CN115481856A

CN115481856A - Comprehensive energy system multi-scale scheduling method and system considering comprehensive demand response

Info

Publication number: CN115481856A
Application number: CN202210905258.0A
Authority: CN
Inventors: 李建杰; 曹金京; 穆明亮; 辛春青; 李蓬; 张鹏; 孙逢麟; 孔庆峰; 吕学志; 李尊华
Original assignee: State Grid Shandong Electric Power Co Ltd
Current assignee: State Grid Shandong Electric Power Co Ltd
Priority date: 2022-07-29
Filing date: 2022-07-29
Publication date: 2022-12-16

Abstract

The invention belongs to the field of comprehensive energy systems, and provides a comprehensive energy system multi-scale scheduling method and system considering comprehensive demand response. The method comprises a day-ahead scheduling stage: the method comprises the steps that the minimum running cost of a power distribution network is taken as an optimization target, and the state of slow-motion equipment on the next day and the slow response speed comprehensive demand response action value are obtained; an intra-day scheduling stage: the method comprises the steps that the minimum running cost of the power distribution network is taken as an optimization target, and the switching power and the medium response speed comprehensive demand response action value among the power transmission and distribution networks are obtained on the basis of the action value of the slow response speed comprehensive demand response; a real-time scheduling stage: and adjusting the output values of the gas turbine, the static reactive power compensation device and the renewable energy source based on the medium response speed comprehensive demand response action value by taking the minimum power fluctuation at the interface of the power transmission network and the power distribution network as an optimization target. The invention realizes the coordination of various comprehensive demand responses in the comprehensive energy system on the types and time, and fully excavates the response capability of the user side.

Description

Comprehensive energy system multi-scale scheduling method and system considering comprehensive demand response

Technical Field

The invention belongs to the field of comprehensive energy systems, and particularly relates to a comprehensive energy system multi-scale scheduling method and system considering comprehensive demand response.

Background

The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

Facing the increasingly serious energy crisis and environmental problems, it is imperative that renewable energy sources are accessed in large quantities at the energy supply side. Due to the inherent uncertainty and randomness of the renewable energy sources, the scheduling control of the power grid becomes gradually complex, and even when the output of large-scale renewable energy sources fluctuates greatly, the power fluctuation at the interface of the power distribution network and the power transmission network is obvious, so that the operation of the power transmission network is influenced. Therefore, the concept of an integrated energy system should be developed. The device utilizes coupling equipment of various energy sources, strengthens physical interconnection among different types of energy sources, integrates supply, conversion, storage and requirements of various energy sources, and effectively solves the problems.

According to the concept of integrated scheduling of source network load storage, the introduction of the demand response project provides a new exploration dimension for system optimization scheduling. The demand response deeply influences various aspects of system operation, and the active participation of the user side in system scheduling can obviously reduce the operation cost of the system and ensure the safe and reliable operation of the system. However, because various energy devices and a large amount of integrated energy loads exist in the integrated energy system, how to coordinate various devices in the integrated energy system to better cooperate with renewable energy sources to achieve power balance and fully exploit the user-side demand response potential is still a key problem to be solved.

To solve the above problems, many studies have been made to improve on the scheduling framework. In the aspect of a scheduling framework, a document, namely a multi-period day-ahead optimization scheduling method, controls the running states of various energy coupling devices in an integrated energy system according to periods, and performs more detailed estimation on the output of renewable energy sources through subdivided periods. However, the error between the result obtained by just using the previous off-line optimization and the actual situation is too large, and the response time of various distributed resources in the system is obviously different, so the method cannot effectively coordinate various resources in the system to fully cooperate with renewable energy sources, and the power balance of the system is influenced.

In order to fully deal with the uncertainty of renewable energy sources, the traditional deterministic optimization model is gradually no longer applicable in the current research, and the uncertain optimization model becomes the focus of attention of scholars at home and abroad. The random optimization is most widely applied, a scene method is usually adopted for random optimization to simulate uncertain variables by using multiple scenes, in order to reduce calculation pressure, scene reduction is carried out to obtain a typical scene, the typical scene is optimized and scheduled, and finally, an expectation is calculated to make a decision.

On the energy supply side, the resistance and reactance values of the power transmission line are close, and the coupling of active power and reactive power is strong, so that unilateral active/reactive power optimization is incomplete and comprehensive. Active and reactive power coordination optimization becomes a mainstream method.

