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Building emission reduction is an important way to achieve China's carbon peaking and carbon neutrality goals. Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system, a multi-time scale optimal scheduling strategy based on model predictive control (MPC) is proposed under the consideration of load optimization. First, load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature, and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost. Second, considering inter-day to intra-day source-load prediction error, an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption. Finally, taking an office building on a summerwork day as an example, the effectiveness of the proposed scheduling strategy is verified. The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%, improves the carbon emission reduction by 7.99%, and the photovoltaic consumption rate reaches 98.57%, improving the system's low-carbon and economic performance. [ABSTRACT FROM AUTHOR]

Artificial intelligence is widely used in mathematical modeling. The technical means in mathematical modeling are more and more diversified, especially the application of artificial intelligence algorithm greatly promotes the development of mathematical modeling. In recent years, because of its great influence on the fuel consumption, output power and exhaust performance of automobiles, the control strategy has become a research hotspot and focus in automobile R&D industry. Therefore, based on the relevant research results in recent years, after studying and analyzing the typical control strategies of hybrid vehicles, this paper finally puts forward the energy management strategy of hybrid vehicles based on model predictive control (MPC), and strives to contribute to the academic research of energy management strategies of hybrid vehicles. [ABSTRACT FROM AUTHOR]

Real‐time control (RTC) of urban drainage systems (UDS) has been proved an efficient tool in combined sewer overflow (CSO) and flooding mitigation. Recently, new RTC approaches based on reinforcement learning (RL) were developed for flooding mitigation in stormwater systems. While these studies have made contributions to enable an improved urban water management, they are insufficient to allow for deeply understanding of the effectiveness of different RLs in UDS. Meanwhile, the risk of handing over the control process to a RL agent is still unavoidable because of the fluctuations of RLs' output and the unknown consequences of implementing RLs control strategy. This study conducted four tasks to address these problems. First, five RTC systems based on five individual RLs were designed to distinguish different RLs' performance in the context of UDS. Then, an independent security system based on SWMM was provided to forecast and evaluate the consequence of RL control strategy. After that, an innovative hybrid RTC system, called Voting, was developed by coupling multiple RLs and the independent security system through a model predictive control framework to avoid the fluctuations of RLs' output. Finally, the robustness of the RL agents was validated using uncertainty analysis. All the RLs were evaluated through simulation based on a Storm Water Management Model of a UDS located in Eastern China. According to the results, (a) different RLs show promise in CSO and flooding mitigation; (b) Voting selects a relatively reliable and optimal control trajectory compared with any single RL agents; (c) the performances of RL agents have certain robustness when facing different rainfall events and imperfect input. Key Points: Different reinforcement learnings (RLs) show promise in CSO and flooding mitigationVoting selects a relatively more reliable and more optimal control trajectory compared with any single RL agentsRLs' performance is influenced by rainfall events and imperfect input, but they still have certain robustness [ABSTRACT FROM AUTHOR]

With the increasing severity of climate change, the power industry, as one of the main sources of carbon emissions, is playing an extremely important role in the process of low-carbon energy transformation. The purpose of this paper is to try to find a general method to solve the optimal path for the low-carbon evolution of the power supply structure so as to meet the challenges faced by the low-carbon transformation of the power industry in the future. This paper first uses the capacity coefficient index (CCI) to represent the power generation ability of different technologies and proposes a forecasting method for the CCI of renewable energy generation. In this paper, a two-layer optimization model considering multiple constraints is established and solved using the MPC method. The results show that China's installed capacity of renewable power could account for more than 50% in 2030, while the carbon emissions will decrease after reaching a peak in 2023. On the premise of ensuring sufficient reserve adjustment capacity of thermal power units, increasing the proportion of renewable energy generation is an important way to realize emission reduction in the power industry. [ABSTRACT FROM AUTHOR]

Offshore wind power is a clean energy source with excellent development prospects that are conducive to realising China's goal of carbon neutrality. However, offshore wind turbines (WTs) are prone to failure and damage owing to their long-term operation in marine environments with high humidity, high wind speeds, and frequent extreme weather. To ensure the safe operation of offshore WTs, a modified dynamic model predictive control (MPC) method was proposed in this study. First, a dynamic model of an offshore WT was established and its nonlinear state-space equations were obtained. These state-space equations were subsequently linearised and discretised, then fast trajectory tracking of the state variables was performed according to the conventional MPC principle. Finally, a control strategy was designed to realise the safe operational control of offshore WT using the safety index as a constraint condition in the MPC. Simulation results confirmed that the proposed MPC strategy using the safety index-based constraint exhibited superior tracking of the set trajectory and achieved the minimum energy loss under varying wind speeds. • Offshore wind power generation system mechanism model is established. • Classical model predictive control (MPC) model and the fast trajectory tracking of wind turbine state variables are given. • Safety index is introduced into the MPC as a constraint condition for the safe operation control of offshore wind turbine. [ABSTRACT FROM AUTHOR]

