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。

基于智能电网构建的欧洲可再生能源电力系统现状和前景展望(英文)

可再生能源在欧盟未来的能源框架中扮演着重要角色。到2020年,在欧盟总的能源消耗量中,预期20%将来自可再生能源,并且能效将提高20%。智能电网将是未来电力系统接纳大规模可再生能源发电并网的基石。文中介绍了欧洲的可再生能源开发现状和规划以及能源政策,并讨论了欧盟在智能电网相关技术方面的研发进展。丹麦是欧盟国家中可再生能源使用和相关关键技术研发的领跑者,文中重点以其为例进行介绍和阐述。

Low-carbon generation expansion planning considering uncertainty of renewable energy at multi-time scales

With the development of carbon electricity,achieving a low-carbon economy has become a prevailing and inevitable trend.Improving low-carbon expansion generation planning is critical for carbon emission mitigation and a lowcarbon economy.In this paper,a two-layer low-carbon expansion generation planning approach considering the uncertainty of renewable energy at multiple time scales is proposed.First,renewable energy sequences considering the uncertainty in multiple time scales are generated based on the Copula function and the probability distribution of renewable energy.Second,a two-layer generation planning model considering carbon trading and carbon capture technology is established.Specifically,the upper layer model optimizes the investment decision considering the uncertainty at a monthly scale,and the lower layer one optimizes the scheduling considering the peak shaving at an hourly scale and the flexibility at a 15-minute scale.Finally,the results of different influence factors on low-carbon generation expansion planning are compared in a provincial power grid,which demonstrate the effectiveness of the proposed model.

柔性直流输电并网风电厂的低电压穿越能力改进(英文)

To achieve active control of the AC voltage magnitude of wind power plant(WPP)collector network and improve the fault ride-through(FRT)capability,an FRT scheme based on feed forward DC voltage control is presented for voltage source converter-high voltage direct current(VSC-HVDC)connected offshore WPPs.During steady state operation,an open loop AC voltage control is implemented at the WPP-side VSC of the HVDC system so that any possible control interactions between WPP-side VSC and VSC of wind turbine are minimized.Whereas during any grid fault,a dynamic AC voltage reference is made according to both the DC voltage error and AC active current from the WPP collector system,thus ensuring fast and robust FRT of the VSC-HVDC-connected offshore WPPs.Under the unbalanced fault condition in the host power system,the resulting oscillatory DC voltage is directly used in the VSC AC voltage controller at the WPP side so that the WPP collector system voltage also reflects the unbalance in the main grid.Time domain simulations are performed to verify the efficacy of the FRT scheme based on the proposed feed forward DC voltage control.Simulation results show satisfactory FRT responses of the VSC-HVDC-connected offshore WPP under balanced and unbalanced faults in the host power system,as is shown under a serious fault in the WPP collector network.

Coordinated voltage control of renewable energy power plants in weak sending-end power grid

The utilization of renewable energy in sending-end power grids is increasing rapidly,which brings difficulties to voltage control.This paper proposes a coordinated voltage control strategy based on model predictive control(MPC)for the renewable energy power plants of wind and solar power connected to a weak sending-end power grid(WSPG).Wind turbine generators(WTGs),photovoltaic arrays(PVAs),and a static synchronous compensator are coordinated to maintain voltage within a feasible range during operation.This results in the full use of the reactive power capability of WTGs and PVAs.In addition,the impact of the active power outputs of WTGs and PVAs on voltage control are considered because of the high R/X ratio of a collector system.An analytical method is used for calculating sensitivity coefficients to improve computation efficiency.A renewable energy power plant with 80 WTGs and 20 PVAs connected to a WSPG is used to verify the proposed voltage control strategy.Case studies show that the coordinated voltage control strategy can achieve good voltage control performance,which improves the voltage quality of the entire power plant.

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified by the 18-node typical system.

