Advanced Energy Management Strategy Research Articles

Case Study on Optimum Utilization of Hybrid Energy for Race Car Performance

—Hybrid energy systems have transformed the auto- motive and motorsport industries, offering an effective balance between performance and sustainability. This paper explores the optimum utilization of hybrid energy in race cars, focusing on advanced energy management strategies and their impact on performance metrics like lap times, fuel efficiency, and emissions. Utilizing insights from Quasi-Pontryagin’s Minimum Principle (Q-PMP) [1], energy allocation frameworks [2], and real-time op- timization techniques under battery constraints [3], this study ex- amines the synergy between internal combustion engines (ICEs) and electric motors in high-performance scenarios. Additionally, the role of hybrid energy storage systems, such as batteries and ultra-capacitors, in enhancing power delivery is analyzed [4]. Results from case studies and simulations demonstrate significant improvements in race performance, including faster lap times and reduced energy consumption, highlighting the practical viability of hybrid systems in motorsports [5][6]. These findings emphasize the importance of adaptive energy strategies in advancing the competitive edge and sustainability of hybrid race cars. Index Terms—Hybrid Energy, Energy Optimization, Fuel Ef- ficiency in Racing, Energy Distribution in Hybrid Vehicles

Advanced energy management strategy for microgrids with integrated battery storage and renewable generation

As electric mobility gains popularity, Electric Vehicles (EVs) and their batteries are becoming more attractive due to their size and energy density advantages. However, the electric grid has not undergone similar improvements, potentially impacting power stability and affecting EV energy usage and availability. The key challenge lies in managing increasing power demands from a fully EV fleet. To address this, efforts are needed to analyze the integration of EVs into the grid and optimize power distribution. In this paper, an innovative Energy Management Strategy (EMS) is proposed to effectively control energy loads, energy sources, and EVs, incorporating Vehicle-to-Grid (V2G) capability. The EMS optimizes energy flow and storage based on time of day, potential energy production, and the cost of grid electricity. The integration of this EMS results in significant benefits, with approximately 12% savings in electricity bills compared to a reference strategy.

The role of advanced energy management strategies to operate flexibility sources in Renewable Energy Communities

Renewable Energy Communities (REC) can largely contribute to building decarbonization targets and provide flexibility through the adoption of advanced control strategies of the energy systems. This work investigates how the role of flexibility sources will be impacted by shifting towards advanced control strategies under a high penetration of variable Renewable Energy Sources, in the following years. A large residential area with diverse energy systems, building envelope configurations, and energy demand patterns is modeled with the simulation environment RECsim, a virtual testbed for the implementation of energy management strategies in REC. Photovoltaic (PV) panels, Battery Energy Storage and Thermal Energy Storage (TES) of different sizes for each household provide a realistic description of a REC which includes both consumers and prosumers.This study explores a scenario in which advanced controllers based on Deep Reinforcement Learning (DRL) replace existing Rule-Based Controllers in building energy systems across a significant number of buildings. These control policies are simulated under three different scenarios that consider consumers with different pricing schemes and TES penetration.Efficient control strategies, have demonstrated significant potential, regardless of the presence of thermal storage and ToU pricing schemes, in reducing energy demand by 12.6%, cutting energy costs by 20.8%, and enhancing self-sufficiency and self-consumption, with minimal impact on Shared Energy. Implementing a flat tariff scheme under DRL enables consumers to increase their energy demand during periods of PV generation, which is particularly advantageous in a REC. Also, this approach lowers overall energy demand by 12.6% and boosts self-sufficiency, and it also decreases electricity exports from the REC to the grid by 18.2% compared to a ToU tariff scheme. When using ToU tariffs, thermal storage can be used to achieve cost savings, but total Shared Energy decreases, as do self-sufficiency and self-consumption of the REC. The results indicate that in a REC with high variable renewable energy and decentralized control, consumers using TES and ToU tariffs with peak prices during high irradiance periods may not be beneficial for the grid compliance.In conclusion, the coupling between DRL and thermal storage should be supported by more innovative pricing schemes for RECs and/or coordinated energy management, although it requires advanced communication and monitoring infrastructure.

