One Step on the Smart Grid Path： Load and Generation Coordination in a Virtual Power System

Worldwide the introduction of dispersed generators （DG） in the distribution network is assuming a significant importance. There is an increasing relevance of the energy process efficiency improvement; as for electric power systems, the most interesting perspective concerns the capability of the system to increase the exploitation of the renewable resources. The integration of DGs in the electric distribution network requires a revision of this infrastructure, so far designed and developed assuming that power flows in one direction： from the high voltage transmission network to the medium voltage distribution, to reach final customers on the low voltage network. The attention to an efficient operation of distribution networks is increasing all over the world; this interest is becoming higher and higher also in Italy, where the high energy prices push in the direction of fostering efficiency as much as possible. This work describes a study developed in the AlpEnergy project framework： an International Cooperation Program aimed at introducing an efficient operational model for the distributed production and consumption. In particular it is proposed a new model for the integration and the management of the DG in the distribution network. The new model （defined VPS： Virtual Power System） is based on a communication channel between the active users （generators）, the loads and, eventually, the Distribution System Operators （DSOs）.

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.

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.

Energy Management System with Power Offering Strategy for a Microgrid Integrated VPP

In the context of both the Virtual Power Plant (VPP) and microgrid(MG), the Energy Management System (EMS) is a key decision-maker forintegrating Distributed renewable Energy Resources (DERs) efficiently. TheEMS is regarded as a strong enabler of providing the optimized schedulingcontrol in operation and management of usage of disperse DERs and RenewableEnergy reSources (RES) such as a small-size wind-turbine (WT) andphotovoltaic (PV) energies. The main objective to be pursued by the EMSis the minimization of the overall operating cost of the MG integrated VPPnetwork. However, the minimization of the power peaks is a new objective andopen issue to a well-functional EMS, along with the maximization of profitin the energy market. Thus, both objectives have to be taken into accountat the same time. Thus, this paper proposes the EMS application incorporatingpower offering strategy applying a nature-inspired algorithm such asParticle Swarm Optimization (PSO) algorithm, in order to find the optimalsolution of the objective function in the context of the overall operating cost,the coordination of DERs, and the energy losses in a MG integrated VPPnetwork. For a fair DERs coordination with minimized power fluctuationsin the power flow, the power offering strategies with an active power controland re-distribution are proposed. Simulation results show that the proposedMG integrated VPP model with PSO-based EMS employing EgalitarianreDistribution (ED) power offering strategy is most feasible option for theoverall operating cost of VPP revenue. The total operating cost of the proposedEMS with ED strategy is 40.98$ compared to 432.8$ of MGs only withoutEMS. It is concluded that each MGs in the proposed VPP model intelligentlyparticipates in energy trading market compliant with the objective function,to minimize the overall cost and the power fluctuation.

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.

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.

Blockchain-assisted virtual power plant framework for providing operating reserve with various distributed energy resources

The paradigm shift from a coal-based power system to a renewable-energy-based power system brings more challenges to the supply-demand balance of the grid.Distributed energy resources(DERs),which can provide operating reserve to the grid,are regarded as a promising solution to compensate for the power fluctuation of the renewable energy resources.Small-scale DERs can be aggregated as a virtual power plant(VPP),which is eligible to bid in the operating reserve market.Since the DERs usually belong to different entities,it is important to investigate the VPP operation framework that coordinates the DERs in a trusted man-ner.In this paper,we propose a blockchain-assisted operating reserve framework for VPPs that aggregates various DERs.Considering the heterogeneity of various DERs,we propose a unified reserve capacity evaluation method to facilitate the aggregation of DERs.By considering the mismatch between actual available reserve capacity and the estimated value,the performance of VPP in the operating reserve market is improved.A hardware-based experimental system is developed,and numerical results are presented to demonstrate the effectiveness of the proposed framework.

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.

A Remote Real-Time Monitoring System for Power Quality

An introduction is made to the composition, design method and engineering application of a remote real time monitoring system of power quality in substations based on internet. With virtual instrument and network technique adopted, this system is characterized by good real time property, high reliability, plentiful functions, and so on. It also can be used to monitor the load of a substation, such as electric locomotives.

A hardware-based algorithm for virtual machine provisioning in a private cloud

Cloud computing is becoming a key factor in the market day by day. Therefore, many companies are investing or going to invest in this sector for development of large data centers. These data centers not only consume more energy but also produce greenhouse gases. Because of large amount of power consumption, data center providers go for different types of power generator to increase the profit margin which indirectly affects the environment. Several studies are carried out to reduce the power consumption of a data center. One of the techniques to reduce power consumption is virtualization. After several studies, it is stated that hardware plays a very important role. As the load increases, the power consumption of the CPU is also increased. Therefore, by extending the study of virtualization to reduce the power consumption, a hardware-based algorithm for virtual machine provisioning in a private cloud can significantly improve the performance by considering hardware as one of the important factors.

