High Operational Flexibility Research Articles

Temperature-Dependent Kinetics of Plasma-Based CO2Conversion: Interplay of Electron-Driven and Thermal-Driven Chemistry.

The transformation of CO2 into chemical building blocks for various industries is considered a key technology in a net-zero energy future. To realize this, plasma discharges are one of the most promising approaches thanks to their electron-driven reactions and high operational flexibility. Most studies focused on room-temperature and vibrationally-excited discharges, however, lately, the importance of thermal reactions is considered. Therefore, we developed a temperature-dependent plasma-chemical reaction mechanism to investigate the temperature dependence of plasma-based CO2 conversion. Here, we present the various effects of thermally-driven reactions on the CO2 conversion as a function of the gas temperature and specific energy input. Our analysis pinpointed the key reactions controlling the plasma-based CO2 conversion, shifting from an electron-driven to a thermal-driven regime. Additionally, we used the mechanism to verify the theoretical upper boundary of the process' energy efficiency, and discussed how our findings could lead to the further development and optimization of plasma discharges for efficient CO₂ conversion in the future.

Enhancing the load cycling rate of subcritical coal-fired power plants: A novel control strategy based on data-driven feedwater active regulation

To accommodate high penetration of renewable power, coal-fired power plants should have high operational flexibility. However, the load cycling rate of subcritical power plants is insufficient, because conventional control strategies cannot perform well when subcritical power plants that have large thermal inertia change load with high rates. To address this issue, a novel control strategy based on idea of data-driven feedwater active regulation was proposed. To evaluate the control performance, dynamic modeling on a reference 330 MW subcritical unit was conducted, and performance indicators including load cycling performance indicator Kp, parameters cumulative deviation, and transient process average coal consumption rate were defined. Results reveal that key factor restricting load cycling rate is the exceeding of key parameters deviations. During 50%–75 % loading up process, the novel control strategy can increase ramp rate by 1.0 % Pe/min, increase Kp by 101.7 %, and reduce parameters cumulative deviations by 58.0 % at maximum and 20.3 % on average. Meanwhile, during 75%–50 % loading down process, the novel control strategy can increase ramp rate by 1.0 % Pe/min, increase Kp by 39.9 %, and reduce parameters cumulative deviations by 32.0 % at maximum and 19.1 % on average. These results demonstrate that feedwater active regulation effectively suppresses the control deviation of key parameters.

Coupling geospatial suitability simulation and life cycle carbon emissions towards uncertain optimization planning for wind-photovoltaic-hydrogen multi-energy complementary system

Building clean and modern multi-energy complementary systems is a direct path to decarbonization in energy sector. The introduction of hydrogen energy with high operational flexibility enables the multi-energy complementary system to fully accommodate renewable energy. The key issues of the optimization planning of the wind-photovoltaic-hydrogen multi-energy complementary system that need to be solved are how to finely characterize the carbon reduction potential of the system, consider the uncertainty of supply and demand in the modeling process, and incorporate the energy supply business of electric vehicles into the planning. This study constructs optimization planning model with the following three progressive stages: geospatial suitability simulation based on geographic information system, uncertainty analysis based on probabilistic scene generation technology, and capacity configuration optimization. Unlike previous studies, this model measures and optimizes the carbon emissions level of the system from a lifecycle perspective. The empirical study results indicate that carbon emissions optimization from the perspective of the entire life cycle would directly affect the system configuration results. From a lifecycle perspective, the carbon emissions accounting result is about 9 times higher than that obtained only during the operation phase. Compared to the traditional distribution system, the carbon emissions of the system constructed in this study decreased by 82.75 %. In addition, considering that the system provides charging services for electric vehicles, which can reduce carbon emissions by about 93.6 % but only increase costs by about 29.4 %. The computational results justify the proposed optimization framework. This study provides optimization models for the system, and the implementation framework can support technical reference for practice.

