CN116632933A - Source network load storage collaborative stability control algorithm based on equivalent model - Google Patents
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Abstract
The invention discloses a source network load storage cooperative stability control algorithm based on an equivalent model, which relates to the technical field of power transmission scheduling, and comprises the following steps: the method comprises the steps of establishing a source network load storage optimization scheduling model, comprehensively considering coordinated interaction characteristics among source network load storages, establishing a source network load storage coordination optimization model, establishing a coordination optimization strategy based on a consistency algorithm, solving the problems of volatility, randomness and the like of renewable energy sources when the renewable energy sources are integrated into a power grid, further improving the stability of the power grid when the renewable energy sources are integrated into renewable resources, fully utilizing the complementary characteristics of various devices to conduct demand side management, improving regulation and control capabilities, carrying out coordination scheduling on the source network load storages based on cluster agents, effectively reducing peak-valley differences of system load, relieving peak regulation pressure of the system, realizing real-time power balance while guaranteeing the running economy and maximizing social benefit of the system, and simultaneously taking flexible load and energy storage into consideration on a load side based on a resource allocation model of the coordination scheduling of the source network load storages.
Description
Technical Field
The invention relates to the technical field of power transmission scheduling, in particular to a source network load storage collaborative stability control algorithm based on an equivalent model.
Background
Along with the coordinated multi-element interaction structure of the source network and the charge storage, the flexibility and the reliability of the system operation are greatly enhanced, but as the participation of the power system in the main body is increased, how to meet different benefit demands among the main bodies and ensure the safe and stable operation of the power grid is the problem to be solved at present, in the aspect of the coordinated optimal scheduling of the source network and the charge storage of the power transmission grid, although partial research is carried out in the aspect of the coordinated optimal scheduling of the source network and the charge storage of the power transmission grid at present, the acceptance of the power grid to multiple types of power sources is improved by utilizing advanced technology, however, the active scheduling strategy of the power transmission network level with four elements of source network load storage is fully considered, and along with the rapid development of a distributed algorithm, the source network load storage collaborative optimization scheduling problem can be efficiently solved through the distributed algorithm, the distributed algorithm has the advantages of protecting user privacy, reducing the communication traffic with a central controller, avoiding system breakdown caused by single-point faults and the like, and the common distributed algorithm has the advantages of an alternate multiplier iteration method, an auxiliary problem principle, optimal condition decomposition, a consistency algorithm and the like, so that the distributed algorithm is not fully researched for the source network load storage multi-element collaborative scheduling problem at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a source network load storage collaborative stability control algorithm based on an equivalent model, and solves the problems in the background art.
In order to achieve the above purpose, the invention is realized by the following technical scheme: the source network load storage collaborative stability control algorithm based on the equivalent model comprises the following steps:
step one: the method comprises the steps of establishing a source network charge storage optimization scheduling model, firstly establishing active power balance constraint of a power grid, then establishing a power supply model, establishing a load model and establishing an energy storage model, finally establishing the source network charge storage optimization scheduling model, obtaining a multi-mode switching control model based on voltage safety event triggering of a power distribution network feeder line, and establishing a power generation facility model, namely a source side model; the power generation facility comprises a generator and a renewable energy source, wherein the cost function of the generator is as followsThe following forms:wherein P is G,i The output active power of the generator i; a, a i ，b i ，C i A fuel cost factor for generator i; omega shape G Is a generator i set;
step two: comprehensively considering the coordination interaction characteristics between source network and load storages, clustering the distributed renewable energy power generation active power by using a multi-scene technology to obtain S typical day PDGs, S (t) of the distributed renewable energy power generation active power and the probability ps of each typical day, wherein s=1, 2, S, dividing the distributed renewable energy power generation active power into a plurality of scenes by using the multi-scene technology and giving out the probability of each scene, predicting the time sequence value of the illumination intensity and the wind speed in a longer time according to a prediction model, analyzing the constraint quantity of a power grid side, and comprehensively considering the coordination interaction characteristics between the source network and load storages;
step three: establishing a source network and load storage collaborative optimization model, establishing the source network and load storage collaborative optimization model of the active power distribution network by taking the minimum daily comprehensive operation cost of the active power distribution network as a target, and establishing a time sequence output model PDG (t) of active power output of distributed renewable energy power generation by taking 15min as a basic step length, wherein the active power output of the distributed renewable energy power generation is considered to be unchanged within 15 min;
step four: establishing a collaborative optimization strategy based on a consistency algorithm, continuously iterating discrete variables by adopting a particle swarm optimization algorithm based on the active power distribution network source load storage collaborative optimization model, solving an optimal solution, then carrying out normalization, establishing a collaborative optimization strategy based on the consistency algorithm, updating marginal cost of each agent, updating output power of each agent, locally updating local mismatch quantity, and updating local mismatch quantity again after neighbor information is obtained.
