Exploiting demand-side heterogeneous flexible resources in risk management of power system frequency

More demand-side flexible resources(DFRs)are participating in the frequency regulation of renewable power systems,whose heterogeneous characteristics have a significant impact on the system frequency response.Consequently,selecting suitable DFRs poses a formidable challenge for independent system operators(ISO).In this paper,a reserve allocation methodology for heterogeneous DFRs is proposed to manage the risk of power system frequency.Firstly,a performance curve is developed to describe the cost,capacity,and response speed of DFRs.Moreover,a clustering method for multiple distributed DFRs is conducted to calculate the aggregated performance curves and uncertainty coefficients.Then,the frequency security criterion considering DFRs’performance is constructed,whose linearity makes it can be easily coupled into the system scheduling model and solved.Furthermore,a risk management model for DFRs considering frequency-chance-constraint is proposed to make a trade-off between cost and frequency security.Finally,the model is transformed into mixed integer second-order cone programming(MISOCP)and solved by the commercial solver.The proposed model is validated by the IEEE 30 and IEEE 118 bus systems.

Delay-aware downlink beamforming with discrete rate adaptation for green cloud radio access network

With the popularity of variety delay-sensitive services, how to guarantee the delay requirements for mobile users （MUs） is a great challenge for downlink beamformer design in green cloud radio access networks （C-RANs）. In this paper, we consider the problem of the delay-aware downlink beamforming with discrete rate adaptation to minimize the power consumption of C-RANs. We address the problem via a mixed integer nonlinear program （MINLP）, and then reformulate the MINLP problem as a mixed integer second-order cone program （MI-SOCP）, which is a convex program when the integer variables are relaxed as continuous ones. Based on this formulation, a deflation algorithm, whose computational complexity is polynomial, is proposed to derive the suboptimal solution. The simulation results are presented to validate the effectiveness of our proposed algorithm.

Renewable Generation Capacity and Reliability Assessments for Resilient Active Distribution Networks Based on Time-sequence Production Simulation

The penetration of renewable generation will affect the energy utilization efficiency,economic benefit and reliability of the active distribution network(ADN).This paper proposes a time-sequence production simulation(TSPS)method for re-newable generation capacity and reliability assessments in ADN considering two operational status:the normal status and the fault status.During normal operation,an optimal dispatch model is proposed to promote the renewable consumption and increase the economic benefit.When a failure occurs,the renewable generators are partitioned into islands for resilient power supply and reliability improvement.A novel dynamic island partition model is presented based on mixed integer second-order cone programming(MISOCP).The effectiveness of the proposed TSPS method is demonstrated in a standard network integrated with historical data of load and renewable generations.