Research on motion planning system for wall-climbing mobile manipulator for large steel structures welding operation

Purpose The purpose of this study is to address the welding demands within large steel structures by presenting a global spatial motion planning algorithm for a mobile manipulator. This algorithm is based on an independently developed wall-climbing robot, which comprises a four-wheeled climbing mobile platform and a six-degree-of-freedom robotic manipulator, ensuring high mobility and operational flexibility. Design/methodology/approach A convex hull feasible domain constraint is developed for motion planning in the mobile manipulator. For extensive spatial movements, connected sequences of convex polyhedra are established between the composite robot’s initial and target states. The composite robot’s path and obstacle avoidance optimization problem are solved by constraining the control points on B-spline curves. A dynamic spatial constraint rapidlye-xploring random trees-connect (RRTC) motion planning algorithm is proposed for the manipulator, which quickly generates reference paths using spherical spatial constraints at the manipulator’s end, eliminating the need for complex nonconvex constraint modeling. Findings Experimental results show that the proposed motion planning algorithm achieves optimal paths that meet task constraints, significantly reducing computation times in task conditions and shortening operation times in non-task conditions. Originality/value The algorithm proposed in this paper holds certain application value for the realization of automated welding operations within large steel structures using mobile manipulator.

A bilevel fast-convergent optimizer via high-fidelity convex models: Application on optimal operation of all-parallel heterogeneous chiller-pump systems

To cope with buildings’ growing and diverse cooling demand, heterogeneous multi-chiller systems with non-dedicated pumps are commonly applied in large-scale centralized chiller plants. All-parallel heterogeneous systems offer high operation flexibility but pose challenges to optimal operation planning. For the deficiencies in convexity tractability, the classical neural networks (NNs) achieved great success in system identification yet were restricted in model-based optimization. In this study, an optimization-oriented convex modeling approach based on input convex neural networks (ICNN) to identify energy consumption models for energy units of chiller-pump systems was presented, which provides convex input–output mappings while leveraging the high-fidelity capability of NNs. Using the convex models, the energy minimization issue subjected to multiple constraints was formulated, the optimality of which was mathematically proven. A bilevel deterministic optimizer was developed to determine the global optimal. Comprehensive data experiments were conducted using practical and simulated operation data to evaluate the modeling and optimization performances of the proposed methods compared with conventional candidates. Numerical results suggest that the ICNN-based convex models exhibit superior modeling performances than physics-based models. A better overall energy saving ratio of 8.86 % and faster convergence time of 3.53 s for average achieved by the proposed bilevel optimizer compared with rule-based and meta-heuristic optimizers under identical external conditions.

Design and performance evaluation of thermal energy storage system with hybrid heat sources integrated within a coal-fired power plant

The operational flexibility of coal-fired power plants (CFPPs) should be effectively enhanced to accommodate large-scale photovoltaic and wind power within the power grid. The integration of thermal energy storage (TES) systems is a potential way to enlarge the load-cycling range of CFPPs. To achieve high operational flexibility of CFPPs and high round-trip efficiency of TES systems, TES systems with hybrid heat sources including the heat converted from power by power-to-heat (P2H) devices and transferred from the reheat steam integrated into CFPP were proposed. Then, simulation and thermo-economic analysis models were developed, and the system design procedure was provided. Results show the minimum power load ratio is decreased from 30 % to about 16 % by storing heat from the reheat steam within the TES system, and then to zero by converting electricity to heat with P2H devices. The mode P-basic constitutes two double-tank molten salt TES systems, which are charged by the heat converted from power by P2H devices and the heat transferred from the reheat steam, respectively. It is discharged by releasing the heat to water/steam cycle of CFPP, and the highest equivalent round-trip efficiency of mode P-basic is up to 62.97 %. Moreover, mode P-basic exhibits the largest total cost of the equipment and storage materials at $58.26 million while it has the largest net present value at $103.79 million and the corresponding payback period is 5 years.

Comparative evaluation of UAV photogrammetry and mobile laser scanner for flexible pavement failure detection in developing countries

Flexible pavements constitute a critical infrastructure that, throughout its life cycle, faces degradation caused by climatic variables and traffic loads. This deterioration affects their mechanical properties, leading to cracks and failures that reduce their functionality and longevity. It is therefore imperative that advanced analytical methodologies are applied to identify the appropriate level of intervention to ensure their optimal serviceability. Recently, technological innovations have emerged that allow the detailed assessment of flexible pavements in an efficient manner, covering large areas in a short time. This research focuses on whether drone photogrammetry (UAV) or mobile laser scanning (MLS) is more appropriate for the diagnosis of surface imperfections in flexible pavements in the context of developing countries, as well as the impacts that its adoption could have on road assessment. The qualitative study is based on a literature review and uses the Choosing by Advantages (CBA) method to evaluate and compare the decisive qualities in the selection of technologies. The results indicate that the mobile laser scanner provides accurate topographic and geometric characterisation at the millimetre level. However, drone photogrammetry, standing out for its high flexibility, low cost and ease of operation, presents itself as the most viable solution for continuous road condition monitoring.