IoT-based framework for optimizing energy efficiency and reliability in acoustic sensor networks using mobile sinks

In today’s world, there is an increasing demand for environmental monitoring, surveillance, and oceanographic research, which poses challenges in improving energy efficiency and data transfer reliability in Acoustic Sensor Networks. Existing methods face hurdles due to limited energy resources and unreliable data transmission. We propose a Reliable and Energy-Efficient Framework with Sink Mobility (REEFSM) to address these issues. This framework optimizes energy consumption and enhances data reliability by incorporating advanced energy management strategies such as adaptive duty cycling and efficient data transmission mechanisms by minimizing forwarding nodes. Simulation results demonstrate that REEFSM reduces energy consumption by up to 43% and increases data reliability by 35% compared to protocols like EERBCR and DEADS. REEFSM ensures zero dead nodes, minimizes packet drops, and maintains high data accuracy throughout the simulation. This research outcome provides a sustainable and reliable solution for energy-efficient data collection in underwater environments. The future research directions, including integrating autonomous path planning, energy harvesting, and machine learning techniques, hold great potential for further advancements in the field.

Advanced energy management scheme for fuel cell-based microgrid using self–regulated controller and switched capacitor inverter

Clean energy production is now the main challenge for sustainable future which can be addressed by introducing renewable energy sources. Fuel cells are critical in providing sustainable energy solutions by enabling efficient energy conversion in microgrids. To enhance the performance of fuel cell-based microgrids, advanced controllers and inverters are necessary to manage power quality and stability. One of the key benefits of this fuel cell is the separation of energy conversion and management, which allows independent optimization for each function of microgrid's performance, cost, or other installation considerations. The ability to individually control of each component of a microgrid can significantly impact various applications positively. Beyond their technological and economic advantages, fuel cells are highly sensitivity to microgrid's characteristics such as current quality and harmonics which creates difficulties for their design and management. Therefore, the entire microgrid must be managed by an effective energy management strategy and integration of advanced inverter topologies. This study aims to improve fuel cell efficiency and power quality within the microgrid through an advanced energy management (AEM) strategy. The proposed AEM structure is developed focusing on two key components: a self-regulated controller (SRC) and a switched capacitor multilevel inverter (SCMLI). These elements work together to mitigate the disturbances and enhance the power quality of the system. The methodology includes simulation and hardware-in-the-loop (HIL) testing under non-linear load conditions. Initial grid-side current total harmonic distortion (THD) of 28.38% was reduced to 2.19% through the proposed system. An impact analysis is also presented to demonstrates that reducing current harmonics and voltage imbalance have improved the efficiency of fuel cell to 50.18% and fuel utilization to 98.05%. The findings show that the proposed scheme significantly reduces current harmonics and improves the overall performance benchmark of the microgrid and fuel cell.

Evaluating Microgrid Investments: Introducing the MPIR Index for Economic and Environmental Synergy

In view of the increasing environmental challenges and the growing demand for sustainable energy solutions, the optimization of microgrid systems with regard to economic efficiency and environmental compatibility is becoming ever more important. This paper presents the Microgrid Performance and Investment Rating (MPIR) index, a novel assessment framework developed to link economic and environmental objectives within microgrid configurations. The MPIR index evaluates microgrid configurations based on five critical dimensions: financial viability, sustainability, regional renewable integration readiness, energy demand, and community engagement, facilitating comprehensive and balanced decision making. The current cases focus on the area of Greece; however, the model can have a wider application. Developed using a two-target optimization model, this index integrates various energy sources—including photovoltaics, micro-wind turbines, and different types of batteries—with advanced energy management strategies to assess and improve microgrid performance. This paper presents case studies in which the MPIR index is applied to different microgrid scenarios. It demonstrates its effectiveness in identifying optimal configurations that reduce the carbon footprint while maximizing economic returns. The MPIR index provides a quantifiable, scalable tool for stakeholders, not only advancing the field of microgrid optimization, but also aligning with global sustainability goals and promoting the transition to a more resilient and sustainable energy future.

