Design of a Photo-Voltaic System to Enhance Network Dynamic Stability

Due to the increasing amount of photovoltaic (PV)-based power generation being connected to power systems, issues pertaining to the integration of the PV-based generators have attracted intense attention. In this connection, the design of a PV-based stabilizer for enhancing power system dynamic stability is examined. The damping action is achieved through the independent control of real power flow from the stabilizer and voltage at the point of common coupling between the stabilizer and grid system. The stabilizer system is designed based on classical frequency response technique. Robustness of the proposed control strategy in enhancing network dynamic stability is demonstrated through computer simulation.

Simulation on DC Current Distribution in AC Power Grid under HVDC Ground-Return-Mode

This paper focus on the Modeling and Calculation of DC current distribution in AC power grid induced under HVDC Ground-Return-Mode. Applying complex image method and boundary element method, a new field-circuit coupling model was set up. Based on the calculation result with complex image method, this paper derived the modification factor for induced earth potential from practical measurement, which increased the accuracy of calculation. The modification method is helpful for evaluation on the effect of means used for blocking the dc-bias current in transformer neutral and also useful for the forecast of the DC current distribution when the power grid is in different line connection mode. The DC distribution character in Guangdong power grid is shown and suggestion is proposed that the mitigation of dc-bias should start from those substations whose earth-potential is highest.

Control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is expected.With conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power systems.New analysis and control methods are needed for power systems to cope with the ongoing transformation.In the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power systems.Specifically,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

Hierarchical Frequency-dependent Chance Constrained Unit Commitment for Bulk AC/DC Hybrid Power Systems with Wind Power Generation

As the steady-state frequency of an actual power system decreases from its nominal value,the composite load of the system generally responds positively to lower power consumption,and vice versa.It is believed that this load frequency damping(LFD)effect will be artificially enhanced,i.e.,sensitivities of loads with respect to operational frequency will increase,in future power systems.Thus,for wind-integrated power systems,this paper proposes a frequency-dependent chance constrained unit commitment(FDCCUC)model that employs the operational frequency as a dispatching variable so that the LFD effect-based load power can act as a supplemental reserve.Because the frequency deviation is safely restricted,this low-cost reserve can be sufficiently exerted to upgrade the wind power accommodation capability of a power system that is normally confined by an inadequate reserve to cope with uncertain wind power forecasting error.Moreover,when the FDCCUC model is applied to a bulk AC/DC hybrid power system consisting of several independently operated regional AC grids interconnected by DC tie-lines,a hierarchically implemented searching algorithm is proposed to protect private scheduling information of the regional AC grids.Simulations on a 2-area 6-bus system and a 3-area 354-bus system verify the effectiveness of the FDCCUC model and hierarchical searching algorithm.

Statistical scenarios forecasting method for wind power ramp events using modified neural networks

Wind power ramp events increasingly affect the integration of wind power and cause more and more problems to the safety of power grid operation in recent years.Several forecasting techniques for wind power ramp events have been reported.In this paper,the statistical scenarios forecasting method is proposed for wind power ramp event probabilistic forecasting based on the probability generating model.Multi-objective fitness functions are established considering cumulative density functions and higher order moment autocorrelation functions with respect to the consistency of distribution and timing characteristics,respectively.Parameters of probability generating model are calculated by the iterative optimization using the modified genetic algorithm with multi-objective fitness functions.A number of statistical scenarios captured bands are generated accordingly.Eventually,ramp event probability characteristics are detected from scenarios captured bands to evaluate the ramp event forecasting method.A wind plant of Bonneville Power Administration with actual wind power data is selected for calculation and statistical analysis.It is shown that statistical results with multi-objective functions are more accurate than the results with single objective functions.Moreover,the statistical scenarios forecasting method can accurately estimate the characteristics of wind power ramp events.The results verify that the proposed method can guide the generation method of statistical scenarios and forecasting models for ramp events.

