关于短期及超短期风电功率预测的评述

讨论风电功率预测及其误差对电力系统的影响,从信息流观点解读风电功率预测过程,归纳影响风电功率预测精度的因素,并对风电功率预测的研究现状加以归类与梳理。在此基础上,讨论对风电功率预测结果评价指标的要求,提出误差评估指标应该反映整个时间窗口内的预报质量,并展望风电功率预测可能的突破。

基于实验经济学仿真构建碳排放交易的多代理模型

碳排放交易的市场效率及减排效果受各类角色及大量参与者决策的影响,参与者特别是市场设计者及监管者必须掌握其行为及机理。实验经济学通过真实实验人参与的实验,反映参与者的有限理性。但其实验规模受限于合格实验者的数量,而多场景实验之间的可比性还受限于实验者的专注度。为此采用了一种混合交互仿真法,先通过实验提炼出真实实验人群在碳排放交易中的不确定行为的关键变量及规律,据此建立具有相同分布特性的多代理随机模型。此后就只需动用少量真实实验人来反映特殊的决策者,并与上述模型生成的大量多代理交互仿真。在保持实验经济学特点的同时,克服了其对实验规模的限制,并保证了重复实验的统计一致性。

Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water Systems in Smart Grids

With the growth of intermittent renewable energy generation in power grids,there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability.The flexibility of demand response(DR)resources has become a valuable solution to this problem.However,existing research indicates that problems on flexibility prediction of DR resources have not been investigated.This study applied the temporal convolution network(TCN)-combined transformer,a deep learning technique to predict the aggregated flexibility of two types of DR resources,that is,electric vehicles(EVs)and domestic hot water system(DHWS).The prediction uses historical power consumption data of these DR resources and DR signals(DSs)to facilitate prediction.The prediction can generate the size and maintenance time of the aggregated flexibility.The accuracy of the flexibility prediction results was verified through simulations of case studies.The simulation results show that under different maintenance times,the size of the flexibility changed.The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.

A Distributed Transactive Energy Mechanism for Integrating PV and Storage Prosumers in Market Operation

The decreasing cost of solar photovoltaics(PVs)and battery storage systems is driving their adoption in the residential distribution system,where more consumers are becoming prosumers.Accompanying this trend is the potential roll-out of home energy management systems(HEMSs),which provide a means for prosumers to respond to externalities such as energy price,weather,and energy demands.However,the economic operation of prosumers can affect grid security,especially when energy prices are extremely low or high.Therefore,it is paramount to design a framework that can accommodate the interests of the key stakeholders in distribution systems—namely,the network operator,prosumer,and aggregator.In this paper,a novel transactive energy(TE)-based operational framework is proposed.Under this frame-work,aggregators interact with the distribution grid operator through a negotiation process to ensure network security,while at the lower level,prosumers submit their schedule to the aggregator through the HEMS.If network security is at risk,aggregators will send an additional price component representing the cost of security(CoS)to the prosumer to stimulate further response.The simulation results show that the proposed framework can effectively ensure the economic operation of aggregators and prosumers in distribution systems while maintaining grid security.

Transient Stability Analysis of Grid-connected Converters in Wind Turbine Systems Based on Linear Lyapunov Function and Reverse-time Trajectory

As the proportion of converter-interfaced renewable energy resources in the power system is increasing,the strength of the power grid at the connection point of wind turbine generators(WTGs)is gradually weakening.Existing research has shown that when connected with the weak grid,the stability of the traditional grid-following controlled converters will deteriorate,and they are prone to unstable phenomena such as oscillation.Due to the limitations of linear analysis that cannot sufficiently capture the stability phenomena,transient stability must be investigated.So far,standalone time-domain simulations or analytical Lyapunov stability criteria have been used to investigate transient stability.However,the time-domain simulations have proven to be computationally too heavy,while analytical methods are difficult to formulate for larger systems,require many modelling assumptions,and are often conservative in estimating the stability boundary.This paper proposes and demonstrates an innovative approach to estimating the transient stability boundary via combining the linear Lyapunov function and the reverse-time trajectory technique.The proposed methodology eliminates the need of time-consuming simulations and the conservative nature of Lyapunov functions.This study brings out the clear distinction between the stability boundaries with different post-fault active current ramp rate controls.At the same time,it provides a new perspective on critical clearing time for wind turbine systems.The stability boundary is verified using time-domain simulation studies.

