Optimize Energy Usage Research Articles

Reducing Uncertainty in Energy Hub Operation: A Demand Response Approach for Improved Cost-Efficiency

Abstract In this paper, a daily programming was performed for an Energy Hub (EH). This EH has a typical electricity generation unit, a boiler, a Combined Heat and Power (CHP), two hectares of solar farm, three wind turbines with different capacities, a battery and a heat storage unit. This hub can purchase electricity from the upstream grid and is also responsible for fully supplying the electrical and thermal loads. Herein, power generation uncertainty by Renewable Energy Sources (RESs), the consumer's electrical and thermal demands as well as the upstream grid electricity market price are modeled by appropriate probability functions. In addition, the combined thermal and electrical energies to consumer are optimized. Moreover, the effect of uncertainty of uncertain parameters is reduced using a Demand Response Program (DRP) by load shifting method. The DRP is applied to both energy forms (electrical and thermal), which reduces hub costs by shifting the load from the hours when energy is expensive and unavailable to the hours when energy is cheap and available. Energy storage devices also shift energy from expensive to cheap hours. The integration of a Demand Response Program effectively minimizes operational costs and enhances the efficiency of the Energy Hub by optimizing energy usage in response to fluctuating prices and availability.

Guest Editorial: Patience in AI Development Is a Virtue: Why 99% Correct Is 100% Wrong

In heavy-asset industries such as oil and gas, precision is crucial. The saying "99% accurate is 100% wrong" reflects this reality. Despite the excitement of new technology, even minor errors can have significant consequences. For example, an artificial intelligence (AI) system inaccurately predicting a critical machinery component's lifespan could lead to unexpected failures, causing costly downtime and safety hazards. Kongsberg Digital, an early adopter of Microsoft's large language models (LLMs), has witnessed AI's transformative power firsthand. Over the past 2 years, these LLMs have driven a resurgence in AI interest. Generative AI's growth presents unprecedented opportunities for the heavy-asset industry, transforming interactions with complex systems. Implementing AI can optimize maintenance schedules, predict failures before they happen, and streamline operations. The rise of generative AI has also spotlighted the more traditional areas of analytics, classification, prediction, and physics-based simulations. This renewed interest has led the oil and gas industry to look for ways to utilize AI to enhance operational efficiency, reduce costs, and improve safety standards. However, AI inaccuracies or “hallucinations” are deal-breakers. AI-generated misinformation can mislead decision-makers, potentially resulting in disastrous outcomes. In some cases, using AI is unnecessary and adds little value. Heavy-asset industries prioritize safety and have historically been conservative in adopting new technologies. While post-ChatGPT developments are significant, the industry lags due to its zero tolerance for failure. This caution is justified; in environments where lives and substantial investments are at stake, even minor errors can be catastrophic. Moreover, precision in AI ensures not only safety and efficiency but also drives sustainability. Accurate AI predictions minimize waste and reduce energy consumption. Inaccurate decisions can lead to excessive energy use and waste, counteracting sustainability goals. AI can lower carbon emissions and reduce the environmental footprint by optimizing energy usage and ensuring regulatory compliance. Building trust in AI requires responsibility in development and implementation, ensuring security without compromise. We must be transparent about data lineage—a principle that guides our transformative journey. For AI to fully integrate into heavy-asset industries, it must be accurate but also secure and trustworthy. This transparency builds confidence among stakeholders, from engineers to executives.

Optimized Smart Home Energy Management System: Reducing Grid Consumption and Costs through Real-Time Pricing and Hybrid Architecture

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management Whale Optimization Algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6%) and energy costs (57.7%) compared to non-scheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

Resilient and Adaptive Secure Routing Protocol for Wireless Sensor Networks Using a Grey Wolf Optimizer and Lightning Search Algorithm

