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CN-121984044-A - Low-carbon transformation-oriented energy storage resource optimal allocation method for receiving-end urban power grid

CN121984044ACN 121984044 ACN121984044 ACN 121984044ACN-121984044-A

Abstract

The invention relates to a low-carbon conversion-oriented energy storage resource optimization configuration method of a receiving-end urban power grid, which comprises the following steps of calculating node carbon potential based on carbon emission flow or marginal emission; and under the constraint of the set, solving the configuration scheme of the output energy storage loader by adopting a two-stage random programming mode. Compared with the prior art, the method has the advantages that the node carbon potential is coupled with the price of the electric power market, the energy storage optimization target is brought in, the target of minimizing the expected total cost and risk is taken as the target, the uncertainty is processed by combining the two-stage stochastic programming, the cooperative optimization of the energy storage capacity, the position and the investment/operation can be realized, and the energy storage configuration scheme which simultaneously meets the safety, economy and low-carbon targets of the power grid is obtained.

Inventors

  • ZHANG HUI
  • FEI DANXIONG
  • CAI JIANFENG
  • HE XINQIN
  • CHEN ZEYUAN
  • ZHANG XINRAN
  • FAN WENWEN
  • ZHANG YANSHI
  • JIANG HAOMIN

Assignees

  • 国网上海市电力公司

Dates

Publication Date
20260505
Application Date
20251208

Claims (10)

  1. 1. The low-carbon transformation-oriented energy storage resource optimization configuration method for the receiving-end urban power grid is characterized by comprising the following steps of: s1, calculating node carbon potential based on carbon emission flow or marginal emission; S2, joint carbon potential of the nodes and price of the electric power market are brought into a configuration target; and S3, solving the configuration scheme of the output energy storage loader by adopting a two-stage random programming mode under the constraint of the set.
  2. 2. The method for optimizing and configuring the energy storage resources of the low-carbon transformation-oriented receiving-end urban power grid according to claim 1, wherein the step S1 is specifically to construct a node-level carbon responsibility allocation model for calculating the node carbon potential based on a carbon emission flow theory or an average emission factor substitution marginal method.
  3. 3. The method for optimizing and configuring energy storage resources of low-carbon transformation-oriented receiving-end urban power grid according to claim 2, wherein the node-level carbon responsibility allocation model is used for calculating extra load of nodes by means of load flow sensitivity or scheduling simulation for each time t and each scene s Resulting in increased carbon emissions from the system To obtain a node carbon potential of: Wherein, the The carbon potential of the node i at time t and scene s is obtained.
  4. 4. The method for optimizing and configuring energy storage resources of low-carbon-conversion-oriented receiving-end urban power grid according to claim 3, wherein the electric power market price in step S2 comprises electricity price under scene S Price of auxiliary service Price of carbon Valuation grids of green 。
  5. 5. The method for optimizing and configuring energy storage resources of a low-carbon transformation-oriented receiving-end urban power grid according to claim 4, wherein the configuration target in the step S2 is specifically that the total cost and risk are expected to be minimized: Wherein, the For the energy storage rated power capacity of node k, To reserve energy capacity for the energy storage of node k, For a binary addressing variable, it is indicated whether an energy storage unit is built at node k, 1 indicates building, 0 indicates non-building, For the stored power output at time t and scene s, For purchasing power from the power grid by the node i under the time t and the scene s, For the energy storage construction investment cost function, For the energy storage operation cost function, For the system carbon emissions at time t and scene s, For the number of green certificates obtained at time t and scene s, In order for the risk avoidance coefficient to be a factor, For conditional Risk value (Conditional Value-at-Risk), the expected loss Risk term at confidence level α is represented.
  6. 6. The method for optimizing and configuring energy storage resources of the low-carbon transformation-oriented receiving-end urban power grid according to claim 5, wherein the system carbon emission under the time t and the scene s is specifically as follows: Wherein, the The net power increment for node i at time t, scene s.
  7. 7. The method for optimizing and configuring energy storage resources of a low-carbon transformation-oriented receiving-end urban power grid according to claim 1, wherein in the step S3, the aggregate constraint includes energy storage dynamic constraint, SOC limit constraint, power upper and lower limit constraint, voltage constraint and voltage-frequency support constraint.
  8. 8. The low-carbon transformation-oriented energy storage resource optimization configuration method for the receiving-end urban power grid of claim 7, wherein the aggregate constraint is specifically: energy storage dynamic: ; SOC limit of 0≤0 ≤ ; Upper and lower power limits: , ; Grid power flow/voltage constraints: ; Voltage-frequency support constraint to meet support index in critical scenarios And (3) with Is set to a threshold value of (2).
  9. 9. The method for optimizing and configuring energy storage resources of a low-carbon transformation-oriented receiving-end urban power grid according to claim 1, wherein the two-stage stochastic programming method in the step S3 comprises a first-stage scale investment decision and a second-stage scene scheduling decision.
  10. 10. The method for optimizing and configuring energy storage resources of low-carbon-conversion-oriented receiving-end urban power grid as set forth in claim 9, wherein the scale investment decision is specifically to select the installed capacity for each candidate point k And energy capacity And determining a binary addressing variable; The scene scheduling decision is specifically to determine the charging and discharging active power, reactive power and charge state corresponding to time t and scene s.

