Energy Management Performance Research Articles

Energy management and demand side management framework for nano-grid under various utility strategies and consumer’s preference

This research proposes a day-ahead scheduling utilizing both demand side management (DSM), and Energy Management (EM) in a grid-tied nanogrid comprises of photovoltaic, battery, and diesel generator for optimizing the generation cost and the energy not supplied (at grid-outage). Wider terminology is introduced to combine both load controllability (considered in traditional DSM), and interval capability to accommodate additional loads defined as flexible, non-flexible, and semi-flexible intervals. Moreover, the user selection for EM or combined operation of EM with DSM at different degrees of interval flexibility is defined as user preference. In addition, three utility’s operations are considered denoted as fixed rate pricing (FRP), time-of-use (ToU) pricing, and FRP with grid-outage. Hence, the suggested framework utilizes the opportunities of generation diversity, the electricity pricing strategy, and the load flexibility. The obtained result show that, DSM with flexible intervals reduces the cost by 21.02%, 25.23%, and 18.15% for FRP, ToU, and FRP with grid-outage scenarios respectively. And cost reduction by 20.41%, 22.42%, and 17.81% for DSM with semi-flexible intervals and 16.24%, 21.15%, and 13.8% for DSM with non-flexible intervals. This cost reduction is associated with full utilization of renewable energy generation and reduction of the energy from/to battery which enhances its lifetime or reduces the required battery size during design stage for cost and provisions saving in flexible and semi-flexible intervals. A hybrid optimization technique of Moth-flame optimization algorithm, and Lagrange’s multiplier is proposed and confirms its effectiveness with detailed comparison with other techniques.

Sustainable Energy Solutions in Sub-Saharan Africa: Integrating Indigenous Knowledge and Climate Resilience for Lower Carbon Emissions

Promoting sustainable energy solutions in sub-Saharan countries is crucial for addressing energy poverty, reducing carbon emissions, and fostering long-term environmental and economic sustainability. This study explored using indigenous knowledge and emerging technologies to reduce carbon footprints and GHG emissions in Africa's climate hotspots in sub-Saharan countries. The study revealed that operating institutions utilize various applications to ensure that energy management resources can mitigate the effects of carbon emissions. The study revealed that the most efficient use of natural resources for energy production requires collaboration among governments, private sectors, NGOs, and local communities. By adopting a holistic and inclusive approach, one can work toward a more sustainable and low-carbon energy future. This paper focuses on carbon footprint analysis and proposes solutions to address environmental issues in implementing sustainable energy solutions in sub-Saharan countries. A multifaceted approach involving effective strategies is needed to lower the carbon footprint. The contribution of this study is to improve energy consumption in communities in Africa by integrating climate resilience considerations into sustainable energy projects to ensure long-term viability. This will involve planning for changing climate conditions, such as extreme weather events, and designing infrastructure that can withstand and adapt to these challenges. It has been concluded that carbon footprint analysis is useful for determining the impacts of carbon particles in the world’s atmosphere. The role of energy management operations seeks to improve the assessment and analysis of carbon footprints by allowing atmospheric measurements of carbon.

Energy management strategy and operation strategy of hybrid energy storage system to improve AGC performance of thermal power units

With the continuous increase of the renewable energy power generation penetration, the intermittency and fluctuation of renewable energy power generation make the traditional thermal power units (TPU) which play the main role of regulation need to undertake more frequent and more severe regulation tasks. In order to improve the automatic generation control (AGC) command response capability of TPU, an operation strategy of hybrid energy storage system (HESS) is proposed in this paper. While assisting TPU to complete the regulation tasks, it gives full play to the advantages of power-type and energy-type energy storage. Moreover, an energy management strategy of energy storage array (ESA) is proposed to improve the overall operation efficiency of ESA while making the state of charge (SOC) of all energy storage subunits consistent. Finally, the effectiveness of the proposed strategy is verified by simulation experiments. The results show that after using HESS to assist TPU operation, the comprehensive regulation performance index of TPU increased from 4.0198 to 7.8112, which significantly improved the operational flexibility of TPU. Meanwhile, the strategy proposed in this paper makes different types of energy storage systems in HESS operate in a relatively healthy SOC range, and the SOC of the flywheel energy storage system (FESS) reaches the limitation value decrease from 6 times to once, ensuring the overall charge and discharge capacity of HESS.

