CN-118970939-B - Power distribution network source network load storage coordination optimization scheduling method and system containing heat storage type industrial load

CN118970939BCN 118970939 BCN118970939 BCN 118970939BCN-118970939-B

Abstract

The invention provides a coordinated optimization scheduling method and system for power distribution network source network load storage containing heat storage type industrial load, wherein the method comprises the steps of obtaining new energy and load history prediction error data; the method comprises the steps of establishing a mathematical model of a gas turbine additionally provided with a flexible carbon capture device, performing convex relaxation treatment on the non-convex problem of the model when the flue gas split ratio of the carbon capture device is adjustable, establishing a heat accumulating type industrial load demand response mathematical model considering daily adjustment capacity, daily adjustment times and daily output constraint, performing piecewise linearization treatment on the heat accumulating type industrial load daily production task nonlinear constraint, acquiring a typical prediction error scene set according to obtained new energy and load historical prediction error data, and establishing a power distribution network load storage coordination optimization scheduling model containing heat accumulating type industrial load demand response. The invention excavates source load storage multi-ring-saving flexible resources, improves new energy consumption rate and economy of system operation, reduces carbon emission, and provides assistance for realizing double-carbon targets and social sustainable development.

Inventors

WANG WENDI
HUANG HAO
QIU YUEMIN
JI YE
LI GANG
YAN SU
WANG SHUFAN
Gu Liuting

Assignees

南京苏逸实业有限公司
南京苏逸实业有限公司科技信息网络分公司
南京华群能源集团有限公司

Dates

Publication Date: 20260505
Application Date: 20240815

Claims (12)

