CN-121643063-B - Energy storage resource cross-period optimal scheduling method and system based on dynamic electricity price

CN121643063BCN 121643063 BCN121643063 BCN 121643063BCN-121643063-B

Abstract

The invention belongs to the technical field of automatic processing of power systems, and particularly relates to a method and a system for energy storage resource cross-period optimal scheduling based on dynamic electricity price, wherein the method comprises the steps of collecting electricity price and load power in real time and synchronously obtaining temperature difference and current intensity inside and outside an energy storage cabin; calculating the electricity price sensitivity coefficient and the energy maintenance cost at any sampling moment, weighting the difference value of the real-time electricity price relative to the reference electricity price by using the electricity price sensitivity coefficient, determining a dispatching priority index based on the energy maintenance cost under the reference electricity price, calculating the execution power according to the difference value of the absolute value and the set dispatching threshold when the absolute value of the dispatching priority index is larger than the set dispatching threshold, and determining the final value of the execution power in combination with the load power. According to the invention, while the economic benefit of cross-period scheduling is improved, the electric quantity feedback and power distribution overload are effectively avoided, and the operation stability of the energy storage system is enhanced.

Inventors

YAN JIANYING
JIANG BIN
Cai Zhanru
HAN WEI
XIONG WEI

Assignees

四联智能技术股份有限公司

Dates

Publication Date: 20260508
Application Date: 20260205

Claims (7)

1. The energy storage resource cross-period optimal scheduling method based on the dynamic electricity price is characterized by comprising the steps of collecting electricity price and load power in real time and synchronously obtaining the temperature difference and the current intensity inside and outside an energy storage cabin; Calculating the relative difference between the real-time electricity price and the average electricity price of a plurality of adjacent sampling moments before the sampling moment to obtain the electricity price deviation degree; Calculating internal resistance heat energy loss according to the extracted physical parameters of the energy storage system and the current intensity at the sampling time, and determining energy maintenance cost by combining the electric energy consumed by the temperature difference inside and outside the energy storage cabin; Acquiring a reference electricity price at a sampling time, weighting a difference value of a real-time electricity price at the sampling time relative to the reference electricity price by using an electricity price sensitivity coefficient, and determining a dispatching priority index based on the weighted value and an energy maintenance cost under the reference electricity price; Responding to the absolute value of the dispatching priority index being larger than a set dispatching threshold value, executing a cross-period dispatching action, calculating execution power according to the difference value between the absolute value and the set dispatching threshold value, and taking the load power as the final value of the execution power if the absolute value of the execution power exceeds the load power at the sampling moment, otherwise, taking the absolute value of the execution power as the final value of the execution power; the electricity price sensitivity coefficient satisfies the expression: ; Is the first The electricity price sensitivity coefficient at each sampling moment; Is the first The electricity price deviation degree of each sampling moment; 、 Is the first Sample time and th Real-time electricity prices at each sampling moment; taking an absolute value; Is a natural exponential function; Is a standard normalization function; the energy maintenance cost satisfies the expression: ; Is the first Energy maintenance costs for each sampling instant; Is the first Current intensity at each sampling instant; Is equivalent internal resistance; Is the temperature control efficiency coefficient; Is the first The temperature difference between the inside and the outside of the energy storage cabin at each sampling moment; is the rated total energy; Is a sampling time interval; the scheduling priority index satisfies the expression: ; Is the first Scheduling priority index of each sampling moment; Is the first Reference electricity prices at the sampling moments; Is a zero avoidance parameter.
2. The energy storage resource cross-period optimal scheduling method based on dynamic electricity prices according to claim 1, wherein the obtaining the electricity price deviation degree comprises the following steps: Calculating the average value of the real-time electricity prices of a plurality of adjacent sampling moments before the sampling moment, obtaining the absolute difference value of the real-time electricity price and the average value of the sampling moment, and taking the ratio of the absolute difference value to the average value as the electricity price deviation degree.
3. The energy storage resource cross-period optimal scheduling method based on dynamic electricity price according to claim 1, wherein the physical parameters of the energy storage system are equivalent internal resistance, temperature control efficiency coefficient and rated total energy.
4. The energy storage resource cross-period optimal scheduling method based on the dynamic electricity price according to claim 1, wherein the reference electricity price at the sampling time is obtained by the following steps: and searching a power market quotation sequence of the power dispatching mechanism on the day of the sampling time, and extracting the average value of the power price in the sequence as a reference power price.
5. The method for cross-period optimal scheduling of energy storage resources based on dynamic electricity prices according to claim 1, wherein the performing the cross-period scheduling action comprises: recording the dispatch priority index of sampling time as Setting the scheduling threshold as If (1) Executing discharge action if And executing the charging action.
6. The energy storage resource cross-period optimal scheduling method based on dynamic electricity prices according to claim 1, wherein the calculating the execution power comprises: , Is the first The execution power at the time of the sampling, As a function of the base power of the power source, Is a coefficient of proportionality and is used for the control of the power supply, Is the first The difference between the absolute value of the scheduling priority index at each sampling instant and the set scheduling threshold.
7. The energy storage resource cross-period optimal scheduling system based on the dynamic electricity price is characterized by comprising a processor and a memory, wherein the memory stores computer program instructions, and the computer program instructions, when executed by the processor, realize the energy storage resource cross-period optimal scheduling method based on the dynamic electricity price according to any one of claims 1-6.

