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.

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).

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.

Optimal Frequency Regulation Based on Characterizing the Air Conditioning Cluster by Online Deep Learning

The air conditioning cluster(ACC)is a potential candidate to provide frequency regulation reserves.However,the effective assessment of the ACC willing reserve capacity is often an obstacle for existing demand response(DR)programs,influenced by incentive prices,temperatures,etc.In this paper,the complex relationship between the ACC willing reserve capacity and its key influence factors is defined as a demand response characteristic(DRC).To learn about DRC along with real-time frequency regulation,an online deep learning-based DRC(ODLDRC)modeling methodology is designed to continuously retrain the deep neural network-based model.The ODL-DRC model trained by incoming new data does not require massive historical training data,which makes it more time-efficient.Then,the coordinate operation between ODL-DRC modeling and optimal frequency regulation(OFR)is presented.A robust decentralized sliding mode controller(DSMC)is designed to manage the ACC response power in primary frequency regulation against any ACC response uncertainty.An ODL-DRC model-based OFR scheme is formulated by taking the learning error into consideration.Thereby,the ODL-DRC model can be applied to minimize the total operational cost while maintaining frequency stability,without waiting for a well-trained model.The simulation cases validate the superiority of the OFR based on characterizing the ACC by online learning,which can capture the real DRC and simultaneously optimize the regulation performance with strong robustness against any ACC response uncertainty and learning error.

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 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.