Optimal Unit Commitment by Considering High Penetration of Renewable Energy and Ramp Rate of Thermal Units-A case study in Taiwan
Source abstract
Abstract
When large amounts of wind power and solar photovoltaic (PV) power are integrated into an independent power grid, the intermittent renewable energy destabilizes power output. Therefore, this study explored the unit commitment (UC) optimization problem; the ramp rate was applied to solve problems with 30 and 10 min of power shortage. The data of actual unit parameters were provided by the Taiwan Power Company. The advanced priority list method was used together with a combination of a generalized Lagrangian relaxation algorithm and a random feasible directions algorithm to solve a large-scale nonlinear mixed-integer programming UC problem to avoid local and infeasible solutions. The results showed that the proposed algorithm was superior to improved particle swarm optimization (IPSO) and simulated annealing (SA) in terms of the minimization of computation time and power generation cost. The proposed method and UC results can be effective information for unit dispatch by power companies to reduce the investment costs of power grids and the possibility of renewable energy being disconnected from the power system. Thus, the proposed method can increase the flexibility of unit dispatch and the proportion of renewable energy in power generation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Electric Power System Optimization · Smart Grid Energy Management · Energy Load and Power Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, National Chin-Yi University of Technology, 57, Sec. 2, Zhongshan Road, Taiping District, Taichung City 41170, Taiwan
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan
Leadtrend Technology Corporation. No.1, Taiyuan 2nd St., Zhubei City Hsinchu County 302, Taiwan
References (24)
Show source-provided references
1. REN21 (Renewables 2018 Global Status Report, 2018). Renewables 2018 Global Status Report, Renewable Energy Policy Network for the 21th Century.
2. (2018, November 07). Bureau of Energy, Ministry of Economic Affairs, R.O.C., Available online: https://www.moeaboe.gov.tw/.
3. Case study of the constraints and potential contributions regarding wind curtailment in Northeast China
4. Dynamic Ramping Model Including Intraperiod Ramp-Rate Changes in Unit Commitment View Document10.1109/TSTE.2016.2578302 ↗
5. Parallel Augmented Lagrangian Relaxation for Dynamic Economic Dispatch Using Diagonal Quadratic Approximation Method
6. An energy storage algorithm for ramp rate control of utility scale PV (photovoltaics) plants
7. Saka, M., Tezcan, S.S., and Eke, I. (2017, January 8–10). Economic load dispatch using vortex search algorithm. Proceedings of the 4th International Conference on Electrical and Electronic Engineering (ICEEE), Ankara, Turkey.10.1109/ICEEE2.2017.7935796 ↗
8. Dynamic Ramping Model Including Intraperiod Ramp-Rate Changes in Unit Commitment10.1109/TSTE.2016.2578302 ↗
9. A coordinated dispatching strategy for wind power rapid ramp events in power systems with high wind power penetration10.1016/j.ijepes.2014.08.019 ↗
10. A Proofreading Tool using Brute Force Techniques10.1109/MPOT.2011.940378 ↗
11. Unit commitment with identical units successive subproblem solving method based on Lagrangian relaxation10.1109/TPWRS.2002.805003 ↗
12. Solving the Hydro Unit Commitment Problem via Dual Decomposition and Sequential Quadratic Programming10.1109/TPWRS.2006.873121 ↗
13. Minimum distance computation of LDPC codes using a branch and cut algorithm10.1109/TCOMM.2010.04.090164 ↗
14. Multistage Artificial Neural Network Short-Term Load Forecasting Engine With Front-End Weather Forecast10.1109/TIA.2007.908190 ↗
15. Unit commitment by heuristics and absolutely stochastic simulated annealing10.1049/iet-gtd:20050574 ↗
16. Shi, Y., and Eberhart, R. (1998, January 4–9). A modified particle swarm optimizer. Proceedings of the IEEE International Conference on Evolutionary Computation, Anchorage, AK, USA.
17. Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, WA, Australia.
18. Application of Combined Feasible-Direction Method and Genetic Algorithm to Optimal Planning of Harmonic Filters Considering Uncertainty Conditions10.1049/ip-gtd:20045203 ↗
19. Yang, T.F., and Ting, T.O. (2008, January 12–14). Methodological Priority List for Unit Commitment Problem. Proceedings of the International Conference on Computer Science and Software Engineering, Hubei, China.
20. Wood, A.J., and Wollenberg, B.F. (1996). Power Generation Operation and Control, John Willey & Sons, Inc.. [2nd ed.].10.1016/0140-6701(96)88715-7 ↗
21. Short-Term Load Forecasting with a New Nonsymmetric Penalty Function10.1109/TPWRS.2011.2142330 ↗
22. Reliability Optimization with the Lagrange-Multiplier and Branch-and-Bound Technique10.1109/TR.1987.5222487 ↗
23. Random feasible directions algorithm with a generalized Lagrangian relaxation algorithm for solving unit commitment problem10.1080/02533839.2014.999865 ↗
24. On the Choice of Random Directions for Stochastic Approximation Algorithms10.1109/TAC.2005.864195 ↗