Design Mode for Component-based Virtual Plant Software

According to the morphological structure characteristics of plants, the de- velopment mode for component-based virtual plants software was put forward, and the internal structure of plant organs component under this mode were analyzed, thereby, the overall design mode for virtual plants software was given out, and its characteristics were estimated. Compared with traditional development modes of virtual plants software, component-based virtual plants software had significant advantages in code reusing, development efficiency and expansion of software functions.

Virtual Plant Modeling Based on Mutual Feedback of Function-structure

[Objective] To study virtual plant modeling based on mutual feedback of function-structure.[Method] With the analysis of the shortcomings of current virtual plant modeling method,the modeling with the idea of function-structure mutual feedback was put forward,and the steps of this modeling were elaborated,including the determination of morphological structure model,biomass production model,biomass allocation model,organ reconstruction model,and the integration method of function model and morphological structure model.[Results] The breakthrough of function-structure mutual feedback based mechanism from the boundaries of physiological ecology model and morphological structure model can solve the difficulty of data transmission between the two models and build an integrated model from the two,which can effectively reflect the incidence relation between plant morphology and function,and more suitable for the growth mechanisms of plants.This modeling approach has significant advantages in the dynamic simulation of plant growth.[Conclusion] The virtual plant modeling based on function-structure mutual feedback provides basis for the simulation of plant growth status in the next stage,and has important significance for the accurate simulation of the dynamic growth process of plant.

Collision detection of virtual plant based on bounding volume hierarchy: A case study on virtual wheat

Visualization of simulated crop growth and development is of significant interest to crop research and production. This study aims to address the phenomenon of organs cross-drawing by developing a method of collision detection for improving vivid 3D visualizations of virtual wheat crops. First, the triangular data of leaves are generated with the tessellation of non-uniform rational B-splines surfaces. Second, the bounding volumes（BVs） and bounding volume hierarchies（BVHs） of leaves are constructed based on the leaf morphological characteristics and the collision detection of two leaves are performed using the Separating Axis Theorem. Third, the detecting effect of the above method is compared with the methods of traditional BVHs, Axis-Aligned Bounding Box（AABB） tree, and Oriented Bounding Box（OBB） tree. Finally, the BVs of other organs（ear, stem, and leaf sheath） in virtual wheat plant are constructed based on their geometric morphology, and the collision detections are conducted at the organ, individual and population scales. The results indicate that the collision detection method developed in this study can accurately detect collisions between organs, especially at the plant canopy level with high collision frequency. This collision detection-based virtual crop visualization method could reduce the phenomenon of organs cross-drawing effectively and enhance the reality of visualizations.

Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

基于Plant Simulation的化纤自动落丝系统仿真实验分析

基于Plant Simulation软件,构建一种化纤自动落丝系统的3D数字化模型,对系统效率关键输入因子进行参数化设置,将系统各工位三维数字模型导入软件中建立层次化运动机构图形结构,并运用Simltalk语言实现三维动作仿真,通过基础物理参数设置,保证了仿真数字化模型与现实系统更具一致性。通过多级实验设计分析了系统影响因子对于系统效率的影响特性曲线,并进一步通过动态参数化实验方法,计算出双输入因子对系统效能影响的敏感度。该实验结果可为化纤自动落丝系统的建设成本控制与可行性分析提供指导,具有较好的工程应用价值。

Peer-to-Peer Energy Trading Method of Multi-Virtual Power Plants Based on Non-Cooperative Game

The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants.Besides,the game relationship between transaction subjects needs to be further explored.This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game.Firstly,a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant,considering the energy consumption characteristics of users.Secondly,the utility functions of multiple virtual power plants are analyzed,and a non-cooperative game model is established to explore the game relationship between electricity sellers in the Peer-to-Peer transaction process.Finally,the influence of user energy consumption characteristics on the virtual power plant operation and the Peer-to-Peer transaction process is analyzed by case studies.Furthermore,the effect of different parameters on the Nash equilibrium point is explored,and the influence factors of Peer-to-Peer transactions between virtual power plants are summarized.According to the obtained results,compared with the central air conditioning set as constant temperature control strategy,the flexible control strategy proposed in this paper improves the market power of each VPP and the overall revenue of the VPPs.In addition,the upper limit of the service quotation of the market operator have a great impact on the transaction mode of VPPs.When the service quotation decreases gradually,the P2P transaction between VPPs is more likely to occur.

