Rolling Optimization Research Articles

Approach to Multi-Timescale Optimization for Distributed Energy Resources Clusters Considering Flexibility Margin

The disordered access of massively distributed energy resources (DERs) brings great challenges to the operation stability of the power grid. This paper puts forward the concept of a cluster, which gathers DERs in large quantities, small capacities, dispersion and disorder to form a large, centralized and orderly whole, namely cluster, with certain incentive measures. In this paper, a multi-timescale optimization method of day-ahead planning and intra-day rolling optimization is proposed according to the characteristics of aggregated clusters and the requirements of China’s power grid architecture. Specifically, the day ahead model is proposed in two steps: the first step is to establish an optimization model with the goal of optimal fitting the target load curve and maximizing the utilization of DERs; The second step is to establish a potential game model considering the reasonable distribution of cluster benefits. Taking the minimum percentage of output correction of each cluster as the objective, considering the deviation of load forecasting and the deviation of day ahead instruction execution, an intra-day rolling optimization model is established. Finally, the application scenario of cluster participation in power grid auxiliary peak shaving is simulated and verified. The simulation results show that the cluster collaborative optimization method proposed in this paper can effectively reduce the load peak valley difference and maximize the use of cluster resources. The optimization tasks can be reasonably allocated while ensuring the stable and reliable operation of the power grid.

A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin

With the growing percentage of the intermittent renewable power generation, the energy system is under increasing pressure in balancing the supply and the demand. As a major part of urban energy consumptions, buildings can provide considerable regulation flexibility for the energy system by actively managing their energy demands. For the building energy flexibility (BEF) provided by thermostatically controlled loads (TCL), its dispatch performance is vulnerable to the building thermal parameter errors, and in some cases, occupants need to provide the critical information related to the indoor temperature state and the occupancy state to the energy management system outside buildings, which decreases the availability of the BEF and raises privacy concerns. For these issues in the BEF utilization, this paper proposes a data-model fusion dispatch strategy based on the digital twin (DT). The proposed strategy is capable of parameter fault tolerance and privacy protection by combining the model-free advantage of the data-driven method with the analytical optimization advantage of the model-driven method. Firstly, a DT-based BEF dispatch framework is proposed. Secondly, the building DT is established by combining the building thermal dynamics (BTD) data-driven model and the TCL operation mechanism model. And the building response deduction is carried out based on the DT. Finally, under the rolling optimization framework, the data-model fusion dispatch strategy is devised by uniting the DT deduction and the optimization constructed by the BTD mechanism model, in which the multi-dimensional modeling of the BTD is carried out from the state dimension and the energy dimension. The simulation results show that the optimization result can reach 98.4% of the ideal result under the scenario with 15% parameter random error, and 98.3% of the ideal result under the scenario with 15% random state noise injection.

Rolling optimization based on holism for the operation strategy of solar tower power plant

Solar tower power plants (STP) with thermal energy storage have the ability to temporally shift power generation, regulate peak load and modulate frequency. The power production of such systems not only depends on the available solar resources, but also on the operation strategies, such as the operational thresholds used to control when the equipment starts or stops in certain conditions. Currently, most of the operational thresholds are determined by operators’ experience or the rated parameters given by the equipment manufacturers, which are usually unoptimized fixed values. Therefore, to guide the optimal operations of STP, optimization methods are developed in this work to optimize the daily operational thresholds according to the solar radiation at present and in the future. Firstly, a well-validated in-house model with easy-to-tune operation strategies for STP is developed to optimize the thresholds. Then, five operational thresholds affecting the power generation of STP are identified. Finally, an optimization algorithm is proposed to optimize the operational thresholds. The proposed optimization algorithm uses a rolling optimization based on holism to enhance regular particle swarm optimization (R-PSO). The power generation of a 50 MW STP under three different operation strategies is compared: (1) unoptimized operation strategy, (2) operation strategy optimized by regular PSO algorithm, and (3) operation strategy optimized by R-PSO algorithm. The average daily power generation under the three operation strategies is 451.16 MWh, 490.25 MWh, and 511.63 MWh, respectively. The results show that the proposed R-PSO could increase averaged daily power production by 13.4 % and 4.36 % when compared with cases with no optimization and PSO optimization, respectively. Therefore, the power production of STP can be significantly improved by optimizing the identified daily operational thresholds using the R-PSO algorithm.

