Energy Optimization Model Research Articles

Energy optimization of building-integrated photovoltaic for load shifting and grid robustness in high-rise buildings based on optimum planned grid output

This study proposes an energy management and optimization model of building-integrated photovoltaic (BIPV) systems integrating static battery storage and electric vehicles considering stochastic parking schedules. A novel energy management strategy of orienting grid robustness with optimum planned grid output is developed to effectively manage BIPV power for load shifting in a typical high-rise building under both low-energy case and zero-energy case. Innovative assessment indicators of load shifting factor and grid robustness factor are proposed to quantify the applicability of the orienting grid robustness strategy compared with the conventional maximizing PV utilization strategy. Multi-objective optimizations are conducted to explore optimum system capacity, planned grid output in cooling and non-cooling seasons balancing the load shifting factor, grid robustness factor and lifetime net present value. The research results indicate that the orienting grid robustness strategy is effective for load shifting in the high-rise building under both low-energy case and zero-energy case with the load shifting factor at about 76.17% and 74.62%, respectively. It achieves obvious grid integration performance reducing the grid robustness factor by 49.34% in the low-energy case and 71.65% in the zero-energy case. The net present value is decreased by about 27.87% and 13.59% in two optimum cases and the annual equivalent carbon emissions are decreased by 10.12% and 65.09%, respectively. The developed energy management and optimization framework with novel strategy and indicators can improve the grid robustness and energy economy of BIPV and storage systems for high-rise buildings towards low-energy and low-carbon operations.

Leveraging AI and Machine Learning Models for Enhanced Efficiency in Renewable Energy Systems

Abstract. Artificial intelligence (AI) and machine learning (ML) have great potential in the management and optimization of renewable energy systems, through more in-depth AI-assisted analysis of store demand in the energy system, and through active learning models for energy optimization and scheduling. Among them, artificial intelligence and active learning can be combined to improve the utilization efficiency of renewable energy in a high range. In particular, recurrent neural network RNN and long- and short-term memory network LSTM in active learning can be used to predict energy and power demand, so as to achieve more effective intelligent power management and system optimization. Two of these models have their own advantages. LSTM and other active learning models can optimize the long-term dependence of traditional time series models and provide more efficient and accurate prediction model results for wind power forecasting and management and other renewable energy forecasting tasks. This study shows that compared with the traditional model, the LSTM model has a high advantage in processing long-term relationships and complex time series data, and the prediction results are more accurate and obvious. Through the dual combination of artificial intelligence and machine learning for renewable energy, this study provides theory, support and certain experimental guidance for the innovation of global energy systems, so as to make certain contributions to the development of renewable energy.

A Multi-Objective Energy Optimization Model for Renewable Energy Systems with Hybrid Energy Storage

Building a Renewable Energy System (RES) is a viable way to address resource depletion and accomplish decarbonization. This study presents a hybrid power system that combines wind, solar, battery, and thermal energy storage. It also examines the co-optimization of scheduling and operation for multiple objectives. The hybrid system combines the affordability of thermal energy storage (TES) with the adaptability of batteries to effectively address the issue of intermittent RES. A new approach for integrated operation is offered, which relies on the electrical block's operation limit. The planning-operation co-optimization system considers minimizing the Net Present Cost (NPC) and reducing power supply likelihood todetermine the best operation limit and sizing decision factors. The co-optimization issue is addressed using novel multi-objective decision-making (MO-DM). This approach incorporates the Decision-Maker (DM) preferences data to direct the evolutionary process toward the desired area. In addition, a data-driven prediction model captures the risks and losses associated with wind generation. The case study's findings indicate the following: (1) The data-driven system has a higher level of precision in wind power projection when compared with commonly used physical simulations. (2) The suggested MO-DM exhibits superior integration, variety, and robustness achievement in the DMchosen area compared to others. (3) The combined battery-thermal energy storing structure achieves improved economy and dependability through the optimum organized operation approach, in contrast to using a single energy storage system under various testing constraints.