Evaluation of Grid Support Capacity From Electrified Railways

High power level and temporal operation flexibility endow electric trains with great potential for improving grid resilience. It becomes crucial to quantify the grid support capacity (GSC) from large-scale electrified railways. This paper innovatively proposes a hierarchical train coordination and trajectory regulation method to evaluate and deliver the GSC from electrified railways. In the light of the mechanical energy consumption analysis of train running, the energy-oriented train operation model is proposed to avoid the nonlinear differential dynamics of train kinetics when coordinating large-scale trains. Then the aggregation and disaggregation of trains for grid support can be formulated as linear and convex quadratic programming problems respectively. To closely follow the reference power and enhance the feasibility of train operation, a novel iterative discretization approach is proposed to generate the precise reference trajectory. A sliding mode controller with disturbance bound adaption is devised to overcome the modeling errors and disturbances when deploying GSC in realistic environment. Comprehensive numerical examples verify the effectiveness of the proposed scheme, and demonstrate the significant grid support potential from collaborative electrified railways.

Unusual Thermal Quenching of Photoluminescence from an Organic–Inorganic Hybrid [MnBr4]2−‐based Halide Mediated by Crystalline–Crystalline Phase Transition

AbstractThe ability to generate and manipulate photoluminescence (PL) behavior has been of primary importance for applications in information security. Excavating novel optical effects to create more possibilities for information encoding has become a continuous challenge. Herein, we present an unprecedented PL temporary quenching that highly couples with thermodynamic phase transition in a hybrid crystal (DMML)2MnBr4 (DMML=N,N‐dimethylmorpholinium). Such unusual PL behavior originates from the anomalous variation of [MnBr4]2− tetrahedrons that leads to non‐radiation recombination near the phase transition temperature of 340 K. Remarkably, the suitable detectable temperature, narrow response window, high sensitivity, and good cyclability of this PL temporary quenching will endow encryption applications with high concealment, operational flexibility, durability, and commercial popularization. Profited from these attributes, a fire‐new optical encryption model is devised to demonstrate high confidential information security. This unprecedented optical effect would provide new insights and paradigms for the development of luminescent materials to enlighten future information encryption.

Unusual Thermal Quenching of Photoluminescence from an Organic-Inorganic Hybrid [MnBr4 ]2- -based Halide Mediated by Crystalline-Crystalline Phase Transition.

The ability to generate and manipulate photoluminescence (PL) behavior has been of primary importance for applications in information security. Excavating novel optical effects to create more possibilities for information encoding has become a continuous challenge. Herein, we present an unprecedented PL temporary quenching that highly couples with thermodynamic phase transition in a hybrid crystal (DMML)2 MnBr4 (DMML=N,N-dimethylmorpholinium). Such unusual PL behavior originates from the anomalous variation of [MnBr4 ]2- tetrahedrons that leads to non-radiation recombination near the phase transition temperature of 340 K. Remarkably, the suitable detectable temperature, narrow response window, high sensitivity, and good cyclability of this PL temporary quenching will endow encryption applications with high concealment, operational flexibility, durability, and commercial popularization. Profited from these attributes, a fire-new optical encryption model is devised to demonstrate high confidential information security. This unprecedented optical effect would provide new insights and paradigms for the development of luminescent materials to enlighten future information encryption.

