Rolling Optimization Research Articles

Two-Time-Scale Energy Management for Microgrids With Data-Based Day-Ahead Distributionally Robust Chance-Constrained Scheduling

The uncertainties arising from both renewable generation and load demand have brought challenges to the reliable and efficient operation of power systems. This paper presents a two-time-scale (i.e., day-ahead and intraday) microgrid energy management model for scheduling with low operational costs and high reliability against uncertainties. For the day-ahead scheduling, we propose a data-based distributionally robust chance-constrained (DRCC) energy dispatch model for grid-connected microgrids, to trade off the economic efficiency and operational risk. This model gains a robust and low conservative day-ahead scheduling solution against uncertainties by formulating the chance-constraint based on Wasserstein ambiguity set into a tractable convex constraint with conditional value-at-risk (CVaR) approximation. For the intraday scheduling, we blend the shorter-time scale prediction with a robust day-ahead scheduling plan as well as the model predictive control (MPC) rolling optimization method. This ensures accurate intraday dispatch solution and balanced supply-demand. Finally, the effectiveness and performance of the proposed method are verified via case studies.

Energy Consumption Optimization of Cold Strip Rolling Lubricated in Mixed Regime

Cold rolling is one of the most popular metal forming methods. Due to mass production of companies which have cold rolling unites, it is essential to make the process optimum to reduce undesirable expense of production. One of the main production expenses is the power which is generally spends in the form of electricity. In the present study, power consumption of rolling is investigated with oil as lubricant

Two-Step Intelligent Control for a Green Flexible EV Energy Supply Station Oriented to Dual Carbon Targets

As China proposes to achieve carbon peak by 2030 and carbon neutrality by 2060, as well as the huge pressure on the power grid caused by the load demand of the energy supply stations of electric vehicles (EVs), there is an urgent need to carry out comprehensive energy management and coordinated control for EVs’ energy supply stations. Therefore, this paper proposed a two-step intelligent control method known as ISOM-SAIA to solve the problem of the 24 h control and regulation of green/flexible EV energy supply stations, including four subsystems such as a photovoltaic subsystem, an energy storage subsystem, an EV charging subsystem and an EV battery changing subsystem. The proposed control method has two main innovations and contributions. One is that it reduces the computational burden by dividing the multi-dimensional mixed-integer programming problem of simultaneously optimizing the 24 h operation modes and outputs of four subsystems into two sequential tasks: the classification of data-driven operation modes and the rolling optimization of operational outputs. The other is that proper carbon transaction costs and carbon emission constraints are considered to help save costs and reduce carbon emissions. The simulation analysis conducted in this paper indicates that the proposed two-step intelligent control method can help green/flexible EV energy supply stations to optimally allocate energy flows between four subsystems, effectively respond to peak shaving and valley filling of power grid, save energy costs and reduce carbon emissions.

Optimization of continuous variable crown work roll shifting strategy using k‐nearest neighbors algorithm for hot strip rolling mill

AbstractAppropriate work roll shifting strategy in the finishing hot strip mill can make roll wear uniform and contour smooth. To eliminate the problem of roll shifting position duplication due to available roll shifting strategy, the optimized work roll shifting strategy is proposed in the hot strip rolling mill with continuous variable crown work rolls, using the k‐nearest neighbors algorithm. The self‐perpetuating parameter is used to describe the concentrated wear of the work roll. The position repeated parameter is put forward to evaluate the roll shifting position history. The relationship between concentrated shifting positions and uneven roll wear is analyzed based on in‐site data collected in a hot strip rolling mill. The program of optimization strategy is coded using language C. Off‐line simulation is made to obtain optimized shifting positions using the program. The comparison between in‐site engineering data and optimized shifting positions has been assessed using the scatter diagram and kernel density estimation. Comparison results show that the new strategy can solve the problem of shifting position duplication well. A simplified method is established to make it easy to apply the strategy to online applications, and the in‐site application shows that the optimization strategy has an excellent practical effect.