A novel learning-based robust model predictive control strategy and case study for application in optimal control of FCEVs

With the gradual implementation of carbon-neutral policies, fuel cell electric vehicles (FCEVs) have gained widespread recognition as a promising solution. Due to multi-source electromechanical coupling, the powertrain of FCEVs constitutes a complex nonlinear system, necessitating an advanced energy management strategy for effective control to enhance economic performance. In this study, a novel learning-based robust model predictive control (LRMPC) is proposed for a 4WD FCEV, integrating a high-confidence velocity estimation model and an accurate state observation model to enhance energy-saving potential, robustness, and real-time application performance under various driving conditions. To enhance robustness and adaptability, deep forest with superior feature fusion abilities is employed to generate future velocity reference datasets based on FCEV operating scenarios. To improve state observation accuracy and control effectiveness, a novel built-in state observation model based on machine learning algorithms is established, leveraging knowledge of nonlinear systems within the FCEV powertrain. Subsequently, to enhance the real-time applicability of LRMPC, an explicit control scheme for multidimensional mapping based on explicit data tables is utilized for accurate state transformation, incorporating input features such as vehicle states and component states. Simulation evaluations and hardware-in-the-loop tests demonstrate the improved economic performance and real-time application ability of LRMPC, showcasing its promising performance.

Thermodynamics analysis of fins integration in sono-PCM reactor: Sonic power influence

This research presents a novel approach of integrating fins into a sonochemical phase change material (PCM) reactor to tackle the challenge of low PCM thermal conductivity, with a focus on cooling the ultrasonic reactor. Through comprehensive numerical simulations using a Fluent-based CFD code, we analyze the thermal performance of the sono-PCM system with and without fins under varying power densities (45.5 and 85.6 kW/m3) and operating times (up to 1 h). Our results highlight the remarkable advantage of fins in accelerating the heat transfer and melting process, reducing the time for a 50% PCM melt by 28.1 and 45.2% for power densities of 45.5 and 85.6 kW/m3, respectively. Enthalpy analysis further confirms the efficiency of the fins, demonstrating a reduction of ∼19% in water enthalpy after 1 h for both power densities. Moreover, the presence of fins significantly enhances the heat recovery by the PCM, leading to 20.1 and 73.2% increase in PCM enthalpy for power densities of 45.5 and 85.6 kW/m3, respectively. These enhancements are attributed to the increased surface area of contact provided by fins, which facilitate efficient heat dissipation within the bulk PCM. This study not only provides crucial insights into PCM systems with fins but also opens up avenues for advanced thermal energy management strategies, revolutionizing heat storage and transfer processes across various engineering applications.

Energy management strategy for a hybrid micro-grid system using renewable energy

This paper introduces an energy management strategy for a hybrid renewable micro-grid system. The efficient operation of a hybrid renewable micro-grid system requires an advanced energy management strategy able to coordinate the complex interactions between different energy sources and loads. This strategy must consider some factors such as weather fluctuations and demand variations. Its significance lies in achieving the overarching objectives of these systems, including optimizing renewable energy utilization, reducing greenhouse gas emissions, promoting energy independence, and ensuring grid resilience. The intermittent nature of renewable sources necessitates a predictive approach that anticipates the energy availability and adjusts the system operation. The aim of this study was to develop an energy management system for a hybrid renewable micro-grid system to optimize the deployment of renewable energy resources and increase their integration in the power system. Therefore, the main objective of this work was to develop an energy management strategy that controls the flow of energy between the hybrid micro-grid system and the load connected directly as well as the load connected to the utility grid using MATLAB/Simulink software. The second objective was to control the charging and discharging of the battery. The results show that the developed algorithm was able to control the energy flow between the hybrid micro-grid system and the utility grid and also to ensure a proper relation between the charging /discharging rate of the battery based on their operating conditions. In this application, the battery was charged at higher power. It was seen that a higher charging power enables to fully recharge the battery in a shorter amount of time than usual. The results have shown that it is possible to maximize the charging time by using a greater power and this algorithm ensures the state of charge (SOC) of battery to remain in the admissible limits (between 20 and 100%).