Multi-Objective Experimental Combustor Development Using Surrogate Model-Based Optimization

Abstract The majority of premixed industrial gas turbine combustion systems feature two or more separately controlled fuel lines. Every additional fuel line improves the operational flexibility but increases the complexity of the system. When designing such a system, the goals are low emissions of various pollutants and avoiding lean blowout or extinction. Typically, these limitations become critical under different load conditions of the machines. Therefore, it is particularly challenging to develop combustors for stable and clean combustion over a wide operating range. In this study, we apply the Gaussian process regression machine learning method for application to burner development, with the aim of improving the process, which is often driven by a trial-and-error approach. To do so, a special pilot unit is installed into a full-scale industrial swirl combustor. The pilot features 61 positions of fuel injection, each of which is equipped with an individual valve, allowing to modify the fuel–air mixture close to the flame root in various degrees. In fully automatized atmospheric tests, we use the pilot system to train two surrogate models for different design objectives of the combustor, relevant for full load and part load operation, respectively. Once trained, the models allow for prediction for any possible injection scheme. In combination, they can be used to identify pilot injector configurations with an improved operation range in terms of low NOx emissions and part load stability. The adopted multimodel approach enables combustor design specifically for high operational flexibility of gas turbines, but can also be extended to other similar industrial development processes.

AGV robot for laser-SLAM based method testing in automated container terminal

PurposeMagnet spot is the primary method to develop the automated guided vehicle (AGV) guidance system for many automated container terminals (ACT). Aiming to improve the high flexibility of AGV operation in ACT, this paper aims to address the problem of technical stability leading to ACT production paralysis and propose a mini-terminal AGV robot for testing laser simultaneous location and mapping (SLAM)-based methods in ACT operation scenarios.Design/methodology/approachThis study developed a physical simulation robot for terminal AGV operations, providing a platform to test technical solutions for applying laser navigation-related technologies in ACTs. Then, the terminal-AGV navigation system framework is designed to apply the laser-SLAM-based method in the physical simulation robot. Finally, the experiment is conducted in the terminal operation scenario to verify the feasibility of the proposed framework for lased-SLAM-based method testing and analyze the performance of the different mini-terminal AGV robots.FindingsA series of experiments are conducted to analyze the performance of the proposed mini-terminal AGV robot for laser-SLAM-based method testing. The experimental results show the validity and effectiveness of the AGV robot and AGV navigation system framework with better local map matching, loopback and absolute positional error.Originality/valueThe proposed mini-terminal AGV robot and AGV navigation system framework can provide a platform for innovative laser-SLAM-based method testing in ACTs applications. Therefore, this study can effectively meet the high requirements of ACT for maturity and stability of the laser navigation technical.

Optimal multi-energy portfolio towards zero carbon data center buildings in the presence of proactive demand response programs

Multiple geographically dispersed data center buildings have been increasingly positioned in renewable-rich areas due to their high energy consumption. This paper proposes an optimal multi-energy portfolio for zero carbon data center buildings in the presence of proactive demand response programs. In the data center buildings, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via multi-energy conversion and storage devices, which are modeled as 100% renewable energy hubs for zero carbon multi-energy supplies. In order to exploit the hub-internal operational dispatchability and flexibility, a comprehensive energy consumption model of data center buildings is proposed to facilitate the demand response in terms of available serves, while the variable speed pumped storage (VSPS) is modeled to capture its dynamic operational characteristics. The energy‑carbon production, conversion, storage, and consumption are formulated as an energy‑carbon matrix, which are solved via a two-stage method to obtain the optimal portfolio. Case studies on southwest China data center buildings are implemented to demonstrate the effectiveness and superiority of the proposed methodology on cost-effective accommodation of RESs. Simulations results show that the system portfolio cost can be reduced by at most 17.8% with a higher operational flexibility.

Dynamic modeling and flexible control of combined heat and power units integrated with thermal energy storage system

The high percentage of renewable energy connected to the grid requires high operational flexibility of combined heat and power (CHP) units. As two products of the CHP units, electricity and heat are coupled, making it challenging to achieve coordinated control. The thermal energy storage (TES) system can smooth out the fluctuations in heat load demand by storing or releasing heat, thus aiding in the decoupling and coordinated control of electricity and heat. In this paper, an optimized control strategy of CHP units integrated with the TES system is proposed to improve the units’ operating flexibility. First, according to the operating mechanism, the dynamic models of the CHP unit and the TES system are established. On this basis, considering the heat source steam active response based on the linear quadratic regulator and the heat-supply support of the TES system, the boiler-turbine-heating-storage coordinated control system of a CHP unit is designed. The simulation experiment is conducted in a 300 MW CHP unit. The results show that compared with the conventional control strategy, the MAE, MAPE, MSE, and RMSE indicators of the electrical power output are decreased by 45.88%, 45.28%, 71.95%, and 47.04%, respectively.