Performance evaluation and energy management strategy of drone methanol reforming fuel cell hybrid power system: A case study

The performance of hydrogen production by methanol steam reforming and high temperature proton exchange membrane fuel cells on drone lacks the comparison of experimental data, resulting in the overall performance improvement of the system is difficult to evaluate. Therefore, the energy management strategy of methanol reforming high temperature proton exchange membrane fuel cell hybrid power system and state machine is designed and optimized. The performance of this system is analyzed and compared with the flight experiment data of a proton exchange membrane fuel cell hybrid UAV. The results show that the SOC of the system can be stabilized between 70 and 80 during the long cruise phase. The test results show that the maximum hydrogen storage mass density of the hydrogen supply system is increased by 67.6%. The flight time of the system under economic cruise flight of the drone is improved by 29.1%–44.9% with different quality of fuel. Under the maximum flight speed of a certain wind speed, the system can meet the flight requirements and increase the flight time by 31.9% and 17.6% respectively under the wind speed of 1.5 m/s and 4 m/s. Its flight time has been greatly improved, and it has great potential as a drone system.

Data-driven adaptive Lyapunov function based graphical deep convolutional neural network for smart grid congestion management

Optimal power flow by leveraging network grid topology will ensure stable operation of the smart grid. Energy management in grid-connected systems aimed to reduce computational non-linearities and ensure reliable operation of the smart grid. The conventional method manages congestion with optimal scheduling for every 10–15 min. Hence congestion in the smart grid occurs during secured energy distribution. In smart grid, instant congestion and energy management are needed. This research, work is devoted to a novel data-driven adaptive Lyapunov function with a Graphical Deep Convolutional Neural Network (GDCNN) regulated optimal flow by accurate energy management. By employing novel Graph theory-based network, the congestion data are obtained to train the proposed GDCNN. A Comparison of obtained results with existing baseline methods has been carried for claiming the novelties of proposed GDCNN. It is observed, that compared to existing machine learning-based extended subspace identification techniques. Our method has better optimal power regulation within 1.8 s by controlling power sources. Also, numerical simulation on IEEE 68 bus system shows the proposed GDCNN have superior performance, reliability, and optimal energy management. This by integrating the benefits of adaptive Lyapunov function and graphical convolutional network.

Optimizing Microgrid Operation: Integration of Emerging Technologies and Artificial Intelligence for Energy Efficiency

Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies published between 2014 and 2024. This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Emerging technologies like artificial intelligence (AI), the Internet of Things, and flexible power electronics are highlighted for enhancing energy management and operational performance. However, challenges persist in integrating AI into complex, real-time control systems and managing distributed energy resources. This review also identifies key research opportunities to enhance microgrid scalability, resilience, and efficiency, reaffirming their vital role in sustainable energy solutions.

Application of energy sustainability model based on optical sensing technology in intelligent warehousing performance management in green manufacturing industry

As the global focus on sustainable development continues to grow, green manufacturing, as a way to optimize resource use and reduce environmental impact, needs to be combined with advanced technologies to improve its efficiency. This study aims to explore the application of energy sustainability model based on optical sensing technology in smart storage of green manufacturing industry, evaluate its impact on storage performance management, and further promote the development of green manufacturing industry. In this paper, an optical sensing system is constructed to monitor and analyze the energy consumption in storage environment in real time. In this study, a green manufacturing enterprise was selected as an example, and the optical sensor was used to monitor energy consumption, inventory status and environmental parameters in real time to ensure the real-time and accuracy of data. The collected data is analyzed to identify patterns and anomalies in energy use to optimize inventory management and logistics scheduling. Evaluate the energy efficiency of storage systems by setting energy efficiency indicators and compare them with traditional models to quantify improvements. On the basis of monitoring and analysis, the energy management strategy is continuously adjusted to enhance the adaptability and flexibility of the intelligent storage system. The research results show that the application of optical sensing technology can significantly improve the energy management performance in warehousing, through real-time monitoring and intelligent optimization, enterprises can achieve more efficient energy use, further promote the implementation of green manufacturing, and provide strong support for the realization of environmental and economic benefits.

An improved data-driven predictive optimal control approach for designing hybrid electric vehicle energy management strategies

This paper proposes an improved “prediction + optimal control” method for energy management in hybrid electric vehicles equipped with planetary gears. A differentiable predictor and a differentiable optimal controller are developed using supervised learning and reinforcement learning approaches, respectively. Three training steps are performed for the initial predictor, the optimal controller, and the final predictor. This method improves the traditional energy management predictive optimal control approach by incorporating an additional step of retraining the differentiable predictor. This adjustment ensures that the predictor does not blindly improve its performance based on evaluation criterion irrelevant to energy management, which was commonly used in previous studies. Instead, it focuses on enhancing the overall performance of energy management under the “prediction + optimal control” framework. The approach introduced in this paper is compared with the globally optimal dynamic programming results and traditional predictive optimal control methods on the Next Generation Simulation (NGSIM) data. Our method outperforms traditional approaches in energy management on both the training dataset and the test dataset. This further illustrates that the conventional practice of presumptuously optimizing predictors in “prediction + optimal control” methods can be improved using the proposed method.