Modelling and Optimization of Process Parameters during Grooved Hot Rolling of SAE 1020 Steel

ABSTRACT The rolled SAE 1020 steel bars are widely used for production of mechanical components, such as, gears, spindles, camshafts, axles, fasteners, and gudgeon pins etc. Steel industries have been striving for productivity and better yield of hot rolled bar products. The roll separating force (RSF), driving torque (DT), end crop length (ECL), and drive energy (DE) are the important response parameters to be controlled for quality production, maximization of yield (i.e. minimization of rolled bar process scrap), safety of mill and reduction in energy consumption, etc. These response parameters depend on several process parameters – rolling speed, billet temperature, reduction ratio, billet cross-section area, roll diameter, etc. This paper presents a study on the effect of the process parameters on the response parameters during rolling of SAE 1020 steel. Simulation of the rolling process has been attempted using FORGE® NxT 1.1. The simulated results so obtained have been validated through experimental results obtained in a rolling mill, following statistical tests. Regression models showing relationship between the process parameters and the response parameters have been developed. Significant model terms have been obtained using ANOVA. The optimum rolling conditions for minimization of the response parameters have been obtained.

Energy Efficiency Optimization Technology of Integrated Energy System Based on Digital Twin Technology

At present, the energy efficiency optimization of an integrated energy system generally needs to optimize the allocation of each unit and structural resources of the system according to the real-time load data of the system to optimize the operation. However, the existing technology for system load forecasting is not accurate enough, resulting in the energy efficiency coefficient is still relatively low after system optimization. On this basis, this paper proposes an integrated energy system energy efficiency optimization technology based on digital twin technology. According to the actual situation of the system, the mathematical model is established. The equivalent analysis of the system is carried out, the digital twin technology is used to forecast the system load in real time, and constraint conditions and the objective function are constructed The optimal energy efficiency optimization strategy is obtained by rolling optimization. The experiment proves that the system energy efficiency coefficient is higher after the application of the design technology.

A model predictive control regulation model for radiant air conditioning system based on delay time

Radiant air conditioning (RAC) systems are widely used because of the advantage of higher thermal comfort compared to convection systems. However, RAC systems exhibit considerable hysteresis and significant time delays in heat transmission from the terminal to the interior environment. Traditional feedback control methods do not adequately account for the system's delay impact while implementing dynamic control, resulting in a mismatch between supply and demand loads in time series, an inefficient impact of interior temperature control, and energy waste. According to this study, the time-delay characteristics of the RAC system are quantified by the startup-process delay time (SPDT) and operating-phase integrated delay time (IDT). The system startup time is set by the SPDT. Based on the IDT, the prediction interval of the model predictive control (MPC) and the step time of rolling optimization are determined. On this basis, a short-term forecasting model of air conditioning operating load and the regulation model of the system using MPC are established and verified by comparing with the conventional rule-based control of constant water temperature (CRBC) and proportionally-integral-differential (PID) control. The results for the RAC system with a large time delay indicate that the stability of indoor temperature under MPC controlling is 18.8% higher than the PID control and 63.5% higher than the CRBC method. Also, the indoor temperature controlled by the MPC is closest to the set value and shows the minimum fluctuation. Furthermore, the overall energy consumption of the system is estimated for reducing by 14.89% in comparison with the CRBC and by 8.51% in comparison with the PID.

Effect of Hot-Rolling Strategy on the Flow Behavior, Productivity, and Mechanical Performance of Ti-6Al-4V Alloy

This work involves studying the effects of applying various designed hot-rolling strategies, using the uniaxial hot compression regimes of the Gleeble 3500 thermo-mechanical simulator on the microstructure, flow behavior, and productivity of Ti-6Al-4V alloy. These strategies were then practically implemented using a rolling mill to produce finished sheets with a thickness of 3 mm. The tensile properties of these finished Ti-6Al-4V sheets were examined, aiming at attaining the optimum rolling strategy conditions that result in upgrading the mechanical performance of the alloy. The undertaken hot-rolling strategies can be divided into two main groups; both comprise applying a total amount of deformation of 75% at a constant strain rate of 0.1 s-1. The first group, isothermal hot rolling regime (IR), includes three strategies and involves applying the total amount of deformation at constant temperatures, i.e., 900, 800, and 750 °C. The second group, non-isothermal hot rolling regime (NIR), includes three strategies and involves partitioning the total amount of deformation into multi-step deformation at variable temperatures in a range of 900-750 °C. The dynamic flow softening is dominant in all IR strategies after the flow stress attains the peak at a low strain value. Then, dynamic flow softening occurs due to the dynamic recrystallization and α phase spheroidization, while a combination of flow softening and hardening takes place on the different passes of the NIR strategies. The designed hot-rolling strategies result in finished sheets with a fine multimodal microstructure that fructifies different mechanical properties that can be employed for different industrial purposes.

