Optimal Energy Research Articles

A wide voltage range non‐isolated continuous input buck‐boost converter for optimal green energy harvesting in LED lighting systems

A wide voltage range non‐isolated continuous input buck‐boost converter for optimal green energy harvesting in LED lighting systems

Customized decentralized autonomous organization based optimal energy management for smart buildings

In recent years, the unprecedented rising urbanization level and proliferation of distributed energy sources (DES) are motivating the transition of smart building from consumer to prosumer. In the context of mitigating upstream emission via reducing reliance on utility grids, the key challenge becomes realizing autonomous and economically optimal energy management within smart buildings. This paper innovatively proposes a Decentralized Autonomous Organization (DAO)-based energy management framework for smart buildings, where the mechanism of day-ahead scheduling DAO considering peer-to-peer (P2P) energy sharing is specifically investigated. Besides, a sequential preference satisfaction mechanism is designed to customize participants’ special demands, thereby achieving effective energy matching. Considering potential communication failures between buildings in practical operations, this study develops a communication failure robust distributed algorithm to minimize operational costs and safeguard building’s privacy. Extensive numerical studies for highly DES penetrated smart buildings successfully offers an operation cost reduction of 33.7% when considering preference satisfaction and P2P energy sharing in the day-ahead scheduling problem. Simulation results also demonstrates that the algorithm in this work excels in resisting potential communication failure during operation.

Electric vehicle eco-driving strategy at signalized intersections based on optimal energy consumption

Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin’s minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time.

Optimization Design and Energy Efficiency Management of Robot Power Systems

Optimization Design and Energy Efficiency Management of Robot Power Systems

Adaptive passive fault tolerant control of DFIG-based wind turbine using a self-tuning fractional integral sliding mode control

Controlling variable wind speed turbine (VWT) system based on a doubly-fed induction generator (DFIG) is a challenging task. It requires a control law that is both adaptable and robust enough to handle the complex dynamics of the closed control loop system. Sliding mode control (SMC) is a robust control technology that has shown good performance when employed as a passive fault-tolerant control for wind energy systems. To improve the closed control loop of VWT based on DFIG with the aim of improving energy efficiency, even in presence of nonlinearities and a certain range of bounded parametric uncertainties, whether electrically or mechanically, an adaptive passive fault tolerant control (AP-FTC) based on a self-tuning fractional integral sliding mode control law (ST-FISMC) developed from a novel hyperbolic fractional surface is proposed in this paper. ST-FISMC introduces a nonlinear hyperbolic function into the sliding manifold for self-tuning adaptation of control law, while fractional integral of the control law smooths discontinuous sign function to reduce chattering. Additionally, this work introduces an adaptive observer, developed and proved based on a chosen Lyapunov function. This observer is designed to estimate variations in electrical parameters and stator flux, ensuring sensorless decoupling in indirect field- oriented control (SI-FOC) of DFIG. Lyapunov theory is also used to prove stability of states vectors in closed control loop with presence of bounded parameters uncertainties or external disturbances. Simulation results show that the proposed approach offers better performance in capturing optimal wind energy, as well as the ability to regulate active/reactive power and high resilience in presence of occurring parameter uncertainties or external disturbances.

Research On Energy Management and Optimization Strategies in Smart Homes

Research On Energy Management and Optimization Strategies in Smart Homes

Application to novel smart techniques for decarbonization of commercial building heating and cooling through optimal energy management

