Coordinated Storage and Flexible Loads as a Network Service Provider: a Resilience-Oriented Paradigm

A resilience-oriented operation multi-time scale scheduling is proposed in this paper that deploys a coordinated storage and flexible loads (CSFLs) structure to act as a network service provider (NSP). The proposed proactive operation strategy can deal not only with intermittent wind generations in hourly operation but also with load supplying resilience for the sub-hourly variations. The energy storage systems (ESSs) can retain the state of charge (SoC) during the sub-hourly fine-tuning periods to supply critical loads for predefined time intervals. In this regard, by integrating the so-called NSPs into a stochastic unit commitment model, the advent-ages of ESSs and flexible loads (FLs) are taken to enrich the short-term scheduling for the different time resolutions. The proposed model is tested by the IEEE RTS-24 standard network. The results show that NSPs can successfully participate in providing coordinated ancillary services in different time-scales, and concurrently by support of FLs, the ESSs proactive role is also retained. The usage of CSFLs reduces the curtailment of wind energy, while they provide a large portion of reserves for the possible fluctuations.

[1]  Wil L. Kling,et al.  Comparison of integration solutions for wind power in the netherlands , 2009 .

[2]  Vahid Vahidinasab,et al.  Valuing consumer participation in security enhancement of microgrids , 2019 .

[3]  Nima Amjady,et al.  Multistage Multiresolution Robust Unit Commitment With Nondeterministic Flexible Ramp Considering Load and Wind Variabilities , 2018, IEEE Transactions on Sustainable Energy.

[4]  Quanyuan Jiang,et al.  Wavelet-Based Capacity Configuration and Coordinated Control of Hybrid Energy Storage System for Smoothing Out Wind Power Fluctuations , 2013, IEEE Transactions on Power Systems.

[5]  B. Dakyo,et al.  Use of Ultracapacitors and Batteries for Efficient Energy Management in Wind–Diesel Hybrid System , 2013, IEEE Transactions on Sustainable Energy.

[6]  Vahid Vahidinasab,et al.  Allocation and Sizing of Energy Storage System Considering Wind Uncertainty: An Approach Based on Stochastic SCUC , 2018, 2018 Smart Grid Conference (SGC).

[7]  Josep M. Guerrero,et al.  Dynamic Pricing: An Efficient Solution for True Demand Response Enabling , 2017 .

[8]  Francisco D. Galiana,et al.  Unit Commitment Incorporating Histogram Control of Electric Loads With Energy Storage , 2016, IEEE Transactions on Power Systems.

[9]  Vahid Vahidinasab,et al.  Customer baseline load models for residential sector in a smart-grid environment , 2016 .

[10]  Xu Andy Sun,et al.  Multistage Robust Unit Commitment With Dynamic Uncertainty Sets and Energy Storage , 2016, IEEE Transactions on Power Systems.

[11]  Amin Khodaei,et al.  Resiliency-Oriented Microgrid Optimal Scheduling , 2014, IEEE Transactions on Smart Grid.

[12]  Yuan Tian,et al.  A Coordinated Multi-Time Scale Robust Scheduling Framework for Isolated Power System With ESU Under High RES Penetration , 2018, IEEE Access.

[13]  Robert J. Thomas,et al.  Secure Planning and Operations of Systems With Stochastic Sources, Energy Storage, and Active Demand , 2013, IEEE Transactions on Smart Grid.

[14]  Silvano Martello,et al.  Decision Making under Uncertainty in Electricity Markets , 2015, J. Oper. Res. Soc..

[15]  A. Conejo,et al.  Decision making under uncertainty in electricity markets , 2010, 2006 IEEE Power Engineering Society General Meeting.

[16]  Emil M. Constantinescu,et al.  Flexible Operation of Batteries in Power System Scheduling With Renewable Energy , 2016, IEEE Transactions on Sustainable Energy.

[17]  Danny H. K. Tsang,et al.  A Two-Stage Approach for Network Constrained Unit Commitment Problem With Demand Response , 2018, IEEE Transactions on Smart Grid.

[18]  C. Buenoa,et al.  Wind powered pumped hydro storage systems , a means of increasing the penetration of renewable energy in the Canary Islands , 2006 .

[19]  Brayima Dakyo,et al.  Energy Management in the Decentralized Generation Systems Based on Renewable Energy—Ultracapacitors and Battery to Compensate the Wind/Load Power Fluctuations , 2015, IEEE Transactions on Industry Applications.

[20]  Xiaodai Dong,et al.  Short-Term Operation Scheduling in Renewable-Powered Microgrids: A Duality-Based Approach , 2014, IEEE Transactions on Sustainable Energy.

[21]  Boming Zhang,et al.  Transmission-Constrained Unit Commitment Considering Combined Electricity and District Heating Networks , 2016, IEEE Transactions on Sustainable Energy.

[22]  B. Hobbs,et al.  Value of Price Responsive Load for Wind Integration in Unit Commitment , 2014, IEEE Transactions on Power Systems.

[23]  Josep M. Guerrero,et al.  Economic demand response model in liberalised electricity markets with respect to flexibility of consumers , 2017 .

[24]  Vahid Vahidinasab,et al.  Robust linear architecture for active/reactive power scheduling of EV integrated smart distribution networks , 2018 .

[25]  Ahmed Yousuf Saber,et al.  Resource Scheduling Under Uncertainty in a Smart Grid With Renewables and Plug-in Vehicles , 2012, IEEE Systems Journal.

[26]  J. Guerrero,et al.  An optimal market-oriented demand response model for price-responsive residential consumers , 2018, Energy Efficiency.

[27]  Daniel S. Kirschen,et al.  Coupling Pumped Hydro Energy Storage With Unit Commitment , 2016, IEEE Transactions on Sustainable Energy.

[28]  Yi Ding,et al.  A Multi-State Model for Exploiting the Reserve Capability of Wind Power , 2018, IEEE Transactions on Power Systems.

[29]  Vahid Vahidinasab,et al.  SoS-based multiobjective distribution system expansion planning , 2016 .

[30]  Mohammad Mardaneh,et al.  Moving beyond the optimal transmission switching: stochastic linearised SCUC for the integration of wind power generation and equipment failures uncertainties , 2017 .

[31]  Marnix C. Vlot,et al.  Economical Regulation Power Through Load Shifting With Smart Energy Appliances , 2013, IEEE Transactions on Smart Grid.