To meet the active regeneration requirements of the diesel particulate filter (DPF), the outlet temperature of the diesel oxidation catalyst (DOC) must be maintained within the range of 585 °C–615 °C. Firstly , this paper explores the DOC system using a set of partial differential equations that describe the coupled flow and heat transfer of both the gas phase and solid phase carriers. Subsequently, the DOC model is significantly simplified, and the equations are solved based on their characteristics. Secondly , a Luenberger observer is designed, and the appropriate range of values for the observer gain is calculated. By minimizing the cost function and adhering to constraints within a finite prediction horizon, the optimal sequence for injecting hydrocarbon(HC) is determined. Thirdly , in this study, a Worldwide Harmonized Transient Cycle test is conducted on the engine, following the China VI emission standard. The computational results demonstrate that the mean absolute errors of the DOC outlet temperatures obtained from the model prediction and the observer are 0.535 °C and 1.994 °C, respectively. Finally , a nonlinear model predictive control is implemented to ensure that the DOC outlet temperature can be maintained between 585 °C and 615 °C. [Display omitted] • The DOC gas phase and solid phase temperature differential equations were simplified. • The Backward Differentiation Formulas method was used to solve the DOC model and the parameters were identified by the Levenberg-Marquardt algorithm. • The Luenberger observer was designed and the Lyapunov function was constructed to prove its stability. • The DOC model was combined with the minimised cost function and NMPC was implemented to optimise the HC injection strategy. [ABSTRACT FROM AUTHOR]

• A surrogate model is proposed to simulate the overflow and downstream outflow processes. • The application range and error source of the surrogate model are analyzed. • The surrogate model can effectively improve the efficiency of real-time scheduling. In the context of global climate change and urbanization, model predictive control (MPC) has been increasingly applied in the field of real-time control of urban drainage systems to cope with frequent urban flooding events caused by extreme rainstorms. However, the computation time of the predictive model calculation greatly hinders further development of MPC in this field. In this study, we propose two surrogate models, namely the water tank-water balance model 1 (TWBM1) and the water tank-water balance model 2 (TWBM2), to replace the storm water management model (SWMM) as the prediction model for MPC, thus improving MPC computational efficiency. Both surrogate models can obtain the total outflow process at the downstream outfall of the study area and the corresponding overflow process. In comparison with TWBM1, TWBM2 requires 26 % fewer calibration parameters, and its structure is simpler. These results have been verified in the real-time optimal scheduling of a rainwater drainage system in the Doumen area of Fuzhou City, China. The results show that TWBM1 and TWBM2 have shorter computation time compared to SWMM, which improves the efficiency of MPC computation. Additionally, in comparison with the existing scheduling results, MPC has better performance in terms of economy, security and computational efficiency. [ABSTRACT FROM AUTHOR]

The energy-saving potential of original equipment manufacturers (OEMs) for electronic products is substantial owing to the huge market size of assembling industry in China. A semi-automatic electronic assembly line (SAEAL) for smart-phones is introduced, and its digital twin (DT) enabled energy-saving platform is developed as the hardware foundation. We propose a stochastic dynamics model via max-plus algebra to characterize the spatio-temporal nature of state transition, which provides dynamical opportunity windows for further energy-saving control strategy. Accordingly, model linearization is conducted to obtain linear state and output equations. Then, a model predictive control (MPC) approach for energy saving optimization is provided to determine the optimal start-stop control signal. The dynamics modeling and MPC approach have been applied and verified in the case of assembly line. The results show that the optimization approach could reach a proportion of 49% for the equipment sleep time of total running time and save a considerable amount of energy for normal production of OEMs. [Display omitted] • A digital twin prototype system of smart-phone assembly line is developed as the testing platform for energy-saving control. • The assembling behaviors of assembly line are depicted by linear state equations via max-plus semiring algebra. • A spatio-temporal dynamics model is provided to evaluate the system performance based on max-plus semiring algebra. • An energy-saving MPC approach is proposed to determine the start-stop control sequences of automatic machines. • The equipment's sleeping time can reach a proportion of 49% of total running time without production efficiency loss. [ABSTRACT FROM AUTHOR]