Development of temperature-speed modes of hot deformation of Co – 28Cr – 6Mo alloy based on processing maps

In the article, the tests of the medical alloy Co – 28Cr – 6Mo after homogenization for uniaxial compression at temperatures of 1000, 1100 and 1200 °C and strain rates of 1, 10, and 50 s­–1 were carried out using the Gleeble System 3800. The stress-strain curves describing the alloy deformation behavior were obtained. The calculations of hot deformation parameters (activation energy, Zener-Hollomon parameter) were performed using three models (power-law, exponential, and hyperbolic sine function) describing the flow stress. The highest degree of convergence was shown by the calculation results based on the power function and the hyperbolic sine function. These models can be used to accurately calculate the flow stress at given temperature and strain rate parameters, or to simulate the deformation process. Also, based on processing maps, the authors developed the deformation-speed modes of hot deformation of the Co – 28Cr – 6Mo alloy. It will make it possible to choose the optimal rolling modes in the future. According to the data obtained, favorable temperature-speed conditions for hot deformation are shifted as deformation accumulates to the region of high temperatures and low strain rates. At the same time, the extremely unfavorable zone with negative values of the ξ-criterion, which appears at e = 0.3 – 0.4, continues to grow quite significantly with an increase in the deformation effect. Hot deformation of the Co – 28Cr – 6Mo alloy at low compression ratios (e < 0.2) is more expedient to perform at temperatures above 1150 °C and strain rates of at least 20 s–1. With an increase in deformation degree, it is necessary to choose lower strain rates (1 – 5 s–1) and higher deformation temperature.

Optimization of roll forming process for different standard sections of steel structures

Optimization of roll forming process for different standard sections of steel structures

Integrated energy scheduling using wind-storage cascade control strategy in dynamic peak regulation right

• Wind-storage integration and electric-heat units participate in dynamic peak regulation right transaction. • Establish wind-storage cascade control strategy for deeply peak regulation. • Based on whole process peak regulation model of electric-heat units to establish scheduling model. • Quantitatively analyzed the benefits of the participants in different scenarios. • Verified the new scheduling strategy can reduce wind curtailment In view of the serious problem of wind curtailment in heating season of the "Three North Regions" in China, an intraday rolling optimization scheduling method is carried out according to wind-storage cascade control strategy combined with the dynamic peak regulation rights transaction which is based on the wind curtailment situation. Determine the operation state of the energy storage by judging when the wind curtailment is generated, and dynamically adjust the capacity of peak regulation rights transaction in-day according to the amount of wind curtailment. Considering the whole process peak regulation cost model of electric-heat units and wind-storage integration cost, and the goal is to maximize the overall benefit in the scheduling cycle, optimize the operation. Finally modeling by YALMIP and solved by CPLEX. The simulation results shown that the benefit improvement verifies the market feasibility, and the reduction of wind curtailment verifies the technical feasibility.

QPSO-MPC based tracking algorithm for cable-driven continuum robots.

Cable-driven continuum robots (CDCRs) can flexibly travel through narrow space for complex workspace tasks. However, it is challenging to design the trajectory tracking algorithm for CDCRs due to their nonlinear dynamic behaviors and cable hysteresis characteristics. In this contribution, a model predictive control (MPC) tracking algorithm based on quantum particle swarm optimization (QPSO) is designed for CDCRs to realize effective trajectory tracking under constraints. In order to make kinematic analysis of a CDCR, the forward and inverse mapping among actuation space, joint space and work space is analyzed by using the piecewise constant curvature method and the homogeneous coordinate transformation. To improve the performance of conventional MPC for complex tracking tasks, QPSO is adopted in the rolling optimization of MPC for its global optimization performance, robustness and fast convergence. Both simulation and operational experiment results demonstrate that the designed QPSO-MPC presents high control stability and trajectory tracking precision. Compared with MPC and particle swarm optimization (PSO) based MPC, the tracking error of QPSO-MPC is reduced by at least 43 and 24%, respectively.