Retraction notice: Intelligent ubiquitous computing model for energy optimization of cloud IOTs in sensor networks

Retraction notice: Intelligent ubiquitous computing model for energy optimization of cloud IOTs in sensor networks

Cost-effective and optimal pathways to selecting building microgrid components – The resilient, reliable, and flexible energy system under changing climate conditions

Amidst the changing climate conditions, electricity retailers-led demand response programs and the emergency backup power for possible grid outages are often discussed in isolation, although several underlying linkages exist, which are important for how the existing building stock evolves with the energy transition. This study navigates through the linkages while investigating the levelized cost of electricity (LCOE)-based building microgrid components and undertakes a comparative analysis of energy optimisation models with and without emergency diesel generators. It also examines the building energy system’s resilience, reliability, and flexibility by using OpenStudio and HOMER Grid to develop energy simulation and optimisation models and other probabilistic models for a chosen building archetype in Sydney, Australia. This study finds that excluding the emergency diesel generator will require a larger battery storage system, increasing LCOE from A$0.17/kWh to A$0.20/kWh across climate scenarios. However, the larger battery storage system increases the probability of surviving outages (> 4 h) by around 10 % across climate scenarios. The LCOE increases up to 45 % for outages up to 8 h across climate scenarios and up to 85 % for outages up to 12 h. Additionally, for the building archetype, the probability of grid purchase (below the current average) is 0.78 in 2030, which drops to 0.55 in 2050. The probability of grid sales (above the current average) also drops from 0.69 to 0.46. Thus, while the narratives around the building energy system’s flexibility overstate the electricity exchange between the building and the grid, this study finds that increasing the on-site production utilisation rate and larger battery throughput contributes to demand response application and flexibility.

Scheme design and assessment of hybrid pump feed system with energy management for throttleable liquid rocket engine

Currently, there is considerable emphasis on the electric pump-fed cycle for liquid engine, primarily due to its design simplicity. However, its development is hindered by the underdeveloped state of power battery technology. Drawing inspiration from hybrid power technology used in electric vehicles and turbochargers, a hybrid pump feed system for throttleable engines is originally proposed as a promising solution. This system integrates the electric motor into the gas generator cycle, with several topologies evaluated. The parallel configuration featuring a mid-motor is selected for its compact structure, efficient power-splitting and energy recovery. Additionally, customized energy management strategies and optimization models are developed to effectively allocate power throughout the operational processes of liquid engines. A comparative analysis of four engine cycles is conducted under the typically variable-thrust mission. The results indicate that attributed to the conservation of turbo-gas and battery energy, the optimized hybrid pump achieves a reduction of 2.39 % compared to the turbopump and 7.15 % to the electric pump in total mass. Adaptability assessment further indicates that the mass advantage of the hybrid pump system is more significant during prolonged engine burning and deep throttling. Specific working conditions are found in which the system prefers electric-motor driving or regenerating turbine energy. Although energy-recovery results in the system efficiency decrease, it serves to lower energy demand of battery pack, thus easing the burden on cell thermal management and structural design. This study provides a practical design framework for hybrid pump-fed rocket engines in future variable-thrust missions.

Mathematical optimization strategy for online scheduling of complex manufacturing systems based on thermal energy optimization and fuzzy mathematical model

With the rapid development of the manufacturing industry and the advancement of the intelligent process, the scheduling problem of complex manufacturing systems becomes more and more complicated, especially in the aspect of energy management and optimization. This study aims to propose an online scheduling mathematical optimization strategy based on thermal energy optimization and fuzzy mathematical model, so as to improve the scheduling efficiency and resource allocation rationality of complex manufacturing systems under different production conditions. A scheduling model considering thermal energy utilization rate, production priority and real-time demand is established by using fuzzy mathematics. The model deals with the uncertain factors by fuzzy logic and solves them by genetic algorithm to realize the dynamic adjustment and optimal scheduling of production resources. Through the experimental verification of a complex manufacturing system, the improvement of scheduling efficiency not only reduces the operating cost, but also improves the flexibility and response speed of the system. The fuzzy mathematical model based on thermal energy optimization provides an effective on-line scheduling strategy for complex manufacturing systems, which can realize efficient scheduling and energy management in dynamic environment.

Thermal optimization of alloy additive manufacturing process and visual design of metal process products based on edge computing

Alloy additive manufacturing technology has been widely used in aerospace, medical equipment and automobile manufacturing. The purpose of this study is to explore the thermal energy optimization scheme in the process of alloy additive manufacturing based on edge computing technology, and to display the metal process products in an all-round way through visual design, so as to promote the knowledge and practice of thermal energy management in the industry. In this paper, the edge computing platform is used to monitor the heat energy data generated in additive manufacturing process in real time, and the heat energy distribution is analyzed with machine learning algorithm. By establishing thermal energy optimization model, adjusting manufacturing parameters to reduce energy consumption, improve material utilization and product quality. At the same time, using visual design technology, the optimization process and results are graphically represented, which provides intuitive decision basis for engineers. The experimental results show that the thermal energy optimization scheme based on edge computing significantly improves the thermal energy efficiency of the alloy additive manufacturing process, and significantly reduces the thermal energy loss. Through the visual design, relevant personnel can understand the thermal energy distribution and process changes more clearly, and promote the collaboration between multiple departments. This study reveals the application potential of edge computing in alloy additive manufacturing and provides an effective thermal optimization strategy.