Integration Challenges and Solutions for Solar-Powered Electric Vehicle Charging Infrastructure: From Panel to Battery

The integration of solar power with electric vehicle (EV) charging infrastructure presents a promising avenue to foster sustainable transportation. This study delves into the multifaceted challenges encountered in the synthesis of solar-powered EV charging stations and proffers solutions that span the complete energy transfer chain from photovoltaic panels to EV batteries. Initial concerns address the intermittent nature of solar energy and its impact on the reliability of power delivery. Advanced energy management strategies are explored, incorporating predictive analytics and real-time adjustment mechanisms to enhance the consistency and efficiency of power flow. The second part of the investigation scrutinizes the power electronics interface, emphasizing the need for high-efficiency converters that can operate effectively over varied solar insolation levels. Novel circuit topologies are presented, alongside adaptive control algorithms designed to optimize the power conversion process. Collectively, the findings underscore the potential of solar-powered EV charging infrastructure to not only support the decarbonization of transportation but also to contribute to the stability and efficiency of the power grid. The intersection of power electronics, energy management, and grid integration forms the cornerstone of this endeavor, with each domain providing critical components to the holistic solution. It culminates in a set of recommendations for policy, design, and operation that can accelerate the adoption of this technology.

Co-optimization of speed planning and cost-optimal energy management for fuel cell trucks under vehicle-following scenarios

Fuel cell hybrid electric heavy-duty vehicles play a key role to realize green and low-carbon travel. To cope with actual traffic environment, an advanced energy management strategy (EMS) is crucial to ensure vehicle’s efficiency and economy. In this paper, a predictive co-optimization control method is designed to achieve speed planning and energy management for the host vehicle with a fuel cell/Li-ion battery hybrid energy storage system under vehicle-following scenarios. Firstly, the host vehicle obtains the real-time speed of leading vehicle by communication technology, and speed prediction for the leading vehicle is implemented by fuzzy C-means clustering enhanced Markov Chain (FCM-MC) considering driving habits. Secondly, based on speed prediction results, the speed planning which aims to ensure safe inter-vehicle distance and minimize the demanded vehicular power is implemented to derive the speed curve of host vehicle. Finally, according to speed planning results, predictive energy management is applied to achieve power allocation between fuel cell (FC) and battery. The simulation results denote that the proposed speed prediction method can decrease forecast error effectively. The speed planning ensures the safe inter-vehicle distance. With speed prediction accuracy improved, the proposed hierarchical energy management strategy can reduce fuel cell degradation cost by 1.31%-3.48% and total operation cost by 0.75%-1.94%.

Learning-based model predictive energy management for fuel cell hybrid electric bus with health-aware control

Advanced energy management strategy (EMS) can ensure healthy, stable, and efficient operation of the on-board energy systems. Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) are two powerful control methods that have been extensively researched in the field of vehicle energy management. However, there are some problems with both approaches. On the one hand, MPC is difficult to cope with the complex systems and the excessive computational load caused by the non-linear solving over long prediction horizon, on the other hand, DRL lacks adaptability to different driving conditions and is poorly interpretable. Therefore, this paper innovatively proposes a learning-based model predictive (L-MPC) EMS for fuel cell hybrid electric bus (FCHEB) with health-aware control. This method effectively merges the advantages of control theory and machine learning. Specifically, firstly, the precise aging models for vehicular energy systems are established and incorporated into the optimization framework along with hydrogen consumption related metrics. Secondly, the principles of the learning-based MPC algorithm are thoroughly elucidated. In addition, to ensure driving details under future conditions, a speed predictor based on a double-layer Bi-directional Long Short-Term Memory (BiLSTM) is proposed at the strategy supervision layer. Finally, the superiority of the proposed strategy in prolonging the lifespan of the energy systems and reducing overall vehicle operating cost is verified by comprehensive comparisons with state-of-the-art MPC and DRL methods under real-world collected driving condition.