The ability of Wireless Sensor Networks (WSNs) to efficiently monitor and collect data from difficult and distant places has made them a notable technical solution for a variety of applications. It is crucial to optimize energy usage and secure data integrity in order to put these technologies to reality with the Internet of Things (IoT). Maintaining a balance between energy economy, flexibility to changing network circumstances, and security is a common difficulty for contemporary routing systems. Because of this, the performance of these protocols drops and they become vulnerable. Specifically, for WSNs, this research presents HGL-ASRP, a state-of-the-art Resilient and Adaptive Secure Routing Protocol. By integrating the Quantum Assisted Grey Wolf Optimizer (GWO) with the Lightning Search Algorithm (LSA), this protocol overcomes the challenges faced by previous routing protocols in the Internet of Things (IoT) setting. Hybrid GWO takes cues from the social behavior of grey wolves and uses them to solve optimization problems very well. As a result of this hybrid GWO and hypercube sampling, grey wolves can only go to a better area to live. With hypercube sampling, the HGL-ASRP method performs better in a number of areas, including speed (90.9 kbps), end-to-end delay (88.8 ms), packet delivery ratio (91.2%), energy efficiency (92.09%), network lifetime (163.63 hours), and security (87.59%). The HGL-ASRP system ensures continuous data transmission even in demanding environments by successfully managing node failures and changing topologies

Optimizing energy efficiency in unrelated parallel machine scheduling problem through reinforcement learning

The industrial sector plays a significant role in global energy consumption and greenhouse gas emissions. To reduce this environmental impact, it's crucial to implement energy-efficient manufacturing systems that utilize sustainable materials and optimize energy usage. This can lead to benefits such as reduced carbon footprints and cost savings.In recent years, metaheuristic approaches have been focused on minimizing energy consumption within the Unrelated Parallel Machine Scheduling Problem (UPMSP). Traditional methods often overlook complex factors like release dates, due dates, and job setup times. This research introduces a novel algorithm that integrates reinforcement learning (RL) with a genetic algorithm (GA) to address this gap.The proposed RLGA algorithm, rooted in the dynamic field of evolutionary reinforcement learning, breaks down policies into smaller components to isolate essential parameters for problem-solving. Through comprehensive analysis, hyperparameters that influence optimal results are identified, facilitating automated hyperparameter selection and optimization. The expert system takes into account problem characteristics such as machine or job saturation, job overlap, and the maximum values of target variables, allowing instances to be grouped into clusters. These clusters are solved using a genetic algorithm with varying combinations of mutation and crossover hyperparameters. The most suitable approach for each cluster is determined by analyzing the results, and this configuration of hyperparameters is applied iteratively to optimize the solution search.The effectiveness of RLGA is evaluated across benchmark instances with different complexities, machine sets, jobs, and constraints. Comprehensive comparisons against existing methods highlight the superior performance and efficiency of RLGA in optimizing energy use and solution quality. Experimental results show that RLGA outperforms well-known solvers like CPO, CPLEX, OR-tools, and Gecode, making it a promising approach for optimizing energy-efficient manufacturing systems.

Beyond Markovian queue: a heuristic optimization of a fluid queue with a delay-aware retrial strategy for unprecedented energy efficiency in 5G-based ad hoc networks

An energy crisis poses a significant challenge in wireless communication networks and looms large due to power-constrained supply and unforeseeable data traffic. As a solution, optimizing energy usage is crucial for enhancing data transmission efficiency and alleviating data overload. Additionally, addressing delays is vital for maintaining energy stability. Our model is dedicated to optimizing energy levels to significantly prolong the system's performance during data transmission. The proposed work thoroughly analyzes the impact of implementing a delay-aware mechanism using a fluid retrial queueing model to enhance energy optimization. Moreover, pioneered a heuristic approach for fluid queueing analysis to effectively reduce energy usage. Our numerical visualizations validate the analytical results, demonstrating an impressive 83.7% accuracy compared to traditional queueing methods.

Effects of electric vehicles on energy sharing for optimal sizing of solar PV and battery energy storage

Energy sharing for homes with electric vehicles (EVs) enhances sustainability by optimizing energy usage, reducing peak demand, and integrating renewable energy sources, thereby lowering costs and improving energy resilience. This study investigates the effects of EVs on optimal sizing problem of solar photovoltaic (SPV) and battery energy storage system (BESS) for grid-tied homes which participate in energy sharing schemes. In this paper, it is assumed that the energy is shared between two homes: home-1 as the prosumer which has an EV and intends to buy SPV and BESS, and house-2 which is a consumer. The optimization problem is formulated to achieve the minimum cost of electricity (COE) for home-1 and to reduce the COE for home-2 while taking consideration of the design constraints over the project lifespan. A rule-based energy management system is developed for different sets of configurations to compare the economic and operational results. The optimization is done by incorporating realistic annual data of the irradiance, temperature, load, and uncertainties of EV. The developed optimization technique is general in nature and can be used for any grid tied homes willing to share the electricity. Sensitivity analyses on costs of SPV-BESS, home energy demand, and grid export constraints are provided. Uncertainty analyses investigates the price of energy sharing and solar PV generation. The impact of various EV models with their respective battery capacity is also analyzed. The results show that the proposed energy-sharing methodology reduces the COE for prosumer and consumer by 1.2 ¢/kWh and 3.6 ¢/kWh, respectively.

