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Supercritical multicomponent thermal fluid injection is a new technology with great potential for offshore heavy oil thermal recovery. In the process of thermal fluid generation, the reaction conditions including temperature, pressure, and the organic mass concentration in the reaction material will significantly affect its composition and injection rate and will further affect the thermal recovery and development quality of heavy oil. However, there is a lack of relevant research on the variation rules and control methods of the composition and injection rate of supercritical multicomponent thermal fluids, resulting in a lack of technical mechanisms for effective optimization. To fill this gap, a reaction molecular dynamics simulation method was used to simulate thermal fluid generation under different temperatures, pressures, and organic mass concentrations. The changes in thermal fluid composition and yield with reaction conditions were studied, and a control model of thermal fluid composition and yield was established. The proportional relationship between the thermal fluid generation scale of an offshore heavy oil platform and the simulated thermal fluid generation scale is analyzed, and a collaborative optimization method of thermal fluid composition and injection rate in field applications is proposed. The results show the following: (1) The higher the mass concentration of organic matter, the higher the content of supercritical carbon dioxide and supercritical nitrogen in thermal fluids, and the lower the content of supercritical water. (2) The higher the temperature and pressure, the higher the thermal fluid yield, and the higher the organic mass concentration, the lower the thermal fluid yield. (3) The component fitting model conforms to the power function relationship, and the coefficient of determination R2 is greater than 0.9; the yield fitting model conforms to the modified inverse linear logarithmic function relationship, the determination coefficient R2 is greater than 0.8, and the fitting degree is high. (4) The ratio between the actual injection rate of thermal fluids in the mine field and the molecular simulated thermal fluid yield is the ratio of organic matter mass in the platform thermal fluid generator and organic matter mass in the simulated box. (5) Based on the composition and yield control model, combined with the simulation of the ratio relationship between yield and injection rate in the field, a collaborative optimization method of thermal fluid composition and injection rate was established. The research results can provide an effective technical method for predicting, controlling, and optimizing the composition and injection rate of supercritical multicomponent thermal fluids. [ABSTRACT FROM AUTHOR]

Compressed cold air-cooling system (CCAS) has been widely employed in dry cutting machine tools to maintain the thermal stability of the cutting space. However, CCAS is an energy-intensive and inefficient thermodynamic system with multi-energy and multi-exergy flows, which poses serious burdens for green manufacturing. To tackle the gap, this research carries out the energy-exergy characteristics analysis and the multi-objective optimization between energy consumption and exergy efficiency for CCAS. Firstly, the energy usage and exergy destruction characteristics for CCAS are analyzed according to the thermodynamic knowledge. Then, the multi-parameter-driven energy consumption and exergy efficiency models are developed, in which the relationships between the thermodynamic parameters and the objective functions are revealed. To obtain a compromise between the high energy use and low thermal energy efficiency, an energy-exergy co-optimization is constructed, and the NSGA-II algorithm combined with the Euclidean technique and TOPSIS decision-making method is adopted to derive the optimal solutions. Finally, a case study is conducted to validate the proposed models. The results show that the relative errors of the proposed energy and exergy models are 1.32% and 1.88%, respectively. The energy consumption is reduced by 10.7%, and the exergy efficiency is improved by 8.9% after optimization. The air compressor consumed the most energy and had the highest exergy destruction. The presented research lays a foundation for exploring the energy-saving potentials and improving the exergy efficiency of CCAS, and provides a theoretical foundation for achieving sustainable manufacturing. [ABSTRACT FROM AUTHOR]

This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees' influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China. [ABSTRACT FROM AUTHOR]

Generation and transmission network expansion planning (GTNEP) co-optimization models have been developed to enable simultaneous decisions considering investment and operational components. Most works solve the GTNEP using linearized models without considering voltage variables, reactive power constraints, and network power losses, thus resulting in a loss of model fidelity and leading to either sub-optimal or non-feasible expansion plans. Therefore, this work addresses the GTNEP problem by integrating the planning of new generation power plants, transmission lines, reactive power allocation, and the evaluation of network power losses, using the AC model to assess the operational problem. Therefore, the integrated AC-GTNEP problem is tackled using meta-heuristic optimization techniques. The approach was implemented using the Julia programming language and evaluated using the 6-bus Garver, the IEEE 24-bus, and the IEEE 118-bus test systems. The results illustrate that the proposed integrated GTNEP approach yields more cost-effective expansion plans compared to the sequential approach, with total savings of approximately 11.30%, 8.8%, and 2.5% when applied to each test system, respectively.