Electricity-Carbon Interactive Optimal Dispatch of Multi-Virtual Power Plant Considering Integrated Demand Response

As new power systems and dual carbon policies develop,virtual power plant cluster(VPPC)provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems.To solve the coordinated optimal operation and low-carbon economic operation problem in multi-virtual power plant,a multi-virtual power plant(VPP)electricity-carbon interaction optimal scheduling model considering integrated demand response(IDR)is proposed.Firstly,a multi-VPP electricity-carbon interaction framework is established.The interaction of electric energy and carbon quotas can realize energy complementarity,reduce energy waste and promote low-carbon operation.Secondly,in order to coordinate the multiple types of energy and load in VPPC to further achieve low-carbon operation,the IDR mechanism based on the user comprehensive satisfaction(UCS)of electricity,heat as well as hydrogen is designed,which can effectively maintain the UCS in the cluster within a relatively high range.Finally,the unit output scheme is formulated to minimize the total cost of VPPC and the model is solved using theCPLEX solver.The simulation results showthat the proposed method effectively promotes the coordinated operation among multi-VPP,increases the consumption rate of renewable energy sources and the economics of VPPC and reduces carbon emissions.

Approach to Modeling and Virtual-reality-based Simulation for Plant Canopy Lighting

Over the past 20 years, significant progress has been made in virtual plant modeling corresponding to the rapid advances in information technology. Virtual plant research has broad applications in agronomy, forestry, ecol- ogy and remote sensing. As many biological processes are driven by light, it is the key for virtual plant to estimate the light absorbed by each organ. This paper presents the radiance equation suitable for calculating sun and sky light intercepted by plant organs based on the principles of the interaction between light and plant canopy firstly; analyzes the process principles of plant canopy primary lighting based on ray casting and projection secondly; describes the multiple scattering of plant lighting based on Monte Carlo ray tracing method and on the radiosity method thirdly; and confirms the research with 3D visualization based on Virtual Reality Modeling Language (VRML) finally. The research is the primary work of digital agriculture, and important for monitoring and estimating corn growth in Northeast China.

A physics-based approach to motion capture data processing for virtual plant modeling and simulation

Dynamic virtual plant simulation is an attractive research issue in both botany and computer graphics.Data-driven method is an efficient way for motion analysis and animation synthesis.As a widely used tool,motion capture has been used in plant motion data acquisition and analysis.The most prominent and important problem in motion capture for plants is primary data processing such as missing markers reconstruction.This paper presents a novel physics-based approach to motion capture data processing of plants.Firstly,a physics-based mechanics model is found by Lagrangian mechanics for a motion captured plant organ such as a leaf,and then its dynamic mechanical properties are analyzed and relevant model parameters are evaluated.Further,by using the physical model with evaluated parameters,we can calculate the next positions of a maker to reconstruct the missing makers in motion capture sequence.We take an example of a maize leaf and pachira leaf to examine the proposed approach,and the results show that the physics-based method is feasible and effective for plant motion data processing.

New advances in virtual plant research

Virtual plants are computer simulations of the growth, development and deployment of plants in a three-dimensional space. Over the past 20 years, significant progress has been made in virtual plant modeling corresponding to the rapid advances in information technology. Virtual plant research has broad applications in agronomy, forestry, ecology, and remote sensing areas. In this review, we attempt to introduce the significance, modeling methodology, and main advances in virtual plant research and applications. The challenges associated with virtual plant modeling in agronomy applications, including the interaction mechanism between plant and environment and root system modeling, are also discussed. Insights into applications of virtual plants in agronomy are given in the areas of performing virtual experiments to accurately quantify the utilization of soil water and nutrients, to design crop ideotype on computers, and to improve crop planting.

Impact of industrial virtual power plant on renewable energy integration

An industrial park is one of the typical en ergy con sumption schemes in power systems owing to the heavy in dustrial loads and their abilities to resp ond to electricity price cha nges.Therefore,en ergy in tegrati on in the industrial sector is significant.Accordingly,the concept of industrial virtual power plant(IVPP)has been proposed to deal with such problems.This study demonstrates an IVPP model to man age resources in an eco-i ndustrial park,including en ergy storage systems,dema nd resp onse(DR)resources,and distributed energies.In addition,fuzzy theory is used to cha nge the deterministic system constraints to fuzzy parameters,considering the uncertainty of renewable energy,and fuzzy chance constraints are then set based on the credibility theory.By maximizi ng the daily ben efits of the IVPP owners in day-ahead markets,DR and energy storage systems can be scheduled economically.Therefore,the energy between the grid and IVPP can flow in both directions:the surplus renewable electricity of IVPP can be sold in the market;when the electricity gen erated in side IVPP is not enough for its use,IVPP can also purchase power through the market.Case studies based on three win d-level scenarios dem on strate the efficie nt syn ergies betwee n IVPP resources.The validatio n results indicate that IVPP can optimize the supply and demand resources in in dustrial parks,thereby decarbonizing the power systems.

Distributionally Robust Optimal Dispatch of Virtual Power Plant Based on Moment of Renewable Energy Resource

Virtual power plants can effectively integrate different types of distributed energy resources,which have become a new operation mode with substantial advantages such as high flexibility,adaptability,and economy.This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal day-ahead dispatch under uncertainties of renewable energy sources.The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments.In this regard,the faults of stochastic optimization and traditional robust optimization can be overcome.Firstly,a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed,and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in day-ahead electricity markets.Then,an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem,which improves the computational efficiency of the model.Finally,the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy.They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output.

