Unit commitment (UC) is an optimal flow problem that determines which generator should be turned on/off on a particular time and how much power should be generated while minimizing the operational cost and satisfying the constraints of the system. Since the presence of renewable energy sources brings more uncertainties to the system, solving UC problem becomes. Providing a good reserve strategy is one of the solutions to the uncertain RES. In this research, feasible reserve is investigated in a simple PV-Diesel system. The problem is solved as a two-stage day ahead robust stochastic unit commitment (SUC) using a metaheuristic variant of particle swarm optimization (PSO). Unlike other researches which deal with the ramping of generators, this research extends ramp rate consideration further to the scheduling of available reserve to deal with hidden infeasible reserve issues found in literatures. This two stage SUC was solved using a new Scouting PSO (SPSO) for the main problem and a repair strategy using priority listing for the subproblem. The SPSO introduces the two-fold scouting mechanisms embedded in the inertia weight and additional rescouting step. It was also proven effective and competitive with other algorithms in literature in solving the IEEE 10-unit UC problem. A new metaheuristic problem formulation was used to deal with the desired conditions for the feasible ramping range revealing more significant advantage of using the proposed method in both deterministic approach and stochastic approach over the conventional computation on the scheduling and allocation of reserve to deal with uncertainties brought by renewables.

Unit commitment (UC) is an optimal flow problem that determines which generator should be turned on/off on a particular time and how much power should be generated while minimizing the operational cost and satisfying the constraints of the system. Since the presence of renewable energy sources brings more uncertainties to the system, solving UC problem becomes. Providing a good reserve strategy is one of the solutions to the uncertain RES. In this research, feasible reserve is investigated in a simple PV-Diesel system. The problem is solved as a two-stage day ahead robust stochastic unit commitment (SUC) using a metaheuristic variant of particle swarm optimization (PSO). Unlike other researches which deal with the ramping of generators, this research extends ramp rate consideration further to the scheduling of available reserve to deal with hidden infeasible reserve issues found in literatures. This two stage SUC was solved using a new Scouting PSO (SPSO) for the main problem and a repair strategy using priority listing for the subproblem. The SPSO introduces the two-fold scouting mechanisms embedded in the inertia weight and additional rescouting step. It was also proven effective and competitive with other algorithms in literature in solving the IEEE 10-unit UC problem. A new metaheuristic problem formulation was used to deal with the desired conditions for the feasible ramping range revealing more significant advantage of using the proposed method in both deterministic approach and stochastic approach over the conventional computation on the scheduling and allocation of reserve to deal with uncertainties brought by renewables.

Department of Energy, Philippines, "2016 Power Statistics," Manila, 2017.
"Energy Statistical Annual Reports," Taipei, 2016.
A. Lee, H. Chen and J. Chen, "Building smart grid to power the next century Taiwan," vol. 68, no. 1, pp. 126-135, 2017.
K. Ngitevelekwa and R. Bansal, "A review of generation dispatch with large-scale photovoltaic systems," Review and Sustainable Energy Reviews, vol. 81, pp. 615-624, 2018.
A. Salisu and T. Ayinde, "Modeling energy demand: some emerging issues," Renewable and Sustainable Energy Reviews, vol. 54, pp. 1470-1480, 2016.
G. Notton, M. Nivet, C. Voyant, C. Paoli, C. Darras, F. Motte and A. Fouilloy, "Intermittent and stochastic character of renewable energy sources: cnsequences, cost of intermittence and benefit of forecasting," Renewable and Sustainable Energy Reviews, vol. 87, pp. 96-105, 2018.
K. Lappalainen and S. Valkealahti, "Output power variation of different PV array configurations during irradiance transitions caused by moving clouds," Applied Energy, vol. 190, pp. 902-910, 2017.
S. Abujarad, M. Mustafa and J. Jamian, "Recent approaches of unit commitment in the presence of intermittent renewable energy resources: A review," Renewable and Sustainable Energy Reviews, pp. 215-223, 2017.