Supercritical multicomponent thermal fluid injection is a new technology with great potential for offshore heavy oil thermal recovery. In the process of thermal fluid generation, the reaction conditions including temperature, pressure, and the organic mass concentration in the reaction material will significantly affect its composition and injection rate and will further affect the thermal recovery and development quality of heavy oil. However, there is a lack of relevant research on the variation rules and control methods of the composition and injection rate of supercritical multicomponent thermal fluids, resulting in a lack of technical mechanisms for effective optimization. To fill this gap, a reaction molecular dynamics simulation method was used to simulate thermal fluid generation under different temperatures, pressures, and organic mass concentrations. The changes in thermal fluid composition and yield with reaction conditions were studied, and a control model of thermal fluid composition and yield was established. The proportional relationship between the thermal fluid generation scale of an offshore heavy oil platform and the simulated thermal fluid generation scale is analyzed, and a collaborative optimization method of thermal fluid composition and injection rate in field applications is proposed. The results show the following: (1) The higher the mass concentration of organic matter, the higher the content of supercritical carbon dioxide and supercritical nitrogen in thermal fluids, and the lower the content of supercritical water. (2) The higher the temperature and pressure, the higher the thermal fluid yield, and the higher the organic mass concentration, the lower the thermal fluid yield. (3) The component fitting model conforms to the power function relationship, and the coefficient of determination R2 is greater than 0.9; the yield fitting model conforms to the modified inverse linear logarithmic function relationship, the determination coefficient R2 is greater than 0.8, and the fitting degree is high. (4) The ratio between the actual injection rate of thermal fluids in the mine field and the molecular simulated thermal fluid yield is the ratio of organic matter mass in the platform thermal fluid generator and organic matter mass in the simulated box. (5) Based on the composition and yield control model, combined with the simulation of the ratio relationship between yield and injection rate in the field, a collaborative optimization method of thermal fluid composition and injection rate was established. The research results can provide an effective technical method for predicting, controlling, and optimizing the composition and injection rate of supercritical multicomponent thermal fluids.

Integrating renewable energy and energy storage systems provides a way of operating the electrical grid system more energy efficiently and stably. Thermal storage and batteries are the most common devices for integration. However, it is not clear which integrated storage system performs better in terms of overall economics. Ice storage has low initial and maintenance costs, but there is an efficiency penalty for charging of storage and it can only shift electrical loads associated with building cooling requirements. A battery's round-trip efficiency, on the contrary, is quite consistent and batteries can be used to shift both HVAC and non-HVAC loads. However, batteries have greater initial costs and a shorter life. This research presents a tool, using model predictive control and optimal sizing, and provides a case study for comparing life-cycle economics of battery and ice storage systems for commercial buildings that have chillers for cooling and an on-site photovoltaic system.

The robot task sequencing problem and trajectory planning problem are two important issues in the robotic optimization domain and are solved sequentially in two separate levels in traditional studies. This paradigm disregards the potential synergistic impact between the two problems, resulting in a local optimum solution. To address this problem, this paper formulates a co-optimization model that integrates the task sequencing problem and trajectory planning problem into a holistic problem, abbreviated as the robot TSTP problem. To solve the TSTP problem, we model the optimization process as a Markov decision process and propose a deep reinforcement learning (DRL)-based method to facilitate problem solving. To validate the proposed approach, multiple test cases are used to verify the feasibility of the TSTP model and the solving capability of the DRL method. The real-world experimental results demonstrate that the DRL method can achieve a 30.54% energy savings compared to the traditional evolution algorithm, and the computational time required by the proposed DRL method is much shorter than those of the evolutionary algorithms. In addition, when adopting the TSTP model, a 18.22% energy reduction can be achieved compared to using the sequential optimization model. [ABSTRACT FROM AUTHOR]

To address the shortcomings of the sine cosine algorithm such as the low search accuracy, slow convergence speed, and easily falling into local optimality, a sine cosine algorithm for elite individual collaborative search was proposed. Firstly, tent chaotic mapping was used to initialize the population and the hyperbolic tangent function was applied non-linearly to adjust the parameters of the sine cosine algorithm, which enhanced the uniformity of population distribution and balanced the global exploration and local exploitation ability. Secondly, the search method of the sine cosine algorithm was improved by combining the search strategy of the sine cosine algorithm, the m-neighborhood locally optimal individual-guided search strategy, and the global optimal individual-guided search strategy, and, then, the three search strategies were executed alternately, which achieved collaboration, improved the convergence accuracy, and prevented the algorithm from falling into local optima. Finally, a greedy selection strategy was employed to select the best individuals for the population, which accelerated the convergence speed of the sine cosine algorithm. The simulation results illustrated that the sine cosine algorithm for elite individual collaborative search demonstrated a better optimization performance than the sine cosine algorithm, the other improved sine cosine algorithms, the other chaos-based algorithms, and other intelligent optimization algorithms. In addition, the feasibility and applicability of the sine cosine algorithm for elite individual collaborative search were further demonstrated by two mechanical optimization design experiments. [ABSTRACT FROM AUTHOR]

