Source-Load Coordinated Optimal Scheduling Considering the High Energy Load of Electrofused Magnesium and Wind Power Uncertainty

In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional units obviously can not solve the new energy as the main body of the scheduling problem.To enhance the systemscheduling ability,based on the participation of thermal power units,incorporate the high energy-carrying load of electro-melting magnesiuminto the regulation object,and consider the effects on the wind unpredictability of the power.Firstly,the operating characteristics of high energy load and wind power are analyzed,and the principle of the participation of electrofusedmagnesiumhigh energy-carrying loads in the elimination of obstructedwind power is studied.Second,a two-layer optimization model is suggested,with the objective function being the largest amount of wind power consumed and the lowest possible cost of system operation.In the upper model,the high energy-carrying load regulates the blocked wind power,and in the lower model,the second-order cone approximation algorithm is used to solve the optimizationmodelwithwind power uncertainty,so that a two-layer optimizationmodel that takes into account the regulation of the high energy-carrying load of the electrofused magnesium and the uncertainty of the wind power is established.Finally,the model is solved using Gurobi,and the results of the simulation demonstrate that the suggested model may successfully lower wind abandonment,lower system operation costs,increase the accuracy of day-ahead scheduling,and lower the final product error of the thermal electricity unit.

End-to-end data-driven modeling framework for automated and trustworthy short-term building energy load forecasting

Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-term building energy loads.Moreover,their predictions are not transparent because of their black box nature.Hence,the building field currently lacks an AutoML framework capable of data quality enhancement,environment self-adaptation,and model interpretation.To address this research gap,an improved AutoML-based end-to-end data-driven modeling framework is proposed.Bayesian optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw data.It bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers.A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation,contributing to the accuracy enhancement of AutoML technologies.Moreover,a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved framework.It overcomes the poor interpretability of conventional AutoML technologies.The performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real building.It is discovered that the accuracy of the improved framework increases by 4.24%–8.79%compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational data.Furthermore,it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved framework.The improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.

Computation and field experiment validation of greenhouse energy load using building energy simulation model

Greenhouse Building Energy Simulation(BES)models were developed to estimate the energy load using TRNSYS(ver.16,University of Wisconsin,USA),a commercial BES program.Validation was conducted based on data recorded during field experiments.The BES greenhouse modeling is reliable,as validation showed 5.2%and 5.5%compared with two field experiments,respectively.As the next step,the heating characteristics of the greenhouses were analyzed to predict the maximum and annual total heating loads based on the greenhouse types and target locations in the Republic of Korea using the validated greenhouse model.The BES-computed results indicated that the annual heating load was greatly affected by the local climate conditions of the target region.The annual heating load of greenhouses located in Chuncheon,the northernmost region,was 44.6%higher than greenhouses in Jeju,the southernmost area among the studied regions.The regression models for prediction of maximum heating load of Venlo type greenhouse and widespan type greenhouse were developed based on the BES computed results to easily predict maximum heating load at field and they explained nearly 95%and 80%of the variance in the data set used,respectively,with the predictor variables.Then a BES model of geothermal energy system was additionally designed and incorporated into the BES greenhouse model.The feasibility of the geothermal energy system for greenhouse was estimated through economic analysis.

Energy-Efficient Operation of Water Systems through Optimization of Load Power Reduction in Electricity Markets

Demand response(DR) is gaining more and more importance in the architecture of power systems in a context of flexible loads and high share of intermittent generation. Changes in electricity markets regulation in several countries have recently enabled an effective integration of DR mechanisms in power systems. Through its flexible components(pumps, tanks), drinking water systems are suitable candidates for energy-efficient DR mechanisms. However, these systems are often managed independently of power system operation for both economic and operational reasons. Indeed, a sufficient level of economic viability and water demands risk management are necessary for water utilities to integrate their flexibilities to power system operation. In this paper,we proposed a mathematical model for optimizing pump schedules in water systems while trading DR blocs in a spot power market during peak times. Uncertainties about water demands were considered in the mathematical model allowing to propose power reductions covering the potential risk of real-time water demand forecasting inaccuracy.Numerical results were discussed on a real water system in France, demonstrating both economic and ecological benefits.

Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest energy.Therefore,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become paramount.In this vein,efforts have been made to predict the HL and CL using a univariate approach.However,this approach necessitates two models for learning HL and CL,requiring more computational time.Moreover,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware requirement.In this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D CNN.Considering the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and CL.As the 1D data are not affected by excessive parameters,the pooling layer is not applied in this implementation.Besides,the use of pooling has been questioned by recent studies.The performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

CALCULATION OF ELASTIC DAMPING CHARACTERISTICS OF ROTOR SUPPORT MADE OF METAL RUBBER MATERIAL UNDER VARIABLE LOADS

A metal rubber（MR） dry friction damper was designed based on the load supported by the rotor. An experimental apparatus for obtaining hysteresis loops of support under the precession load was designed. The elastic-damping characteristics of the ring-shaped MR damper used as a rotor support under variable loads were presented by studying the hysteresis loops of the damper. The vibration rigidity and the energy dissipation coefficient were calculated from the hysteresis loops, based on the description of the deformation process of the MR element with simple structure in a dimensionless coordinating system. The calculation results showed that the energy dissipation coefficient in the inner of MR element and on the boundary between the damper and the frame of the rotor support were approximately equal. The comparison of the hysteresis loops for a precession load and a one-axial load indicated a large difference when the coefficient of the energy dissipation and the stiffness of the MR damper were concerned.