Robust N−k Security-constrained Optimal Power Flow Incorporating Preventive and Corrective Generation Dispatch to Improve Power System Reliability

As extreme weather events have become more frequent in recent years,improving the resilience and reliability of power systems has become an important area of concern.In this paper,a robust preventive-corrective security-constrained optimal power flow(RO-PCSCOPF)model is proposed to improve power system reliability under N−k outages.Both the short-term emergency limit(STL)and the long-term operating limit(LTL)of the post-contingency power flow on the branch are considered.Compared with the existing robust corrective SCOPF model that only considers STL or LTL,the proposed ROPCSCOPF model can achieve a more reliable generation dispatch solution.In addition,this paper also summarizes and compares the solution methods for solving the N−k SCOPF problem.The computational efficiency of the classical Benders decomposition(BD)method,robust optimization(RO)method,and line outage distribution factor(LODF)method are investigated on the IEEE 24-bus Reliability Test System and 118-bus system.Simulation results show that the BD method has the worst computation performance.The RO method and the LODF method have comparable performance.However,the LODF method can only be used for the preventive SCOPF and not for the corrective SCOPF.The RO method can be used for both.

A review of offshore wind farm layout optimization and electrical system design methods

There is more wind with less turbulence offshore compared with an onshore case,which drives the development of the offshore wind farm worldwide.Since a huge amount of money is required for constructing an offshore wind farm,many types of research have been done on the optimization of the offshore wind farm with the purpose of either minimizing the cost of energy or maximizing the total energy production.There are several factors that have an impact on the performance of the wind farm,mainly the energy production of wind farm which is highly decided bythe wind condition of construction area and micro-siting of wind turbines(WTs),as well as the initial investment which is influenced by both the placement of WTs and the electrical system design,especially the scheme of cable connection layout.In this paper,a review of the state-of-the-art researches related to the wind farm layout optimization as well as electrical system design including cable connection scheme optimization is presented.The most significant factors that should be considered in the optimization work of the offshore wind farm is highlighted after reviewing the latest works,and the future needs are specified.

Multi-agents modelling of EV purchase willingness based on questionnaires

Traditional experimental economics methods often consume enormous resources of qualified human participants,and the inconsistence of a participant’s decisions among repeated trials prevents investigation from sensitivity analyses.The problem can be solved if computer agents are capable of generating similar behaviors as the given participants in experiments.An experimental economics based analysis method is presented to extract deep information from questionnaire data and emulate any number of participants.Taking the customers’willingness to purchase electric vehicles(EVs)as an example,multi-layer correlation information is extracted from a limited number of questionnaires.Multiagents mimicking the inquired potential customers are modelled through matching the probabilistic distributions of their willingness embedded in the questionnaires.The authenticity of both the model and the algorithmis validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results.With the aid of agent models,the effects of minority agents with specific preferences on the results are also discussed.

Transactive control:a framework for operating power systems characterized by high penetration of distributed energy resources

The increasing number of distributed energy resources connected to power systems raises operational challenges for the network operator, such as introducing grid congestion and voltage deviations in the distribution network level, as well as increasing balancing needs at the whole system level. Control and coordination of a large number of distributed energy assets requires innovative approaches. Transactive control has received much attention due to its decentralized decision-making and transparent characteristics. This paper introduces the concept and main features of transactive control, followed by a literature review and demonstration projects that apply to transactive control. Cases are then presented to illustrate the transactive control framework. At the end, discussions and research directions are presented, for applying transactive control to operating power systems, characterized by a high penetration of distributed energy resources.

