Real-time Optimization Strategy Research Articles

A bi-level real-time optimal control strategy for thermal coupled multi-zone dedicated outside air system-assisted HVAC systems

This paper proposes a bi-level optimal control strategy for thermally coupled multi-zone dedicated outside air system (DOAS)-assisted HVAC systems, which integrates the deep reinforcement learning (DRL) algorithm with Lyapunov function-based reward shaping (LRS) and an augmented genetic algorithm. The proposed strategy aims to maintain indoor air quality (IAQ) and thermal comfort while reducing the overall energy cost. On the upper level, the LRS method enhances the convergence of the DRL algorithm, ensuring satisfactory IAQ with reduced electricity cost. On the lower level, the embedding of the pre-trained optimal agent achieves a unidirectional decoupling, which mitigates model dependency and accurately control thermal comfort under thermal coupling. The proposed strategy offers the following advantages: a) It can efficiently maintain IAQ in multiple zones and high energy efficiency by optimizing the supply airflow, in response to real-time environmental changes. b) It can achieve satisfactory thermal comfort by regulating the fan coil unit power, while implicitly reducing the overall energy costs via unidirectional decoupling. c) Experimental results demonstrate that the proposed strategy achieves a maximum energy savings of 33.13 % and electricity savings of 34.14 %, compared with the traditional strategy. d) The proposed strategy exhibits good generalization ability under different test climates and occupancy scenarios.

Real-time V2G simulator for grid system frequency and voltage regulation with fault ride-through capabilities

Electric vehicles (EVs) have become one of the promising power grid ancillary services. They can help reduce the intermittent of renewable energy sources by supporting active power and frequency control services. Additionally, bi-directional chargers included into EV batteries can supply/consume reactive power for voltage regulation. To improve voltage quality, a unique control approach is devised to offer fault ride-through. In this study, real-time charging optimization schemes based on particle swarm optimization are developed to coordinate the EVs charging/discharging power with the grid power dispatches depending on the charging priority and preferential electricity prices. Five different car user profiles including different initial state of charge and plug in state have been depicted. Different optimum issues are carried out with comprehensive simulations employing hardware in the loop via real-time simulation for a modified IEEE 14-bus test system. Where different frequency and voltage controls, management strategies, variant fault scenarios, benefits, and challenges for power system regulation with EVs are illustrated. According to the simulation results, the proposed optimized charging strategies reduce the total expenses of EV parking station by 40 % compared to other cases, hence maximizing EV owner satisfaction. The net load curve is smoothed and reduced by 11 %, particularly during peak period.

A new real-time optimal energy management strategy for range extended electric vehicles considering battery degradation and engine start-up

A new real-time optimal energy management strategy for range extended electric vehicles considering battery degradation and engine start-up

Optimization Strategies for Real-time Energy Management of Electric Vehicles Based on LSTM Network Learning

The orderly control of electric vehicle load can improve the load characteristics of regional power grid and reduce the charging cost. Since it is impossible to predict the accurate access time and charging demand of electric vehicles in the future, it is impossible to make a global optimal arrangement for accessing the grid when electric vehicles are charging. Aiming at this problem, a real-time energy management system and optimization strategy of electric vehicle based on deep long-term and short-term memory neural network are proposed. Firstly, a three-tier electric vehicle management architecture including power grid layer, regional energy management system and charging station energy management system is constructed to manage large-scale electric vehicles in layers and regions; Then, a region station interaction strategy based on deep long-term and short-term memory neural network is proposed. The historical optimal solution solved by historical load information is used to train the learning network to guide the new real-time optimization; The proposed strategy can further reduce the charging cost and improve the peak valley characteristics of regional load on the premise of ensuring the charging demand of users. Finally, a simulation example is given to verify the effectiveness and superiority of the proposed layered architecture and management strategy.