In this paper, a co-optimization design method for thermal-stress coupling 3-dimensional integrated system with through silicon via is proposed based on the finite element method, support vector machine model and modified particle swarm optimization algorithm. In the cause of analyzing the effects of geometrical parameters (radius of through silicon via, oxide thickness and the height of oxide insolation layer) on the thermal-stress distribution, the finite element method based COMSOL software is conducted to simulate the thermal-stress coupling 3-dimensional integrated system. Based on the simulation data obtained by COMSOL, the support vector machine models are adopted to establish the database for describing the relationships between the geometrical parameters and key indexes (peak temperature, peak stress and temperature difference) to improve the design efficiency. Based on the desired key indexes of thermal-stress coupling 3-dimensional integrated system, the multi-objective evaluation function is formulated. Then, the geometrical parameters are optimized by the modified particle swarm optimization algorithm. The finite element simulation is conducted to verify the effectiveness of the proposed strategy. In three cases, the errors between the simulated and desired temperature differences are all less than 0.28 K, and the relative errors between the simulated and desired peak temperature/peak stress are all less than 4.78%, which indicates that the geometrical parameters can be optimized to control the key indexes of thermal-stress coupling 3-dimensional integrated system. Therefore, the developed method can be used in the design and manufacture of 3-dimensional integrated system. [ABSTRACT FROM AUTHOR]

This work provides a complete framework for the simulation, co-optimization, and sim-to-real transfer of the design and control of soft legged robots. Soft robots have "mechanical intelligence": the ability to passively exhibit behaviors that would otherwise be difficult to program. Exploiting this capacity requires consideration of the coupling between design and control. Co-optimization provides a way to reason over this coupling. Yet, it is difficult to achieve simulations that are both sufficiently accurate to allow for sim-to-real transfer and fast enough for contemporary co-optimization algorithms. We describe a modularized model order reduction algorithm that improves simulation efficiency, while preserving the accuracy required to learn effective soft robot design and control. We propose a reinforcement learning-based co-optimization framework that identifies several soft crawling robots that outperform an expert baseline with zero-shot sim-to-real transfer. We study generalization of the framework to new terrains, and the efficacy of domain randomization as a means to improve sim-to-real transfer. [ABSTRACT FROM AUTHOR]

Legged robots are constantly evolving, and energy efficiency is a major driving factor in their design. However, combining mechanism efficiency and trajectory planning can be challenging. This work proposes a computational optimization framework for optimizing leg design during basic walking while maximizing energy efficiency. We generalize the robotic limb design as a four-bar linkage-based design pool and optimize the leg using an evolutionary algorithm. The leg configuration and design parameters are optimized based on user-defined objective functions. Our framework was validated by comparing it to measured data on our prototype quadruped robot for forward trotting. The Bennett robotic leg was advantageous for omni-directional locomotion with enhanced energy efficiency. [ABSTRACT FROM AUTHOR]

The requirement for the integration of power plants due to the cyclical rise in electrical energy consumption is due to the fluctuating load demand experienced with the current grid systems. This integration necessitates effectively allocating loads to the power plants for a minimum grid-tied transmission line cost, while meeting the network constraints. In this paper, we formulate an optimization problem of minimizing the total operational cost of all committed plants transmitted to the grid, while also meeting the network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed particle swarm optimization (PSO) method resolves the optimization problem using a piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction, with a connected load of 850 MW, 1263 MW, and 2630 MW of power demand, contrasted with previous PSO application cost values percentage, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), and (73.86%, 89.10), respectively, and significant environmental benefits. The proposed co-optimization approach can significantly enhance the self-consumption ratio compared to the baseline method. [ABSTRACT FROM AUTHOR]