Description

Energy storage resource cross-period optimal scheduling method and system based on dynamic electricity price Technical Field The invention relates to the technical field of automatic processing of power systems. More particularly, the invention relates to a method and a system for energy storage resource cross-period optimal scheduling based on dynamic electricity price. Background In a modern power system, an energy storage system is used as a key resource for regulating power supply and demand balance, and plays an important role in improving the stability of a power grid and reducing the electricity consumption cost of a user through a peak clipping and valley filling mode of energy storage in a low-electricity-price period and energy release in a high-electricity-price period. At present, the traditional energy storage scheduling mode is to preset a charging and discharging electricity price threshold value, when the real-time electricity price is lower than the charging threshold value, charging is started, and when the real-time electricity price is higher than the discharging threshold value, discharging is executed. The dynamic electricity price is influenced by multiple factors such as new energy access, load mutation and the like, the characteristics of nonlinearity and high-frequency fluctuation are presented, the traditional energy storage scheduling mode cannot adapt to the complicated electricity price distribution, so that the electricity price is discharged in advance at a secondary high point or is charged in advance at a secondary low point, the energy storage resources are not occupied effectively, meanwhile, the total energy utilization rate in the actual scheduling process is low due to the fact that the heat loss difference of the energy storage medium under different powers is ignored, the optimal charging and discharging time cannot be identified, and the economy and response accuracy of resource cross-period scheduling are reduced. Disclosure of Invention In order to solve the technical problem that energy storage scheduling lacks flexibility and energy loss is ignored under the dynamic electricity price to cause unreasonable cross-period resource allocation, the invention provides a scheme in the following aspects. The invention provides a method for optimizing and dispatching energy storage resources in a time span based on dynamic electricity prices, which comprises the steps of collecting electricity prices and load power in real time, synchronously obtaining temperature difference and current intensity inside and outside an energy storage cabin, calculating relative difference between the real-time electricity prices and average electricity prices of a plurality of adjacent sampling moments before the sampling moments at any sampling moment, obtaining electricity price deviation degree, determining an electricity price sensitivity coefficient according to instantaneous change rate of the real-time electricity prices and the electricity price deviation degree, calculating internal resistance heat energy loss according to physical parameters of an extracted energy storage system and the current intensity of the sampling moments, determining energy maintenance cost in combination with electric energy consumed by temperature difference inside and outside the energy storage cabin, obtaining reference electricity prices of the sampling moments, weighting the difference of the real-time electricity prices relative to the reference electricity prices by the electricity price sensitivity coefficient, determining a dispatching priority index based on the weighted value and the energy maintenance cost under the reference electricity prices, responding to the fact that the absolute value of the dispatching priority index is larger than a set dispatching threshold, executing a time span dispatching action, calculating execution power according to the difference between the absolute value and the set dispatching threshold, and taking the absolute value as the execution power when the absolute value exceeds the absolute value, otherwise taking the execution power as the final execution power. The invention captures nonlinear fluctuation and instantaneous change trend of dynamic electricity price by introducing self-adaptive electricity price sensitivity coefficient, solves the problem of ineffective occupation of resources caused by insufficient flexibility of traditional scheduling, overcomes the technical defect of low efficiency caused by neglecting energy loss by analyzing internal resistance heat loss and temperature control energy consumption to construct energy maintenance cost, further comprehensively analyzes loss cost and profit space, constructs scheduling priority index, introduces load power to realize boundary constraint of execution power, and remarkably improves economy and distribution safety of resource scheduling in a cross-period. Preferably, the acquiring the electricity