Optimal Energy Management for Virtual Power Plant with Renewable Generation

The nature of variable and uncertainty from renewable energy sources (RESs) makes them challenging to be integrated into the main grid separately. A Virtual Power Plant (VPP) is proposed to aggregate the capacities of RESs and facilitate the integration and management in a decentralized manner. In this paper, a novel framework for optimal energy management of VPP considering key features such as handling uncertainties with RESs, reducing operating costs and regulating system voltage levels is proposed, and a two-stage stochastic simulation is formulated to address the uncertainties of RESs generation and electricity prices. Simulation result show that the framework can benefit from ensuring the energy balance and system security, as well as reducing the operation costs.

Optimal Control and Bidding Strategy of Virtual Power Plant with Renewable Generation

A Virtual Power Plant (VPP), aggregating the capacities of distributed energy resources (DER) as a single profile, provides presence of DERs in the electricity market. In this paper, a stochastic bidding model is proposed for the VPP to optimise the bids in the day-ahead and balancing market, with the objective to maximise its expected economic profit. The performance of proposed strategy has been assessed in a modified commercial VPP (CVPP) system with wind generation installed, and also the results are compared with the ones achieved from other commonly-used strategies to verify its feasibility.

Design and Realization of 3D Management Information System for Ancient and Famous trees Based on Virtual Plant

The modernization and informatization management of ancient and famous trees’ management is an important standard with which the municipal public resource management level of a region or city can be measured.Design and development of ancient and famous trees’three-dimensional management information system was realized based on virtual plant by using integrated techniques of virtual plants and Geographic Information System.The system architecture design was developed on the basis of functional requirements,and the practical system was achieved in Visual Studio 2008 development tools and OpenGL graphics standards.The system has function of ancient trees archives management,three-dimensional reconstruction of the trees surrounding environment,individual tree information inquiry in three-dimensional scene,etc.Application and dissemination of this system will greatly promote the management and protection of ancient and famous trees standardization,informalization and

Accelerated Particle Swarm Optimization for Controlling Virtual Power Plant Consisting of Renewable Energy Sources

RES （renewable energy sources）, such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of VPP （virtual power plant） has been developed. The VPP is composed of several RES, from which at least one of them is fully controllable. Because the production of noncontrollable RES can not be forecasted perfectly, therefore an optimal dispatch schedule within VPP is needed. To address this problem, an APSO （accelerated particle swarm optimization） is used to solve the constrained optimal dispatch problem within VPP. The experimental results show that the proposed optimization method provides high quality solutions while meeting constraints.

Battery Energy Storage System and Demand Response Based Optimal Virtual Power Plant Operation

With certain controllability of various distribution energy resources (DERs) such as battery energy storage system (BESS), demand response (DR) and distributed generations (DGs), virtual power plant (VPP) can suitably regulate the powers access to the distribution network. In this paper, an optimal VPP operating problem is used to optimize the charging/discharging schedule of each BESS and the DR scheme with the objective to maximize the benefit by regulating the supplied powers over daily 24 hours. The proposed solution method is composed of an iterative dynamic programming optimal BESS schedule approach and a particle swarm optimization based (PSO-based) DR scheme approach. The two approaches are executed alternatively until the minimum elec-tricity cost of the whole day is obtained. The validity of the proposed method was confirmed with the obviously decreased supplied powers in the peak-load hours and the largely reduced electricity cost.

基于改进深度Q网络的虚拟电厂实时优化调度

深度强化学习算法以数据为驱动,且不依赖具体模型,能有效应对虚拟电厂运营中的复杂性问题。然而,现有算法难以严格执行操作约束,在实际系统中的应用受到限制。为了克服这一问题,提出了一种基于深度强化学习的改进深度Q网络(improved deep Q-network,MDQN)算法。该算法将深度神经网络表达为混合整数规划公式,以确保在动作空间内严格执行所有操作约束,从而保证了所制定的调度在实际运行中的可行性。此外,还进行了敏感性分析,以灵活地调整超参数,为算法的优化提供了更大的灵活性。最后,通过对比实验验证了MDQN算法的优越性能。该算法为应对虚拟电厂运营中的复杂性问题提供了一种有效的解决方案。

面向综合能源协调的虚拟电厂调控平台设计与规划优化

为解决综合能源协调调度难题,文章提出了基于虚拟电厂调控平台设计和综合能源协调调度模型。通过分析虚拟电厂的基本网架结构,设计了虚拟电厂总体架构,分为资源层、平台层和应用层,并设计了网络架构和数据流架构。基于该架构建立了面向综合能源协调的虚拟电厂调度应用模型。针对模型内光伏、风力发电、电解器、燃料电池、氢气储能、电池储能,以可靠性指标作为切入点,提出了全生命周期内成本最小的目标函数。通过算例分析,验证了文章所提出的模型在可靠性和成本方面的优势。