Deep Learning-Infused Hybrid Security Model for Energy Optimization and Enhanced Security in Wireless Sensor Networks

Deep Learning-Infused Hybrid Security Model for Energy Optimization and Enhanced Security in Wireless Sensor Networks

Bundle-specific tractogram distribution estimation using higher-order streamline differential equation

Streamline tractography locally traces peak directions extracted from fiber orientation distribution (FOD) functions, lacking global information about the trend of the whole fiber bundle. Therefore, it is prone to producing erroneous tracks while missing true positive connections. In this work, we propose a new bundle-specific tractography (BST) method based on a bundle-specific tractogram distribution (BTD) function, which directly reconstructs the fiber trajectory from the start region to the termination region by incorporating the global information in the fiber bundle mask. A unified framework for any higher-order streamline differential equation is presented to describe the fiber bundles with disjoint streamlines defined based on the diffusion vectorial field. At the global level, the tractography process is simplified as the estimation of BTD coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors. Experiments are performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography Challenge data, FiberCup data, and in vivo data from the Human Connectome Project (HCP) for qualitative and quantitative evaluation. Results demonstrate that our approach reconstructs complex fiber geometry more accurately. BTD reduces the error deviation and accumulation at the local level and shows better results in reconstructing long-range, twisting, and large fanning tracts.

Self-operation and low-carbon scheduling optimization of solar thermal power plants with thermal storage systems

Photo thermal power generation, as a renewable energy technology, has broad development prospects. However, the operation and scheduling of photo thermal power plants rarely consider their internal structure and energy flow characteristics. Therefore, this study explains the structure of a solar thermal power plant with a thermal storage system and analyzes its main energy flow modes to establish a self-operation and low-carbon scheduling optimization model for the solar thermal power plant. The simulation results of the example showed that for the self-operating model oriented towards power generation planning and peak valley electricity prices, the existence of a thermal storage system could improve the power generation capacity and revenue of the photovoltaic power plant. For example, when the capacity of the thermal storage system was greater than 6 h, the penalty for insufficient power generation in the simulation result was 0 $, and the maximum increase in revenue reached 84.9% as the capacity of the thermal storage system increased. In addition, when the capacity of the thermal storage system increased from 0 to 8 h, the comprehensive operating cost decreased from 1635.2 k $ to 1224.6 k $, and the carbon emissions decreased from 26.4 × 103 ton to 22.1 × 103 ton. Compared with the existing literature, this study provides a more comprehensive and systematic solution through detailed energy flow analysis and optimization model. The research has practical and far-reaching significance for promoting the development of clean energy technology, improving the sustainable utilization of renewable energy, and optimizing the overall performance of the energy system.

Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings

In the current context of energy transition and increasing climate change, optimizing building performance has become a critical objective. Efficient energy use and occupant comfort are paramount considerations in building design and operation. To address these challenges, this study introduces a predictive model leveraging Machine Learning (ML) algorithms. The model aims to predict thermal comfort levels and optimize energy consumption in Heating, Ventilation, and Air Conditioning (HVAC) systems. Four distinct ML algorithms Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and EXtreme Gradient Boosting (XGBOOST) are employed for this purpose. Data for the model is collected using a network of Raspberry Pi boards equipped with multiple sensors. Performance evaluation of the ML algorithms is conducted using statistical error metrics, including, Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Results reveal that the RF and XGBOOST algorithms exhibit superior performance, achieving accuracies of 96.7 % and 9.64 % respectively. In contrast, the SVM algorithm demonstrates inferior performance with a R2 of 81.1 %. These findings underscore the predictive capability of the RF and XGBOOST model in forecasting Predicted Mean Vote (PMV) values. The proposed model holds promise for enhancing occupant thermal comfort in buildings while simultaneously optimizing energy consumption in HVAC systems. Further research could explore the practical applications of these findings in building design and operation.