• A grey box model of a district thermal station was established. • The feasibility of the method was verified using an existing district heating substation. • The proposed method was flexible and performed well in terms of anti-interference. • The proposed method can reduce thermal energy consumption by 7.4%. In China, the regulation of a district heating (DH) station is mainly based on weather compensation control. This control method leads to large room temperature fluctuations and high energy consumption as the regulation of supply water temperature is solely based on outdoor meteorological parameters, which the influence of pipe delay, thermal inertia of buildings, and indoor temperature were not considered. In this study, a model predictive control (MPC) method was adopted to achieve high room temperature stability and high flexibility in DH station regulation. A dynamic model of the entire DH station, which considers the thermal inertia of buildings and delay of pipe, was established and verified using actual operation data. The performance of the MPC method was then evaluated and compared with that of the conventional control method (CCM) using the dynamic system model. When thermal comfort was considered as the single objective of the MPC, the optimal control step of the DH station was 8 h. The room temperature stability improved significantly as the room temperature fluctuation amplitude reduced from 2.64 °C to 0.35 °C. When thermal comfort and energy consumption were combined by the objective function, the MPC method could reduce the system energy consumption by 7.4 %. The MPC method could help improve the stability of the pipe network and had a strong anti-interference ability. In practical applications, during the two years after renovation, energy consumption was reduced by 5.9 % and 7.9 %, respectively. Further, the thermal comfort rate of households was improved by 0.27 % and 0.03 %, with relative incomes of 0.72 yuan/m2 and 1.04 yuan/m2, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • The unrealistic offtake assumption of the Integrator Delay model is analyzed for the first time. • The Integrator Dual-Delay model is developed by innovatively considering the offtake flow delay. • Centralized model predictive controllers are designed for cascade canals with multiple offtakes. • The Integrator Dual-Delay model remarkably enhances the water level control stability. The Integrator Delay (ID) model is widely used for advanced controller design in canal automation projects, assuming that all offtakes are aggregated into one at the most downstream end for each canal pool. However, such an unrealistic assumption differs greatly from the actual location distribution of the offtakes in most open canal irrigation systems (OCIS), leading to increased uncertainties and inaccurate canal operations. For this reason, this study introduced the Integrator Dual-Delay (IDD) model, capable of innovatively considering the lag-effect of the offtake flow changes alongside the irrigation canal. To investigate the effectiveness of the modified control-oriented model in upgrading the canal automatic operating systems, two centralized model predictive controllers were designed with the ID model and the IDD model, respectively. Then the simulation tests were carried out on the Third Main Canal of the Zhanghe Irrigation System in Southern China. Two performance indicators of Non-dimensional Integrated Square of Error (NISE) and Non-dimensional Integrated Square of Discharge Change (NISQ) were adopted to measure the water level control stability and flow control stability. The results show that with the IDD model, the NISE is reduced from 3.53 × 10-5 to 1.88 × 10-5 and the NISQ is reduced from 3.23 × 10-6 to 2.88 × 10-6, indicating that the proposed IDD model can help the control stabilities of water level and flow to be improved by 46.7 % and 10.8 %, respectively. The IDD model constructs a more accurate state-space prediction process for advanced controller design and contributes to more efficient canal automatic operations, showing great potential in precision irrigation for various OCIS. [ABSTRACT FROM AUTHOR]

In recent years, radiant floor heating systems have been favored by more and more consumers because of their better comfort, high stability, and energy efficiency. This is especially true in southern China, where heating demand is increasing, and its application is becoming more common. This study explored the method of establishing a state-space model of a variable-flow radiant heating system without selecting and applying extensive measurement data and the application of the model predictive control method to its control optimization. The average errors between the state-space model and the experimental data in terms of the zone air temperatures and radiant floor surface temperatures were only −0.21–0.07 °C. The state-space model saves 76%~95% of the computation time compared to the Trnsys model, particularly suitable for large volumes and longtime building simulations. For the intermittent operation of the radiant floor heating with an air-source heat pump, the MPC controller reduced the response time by about 90 min compared with that with the PID controller, a reduction of approximately 56%. Besides, the MPC controller can effectively reduce energy consumption by 14.9% and improve the COP of the air-source heat pump by 24.5% compared with the PID controller. [ABSTRACT FROM AUTHOR]