Using architectural glazing systems to harness solar thermal potential for energy savings and indoor comfort

The capacity of windows to transmit solar irradiance contributes significantly to indoor environments, which facilitates supplemental warmth courtesy of shortwave solar irradiance, impacting both human comfort and building energy dynamics. This study explores the impact of window glazing on indoor thermal comfort and building energy dynamics through shortwave solar irradiance. It presents a comprehensive analysis of 5,138 glazing systems and introduces a novel thermal effect index. This index was generated using a spectrally-resolved method to classify windows based on their capacity to transmit solar energy. The index divides the quantified thermal effects into 10 equally-sized ranges and assigns corresponding indices. In addition to recognizing the influence of specific solar spectral distributions on the thermal effects of glazings, this work also proposes a method for computing effective wavebands of spectral transmittance, allowing for the rapid evaluation of a window's thermal effects under diverse solar spectra. In essence, this study emphasizes the importance of the thermal effect in understanding solar energy utilization and determining implications for energy savings and enhanced indoor thermal comfort. By addressing the interplay between solar energy use and human comfort, this research offers valuable insights for future advanced envelope design and energy management strategies. With this knowledge, we will be better able to tailor advanced envelope products to meet both human needs and energy efficiency goals by harnessing solar energy.

Innovative powertrain and advanced energy management strategy for hybrid hydraulic excavators

This paper presents an innovative powertrain managed by an advanced energy management strategy for hybrid hydraulic excavators to improve the overall energy efficiency. Here, a main pump is driven via a planetary gear by an engine and an electric motor/generator. A double clutch is integrated to adjust torque and speed of engine according to efficiency map enabling higher working efficiency. Potential energy of the boom cylinder can be converted and stored in a battery through an energy regeneration system. The advanced energy management strategy is designed by utilising extremum seeking and fuzzy techniques to optimally distribute power requirement. A fuzzy logic system is designed based on consideration of battery performance and aging to adjust a penalty function subsequently improving the overall system performance while prolonging the component lifetime. Simulations and real-time experiments have been carried out to evaluate the effectiveness of the proposed powertrain managed by the advanced energy management strategy.

Frontier ocean thermal/power and solar PV systems for transformation towards net-zero communities

Ocean thermal and power energy systems are promising driving forces for seashore coastal communities to achieve net-zero energy/emission target, whereas energy planning and management on ocean thermal/power and distributed building integrated photovoltaic (BIPV) systems are critical, in terms of serving scale sizing and planning on geographical locations of district building community, and cycling aging of battery storages. However, the current literature provides insufficient studies on this topic. This study aims to address this research gap by transforming towards zero-energy coastal communities from the district level in subtropical regions, including centralised seawater-based chiller systems, distributed BIPVs and coastal oscillating water column technologies, as well as multi-directional Vehicle-to-Building energy interaction paradigms. Advanced energy management strategies were explored to enhance renewable penetration, import cost-saving, and deceleration of battery cycling aging, in response to relative renewable-to-demand difference, off-peak grid information with low price, and real-time battery cycling aging. Furthermore, in accordance with the power generation characteristic of two wave stations (i.e., Kau Yi Chau (KYC) and West Lamma Channel (WLC)) in Hong Kong, energy system planning and structural configurations of the coastal community were proposed and comparatively studied for the multi-criteria performance improvement. Research results showed that, compared to an air-cooled chiller, the water-cooled chiller with a much higher Coefficient of Performance (COP) will reduce the energy consumption of cooling systems, leading to a decrease in total electric demand from 134 to 126.5 kWh/m2·a. The scale for the net-zero energy district community with distributed BIPVs and oscillating water column was identified as 5 high-rise office buildings, 5 high-rise hotel buildings, 150 private cars and 120 public shuttle buses. Furthermore, the geographical location planning scheme on the Case 1 (office buildings close to KYC, and hotel buildings close to WLC) was identified as the most economically and environmentally feasible scheme, whereas the Case 3 (only office buildings are planned close to all power supply with oscillating water column) showed the highest flexibility in grid electricity shifting, together with the highest value of equivalent battery relative capacity. This study demonstrates techno-economic performances and energy flexibility of frontier ocean energy technologies in a coastal community under advanced energy management strategies, together with technical guidance for serving scale sizing and planning on geographical locations. The research results highlight the prospects and promote frontier ocean energy techniques in subtropical coastal regions.