Multi-time Scale Energy Management Strategy of Aggregator Characterized by Photovoltaic Generation and Electric Vehicles

The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of PV generation and EVs.However,the daily management of the Agg is challenged by uncertain PV fluctuations.To address this problem,a robust multi-time scale energy management strategy for the Agg is proposed.In a day-ahead phase,robust optimization is developed to determine the power schedule.In a real-time phase,a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs.A case study indicates a good scheduling performance under an uncertain PV output.Through the convexification,the solving efficiency of the real-time operation model is improved,and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent.Moreover,the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient.The strategy can ensure the flexibility of the Agg for real-time operation.

Multi-agent modeling and analysis of EV users' travel willingness based on an integrated causal/statistical/behavioral model

An electric vehicle(EV) centred ecosystem has not yet been formed, the existing limited statistic data are far from enough for the analysis of EV users' travel and charge behaviors, which however tends to be affected by many certain and uncertain factors. An experimental economics(EE)-based simulation method can be used to analyze thebehaviors of key participants in a system. However, it is restricted by the system size, experimental site and the number of qualified human participants. Therefore, this method is hard to be adopted for the behavioral analysis of a large number of human participants. In this paper, a new method combining a questionnaire statistics and the EEbased simulation is proposed. The causal relationship is considered in the design of the questionnaires and data extraction, then a multi-agent modeling integration method is introduced in the EE-based simulation, which enables the integration of causal/statistical/behavioral models into the multi-agent framework to reflect the EV users' travel willingness statistically. The generated multi-agents are used to replace human participants in the EE-based simulation in order to evaluate EV users' travel demands in different scenarios, and compare the differences of simulated or measured travel behaviors between potential EV users and internal combustion engine(ICE) vehicle users.

LSTM-based Energy Management for Electric Vehicle Charging in Commercial-building Prosumers

As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution.To address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers.Under the proposed system control structure,the LSTM algorithm can be separated into offline and online stages.At the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network training.At the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network.The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.

Voltage regulation in LV grids by coordinated volt-var control strategies

The increasing penetration level of photovoltaic(PV)power generation in low voltage(LV)networks results in voltage rise issues,particularly at the end of the feeders.In order to mitigate this problem,several strategies,such as grid reinforcement,transformer tap change,demand-side management,active power curtailment,and reactive power optimization methods,show their contribution to voltage support,yet still limited.This paper proposes a coordinated volt-var control architecture between the LV distribution transformer and solar inverters to optimize the PV power penetration level in a representative LV network in Bornholm Island using a multi-objective genetic algorithm.The approach is to increase the reactive power contribution of the inverters closest to the transformer during overvoltage conditions.Two standard reactive power control concepts,cosu(P)and Q(U),are simulated and compared in terms of network power losses and voltage level along the feeder.As a practical implementation,a reconfigurable hardware is used for developing a testing platform based on real-time measurements to regulate the reactive power level.The proposed testing platform has been developed within PVNET.dk project,which targets to study the approaches for large PV power integration into the network,without the need of reinforcement.

Voltage rise mitigation for solar PV integration at LV grids Studies from PVNET.dk

Solar energy from photovoltaic(PV)is among the fastest developing renewable energy systems worldwide.Driven by governmental subsidies and technological development,Europe has seen a fast expansion of solar PV in the last few years.Among the installed PV plants,most of them are situated at the distribution systems and bring various operational challenges such as power quality and power flow management.The paper discusses the modelling requirements for PV system integration studies,as well as the possible techniques for voltage rise mitigation at low voltage(LV)grids for increasing PV penetration.Potential solutions are listed and preliminary results are presented.

Convex optimization of virtual storage system scheduling in market environment

Due to the popularization of distributed energy resources(DERs),the aggregated prosumer effect excels a general energy storage system characteristic.Virtual energy storage system(VESS)concept is proposed hereby that mimics an actual storage unit and incorporates the same charging(consumer)and discharging(producer)modes.It is possible to provide ancillary services via VESS by exploiting the flexibility and thus much research has been proposed on the optimization of the VESS scheduling.In general,the charging and discharging efficiencies of VESS are different and there can be only one status at a time slot.To achieve the optimal schedule while considering the constraints above,binary terms should be introduced into the optimization problem which end up with a nonconvex problem.In this paper,a complimentary mathematical proof is given for the convexification of this mixed-integer linear programming(MILP)problem so that the linear programming(LP)method can be applied instead if the objective function is linear.The proposed proof is validated through a case study and the simulation results show the effectiveness of the proposed method.