Enhancing sustainability in electric mobility: Exploring blockchain applications for secure EV charging and energy management

The global shift towards reducing carbon emissions has spurred a growing interest in electric vehicles (EVs) over traditional fossil fuel-based internal combustion engines. Concurrently, blockchain technology has emerged as a transformative solution across various sectors, characterized by its decentralized database and distributed ledger. Its notable attributes, including transparency, immutability, traceability, security, auditability, and authenticity, have led major companies to recognize its potential integration into the EV sector. As a result, there has been a steady increase in case studies and research initiatives exploring blockchain applications in EVs. This article provides a detailed overview of the use of blockchain in the EV domain, focusing on the challenges and applications in four key areas: EV charging infrastructure, energy trading, supply chain management, and energy management. Initially, the basic concepts of blockchain technology are briefly explained, followed by its applications in different fields. The study presents the opportunities and crucial requirements for implementing blockchain technology for secure energy trading, charging, energy management, and supply chain operations. While blockchain can improve security and efficiency in the EV domain, it remains vulnerable to various attacks. This article explores these attacks and other potential challenges in integrating blockchain technology in the EV sector. The main findings of this rEView provide insights into the importance of blockchain in the EV sector and underscore the need to overcome existing challenges for the widespread adoption of this technology in the near future.

Two-stage data-driven optimal energy management and dynamic real-time operation in networked microgrid based on a deep reinforcement learning approach

Given the significant challenges posed by the vast and diverse data in energy management, this study introduces a two-stage approach: optimal energy management system (OEMS) and dynamic real-time operation (DRTOP). These stages employ a multi-agent policy-oriented deep reinforcement learning (DRL) approach, aiming to minimize operating and energy exchange costs through interactions in the networked microgrid (NMG) energy market. The primary objectives include minimizing the distribution system operator (DSO) cost and optimizing the exchanged power between DSO and NMG, and the power transmission losses and the secondary include minimizing MG’s operating cost, optimal use of renewable energy resources (RER) and energy storage systems (ESS), minimizing the exchanged power cost with the main grid and, risk analysis. The OEMS&DRTOP model is developed based on the Stackelberg game theory and the DRL structure. The DRL model is developed in two offline learning and online distributed operation phases to minimize the computational burden, time, and DRL operation process. This study’s results show the high efficiency of the presented approach to minimizing the operating cost, the exchanged power based on the price uncertainty, power transmission losses, and, RER and ESSs optimal participation. In addition, regarding computational load, the proposed concept demonstrates a 12.9% reduction compared to the dueling deep Q-network method and a 17% reduction compared to the deep Q-network method. Also regarding computational time, the proposed concept demonstrates a 17.13% reduction compared to the dueling deep Q-network method and a 25.6% reduction compared to the deep Q-network method.

A reinforcement learning-based online learning strategy for real-time short-term load forecasting

Real-time Short-Term Load Forecasting (STLF) is crucial for energy management and power system operations. Conventional Machine Learning (ML) methodologies for STLF are often challenged by the inherent variability in energy demand. To tackle the challenge associated with inherent variability, this paper presents a novel Reinforcement Learning (RL)-enhanced STLF method. Different from conventional methods, our method dynamically improves the STLF model by selecting optimal training data to capture recent power usage trends and possible variations in demand patterns. By doing so, our method can significantly reduce the impact of unforeseen fluctuations in real-time forecasting. In addition to the novel RL-enhanced STLF method, we propose a comprehensive evaluation framework, encompassing three key dimensions: accuracy, runtime efficiency, and robustness. Tested on three distinct real-world energy datasets, our RL-enhanced method demonstrates superior forecasting performance across three evaluation metrics by achieving accurate and robust predictions in real-time under varying scenarios. Furthermore, our approach provides uncertainty bounds for practical predictions applications. These results underscore the significant advancements made by our RL-based method in forecasting precision, efficiency, and robustness. We have made our algorithm openly accessible online to promote continued development and advancement of STLF methods.