Intelligent Energy Management: Leveraging an Effective Machine Learning for Predictive Appliance Energy Optimization in Smart Homes

In the context of escalating energy demands and the proliferation of smart home technologies, this study introduces a novel approach to energy management using the Random Forest machine learning model. Our research focuses on optimizing household appliance energy use, harmonizing efficiency with user comfort. By analyzing data on appliance usage patterns, environmental conditions, and user preferences, the Random Forest model predicts future energy needs, enabling the intelligent scheduling of appliances to reduce unnecessary consumption. The model’s strength lies in its capacity to unravel complex, non-linear relationships in high-dimensional data typical of household energy usage scenarios. Initial results demonstrate a notable decrease in energy consumption, affirming the model’s efficacy in enhancing energy efficiency without diminishing user convenience. This research not only highlights the potential of machine learning in energy management but also sets a foundation for future exploration into adaptive, real-time energy optimization strategies in smart homes. In this research, we compared our model with other machine learning models, and our model got a good accuracy of 95.71%, while for time series data, we got 99.71%.

Minimum decision of collision injury based on drivable area in unavoidable scene

The existing technology for assisted driving collision avoidance has improved the active safety of vehicles, but it is primarily designed for scenarios where collisions can be avoided. It is not suitable for situations where pedestrians suddenly intrude. Therefore, there is a need to develop a control strategy that can minimize collision injuries in unavoidable situations. This study focuses on typical collision scenarios where pedestrians suddenly intrude. It proposes a real-time online double-layer optimal pre-collision control strategy based on drivable area, using the Head Injury Criteria ( HIC) as a quantifiable measure of collision injury. The upper-layer controller designs a cost function based on a three Degrees of Freedom (DOF) collision solving model to minimize collision injuries. It takes the constraints of vehicle dynamics, maximum front wheel steering angle, pedestrian avoidance into account, and the optimal pre-impact state point with the minimum HIC value is calculated in real-time. Meanwhile, the lower-layer controller is designed to track the optimal pre-impact state point using Model Predict Control (MPC) algorithm. The effectiveness of the proposed control strategy is verified through Simulink simulation, achieving the dual objectives of avoiding pedestrians suddenly intruding and minimizing passenger collision injuries.

AI-Based Holistic Model for Decision-Support System in Inventory Optimization using deep Learning

Abstract: This study introduces an innovative artificial intelligence-driven comprehensive model designed for a decision-support system in inventory optimization. This model was developed using advanced deep learning techniques. The model aims to surpass the constraints of typical inventory management systems by effectively capturing temporal correlations and nuanced patterns in the data. This is achieved by integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. To navigate modern supply chains effectively, the primary focus should be on offering real-time decision support. The approach aims to enhance the decision-making processes in inventory management. This brief introduction offers insight into a distinctive method aimed at addressing the challenges of modern supply networks. The strategy focuses on providing realtime decision support and optimization strategies to enhance inventory management efficiency. The model's effectiveness is demonstrated by thorough performance evaluations, showing higher accuracy, precision, and recall compared to existing logistic regression models. The suggested LSTM model achieves an accuracy of 0.99, precision of 0.96, recall of 0.94, and an F1 Score of 0.97, surpassing existing models with higher scores in all metrics. The GRU model, albeit somewhat less effective than the LSTM, shows great overall performance, especially in recall with a score of 0.97. This investigation compares the effectiveness of different models, focusing on the potential advancements made possible by LSTM and GRU architectures in improving decision-support systems for inventory optimization. The AI-Based Holistic Model provided is at the forefront of technology advancements, offering a comprehensive solution for making educated decisions and optimizing inventories sustainably in today's highly competitive corporate climate.