1. The power distribution network source network load storage coordination optimization scheduling method for the heat storage type industrial load is characterized by comprising the following steps of: S1, acquiring new energy and load history prediction error data; S2, establishing a mathematical model of the gas turbine additionally provided with the flexible carbon capture device, and performing convex relaxation treatment aiming at the problem that the model is not convex when the flue gas split ratio of the carbon capture device is adjustable; S3, constructing a heat accumulating type industrial load demand response mathematical model considering daily adjustment capacity, daily adjustment times and daily output constraint, and performing piecewise linearization processing on the heat accumulating type industrial load daily output nonlinear constraint; s4, acquiring a typical prediction error scene set according to the new energy and the load history prediction error data acquired in the S1; s5, constructing a power distribution network source network load storage coordination optimization scheduling model with heat storage type industrial load demand response according to the new energy source provided by the power distribution network scheduling center, the day-ahead prediction curve of the load power and the typical prediction error scene set obtained in the S4; the power distribution network source network load storage coordination optimization scheduling model with the heat storage type industrial load demand response comprises the following steps: step S501, constructing an objective function of a power distribution network optimization scheduling model containing a flexible carbon capture gas turbine, a heat accumulating type industrial load demand response and an energy storage device; And S502, constructing constraint conditions of a power distribution network source network load storage coordination optimization scheduling model containing heat storage type industrial loads.
2. The power distribution network source network load storage coordination optimization scheduling method for the heat storage type industrial load according to claim 1, wherein the power distribution network source network load storage coordination optimization scheduling method is characterized by comprising the following steps of: in S1, the new energy, load history prediction error data includes respective hour-by-hour power prediction error data that was pushed at least one year forward from the start of the scheduled day.
3. The power distribution network source network load storage coordination optimization scheduling method for the heat storage type industrial load according to claim 1, wherein the power distribution network source network load storage coordination optimization scheduling method is characterized by comprising the following steps of: In S2, the flexible carbon trapping device additionally arranged on the gas turbine comprises an absorption tower, a liquid storage tank and a regeneration tower, CO2 absorbed by the absorption tower is stored in the liquid storage tank and is not processed in the peak load period, carbon trapping power of the gas turbine in the peak load period is reduced, internet surfing power is improved, and in the low load period, the stored CO2 is released from the liquid storage tank and is sent into the regeneration tower, and the power of the gas turbine is consumed for subsequent carbon trapping processing.
4. The power distribution network source network load storage coordination optimization scheduling method for the heat storage type industrial load according to claim 1, wherein the power distribution network source network load storage coordination optimization scheduling method is characterized by comprising the following steps of: In S2, the mathematical model of the gas turbine to which the flexible carbon capture apparatus is added is constructed as follows: Wherein, the For the total output power of the gas turbine with the flexible carbon capture device in the t period, For the on-line power of the gas turbine at time t, The energy consumption for the carbon capturing operation of the carbon capturing equipment in the t period, For a fixed energy consumption of the carbon capture device for the period t, The operation energy consumption of the unit CO 2 is captured by the carbon capture equipment, For the total mass of CO 2 captured during period t, The mass of CO 2 entering the carbon capture device after being shunted by the flue gas bypass system in the t period, The rich liquid tank equipped for the carbon capturing device is supplied with the amount of CO 2 in the t period, Is the absorption capacity of CO 2 of the rich liquid tank in the period t, Is the split ratio of the flue gas, For the maximum level of carbon capture, For the total mass of CO 2 actually produced by the gas turbine during the t-period, CO 2 generated for the unit power of the gas turbine, The net carbon emission of the gas turbine is the mass of CO 2 discharged into the air after being branched by the flue gas bypass system in the period of t.
5. The power distribution network source network load storage coordination optimization scheduling method for the heat storage type industrial load according to claim 1 or 4, wherein the power distribution network source network load storage coordination optimization scheduling method is characterized by comprising the following steps of: In S2, the convex relaxation process includes constructing constraints for a mathematical model of the gas turbine according to the following formula: Wherein, the The split ratio of the flue gas is shown, Indicating the total output power of the carbon capture gas turbine for period t, 、 Representing the lower and upper limits of the carbon capture gas turbine output respectively, Representation for substitution The auxiliary variable of the bilinear term, 、 Respectively represent the minimum and the maximum of the split ratio of the flue gas.
6. The power distribution network source network load storage coordination optimization scheduling method for the heat storage type industrial load according to claim 1, wherein the power distribution network source network load storage coordination optimization scheduling method is characterized by comprising the following steps of: In S3, the piecewise linearizing process for the daily output nonlinear constraint includes introducing auxiliary variables according to the following formula to perform piecewise linearizing conversion: Wherein x 1 、x 2 、x 3 is three 0-1 auxiliary variables introduced for piecewise linearization, For the yield of the m-th heat-accumulating industrial load at the moment t of the kth prediction error scene, O m is the yield requirement of the m-th heat-accumulating industrial load, lambda 1 、λ 2 、λ 3 is the product yield under the power down-regulating state, rated power and up-regulating state of the heat-accumulating industrial load equipment respectively, Is the reference power of the operation of the heat accumulating type industrial load equipment, 、 The maximum up-regulation and down-regulation power of the heat accumulating type industrial load under the premise of ensuring safe operation is realized.
7. The power distribution network source network load storage coordination optimization scheduling method for the heat storage type industrial load according to claim 1, wherein the power distribution network source network load storage coordination optimization scheduling method is characterized by comprising the following steps of: In S4, according to the new energy source and the load history prediction error data obtained in S1, obtaining a typical prediction error scene set includes: step S401, calculating Euclidean distance between any two source load history prediction error vectors; step S402, calculating local density of source load historical prediction error data corresponding to the historical prediction error vector; step S403, determining a clustering center distance of source load historical prediction error data; Step S404, traversing all the historical prediction error data of new energy and load by taking the normalized numerical values of the local density and the cluster center distance as horizontal and vertical coordinates, and drawing corresponding points of the historical prediction error data of the source load in a two-dimensional space; And step 405, arranging the products of the horizontal and vertical coordinates in the two-dimensional space, which are drawn in the step 404, from high to low, and taking a set formed by samples corresponding to the points which are ordered in the first 5% as a typical prediction error scene set.
8. The power distribution network source network load storage coordination optimization scheduling method for the heat storage type industrial load according to claim 1, wherein the power distribution network source network load storage coordination optimization scheduling method is characterized by comprising the following steps of: In step S501, the construction formula of the objective function of the power distribution network optimization scheduling model including the flexible carbon capture gas turbine, the heat accumulating type industrial load demand response and the energy accumulating device is as follows: Wherein, the Indicating the start-stop cost of the gas turbine, Representing the operating costs of the carbon capture gas turbine in the kth typical scenario, Representing the purchase cost of the distribution network in the kth typical scenario, Representing the subsidy cost of the distribution network in the kth typical scenario for paying the heat accumulating type industrial load to participate in the demand response, Representing the carbon emission costs in the kth typical scenario, Representing the wind and light curtailment penalty cost in the kth typical scenario, Representing the cut load cost in the kth typical scenario.
9. The power distribution network source network load storage coordination optimization scheduling method for the heat storage type industrial load according to claim 1, wherein the power distribution network source network load storage coordination optimization scheduling method is characterized by comprising the following steps of: In step S502, the constraint condition of constructing the power distribution network source network load storage coordination optimization scheduling model containing the heat storage type industrial load includes: constructing a power balance constraint condition; Constructing a line transmission capacity constraint condition; Constructing constraint conditions of wind discarding, light discarding and load shedding; constructing a carbon capture gas turbine output constraint condition, a climbing constraint condition and a minimum start-stop time constraint condition; and constructing power and energy constraints of the energy storage device.
10. A power distribution network source network load storage optimization scheduling system for a heat storage type industrial load by using the method of any one of claims 1-9, wherein the system comprises a data acquisition module, a gas turbine mathematical model construction module, a demand response mathematical model construction module, a prediction error scene set acquisition module and an optimization scheduling model construction module, and is characterized in that: the data acquisition module is used for acquiring new energy and load historical data, acquiring new energy and load historical prediction error data according to the acquired historical data, and clustering the new energy and load historical prediction error data to acquire a typical prediction error scene set; The gas turbine mathematical model construction module is used for establishing a mathematical model of the gas turbine with the flexible carbon capture device, and carrying out convex relaxation treatment on the non-convex problem of the model when the flue gas split ratio of the carbon capture device is adjustable; The demand response mathematical model construction module is used for constructing a heat accumulating type industrial load demand response mathematical model considering daily adjustment capacity and daily adjustment frequency constraint and carrying out piecewise linearization treatment on daily output nonlinear constraint; the prediction error scene set acquisition module is used for acquiring a typical prediction error scene set according to new energy and load history prediction error data; the optimal scheduling model construction module is used for constructing a power distribution network source network load storage coordination optimal scheduling model of the heat-storage-containing industrial load according to a new energy source provided by a power distribution network scheduling center, a daily prediction curve of load power and an obtained typical prediction error scene set; the power distribution network source network load storage coordination optimization scheduling model with the heat storage type industrial load demand response comprises the following steps: and constructing a constraint function of a power distribution network source network load storage coordination optimization scheduling model containing the heat storage type industrial load.
11. A terminal comprises a processor and a storage medium, and is characterized in that: The storage medium is used for storing instructions; The processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-9.
12. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1-9.