Quantifying the contribution of EV battery swapping stations to the economic and reliability performance of future distribution system

This paper proposes a comprehensive framework to investigate the potential role of grid-connected battery swapping station (BSS) with vehicle-to-grid (V2G) in improving the economy and reliability of the distribution system. For this aim, we first develop an empirical operating model considering operating characteristics of the distribution system with the BSS explicitly. Then, on this basis, a rolling optimization scheduling model of the distribution network is established. In particular, a refined BSS model including a new calculation method of available generation capacity (AGC) is included. Finally, a quantitative method to quantify the effect of BSS on reliability and economy of distribution networks is proposed. Compared with existing works, the main contribution of this paper is threefold. (i) A battery-level BSS operational model including AGC calculation is proposed to fully explore the V2G potential of the BSS. (ii) A rolling optimization scheduling model is established, which fully exploits the potential interaction between economy and reliability of system operation. (iii) An assessment framework is proposed to analyze the special value of BSS resources from aspects of both economy improvement and reliability enhancement, which can provide more practical instruction for BSS planning. Numerical studies are conducted based on IEEE 33-bus distribution network to verify the accuracy and efficiency of our approach. The simulation results show that if V2G of the BSS is orderly exploited and managed, the net profit of the distribution system will increase by 11.97%, and system expected energy not supplied (SEENS) will decrease by 14.34%. The reliability and economy are two contradictory factors, the dispatch model we proposed can achieve better economy with the same reliability level. The location of the BSS also has important effect of the economy and reliability of distribution system.© 2017 Elsevier Inc. All rights reserved.

Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction

With the integration of Renewable Energy Resources (RERs), the Day-Ahead (DA) scheduling for the optimal operation of the integrated Isolated Microgrids (IMGs) may not be economically optimal in real time due to the prediction errors of multiple uncertainty sources. To compensate for prediction error, this paper proposes a Robust Model Predictive Control (RMPC) based on an interval prediction approach to optimize the real-time operation of the IMGs, which diminishes the influence from prediction error. The rolling optimization model in RMPC is formulated into the robust model to schedule operation with the consideration of the price of robustness. In addition, an Online Learning (OL) method for interval prediction is utilized in RMPC to predict the future information of the uncertainties of RERs and load, thereby limiting the uncertainty. A case study demonstrates the effectiveness of the proposed with the better matching between demand and supply compared with the traditional Model Predictive Control (MPC) method and Hard Charging (HC) method.

Optimal speed planning and collision avoidance control for electric vehicles considering traffic lights

<p indent=0mm>To reduce the energy consumption and avoid the risk of rear-end collisions for intelligent electric vehicles (EVs) during cruising under urban conditions, a hierarchical control system for energy saving and collision avoidance for EVs considering the information of traffic lights is proposed. The upper layer of the system plans the vehicle speed based on the aforementioned traffic information and a vehicle dynamic model and obtains the reference speed sequence with the minimum energy consumption through online rolling optimization; the bottom layer calculates the optimal acceleration of the vehicle in real-time along with the reference speed sequence and multiobjective function and performs collision avoidance control. Finally, the simulation model of the control system was built based on the Matlab/Simulink platform, and the effectiveness of the proposed control system was verified based on conditions of the presence and absence of human-driven vehicles in front of the EV. The upper layer can realize the optimal reference speed in vehicle speed planning according to the change in road traffic information and improve the energy consumption characteristics of the EV; the collision avoidance control by the lower layer can calculate the optimal acceleration control input by combining the optimal reference speed and the multiobjective function evaluation results. Thus, it is clear that the designed system can ensure energy-efficient, safe, and stable driving of EVs.

Online Optimal Energy Distribution of Composite Power Vehicles Based on BP Neural Network Velocity Prediction

In this paper, an online optimal energy distribution method is proposed for composite power vehicles using BP neural network velocity prediction. Firstly, the predicted vehicle speed in the future period is obtained via the output of a BP neural network, where the current vehicle driving state and elapsed vehicle speed information is used as the input. Then, according to the predicted vehicle speed, an energy management method based on model predictive control is proposed, and online real-time power distribution is carried out through rolling optimization and feedback correction. Cosimulation results under urban drive cycle show that the proposed method can effectively improve the energy efficiency of composite power sources compared with the commonly used method with the assumption of prior known driving conditions.

Model predictive control strategy of head rice yield in paddy rice intermittent drying

This paper proposes a paddy rice model predictive control (MPC) drying strategy to achieve the real-time rolling optimization control of paddy rice intermittent drying process for the maximization of head rice yield (HRY). In the rolling optimization process, the adjusted evaluation index of predicted HRY ( AEHRY ) is used as the performance index to calculate the optimal control law in real-time. By comparing the simulation results of proposed drying strategies, the optimal drying strategy was chosen. Intermittent drying experiments of proposed MPC drying strategy were carried out to evaluate its performance. Experimental results showed that the proposed MPC drying strategy had the greatest AEHRY and HRY. In terms of Nanjing 9108 with initial moisture content of 22.88% wet basis, the average AEHRY s of 50 °C, 55 °C, 60 °C constant temperature drying, and MPC drying were 0.0773, 0.2130, 0.2283 and 0.3147, with average HRYs of 0.7121, 0.7129, 0.7163 and 0.7202, respectively. The feasibility of the proposed paddy rice MPC drying strategy was proved. The paddy rice intermittent drying processes under different application conditions have different drying requirements and restrictions. By modifying constraint part of the proposed MPC drying strategy, the ranges of drying parameters and the relationship during the drying parameters can be constrained to realize the real-time online optimization of these intermittent drying processes.