Research on adaptive prediction model of rate of penetration under dynamic formation conditions

Rate of Penetration (ROP) prediction is a crucial for optimizing drilling processes and enhancing drilling efficiency. However, most existing ROP prediction models are static, relying on fixed neighboring well datasets and incapable of adapting to real-time changes in formation conditions during drilling. The dynamic and uncertain nature of geological conditions and formation heterogeneity further limits the extension and application of current ROP prediction models to target wells. To address these challenges, this study introduces an Adaptive Random Forest (ARF) algorithm to establish an ROP adaptive prediction model under dynamic formation conditions during real-time drilling operations. The performance of the ARF model is compared with the Moving-Window Random Forest (MW-RF) ROP dynamic prediction model, using data from the W1 well in the Sichuan basin as an example. Experimental results demonstrate that the ARF model outperforms the MW-RF model, exhibiting smaller overall error and reduced error fluctuation during formation condition changes. The ARF model's dynamic adaptability allows it to respond to the evolving trends of the ROP prediction model, rapidly adjusting to changes in formation conditions based on real-time drilling data flow. In contrast, the MW-RF model exhibits slower adaptability and delays in updating, reacting passively to window movements. The proposed ARF model overcomes limitations associated with static ROP prediction models, making it applicable to practical drilling scenarios and providing a theoretical foundation for real-time drilling parameter optimization and intelligent drilling strategies.

Iterative real-time optimization of a membrane pilot plant

In this paper, we describe the implementation and experimental validation of iterative real-time optimization (RTO) at a continuously operated membrane separation pilot plant. The objective is to achieve an economically optimal operation using the transmembrane pressure and the feed flow rate as adjustable operational parameters (set-points for the lower-level plant controllers). In spite of the popularity of RTO based on rigorous nonlinear plant models in the research literature, its practical use so far remains limited to large scale plants in the petrochemical industries. The main issue behind the limited application of RTO schemes is the necessary effort to derive and maintain sufficiently accurate models of the plants. In case of a significant plant-model mismatch, the results are not reliable, the optimal performance is not reached and even systematic violations of constraints may occur. The applicability of RTO could be extended significantly if simple process models sufficed for its successful application. Then an adaptation of the model or of the optimization problem to the real behavior of the plant is necessary to achieve an optimal operation. For this example, we tackle this issue by applying an iterative scheme called modifier-adaptation with quadratic approximation in combination with model adaptation to ensure the convergence of the plant to the true process optimum despite the fact that the model has deficiencies. The experimental results show that, in spite of a considerable plant-model mismatch, the presented RTO scheme is capable of boosting the process economics.

Data-driven assisted real-time optimal control strategy of submerged arc furnace via intelligent energy terminals considering large-scale renewable energy utilization

This study presents a data-driven assisted real-time optimization model which is an innovative approach to address the challenges posed by integrating Submerged Arc Furnace (SAF) systems with renewable energy sources, specifically photovoltaic (PV) and wind power, with modern intelligent energy terminals. Specifically, the proposed method is divided into two stages. The first stage is related to data-driven prediction for addressing local time-varying renewable energy and electricity market prices with predicted information, and the second stage uses an optimization model for real-time SAF dispatch. Connections between intelligent energy terminals, demand-side devices, and load management systems are established to enhance local renewable resource utilization. Additionally, mathematical formulations of the operating resistance in SAF are explored, and deep neuron networks are employed and modified for dynamic uncertainty prediction. The proposed approach is validated through a case study involving an intelligent energy terminal with a 12.5 MVA SAF system and 12 MW capacity renewable generators in an electricity market with fluctuating prices. The findings of this research underscore the efficacy of the proposed optimization model in reducing operational costs and enhancing the utilization of localized renewable energy generation. By integrating four distinct dissatisfaction coefficients into the optimization framework, we demonstrate the model's adaptability and efficiency. The application of the optimization strategy delineated herein results in the SAF system's profitability oscillating between $111 and $416 across various time intervals, contingent upon the coefficient settings. Remarkably, an aggregate daily loss recovery amounting to $1,906.84 can be realized during the optimization period. Such outcomes not only signify considerable economic advantages but also contribute to grid stability and the diminution of renewable energy curtailment, thereby underscoring the dual benefits of economic efficiency and sustainability in energy management practices.

