Energy storage optimization configuration method considering reliability cost
A technology for optimizing configuration and reliability, applied in climate sustainability, neural learning methods, design optimization/simulation, etc., can solve the problems of less research and achieve the effect of improving reliability
Pending Publication Date: 2022-02-08
NANJING INST OF TECH
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Problems solved by technology
There are methods using long-term short-term memory model and time series modeling to conduct research and exploration in wind power forecasting, but there are relatively few related studies on applying LSTM to the field of photovoltaic power forecasting
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Embodiment 1
[0015] Taking photovoltaic output prediction as an example, a solar irradiance, b module temperature, c air temperature, d relative humidity, e atmospheric pressure, f photovoltaic power are used as input, and model evaluation indicators RMSE, MAE, R2 are used as output. The prediction steps are:
[0016] Step S21, data cleaning: clean the collected on-site photovoltaic power data f and environmental data a, b, c, d, e, and remove "bad data" caused by communication failures in actual production in units of days ".
[0017] Step S22 uses the EMD algorithm to decompose the environmental data into eigenmode components {IMF1, IMF2, ..., IMFm} of different frequencies and the residual component rn, and decompose the original environmental sequence into various characteristic fluctuation sequences, so that the original environmental The different scale fluctuations or trends existing in the signal are decomposed step by step.
[0018] Step S23, perform PCA dimensionality reduction...
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The invention provides an energy storage optimization configuration method considering reliability cost. The method comprises the following steps: step S01, predicting new energy output; S02, establishing a system optimization configuration model by taking an expected value of insufficient electric quantity as an evaluation index, wherein the system optimization configuration model specifically comprises an energy storage system model, a reliability cost model, a power generation system cost model and a constraint condition; and S03, configuring energy storage optimization based on the system optimization configuration model. According to the method for collaborative optimization of the energy storage capacity of the wind-solar-energy-storage complementary micro-grid system, reliability cost is considered, wind and light resource multi-time-scale uncertainty is considered at the same time, and aiming at how to improve the operation reliability of a power system, the operation reliability of the micro-grid is evaluated by taking an expected value of insufficient electric quantity as an index; and the reliability is quantized into reliability cost, a reliability cost function is established, the reliability cost function is added into the total cost of system configuration, and then optimization is performed.
Description
technical field [0001] The invention belongs to the field of energy storage optimization configuration for new energy power generation, and relates to an energy storage optimization configuration method considering reliability cost. Background technique [0002] Adding energy storage units to the new energy power generation system can effectively alleviate the problem of reducing the stability of the power system during the process of new energy grid connection, reduce the phenomenon of "abandoning wind and light", and fully consider the operation reliability of the power system. Reasonable optimization of energy capacity can reduce the total cost of microgrid system construction while solving resource and environmental problems. [0003] Aiming at the optimal configuration of energy storage, some methods use particle swarm optimization algorithm to study the capacity configuration of microgrid, construct a model with the lowest configuration cost as the goal, but do not tak...
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Login to View More IPC IPC(8): G06F30/27G06F17/14G06K9/62G06N3/04G06N3/08G06Q10/04G06Q30/02G06Q50/06H02J3/00H02J3/28G06F111/04G06F113/04G06F119/02
CPCG06F30/27G06F17/14G06Q10/04G06Q30/0206G06Q50/06G06N3/08H02J3/008H02J3/28G06F2113/04G06F2119/02G06F2111/04H02J2300/24H02J2300/28H02J2203/20G06N3/044G06F18/2135Y02E10/56Y02E40/70Y02E70/30Y04S10/50
Inventor 王新迪卞海红潘柯言王新策董文超
Owner NANJING INST OF TECH