Optimal hydroelectric energy utilization with ATDOA: a case study of the Bumbuna Dam

ABSTRACT Given that hydroelectric energy is regarded as the tertiary means of electricity production and furthermore the most crucial renewable energy producer globally, the imperative of maximizing the utilization of the vast and costly water resource, which depletion is increasingly conspicuous each passing day, is more pressing than before. In this study, the Advanced Tasmanian Devil Optimization Algorithm (ATDOA) is employed to optimize the utilization of hydroelectric energy derived from the Bumbuna Dam reservoir. This reservoir is situated in the Seli River, located in the Northern Province of Sierra Leone. The research period includes 138 months, covering the duration from October 2010 to April 2022. The Tasmanian Devil Optimization Algorithm, which is bio-inspired algorithm, has been shown to be an effective optimization technique in multiple scholarly researches. The decision variables in the hydroelectric energy optimization model from the reservoir are the optimal release values of the monthly hydroelectric output from the reservoirs of the dams. After verifying the correctness of the ATDOA algorithm by using several standard benchmark functions, a model was developed for optimal hydroelectric utilization of the Bumbuna Dam.

A study of home energy management considering carbon quota

The household energy management system (HEMS) has become an important system for energy conservation and emission reduction. In this study, home energy management considering carbon quota has been established. Firstly, the household photovoltaic output model, load model of various electrical appliances, battery load model, and charging and discharging of electric vehicles (EVs) model are established. Secondly, the carbon emission and carbon quota of household appliances and EVs are considered in these models. Thirdly, the energy optimization model of minimum the household user’s total comprehensive operation cost with the minimum total electricity consumption, carbon trading cost, battery degradation cost, and carbon quota income are proposed, taking into account constraints such as the comfort of users’ energy use time. Subsequently, the improved particle swarm optimization (IPSO) algorithm is used to tackle the problem. Compared to the standard particle swarm optimization (PSO), the IPSO has significantly improved the optimization effect. By comparing the optimization results in different scenarios, the effectiveness of the strategy is verified, and the influence of different carbon trading prices on optimal energy scheduling has been analyzed. The result shows that the comprehensive consideration of carbon trading cost and total electricity cost can reduce the household carbon emissions and the total electricity cost of the household user. By increasing the carbon trading price, the user’s carbon trading income and the EV carbon quota income increase, and the overall operating cost decreases; the guidance and regulation of carbon trading price can make a valuable contribution to HEMS optimization. Compared to the original situation, the household carbon emissions are reduced by 14.58 kg, a decrease of over 21.47%, while the total comprehensive operation cost are reduced by 14.12%. Carbon quota trading can guide household users to use electricity reasonably, reducing household carbon emissions and the total cost of household electricity.

Stackelberg game-based optimal electricity trading method for distribution networks with small-micro industrial parks

In order to improve the operating benefits of the distribution network (DN) and reduce the energy consumption costs of small-micro industrial parks (SMIPs), a two-layer optimal electricity trading method for DN with SMIPs is proposed. First, based on the Stackelberg game, a multi-objective two-layer optimal trading model for DN and SMIP is established. In the upper layer, the DN agent is regarded as the leader, and a trading model is established with the goal of maximizing the profits of agents. In the lower layer, an energy optimization model is proposed for the SMIP operators, which are regarded as the followers, with the goal of minimizing the operating costs. According to the buying and selling electricity prices at the upper and lower layers, a dynamic pricing strategy is formulated. The Karush–Kuhn–Tucker condition (KKT) is introduced to transform the two-layer model into a single-layer model, and based on linear transformations, the model is further converted into a mixed-integer linear programming model. The transformations aim to address the non-linear issues arising from multivariable coupling between the upper and lower-layer trading models. The simulation results show that the trading strategy proposed in this paper can effectively increase the profit of DNs while reducing the operating costs of SMIPs and can provide a reference for decision-making in the electricity market (EM) with the participation of SMIP.

A Mathematical Programming Approach for IoT-Enabled, Energy-Efficient Heterogeneous Wireless Sensor Network Design and Implementation.

The Internet of Things (IoT) is playing a pivotal role in transforming various industries, and Wireless Sensor Networks (WSNs) are emerging as the key drivers of this innovation. This research explores the utilization of a heterogeneous network model to optimize the deployment of sensors in agricultural settings. The primary objective is to strategically position sensor nodes for efficient energy consumption, prolonged network lifetime, and dependable data transmission. The proposed strategy incorporates an offline model for placing sensor nodes within the target region, taking into account the coverage requirements and network connectivity. We propose a two-stage centralized control model that ensures cohesive decision making, grouping sensor nodes into protective boxes. This grouping facilitates shared resource utilization, including batteries and bandwidth, while minimizing box number for cost-effectiveness. Noteworthy contributions of this research encompass addressing connectivity and coverage challenges through an offline deployment model in the first stage, and resolving real-time adaptability concerns using an online energy optimization model in the second stage. Emphasis is placed on the energy efficiency, achieved through the sensor consolidation within boxes, minimizing data transmission hops, and considering energy expenditures in sensing, transmitting, and active/sleep modes. Our simulations on an agricultural farmland highlights its practicality, particularly focusing on the sensor placement for measuring soil temperature and humidity. Hardware tests validate the proposed model, incorporating parameters from the real-world implementation to enhance calculation accuracy. This study provides not only theoretical insights but also extends its relevance to smart farming practices, illustrating the potential of WSNs in revolutionizing sustainable agriculture.