The present article proposes a novel smart building energy system utilizing deep geothermal resources through naturally-driven borehole thermal energy storage interacting with the district heating network. It includes an intelligent control strategy for lowering operational costs, making better use of renewables, and avoiding CO2 emissions by eliminating heat pumps and cooling machines to address the heating and cooling demands of a commercial building in Uppsala, a city near Stockholm, Sweden. After comprehensively conducting techno-environmental and economic assessments, the system is fine-tuned using artificial neural networks (ANN) for optimization. The study aims to determine which ANN design and training procedure is the most efficient in terms of accuracy and computing speed. It also assesses well-known optimization algorithms using the TOPSIS decision-making technique to find the best trade-off among various indicators. According to the parametric results, deeper boreholes can collect more geothermal energy and reduce CO2 emissions. However, deep drilling becomes more expensive overall, suggesting the need for multi-objective optimization to balance costs and techno-environmental benefits. The results indicate that Levenberg-Marquardt algorithms offer the optimum trade-off between computation time and error minimization. From a TOPSIS perspective, while the dragonfly algorithm is not ideal for optimizing the suggested system, the non-dominated sorting genetic algorithm is the most efficient since it yields more ideal points rated below 100. The optimization yields a higher energy production of 120 kWh/m2, as well as a decreased levelized cost of energy of 57 $/MWh, a shorter payback period of two years, and a reduced CO2 index of 1.90 kg/MWh. The analysis reveals that despite the high investment costs of 382.50 USD/m2, the system is financially beneficial in the long run due to a short payback period of around eight years, which aligns with the goals of future smart energy systems: reduce pollution and increase cost-effectiveness.

An enhanced salp swarm algorithm with chaotic mapping and dynamic learning for optimizing purge process of proton exchange membrane fuel cell systems

Swarm intelligence algorithm is now a research focus for the energy optimization of fuel cell systems to improve hydrogen utilization efficiency. However, conventional algorithms exhibit poor robustness and low execution efficiency since complex constraints are involved in the fuel cell cathode purge process. Given that, this paper innovatively introduces chaotic mapping and dynamic learning mechanisms into the Salp swarm algorithm to enhance the algorithm's global search and local exploitation capabilities under complex constraints. First, we developed a three-stage nonlinear cathode purge model to describe the different water transfer behaviors in the three purging stages. Then, an optimizing function is created under the complex staged constraints to accurately reveal the energy consumption mechanism. Finally, the enhanced Salp swarm algorithm enables the rapid and accurate determination of the three-stage dynamic purge flows based on minimizing energy consumption. Compared with the fixed flow purge scheme, we found that energy consumption is reduced by 3.6 %–8.2 % under various constraints. The enhanced Salp swarm algorithm proposed in this work demonstrates an outstanding performance in the energy consumption optimization of fuel cell systems.

Deep reinforcement learning-based resource scheduling for energy optimization and load balancing in SDN-driven edge computing

Traditional techniques for edge computing resource scheduling may result in large amounts of wasted server resources and energy consumption; thus, exploring new approaches to achieve higher resource and energy efficiency is a new challenge. Deep reinforcement learning (DRL) offers a promising solution by balancing resource utilization, latency, and energy optimization. However, current methods often focus solely on energy optimization for offloading and computing tasks, neglecting the impact of server numbers and resource operation status on energy efficiency and load balancing. On the other hand, prioritizing latency optimization may result in resource imbalance and increased energy waste. To address these challenges, we propose a novel energy optimization method coupled with a load balancing strategy. Our approach aims to minimize overall energy consumption and achieve server load balancing under latency constraints. This is achieved by controlling the number of active servers and individual server load states through a two stage DRL-based energy and resource optimization algorithm. Experimental results demonstrate that our scheme can save an average of 19.84% energy compared to mainstream reinforcement learning methods and 49.60% and 45.33% compared to Round Robin (RR) and random scheduling, respectively. Additionally, our method is optimized for reward value, load balancing, runtime, and anti-interference capability.

Compression and energy absorption of wood-based reinforced 3D Kagome lattice structures

3D Kagome lattice sandwich structure is recognized as the most excellent lattice configuration. However, the preparation method of 3D Kagome is complicated and the raw materials for its preparation are limited to materials with high plasticity. In this study, we developed a wood-based 3D Kagome lattice structure that combines discrete rods into a continuous core using reinforcement. Orthogonal tests, theoretical analysis, and finite element simulations were performed to investigate the correlation between the dimensional parameters, the mechanical properties, and the energy absorption capacity. The damage modes were found to be mainly bending fracture, core shear, and panel rupture, with the use of reinforcement affecting the damage modes. Compressive properties of the 3D Kagome lattice structure are increased by increasing core diameter and inclination degree, decreasing core in-cut diameter, and using high-strength reinforcements. Finite element simulations further confirm that the use of high-strength reinforcements changes the stress distribution of the lattice structure. The 3D Kagome lattice structure with an inclination degree of 65°, a core diameter of 10 mm, a reinforcement wall thickness of 2 mm, and a core in-cut diameter of 2 mm has the optimal compression performance and energy absorption capacity.