Plan the evacuation of a hospital at imminent risk: A multimodal assessment for each hospital

BackgroundThe increasing frequency and intensity of climatic events and the number of infrastructures involved require standardization and multiplication of the evaluation of hospital evacuation plans. This article presents an automated simulation method dedicated to guiding experts in the development of a hospital selective evacuation plan. MethodsA pragmatic adaptation of the 13 principles of the international guidelines by a local operational expert working group led to the implementation of a computer-assisted simulation model. An application for a public hospital at significant risk of flooding is provided. ResultsThe expert group classifies each patient to be transferred according to 3 priorities (P1 to P3), 3 age categories (adult, paediatric, neonatal), specific transfer equipment and team required, and type of reception unit in hospitals. Ten specific profiles for transport conditions or destination hospitals were considered. The model can be used either to evaluate the minimal number of evacuation means necessary to proceed with the evacuation in a few hour of all the patients who require evacuation (output) or alternatively, the number of patients out of delay considering the number of evacuation means available (constraint). The model can be adapted with real-time constraints or optimization strategies such as traffic conditions, convoys, ephemeral proximity units, and reinforcements. ConclusionThe definition of the criteria that impact hospital intensive care unit evacuation, followed by the identification of these elements in the case of each hospital concerned, allows the construction of a homogeneous and transposable planning model and identifies the best evacuation strategies for each hospital.

Multiple-Zone Synchronous Voltage Regulation and Loss Reduction Optimization of Distribution Networks Based on a Dual Rotary Phase-Shifting Transformer

For the problem in which accessing a high proportion of renewable energy results in exceeding the limit in distribution network voltage, the existing regulating method experiences difficulty in considering the two-way voltage regulation and loss reduction optimization function. This study proposes a series-type dual rotary phase-shifting transformer (DRPST) based on the principle of phase volume synthesis. This transformer exhibits bidirectional voltage regulation, high reliability, and low cost. First, the topology, operating principle, and equivalent circuit of DRPST are introduced, and its simplified circuit model is established. On the basis of this model, the causes of voltage exceeding the limits are analyzed and the active distribution network model that contains DRPST is constructed. A real-time rolling two-layer optimization strategy based on DRPST is proposed. The inner layer model is solved using the multi-objective particle swarm optimization algorithm with the objective of minimizing voltage deviation and line loss. The optimal compromise solution of the Pareto solution set of the inner layer model is determined using the fuzzy subordinate degree function method. The outer model is based on the optimal compromise solution of the inner model, and the DRPST output rotor angle is controlled without deviation through double closed-loop proportional–integral regulation. Finally, the correctness and effectiveness of the proposed topology and control method are verified via simulation and experimental analysis.

Real-time optimization of wing drag and lift performance using a movable leading edge

A real-time optimization strategy can provide any system with a considerable boost in performance on the fly, which in real-world applications can be translated to lower energy consumption or higher efficiency. This study investigates the particular case of using real-time optimization to improve wing aerodynamic performance with a dynamically activated deflectable leading edge. Its activation aims to minimize drag and maximize lift and is governed by real-time and gradient-based optimization. An extension to a classic method is suggested to enhance gradient estimation accuracy. Experimental data are obtained at a Reynolds number of 2.0×105 with the wing fixed at five positions. For each of these positions, optimal leading-edge deflections are found. The results indicate that deflecting the leading edge has a negligible impact on drag and lift before the stall onset. However, the reduction in the pitching moment cannot be ignored. When the wing is experiencing a proper stall, the movable leading edge yields remarkable enhancements, with the lift being approximately raised by 45% together with a substantial increase in the critical angle of attack. The findings highlight the potential of real-time optimization in experimental aerodynamic studies, reinvigorating its application in improving aircraft performance.