Wind power generation is characterized by high intermittency and volatility owing to the stochastic nature of wind. In addition to forecasting accuracy, forecasting uncertainty quantification can have a major impact on power system energy management and operations planning. In this paper, a new fully co-optimized hybrid short-term probabilistic Wind Power Forecasting (WPF) model is proposed for the construction of Prediction Intervals (PIs) in a spatiotemporal framework. A Multi-Objective Improved Adaptive Particle Swarm Optimization (MOIAPSO) algorithm is developed to optimize the model’s parameters. PIs are generated by nonlinear autoregressive networks with exogenous inputs (NARX) using the Lower Upper Bound Estimation (LUBE) method. Unlike previous related work, the components of the proposed hybrid NARX-LUBE-MOIAPSO model, as well as the initial settings and the configuration of the parameters, are determined based on a full co-optimization approach. The co-optimization is performed from a forecasting quality and training time trade-off perspective. Furthermore, a spatiotemporal framework is introduced to improve forecasting performance and comprehension of regional spatiotemporal uncertainty dynamics. The spatiotemporal framework comprises a novel conditional spatiotemporal forecasting methodology and the modeling of spatiotemporal dependencies based on a binary Probabilistic Forecasting Error (PFE) metric. The proposed model and spatiotemporal framework are tested on publicly available datasets consisting of turbine-specific measurements and generate accurate forecasts with efficient uncertainty quantification, while maintaining computational complexity at relatively low levels compared to other state-of-the-art hybrid probabilistic WPF models.

Reinforcement Learning (RL) is a popular approach for deciding on an optimum traffic signal control policy to alleviate congestion in a road network. However, the traffic signal control policy can also be optimized in conjunction with the design of vehicular flow directions to further improve traffic performance. The design of vehicular flow directions refers to the right of way or directional restriction imposed in a road network. Here, a new RL-based technique is presented for co-optimization of the design of vehicular flow directions and control policy for traffic signals. This technique consists of a two-step iterative process, wherein a set of vehicular flow directions for a road network is generated, then a RL-based approach is used to train the traffic signal control policy over the given set of vehicular flow directions. Following the proposed technique, the vehicular flow directions with poor traffic performance are iteratively eliminated, while new vehicular flow directions are generated to achieve better traffic performance and realize convergence to a maximum possible expected traffic performance. The proposed RL-based technique is evaluated by using two examples under rush hour and non-rush hour traffic conditions. It is found that, compared to a RL-based approach in which only traffic signal control policy is considered, the proposed approach can be used to obtain a better traffic performance in terms of vehicular queue length and throughput.

Abstract Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system (DS) in extreme weather and with equipment faults. Previous studies have separately considered network reconfiguration (NR) and dispatching mobile power sources (MPS) to restore the outage load. However, NR cannot deal with the scenario of an electrical island, while dispatching MPS results in a long power outage. In this paper, a resilient outage recovery method based on co-optimizing MPS and NR is proposed, where the DS and traffic network (TN) are considered simultaneously. In the DS, the switch action cost and power losses are minimized, and the access points of MPSs are changed by carrying out the NR process. In the TN, an MPS dispatching model with the objective of minimizing power outage time, routing and power generation cost is developed to optimize the MPSs’ schedule. A solution algorithm based on iteration and relaxation methods is proposed to simplify the solving process and obtain the optimal recovery strategy. Finally, numerical case studies on the IEEE 33 and 119-bus systems validate the proposed resilient outage recovery method. It is shown that the access point of MPS can be changed by NR to decrease the power outage time and dispatching cost of MPS. The results also show that the system operation cost can be reduced by considering power losses in the objective function.

Co-optimization for memory bank compilation and placement was suggested as a way to improve performance and power and reduce the size of a memory subsystem. First, a multi-configuration SRAM compiler was realized that could generate memory banks with different PPA by splitting or merging, upsizing or downsizing, threshold swapping, and aspect ratio deformation. Then, a timing margin estimation method was proposed for the memory bank based on placed positions. Through an exhaustive enumeration of various configuration parameters under the constraint of timing margins, the best SRAM memory compilation configuration was found. This method could be integrated into the existing physical design flow. The experimental results showed that this method achieved up to an 11.1% power reduction and a 7.6% critical path delay reduction compared with the traditional design method. [ABSTRACT FROM AUTHOR]

3D monocular detection remains to be a focal point of research, particularly due to its capacity to deliver available precision under conditions of low cost and simplified configurations, making it especially valuable in fields like autonomous driving. Current 3D object detection methods often overlook the spatial information missing from images, which is critical to spatial perception, and optimize bounding box attributes separately, failing to meet the requirements of autonomous driving. We introduce MonoCAPE, a novel 3D detection framework addressing these issues by encoding spatial information and co-optimizing attributes through a Coordinate-Aware Position Encoding (CAPE) Generator and a Task Co-optimization Strategy (TCS). The CAPE Generator produces sparse positional embeddings, enabling spatial awareness with low computational cost, while the TCS utilizes Gaussian modeling to prevent suboptimal outputs. In this way, our framework comprehensively takes into account what existing approaches ignore. Extensive experiments on the KITTI dataset demonstrate MonoCAPE significantly improves A P 3 D and A P B E V metrics compared to existing advanced methods. [ABSTRACT FROM AUTHOR]

