Wind-Thermal-Nuclear-Storage Combined Time Division Power Dispatch Based on Numerical Characteristics of Net Load

In order to satisfy the strategic needs of energy sustainable development, renewable energy has developed rapidly and the power systems have been transformed to a new generation of power systems. In the renewable energy power generation technologies, the fastest developing wind power generation are highly intermittent and fluctuating. When high penetration of renewable power connects to the power grid and participates in the system dispatch, there will be more difficulties and challenges in the energy balance control. In this paper, a wind-thermal-nuclear-storage combined time division power dispatch strategy based on numerical characteristics of net load is proposed, where a specific thermal generating mode and an unconventional nuclear generating mode are discussed. In the strategy, the dispatch time division method is introduced in detail and the sample entropy theory is used to calculate the net load complexity. An adaptive thermal generating mode is determined according to the numerical characteristics of the net load. The nuclear generating modes of constant power operation, time division operation, and net load tracking time division operation are compared and analyzed, respectively. Finally, the wind-thermal-nuclear-storage combined time division power dispatch strategy aiming at decreasing the ramping power of thermal generators is achieved, and the increasing of the participation of pumped storage and improving of the continuous and steady operation time of thermal generators are realized. The experiment simulation is developed on an actual provincial power system in the northeast of China. The results verify that the thermal generator ramping power in the case based on SampEn are reduced, and the participation of pumped storage is improved. When both of the thermal generating mode and nuclear generating mode are according to the changing of net loads, the ramping powers of thermal generators are further decreased.

[1]  Yu Liu,et al.  Incorporating Concentrating Solar Power into High Renewables Penetrated Power System: A Chance-Constrained Stochastic Unit Commitment Analysis , 2019, Applied Sciences.

[2]  Ruiwei Jiang,et al.  Robust Unit Commitment With Wind Power and Pumped Storage Hydro , 2012, IEEE Transactions on Power Systems.

[3]  Guangchao Geng,et al.  Hydro-Thermal-Wind Coordination in Day-Ahead Unit Commitment , 2016, IEEE Transactions on Power Systems.

[4]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[5]  Wei Zhou,et al.  Optimization Strategy for Economic Power Dispatch Utilizing Retired EV Batteries as Flexible Loads , 2018, Energies.

[6]  Hong Li,et al.  A Distributed Energy Resources Aggregation Model Based on Multi-Scenario and Multi-Objective Methodology , 2019, Applied Sciences.

[7]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[8]  Hamid Shaker,et al.  Impacts of large-scale wind and solar power integration on California׳s net electrical load , 2016 .

[9]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[10]  Jingjing Yang,et al.  Multi-time scale optimal dispatch in ADN based on MILP , 2018, International Journal of Electrical Power & Energy Systems.

[11]  Carlos M. Correa-Posada,et al.  Integrated Power and Natural Gas Model for Energy Adequacy in Short-Term Operation , 2015, IEEE Transactions on Power Systems.

[12]  Matti Lehtonen,et al.  Optimal location-allocation of storage devices and renewable-based DG in distribution systems , 2019, Electric Power Systems Research.

[13]  Yuwei Wang,et al.  Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network , 2018 .

[14]  Yuan-Kang Wu,et al.  Assessment of Potential Variability of Net Load Following the Integration of 3 GW Wind Power in Taiwan , 2016 .

[15]  Jia Hongji,et al.  Research on Some Key Problems Related to Integrated Energy Systems , 2015 .

[16]  Dragana Bajic,et al.  Binarized cross-approximate entropy in crowdsensing environment , 2017, Comput. Biol. Medicine.

[17]  K. Myung-Whun,et al.  An information entropy interpretation of photon absorption by dielectric media , 2019, Optics Communications.

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

[19]  A. Papavasiliou,et al.  Reserve Requirements for Wind Power Integration: A Scenario-Based Stochastic Programming Framework , 2011, IEEE Transactions on Power Systems.

[20]  Chen Xiaoxia.  A Method for AGC Reserve Capacity Analysis Considering Photovoltaic Power Fluctuation Characteristics , 2015 .

[21]  Huang Yong.  Analysis of Nuclear Power Plant Participating in Peak Load Regulation of Power Grid and Combined Operation With Pumped Storage Power Plant , 2011 .

[22]  E. Lannoye,et al.  Assessment of power system flexibility: A high-level approach , 2012, 2012 IEEE Power and Energy Society General Meeting.

[23]  T. Yong,et al.  Ramp enhanced unit commitment for energy scheduling with high penetration of renewable generation , 2015, 2015 IEEE Power & Energy Society General Meeting.

[24]  M A Matos,et al.  Setting the Operating Reserve Using Probabilistic Wind Power Forecasts , 2011, IEEE Transactions on Power Systems.

[25]  Unit Commitment Problem with Pumped-storage Units , 2012 .

[26]  Jinghua Li,et al.  A coordinated dispatch method with pumped-storage and battery-storage for compensating the variation of wind power , 2018 .

[27]  Manuel Chazarra,et al.  Optimal Joint Energy and Secondary Regulation Reserve Hourly Scheduling of Variable Speed Pumped Storage Hydropower Plants , 2018, IEEE Transactions on Power Systems.