Extreme Scenarios Based Data-adaptive Probability Uncertainty Set for Distributionally Robust Transmission Expansion Planning

Increasing penetration of renewable energy into power systems is the development trend of future energy systems.One of the main challenges is to plan the expansion scheme of transmission systems to accommodate uncertainties of wind power.In this letter,we propose a novel extreme scenarios(ESs)based data-adaptive probability uncertainty set for the transmission expansion planning problem.First,available historical data are utilized to identify data-adaptive ESs through the convex hull technology,and the probability uncertainty set with respect to the obtained ESs is then established,from which we draw the final expansion decision based on the worst-case distribution.The proposed distributionally robust transmission expansion planning(DRTEP)model can guarantee optimality of expected cost under the worst-case distribution,while ensuring feasibility of all possible wind power generation.Simulation studies are carried out on a modified IEEE RTS 24-bus system to verify the effectiveness of the proposed DRTEP model.

Interdependence between transportation system and power distribution system: a comprehensive review on models and applications

The rapidly increasing penetration of electric vehicles(EVs) in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment pro-tection. Integrating charging facilities, especially highpower chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced,especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted.Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.

Power hardware in the loop validation of fault ride through of VSC HVDC connected offshore wind power plants

This paper presents the power hardware in the loop(PHIL)validation of a feed forward DC voltage control scheme for the fault ride through(FRT)of voltage source converter(VSC)high voltage DC(HVDC)connected offshore wind power plants(WPPs).In the proposed FRT scheme,the WPP collector network AC voltage is actively controlled by considering both the DC voltage error and the AC current from the WPP AC collector system which ensures fast and robust FRT of the VSC HVDC connected offshore WPPs.The PHIL tests were carried out in order to verify the efficacy of the proposed feed forward DC voltage control scheme for enhancing the FRT capability of the VSC HVDC connected WPPs.The PHIL test results have demonstrated the proper control coordination between the offshore WPP and the WPP side VSC and the efficient FRT of the VSC HVDC connected WPPs.

Transition towards higher penetration of renewables: an overview of interlinked technical, environmental and socio-economic challenges

Investment for renewables has been growing rapidly since the beginning of the new century, and the momentum is expected to sustain in order to mitigate the impact of anthropogenic climate change.Transition towards higher renewable penetration in the power industry will not only confront technical challenges, but also face socio-economic obstacles.The connected between environment and energy systems are also tightened under elevated penetration of renewables.This paper will provide an overview of some important challenges related to technical, environmental and socio-economic aspects at elevated renewable penetration.An integrated analytical framework for interlinked technical, environmental and socio-economic systems will be presented at the end.

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.

Fuzzy logic based coordinated control of battery energy storage system and dispatchable distributed generation for microgrid

Microgrid is a good option to integrate renewableenergy sources (RES) into power systems. Inorder to deal with the intermittent characteristics of therenewable energy based distributed generation (DG) units,a fuzzy-logic based coordinated control strategy of a batteryenergy storage system (BESS) and dispatchable DGunits is proposed for the microgrid management system(MMS). In the proposed coordinated control strategy, theBESS is used to minimize active power exchange at thepoint of common coupling of the microgrid for grid-connectedoperation, and is used for frequency control for islandoperation. The efficiency of the proposed controlstrategy was tested by case studies using DIgSILENT/PowerFactroy.

Review of Service Restoration for Distribution Networks

With the rapid deployment of the advanced metering infrastructure(AMI)and distribution automation(DA),selfhealing has become a key factor to enhance the resilience of distribution networks.Following a permanent fault occurrence,the distribution network operator(DNO)implements the selfhealing scheme to locate and isolate the fault and to restore power supply to out-of-service portions.As an essential component of self-healing,service restoration has attracted considerable attention.This paper mainly reviews the service restoration approaches of distribution networks,which requires communication systems.The service restoration approaches can be classified as centralized,distributed,and hierarchical approaches according to the communication architecture.In these approaches,different techniques are used to obtain service restoration solutions,including heuristic rules,expert systems,metaheuristic algorithms,graph theory,mathematical programming,and multi-agent systems.Moreover,future research areas of service restoration for distribution networks are discussed.

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.

