计及风电场涉网功率平抑与AGC考核的混合储能容量优化配置

doi:10.19595/j.cnki.1000-6753.tces.242194

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Abstract

Addressing the issue of optimal capacity configuration for hybrid energy storage systems (HESS) combining lithium-ion batteries and supercapacitors in wind farms, this study focuses on scenarios involving grid-connected power smoothing for wind farms, assessment of automatic generation control (AGC) power instructions, and ancillary services. Considering the life-cycle cost of the HESS, as well as integrating multiple revenue streams such as wind power consumption, peak-shaving and valley-filling arbitrage, carbon trading, power smoothing, and assessment of AGC power instructions along with ancillary services, a bi-level optimal capacity configuration model for the HESS in wind farms is proposed. The aim is to enhance the economic benefits of the combined wind-storage system. The upper layer considers market electricity prices and wind power output data, aiming to maximize the arbitrage revenue from energy storage. Based on optimal energy storage arbitrage, a model for optimizing the capacity of the hybrid energy storage system is established. The lower layer comprehensively considers the full life-cycle cost of energy storage, carbon trading rules, wind power smoothing, and the assessment and compensation mechanism for AGC power instructions. Using predictive control methods, a capacity configuration model is constructed with the objective of maximizing the benefits of the combined wind-storage system. Iterative solutions are then sought between the upper and lower layers. In order to solve the problem of command allocation of lower-level hybrid energy storage power, the improved Hilbert-Huang Transform (HHT) is employed to determine the power instructions for lithium-ion batteries and supercapacitors. Finally, through the analysis of examples, the capacity optimization allocation method of HESS in wind farms is verified, which effectively improves the economic benefits of grid-related power stabilization and AGC power command assessment of the combined wind storage system.
The upper-level model considers market electricity prices and wind power output characteristics, aiming to maximize the profit of energy storage arbitrage, and constructs a model to solve the optimal energy storage charge-discharge schedule, determining capacity allocation schemes to be passed to the lower level. The lower-level model takes into account the full lifecycle cost of energy storage, carbon trading rules, power smoothing, AGC power instruction assessment, and compensation mechanisms. To avoid issues such as insufficient charge-discharge response capability due to State of Charge (SOC) exceeding limits, a predictive control method is employed to construct a capacity optimization allocation model with the goal of maximizing the net benefits of the wind-energy storage hybrid system. The capacity allocation results are adjusted and returned to the upper level, achieving optimal configuration of mixed energy storage through iterative solving. Regarding the power instruction allocation issue in the lower-level model, an improved HHT method is adopted to decompose wind power instructions and AGC power instructions, determining the instantaneous frequency of IMF components. Based on the principle of minimizing overlap between instantaneous frequency-time curves, the sub-frequency frequencies of lithium-ion batteries and supercapacitors are determined.
For a 100MW wind farm, simulation was conducted to verify the economic and operational effectiveness of the proposed wind farm HESS capacity optimization allocation scheme. This paper performs a comparative analysis among multiple scenarios considering power smoothing of wind power and the assessment of AGC power instructions along with auxiliary services. The effectiveness of the proposed capacity optimization allocation method for the wind farm's lithium-ion batteries and supercapacitors hybrid energy storage system is validated. Simulation results demonstrate that the proposed wind farm HESS capacity optimization allocation model can effectively reduce wind power fluctuations, accurately respond to AGC power instructions, and enhance the overall economic benefits of the wind farm.
The conclusions of this paper are given as follows: 1) The proposed two-layer capacity optimization scheme for the wind farm HESS ensures the revenue of wind power generation, meets the grid requirements for power smoothing and AGC power instruction tracking as outlined in the "two regulations," and enhances the overall economic benefits of the wind farm. 2) The HESS configuration scheme effectively smooths out fluctuations in wind power, addressing the requirements for mitigating fluctuations in wind power and avoiding penalties in electricity assessments. 3) Building upon ensuring the smooth integration of wind power into the grid, the HESS configuration scheme responds to AGC power instructions, reducing penalties in electricity assessments, acquiring compensation revenue for auxiliary services, and enhancing the economic benefits of wind farms in responding to AGC power instructions.

Key words： Wind power suppression AGC power order ancillary services grid-connected assessment hybrid energy storage optimization allocation

Received: 05 December 2024

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TM711

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	Yang Hao
	Song Zhenhan
	Shi Xiaohan
	Yi Wenfei
	Song Haoyu

Cite this article:

Yang Hao,Song Zhenhan,Shi Xiaohan等. Considering the grid-related power stabilization of wind farms and the optimal allocation of hybrid energy storage capacity assessed by AGC[J]. Transactions of China Electrotechnical Society, 0, (): 250435-.

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