Model-based control strategy with linear parameter-varying state-space model for rack-based cooling data centers

Cooling system energy consumption occupies approximately 40% of the total energy consumption in data centers, and efficient control is crucial for improving cooling efficiency and reducing the operational costs of data centers. This paper introduced a novel economic model predictive control (EMPC) strategy based on a linear parameter-varying state-space model. The primary objective was to maintain the thermal environment of data centers while minimizing the energy consumption of the cooling system. Therein, a linear parameter-varying state-space model was developed based on the parameter identification method to precisely predict the temperature field of the rack-based cooling data center in real-time. The energy management and thermal regulation performance of the EMPC and the proportion integration differentiation (PID) controller were comparatively analyzed through simulation experiments. Moreover, the impacts of the server workload level and optimization time domain on the performance of EMPC were comprehensively investigated. The results show that, the proposed EMPC was able to minimize the energy consumption by optimizing the supplied cold air temperature and airflow rate simultaneously. In comparison with the PID controller, EMPC not only achieves a 9.7% energy saving by preventing server over-cooling but also diminishes server temperature fluctuations, promoting the safe and stable operation of the server. The research shows that the EMPC reveals excellent performance along with all server workload levels. In addition, the length of the prediction horizon has a notable impact and three minutes is suggested for the rack-based cooling system.

Energy management based on safe multi-agent reinforcement learning for smart buildings in distribution networks

The rapid urbanization and increasing use of distributed renewable energy resources have imposed a significant burden on power networks. Smart buildings equipped with artificial intelligence technology can play a pivotal role in energy management, ultimately enhancing energy efficiency and voltage quality. However, ensuring voltage stability within large-scale smart building systems presents challenges due to the coexistence of diverse energy sources and the fluctuating nature of renewable energy. This paper proposes a safe multi-energy management framework achieved by online decentralized execution and centralized training for large scale smart buildings in distribution networks. The energy management problem is formulated as a safety-augmented Markov decision process, presenting intractability for dynamic programming due to its extensive continuous state space. To solve this issue and improve the convergence speed and training process stability, a safety-augmented constrained multi-agent reinforcement learning algorithm based on reward extrapolation is proposed. In this algorithm, hazard values are introduced to enhance non-safe multi-agent reinforcement learning algorithms and meet safety constraints. A novel reward network is designed by imitating expert underlying intentions to ensure the rationality of the reward function for multi-objective tasks. Additionally, the loss function for estimating the Q-network is redesigned during training process to guarantee effective convergence. Theoretical analysis is conducted to provide the convergence guarantee. Numerical case studies based on actual data are performed to validate the effectiveness and scalability of our approach, showing that smart buildings can achieve superior energy management performance while ensuring voltage safety for distribution networks. The source code of the proposed algorithm is available at https://github.com/SYiyun/CMARL-EX.

Stochastic operation of Volt-VAr optimization and network reconfiguration-based energy management under different loading patterns

Stochastic operation of Volt-VAr optimization and network reconfiguration-based energy management under different loading patterns

Research on the impact of sliding window and differencing procedures on the support vector regression model for load forecasting

Load forecasting is a critical aspect of energy management and grid operations. Machine learning techniques as support vector regression (SVR), have been widely used for load forecasting. However, the effectiveness of SVR is highly dependent on its hyperparameters, including the error sensitivity parameter, penalty factor, and kernel function. Furthermore, as the load data follows a time series pattern, the accuracy of the SVR model is influenced by the data's characteristics. In this regard, the present study aims to investigate the impact of combining the sliding window procedure and differencing the input data on the prediction accuracy of the SVR model. The study utilizes daily maximum load data from the Queensland and Victoria states in Australia. The analyses revealed that while the sliding window procedure had a minimal impact on the prediction results, the differencing of the input data significantly improved the accuracy of the predictions.

Enhancing the performance of sustainable energy management of buildings in smart cities

Energy utilization has been the most influential parameter in recent decades, especially in the smart city model. The energy management system has been a more attractive research problem due to its utility, ability, and applications. This paper has an objective that the article discusses innovative energy management methods for sustainability and highlights the potential for integrated smart energy sources. The discussion also touches on the understanding of energy management and production, various storage systems, and their potential future applications. This paper explores challenges in sustainable smart energy management, focusing on methodologies like smart energy systems, PV calculations, electric grid models, and energy management strategies in smart cities. The passive infrared receiver (PIR) sensor has been used in real-time energy management systems to integrate these methodologies into the city's infrastructure. The energy management design aims to coordinate electrical appliances such as fans and lights to minimize energy consumption. The article proposes new energy management and security techniques based on data sources to enhance city intelligence, adaptability, and sustainability by reducing human involvement in controlling electrical appliances in residential buildings. The proposed design and development system optimizes energy utilization more efficiently and effectively than conventional systems, meeting real-time energy management objectives.