A soft actor-critic reinforcement learning framework for optimal energy management in electric vehicles with hybrid storage

The efficient energy management of electric vehicles (EVs) equipped with hybrid energy storage systems (HESS) poses a significant challenge due to its vast search space, numerous control variables, and intricate driving conditions. In response to this challenge, this paper introduces a novel deep reinforcement learning (DRL) algorithm, specifically the soft actor-critic (SAC), tailored for optimizing energy distribution within EVs equipped with battery-supercapacitor (SC) HESS. The proposed SAC-based energy management system (EMS) is designed to address inherent limitations present in most existing DRL algorithms, such as slower convergence rates, discretization errors, unstable training dynamics, and suboptimal optimization effects. The SAC-based EMS undergoes training in a continuous action space through self-play, utilizing a complex driving cycle and a novel reward function. The algorithm demonstrates its efficiency by rapidly maximizing cumulative rewards and refining its decision-making policies. Afterward, extensive experiments showcase the superiority of the proposed SAC-based EMS over rule-based (RB) techniques, deep deterministic policy gradient (DDPG), and battery-only configurations across various driving cycles. Notably, the approach effectively allocates high-power surges and braking energy to the SC, reducing the frequency of charge/discharge cycles and thereby extending the battery's lifespan. Additionally, the learned SAC-based EMS policy achieves substantial reductions in electricity consumption by 39.6 %, 47.87 %, and 45 % compared to battery-only configuration, and 42.56 %, 47.87 %, and 45.83 % compared to RB technique under the NYCC, LA92, and FTP cycles, respectively. In contrast, the DDPG algorithm tends to rely heavily on the SC to deliver the total power required, even under normal driving conditions.

Enhanced Indoor Positioning System Using Ultra-Wideband Technology and Machine Learning Algorithms for Energy-Efficient Warehouse Management

The following article presents a proprietary real-time localization system using temporal analysis techniques and detection and localization algorithms supported by machine learning mechanisms. It covers both the technological aspects, such as proprietary electronics, and the overall architecture of the system for managing human and fixed assets. Its origins lie in the ever-increasing degree of automation in the management of company processes and the energy optimization associated with reducing the execution time of tasks in an intelligent building supported by in-building navigation. The positioning and tracking of objects in the presented system was realized using ultra-wideband radio tag technology. An exceptional focus has been placed on reducing the energy requirements of the components in order to maximize battery runtime, generate savings in terms of more efficient management of other energy consumers in the building and increase the equipment’s overall lifespan.

M (M=Mn, Fe, and Cu)-doped Ni3S2@Co9S8 grown on Ni foam with different heterostructures as catalysts for overall seawater splitting

The increasing global energy demand and the environmental impact of fossil fuels have prompted scientists to seek clean and renewable energy solutions. Hydrogen energy, known for its efficiency and environmental benefits, is considered an ideal choice. Given the limited availability of freshwater resources, this study uses abundant seawater as the feedstock for water electrolysis. Our research focuses on developing non-precious metal electrocatalysts to enhance the efficiency and economic viability of water electrolysis for hydrogen production. Using Ni3S2@Co9S8 as the base material, we regulate the catalytic performance by doping with transition metals such as Mn, Fe, and Cu. The Mn-Ni3S2@Co9S8/NF electrode exhibits excellent hydrogen evolution reaction (HER) (overpotential of 76 mV @10 mA cm−2) and oxygen evolution reaction (OER) (overpotential of 261 mV @10 mA cm−2) activity in seawater electrolytes, significantly reducing the overpotential requirements. Density Functional Theory (DFT) assessments reveal that Co9S8 possesses optimal gibbs free energy capabilities and Mn-Ni3S2 possesses more electron distribution, explaining its high catalytic efficiency. This study provides an in-depth analysis of the catalyst structure and performance, offering valuable insights for developing efficient and stable electrocatalysts for water electrolysis, thereby contributing to the sustainable development of the hydrogen economy and advancements in clean energy technology.

