Emergency reserve activation considering demand-side resources and battery storage in a hybrid power system

Optimal sequential and dynamic emergency reserve scheduling and activation plans considering the spinning reserves, demand-side resources and battery storage in a hybrid power system are proposed in this paper. The hybrid power system consists of conventional thermal generating units, wind energy generators, solar photovoltaic plants and electric vehicles. An optimal emergency plan has been developed using the coordinated action of slow and fast reserves for the secure power system operation with optimum overall cost. In this paper, we have considered reserves from thermal generators (i.e., spinning reserves), demand-side resources and also the reserves from battery storage. Here, an optimal dynamic and sequential reserve activation plans are developed by using the optimal coordination of fast reserves (i.e., demand-side resources and battery storage) and slow reserves (i.e., spinning reserves). The optimization problem is solved using the MATLAB optimization toolbox (Fmincon function). The effectiveness and suitability of the proposed approach is tested on IEEE 30, 118 and 300 bus test systems. The simulation results show the advantage of dynamic emergency reserve activation approach over the sequential reserve activation approach.

[1]  Maria Vrakopoulou,et al.  Spinning and non-spinning reserve allocation for stochastic security constrained unit Commitment , 2014, 2014 Power Systems Computation Conference.

[2]  P. R. Bijwe,et al.  Coordinated Action of Fast and Slow Reserves for Optimal Sequential and Dynamic Emergency Reserve Activation , 2016 .

[3]  B. Hobbs,et al.  Benefits of coordinating sizing, allocation and activation of reserves among market zones , 2017 .

[4]  H. Gooi,et al.  Spinning Reserve Estimation in Microgrids , 2011 .

[5]  S. Surender Reddy,et al.  Day-Ahead and Real Time Optimal Power Flow considering Renewable Energy Resources , 2016 .

[6]  S. Surender Reddy,et al.  Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm , 2016 .

[7]  M. Shahidehpour,et al.  Short-term scheduling of battery in a grid-connected PV/battery system , 2005, IEEE Transactions on Power Systems.

[8]  Nit Petcharaks.  Optimal spinning reserve under load and intermittent generation uncertainty using Monte Carlo simulation , 2015, 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[9]  Seema Singh,et al.  Multi-objective unit commitment with renewable energy using hybrid approach , 2016 .

[10]  Manisha Govardhan,et al.  Joint energy and spinning reserve scheduling integrating uncertain wind power , 2016, 2016 IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS).

[11]  O. Alsac,et al.  Optimal Load Flow with Steady-State Security , 1974 .

[12]  Kevin Tomsovic,et al.  Quantifying Spinning Reserve in Systems With Significant Wind Power Penetration , 2012 .

[13]  Kenneth Bruninx,et al.  Endogenous probabilistic reserve sizing and allocation in unit commitment models: Cost-effective, reliable and fast , 2017, 2017 IEEE Power & Energy Society General Meeting.

[14]  David C. Yu,et al.  An Economic Dispatch Model Incorporating Wind Power , 2008, IEEE Transactions on Energy Conversion.

[15]  Víctor Manuel Fernandes Mendes,et al.  Bilevel approach to wind-CSP day-ahead scheduling with spinning reserve under controllable degree of trust , 2016 .

[16]  Daniel S. Kirschen,et al.  Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration , 2009, IEEE Transactions on Power Systems.

[17]  Michael Milligan,et al.  Operating Reserves and Wind Power Integration: An International Comparison , 2010 .

[18]  S. Surender Reddy,et al.  Optimal power flow with renewable energy resources including storage , 2016, Electrical Engineering.

[19]  Je-Seok Shin,et al.  Operation Strategy of Multi-Energy Storage System for Ancillary Services , 2017, IEEE Transactions on Power Systems.

[20]  James A. Momoh,et al.  Realistic and Transparent Optimum Scheduling Strategy for Hybrid Power System , 2015, IEEE Transactions on Smart Grid.

[21]  S. Surender Reddy,et al.  Optimal dynamic emergency reserve activation using spinning, hydro and demand-side reserves , 2016 .

[22]  Mark O'Malley,et al.  Reserves in Stochastic Unit Commitment: An Irish System Case Study , 2015, IEEE Transactions on Sustainable Energy.

[23]  Qian Xu,et al.  Joint day-ahead energy and reserve scheduling model with demand response , 2015, 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT).

[24]  Lawrence E. Jones,et al.  Renewable Energy Integration: Practical Management of Variability, Uncertainty, and Flexibility in Power Grids , 2014 .

[25]  Nilay Shah,et al.  Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies , 2017 .

[26]  Nuntiya Chaiyabut,et al.  Optimal spinning reserve for wind power uncertainty by unit commitment with EENS constraint , 2014, ISGT 2014.

[27]  William D'haeseleer,et al.  Optimization and allocation of spinning reserves in a low-carbon framework , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[28]  Pierluigi Siano,et al.  Evaluating the impact of sub-hourly unit commitment method on spinning reserve in presence of intermittent generators , 2016 .

[29]  Weiwei Miao,et al.  Hierarchical market integration of responsive loads as spinning reserve , 2013 .

[30]  P. R. Bijwe,et al.  Real time economic dispatch considering renewable energy resources , 2015 .

[31]  Víctor Manuel Fernandes Mendes,et al.  Self-scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach , 2014 .

[32]  Mario A. Rios,et al.  Spinning reserve computation for a power system with WPG using Point-Estimate Methods , 2015, 2015 IEEE Eindhoven PowerTech.

[33]  Yue Wang.  Risk assessment of stochastic spinning reserve of a wind-integrated multi-state generating system based on a cross-entropy method , 2017 .

[34]  S. Surender Reddy,et al.  Optimal scheduling of thermal-wind-solar power system with storage , 2017 .

[35]  R. Entriken,et al.  Reserve determination for system with large wind generation , 2009, 2009 IEEE Power & Energy Society General Meeting.