Description

Power distribution network source network load storage coordination optimization scheduling method and system containing heat storage type industrial load Technical Field The invention belongs to the field of power systems, relates to a power distribution network scheduling technology, and particularly relates to a power distribution network source network load storage coordination optimization scheduling method and system containing heat accumulation type industrial loads. Background The power industry is mainly provided with carbon emission, the direct emission of CO 2 is reduced by adopting a carbon trapping technology on the power generation side, new energy is utilized to generate electricity to replace fossil energy to generate electricity, and the method has important emission reduction significance, however, the new energy output has uncertainty and even has certain anti-peak regulation characteristics, namely, the new energy generating capacity in the load peak period is low, the new energy generating capacity in the load valley period is high, the new energy utilization rate is low, moreover, if the new energy generating capacity in the load peak period is low, the increasing of the generating capacity and the carbon emission of a fossil fuel unit is caused, more CO 2 needs to be trapped and treated, but the increasing of the power consumption of the carbon trapping device causes the adverse phenomenon of peak-to-peak peaking, huge peak regulation pressure is brought to the system operation, and the decoupling of the trapped and treated CO 2 can be realized by increasing the liquid storage tank, so that the flexibility of the carbon trapping device is increased, and the situation is relieved. Meanwhile, the thermal inertia of the heat accumulating type industrial load resource is large, after the energy accumulating equipment is additionally arranged, the potential of participating in flexible regulation of a system is huge, if the participation in a demand response plan can be promoted, the peak regulation pressure of the system can be greatly relieved, however, the large-scale excavation of the source load flexible resource inevitably leads to the complexity rise of a system optimal scheduling model, an unreasonable modeling method and an optimal scheduling algorithm, the economy of a scheduling scheme cannot be improved, and even the safety of system operation and load electricity utilization is threatened. In recent years, energy conservation, carbon reduction and clean energy consumption become common knowledge gradually, therefore, an electric power system needs to simultaneously generate power from a source load at multiple angles, the source end can use a carbon capture device to seal and store CO 2 discharged by a traditional fossil energy unit and greatly popularize new energy for power generation, the load end can fully regulate loads, particularly thermal storage type loads with huge potential participate in demand response projects, and the storage end promotes the installation and utilization of energy storage at a user side through mechanisms such as two electricity price making and the like, so that an electricity utilization curve is optimized and new energy power is consumed in situ. However, the flexible carbon capture device and the mathematical model of the heat accumulating type industrial load participation demand response have non-convex and nonlinear characteristics with different degrees, the problem of difficult solution is brought by directly incorporating the flexible carbon capture device and the heat accumulating type industrial load into the power distribution network optimization scheduling model, and in addition, the problem of uncertainty of the operation of the power distribution network, which is brought by the improvement of the permeability of new energy, is also necessary to be considered in the power distribution network optimization scheduling model. CN202011490794.6A discloses a wind power consumption coordination control method based on electric smelting magnesium load, which comprises the steps of predicting a day-ahead wind power output curve, combining actual production running conditions of the electric smelting magnesium load, regarding the electric smelting magnesium load as a controllable load during day-ahead scheduling, regulating and controlling the high energy consumption load in a wind discarding time period, taking the minimum sum of the air discarding quantity in the whole day of the next day as an optimization target, introducing upper and lower limit constraint of electric smelting magnesium load power, regulation duration constraint and regulation times constraint and running constraint of wind power and thermal power, and obtaining the regulation quantity of each period of the electric smelting magnesium load through optimization calculation to realize the consumption of wind power. The actual situation that the carbon capture device is