The growing adoption of renewable energy-based industrial microgrids challenges grid frequency stability. It highlights the need for microgrids to participate in Frequency Regulation (FR) service while preserving their economic benefits. Current policies, which prioritize FR participation based on revenue incentives, result in a suboptimal balance between economic and FR objectives. To address this issue, this study introduces a co-optimization approach and underscores its role in trade-off exploration. An actual case study validates the approach and reveals the conflict between these two objectives. The results of co-optimization reveal the impacts of Renewable Energy Sources (RES) and Energy Storage Systems (ESS) through Pareto front analysis. While RES can significantly reduce the power cost of EIIMs by up to 96.1%, it has a negative impact on frequency stability. This negative impact, measured through an energy-based method, results in deviations from the grid's preferred frequency regulation power by up to 154.0%. Meanwhile, the positive impact of ESS on improving the trade-off situation is demonstrated through the "Least Misery" principle. Compared to having no ESS, incorporating a 100 kW·h ESS achieves win-win outcomes, enhancing minimal dissatisfaction by 10.16%. Furthermore, a comparative analysis with the current policy approach showcases the risks of obtaining suboptimal solutions under existing policies. Some radical policy settings result in a dissatisfaction rate larger than 100%. This study elucidates the role of co-optimization by advancing comprehension of the impacts of RES and ESS while providing a method to achieve the appropriate trade-off. • Co-optimization balances economic and frequency regulation performance. • The proposed two-stage framework delineates the Pareto front efficiently. • Renewable energy reduces cost by up to 96.1% but affects stability by up to 154.0%. • Energy storage enhances minimal dissatisfaction by up to 10.16%. • Some radical revenue settings result in a dissatisfaction rate larger than 100%. [ABSTRACT FROM AUTHOR]

The robot task sequencing problem and trajectory planning problem are two important issues in the robotic optimization domain and are solved sequentially in two separate levels in traditional studies. This paradigm disregards the potential synergistic impact between the two problems, resulting in a local optimum solution. To address this problem, this paper formulates a co-optimization model that integrates the task sequencing problem and trajectory planning problem into a holistic problem, abbreviated as the robot TSTP problem. To solve the TSTP problem, we model the optimization process as a Markov decision process and propose a deep reinforcement learning (DRL)-based method to facilitate problem solving. To validate the proposed approach, multiple test cases are used to verify the feasibility of the TSTP model and the solving capability of the DRL method. The real-world experimental results demonstrate that the DRL method can achieve a 30.54% energy savings compared to the traditional evolution algorithm, and the computational time required by the proposed DRL method is much shorter than those of the evolutionary algorithms. In addition, when adopting the TSTP model, a 18.22% energy reduction can be achieved compared to using the sequential optimization model.

The requirement for the integration of power plants due to the cyclical rise in electrical energy consumption is due to the fluctuating load demand experienced with the current grid systems. This integration necessitates effectively allocating loads to the power plants for a minimum grid-tied transmission line cost, while meeting the network constraints. In this paper, we formulate an optimization problem of minimizing the total operational cost of all committed plants transmitted to the grid, while also meeting the network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed particle swarm optimization (PSO) method resolves the optimization problem using a piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction, with a connected load of 850 MW, 1263 MW, and 2630 MW of power demand, contrasted with previous PSO application cost values percentage, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), and (73.86%, 89.10), respectively, and significant environmental benefits. The proposed co-optimization approach can significantly enhance the self-consumption ratio compared to the baseline method.

Carbon fiber is becoming a key material for engineering applications due to its excellent comprehensive properties. The process parameter optimization is an important step in the polymerization process of carbon fiber production. At present, most of the research on process parameter optimization is usually carried out on a single production line, without considering the correlation between optimization problems. In this paper, a multiobjective mechanism model for the co-optimization of the polymerization process of carbon fiber production is established. Each of these submodels is a multiobjective process parameter optimization task, corresponding to the polymerization process of a production line. In order to solve the model effectively, we also designed an evolutionary multitasking algorithm based on transfer learning, which reuses the past experiences of one task to generate a population pool for the next iteration of another task, enabling explicit genetic transfer between different tasks and accelerating the population convergence speed. The proposed multitasking framework for operation optimization has been conducted on 10 different production conditions of the polymerization process. Experimental results show that compared with other implicit and explicit genetic algorithms, this algorithm is very competitive in generating effective solutions. This research provides important support for process parameter optimization and manufacturing of carbon fiber production, which will help engineers and technicians to make informed decisions. [ABSTRACT FROM AUTHOR]

Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system (DS) in extreme weather and with equipment faults. Previous studies have separately considered network reconfiguration (NR) and dispatching mobile power sources (MPS) to restore the outage load. However, NR cannot deal with the scenario of an electrical island, while dispatching MPS results in a long power outage. In this paper, a resilient outage recovery method based on co-optimizing MPS and NR is proposed, where the DS and traffic network (TN) are considered simultaneously. In the DS, the switch action cost and power losses are minimized, and the access points of MPSs are changed by carrying out the NR process. In the TN, an MPS dispatching model with the objective of minimizing power outage time, routing and power generation cost is developed to optimize the MPSs' schedule. A solution algorithm based on iteration and relaxation methods is proposed to simplify the solving process and obtain the optimal recovery strategy. Finally, numerical case studies on the IEEE 33 and 119-bus systems validate the proposed resilient outage recovery method. It is shown that the access point of MPS can be changed by NR to decrease the power outage time and dispatching cost of MPS. The results also show that the system operation cost can be reduced by considering power losses in the objective function. [ABSTRACT FROM AUTHOR]

In this article, a novel p-type trapezoidal gate (PTG) lateral double-diffused MOSFET (LDMOS) is proposed and investigated by the 3-D TCAD simulation. The results reveal that the PTG LDMOS boasts a reduced gate-to-drain charge (${Q}_{\text {GD}}$) while maintaining an acceptable breakdown voltage (BV) and specific ON-resistance (${R}_{ \text{ON},\text {sp}}$). Compared with conventional LDMOS, a better tradeoff between the static figure of merit (FOMS, FOM $_{\text {S}}\,\,=$ BV2/ ${R}_{ \text{ON},\text {sp}}$) and the dynamic figure of merit (FOMD, FOM $_{\text {D}} = {R}_{ \text{ON},\text {sp}} \cdot {Q}_{\text {GD}}$) is realized. In the ON-state, the p-type polysilicon gate embedded in the drift region induces multiple plane majority-carrier accumulation layers, leading to a decrease in ${R}_{ \text{ON},\text {sp}}$. In the OFF-state, the metal–insulator–semiconductor (MIS) capacitor, which is composed of extended trench gate, gradual trapezoidal oxide, and N-drift. assists in depleting the drift region. Therefore, the doping concentration of drift region can be significantly lifted, and the BV is increased. Besides, the p-n junction capacitor composed of p-type and n-type polysilicon isolates the field coupling between gate and drain, and the gate-to-drain capacitor (${C}_{\text {GD}}$) is thus reduced. Compared with multiple-plane electron accumulation layer LDMOS (MAL LDMOS) and split triple-gate LDMOS (STG-LDMOS), ${Q}_{\text {GD}}$ and ${R}_{ \mathrm{\scriptscriptstyle ON},\text {sp}}$ of our proposed PTG LDMOS are shrunk by 34.3% and 54.4%, respectively. In general, the proposed PTG LDMOS achieves a better tradeoff between the static and switching characteristics. [ABSTRACT FROM AUTHOR]

Planar electromagnetic vibration energy harvester (EVEH) is a promising technology for compact, low form factor self-powered wireless devices. However, planar EVEH has the limitation of non-fully utilized magnetic flux gradient and low output power due to its structure constraint and weak magnetic flux gradient capturing capability. Planar coil is the key component in harvesting magnetic flux gradient and determining the output electromotive force (EMF) voltage and output power. A novel non-uniform distributed planar (NUDP) coil is proposed to break the current limitation of output power for the planar coil. The boost in output power is realized with tight inner windings and loose outer windings. The breakpoint is determined based on power increasing rate for each winding. The optimal winding radius for both inner and outer coils is investigated using discrete non-linear constraint heuristic optimization method. The designed NUDP coil-based EVEH achieves peak-to-peak output voltage of 305 mV and output power of 1.163 mW at 64.3 Hz resonant frequency and 0.05 g acceleration. It realizes 16.3% higher output power and 19.4% higher output voltage than uniform distributed planar (UDP) coil under the same spring and magnet. The power density of the proposed prototype reaches 9.616 mW/cm3g2, which is 15.025 times higher than the state-of-the-art EVEHs with the planar coil. [ABSTRACT FROM AUTHOR]

This article proposes a co-optimization model that allows evaluating the simultaneous participation of energy storage systems (ESS) in arbitrage applications, secondary frequency regulation, and transmission investment deferral. The model considers the ESS degradation through an upper-piecewise linear approximation; constraints related to primary frequency energy reserve are also included in the model. The formulation is evaluated using historical data from the Colombian wholesale energy market. Eight case studies corresponding to the possible combinations of ESS participation in the three applications are analyzed. Subsequently, a financial evaluation is carried out for each case study, both from the system and the investor's point of view. The results show that the exclusive use of ESS to provide arbitrage is not economically viable in Colombia, while simultaneously providing secondary frequency regulation and restriction relief services is profitable, both for the system and for an investor agent. [ABSTRACT FROM AUTHOR]