Preferably, in the first step, a network side power balance constraint is established; since economic dispatch generally dispatches active power, reactive power is temporarily overridden, and thus the following active power balance constraint is established:
wherein P is G,i ，P w,r ，P B,s And P D,l The active power of the generator i, the renewable energy r, the energy storage equipment s and the electric load l are respectively; omega shape G ，Ω w ，Ω b ，Ω D Respectively a generator i, a renewable energy source r, an energy storage device s and an electric load l.
Preferably, in the first step, the method performs reduction analysis on multiple scenes based on a parallel iterative bipartite K-means- + enhancement clustering algorithm, and simulates time sequence characteristics and uncertainty of illumination intensity and load demands; constructing a power distribution network light storage joint optimization configuration model based on source network load collaborative optimization; and providing a parallel double-quantum differential evolution algorithm to carry out efficient solution on a power distribution network light-storage joint configuration model based on source network load collaborative optimization.
Preferably, the first step further includes analyzing the response performance characteristics of the demand side, establishing a demand side response scheduling model to realize coordinated scheduling and effective interaction between supply and demand sides of the power grid, analyzing the characteristics of power generation equipment of the power generation side in the power grid, obtaining typical power generation characteristics of renewable energy sources wind power, photovoltaic power generation and nonrenewable energy sources, obtaining the output conditions of each power source in each period of a typical day, and establishing a model for analyzing the characteristics of the energy storage unit, thereby laying a foundation for establishing an optimal control model.
Preferably, the multi-mode switching control model based on voltage security event triggering in the first step optimizes multiple targets considering source, load, storage cost and network transmission loss under each operation mode to obtain power optimization values of each end of source load storage under long time scale.
Preferably, the fourth step further includes establishing a collaborative optimization model for source network load reserve with the minimum running cost of the power system in the scheduling period; and carrying out decoupling analysis on a power generation cost function model, a power generation side standby operation cost function model, a load side standby operation cost function model and a source network load reserve cooperative optimization model of the power system by adopting a source network load reserve cooperative optimization algorithm based on standby value discrimination, so as to obtain the standby capacities of the power generation side, the load side, the power grid side and the energy storage side.
Preferably, the first step further includes determining resource evaluation indexes based on the functions of the distributed power source, the power grid, the power load and the energy storage in the cooperative interactive regulation and control process, and dividing the resource evaluation indexes into two layers to coordinate control capacity, resource interaction capacity and resource utilization capacity as first layer resource evaluation indexes; the comprehensive peak regulation capacity, the energy storage quick response capacity, the reactive power supporting capacity, the voltage stability, the distributed energy permeability, the load peak-valley difference, the distributed energy consumption rate, the DG utilization rate and the load resource utilization rate of the system are used as second-layer resource evaluation indexes.