Indoor thermal energy optimization and 3D visualization based on ambient light sensing in the protection of historical architectural landscapes

While preserving cultural heritage, historic buildings also face the challenge of modern comfort and energy efficiency. Thermal energy optimization of indoor environment is an important means to improve living comfort and reduce energy consumption. Especially when protecting historical buildings, it is necessary to analyze how to carry out effective thermal energy management without affecting their appearance and structure. This paper discusses the optimization strategy of indoor thermal energy based on ambient light sensing technology, combined with 3D visualization to achieve the balance between energy conservation and comfort in historic buildings. Through the dynamic monitoring and analysis of indoor thermal environment, a feasible solution is provided for the protection and utilization of historical buildings. In this study, ambient light sensors were deployed in a historical building to monitor indoor light, temperature and humidity in real time. Through data collection and analysis, the main factors affecting indoor thermal environment are identified. 3D visualization technology is used to visualize the indoor heat energy distribution, and specific optimization suggestions are put forward. The research shows that the application of ambient light sensor significantly improves the monitoring accuracy of indoor heat energy, and the implementation of optimization measures reduces the indoor temperature fluctuation and energy consumption. The 3D visualization visually shows the thermal energy changes before and after optimization, providing a basis for managers to make decisions. Combined with 3D visualization technology, it not only enhances the comprehensibility of data, but also provides innovative ideas for the protection and rational use of historical buildings.

Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm’s efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

The impact of ammonia and hydrogen additives on the combustion characteristics, performance, stability and emissions of an industrial DF diesel engine

Both hydrogen and ammonia are potential fuels that can significantly replace fossil fuels in the automotive and energy sectors. In compression ignition engines, they can be effectively and efficiently co-combusted with diesel fuel. This study presents experimental comparative research on the impact of alternative fuels, namely ammonia and hydrogen, on the performance, stability, and emissions of an industrial dual-fuel compression ignition engine. The research was conducted on an engine operating under constant load (0.7 MPa) and constant speed (1500 rpm), comparing scenarios of diesel-only operation and alternative fuel energy shares of 8 %, 12 %, 24 %, and 32 %. For both ammonia/diesel and hydrogen/diesel engines, combustion characteristics, performance, and emissions were determined and analyzed. Additionally, an evaluation of the dual-fuel engine’s operational stability was conducted. For the compression ignition engine co-combusting ammonia with diesel, the most favorable solution was using a 32 % energy share of NH3. In this configuration, the engine achieved nearly the highest efficiency (38 %), the most stable operation, and the lowest emissions of harmful and toxic exhaust components (NO = 2.23 g/kWh, HC = 0.2 g/kWh, CO = 24.9 g/kWh, CO2 = 512 g/kWh, Soot = 0.51 g/kWh). For the engine co-combusting hydrogen with diesel, the optimal energy share of H2 was 24 %. In this configuration, the engine achieved the highest efficiency (39.8 %), almost the most stable operation, and the lowest emissions of HC = 0.22 g/kWh, CO = 15.8 g/kWh, CO2 = 434.7 g/kWh and Soot = 0.52 g/kWh. Unfortunately, a significant drawback of using H2 in a compression ignition engine is the substantial increase in NO emissions.