Wind/Storage Power Scheduling Based on Time–Sequence Rolling Optimization

Wind/Storage Power Scheduling Based on Time–Sequence Rolling Optimization

Cooperative Optimal Control of the Following Operation of High-Speed Trains

The following operation of multiple high-speed trains (HSTs) with high speed and great density based on the moving block and virtual coupling control system can significantly improve the performance of railway systems concerning operation safety and line capacity. However, it is difficult for HST’s conventional control approach to satisfy the service demand, including safety, energy-saving, and ride comfort, due to the significantly increasing longitudinal interaction between adjacent trains in the following operating conditions. In this paper, the characteristic model of multiple HSTs’ following operation was formulated to effectively and accurately describe train control constraints derived from the coupling relationship among adjacent trains’ operation states. Then, the cooperative model predictive control (CMPC) strategy with a multiobjective rolling optimization scheme is developed. The overall line capacity, energy consumption, and ride comfort of train platoons in the railway section are defined as optimization indexes. Furthermore, some sufficient conditions for the consensus stability of the CMPC of HST are given, based on the proposed multiple HSTs following characteristic model and cooperative optimal control strategy. Simulation experiments are carried out by using HST’s operation data collected from real-world runs. Results demonstrate the effectiveness and feasibility of the proposed modeling and control method.

Study on the multitime scale rolling optimization operation of a near-zero energy building energy supply system

The combined cooling, heating and power system of near-zero energy buildings can provide cold energy, heat energy and electricity to users at the same time, realizing combined cold, heat and power generation. However, the randomness of renewable energy output and the volatility of user loads lead to an imbalance between energy supply and demand. Therefore, a three-stage multitime scale optimization operation method is proposed. This method establishes a three-stage optimization model of ‘day-ahead, day-in rolling, real-time feedback’ to optimize the output of the unit step by step. In the day-ahead phase, the optimization goal is to take the lowest daily operating cost while considering the environmental cost. In the day-in rolling stage, the optimization goal is to control the minimum cost of energy purchase in the time domain and the minimum penalty cost of unit output change. The real-time feedback phase optimizes with the minimum total device power adjustment. The results show that the cost saving rates of multitime scale optimization strategies on typical winter days, typical transition season days and typical summer days are 0.90%, 2.32% and 2.12%, respectively. Compared with the ideal cost of different typical days, the cost deviation rates are 0.21%, 0.51% and 1.97%, respectively. Compared with the optimization of the previous day, the total power of the grid connection line on different typical days is −8.0%, −10.65% and −6.23%, respectively. Therefore, this novel optimization method has good economy, stability and accuracy and has a guiding effect on the formulation of each equipment operation plan.

Shifting strategy optimization of the thermal work roll in hot strip rolling based on three particle swarms and differential evolution algorithm

ABSTRACT During fast-paced rolling of the same type strip of hot tandem rolling, the thermal expansion of the work rolls has a significant influence on the shape of the on-load roll gap, which needs to be considered in the free shifting of the work rolls. In this paper, the thermal crown evaluation index and the thermal expansion simulation model of the work roll are established, and the influence of different roll shifting parameters on the thermal crown of the roll in the service cycle of the work roll is analyzed. A special roll shifting strategy of the downstream stand is designed, and intelligent optimization is carried out with the goal of thermal roll shape and uniform wear of work rolls. The optimized roll shifting strategy can significantly improve the strip shape quality.

A two-stage rolling optimization strategy for park-level integrated energy system considering multi-energy flexibility

Due to the advantages, such as being environment-friendly, the penetration of renewable energy has increased rapidly. However, the uncertainty and randomness of renewable energy source output severely impact economic dispatch, especially for those park-level integrated energy systems. To improve the economy of system operation and the accuracy of scheduling results, this paper presents a two-stage rolling dispatch strategy, in which a refined model of power flow, natural gas pipelines, and heat flow are established. The multi-energy flow model is linearized by the piecewise linearization method and second-order cone relaxation method. Moreover, a virtual energy storage model considering the thermal inertia in district heat systems and building enclosures is constructed. Further, to quantify the capacity of adjustable resources in park-level integrated energy systems, a multi-energy flexible source model is set up and integrated. Ultimately, to even out the prediction errors, a two-stage rolling dispatch is carried out, which corrects the results of day-ahead schedule plans. Based on the aforementioned works, case results demonstrate the effectiveness of the presented approach, both in renewable energy accommodation rate (with a promotion of 13.3%) and system operation economy (with a reduction of 14.55% in operation costs). It is recognized that the constructed models play important roles in improving the system flexibility and the presented operation strategy can effectively enhance the accuracy of the dispatch plans in real-time operation.