Real-time processing and optimization strategies for IoT data streams

Abstract With the development of industrial IoT and the arrival of smart manufacturing, the field of edge computing has gained more and more attention. However, traditional industrial computing scenarios relying on industrial clouds make data latency a greater challenge. In this paper, for the contradiction between edge devices and task resource allocation encountered in edge computing scenarios in smart manufacturing, we propose an industrial internet task scheduling model for smart manufacturing and introduce a scheduling node state matrix to realize the state management of each scheduling subtask. Aiming at the problem of multiple tasks seizing resources in a complex, intelligent manufacturing environment, the study combines the caching mechanism to realize the task offloading computational processing of order scheduling, in which the caching mechanism is used to solve the problem of computational resource limitations at the edge. It is found through simulation that when the computational task factor ξk =2 is larger, more offloading power is allowed to be transmitted to the edge ser ver for computation. For computational tasks with smaller task factor ξk , the device tends to allocate more computational rate to that computational task. Eventually the data queue length will be continuously reduced and the data queue is concentrated in the interval of very small values, this result verifies that the task scheduling algorithm is able to perform task scheduling efficiently and reduce the latency.

Integrated real-time optimal energy management strategy for plug-in hybrid electric vehicles based on rule-based strategy and AECMS

Integrated real-time optimal energy management strategy for plug-in hybrid electric vehicles based on rule-based strategy and AECMS

Real-Time Global Optimal Energy Management Strategy for Connected PHEVs Based on Traffic Flow Information

Real-Time Global Optimal Energy Management Strategy for Connected PHEVs Based on Traffic Flow Information

Integrated real-time optimal energy management strategy for plug-in hybrid electric vehicles based on rule-based strategy and AECMS

Integrated real-time optimal energy management strategy for plug-in hybrid electric vehicles based on rule-based strategy and AECMS

A real-time PMU-based optimal operation strategy for active and reactive power sources in smart distribution systems

A real-time PMU-based optimal operation strategy for active and reactive power sources in smart distribution systems

A method for real-time optimal heliostat aiming strategy generation via deep learning

Optimal aiming strategies are essential for efficient solar power tower technology operation. However, the high calculation complexity makes it difficult for existing optimization methods to solve the optimization problem in real-time directly. This work proposes a real-time optimal heliostat aiming strategy generation method via deep learning. First, a two-stage learning scheme where the neural network models are trained by genetic algorithm (GA) benchmark solutions to produce an optimal aiming strategy is presented. Then, an end-to-end model without needing GA solutions for training is developed and discussed. Furthermore, a robust end-to-end training method using randomly sampled flux maps is also proposed. The proposed models demonstrated comparable performance as GA with two orders of magnitude less computation time through case studies. Among the proposed models, the end-to-end model shows significantly better generalization ability than the pure data-driven two-stage model on the test set. A robust end-to-end model with data enhancement has better robustness on unseen flux maps.

Digital twin assisted decision support system for quality regulation and leak localization task in large-scale water distribution networks

Effective water resource management is essential in large metropolitan cities. Digital Twins (DT), supported by IIoT and machine learning technologies, provide opportunities for real-time prediction and optimization for effective decision-making in water distribution systems. A framework for the digital twin of the Water Distribution Network (WDN) is developed in this paper to achieve higher operational efficiency using ‘WNTR’, the Python-based library of EPANET. All computational experiments and methods were validated on the benchmark hydraulic C-TOWN network (Ostfeld et al., 2011). The hydraulic parameters and quality parameters of the DT model for the water network were calibrated using the Differential Evolution (DE) algorithm. The calibrated DT served as a real-time proxy to generate simulation data, which is used for two different applications in large-scale water networks: (i) Disinfectant dosage regulation task using booster stations and (ii) pipe leakage localization task. The calibrated DT was utilized to estimate the optimal disinfectant dosing rates, ensuring water quality control within an acceptable range using optimization. The results highlight the effectiveness of the neural network and real-time optimization strategy to achieve the optimal dosing rate. For the leakage localization task, the Graph Convolution Networks (GCN) based neural network trained on the DT was found to predict leakage location very accurately.