The invention provides a source network load storage collaborative stability control algorithm based on an equivalent model. The beneficial effects are as follows:
the source network load storage cooperative stability control algorithm based on the equivalent model is based on economic dispatch, realizes real-time power balance while ensuring the running economy of a system and maximizing social benefit, simultaneously considers flexible load and energy storage on a load side based on a source network load storage cooperative dispatch resource allocation model, considers different power supplies and output characteristics thereof on a power supply side, solves the problems of fluctuation, randomness and the like of renewable energy sources when the renewable energy sources are integrated into a power grid through the cooperative complementation of flexible load resources and clean renewable energy sources, further improves the stability when the power grid is integrated into renewable resources, fully utilizes the complementation characteristics of various devices, manages the demand side, improves the regulation and control capability, and effectively reduces the peak-valley difference of the system load and relieves the peak regulation pressure of the system when the source network load storage based on the cluster agent is cooperatively dispatched; by adopting the consistency algorithm, the problem solving speed is increased, the privacy of each cluster is ensured, the network loss is considered at the network side, the system running cost is further optimized, and the stability of the renewable energy grid connection is improved.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a diagram of a system partition control scheme in accordance with the present invention;
fig. 3 is a diagram of a distributed control architecture of the power distribution system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, the embodiment of the invention provides a technical scheme: the source network load storage collaborative stability control algorithm based on the equivalent model comprises the following steps:
step one: the method comprises the steps of establishing a source network charge storage optimization scheduling model, firstly establishing active power balance constraint of a power grid, then establishing a power supply model, establishing a load model and establishing an energy storage model, finally establishing the source network charge storage optimization scheduling model, obtaining a multi-mode switching control model based on voltage safety event triggering of a power distribution network feeder line, and establishing a power generation facility model, namely a source side model; the power generation facility includes a generator and a renewable energy source, wherein a cost function of the generator is of the form:wherein P is G,i The output active power of the generator i; a, a i ，b i ，C i A fuel cost factor for generator i; omega shape G Is a generator i set;
step two: comprehensively considering the coordination interaction characteristics between source network and load storages, clustering the distributed renewable energy power generation active power by using a multi-scene technology to obtain S typical day PDGs, S (t) of the distributed renewable energy power generation active power and the probability ps of each typical day, wherein s=1, 2, S, dividing the distributed renewable energy power generation active power into a plurality of scenes by using the multi-scene technology and giving out the probability of each scene, predicting the time sequence value of the illumination intensity and the wind speed in a longer time according to a prediction model, analyzing the constraint quantity of a power grid side, and comprehensively considering the coordination interaction characteristics between the source network and load storages;
step three: establishing a source network and load storage collaborative optimization model, establishing the source network and load storage collaborative optimization model of the active power distribution network by taking the minimum daily comprehensive operation cost of the active power distribution network as a target, and establishing a time sequence output model PDG (t) of active power output of distributed renewable energy power generation by taking 15min as a basic step length, wherein the active power output of the distributed renewable energy power generation is considered to be unchanged within 15 min;
step four: establishing a collaborative optimization strategy based on a consistency algorithm, continuously iterating discrete variables by adopting a particle swarm optimization algorithm based on the active power distribution network source load storage collaborative optimization model, solving an optimal solution, then carrying out normalization, establishing a collaborative optimization strategy based on the consistency algorithm, updating marginal cost of each agent, updating output power of each agent, locally updating local mismatch quantity, and updating local mismatch quantity again after neighbor information is obtained.
Establishing network side power balance constraint in the first step; since economic dispatch generally dispatches active power, reactive power is temporarily overridden, and thus the following active power balance constraint is established:
wherein P is G,i ，P w,r ，P B,s And P D,l The active power of the generator i, the renewable energy r, the energy storage equipment s and the electric load l are respectively; omega shape G ，Ω w ，Ω b ，Ω D Respectively a generator i, a renewable energy source r, an energy storage device s and an electric load l.
The method in the first step carries out reduction analysis on multiple scenes based on a parallel iterative bipartite K-means- + enhancement clustering algorithm, and simulates the time sequence characteristics and uncertainty of illumination intensity and load demands; constructing a power distribution network light storage joint optimization configuration model based on source network load collaborative optimization; and providing a parallel double-quantum differential evolution algorithm to carry out efficient solution on a power distribution network light-storage joint configuration model based on source network load collaborative optimization.
The first step further comprises analyzing the response performance characteristics of the demand side, establishing a demand side response scheduling model to achieve coordinated scheduling and effective interaction of supply and demand sides of the power grid, analyzing the characteristics of power generation equipment of the power generation side in the power grid, obtaining the typical power generation characteristics of renewable energy wind power, photovoltaic power generation and non-renewable energy, obtaining the output condition of each power supply in each period of typical days, and establishing a model for analyzing the characteristics of the energy storage unit to lay a foundation for establishing an optimal control model.