M. I. Alizadeh, M. P. Moghaddam and N. Amjady, "Multistage multiresolution robust unit commitment with nondeterministic flexible ramp considering load and wind variables," IEEE Transactions on Sustainable Energy, vol. 9, no. 2, pp. 872-883, 2018.
Z. Guo, R. Chen, N. Fan and J. Watson, "Contingency-constrained uit commitment with intervening time for system adjustments," IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 3049-3059, 2017.
C. Shao, X. Wang, M. Shahidehpour, X. Wang and B. Wang, "Security-constrained unit commitment with flexible uncertainty set for variable wind power," IEEE Transactions on Sustainable Energy, vol. 8, no. 3, pp. 1237-1246, 2017.
Y. Zhang, J. Wang, T. Ding and X. Wang, "Conditional value at risk-based stochastic unit commitment considering the uncertainty of wind power generation," IET Journals, vol. 12, no. 2, pp. 482-489, 2018.
C. Correa-Posada, G. Morales-Espana, P. Duenas and P. Sanches-Martin, "Dynamic Ramping Model Including Intraperiod Ramp-Rate Changes in Unit Commitment," IEEE Transactions on Sustainable Energy, vol. 8, no. 1, pp. 43-50, 2017.
Q. Zhai, X. Li, X. Lei and X. Guan, "Transmission constrained UC with wind power: an all-scenario-feasible MILP formulation with strong non-anticipativity," IEEE Transactions on Power Systems, vol. 32, no. 3, pp. 1805-1817, 2017.
Z. Soltani, M. Ghaljehei, G. Gharehpetian and H. Aalami, "Integration of smart grid technologies in stochastic multi-objective unit commitment: An economic emission analysis," vol. 100, pp. 565-590, 2018.
H. Quan, D. Srinivasan and A. Khosravi, "Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: a comparative study," Energy, vol. 103, pp. 735-745, 2016.
N. Saber, M. Salimi and D. Mirabbasi, "A priority list based approach for solving thermal unit commitment problem with novel hybrid genetic-imperialist competitive algorithm," Energy, vol. 117, pp. 272-280, 2016.
A. Shukla and S. Singh, "Multi-objective unit commitment with renewable energy using hybrid approach," IET Journal, vol. 10, no. 3, pp. 327-338, 2016.
K. Srikanth, L. Panwar, B. Panigrahi and E. Herrera-Viedma, "Meta-heuristic framework: quantum inspired binary grey wolf optimizer for unit commitment problem," Computers and Electrical Engineering, pp. 1-18, 2017.
W. Wang, C. Li, X. Liao and H. Qin, "Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm," Applied Energy, pp. 612-626, 2017.
F. Barani, M. Mirhosseini, H. Nezamabadi-puor and M. Farsangi, "Unit commitment by an improved binary quantum GSA," Applied Soft Computing, vol. 60, pp. 180-189, 2017.
A. Azkarzadeh, "Distribution generation by photovoltaic and diesel generator systems: Energy management and size optimization by a new approach for a stand alone application," Energy, vol. 122, pp. 542-551, 2017.
L. Panwar, R. Srikanth, A. Verma, B. Panigrahi and K. Rajesh, "Binary grey wolf optimizer for large scale unit commitment problem," Swarm and Evolutionary Computation, vol. 38, pp. 251-266, 2018.
M. Hedayati-Mehdiabadi, K. Hedman and J. Zhang, "Reserve Policy Optimization for scheduling wind energy and reserve," IEEE Transactions on Power System, vol. 33, no. 1, pp. 19-31, 2018.
H. Ye, J. Wang, Y. Ge, J. Li and Z. Li, "Robust integration of high-level dispatchable renewables in power system operation," IEEE Transactions on Sustainable Energy, vol. 8, no. 2, pp. 826-835, 2017.