A multi-state model for wind farms considering operational outage probability

As one of the most important renewable energy resources,wind power has drawn much attention in recent years.The stochastic characteristics of wind speed lead to generation output uncertainties of wind energy conversion system(WECS)and affect power system reliability,especially at high wind power penetration levels.Therefore,a more comprehensive analysis toward WECS as well as an appropriate reliability assessment model are essential for maintaining the reliable operation of power systems.In this paper,the impact of wind turbine outage probability on system reliability is firstly developed by considering the following factors:running time,operating environment,operating conditions,and wind speed fluctuations.A multistate model for wind farms is also established.Numerical results illustrate that the proposed model can be well applied to power system reliability assessment as well as solving a series of reliability-centered decision-making problems of power system scheduling and maintenance arrangements.

An on-line power/voltage stability index for multi-infeed HVDC systems

High-voltage direct current(HVDC)transmission is playing an increasingly important role in modern power systems,and the resulted power/voltage stability issue has raised widespread concern.This paper presents an on-line power/voltage stability index(PVSI)for multi-infeed HVDC(MIDC)systems.Different from the existing indices which are developed mainly for off-line and static analysis,the proposed PVSI can be applied in real time.Effects of system changes on stability assessment such as change of system states and control strategies are considered.Thus,helpful guidance can be provided for on-line HVDC stability and controls.The PVSI is originally deduced for single-infeed HVDC systems in an‘‘AC way’’by analyzing the power and voltage stability of both pure AC systems and HVDC systems.Moreover,its on-line application in practical MIDC systems is realized by building an equivalent single-infeed model,and utilizing nowadays measurement and communication infrastructures such as wide-area measurement system(WAMS).The effectiveness of the PVSI is verified through simulations in real-time digital simulator(RTDS).

A Day-ahead Economic Dispatch Method Considering Extreme Scenarios Based on Wind Power Uncertainty

As the intermittency of wind power is a growing concern in the day-ahead economic dispatch,this paper proposes a day-ahead economic dispatch method considering extreme scenarios of wind power by using an uncertainty set.The uncertainty set inspired by robust optimization is used to describe wind power intermittency in this paper.Four extreme scenarios based on the uncertainty set are formulated to represent the worst cases of wind power fluctuation.An economic dispatch method considering the costs of both load shedding and wind curtailment is proposed.The economic dispatch model can be easily solved by a quadratic programming method owing to the introduction of four extreme scenarios and the uncertainty set of wind power.Simulation is done using the IEEE 30-bus system and the results verify the effectiveness of the proposed method.

Active Power Correction Strategies Based on Deep Reinforcement Learning Part I:A Simulation-driven Solution for Robustness

Employing the novel Deep Reinforcement Learning approach,this paper addresses the active power corrective control in modern power systems.Seeking to minimize the joint effect engendered by operation cost and blackout penalty,this correction strategy focuses on evaluating the robustness and adaptability aspects of the control agent.In Part I of this paper,where robustness is the primary focus,the agent is developed to handle unexpected incidents and guide the stable operation of power grids A Simulation-driven Graph Attention Reinforcement Learning method is proposed to perform robust active power corrective control.The aim of the graph attention networks is to determine the representation of power system states considering the topological features.Monte Carlo tree search is adopted to select the best suitable action set out of the large action space,including generator redispatch and topology control actions.Finally,driven by simulation,a guided training mechanism along with a long-short-term action deployment strategy are designed to help the agent better evaluate the action set while training and to operate more stably when deployed.The efficacy of the proposed method has been demonstrated in the“2020 I earning to Run a Power Network.Neurips Track 1”global competition and the associated cases.Part II of this paper deals with the adaptability case,where the agent is equipped to better adapt to a grid that has an increasing share of renewable energies through the years.