Description

Low-carbon transformation-oriented energy storage resource optimal allocation method for receiving-end urban power grid Technical Field The invention relates to the technical field of energy storage regulation and control, in particular to a low-carbon transformation-oriented energy storage resource optimization configuration method for a receiving-end urban power grid. Background In recent years, new energy with strong randomness, volatility and intermittence is rapidly developed, the output of a traditional unit represented by coal electricity is limited, and the adoption of energy storage resources with low carbon attribute, large adjustment range, high adjustment speed and long duration to participate in the balance of an electric power system is a trend. In the aspect of the energy storage resource optimal configuration strategy of the receiving-end urban power grid, the energy storage resource can effectively stabilize wind and light fluctuation, promote new energy consumption, and have obvious low-carbon characteristics. Aiming at reducing the carbon emission of an electric power system, the prior art research is mainly conducted around economic cost, carbon emission and fluctuation new energy consumption multi-dimension, the prior art research is less in considering the optimal configuration of energy storage resources by system voltage-frequency support and composite carbon regulation, and the coupling characteristic consideration among the safety, economy and low-carbon targets of a receiving-end urban power grid is more shallow. However, as the new energy permeability of the receiving urban grid increases, the energy storage configuration decision is no longer pursuing purely the electricity economic profit, and there is an urgent need to trade off between grid safety (voltage/frequency support), economy (electricity/auxiliary service market revenue) and low carbon goals (carbon tax/carbon trading/green evidence). Disclosure of Invention The invention aims to overcome the defects of the prior art and provide the energy storage resource optimal configuration method of the receiving-end urban power grid facing the low-carbon transformation, which can obtain an energy storage configuration scheme meeting the aims of safety, economy and low carbon of the power grid. The invention aims at realizing the following technical scheme that the energy storage resource optimal configuration method of the receiving-end urban power grid facing the low-carbon transformation comprises the following steps: s1, calculating node carbon potential based on carbon emission flow or marginal emission; S2, joint carbon potential of the nodes and price of the electric power market are brought into a configuration target; and S3, solving the configuration scheme of the output energy storage loader by adopting a two-stage random programming mode under the constraint of the set. Further, the step S1 is specifically to construct a node-level carbon responsibility allocation model for calculating the node carbon potential based on the carbon emission flow theory or the average emission factor substitution marginal method. Further, the node-level carbon responsibility allocation model calculates the node extra load by simulating based on power flow sensitivity or scheduling for each time t and scene sResulting in increased carbon emissions from the systemTo obtain a node carbon potential of: Wherein, the The carbon potential of the node i at time t and scene s is obtained. Further, the electricity market price in the step S2 includes electricity price under scene SPrice of auxiliary servicePrice of carbonValuation grids of green。 Further, the configuration objective in step S2 is specifically to minimize the desired total cost and risk: Wherein, the For the energy storage rated power capacity of node k,To reserve energy capacity for the energy storage of node k,For a binary addressing variable, it is indicated whether an energy storage unit is built at node k, 1 indicates building, 0 indicates non-building,For the stored power output at time t and scene s,For purchasing power from the power grid by the node i under the time t and the scene s,For the energy storage construction investment cost function,For the energy storage operation cost function,For the system carbon emissions at time t and scene s,For the number of green certificates obtained at time t and scene s,In order for the risk avoidance coefficient to be a factor,For conditional Risk value (Conditional Value-at-Risk), the expected loss Risk term at confidence level α is represented. Further, the system carbon emission under the time t and the scene s is specifically as follows: Wherein, the The net power increment for node i at time t, scene s. Further, in the step S3, the aggregate constraint includes an energy storage dynamic constraint, an SOC limit constraint, a power upper and lower limit constraint, a voltage constraint, and a voltage-frequency support const