Influence of Roll Diameter on Material Deformation and Properties during Wire Flat Rolling

The influence of roll diameter on the strain distribution, shape change, contact pressure, and damage value of a workpiece was investigated during wire flat rolling to control the material properties of the flattened wire. The flattened wires fabricated by the four different rolls were compared using finite element analysis. The strain inhomogeneity of the flat-rolled wire increased with the roll diameter; thus, the macroscopic shear bands were strengthened as the roll diameter increased during wire flat rolling. The contact width and lateral spreading of the flattened wire increased with the roll diameter; therefore, the reduction in area decreased with the roll diameter. The contour of the normal contact pressure on the wire surface exhibited a similar pattern regardless of the roll diameter. The contact pressure showed higher values at the entry, edge, and exit zones in the contact area. The distribution of the damage value varied with the roll diameter. The free surface region tended to have the peak damage value during the process; however, the center region exhibited the maximum damage value with the roll diameter. From the perspective of the damage value, the optimum roll diameter was in existence during wire flat rolling. The underlying cause of the different strain distributions, shape changes, and damage values of the flat-rolled wire was the different contact lengths originating from the different roll diameters during wire flat rolling.

Mathematical Models for Forecasting of 10Mn2VNb Steel Heavy Plates Mechanical Properties

The problem urgency for determining the optimal rolling and heat treatment schedules for providing the required indices of heavy plates physical and mechanical properties is shown. The use of statistical mathematical models for solving this problem is substantiated and the methodology for their design is described. Statistical mathematical models were designed using the mathematical statistics methods and Data Mining tools to determine the yield strength, ultimate tensile strength and percent elongation for 10Mn2VNb steel plates rolled under 3600 heavy plate mill conditions. Software for the numerical implementation of these statistical mathematical models has been developed. Applied software has been developed for the numerical implementation of the statistical mathematical models for predicting the heavy plate’s mechanical properties, and high calculation accuracy has been confirmed with the ones help: 95.82% for the yield strength, 96.78% for the ultimate tensile strength, and 91.48% for the percent elongation. The regularities of the influence for finish rolling factual temperature in the finishing stand of 3600 heavy plate mill and the plate thickness on 10Mn2VNb pipe steel physical and mechanical properties were identified by processing the database and using the designed software.

Design of a deep inference framework for required power forecasting and predictive control on a hybrid electric mining truck

Accurate required power forecasting is critical to ensure the recharge mileage and to optimize the energy utilization of heavy-duty vehicles. This paper proposed an artificial intelligence model predictive control framework for the energy management system (EMS) of the series hybrid electric vehicle with terrain and load information. It aims to achieve optimal energy distribution with increased required power prediction accuracy and optimized SoC sequence by fast analyzing high-dimensional information. Firstly, the required power is predicted by a deep inference framework (LASSO-CNN), which integrates the least absolute shrinkage selection operator (LASSO) and convolutional neural network (CNN). Secondly, the optimal SoC sequence on hilly roads is planned in advance, with the SHEV's recuperated energy being predicted based on the current driving state. Finally, MPC-based predictive energy management is achieved by combining required power prediction and SoC planning with rolling optimization and feedback correction. Simulation results show that the fuel consumption is 2.51% lower than the deterministic dynamic programming-based controller, and the computation time is decreased by 48.1%. These promising results suggest that the proposed predictive energy management strategy can play a critical role in predicting power demand, which further reduces the fuel consumption of the hybrid electric mining truck.

Discrete Element Modeling of Vibration Compaction Effect of the Vibratory Roller in Roundtrips on Gravels

Abstract This paper aims to study the vibration compaction mechanism of the vibratory roller on gravels using a two-dimensional discrete element method. The roadbed model was established by gravel particles with irregular shapes, which was closer to reality. The performance parameters of the vibratory roller, such as operating frequency and rolling velocity, were investigated to explore their influences on the operating efficiency of the vibratory roller in roundtrips. The frequencies of 15 Hz and 17 Hz were proved to be the optimal frequency and resonance frequency in the current simulations, respectively. The vibratory roller could achieve a better vibration compaction effect with less power consumption at the optimal frequency. In addition, the number of roundtrips and power consumption should be considered in the selection of the optimal rolling velocity. The movement direction and the contact force distribution of gravels were illustrated by the displacement field, velocity field, as well as the contact force chains. Our results provide a better understanding of the mechanical behavior of gravel particles and their interactions with the vibratory roller.