Role of Artificial Intelligence in IoT

Artificial Intelligence (AI) and the Internet of Things (IoT) are two revolutionary technologies shaping modern industries. AI enables machines to learn from data and make autonomous decisions, while IoT connects devices, allowing them to communicate and exchange information. The convergence of these technologies, known as AIoT (Artificial Intelligence of Things), has immense potential to transform daily life and business operations. AI enhances IoT by adding intelligence to device networks, improving efficiency and automating processes. For example, AI-powered IoT systems can perform predictive maintenance in industrial settings, preventing costly machine failures. In smart buildings, AI can optimize energy usage, reducing both costs and environmental impact. However, integrating AI with IoT poses significant challenges. One major issue is the vast amount of data generated by IoT devices, which AI requires to function effectively. Managing this data efficiently is critical. Additionally, IoT devices are vulnerable to cyber-attacks, making security a key concern. Protecting these systems from potential threats is essential to safely realize the benefits of AIoT. Despite these challenges, the combination of AI and IoT promises to drive future advancements across industries, enabling smarter, more connected systems that optimize decision-making, reduce costs, and improve overall efficiency.

An efficient neural network LEACH protocol to extended lifetime of wireless sensor networks

This paper presents NN_ILEACH, a novel neural network-based routing protocol designed to enhance the energy efficiency and longevity of Wireless Sensor Networks (WSNs). By integrating the Energy Hole Removing Mechanism (EHORM) with a sophisticated neural network for cluster head selection, NN_ILEACH effectively addresses the energy depletion challenges associated with traditional protocols like LEACH and ILEACH. Our extensive simulations demonstrate that NN_ILEACH significantly outperforms these classical protocols. Specifically, NN_ILEACH extends the network lifetime to an impressive 11,361 rounds, compared to only 505 rounds achieved by LEACH under identical conditions—representing a more than 20-fold improvement. Additionally, NN_ILEACH achieves a 30% increase in throughput and a 25% enhancement in packet delivery ratio, while reducing overall energy consumption by 40%. These results underscore the protocol’s potential to optimize energy usage and maintain network stability, paving the way for more resilient IoT systems in dynamic environments. Future work will explore further integration of machine learning techniques to enhance adaptability and performance in WSNs.

Low-carbon transformation and ecological safeguarding in the Yellow River Basin: Integrating biomechanical and biological insights

This research, titled “Low-carbon transformation and ecological safeguarding in the Yellow River Basin: Integrating biomechanical and biological insights” explores the interplay between economic activities, land use changes, and environmental impact. Through regression analyses and assessments of land use alterations, the study identifies significant provincial variations in factors influencing carbon emissions. In addition to the socio-economic factors, the research incorporates insights from biomechanics and biology, drawing parallels between the ecological systems of the Yellow River Basin and biological processes such as energy efficiency and resource allocation in living organisms. For instance, just as organisms optimize energy usage and adapt to external stressors, the proposed low-carbon strategies aim to optimize resource use and improve the resilience of the basin’s ecosystem. Proposed strategies for low-carbon transformation provide a practical roadmap for sustainable development, informed by biological principles like ecological balance, regeneration, and the importance of maintaining biodiversity. These principles reflect how biomechanical systems, such as musculoskeletal structures, balance energy expenditure and repair to maintain functionality under strain, similar to how ecosystems must manage resource cycles to withstand environmental stressors. The integration of socio-economic indicators, alongside biological and biomechanical insights, underscores the need for region-specific policies that consider not only economic factors but also the natural regenerative capacities of the ecosystem. The study suggests that, like biological systems that repair and adapt to maintain homeostasis, the Yellow River Basin’s ecological processes can be guided by sustainable management practices to ensure long-term resilience and stability. In conclusion, the research contributes valuable insights to the global discourse on balancing economic growth with ecological preservation in the ecologically vital Yellow River Basin, highlighting how the integration of biomechanical and biological principles can enhance both ecological safeguarding and low-carbon transformation strategies.