Study on optimal allocation of energy storage in multi-regional integrated energy system considering stepped carbon trading

Abstract In this study, an energy storage configuration optimization model of multi regional integrated energy system based on integrated scheduling and stepped Carbon emission trading is proposed. By analyzing the steady-state, dynamic, and variable operating characteristics of multi regional integrated energy systems, a distributed energy planning modeling method was adopted to construct energy storage configuration constraint objects and optimization models. The target scheme of energy storage configuration is optimized by using the results of integrated scheduling scheme and dynamic distribution analysis of ladder Carbon emission trading, and the parameters are optimally estimated by using Monte Carlo method and quadratic fitting algorithm. The simulation results show that this method has a strong scheduling capability in the context of stepped Carbon emission trading, can accurately assess the load demand and supply demand relationship, reduce carbon emissions, and improve energy efficiency, with an average of 0.9121.

Active distribution network operational optimization problem: A multi-objective tuna swarm optimization model

This study proposes multi-objective tuna swarm optimization through multi-objective transformation, initialization improvement and population variation for active distribution network (ADN). The ADN energy optimization model with dynamic reconfiguration, reactive power compensation, on-load tap changer and controllable load coordinated control are established with the minimum economic and environmental cost. Several controllable resources increase and increase the complexity of energy optimization problem in realized the effective clean energy consumption and the power grid stable operation. The minimum control cost and the minimum node voltage deviation are proposed. This study also proposes a decision-making method based on pareto front and an index to evaluate the maximum extensible dimension of intelligent algorithms in the process of analyzing energy optimization problems with intelligent algorithms. The proposed ADN energy optimization method based on multi-objective tuna swarm optimization shows excellent results after testing with the improved IEEE33 system. The voltage deviation and network loss are reduced by 51.62% and 22.16% on average compared with the single means control. The proposed model provides the support for the new energy consumption and the power grid stable operation, has strong engineering significance in the intelligent upgrading and power grid active control transformation.

Joint energy-frequency regulation electricity market design for the transition towards a renewable-rich power system

Striving to develop renewable energy generation (REG) has become a global trend. Due to high carbon emissions, fossil-fueled generation units (FFGUs) in an electrical energy market such as the day-ahead and real-time electricity markets are facing unprecedented constraints. From the perspective of FFGUs, an effective way is to change their market roles from energy suppliers with high environmental costs to secure and reliable frequency regulation service providers. To help FFGUs successfully complete the transition, a bi-level optimization model for the joint energy and regulation service market is proposed in this paper. Different from existing joint markets, both the environmental cost of FFGUs and the frequency regulation service (FRS) cost of REG units are considered in this research for market operators to determine the scheduling priorities of submitted bids from generation units. In the frequency regulation service market model, a dynamic approach to determining the FRS demand is applied, which not only incentivizes REG units to initiatively manage their outputs, but also derives the price signals through the equilibrium of the FRS demand and supply to ensure the revenue of FFGUs in the FRS market. Meanwhile, the decision-making model of the FFGUs under the proposed market mechanism is also presented. Finally, extensive numerical experiments demonstrate the feasibility and efficiency of the mechanism. Simulation results show that the proposed mechanism can stimulate a smooth transition of FFGUs by adapting to actual situations and policy changes.

Speed and energy optimization method for the inland all-electric ship in battery-swapping mode

Zero-emission battery-powered ship is considered the ideal technical solution to achieve emission reduction and energy conservation in inland shipping. However, due to long charging time, limited onboard space, and initial investment cost, the all-electric ship in battery charging mode may not be suitable for inland long-distance navigation. Therefore, the all-electric ship in battery-swapping mode is proposed, and a joint optimization method is designed to acquire optimal voyage scheduling and energy management. First, considering the environmental factors of water speed and water depth and cargo amount, the energy consumption model for the all-electric ship in battery-swapping mode is established by analyzing its energy transfer process. Second, a speed and energy optimization model is established to minimize the battery-swapping cost, considering the constraints of sailing speed, sailing time, and battery energy use. Third, differential evolution with neighborhood search (NSDE) is adopted to solve this nonlinear and complex problem. Finally, a case study for the ship in the Yangtze River is studied to verify the effectiveness of the proposed method. The results show that the operation cost could be minimized by this method effectively.