Allocation of synchronous condensers for restoration of system short-circuit power

Modern power systems,employing an increasing number of converter-based renewable energy sources(RES)and decreasing the usage of conventional power plants,are leading to lower levels of short-circuit power and rotational inertia.A solution to this is the employment of synchronous condensers in the grid,in order to provide sufficient short-circuit power.This results in the increase of the short-circuit ratio(SCR)at transmission system busbars serving as points of interconnection(POI)to renewable generation.Evaluation of the required capacity and grid-location of the synchronous condensers,is inherently a mixed integer nonlinear optimization problem,which could not be done on manual basis considering each type of machine and all bus-bars.This study therefore proposes a method of optimal allocation of synchronous condensers in a hypothetic future scenario of a transmission system fed by renewable generation.Total cost of synchronous condenser installations in the system is minimized and the SCRs at the POIs of central renewable power plants are strengthened.The method has potential for application on larger grids,aiding grid-integration of RES.

Adaptive Ultra-short-term Wind Power Prediction Based on Risk Assessment

A risk assessment based adaptive ultra-short-term wind power prediction(USTWPP)method is proposed in this paper.In this method,features are first extracted from the historical data,and then each wind power time series(WPTS)is split into several subsets defined by their stationary patterns.A WPTS that does not match any of the stationary patterns is then included in a subset of non-stationary patterns.Each WPTS subset is then related to a USTWPP model that is specially selected and optimized offline based on the proposed risk assessment index.For online applications,the pattern of the last short WPTS is first recognized,and the relevant prediction model is then applied for USTWPP.Experimental results confirm the efficacy of the proposed method.

Effects of centralized and local PV plant control for voltage regulation in LV feeder based on cyber-physical simulations

In the modern power system, both local and centralized reactive power control strategies for photovoltaic(PV) plants, are proposed and compared. While local control improves the network security, it lacks the optimization benefits from centralized control strategies.Therefore, this paper considers the coordination of the two control strategies, depending on external impact from the weather system and consumer behavior, in a low voltage(LV) distribution feeder. Through modeling and simulation in an established real-time cyber-physical simulation platform, the LV network is evaluated with both local and centralized control. A set of boundaries for coordinating between the two strategies are identified, which can help network operators decide suitable control in different operating situations. Furthermore, the cyber-physical simulation platform, is used to study the impact of physical perturbations, i.e. changes in irradiance and consumption,and cyber disturbances, in form of communication channel noise, is evaluated for the control strategies. Results show how small and large disturbances in the cyber system affect the centralized control strategy optimizer performance.

Applying Synchronous Condenser for Damping Provision in Converter-dominated Power System

The dynamic characteristics of converter-dominated systems are governed by controlling power converters and the interactions between converter systems and conventional alternators.Frequency oscillations can appear under dynamic operation conditions caused by the phase-locked loop dynamics and interactions among the converter control systems.The oscillations may be poorly damped,which can result in reduced power generation,longer settling time,or disconnections of sensitive components.It is foreseeable that damping services will be critical for power grid stabilization in the future with high penetration of renewable generation.In this work,synchronous condensers(SCs)are evaluated and applied to provide damping services to the power grid under post-event conditions.An innovative supplementary controller for the automatic voltage regulator of SCs is proposed to improve the frequency stabilization in a converter-dominated system after disturbances.Using local and remote measurements,SCs are able to modulate the reactive power output and hence,the terminal bus voltage,which further impacts the power flow in the system;therefore,damping can be provided to the frequency oscillations.The control is implemented on an industrial-level hardware platform,and the performance is verified by the hardware-in-the-loop simulation.