Multipath Routing with Dynamic Load Balancing for Enhanced Energy Efficiency in Zone-Based Wireless Ad Hoc Networks

Mobile Ad hoc Networks (MANETs) consist of independent, self-organized mobile nodes capable of wirelessly communicating with each other in a reliable and secure manner. One of the major challenges in MANETs is energy consumption, as the lifetime of a node's battery is limited. This limitation can affect both network connectivity and the lifetime of individual nodes. Various protocols, proposed by the IETF and other solution providers, have aimed to optimize bandwidth, transmission quality, and power control, but none have significantly improved energy consumption. In response to these challenges, the proposed protocol is the Dynamic Load Balancing in Multipath Energy-Consuming Routing Protocol (DLB-MERP) for Wireless Ad hoc Networks. DLB-MERP can be viewed as an extension of the AOMDV protocol and is classified as a zone-based routing protocol, though it incorporates superior load balancing and energy management techniques. It introduces the concept of virtual zones to manage and distribute energy consumption in an organized way. The protocol selects communication paths based on the availability of node energy and power levels. A leader node is defined within each zone, chosen based on its energy level, load, and channel strength, to optimize energy usage. This method increases network resiliency through multipath routing, thereby enhancing overall network performance and extending network lifetime. DLB-MERP effectively addresses the limitations of existing energy-aware routing methodologies by balancing energy consumption, leading to prolonged network operation.

Web Server Based Electrical Control System Analysis for Smart Buildings

Energy management in smart buildings still faces challenges in optimizing energy use, particularly for high-load devices, such as HVAC systems and lighting. Conventional control systems are often inadequate for optimizing energy usage based on the operational needs of the building. This study aims to develop and analyze a web server-based electrical energy control system that can be accessed in real time to improve the energy efficiency of smart buildings. This study employed a quasi-experimental method by implementing a web server-based control system in a smart building and comparing the energy consumption before and after the application of the system. The results show that the system reduces the energy consumption by up to 25%, particularly for HVAC systems and lighting. The most significant energy savings occurred during off-peak hours, when the system automatically reduced power for unnecessary devices. The implications of this research suggest that a web server-based control system not only enhances energy efficiency and reduces operational costs, but also provides greater flexibility in energy management through more adaptive and responsive remote control. This research contributes to the development of more sustainable energy management technologies for smart buildings, with wide potential applications in commercial and institutional building scenarios

Impact of urbanization, economic growth, FDI, and trade openness on energy demand in Ireland: An ARDL approach

Abstract This research study uses an autoregressive distributed lag (ARDL) paradigm to examine the effects of trade openness, urbanization, economic growth, and foreign direct investment (FDI) on energy demand in Ireland. This thorough theoretical framework anticipates a relationship between energy demand and trade, foreign direct investment, and urbanization. This study demonstrates both short- and long-run cointegration among the variables employing an ARDL (autoregressive distributed lag) bounds-testing method for the data set period from 1972 to 2021. Additionally, Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Square (DOLS), and Canonical Cointegrating Regression (CCR) were utilized in the study to verify the results' robustness. Nonetheless, this analysis discovers that trade openness and rising GDP (Gross Domestic Product) per capita increase energy consumption. The results show that for every 1% increase in GDP, energy consumption increases by 0.967%. On the flip side, energy consumption in Ireland is declining due to increased urbanization and foreign direct investment. The results show that for every 1% increase in urbanization, energy usage decreases by 1.738%. The investigation shows that Ireland's energy usage is strongly impacted by trade liberalization, FDI, urbanization, and economic expansion. Findings like these can help policymakers and stakeholders manage and optimize energy usage in the context of urbanization, economic expansion, foreign direct investment, and global trade. The report recommends sustainable energy planning and policy initiatives to strike a healthy balance between meeting the increasing demand for energy and protecting the environment.