The future technological potential of hydrogen fuel cell systems for aviation and preliminary co-design of a hybrid regional aircraft powertrain through a mathematical tool

The growing demand for air travel in the commercial sector leads to an increase in global emissions, whose mitigation entails transitioning from the current fossil-fuel based generation of aircrafts to a cleaner one within a short timeframe. The use of hydrogen and fuel cells has the potential to reach zero emissions in the aerospace sector, provided that required innovation and research efforts are substantially accomplished. Development programs, investments and new regulations are needed for this technology to be safe and economical. In this context, it makes sense to develop a model-based preliminary design methodology for a hybrid regional aircraft assisted by a battery hybridized fuel cell powertrain. The technological assumptions underlying the study refer to both current and expected data for 2035. The major contribution of the proposed methodology is to provide a mathematical tool that considers the interactions between the choice of components in terms of installed power and energy management. This simultaneous study is done because of the availability of versatile control maps. The tool was then deployed to define current and future technological scenarios for fuel cell, battery and hydrogen storage systems, by quickly adapting control strategies to different sizing criteria and technical specifications. In this way, it is possible to facilitate the estimation of the impact of different sizing criteria and technological features, at the aircraft level, on the onboard electrical system, the management of in-flight power, the propulsion methods, the impact of the masses on consumption and operational characteristics in a typical flight mission. The proposed combination of advanced sizing and energy management strategies allowed meeting mass and volume constraints with state-of-the-art PEM fuel cell and Li-ion battery specifications. Such a solution corresponds to a high degree of hybridization between the fuel cell system and battery pack (i.e., 300 kW and 750 kWh), whereas projected 2035 specs were demonstrated to help reduce mass and volume by 23 % and 40 %, respectively.

Day-ahead scheduling of 100% isolated communities under uncertainties through a novel stochastic-robust model

Energy communities enable effective coordination among prosumers on pursuing collective targets. This paper focuses on isolated 100% renewable communities, involving individual (controllable appliances and small generators) and collective (wind generators and battery banks) assets. To effectively coordinate the agents involved in these structures, advanced energy management strategies are necessary. This work develops a three-stage day-ahead scheduling strategy for isolated 100% energy communities, involving peer-to-peer transactions among prosumers. The different uncertainties involved are incorporated through a novel stochastic-robust formulation, that results in a computationally tractable optimization framework. To validate the new model, a case study on a six-prosumer benchmark community is analysed. Results reveal the importance of collective assets and peer-to-peer exchanges among prosumers as well as the effectiveness of the developed formulation. The role of batteries is also discussed, helping to reduce the total unserved energy and operating cost by 20% and 19%, respectively, as well as enabling a more efficient use of wind energy. The impact of robustness is also studied, incrementing the expected importable energy by 28% compared to the deterministic case, while the exportable energy from prosumers is notably reduced by 40%. However, uncertainty-aware strategies have a direct impact on operational costs, incrementing the expenditures by 37% when uncertainties are considered.