Load Shedding and Restoration for Intentional Island with Renewable Distributed Generation

Due to the high penetration of renewable distributed generation(RDG),many issues have become conspicuous during the intentional island operation such as the power mismatch of load shedding during the transition process and the power imbalance during the restoration process.In this paper,a phase measurement unit(PMU)based online load shedding strategy and a conservation voltage reduction(CVR)based multi-period restoration strategy are proposed for the intentional island with RDG.The proposed load shedding strategy,which is driven by the blackout event,consists of the load shedding optimization and correction table.Before the occurrence of the large-scale blackout,the load shedding optimization is solved periodically to obtain the optimal load shedding plan,which meets the dynamic and steady constraints.When the blackout occurs,the correction table updated in real time based on the PMU data is used to modify the load shedding plan to eliminate the power mismatch caused by the fluctuation of RDG.After the system transits to the intentional island seamlessly,multi-period restoration plans are generated to optimize the restoration performance while maintaining power balance until the main grid is repaired.Besides,CVR technology is implemented to restore more loads by regulating load demand.The proposed load shedding optimization and restoration optimization are linearized to mixed-integer quadratic constraint programming(MIQCP)models.The effectiveness of the proposed strategies is verified with the modified IEEE 33-node system on the real-time digital simulation(RTDS)platform.

Active Power Correction Strategies Based on Deep Reinforcement Learning Part II:A Distributed Solution for Adaptability

This article is the second part of Active Power Correction Strategies Based on Deep Reinforcement Learning.In Part II,we consider the renewable energy scenarios plugged into the large-scale power grid and provide an adaptive algorithmic implementation to maintain power grid stability.Based on the robustness method in Part I,a distributed deep reinforcement learning method is proposed to overcome the infuence of the increasing renewable energy penetration.A multi-agent system is implemented in multiple control areas of the power system,which conducts a fully cooperative stochastic game.Based on the Monte Carlo tree search mentioned in Part I,we select practical actions in each sub-control area to search the Nash equilibrium of the game.Based on the QMIX method,a structure of offine centralized training and online distributed execution is proposed to employ better practical actions in the active power correction control.Our proposed method is evaluated in the modified global competition scenario cases of“2020 Learning to Run a Power Network.Neurips Track 2”.

Demand-side Management Based on Model Predictive Control in Distribution Network for Smoothing Distributed Photovoltaic Power Fluctuations

With the rapid increase of distributed photovoltaic(PV) power integrating into the distribution network(DN), the critical issues such as PV power curtailment and low equipment utilization rate have been caused by PV power fluctuations. DN has less controllable equipment to manage the PV power fluctuation. To smooth the power fluctuations and further improve the utilization of PV, the regulation ability from the demandside needs to be excavated. This study presents a continuous control method of the feeder load power in a DN based on the voltage regulation to respond to the rapid fluctuation of the PV power output. PV power fluctuations will be directly reflected in the point of common coupling(PCC), and the power fluctuation rate of PCCs is an important standard of PV curtailment.Thus, a demand-side management strategy based on model predictive control(MPC) to mitigate the PCC power fluctuation is proposed. In pre-scheduling, the intraday optimization model is established to solve the reference power of PCC. In real-time control, the pre-scheduling results and MPC are used for the rolling optimization to control the feeder load demand. Finally,the data from the field measurements in Guangzhou, China are used to verify the effectiveness of the proposed strategy in smoothing fluctuations of the distributed PV power.

A Comprehensive AC Fault Ride-through Strategy for HVDC Link with Serial-connected LCC-VSC Hybrid Inverter

An HVDC link with line-commutated converter(LCC)as a rectifier at the power sending end and with a serial-connected LCC and voltage source converter(VSC)hybrid inverter(SLVHI)at the power receiving end,has been adopted for the forthcoming Baihetan HVDC engineering project in China.To realize the AC fault ride-through(FRT)of SLVHI,a new strategy based on DC chopper(DCC)is proposed in this paper.Firstly,the mathematical model is built to investigate the VSC DC overvoltage mechanism after SLVHI commutation failure(CF)and related factors.Secondly,a modified DCC topology of SLVHI is designed to adjust the unbalanced power dissipation based on voltage drop depth.Thirdly,an enhanced voltage-dependent current order limiter(VDCOL)is proposed to deal with the unbalanced power after DCC switch-off.With the cooperation between modified DCC and enhanced VDCOL,the proposed FRT strategy can realize CF mitigation and VSC DC overvoltage suppression simultaneously.Finally,the inverter side AC FRT performances of the HVDC link with SLVHI were studied using PSCAD software/EMTDC algorithm.The simulation results validate the effectiveness and superiority of the proposed strategy,with better CF mitigation and VSC DC overvoltage suppression abilities than offered by other existing FRT strategies under different fault scenarios.