Energy consumption modeling in industrial sewing operations: A case study on carbon footprint measurement in the apparel industry

In the textile supply chain, the apparel manufacturing industry heavily relies on energy consumption (EC), contributing to greenhouse gas emissions. Understanding and optimizing energy usage in apparel manufacturing is crucial for sustainable and energy-efficient manufacturing. The EC in the industrial sewing operations is directly linked with sewing parameters, including fabric seam length, standard sewing speed, and stitching time. In general, the carbon footprint of an apparel industry is measured by considering the standard minute value (SMV) and specific energy consumption (SEC). However, these metrics include non-productive time and do not accurately measure actual EC or assess the industry’s carbon footprint. To address these limitations, this case study tackles this technical issue by calculating the stitching time of an industrial sewing machine based on its actual EC derived from the sewing machine’s motor power. This study employs EC modeling for industrial sewing parameters, including fabric seam length, standard sewing speed, and stitching time. This proposed model utilizes real-time data collected during the garment-making process, including fabric spreading, cutting, sewing, and ironing. The study allocates eleven sewing machines for a specific garment style to stitch a basic knit fabric t-shirt. The total EC for the entire garment-making (t-shirt) process at a single line is 0.1052 kWh, equivalent to 59.97 g of CO2 emissions (Bangladesh’s CO2/kWh emission factor). This study explores that a direct-drive servo motor consumes approximately five times less than its maximum motor load power, making it more energy-efficient than other sewing machines. Therefore, this study demonstrates the effectiveness of EC modeling by incorporating industrial sewing parameters and selecting energy-efficient sewing machines to reduce carbon footprints and enhance environmental sustainability in the apparel manufacturing industry.

Communication Technology for Renewable Energy Access Grid System Based on Multi-source Heterogeneous Data Fusion

Abstract Incorporating renewable energy solutions into the power grid is essential for facilitating a shift in energy sources. Ensuring the effectiveness and dependability of communication technologies within smart distribution grids is vital, as these grids serve as the link between end users and the central station, thereby contributing to the grid’s stable functioning and the effective dissemination of energy. This study introduces a framework for communication that tackles the challenges associated with the integration of multi-source, heterogeneous data in smart distribution grids. The framework leverages multi-source heterogeneous data fusion and capitalizes on edge computing to efficiently process and consolidate data from diverse sensors and devices. An optimal weight allocation algorithm is proposed to mitigate data redundancy and to improve the energy efficiency of communication. The paper demonstrates, through theoretical examination and empirical tests, the proposed system’s enhanced communication capabilities, especially in terms of data transmission effectiveness and energy usage optimization. The discussion highlights the advantages of smart distribution networks over conventional communication methods and offers insights for further advancements in communication technologies within this sector.

ENIAO: Energy Aware Faulty Node Re-Placement Integrated With Duty Cycling and Improved Harmony Search Based Clustering through Adaptive Fish School Search Routing for WSN Optimization

In wireless sensor networks (WSNs), sensor nodes are constrained by resource constraints. The limited energy supply and susceptibility to failure greatly affect WSN lifespan, hindering long-term deployment. ENIAO is an Integrated cross-layer Optimized Routing Approach for WSNs that is fault-tolerant and energy-efficient. A bio-inspired clustering architecture and adaptive duty cycling are incorporated into ENIAO's routing optimization. In a clustering protocol, the network is partitioned, and paths are dynamically optimized within and between clusters. By optimizing active/sleep schedules, duty cycling optimizes energy efficiency. A variety of network conditions have been simulated to assess ENIAO's performance. Regarding fault tolerance and energy consumption, ENIAO significantly prolongs the network lifetime. As compared to benchmark protocols, it achieves higher throughput. As a result of the cross-layer design, ENIAO is automatically adapted to optimize energy usage and routing reliability. In the long run, large-scale IoT deployments are possible with ENIAO due to the integrated approach.

