resource planning. This research endeavours to enhance forecasting precision by developing an advanced electrical load forecasting model that integrates improved feature selection and hybrid deep learning algorithms. In this study, a novel Electrical Load Forecasting model is meticulously crafted, encompassing phases of pre-processing, feature extraction, feature selection, and the prediction phase. The process initiates with the pre-processing of raw data, employing data cleaning and normalization techniques for ensuring data quality and consistency. Relevant features for load forecasting, including Improved Principal Component Analysis, Skewness, Kurtosis, and Variance-based features, are subsequently extracted. The research introduces a novel Improved Information Gain approach for feature selection, aiming to discern the most informative and discriminative features crucial for precise load forecasting. Additionally, a hybrid classifier model is implemented in the load forecasting phase, synergizing the strengths of deep learning classifiers - the Artificial Neural Network and Long Short-Term Memory. The ANN and LSTM models are adeptly trained using the selected features acquired from the Improved Information Gain approach. The predictions from both LSTM and ANN models are intelligently fused to yield the final load forecasting outcome. This comprehensive approach contributes to the advancement of electrical load forecasting models, providing a robust framework for accurate predictions and informed decision-making in the realm of energy management and planning.

Reinforcement learning-based heuristic planning for optimized energy management in power-split hybrid electric heavy duty vehicles

In this work, we systematically integrate relevant expertise, specifically on the optimal brake-specific fuel consumption (BSFC) curve, battery characteristics and terrain information into the development of an energy management plan for heavy-duty power-split hybrid electric vehicles. We utilize deep deterministic policy gradient (DDPG) algorithm as one of the most sophisticated reinforcement learning technique. Initially, we begin by explaining the vehicle configuration's system modeling. Subsequently, we then present an energy management approach based on deep Q-learning concepts. A novel algorithm, Deep Deterministic Policy Gradient, for energy management control, have been created to combat the “curse of dimensionality” in reinforcement learning. The new AMSGrad optimization technique is used by DDPG algorithm to update the weight of neural networks. We robustly train the proposed control system in realistic driving environment. The Knowledge Incorporation (KI) based DDPG based system is compared systematically to the conventional DDDPG methodology and the benchmark Dynamic Programming (DP) method, the latter of which usually uses the RMSProp Optimizer in its formulation. The results show that as compared to the typical DQL policy, deep reinforcement learning approaches, notably expert Knowledge incorporation KI-DDPG and terrain information with the AMSGrad optimizer, achieve faster training speeds and reduced fuel consumption while maintaining the terminal state of charge (SOC).

Advanced intelligent optimization for electric vehicle charging stations integrated with photovoltaic system and battery storage in commercial buildings

This paper presents a sophisticated four-stage optimization and intelligent control algorithm tailored for two-way electric vehicle charging (EVC) stations integrated with advanced photovoltaic systems and fixed battery energy storage in commercial buildings. The primary objective is to minimize operating costs while prioritizing customer satisfaction within a dynamic and uncertain energy landscape. Our algorithm optimizes the scheduled charging and discharging of electric vehicles (EVs), local battery storage (BS) units, grid power supply, and deferred loads to balance instantaneous supply and demand. The first stage focuses on developing optimal energy management plans for the day ahead, considering factors such as projected energy production, anticipated EVC demand, and building energy consumption patterns. Building on this foundation, the second stage introduces multilayer EV charging price structures and optimizes participation rewards for discharging, dynamically addressing EV charging patterns and price sensitivities. Approaching the commissioning timeline, the third stage refines energy management plans for the upcoming hours using real-time data and forecasts, adapting to evolving conditions for optimal resource allocation. The final stage involves real-time control and the implementation of optimized programs, dynamically adjusting charge/discharge processes, grid interactions, and load deferral to maintain supply-demand balance and minimize operating costs. Our algorithm enhances system resilience in unpredictable conditions, providing compelling incentives for active EV user participation. Coordinating the integrated system efficiently, including the commercial building’s energy load, ensures reliable service to customers while reducing costs. Extensive case studies and a comparative analysis validate the algorithm’s efficiency in significantly reducing operating costs and enhancing resilience to uncertainty. The paper concludes by highlighting the algorithm’s pioneering role in intelligent EV charging station (CHS) management, offering a cost-effective, customer-oriented, and dynamic energy control strategy for advancing global energy practices.

