Resilience-Constrained Unit Commitment Considering Demand Response

Increasing number of outages due to natural disasters shows the vulnerability of power systems to extreme events and brings an awareness of operating the system proactively. In this paper a new resilience-constrained unit commitment (RCUC) model is proposed by incorporating emergency demand response program (EDRP), named as RCUCDR. A sequential and Monte Carlo Simulations-based framework is used in the proposed RCUC-DR model. High loading rates of transmission lines are one of the main reasons of cascading outages. In order to have homogenous distribution of power flow, loading reduction in the areas affected by extreme events and demand response programs (DRPs) are considered simultaneously in the proposed RCUC-DR framework. The proposed RCUCDR model is formulated as a mix-integer programming (MIP) optimization model. Single-period and multi-period responsive loads are considered in the utilized DRPs. Capability of the proposed model is tested on the IEEE-24 bus system and compared with RCUC and security-constrained unit commitment (SCUC). The results demonstrate the effectiveness of utilizing DRPs in the case of extreme events.

[1]  Shaolei Ren,et al.  Incentivizing Energy Reduction for Emergency Demand Response in Multi-Tenant Mixed-Use Buildings , 2018, IEEE Transactions on Smart Grid.

[2]  Payman Dehghanian,et al.  Maintaining Electric System Safety Through An Enhanced Network Resilience , 2018, IEEE Transactions on Industry Applications.

[3]  Mohammad Shahidehpour,et al.  Resilience-Constrained Hourly Unit Commitment in Electricity Grids , 2018, IEEE Transactions on Power Systems.

[4]  R. Dawson,et al.  Power System Resilience to Extreme Weather: Fragility Modeling, Probabilistic Impact Assessment, and Adaptation Measures , 2017, IEEE Transactions on Power Systems.

[5]  Alireza Soroudi,et al.  Power System Optimization Modeling in GAMS , 2017 .

[6]  Chong Wang,et al.  Resilience Enhancement With Sequentially Proactive Operation Strategies , 2017, IEEE Transactions on Power Systems.

[7]  Amir Abdollahi,et al.  Demand Response Resources' Allocation in Security-Constrained Preventive Maintenance Scheduling via MODM Method , 2017, IEEE Systems Journal.

[8]  Jianhui Wang,et al.  Integration of Preventive and Emergency Responses for Power Grid Resilience Enhancement , 2017, IEEE Transactions on Power Systems.

[9]  Pierluigi Mancarella,et al.  Metrics and Quantification of Operational and Infrastructure Resilience in Power Systems , 2017, IEEE Transactions on Power Systems.

[10]  Pierluigi Mancarella,et al.  Boosting the Power Grid Resilience to Extreme Weather Events Using Defensive Islanding , 2016, IEEE Transactions on Smart Grid.

[11]  Frances M. T. Brazier,et al.  An entropy-based metric to quantify the robustness of power grids against cascading failures , 2013 .

[12]  Mohammad Kazem Sheikh-El-Eslami,et al.  Investigation of Economic and Environmental-Driven Demand Response Measures Incorporating UC , 2012, IEEE Transactions on Smart Grid.

[13]  Zhejing Bao,et al.  Analysis of cascading failure in electric grid based on power flow entropy , 2009 .

[14]  Waltraud Kahle,et al.  A general repair, proportional-hazards, framework to model complex repairable systems , 2003, IEEE Trans. Reliab..

[15]  N. J. Balu,et al.  Composite generation/transmission reliability evaluation , 1992, Proc. IEEE.

[16]  R. H. Kerr,et al.  Unit commitment , 1966, Mathematical Programming for Power Systems Operation with Applications in Python.