A critical insight into the intersection of sustainability and technology

Purpose This paper aims to explore the role of blockchain (BCT) and Artificial Intelligence (AI) technologies in enhancing and incentivizing environmentally responsible, socially inclusive and economically viable tourism practices within the hospitality and tourism (H&T) industry. Design/methodology/approach This study is based on a critical reflection research approach that enables a synthesis of information derived from existing literature's insights and the authors' experiences and observations. By examining frameworks and theories in the literature, critical reflection also helps develop a more comprehensive understanding of the topic. Findings This study portrays how BCT could be used to track the sustainability credentials of tourism providers and how AI can optimize energy usage in hotels. This study depicts how adopting technology-driven sustainable practices in the H&T industry can increase profitability, improve reputation, compliance with regulations, efficiency and a better guest experience. Practical implications The study suggests targeted actions and policy frameworks that can be tailored to different stakeholder groups (hospitality businesses, policymakers and tourists) to overcome barriers and maximize the positive societal and environmental impacts of adopting BCT and AI for sustainable tourism. Originality/value The originality of this study lies in its ability to offer new perspectives and novel recommendations on the diverse uses of AI and BCT in the context of sustainable tourism. Furthermore, the study provides strategic and policy elements (Targeted actions and policy frameworks) for stakeholders to integrate sustainable tourism practices using BCT and AI successfully. This study differs from earlier review studies that primarily focused on adopting emerging technologies and ignoring the sustainability angle in the use of technology.

AI for Sustainable University of Cebu Smart Buildings: Optimizing Energy Consumption through Machine Learning

Artificial Intelligence (AI) technologies to optimize energy consumption in smart buildings. The University of Cebu, one of the leading and largest universities in Asia, is the focus of this study. This study focuses on developing an AI-based system that leverages machine learning algorithms to analyze energy usage patterns, identify inefficiencies, and recommend strategies to reduce energy consumption and promote sustainability in building operations at the University of Cebu. By collecting real-time data from sensors and integrating them with weather conditions and energy tariffs, we aim to create predictive models that optimize energy usage based on various factors. The objective of this research is to design an intelligent system that maximizes energy efficiency, reduces carbon footprint, and minimizes operating costs for the University of Cebu smart buildings. Through a combination of data analysis, algorithm development, and system implementation, we seek to provide a practical and scalable solution for sustainable energy management in a built environment. This study also addresses the ethical considerations of Artificial Intelligence adoption in smart buildings, emphasizing transparency, user Artificial Intelligence in optimizing energy consumption, and fostering environmentally friendly practices in the built environment of the University of Cebu centricity, and privacy.

Nonlinear FOPID controller design for pressure regulation of steam condenser via improved metaheuristic algorithm

Shell and tube heat exchangers are pivotal for efficient heat transfer in various industrial processes. Effective control of these structures is essential for optimizing energy usage and ensuring industrial system reliability. In this regard, this study focuses on adopting a fractional-order proportional-integral-derivative (FOPID) controller for efficient control of shell and tube heat exchanger. The novelty of this work lies in the utilization of an enhanced version of cooperation search algorithm (CSA) for FOPID controller tuning, offering a novel approach to optimization. The enhanced optimizer (en-CSA) integrates a control randomization operator, linear transfer function, and adaptive p-best mutation integrated with original CSA. Through rigorous testing on CEC2020 benchmark functions, en-CSA demonstrates robust performance, surpassing other optimization algorithms. Specifically, en-CSA achieves an average convergence rate improvement of 23% and an enhancement in solution accuracy by 17% compared to standard CSAs. Subsequently, en-CSA is applied to optimize the FOPID controller for steam condenser pressure regulation, a crucial aspect of heat exchanger operation. Nonlinear comparative analysis with contemporary optimization algorithms confirms en-CSA’s superiority, achieving up to 11% faster settling time and up to 55% reduced overshooting. Additionally, en-CSA improves the steady-state error by 8% and enhances the overall stability margin by 12%.

Reviewing Demand Response for Energy Management with Consideration of Renewable Energy Sources and Electric Vehicles

This review paper critically examines the role of demand response (DR) in energy management, considering the increasing integration of renewable energy sources (RESs) and the rise in electric vehicle (EV) adoption. As the energy landscape shifts toward sustainability, recognizing the synergies and challenges offered by RESs and EVs becomes critical. The study begins by explaining the notion of demand response, emphasizing its importance in optimizing energy usage and grid stability. It then investigates the specific characteristics and possible benefits of incorporating RESs and EVs into DR schemes. This assessment evaluates the effectiveness of DR techniques in leveraging the variability of renewable energy generation and managing the charging patterns of electric vehicles. Furthermore, it outlines important technological, regulatory, and behavioral impediments to DR’s mainstream adoption alongside RESs and EVs. By synthesizing current research findings, this paper provides insights into opportunities for enhancing energy efficiency, lowering greenhouse gas emissions, and advancing sustainable energy systems through the coordinated implementation of demand response, renewable energy sources, and electric vehicles.