Performance evaluation and energy management of an automobile exhaust thermoelectric generator for ISG mild HEV application

Application of automobile exhaust thermoelectric generator (AETEG) has certain impact on the fuel economy of vehicle, and to further improve the fuel-saving performance of the traditional integrated starter and generator (ISG) based mild hybrid electric vehicle (HEV), an AETEG was developed and integrated into its powertrain system. Considering model predictive control (MPC) strategy has the advantages of high accuracy, strong reliability and wide application range, it was proposed to manage the energy distribution of ISG mild HEV equipped with AETEG. Firstly, the demand power of ISG mild HEV was predicted with Markov model. Then, a maximum power point tracking (MPPT) method of AETEG was applied to charge the batteries and propel the hybrid power system. Finally, the fuel consumption was treated as the objective function for the optimal torque distribution between internal combustion engine (ICE) and ISG based on a dynamic programming (DP) algorithm. Results demonstrate that the MPC-DP strategy with AETEG decreases the fuel consumption of ISG mild HEV by 5.63%, and ensures the maximum state of charge (SOC) drop-down of 22.67% and final SOC of 0.529 with same initial SOC value of 0.6. Contrast to the fuzzy logic control (FLC) and rule-based energy management strategy (REMS) applied under a composite driving cycle, the proposed MPC-DP method has obvious advantage in SOC stability and dynamic performance, the emission is decreased by at least 2.3%, and the average fuel economy is improved by 6.25% and 7.53%, respectively.

Prioritizing the indicators of energy performance management: a novel fuzzy decision-making approach for G7 service industries

Ensuring energy performance management is important in many ways, such improvement of energy efficiency and decrease of energy costs are reduced. There are various indicators of the effectiveness of energy performance management of buildings. Due to this situation, businesses need to make the necessary improvements for the development of these factors. Nonetheless, these actions cause an increase in the costs of the companies. Hence, among these actions, the more important ones need to be identified. Owing to this issue, businesses can use their limited budgets for more priority indicators. The purpose of this study is to evaluate the main indicators of energy performance management systems. In this way, a new model is proposed to make a priority analysis for the hospitals. Firstly, five indicators of energy performance management systems are selected by considering ISO 50006 standards. Furthermore, these indicators are weighted by using Spherical fuzzy CRITIC. Secondly, G7 countries are examined with fuzzy RATGOS technique. Identification of the most significant indicators of the energy performance systems is an important novelty of this study. The most significant methodological novelty of this study is proposing a new technique to the literature named RATGOS. It is understood that energy efficiency is the most crucial indicator of energy performance management. Furthermore, it is also identified that France is the most successful G7 economy with respect to the energy performance management. Japan and United States have also high performance in this respect. It is recommended that necessary actions should be taken to increase energy efficiency. By conducting an energy audit, energy consumption data is analyzed so that energy losses and inefficiencies can be detected. This assessment provides opportunities for energy efficiency and helps identify improvement strategies.

Enhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks

Power distribution grids experiences proliferation of solar photovoltaics (PV) at the system edge. However, its counterpart of sparse meter deployment provides insufficient monitoring of PVs, for which the potential violations challenge the operators for energy management and stable operation. Some previous works use satellite imagery to detect distributed PVs for the easy access of data. However, their PV localization methods rely on label-rich area with unitary background/environment to implement well; even further/harder, they do not provide precise metered-PV detection and quantification to estimate/know PV generation outputs in unobservable area, which is essential to prevent the edge from excessive two-way power flow and other violations. Thus, we combine the two steps of detecting PV existence and quantify PV amount into one classification task. To boost the classification performance in unobservable edge area, we construct a generative adversarial network that simultaneous augments the diversity of labelled PV satellite images and embed distinct PV characteristics/features for training the classifier. Furthermore, the PV localization and quantification result is combined with geographic information, historical weather conditions and neighboring generation patterns to estimate power output at the system edge. We validate the proposed approaches on PV systems in the southwest of the U.S. Experiment results show high accuracy and robustness in predicting distributed solar power without sufficient prior information.

Two-Stage Data-Driven Optimal Energy Management and Dynamic Real-Time Operation in Networked Microgrid Based Deep Reinforcement Learning Approach

Two-Stage Data-Driven Optimal Energy Management and Dynamic Real-Time Operation in Networked Microgrid Based Deep Reinforcement Learning Approach