Efficient utilization of wind power： Long-distance transmission or local consumption？

Excess wind power produced in wind-inten- sive areas is normally delivered to remote load centers via long-distance transmission lines. This paper presents a comparison between long-distance transmission, which has gained popularity, and local energy consumption, in which a fraction of the generated wind power can be locally consumed by energy-intensive industries. First, the challenges and solutions to the long-distance transmission and local consumption of wind power are presented. Then, the two approaches to the utilization of wind power are compared in terms of system security, reliability, cost, and capability to utilize wind energy. Finally, the economic feasibility and technical feasibility of the local consumption of wind power are demonstrated by a large and isolated industrial power system, or supermicrogrid, in China. The coal-fired generators together with the shortterm interruptible electrolytic aluminum load in the supermicrogrid are able to compensate for the intermit- tency of wind power. In the long term, the transfer of high- energy-consumption industries to wind-rich areas and their local consumption of the available wind power are beneficial.

A Multi-time Scale Tie-line Energy and Reserve Allocation Model Considering Wind Power Uncertainties for Multi-area Systems

Continued expansion of the power grid and the increasing proportion of wind power centralized integration leads to requirements in sharing both energy and reserves among multiple areas under a hierarchical control structure,which successively requires a correction between schedule plans within multi-time scale.In order to address this problem,this paper develops an information integration method integrating complicated relationships among fuel cost,total thermal power output,reserve capacity,owned reserves and expectations of load shedding and wind curtailment,into three types of time-related relationship curves・Furthermore,a multi-time scale tieline energy and reserves allocation model is proposed,which contains two levels in the control structure,two time scales in dispatch sequence and multiple areas integrated within wind farms as scheduling objects・The efficiency of the proposed method is tested in a 9-bus test system and IEEE 118-bus system.The results show that a cross-regional control center is able to approach the optimal scheduling results of the whole system with the integrated uploaded relationship curves.The proposed model not only relieves energy and reserve shortages in partial areas but also allocates them to more urgent need areas in a high effectivity manner in both day-ahead and intraday time scales.

A demand side controller of electrolytic aluminum industrial microgrids considering wind power fluctuations

Direct wind power purchase for large industrial users is a meaningful way to improve wind power consumption and decrease industrial production costs.Short-term wind power fluctuations may lead to large-scale wind power curtailment problems.To promote use of wind energy,a demand side control method is proposed based on output regulator theory for a grid-connected industrial microgrid with electrolytic aluminum loads to continuously track and respond to wind power fluctuations.The control model of the EALs and the dominant frequencies of the wind power fluctuation signals are analyzed and incorporated into the demand side control plant.The feedback control signals with active power deviations on the tie-line are used to design the demand side controller.Simulations are conducted for an actual industrial microgrid to validate the feasibility and effectiveness of the proposed method.The results demonstrate that the proposed controller based on output regulator theory is able to effectively track wind power fluctuations.

Detection Method for Cascading Failure of Power Systems Based on Epidemic Model

The early detection of cascading failure plays an important role in the safe and stable operation of the power system with high penetration of renewable energy.This paper proposes a fault propagation dynamic model based on the epidemic model,and further puts forward a method to detect the development of cascading failures.Through the simulation of the IEEE 39-bus and 118-bus systems,this model is proven to be valid and capable of providing practical technical support for the prevention of cascading failures in power systems with high penetration of renewable energy.This paper also provides an analysis method for the choice of different protection and control measures at each stage of cascading failure,which has critical significance and follow-up value.