Transitioning from fossil fuel dominated power systems to high penetrations of intermittent renewable generation is affecting classical electricity market designs. Here, a method is proposed to model and assess the absence of co-optimization of energy and reserve that prevails in the European system. Co-optimization is formulated through an optimization problem (COM), and its absence as an equilibrium problem (EQM) built on Karush-Kuhn-Tucker conditions of agents’ optimization and market clearing equations. EQM cannot be reformulated as a single optimization problem. Market distortions are identified by comparing the complementarity conditions of both models. These are then discussed on system with Feed in Premium to wind generation. Parameters in the models allow to represent different market configurations regarding: available wind generation, Feed in Premium to wind, generators’ risk aversion, and required reserve from wind generation. [ABSTRACT FROM AUTHOR]

With increasing concerns on transportation decarbonization, fuel cell hybrid trains (FCHTs) attract many attentions due to their zero carbon emissions during operation. Since fuel cells alone cannot recover the regenerative braking energy (RBE), energy storage devices (ESDs) are commonly deployed for the recovery of RBE and provide extra traction power to improve the energy efficiency. This paper aims to minimize the net hydrogen consumption (NHC) by co-optimizing both train speed trajectory and onboard energy management using a time-based mixed integer linear programming (MILP) model. In the case with the constraints of speed limits and gradients, the NHC of co-optimization reduces by 6.4% compared to the result obtained by the sequential optimization, which optimizes train control strategies first and then the energy management. Additionally, the relationship between NHC and employed ESD capacity is studied and it is found that with the increase of ESD capacity, the NHC can be reduced by up to 30% in a typical route in urban railway transit. The study shows that ESDs play an important role for FCHTs in reducing NHC, and the proposed time-based co-optimization model can maximize the energy-saving benefits for such emerging traction systems with hybrid energy sources, including both fuel cells and ESD. [ABSTRACT FROM AUTHOR]

Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. However, the diverse lead-specific features (known as diversity) among 12 leads were neglected, causing inadequate information learning for 12-lead ECG. To maximize the information learning of multi-lead ECG, the information fusion of comprehensive features with integrity and lead-specific features with diversity should be taken into account. In this paper, we propose a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG. MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network. We demonstrate our MLBF-Net on China Physiological Signal Challenge 2018 which is an open 12-lead ECG dataset. The experimental results show that MLBF-Net obtains an average $F_{1}$ score of 0.855, reaching the highest arrhythmia classification performance. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.

Generation self-scheduling and coal supply in coal-fired power plants are closely related but typically optimized separately. To enhance the optimal operation of power plants, we propose a coordinated optimal operation strategy of generation and coal management in this paper. Uncertainties in electricity prices and demands, coal prices, and coal inventory holding costs are captured and modeled by discrete scenarios. Emission constraints are introduced to control generation emissions. The heat loss caused by the weathering of coal during the storage is taken into account, which distinguishes the considered co-optimization problem from the previous ones. The proposed strategy is built on a mixed-integer linear programming-typed two-stage stochastic programming model, in which whether to purchase coal is determined in the first stage and the quantity of coal purchase, the coal inventory, and the economic generation dispatch are determined in the second stage. The objective is to maximize the expected profits. An improved Benders decomposition algorithm is developed to solve the problem where multiple Benders cuts are added in each iteration and valid inequalities are introduced to speed up the convergence of the algorithm. Numerical experiments demonstrate the effectiveness of the proposed strategy and algorithm.

A tram with an on-board energy storage system is a promising candidate for urban traffic systems. The co-optimization of speed and voltage trajectories for a catenary-supercapacitors hybrid electric tram to minimize energy consumption from traction substations is presented in this article. A source-catenary-load-storage integrated optimization model is proposed to reflect the coupling performance between vehicle dynamics and power sources. Two different methods are designed to address this problem. First, a hierarchical method based on dynamic programming is proposed, and the optimal control problem is recast into two subproblems of speed trajectories optimization and supercapacitors voltage trajectories optimization. Efforts are made to reduce the computation burden. To further investigate the optimality of the hierarchical method, a coupling optimization method is designed using the three-dimensional dynamic programming. The performances of the two methods are evaluated by different track conditions and compared with the only optimizing speed profiles based method, which shows that the proposed methods can improve energy efficiency and reduce the fluctuation of catenary voltage. Meanwhile, the coupling method has lower energy consumption than the hierarchical method, but suffers from more considerable computation time. Finally, the influences of tram weight and dynamic traffic constraints are investigated. [ABSTRACT FROM AUTHOR]