H. Pandzic, Y. Dvorkin, T. Qui, Y. Wang and D. Kirschen, "Toward cost-efficient and reliable unit commitment under uncertainty," IEEE Transactions on Power Systems, vol. 31, no. 2, pp. 970-981, 2016.
W. Yuan and Q. Zhai, "Power-based transmission constrained unit commitment with energy based reserve," IET Journal, vol. 11, no. 2, pp. 409-418, 2017.
NASA, "Surface Meteorology and Solar Energy," [Online]. Available: https://eosweb.larc.nasa.gov/sse/.
H. Abdi, S. Beigvand and M. La Scala, "A review of optimal power flow studies applied to smart grids and microgrids," Renewable and Sustainable Energy Reviews, vol. 71, pp. 742-766, 2017.
A. Alqurashi, A. Etemadi and A. Khodaei, "Treatment of uncertainty for next generation power systems:State-of-the-art in stochastic optimization," Electric Power System Research, pp. 233-245, 2016.
K. Kumar and B. Saravanan, "Recent technologies to model uncertainties inpower genrationfrom renewable energy sources and loads in microgrid-a review," Renewable and Sustainable Energy Reviews, vol. 71, pp. 348-358, 2017.
M. Emmanuel and R. Rayudu, "Evolution of dispatchable photovoltaic system integration with electric power netwrok for smart grid applications: a review," Renewable and Sustainable Energy Reviews, vol. 67, pp. 207-224, 2017.
X. Yang, Nature-Inspired Optimization Algorithms, London: Elsevier, 2014.
Z. Abdmouleh, A. Gastli, L. Ben-Brahim and M. Haouari, "Review of optimization techniques applied for the integration of distributed generation from renewable energy sources," Renewable Energy, vol. 113, pp. 266-280, 2017.
E. Portilla-Flores, A. Sanchez-Marquez, L. Flores-Pulido, E. Vega-Alvarado, M. Yanez and J. Aponte-Rodriguez, "Enchancing the Harmony Search Algorithm Performance on Constrained Numerical Optimization," IEEE Access, vol. 5, pp. 25759-25780, 2017.
M. Mafarja and S. Mirjalili, "Hybrid whate optimization algorithm with simulated annealing for feature selection," Neurocomputing, vol. 260, pp. 302-312, 2017.
H. Xiong, Z. Wu, Z. Fan, G. Li and G. Jiang, "Quantum rotation gate in quantum-inspired evolutionary algorithm: A review, analysis and comparison study," Swarm and Evolutionary Computation, pp. 1-15, 2017.
J. Kennedy and R. Eberhart, "Particle Swarm Optimization," IEEE International Conference on Neural Networks, 1995.
Z. Geem, K. Lee and G. Loganathan, "A New Heuristic Optimization Algorithm: Harmony Search," Simulation, vol. 76, no. 2, pp. 60-68, 2001.
V. Kamboj, S. Bath and J. Dhillon, "Implementation of hybrid harmony search/random search algorithm for single area unit commitment problem," International Journal of Electrical Power & Energy Systems, vol. 77, pp. 228-249, 2016.
M. Nazari-Heris, A. Fakmir-Babaei and B. Mohammadi-Ivatloo, "A novel hybrid harmony search and particle swarm optimization method for solving combined heat and power economic dispatch," in Smart Grid Conference, Tehran, 2017.
Y. Liang and J. Cuevas-Juarez, "Harmony search and virus optimization algorithm for multi-objective combined economic energy dispatching problems," in Evolutionary Computation, Vancouver, 2016.
M. Kheshti, X. Kang, J. Li, P. Regulski and V. Terzija, "Lightning flash algorithm for solving non-convex combined emission economic dispatch with generator constraints," IET Generation, Transmission & Distribution, vol. 12, no. 1, pp. 104-116, 2018.