Rancang Bangun Mesin Pengerolan Pipa 1,5 Inci Menggunakan Motor Listrik Sebagai Penggerak dan Dongkrak 2 Ton Sebagai Penekan Pipa

The 1.5 inch pipe rolling machine utilizes an electric motor to drive the pipe holder and a 2ton jack as a pipe press. Although the roller machine is widely used in conventional workshops by using human power, it causes the pipe rolling time to be long and the machine user gets tired quickly. This is due to lack of resources to think about developing and improving the performance of these machines. Therefore, the focus of this research is the design of a 1.5 inch pipe rolling machine using an electric motor as a drive and a 2 ton jack as a pipe press. This research aims to produce a design and machine for 1.5 inch pipe rolling and to help accelerate the pipe rolling process and reduce worker fatigue. This pipe rolling machine consists of several components, namely, the engine frame, electric motor, gearbox reducer, chain-sprocket, shaft, pipe drive wheel, hydraulic pump and so on. The working process of this machine begins by preparing a 1.5 inch x 3 meter pipe then the pipe is positioned above the drive wheel, lowers the upper pressure wheel to the desired depth with a 2ton jack power, turn on the electric motor and press the button to roll the pipe back and forth and so on. up to pipe roll size as needed. The stages in making this machine are design, detailed drawing, manufacturing process, quality control, assembly and testing, data retrieval and data analysis. The research resulted in drawings of the design and manufacture of the machine, the calculation of the rotational speed of the machine for optimal rolling obtained 20.46 rpm and an average pipe rolling time of 6.80 minutes for one workpiece. This machine can still be optimized by adding a workpiece movement limiter when rolling.

A multi-objective optimization energy management strategy for power split HEV based on velocity prediction

Under the complicated driving conditions, the sharp acceleration and deceleration actions would cause the high-rate charge and discharge current of electric driving system in hybrid electric vehicle (HEV), which brings about a serious impact on the battery lifetime. The hybrid energy storage system (HESS) combined with battery and ultracapacitor (UC), would be a possible solution to this problem. For HEV with HESS, in addition to improving fuel economy, realizing the protection of battery is also an important objective. However, improving one aspect performance may sacrifice another aspect performance. The tradeoff between multiple optimization objectives remains a challenge for energy management design. Aiming at this problem, a multi-objective optimization energy management strategy based on velocity prediction for a dual-mode power split HEV with HESS is proposed in this paper. Firstly, to get the precise predictive input sequence, generalized regression neural network (GRNN) is used to predict future velocity. Secondly, the power distribution of dual-mode power spilt HEV with HESS is described as a rolling optimization problem in the prediction horizon of model predictive control (MPC). A new cost function considering the fuel consumption and the protection of the battery is brought forward, and the optimization problem is solved using Pontryagin's minimum principle (PMP). Moreover, the Powell-Modified algorithm is introduced to execute the solving process of PMP. Finally, the proposed strategy is verified by comparing it with four other strategies under four different driving cycles. Compared to the rule-based strategy, the proposed strategy reduces root mean square (RMS) of battery current and fuel consumption by up to 18.5 % and 18.9 %, respectively.

Multi-objective collaborative optimization in cement calcination process: A time domain rolling optimization method based on Jaya algorithm

Coal consumption and free calcium oxide (f-CaO) content are two important production indicators in the cement calcination process, and its collaborative optimization is of great significance to improve the production performance. However, due to the multiple dynamic interferences and multiple conflicts between coal consumption and f-CaO, product quality is unstable and coal consumption becomes high. To address the problems, this paper proposes a multi-objective collaborative optimization method for cement calcination process. This method takes the coal consumption and f-CaO content as the optimization objectives to establish a multi-objective optimization model. In addition, to solve the problem that static single-step optimization is difficult to track the continuous change of working conditions, we propose a time domain rolling multi-objective Jaya algorithm (TDRM-Jaya) to optimize the model. The algorithm, involving a population update strategy, a selection strategy and a time domain rolling strategy, realizes the dynamic rolling optimization for the cement calcination process. The test comparison with several common intelligent optimization algorithms demonstrates the advantages of using TDRM-Jaya. Besides, practical industrial data validates the effectiveness of the proposed optimization method.