Deep Reinforcement Learning Based Approach for Optimal Power Flow of Distribution Networks Embedded with Renewable Energy and Storage Devices

This study proposes a deep reinforcement learning(DRL)based approach to analyze the optimal power flow(OPF)of distribution networks(DNs)embedded with renewable energy and storage devices.First,the OPF of the DN is formulated as a stochastic nonlinear programming problem.Then,the multi-period nonlinear programming decision problem is formulated as a Markov decision process(MDP),which is composed of multiple single-time-step sub-problems.Subsequently,the state-of-the-art DRL algorithm,i.e.,proximal policy optimization(PPO),is used to solve the MDP sequentially considering the impact on the future.Neural networks are used to extract operation knowledge from historical data offline and provide online decisions according to the real-time state of the DN.The proposed approach fully exploits the historical data and reduces the influence of the prediction error on the optimization results.The proposed real-time control strategy can provide more flexible decisions and achieve better performance than the pre-determined ones.Comparative results demonstrate the effectiveness of the proposed approach.

Optimized dispatch of wind farms with power control capability for power system restoration

As the power control technology of wind farms develops,the output power of wind farms can be constant,which makes it possible for wind farms to participate in power system restoration.However,due to the uncertainty of wind energy,the actual output power can’t reach a constant dispatch power in all time intervals,resulting in uncertain power sags which may induce the frequency of the system being restored to go outside the security limits.Therefore,it is necessary to optimize the dispatch of wind farms participating in power system restoration.Considering that the probability distribution function(PDF)oftransient power sags is hard to obtain,a robust optimization model is proposed in this paper,which can maximize the output power of wind farms participating in power system restoration.Simulation results demonstrate that the security constraints of the restored system can be kept within security limits when wind farm dispatch is optimized by the proposed method.

ADMM-based Distributed Active and Reactive Power Control for Regional AC Power Grid with Wind Farms

A distributed active and reactive power control(DARPC)strategy based on the alternating direction method of multipliers(ADMM)is proposed for regional AC transmission system(TS)with wind farms(WFs).The proposed DARPC strategy optimizes the power distribution among the WFs to minimize the power losses of the AC TS while tracking the active power reference from the transmission system operator(TSO),and minimizes the voltage deviation of the buses inside the WF from the rated voltage as well as the power losses of the WF collection system.The optimal power flow(OPF)of the TS is relaxed by using the semidefinite programming(SDP)relaxation while the branch flow model is used to model the WF collection system.In the DARPC strategy,the large-scale strongly-coupled optimization problem is decomposed by using the ADMM,which is solved in the regional TS controller and WF controllers in parallel without loss of the global optimality.The boundary information is exchanged between the regional TS controller and WF controllers.Compared with the conventional OPF method of the TS with WFs,the optimality and accuracy of the system operation can be improved.Moreover,the proposed strategy efficiently reduces the computation burden of the TS controller and eliminates the need of a central controller.The protection of the information privacy can be enhanced.A modified IEEE 9-bus system with two WFs consisting of 64 wind turbines(WTs)is used to validate the proposed DARPC strategy.

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.

Two-step Optimal Allocation of Stationary and Mobile Energy Storage Systems in Resilient Distribution Networks

Energy storage systems (ESSs) are acknowledged to be a promising option to cope with issues in high penetration of renewable energy and guarantee a highly reliable power supply. In this paper, a two-step optimal allocation model is proposed to obtain the optimal allocation (location and size) of stationary ESSs (SESSs) and mobile ESSs (MESSs) in the resilient distribution networks (DNs). In the first step, a mixed-integer linear programming (MILP) problem is formulated to obtain the preselected location of ESSs with consideration of different scenarios under normal operation conditions. In the second step, a two-stage robust optimization model is established to get the optimal allocation results of ESSs under failure operation conditions which are solved by column-and-constraint generation (C&CG) algorithm. A hybrid ESS allocation strategy based on the subjective and objective weight analysis is proposed to give the final allocation scheme of SESSs and MESSs. Finally, the proposed two-step optimal allocation model is demonstrated on a modified IEEE 33-bus system to show its effectiveness and merits.