Y. Shi and R. Eberhart, "A modified particle swarm optimizer," in Evolutionary Computation Proceedings; IEEE World Congress on Computaional Intelligence, AK, USA, 1998.
M. R. Bonyadi and Z. Michalewicz, "Analysis of stability, local convergence, and transformation sensitivity of a variant of the particle swarm optimization algorithm," IEEE Transactions on Evolutionary Computation, vol. 20, no. 3, pp. 370-385, 2016.
J. J. Liang, A. K. Qin, P. Suganthan and S. Baskar, "Comprehensive learning particle swarm optimizer for global optimization of multimodal functions," IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281-295, 2006.
Y. Li, Z. Zhan, S. Lin, J. Zhang and X. Luo, "Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems," Information Sciences, vol. 293, pp. 370-382, 2015.
H. Han, W. Lu, Y. Hou and J. Qiao, "An adaptive-PSO-based self-organizing RBF neural network," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 1, pp. 104-117, 2018.
Y. Chen, L. Li, J. Xiao, Y. Yang, J. Liang and T. Li, "Particle swarm optimization with crossover operation," Engineering Applications of Artificial Intelligence, vol. 70, pp. 159-169, 2018.
Q. Zheng, J. Wang and A. Liu, "Stochastic optimization for unit commitment - a review," IEEE Transactions on Power Systems, vol. 30, no. 4, pp. 1913-1924, 2015.
M. Kazemi, P. Siano, D. Sarno and A. Goudarzi, "Evaluating the impact of sub-hourly unit commitment method on spinning reserve in presence of intermittent generators," Energy, vol. 113, pp. 338-354, 2016.
S. Abujarad, M. Mustafa and J. Jamian, "Unit commitment problem solution in the presence of solar and wind integration by an imporves priority list method," in Intelligent and Advanced Systems, Kuala Lumpur, Malaysia, 2016.
A. Elsayed, A. Maklad and S. Farrag, "A new priority list unit commitment method for large scale power systems," in Power Systems Conference (MEPCON), Cairo, Egypt, 2017.
Y. Xiang, J.-Y. Liu and Y.-L. Liu, "Robust Energy Management of Microgrid with Uncertain Renewable Generation and Load," IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 1034-1043, 2016.
I. Blanco and J. Morales, "An efficient robust solution to the two-stage stochastic unit commitment problem," IEEE transaction on Power System, vol. 32, no. 6, pp. 4477-4488, 2017.
K. Bruninx and E. Delarue, "Endogenous prababilistic reserve sizing and allocation in unit commitment models: costeffective, reliable and fast," IEEE Transactions on Power System, vol. 32, no. 4, pp. 2593-2603, 2017.
Y. Zhang, J. Wang, B. Zeng and Z. Hu, "Chance-Constrained Two-stage Unit Commitment under uncertain Load and Wind Power Output Using Bilinear Benders Decomposition," IEEE Transactions on Power Systems, vol. 32, no. 5, pp. 3637-3647, 2017.
C. Peng, Y. Hou, N. Yu and W. Wang, "Risk-limiting unit commitment in smart grid with intelligent periphery," IEEE Transactions on Power System, vol. 32, no. 6, pp. 4696-4707, 2017.
J. Morales, S. Pineda, A. Conejo and M. Carrion, "Scenario Reduction for Future Market Trading in Electricity Markets," IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 878-888, 2009.
J. Zhan, C. Chung and A. Zare, "A Fast Solution Methods for Stochastic Transmission Expansion Planning," IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4684-4695, 2017.
N. Growe-Kuska, H. Heitsch and W. Romisch, "Scenario Reduction and Scenario Tree Construction for Power Management Problems," in IEEE Bologna PowerTech Conference, Bologna, 2003.
Y. Wang, Y. Liu and D. S. Kirschen, "Scenario Reduction With Submodular Optimization," IEEE Transactions on Power Systems, vol. 32, no. 3, pp. 2479-2480, 2017.