Dual−Layer Distributed Optimal Operation Method for Island Microgrid Based on Adaptive Consensus Control and Two−Stage MATD3 Algorithm

Island microgrids play a crucial role in developing and utilizing offshore renewable energy sources. However, high operation costs and limited operational flexibility are significant challenges. To address these problems, this paper proposes a novel dual−layer distributed optimal operation methodology for islanded microgrids. The lower layer is a distributed control layer that manages multiple controllable distributed fuel−based microturbines (MTs) within the island microgrids. A novel adaptive consensus control method is proposed in this layer to ensure uniform operating status for each MT. Moreover, the proposed method can achieve the total output power of MTs to follow the reference signal provided by the upper layer while ensuring plug−and−play capability for MTs. The upper layer is an optimal scheduling layer that manages various forms of controllable distributed power sources and provides control reference signals for the lower layer. Additionally, a two−stage twin−delayed deterministic policy gradient (MATD3) algorithm is utilized in this layer to minimize the operating costs of island microgrids while ensuring their safe operation. Simulation results demonstrate that the proposed methodology can effectively reduce the operating costs of island microgrids, unify the operational status of MTs, and achieve plug−and−play capability for MTs.

Two-layer management of HVAC-based Multi-energy buildings under proactive demand response of Fast/Slow-charging EVs

Building heating, ventilation, and air conditioning (HVAC) and associated energy consumption make up the more and more important part of the world, whose reduction provides a cost-effective path to the “dual carbon” goal. This paper proposes a two-layer management of HVAC-based multi-energy buildings under proactive demand response of fast/slow-charging electric vehicles (EVs). In this paper, the building HVAC is mathematically formulated via a R-C thermodynamic model, which coordinates with multi-energy converters and storages to form a 100% renewable building. The building management is a challenging optimization problem due to its severe constraints and strong spatio-temporal couplings. In the first layer, a day-ahead multi-energy dispatch is formulated to economically optimize the electrical, heat, gas energy carriers. In the second layer, alternating current (AC) slow charging and direct current (DC) fast charging types of EVs are considered and managed via a novel real-time supply–demand pricing mechanism. After acquiring the economical dispatch references in the first layer, the second layer implements a model predictive control (MPC)-based real-time scheduling to handle the multi-energy supply–demand fluctuations. The original two-layer optimization is further handled via mixed-integer linear program (MILP) reformulation for high-efficient solving. Comparisons have shown the advantageous performances of the proposed two-layer optimization over economics and practicability. Simulations results show that the overall system operating cost can be reduced by at most 3.01% with a higher operational flexibility in building management.

Nonlinear operational optimization of an industrial power-to-heat system with a high temperature heat pump, a thermal energy storage and wind energy

The heat demand for industrial processes is often provided in the form of steam generated by various fossil fueled equipment. In order to reduce CO2 emissions, the heat demand has to be covered by renewable energy sources. Electrified steam generation relies on complex energy systems, that can be operated according to energy availability and cost developments. However, such a multi component industrial energy system poses a challenge in modeling and determining the cost- or emission-optimal operation of the system. This study develops a methodology to model a multi component industrial energy system on the basis of a case study. By optimal system operation, either costs or emissions are minimized in response to fluctuating renewable wind energy and electricity prices.A high temperature heat pump (HTHP), a sensible thermal energy storage (TES) and a wind turbine are combined to create an electrified energy system to supply super-heated steam. During periods of low wind speed, additional grid electricity is purchased to ensure a steady heat supply. The HTHP offers a high operational flexibility and thus, enables the charging and discharging of the TES. A model of the closed reverse Brayton cycle HTHP, which is able to simulate part load behavior, is created in a process simulation software and consolidated in nonlinear surrogate models. The component behavior of a TES is represented by a combination of equations based on heat exchanger relations. Finally, the resulting algebraic nonconvex, nonlinear optimization problem based on the proposed system is solved using the local interior point optimizer (IPOPT) solver equipped with a multi-start approach to determine an optimal operation over a reference week with respect to the current wind power generation, grid emissions and electricity prices.The results of the optimization show, that optimal operating strategies enable a high potential to decarbonize future industries at minimum operational costs or emissions.