Multi-objective optimization for energy-efficient building design considering urban heat island effects

Building energy performance (BEP) associated with climate change and urban heat island effects (UHI) play an important role in urban sustainable development. To predict and optimize BEP under various socioeconomic scenarios, a new framework combining the physical simulation modeling integrated explainable machine learning and multi-objective optimization is proposed in this study. A Grasshopper-based simulation model incorporates BO-LGBM (Bayesian optimization-LightGBM) is developed to construct a solid prediction system, which tends to tune the hyperparameters accurately and explain more details with the aid of SHapley Additive explanation (SHAP). Two major aspects, including the building energy use intensity and indoor thermal comfort, are modeled by considering the different Shared Socioeconomic Pathways (SSPs) climate change scenarios in the near and far future. A multi-objective optimization method is employed to find an optimal solution for energy-efficient building design under constraints or uncertainties. Key findings include a 54% improvement in the Pareto front for building energy optimization and a significant impact of SSP585 scenarios on future energy consumption. The main novelty lies in the incorporation of machine learning into a physical model to achieve energy-efficient building design in urban contexts by considering UHI effects and climate change, offering actionable strategies for BEP assessment and promoting sustainable city planning.

Single-, double-, and triple-atom catalysts on PC6 for nitrate reduction to ammonia: A computational screening

Single-atom catalysts (SACs) exhibit higher atomic utilization, while double-atom catalysts (DACs) and triple-atom catalysts (TACs) have an advantage due to the synergistic effect of multiple atomic centers. However, the current research on the mechanism of multiple-atom catalysts in electrocatalytic nitrate reduction to ammonia (NRA) remains insufficient. Herein, we investigated a series of homonuclear single-, double-, and triple-atom catalysts on carbon phosphide (PC6) monolayers (TMn@PC6, where TM = non-noble transition metals; n = 1, 2, or 3). The NRA activity of these catalysts was assessed by the density functional theory calculations, Co3@PC6 and Ru3@PC6 exhibit superior limiting potentials of −0.34 V and −0.36 V, respectively. Their capability to produce *H with optimal free energy on their surface enhances the electrochemical potential-determining step of *NO to *NOH while simultaneously inhibiting the hydrogen evolution reaction. The movement of the d-band center, induced by metal atom doping, regulates the adsorption strength of *NO, thus achieving a lower barrier for the hydrogenation step. This work provides theoretical guidance for the development of highly efficient multiple-atom catalysts for NRA.

Comparison and Optimization of Energy’s Source Consumption

In the period of continually evolving electronic engineering, devices are becoming more intelligent and more multifunctional. However, this increased functionality enhances companies with higher energy consumption which places pressure on energy resources and poses sustainability challenges. Due to this point, this passage is going to discuss three different types of energy resources—batteries, power plants, and renewable energy—outlining their advantages and drawbacks while considering their shortcuts on environmental impacts. Batteries face limitations in capacity and disposal concerns. Power plants, especially those reliant on fossil fuels, offer reliability but contribute to emissions and resource limitation. Renewable energy, like solar and wind power, provides sustainable solutions but may be intermittent due to environmental effects. After that, this passage will determine the optimum choice under different circumstances to reach its maximum productivity.

Enhancing local energy sharing reliability within peer-to-peer prosumer communities: A cellular automata and deep learning approach

This study introduces a significant advancement in peer-to-peer (P2P) energy trading systems within smart grids, addressing a crucial gap in existing research by incorporating optimal energy storage capacities to accommodate varying energy demands resulting from lifestyle changes. Through a two-level optimization approach, aimed at maximizing self consumption and optimizing energy flow within the grid, we propose a novel energy management strategy. Our contribution lies in the introduction of a new layer of deep learning and rules control, forming a self-energy sharing system for each prosumer. This architecture, termed the smart node, integrates deep learning techniques, to predict and customize energy services through dynamic adjustment of lower and upper bounds of battery capacities. Additionally, we leverage cellular automaton (CA) approaches to establish sustainable consensus among P2P network users, enhancing the adaptability and efficiency of the energy management system. The results show that the proposed algorithm could reduce the energy consumed by the P2P community from the utility by around 20% and maximize the collective self-consumption by around 8% compared to conventional energy trading in microgrids.