Adaptive model predictive scheduling of flexible interconnected low-voltage distribution networks considering charging preferences of electric vehicles

DC interconnection at the second side of distribution transformers helps achieve power sharing among nearby low-voltage distribution networks (LVDNs) and promote integration of intermittent inverter-based distributed generators (DGs). This paper proposes an adaptive model predictive scheduling method for flexible interconnected LVDNs considering charging preferences of electric vehicles (EVs). Firstly, the steady-state models of flexible resources including voltage source converters, energy storage systems along with AC and DC power flow models are established. Then, a model predictive control (MPC)-based rolling optimization model is formulated aiming to minimize the daily energy loss considering uncertainties of DGs, load and each charging station as a whole. To further explore the flexibility and dispatchability of each charging station, an adaptive MPC-based rolling optimization model is built considering three types of EVs with different charging preferences, i.e., uncontrollable EVs, charging-only EVs and vehicle-to-grid EVs. The scheduling window of the adaptive MPC-based scheduling is dynamically updated according to the maximum departure time of currently charging EVs to fulfill expected energy requirements of all EVs. Simulation results on a typical flexible LVDN show that the daily energy loss and total load fluctuation can be further reduced through real-time scheduling of controllable EVs in addition to existing flexible resources.

Multilayer Game Collaborative Optimization Based on Elman Neural Network System Diagnosis in Shared Manufacturing Mode.

With the dynamic sharing of global resources shifted from the independent production of a single enterprise to and the development of collaborative manufacturing, today's manufacturing industry has collaborative production among multiple enterprises. This paper extends the optimization method of the multienterprise dynamic equipment collaborative scheduling system with the dynamic manufacturing network theory approach, combining the neural network system and model predictive control theory to continuously and dynamically diagnose the changes of demand and capacity in the actual production process and design the corresponding multilayer game collaborative optimization robust model according to the uncertainty relationship. The study considers the production characteristics and management needs of multiple enterprises within the manufacturing industry, as well as the optimization of the dynamic equipment collaborative scheduling system, uncertain demand, dynamic equipment matching effectiveness, and equipment failure under the shared manufacturing model, deduces the market capacity demand for manufacturing enterprises by inverse prediction of dynamic market demand through the simple recurrent neural network (Elman network), and then maximizes the idle equipment utilization and load capacity under the shared manufacturing model in model rolling optimization. This paper gives model solutions based on big data analysis and combines improved population intelligence algorithms to determine the dynamic matching of manufacturing resources, product supply and demand paths, and transportation flows. The simulation results show that the application of model predictive control theory to the model of Elman neural network system diagnosis can effectively reduce the adverse effects of the two-way uncertainty environment on the profitability of enterprises. Meanwhile, considering the maximum performance matching of dynamic equipment scheduling and combining with robust optimization methods will reduce the cost loss of manufacturing enterprises in the worst case, and the cost loss reduction can reach 24.73%. The analysis results verify that the method is of great practical significance in the direction of intelligent production collaboration and industrial digital transformation.

UAV Formation Control under Communication Constraints Based on Distributed Model Predictive Control

A distributed model predictive control method is used to transform the formation and maintenance problem of fixed-wing UAV formation during flight into an online rolling optimization problem to solve in this paper. Firstly, the state estimation model of the neighborhood UAV is established according to the relative information of the UAV. Secondly, the error state model in the three-dimensional inertial coordinate frame of the UAV is established without considering the time delay, sensor error, and external interference. Thirdly, a cost function is designed by introducing the error state of the UAV in the neighborhood. Finally, four UAVs are used to verify that under the action of the controller, the UAVs can quickly form and maintain the desired formation while tracking the reference line.

Optimization of Rolling Schedule for Single-Stand Reversible Cold Rolling Mill Based on Multiobjective Artificial Fish Swarm Algorithm

The single-stand reversible cold rolling mill is important equipment in the production of steel strips. The rolling schedule is the core technological content in the strip production of the single-stand reversible cold rolling mill. The scientific rolling schedule is the fundamental guarantee for the production capacity of the rolling mill, product quality, accuracy, shape quality, energy saving, and consumption reduction. This paper takes the dynamic rolling process of single-stand cold rolling as the research object, the purposes of increasing production capacity, saving energy, and reducing consumption are achieved by optimizing the rolling schedule. Based on the study of the mechanism model and the analysis of a large number of field measured data, a slice of mathematical models of the rolling process suitable for engineering calculation are proposed, and a few objective functions suitable for the single-stand reversible cold rolling process are designed. On this basis, the artificial fish swarm algorithm is improved into a multiobjective optimization algorithm for the optimization of rolling schedule, and the optimal rolling load distribution scheme is obtained. Finally, the optimization method of rolling schedule proposed in this paper is applied to the actual rolling production. The results show that the proposed method can improve productivity and save energy compared with the empirical rolling schedule, and the feasibility and validity of the proposed algorithm are verified.