Scenario analysis of energy consumption and related emissions in the transportation industry-a case study of Shaanxi Province.

In the context of pursuing carbon neutrality and balancing the use of fossil fuels with renewable energy, the transportation industry faces the challenge of accurately predicting energy demand, related emissions, and assessing the effectiveness of energy technologies and policies. This is crucial for formulating energy management plans and reducing carbon dioxide (CO2) and atmospheric pollutant emissions. Currently, research on energy consumption and emission forecasting primarily relies on energy consumption quantities and emission factors, which lack precision. This study employs the low emissions analysis platform (LEAP) model, utilizing a "bottom-up" modeling approach combined with scenario analysis to predict and analyze the energy demand and related emissions in the transportation industry. Compared to previous studies, the methodological framework proposed in this research offers higher precision and can explore energy-saving and emission-reduction pathways for different modes of transport, providing a valuable energy forecasting tool for transport policy formulation in other regions. The forecast results indicate that under the business-as-usual (BAU) scenario, by 2049, the energy consumption and related emissions in Shaanxi Province's transportation industry are expected to increase by 1.15 to 1.85 times compared to the baseline year. In the comprehensive (CP) scenario, the industry is projected to reach a carbon peak around 2033. The study also finds that energy consumption and emissions predominantly originate from private passenger vehicles, highway freight, and civil aviation passenger, which have the greatest potential for emission reduction under the transport structure optimized (TSO) scenario. Therefore, policymakers should consider regional development characteristics, combine different transportation modes, and specifically analyze the emission reduction potential of the transportation industry in various regions, formulating corresponding reduction policies accordingly.

A Digital Transformation Framework for Smart Municipalities

Smart cities, as defined by Sustainable Development Goal 11, strive to make cities more inclusive, safe, resilient, and sustainable. Digital technologies addresses urbanisation concerns, such as rising energy use, pollution, waste disposal, and social inequities. The Internet of Things (IoT) and data-driven technologies are essential drivers, with a focus on infrastructure and decision-making in smart cities and municipalities. Digital Transformation (DT) is a prerequisite for becoming a Smart Municipality. The research objective of this paper is to investigate the role of digital technologies in improving urban processes, focusing on Smart City dimensions for municipalities, namely smart governance, environment, living, and technology. Municipalities in South Africa, particularly in the Eastern Cape, used digital adoption to boost productivity and skill development. However, the need for standardised DT principles presents problems for transitioning municipalities into data-driven organisations. The paper also examines the global energy issue and how smart cities can contribute to energy solutions. Finally, the paper addresses the following research question: ‘How can lessons learned from the Eastern Cape Municipalities digital adoption be scaled to other developing nations facing similar challenges in energy management and urban planning?’ Using a survey method, it provides guidelines in the DT framework, offering empirical insights into Smart Municipality digitalisation.

China's local-level monthly residential electricity power consumption monitoring

Timely implementation of electricity power consumption (EPC) analysis and evaluation helps assess energy-saving potential and develop energy management plans. Yet, timely EPC data of subnational regions are usually public unavailable, especially for developing countries. Lacking timely and local EPC data makes it difficult to satisfy the requirements for local governments to quickly monitor energy consumption and establish early warning mechanisms. As a solution, this study establishes a quick accounting method for local residential EPC utilizing nighttime light data, based on which we investigate the variations in residential EPC before and after the COVID-19 pandemic. Our method has demonstrated robust performance in estimation and proven its reliability. Compared to previous studies, the proposed methodology has four advantages. First, it monitors local-level residential EPC data at a monthly scale, which allows for observing EPC statistics at a higher frequency. Second, it separates residential EPC from urban and rural sectors, which is useful for analyzing urban-rural dichotomy energy consumption patterns. Third, it provides a less costly and time-consuming approach to estimating energy consumption over space and time. Fourth, it covers almost the whole China (except Tibet, Hongkong, Macao, and Taiwan due to data unavailability), providing useful information for achieving national energy-saving goals. Based on the methodology, we see great potential for developing “global-country-region” energy monitoring through satellite image data service globally.