基于虚拟电力系统稳定器的低频振荡抑制策略

基于虚拟同步发电机(VSG)控制的新能源机组虽可增强电网惯量及频率稳定性,但可能继承与同步机类似的低频振荡问题。针对该问题,首先基于单台VSG并网的线性化模型,构建了适用于VSG的Phillips-Heffron模型,借助阻尼转矩法挖掘了VSG低频振荡的诱发机理。进一步提出一种附加于VSG无功环节的虚拟电力系统稳定器(VPSS),并给出了控制结构及基于相位补偿法的参数设计过程。研究结果表明:VSG发生低频振荡是由于系统受扰后,虚拟功角的振荡信号经过虚拟励磁调节器对VSG机电振荡回路产生了负阻尼转矩。将所提VPSS接入系统可在几乎不影响振荡频率的前提下,实现0.1~3 Hz下VSG低频振荡的有效抑制。

电厂锅炉实验虚拟仿真系统建设与应用

为开展能动专业核心课程“锅炉原理”的实验教学,校企合作开发了电厂锅炉实验虚拟仿真系统。基于两相流动与辐射对流传热原理构建的仿真系统,由风烟和汽水两大模型构成,严格遵守质量、能量、动量守恒方程。变负荷虚拟实验显示,随锅炉负荷增加,热效率降低、燃料耗量增加。根据辐射传热原理,结合火焰平均温度计算发现:只有炉膛出口烟温增加,才能保证炉膛传热负荷增加,继而抬升排烟温度,导致排烟热损失变大、锅炉热效率降低。锅炉原理实验虚拟仿真,可训练学生运用锅炉本体热力计算原理分析问题的思维,是落实创新能力培养的重要举措。

Virtual Machine软件在火力发电厂控制系统及课件培训中的应用

论文就Virtual Machine软件在火力发电厂Windows XP控制系统中应用遇到的问题进行了论述,并针对问题提出了相应的措施及方案,以期为Virtual Machine软件在更多领域中应用提供参考资料。

Low-frequency Oscillation Damping Control for Large-scale Power System with Simplified Virtual Synchronous Machine

This study focuses on a virtual synchronous machine(VSM) based on voltage source converters to mimic the behavior of synchronous machines(SMs) and improve the damping ratio of the power system. The VSM model is simplified according to some assumptions(neglecting the speed variation and the stator transients) to allow for the possibility of dealing with low-frequency oscillation in large-scale systems with many VSMs. Furthermore, a virtual power system stabilizer(VPSS) structure is proposed and tuned using a method based on a linearized power system dynamic model. The linear and nonlinear analyses examine the stability of two modified versions of a 16-machine power system in which, in the first case, partial classical machines are replaced by VSMs, and in the second case, all SMs are replaced by VSMs. The simulation results of the case studies validate the efficiency of the proposed control strategy.

电力系统状态感知虚拟仿真实验设计

针对传统电力系统状态感知实验中系统建设成本高、操作危险系数大、实验模式单一等问题,开发了电力系统状态感知虚拟仿真实验。具体地,设计了状态感知原理与实验准备模块、智能感知实验操作模块、实验考核和评价模块等实验内容,提出了以目标成果导向为原则的实验评价考核方法,构建了与之相适应的虚拟仿真实验教学方法。实践表明,该虚拟实验突破了传统实验中电气设备高压操作和高危实验环境等限制,延伸了实验教学的时间和空间,有助于培养学生深入开展电力系统状态感知与分析的综合实践能力,提升了电力系统实验教学效果。

面向系统响应能力提升的虚拟电厂聚合特性及交易机制综述

在新型电力系统频率动态品质下降、频率稳定问题突出的背景下,文中综述了面向系统频率响应能力提升的虚拟电厂聚合响应特性与市场交易机制,为当前由于市场机制缺乏导致的分布式资源频率支撑技术应用难题提供市场化解决思路。首先,总结了分布式资源频率动态响应基本原理以及虚拟电厂所需关键支撑技术,剖析了虚拟电厂在提供系统频率支撑方面存在的问题。其次,从时间、空间、成本3个维度提炼了虚拟电厂的频率聚合响应特性,并综述了虚拟电厂提升系统频率响应能力的市场交易机制,涵盖产品设计、出清定价、贡献量化、结算考核4个关键环节。然后,探讨了虚拟电厂聚合响应特性与市场交易机制间的交互作用关系。最后,展望了虚拟电厂提升系统频率响应能力的未来研究重点。

基于虚拟直流电机控制的船舶高频高峰值脉冲负载冲击平抑控制策略研究

高频高峰值脉冲负载对具有弱惯量特性的船舶电力系统呈现较大的功率冲击影响,严重时会造成船舶发电机组振荡停机。针对这一问题,该文首先建立典型脉冲负载的数学模型,研究脉冲负载功率特性及其对船舶电力系统冲击机理。根据研究结果,提出采用超级电容冲击平抑装置平抑脉冲功率。其次,通过分析虚拟直流电机(VDG)运行机理,确定通过VDG控制提升双向DC-DC变换器惯量的可行性。基于以上研究,推导双向DC-DC变换器的小信号模型及系统传递函数,分析虚拟转动惯量及虚拟阻尼对平抑装置稳定性的影响。然后,提出一种适用于脉冲负载稳态运行和暂态突变的平抑装置控制策略。最后,基于RT Box 3半实物仿真平台,搭建了具有平抑装置的船舶电力系统模型,验证了所提控制策略的有效性。