This paper considers the co-operation of distributed generators (DGs), battery energy storage systems (BESSs) and voltage regulating devices for integrated peak shaving and voltage regulation in distribution grids through a co-optimization framework, which aims to minimize the operational costs while fulfilling the operational constraints of network and devices. To account for the uncertainties of load demand and generation, we then convert the co-optimization model into a two-stage stochastic program where state-of-charge (SoC) trajectories of BESSs and operation of voltage regulating devices are optimized at the first stage for day-ahead scheduling, while the reactive powers of DGs and BESSs are left at the second stage for potential intra-day scheduling to handle short-term voltage issues. The proposed co-optimization scheme is validated on the IEEE 37-node test feeder and compared with other practices. [ABSTRACT FROM AUTHOR]

Market and network integration of distributed energy resources can be facilitated by their coordination within a virtual power plant (VPP). However, VPP operation subject to network limits and different market and physical uncertainties is a challenging task. This paper introduces a framework that co-optimizes the VPP provision of multiple market (e.g., energy, reserve), system (e.g., fast frequency response, inertia, upstream reactive power), and local network (e.g., voltage support) services with the aim of maximizing its revenue. To ensure problem tractability, while accommodating the uncertain nature of market prices, local demand, and renewable output and while operating within local network constraints, the framework is broken down into three sequentially coordinated optimization problems. Specifically, a scenario-based robust optimization for day-ahead resource scheduling, with linearized power flows, and two receding horizon optimizations for close-to-real-time dispatch, with a more accurate second-order cone relaxation of the power flows. The results from a real Australian case study demonstrate how the framework enables effective deployment of VPP flexibility to maximize its multi-service value stack, within an uncertain operating environment, and within technical limits. [ABSTRACT FROM AUTHOR]

A gallium nitride (GaN) boundary conduction mode (BCM) dual-boost ac–dc converter for low-voltage electromagnetic energy harvesting (EH) is presented in this article. The μW-to-mW EH system is very susceptible to any tiny losses from power converters, interconnections, and system parasitic. To have better utilizations of harvested energy, an output power enhanced power conversion design is proposed. First, impedance matching is satisfied between electromagnetic generator and ac–dc converter to achieve maximum delivered power. The ac–dc converter is designed under BCM for constant input impedance. EH's winding inductance is employed as the boost inductor in the ac–dc converter. A codesign principle for converter on-time with electromagnetic generator's winding characteristics is revealed. Second, power transistors in EH low-voltage conversion are optimized to realize high efficiency. Power transistors’ low-voltage switching characteristics are significantly different from high-voltage situation with no or little miller plateau region. Low-voltage power transistors are compared from both device characteristics and calculated power loss to provide device selection guidance. A GaN-based dual-boost ac–dc converter under BCM operation with constant on-time controller is designed in this article to provide 3.3 V output for a 500-Ω load resistor. It achieves peak efficiency of 82.4% for open-loop operation at 0.6 V/100 Hz input and peak efficiency of 72.3% for close-loop operation at 0.75 V/100 Hz. [ABSTRACT FROM AUTHOR]

Currently, the synergistic interactions between plant polyphenols and probiotic bacteria are gaining interest with diverse perspectives of applications of both functional ingredients. This study aims to explore and optimize the interaction between pomegranate peel polyphenols and the probiotic strain Lactobacillus rhamnosus ATCC 53103, throughout the co-optimization of the extraction/probiotication techniques. In this context, the response surface methodology (RSM) was applied to find the suitable extraction conditions to obtain phenolic extracts exhibiting a synergistic interaction when fermented with the probiotic strain. A ß-Cyclodextrin ultrasound-assisted extraction technique has been applied for pomegranate polyphenols extraction, followed by the probiotic fermentation of extracts. Results of RSM indicate that for all chosen design responses, the experimental values are in good agreement with the predicted one, with R squared values above 0.8 for all prediction models. Using optimal conditions predicted by the desirability function, it has been found that after probiotication of optimized phenolic extracts, the growth of probiotic strain at presence of polyphenols was possible. Moreover, a significant (p < 0.05) improvement of in vitro antioxidant activity of probioticated extracts was confirmed (an increase on in vitro free radical scavenging activity up to 51.37%). Results of this study, demonstrate for the first time, the feasibility of RSM approach to perform simultaneously the optimization of extraction and probiotication processes of phenolic compounds with the perspective to develop a new bioprocess aiming to produce functional food ingredient or novel nutraceutical products containing both bioactive polyphenols and beneficial probiotic bacteria. [ABSTRACT FROM AUTHOR]