The multi-mode switching control model based on the triggering of the voltage safety event in the first step optimizes the multi-objective considering the source, the load, the storage cost and the network transmission loss under each operation mode to obtain the power optimization value of each end of the source load storage under a long time scale.
The fourth step further comprises the step of establishing a collaborative optimization model for source network load reserve, wherein the operation cost of the source network load reserve is minimum in a scheduling period; and carrying out decoupling analysis on a power generation cost function model, a power generation side standby operation cost function model, a load side standby operation cost function model and a source network load reserve cooperative optimization model of the power system by adopting a source network load reserve cooperative optimization algorithm based on standby value discrimination, so as to obtain the standby capacities of the power generation side, the load side, the power grid side and the energy storage side.
The first step further comprises determining resource evaluation indexes based on the functions of the distributed power supply, the power grid, the power load and the energy storage resources in the cooperative interactive regulation and control process, and dividing the resource evaluation indexes into two layers, wherein the coordination control capacity, the resource interaction capacity and the resource utilization capacity are used as first-layer resource evaluation indexes; the comprehensive peak regulation capacity, the energy storage quick response capacity, the reactive power supporting capacity, the voltage stability, the distributed energy permeability, the load peak-valley difference, the distributed energy consumption rate, the DG utilization rate and the load resource utilization rate of the system are used as second-layer resource evaluation indexes.
The method is based on economic dispatch, realizes real-time power balance while ensuring the running economy of the system and maximizing social welfare, simultaneously considers flexible load and energy storage on the load side and considers different power supplies and output characteristics thereof on the power supply side based on a resource allocation model of source network load storage coordination dispatch, solves the problems of fluctuation, randomness and the like of renewable energy sources when the renewable energy sources are integrated into a power grid through coordination complementation of flexible load resources and clean renewable energy sources, further improves the stability of the power grid when the renewable energy sources are integrated into the power grid, fully utilizes the complementation characteristics of various devices, carries out demand side management, improves regulation and control capability, and effectively reduces the peak-valley difference of the system load when the source network load storage based on a cluster agent is subjected to cooperative dispatch, and relieves the peak regulation pressure of the system; by adopting the consistency algorithm, the problem solving speed is increased, the privacy of each cluster is ensured, the network loss is considered at the network side, the system running cost is further optimized, and the stability of the renewable energy grid connection is improved.
While the fundamental and principal features of the invention and advantages of the invention have been shown and described, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (7)
1. A source network load storage cooperative stability control algorithm based on an equivalent model is characterized in that: the steps include the following:
step one: the method comprises the steps of establishing a source network charge storage optimization scheduling model, firstly establishing active power balance constraint of a power grid, then establishing a power supply model, establishing a load model and establishing an energy storage model, finally establishing the source network charge storage optimization scheduling model, obtaining a multi-mode switching control model based on voltage safety event triggering of a power distribution network feeder line, and establishing a power generation facility model, namely a source side model; the power generation facility includes a generator and a renewable energy source, wherein a cost function of the generator is of the form:wherein P is G,i The output active power of the generator i; a, a i ，b i ，C i A fuel cost factor for generator i; omega shape G Is a generator i set;
step two: comprehensively considering the coordination interaction characteristics between source network and load storages, clustering the distributed renewable energy power generation active power by using a multi-scene technology to obtain S typical day PDGs, S (t) of the distributed renewable energy power generation active power and the probability ps of each typical day, wherein s=1, 2, S, dividing the distributed renewable energy power generation active power into a plurality of scenes by using the multi-scene technology and giving out the probability of each scene, predicting the time sequence value of the illumination intensity and the wind speed in a longer time according to a prediction model, analyzing the constraint quantity of a power grid side, and comprehensively considering the coordination interaction characteristics between the source network and load storages;
step three: establishing a source network and load storage collaborative optimization model, establishing the source network and load storage collaborative optimization model of the active power distribution network by taking the minimum daily comprehensive operation cost of the active power distribution network as a target, and establishing a time sequence output model PDG (t) of active power output of distributed renewable energy power generation by taking 15min as a basic step length, wherein the active power output of the distributed renewable energy power generation is considered to be unchanged within 15 min;
step four: establishing a collaborative optimization strategy based on a consistency algorithm, continuously iterating discrete variables by adopting a particle swarm optimization algorithm based on the active power distribution network source load storage collaborative optimization model, solving an optimal solution, then carrying out normalization, establishing a collaborative optimization strategy based on the consistency algorithm, updating marginal cost of each agent, updating output power of each agent, locally updating local mismatch quantity, and updating local mismatch quantity again after neighbor information is obtained.