Peak shaving strategy for renewable hybrid system driven by solar and radiative cooling integrating carbon capture and sewage treatment

Although considered to be one of the most valuable alternative energy sources, the utilization of renewable energy still confronts challenges such as low conversion efficiency and large transient fluctuations. To address this issue, we propose a renewable hybrid system driven by solar and radiative cooling, develop advanced energy management and peak shaving strategies. After establishing and validating the mathematical model, the renewable energy recovery, subsystem variation law analysis, and hybrid system performance calculation are computed. Three-objective optimization under four cases is implemented after understanding the change rules. Subsequently, the energy management and peak shaving are performed, as well as the positive contribution of the storage system to the renewable hybrid system is discussed. Based on the equivalent coefficient, the renewable hybrid system benefits are finally assessed. The numerical results conclude that the renewable hybrid system has excellent peak shaving characteristics, can fulfill dynamically changing energy demands. Its equivalent work, energy efficiency, and exergy efficiency reach 155.11 kW, 23.17%, and 70.61%, respectively, with additional emission reduction, carbon capture, and sewage treatment capacities of 131.69 kg/h, 131.82 kg/h, and 1.509 kg/s, can recover the cost within 4.29 years. It complies with sustainable development models, conforms to carbon neutrality requirements, as well as provides meaningful guidelines for renewable energy utilization.

A regression learner-based approach for battery cycling ageing prediction―advances in energy management strategy and techno-economic analysis

Renewable energy planning, electrochemical battery storages and advanced energy management strategies are flexible solutions for transformation towards smart grids, whereas the complex battery cycling ageing is nonlinearly dependent on intermittent renewable supply, stochastic load profiles and dynamic charging/discharging behaviors. In this study, a nonlinear mathematical model is developed to explore effective strategies for smart grids. A general regression learner-based battery cycling ageing prediction method is proposed for quantifications of lifetime battery cycling ageing and battery replacement times, including the database preparation, surrogate model training with typical feature extraction and classification, cross-validation, and performance prediction in various battery groups. A machine learning (ML) algorithm selection approach is proposed through the statistical analysis, to guide the accurate surrogate model development, considering the diversity in dynamic charging/discharging behaviours and intrinsic cycling ageing performances of each battery. Afterwards, a novel battery discharging control strategy is proposed, to address the contradiction between the economic cost-saving and the associated battery replacement cost. Last but not the least, the machine learning-based models are thereafter integrated in the district energy community for technical performance analysis. This study can provide a regression learner-based battery cycling ageing modelling method, a machine learning algorithm selection approach, and a holistic framework for systematic integration with avoidance on techno-economic performance overestimation, which is critical to guide renewable energy planning, electrochemical battery storages, and advanced energy management strategies.

Advanced energy management strategy for microgrid using real-time monitoring interface

Recently, the integration of renewable energy sources in microgrids has seen a significant rise due to their attractive prices, reliability etc. However, besides the techno-economic benefits, the renewable energy sources are intermittent, and the high penetration of renewable sources into the microgrid poses design and operation challenges. Indeed, an efficient energy management strategy (EMS) is required to govern power flows across the entire microgrid. This paper introduces an advanced EMS design with a real-time monitoring interface for the effective operation of the hybrid microgrid and data analysis. The proposed advanced EMS model uses a real-time monitoring interface, and it provides the optimum operation and control in terms of balanced power supply and voltage profile with stable frequency. We designed the microgrid, which comprises hybrid sources such as solar and wind power sources, Li-ion battery storage system, backup electrical grids, and AC/DC loads, considering the functional constraints of a microgrid energy management and stability. In addition, the battery energy storage is effectively managed through the performance control of battery charging and discharging using an efficiency controller. The proposed system control is based on the optimum power supply of loads through the available renewable sources and the battery State of Charge (SOC). The microgrid measurement data is transmitted through the Python platform and a graphical user interface (GUI) software developed for data analysis. The simulation results using Matlab Simulink and Python platforms demonstrate the relevance and effectiveness of the proposed EMS and monitoring interface for the stable and reliable operation of the developed hybrid microgrid.