A Predictive Control Model for Master Slave Robotic Manipulator with RBF Neural Network

In recent years, manipulator control has been widely concerned, and its uncertainty is one of the focuses. As we all know, the manipulator is a MIMO nonlinear system, which has the characteristics of severe variable coupling, large time-varying amplitude of parameters and high degree of nonlinearity. Therefore, a lot of uncertain factors must be considered when designing the control algorithm of manipulator system. The predictive control algorithm adopts online rolling optimization, and in the process of optimization, feedback correction is carried out by the difference between the actual output and the reference output. It can iterate the predictive model and suppress the influence of some uncertain disturbances to a certain extent. Therefore, the design of predictive controller for robot is not only of theoretical significance, but also of great practical significance. The trajectory tracking problem is proposed in this paper, and a predictive control method for master slave robotic manipulator with sliding mode controller is designed. In addition, when external disturbances occurred, the approximation errors are compensated by the proposed control method. Finally, The results demonstrate that the stability of the controllers can be improved for the trajectory tracking errors.

Study of wear of cylindrical plugs of a helical rolling mill

The present work is devoted to the wear process of mandrels of screw rolling mill &ldquo;MISIS 130D&rdquo; during seamless pipes manufacturing of stainless steel X12CrNiTi18-9. The software application QFORM was used to simulate the process of mandrel wear during rolling of billets of 700 mm long based on the finite element method was carried. The results showed that the wear of the mandrels was not irregular along their length. Resulting in mathematical analysis data of modeling the wear dependence of mandrels of high-temperature alloy X38CrMoV5-1 on the major various conditions of the rolling process was established. The results showed that the wear of the mandrels of the rolling mill can be diminished by the selection of optimal rolling conditions and using the moving mandrels.

Optimal dispatching of an energy system with integrated compressed air energy storage and demand response

The integrated energy system is considered to be an important way to avoid energy supply risks by virtue of advantages in meeting diversified energy demand and improving energy utilization efficiency. Energy storage enables microgrid operators to respond to variability or loss of generation sources. In view of the difficulty of battery to fully improve the energy utilization efficiency and solve the problems of clean energy power large-scale consumption, and the difficulty of single demand response to significantly improve the operational reliability and economy of the integrated energy system, a rolling optimization planning framework and model of an integrated energy system considering compressed air energy storage and sliding time window-based electric and heating integrated response demand is proposed, which can obtain both optimal resource configuration and energy management strategy. The planning problem is to coordinate the output of various dispatching resources on the basis of satisfying the constraints, so as to minimize the total cost. Taking into account the uncertainty of wind power and photovoltaic output and the unplanned outage risk of the unit, a stochastic optimization model is constructed based on the probability density function of the risk variable, and finally the cost probability distribution is obtained. The results verify the universality of the planning model. The comparison of five different energy system configuration schemes and multiple time window length schemes verifies the validity and superiority of the model. The simulation results show that the integrated energy system scheme proposed by this planning model has better economy than the scheme without compressed air energy storage, and the operating cost decreases by 11.9% and the total cost decreases by 4.5%. In addition, the results show that through the introduction of demand response, the operating economy and flexibility of the integrated energy system are improved, and as the sliding window length increases, the total cost gradually decreases.

A Novel Optimization Method for a Multi-Year Planning Scheme of an Active Distribution Network in a Large Planning Zone

Electric power distribution networks plays a significant role in providing continuous electrical energy to different categories of customers. In the context of the present advancements, future load expansion in the active distribution networks (ADNs) poses the key challenge of planning to be derived as a multi-stage optimization task, including the optimal expansion planning scheme optimization (EPSO). The planning scheme optimization is a multi-attribute decision-making issue with high complexity and solving difficulty, especially when it involves a large-scale planning zone. This paper proposes a novel approach of a multi-year planning scheme for the effective solution of the EPSO problem in large planning zones. The proposed approach comprises three key parts, where the first part covers two essential aspects, i.e., (i) suggesting a project condition set that considers the elements directly related to a group of specific conditions and requirements (collectively referred to as conditions) to ADN planning projects; and (ii) Developing a condition scoring system to evaluate planning projects. The second part of our proposed scheme is a quantization method of correlativity among projects based on two new concepts: contribution index (CI) and dependence index (DI). Finally, considering the multi-year rolling optimization, a detailed mathematical model of condition evaluation and spatiotemporal optimization sequencing of ADN planning projects is developed, where the evaluation and optimization are updated annually. The proposed model has been successfully validated on a practical distribution network located in Xiantao, China. The investigated case study and comparisons verify the various advantages, suitability, and effectiveness of the proposed planning scheme, consequently saving more than 10% of the investment compared with the existing implemented scheme.