Multi-parameter control-based operation strategy for mainstream deammonification in an integrated anaerobic biofilm reactor-step feed MBR

The mainstream deammonification of municipal wastewater has been recognized as one of the greatest challenges in wastewater engineering. The conventional activated sludge process has disadvantages of high energy input and sludge production. To tackle this situation, an innovative A-B process, where an anaerobic biofilm reactor (AnBR) functioned as the A stage for energy recovery, and a step-feed membrane bioreactor (MBR) functioned as the B stage for mainstream deammonification, was constructed for carbon-neutral wastewater treatment. For addressing the challenge associated with selective retention of ammonia-oxidizing bacteria (AOB) over nitrite oxidizing bacteria (NOB), a multi-parameter control-based operation strategy was developed with synergistic control of influent COD redistribution, dissolved oxygen (DO) concentration and sludge retention time (SRT) in the innovative AnBR – step-feed MBR system. Results showed that more than 85% of wastewater COD could be removed with the direct production of methane gas in the AnBR. A relatively stable partial nitritation, which is a prerequisite of anammox, was achieved with the successful suppression of NOB, leading to 98% of ammonium-N and 73% of total nitrogen removed. Anammox bacteria could well survive and enrich in the integrated system, and the contribution of anammox to the total nitrogen removal was more than 70% at optimal conditions. Reactions network involved in the nitrogen transformation in the integrated system was further constructed through the mass balance and microbial community structure analyses. Consequently, this study demonstrated a practically feasible process configuration with high operation and control flexibility towards stable mainstream deammonification of municipal wastewater.

Tamm-plasmon-polariton biosensor based on one-dimensional topological photonic crystal

Tamm-plasmon-polariton (TPP) has strong light absorption and polarization-independence, shows potential application in the optical biosensors. However, with the weak field confinement of the distributed Bragg reflector (DBR), the sensitivity enhancement of TPP biosensors require defect layers or nano-materials with strong light absorption. Herein, we propose a novel TPP biosensor by using one-dimensional topological photonic crystal (1D TPhC) as DBR, the working wavelength is 633 nm, where 1D TPhC is composed by two 1D photonic crystals (1D PhCs) with different topological invariants. The strong field confinement of 1D TPhC is employed to improve the light absorption of TPP, and enhance the susceptibility of the biosensor to the analyte. Since TPP is polarization-independent, it has superior sensing performance in both transverse electric (TE) and transverse magnetic (TM) polarization modes. The sensitivity and Figure of Merit (FOM) are 2.6553 × 104 RIU−1 (1.3349 × 104 RIU−1) and 3.1238 × 107 RIU−1deg−1 (6.6745 × 108 RIU−1deg−1) for TM(TE)-polarization mode. The proposed TPP biosensor without defect layer, doesn’t need consider the thickness and position of the sensing medium layer, shows high operational flexibility. Besides, with the protection of the topological edge state, this biosensor has high tolerance to the thickness deviations, which can reduce the requirements on fabrication. It is anticipated that the proposed TPP biosensor has excellent sensing performances, possesses great potentials in environmental monitoring, biological detection, etc.

Technical and economic analysis of energy storage in the compressed air technology with low capacity for the production plant

Compressed air energy storage (CAES) system is a promising technology due to its numerous advantages, including relatively low maintenance cost, a long lifespan and high operational flexibility. This article explores the possibility of designing a CAES power plant as a source of electricity and heat for an existing industrial plant. The study involves the technical analysis of the power plant parameters and the economic analysis of the project’s feasibility. The proposed power plant is an innovative solution with an air expander with an external combustion chamber and a bypass that allows the combustion of virtually any fuel, making it particularly environmentally friendly. In the system, the use of a combustion chamber at the outlet of the turbine makes the chamber operate at a constant pressure that is close to atmospheric pressure. The designed power plant has a capacity of approx. 3.1 MW. Turbine operation reaches an efficiency of about 76%. Additional modification of the power plant and the use of heat from compressor cooling could increase the power of the power plant by about 0.5 MW. The conducted financial analysis showed that the project is economically feasible under the adopted assumptions in three modeled scenarios. Under the most optimistic scenario, the internal rate of return (IRR) reached 14.27%, and the investment return time was 10 years. When using long-term energy prices data, it was 7.46% and 23 years, respectively. The proposed CAES system is original and competitive in comparison to the currently used solutions.