2. The source network load storage collaborative stability control algorithm based on the equivalent model according to claim 1, which is characterized in that: establishing network side power balance constraint in the first step; since economic dispatch generally dispatches active power, reactive power is temporarily overridden, and thus the following active power balance constraint is established:
wherein P is G,i ，P w,r ，P B,s And P D,l The active power of the generator i, the renewable energy r, the energy storage equipment s and the electric load l are respectively; omega shape G ，Ω w ，Ω b ，Ω D Respectively a generator i, a renewable energy source r, an energy storage device s and an electric load l.
3. The source network load storage collaborative stability control algorithm based on the equivalent model according to claim 1, which is characterized in that: the method in the first step carries out reduction analysis on multiple scenes based on a parallel iterative bipartite K-means- + enhancement clustering algorithm, and simulates the time sequence characteristics and uncertainty of illumination intensity and load demands; constructing a power distribution network light storage joint optimization configuration model based on source network load collaborative optimization; and providing a parallel double-quantum differential evolution algorithm to carry out efficient solution on a power distribution network light-storage joint configuration model based on source network load collaborative optimization.
4. The source network load storage collaborative stability control algorithm based on the equivalent model according to claim 1, which is characterized in that: the first step further comprises analyzing the response performance characteristics of the demand side, establishing a demand side response scheduling model to achieve coordinated scheduling and effective interaction of supply and demand sides of the power grid, analyzing the characteristics of power generation equipment of the power generation side in the power grid, obtaining the typical power generation characteristics of renewable energy wind power, photovoltaic power generation and non-renewable energy, obtaining the output condition of each power supply in each period of typical days, and establishing a model for analyzing the characteristics of the energy storage unit to lay a foundation for establishing an optimal control model.
5. The source network load storage collaborative stability control algorithm based on the equivalent model according to claim 1, which is characterized in that: the multi-mode switching control model based on the triggering of the voltage safety event in the first step optimizes the multi-objective considering the source, the load, the storage cost and the network transmission loss under each operation mode to obtain the power optimization value of each end of the source load storage under a long time scale.
6. The source network load storage collaborative stability control algorithm based on the equivalent model according to claim 1, which is characterized in that: the fourth step further comprises the step of establishing a collaborative optimization model for source network load reserve, wherein the operation cost of the source network load reserve is minimum in a scheduling period; and carrying out decoupling analysis on a power generation cost function model, a power generation side standby operation cost function model, a load side standby operation cost function model and a source network load reserve cooperative optimization model of the power system by adopting a source network load reserve cooperative optimization algorithm based on standby value discrimination, so as to obtain the standby capacities of the power generation side, the load side, the power grid side and the energy storage side.
7. The source network load storage collaborative stability control algorithm based on the equivalent model according to claim 1, which is characterized in that: the first step further comprises determining resource evaluation indexes based on the functions of the distributed power supply, the power grid, the power load and the energy storage resources in the cooperative interactive regulation and control process, and dividing the resource evaluation indexes into two layers, wherein the coordination control capacity, the resource interaction capacity and the resource utilization capacity are used as first-layer resource evaluation indexes; the comprehensive peak regulation capacity, the energy storage quick response capacity, the reactive power supporting capacity, the voltage stability, the distributed energy permeability, the load peak-valley difference, the distributed energy consumption rate, the DG utilization rate and the load resource utilization rate of the system are used as second-layer resource evaluation indexes.
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