Performance assessment of cross office building energy prediction in the same region using the domain adversarial transfer learning strategy

Precise and reliable building energy consumption prediction is of great significance for building energy management and future energy planning. To mitigate the adverse impact of data limitation on the performance of building energy consumption prediction model, a transfer learning strategy was introduced. However, the operation modes of different building energy systems are different, resulting in a large difference in the distribution of building energy consumption related data. In this paper, the relevant data of 25 buildings from the same region of the United States were selected. On this basis, two similarity calculation strategies are compared, and three similarity measurement methods were used to analyze the impact of different source buildings on the performance improvement of transfer learning models. The results show that choosing the appropriate source building can improve the transfer learning performance by up to 82.56% when compared to the no-transfer model (the baseline model). When higher similarity source buildings are selected, the corresponding average PIR is about 24.7% higher than the total average, which can make the transfer model performance improvement more obvious. This paper provides insights into the selection of source domains for transfer learning strategies under the same region.

Etude and Optimization of MPPT Controllers in Photovoltaic Systems with Battery Energy Storage

Abstract Three different types of MPPT techniques will be studied and compared in this work. MPPT is a technology for maximizing the power produced by solar energy systems. Fuzzy logic-based control (FLC), one of the most widely utilized MPPT approaches, is examined, perturbation and monitoring (P&O), and plug-in (Inc). The power and effectiveness of a solar energy system with lead-acid batteries for storage, an energy management plan, and associated photovoltaics In order to confirm the benefit, FL-MPPT was examined in this study, and the performance of its algorithm was compared and evaluated. Using both techniques, P&O and Inc. Using Matlab/Simulink 2018-a tools, simulation results showed that the power quality was higher using MPPTFLC.

신재생 에너지 최적 활용을 위한 축열조 온도 예측 모델 연구

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

Improved energy management of chiller system with AI-based regression

This research aims to enhance energy management in commercial building air-conditioning systems, specifically focusing on chillers, which are significant energy consumers. This study evaluates various regularized regression models using comprehensive time series operating data from a system comprising five chillers of two distinct capacities. Compared with lasso and elastic net regression, ridge regression exhibits superior performance metrics when optimized with the appropriate hyperparameter. This makes it the most suitable method for modeling the system coefficient of performance (SCOP), thereby facilitating the development of an effective energy management plan. Key variables that strongly influence SCOP include the part load ratios of operating chillers, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. This study further identifies July as the month with the highest potential for performance improvement based on the predicted benchmark regions. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters. It highlights the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation, taking into account the distinct performance characteristics and time series features of individual system components. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.

A Short-term Load Curve Intelligent Prediction Method Based on FFT and GA Composite Algorithm and Its Application

In order to reduce the system operation cost of predicting short-term load curves and improve the feasibility of artificial intelligence prediction, the FFT and GA composite algorithm is designed as an artificial intelligence algorithm with the goals of low time complexity, small required sample data, and high flexibility. The algorithm uses mature Fast Fourier Transform and Genetic Algorithm, and improves the flexibility of the prediction method by dynamically adding kernel functions. It can be used for load curve prediction of 96 data points or smaller granularity, and can also reuse the calculation results of big data prediction methods for larger data granularity. By improving the feasibility of individual low-reward data predictions, it can improve the accuracy, sensitivity, and adaptability of energy management plans, accelerate the high-quality development of the power grid, improve the level of digitization, and promote demand response.