Energy dissipation and contour integral characterizing fracture behavior of incremental plasticity

J ep -integral is derived for characterizing the frac- ture behavior of elastic-plastic materials. The J ep -integral differs from Rice’s J-integral in that the free energy density rather than the stress working density is employed to define energy-momentum tensor. The J ep -integral is proved to be path-dependent regardless of incremental plasticity and deformation plasticity. The J epintegral possesses clearly clear physical meaning： （1） the value J ep tip evaluated on the infinitely small contour surrounding the crack tip represents the crack tip energy dissipation; （2） when the global steadystate crack growth condition is approached, the value of J ep farss calculated along the boundary contour equals to the sum of crack tip dissipation and bulk dissipation of plastic zone. The theoretical results are verified by simulating mode I crack problems.

EFFECTS OF SPLITTER BLADES ON THE LAW OF INNER FLOW WITHIN CENTRIFUGAL PUMP IMPELLER

Analysis on the inner flow field of a centrifugal pump impeller with splitter blades is carfled out by numerical simulation. Based on this analysis, the principle of increasing pump head and efficiency are discussed. New results are obtained from the analysis of turbulence kinetic energy and relative velocity distribution： Firstly, unreasonable length or deviation design of the splitter blades may cause great turbulent fluctuation in impeller channel, which has a great effect on the stability of impeller outlet flow; Secondly, it is found that the occurrence of flow separation can be decreased or delayed with splitter blades from the analysis of blade loading; Thirdly, the effect of splitter blades on reforming the structure of ＂jet-wake＂ is explained from the relative velocity distribution at different flow cross-sections, which shows the flow process in the impeller. The inner flow analysis verifies the results of performance tests results and the PIV test.

Experimental Study on Load Characteristics in a Floating Type Pendulum Wave Energy Converter

A floating type pendulum wave energy converter(FPWEC) with a rotary vane pump as the power take-off system was proposed by Watabe et al.in 1998.They showed that this device had high energy conversion efficiency.In the previous research,the authors conducted 2D wave tank tests in regular waves to evaluate the generating efficiency of FPWEC with a power take-off system composed of pulleys,belts and a generator.As a result,the influence of the electrical load on the generating efficiency was shown.Continuously,the load characteristics of FPWEC are pursued experimentally by using the servo motors to change the damping coefficient in this paper.In a later part of this paper,the motions of the model with the servo motors are compared with that of the case with the same power take-off system as the previous research.From the above experiment,it may be concluded that the maximum primary conversion efficiency is achieved as high as 98%at the optimal load.

Describing failure in geomaterials using second-order work approach

Geomaterials are known to be non-associated materials. Granular soils therefore exhibit a variety of failure modes, with diffuse or localized kinematical patterns. In fact, the notion of failure itself can be confusing with regard to granular soils, because it is not associated with an obvious phenomenology. In this study, we built a proper framework, using the second-order work theory, to describe some failure modes in geomaterials based on energy conservation. The occurrence of failure is defined by an abrupt increase in kinetic energy. The increase in kinetic energy from an equilibrium state, under incremental loading, is shown to be equal to the difference between the external second-order work,involving the external loading parameters, and the internal second-order work, involving the constitutive properties of the material. When a stress limit state is reached, a certain stress component passes through a maximum value and then may decrease. Under such a condition, if a certain additional external loading is applied, the system fails, sharply increasing the strain rate. The internal stress is no longer able to balance the external stress, leading to a dynamic response of the specimen. As an illustration, the theoretical framework was applied to the well-known undrained triaxial test for loose soils. The influence of the loading control mode was clearly highlighted. It is shown that the plastic limit theory appears to be a particular case of this more general second-order work theory. When the plastic limit condition is met, the internal second-order work is nil. A class of incremental external loadings causes the kinetic energy to increase dramatically, leading to the sudden collapse of the specimen, as observed in laboratory.

Energy Management System Design and Testing for Smart Buildings Under Uncertain Generation (Wind/Photovoltaic) and Demand

This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.

Experimental investigations of a prototype reversible pump turbine in generating mode with water head variations

Influences of water head variations on the performances of a prototype reversible pump turbine are experimentally studied in generating mode within a wide range of load conditions(from 25% to 96% of the rated power). The pressure fluctuations of the reversible pump turbine at three different water heads(with non-dimensional values being 0.48, 0.71 and 0.90) are measured and compared based on the pressure data recorded in the whole flow passage of the turbine. Furthermore, effects of monitoring points and load variations on the impeller-induced unstable behavior(e.g. blade passing frequency and its harmonics) are quantitatively discussed. Our findings reveal that water head variations play a significant role on the pressure fluctuations and their propagation mechanisms inside the reversible pump turbine.