Dynamic grouping control of electric vehicles based on improved k-means algorithm for wind power fluctuations suppression

To address the significant lifecycle degradation and inadequate state of charge(SOC)balance of electric vehicles(EVs)when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm.First,a swing door trending(SDT)algorithm based on compression result feedback was designed to extract the feature data points of wind power.The gating coefficient of the SDT was adjusted based on the compression ratio and deviation,enabling the acquisition of grid-connected wind power signals through linear interpolation.Second,a novel algorithm called IDOA-KM is proposed,which utilizes the Improved Dingo Optimization Algorithm(IDOA)to optimize the clustering centers of the k-means algorithm,aiming to address its dependence and sensitivity on the initial centers.The EVs were categorized into priority charging,standby,and priority discharging groups using the IDOA-KM.Finally,an two-layer power distribution scheme for EVs was devised.The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals.The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles.The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals,smoothing wind power fluctuations,mitigating EV degradation,and enhancing the SOC balance.

Optimal dispatching method for integrated energy system based on robust economic model predictive control considering source-load power interval prediction

Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.

Research on Link Mechanism of China's Regional Carbon Emission Trading System

The construction of China's carbon emission trading market has gone through a process from one-way participation in the international carbon market to voluntary emission reduction trading at home, and then to the total carbon emission right trading as the main and voluntary emission reduction trading as the auxiliary. The link of regional carbon emission trading system is conducive to reducing the cost of carbon emission reduction, promoting market liquidity, reducing operational risks of carbon market and reducing carbon leakage. The link of inter-provincial carbon market needs to break down administrative barriers and form a consensus on the link elements of carbon emission trading under the unified legal framework. The link of regional carbon emission trading system should be carried out from the aspects of legal mechanism construction, link mode selection and mutual recognition and assimilation of carbon emission trading system.

Data-driven source-load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling

A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems(IESs)in the operation scheduling problem of integrated energy production units(IEPUs).First,to solve the problem of inaccurate prediction of renewable energy output,an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction.Subsequently,to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs,a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established.The system considers the further utilization of energy using hydrogen energy coupling equipment(such as hydrogen storage devices and fuel cells)and the comprehensive demand response of load-side schedulable resources.The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions,improve the source-load interaction of the IES,realize the efficient use of hydrogen energy,and improve system robustness.

Bi-Level Energy Management Model of Grid-Connected Microgrid Community

As the proportion of renewable energy power generation continues to increase,the number of grid-connected microgrids is gradually increasing,and geographically adjacent microgrids can be interconnected to form a Micro-Grid Community(MGC).In order to reduce the operation and maintenance costs of a single micro grid and reduce the adverse effects caused by unnecessary energy interaction between the micro grid and the main grid while improving the overall economic benefits of the micro grid community,this paper proposes a bi-level energy management model with the optimization goal of maximizing the social welfare of the micro grid community and minimizing the total electricity cost of a single micro grid.The lower-level model optimizes the output of each equipment unit in the system and the exchange power between the system and the external grid with the goal of minimizing the operating cost of each microgrid.The upper-level model optimizes the goal ofmaximizing the socialwelfare of themicrogrid.Taking amicrogrid community with four microgrids as an example,the simulation analysis shows that the proposed optimization model is beneficial to reduce the operating cost of a single microgrid,improve the overall revenue of the microgrid community,and reduce the power interaction pressure on the main grid.

Research on Energy Decision System based on Network Technology

This paper analyzes the C/S structure and B/S structure of their own characteristics, building energy decision support system based on network technology, elaborated Web GIS and database connections and other technical principles and applications. Mixed B/S structure mode development to achieve information management, energy consumption is forecast to show the query using the amomat of energy, energy supply and demand dynamic equilibrimn analysis and other functions, with simple, efficient, easy to operate and so on, and has good scalability and maintainability.

A Three-Dimensional Case System Model for Programming Course

This research aims to construct a case resource library for programming course,which can be used for either teachers’teaching or students’learning.The cases cannot be simply piled up but rather require a systematic planning.The solution to this is to design a case system model.The outcome-based education(OBE)concept is adopted to guide the research,and a three-dimensional case system model matching the course objectives is designed.Under the guidance of the model,the case resource library construction is more planned.Cases based on the model can provide all-round support for the cultivation of students’ability by gradually promoting knowledge and technology,frequently exercising one’s abilities,as well as expanding diverse and innovative problems.

Revisiting BCC-SESM parameters sensitivity with BCC-CSM1.1 co_(2)-concentration-driven simulations

Based on the results of the complex climate model BCC-CSM,the Beijing Climate Center Simple Earth System Model(BCC-SESM)was developed for climate system simulations in Integrated Assessment Models(IAMs).The first version of the BCC-SESM model was based on a high-emissions scenario(ESMRCP8.5)and tends to overestimate the temperatures in low and medium emissions scenarios.To address this problem,this study uses three CO_(2)-concentration-driven simulations under different RCP scenarios of complex climate models to evaluate parameters sensitivity and their impacts on projection efficacy.The results show that the new version of the BCC-SESM(denoted as BCC-SESM1.1)model based on a medium-emissions scenario experiment(RCP4.5)is more suitable for temperature projections for various climate scenarios.It can well reproduce the original value of complex climate model.At the same time,it also has high predictive efficacies for medium(RCP4.5)and low(RCP2.6)emissions scenarios,although it tends to underestimate for high emissions scenario(RCP8.5).The sensitivity tests for different RCP scenarios shows that the BCC-SESM1.1 has higher efficacy in projections of future climate change than those model versions based on the other scenarios.The projection deviations for the global average temperature by the BCC-SESM1.1(<2%)are better than the previous BCC-SESM(<5%).In light of recent progress in climate policy,the BCC-SESM1.1 is hence more suitable for coupling with IAMs for the purposes of assessing climate outcomes.