As an extension of the traditional Demand Response (DR), the Integrated Demand Response (IDR) optimizes the operation of flexible load, energy storage and energy conversion equipment on the demand side by using the coupling and complementary relationship of multiple energy sources, and finally improves the flexibility of system operation and the energy utilization efficiency. The document, based on multi-energy complementary electricity/heat comprehensive demand response, provides a comprehensive energy system optimization operation model considering price demand response so as to improve the energy utilization efficiency. Because the various comprehensive demand response projects have great difference in response speed, how to coordinate the various comprehensive demand response projects on different time scales to participate in the optimized scheduling on the system level is a problem worthy of focusing attention.

Disclosure of Invention

In order to solve the technical problems in the background art, the invention provides a comprehensive energy system multi-scale scheduling method and system considering comprehensive demand response, which can fully mobilize various resources in the comprehensive energy system, better cooperate with renewable energy sources to realize power balance and fully explore the demand response potential of a user side.

In order to achieve the purpose, the invention adopts the following technical scheme:

the invention provides a comprehensive energy system multi-scale scheduling method considering comprehensive demand response.

The comprehensive energy system multi-scale scheduling method considering comprehensive demand response comprises the following steps:

a day-ahead scheduling stage: the method comprises the steps that the minimum running cost of a power distribution network is taken as an optimization target, and the state of slow-motion equipment on the next day and the slow response speed comprehensive demand response action value are obtained;

scheduling stage in day: the method comprises the steps that the minimum running cost of the power distribution network is taken as an optimization target, and the switching power and the medium response speed comprehensive demand response action value among the power transmission and distribution networks are obtained on the basis of the action value of the slow response speed comprehensive demand response;

a real-time scheduling stage: and adjusting the output values of the gas turbine, the static reactive power compensation device and the renewable energy source based on the medium response speed comprehensive demand response action value by taking the minimum power fluctuation at the interface of the power transmission network and the power distribution network as an optimization target.

A second aspect of the invention provides an integrated energy system multi-scale dispatch system that takes into account integrated demand response.

An integrated energy system multi-scale dispatch system that considers integrated demand response, comprising:

a day-ahead scheduling module configured to: the method comprises the steps that the minimum running cost of a power distribution network is taken as an optimization target, and the state of slow-motion equipment on the next day and the slow response speed comprehensive demand response action value are obtained;

an intra-day scheduling module configured to: the method comprises the steps that the minimum running cost of the power distribution network is taken as an optimization target, and the switching power and the medium response speed comprehensive demand response action value among the power transmission and distribution networks are obtained on the basis of the action value of the slow response speed comprehensive demand response;

a real-time scheduling module configured to: and adjusting the output values of the gas turbine, the static reactive power compensation device and the renewable energy source based on the medium response speed comprehensive demand response action value by taking the minimum power fluctuation at the interface of the power transmission network and the power distribution network as an optimization target.

A third aspect of the invention provides a computer-readable storage medium.

A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for multi-scale scheduling of an integrated energy system taking into account an integrated demand response as described in the first aspect above.

A fourth aspect of the invention provides a computer apparatus.

A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method for multi-scale scheduling of an integrated energy system taking into account an integrated demand response as described in the first aspect above when executing the program.

Compared with the prior art, the invention has the beneficial effects that:

due to the large access of renewable energy sources on the energy supply side, the power balance of the system is greatly affected. The scheduling method provided by the invention combines a multi-time scale scheduling framework and comprehensive demand response, realizes coordination of various devices under multiple time scales to balance randomness and volatility of output of renewable energy sources, fully excavates the adjustability of a load side, realizes source-load cooperative optimization, and further reduces the influence of the volatility of the renewable energy sources on system operation. Meanwhile, an active and reactive power coordinated optimization method is adopted, and a power grid model in the comprehensive energy system is more accurately described, so that the aims of reducing the network loss and improving the operating economy of the system are fulfilled.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are included to illustrate an exemplary embodiment of the invention and not to limit the invention.

FIG. 1 is a frame diagram of a multi-time scale scheduling method for an integrated energy system with integrated demand response taken into account according to an embodiment of the present invention;

FIG. 2 is a graph illustrating the effect of the method of the present invention and the comparative method in limiting the grid voltage according to an embodiment of the present invention;

FIG. 3 is a graph illustrating the effect of the method of the present invention and the comparative method in limiting active power fluctuation between power transmission and distribution networks, according to an embodiment of the present invention;

fig. 4 is a diagram illustrating an active and reactive power output effect of distributed resources in the method of the present invention and the comparative method according to an embodiment of the present invention.

Detailed Description

The invention is further described with reference to the following figures and examples.

It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

It is noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the logical function specified in the various embodiments. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Example one

As shown in fig. 1, the embodiment provides a multi-scale scheduling method of an integrated energy system considering integrated demand response, and the embodiment is illustrated by applying the method to a server, it is to be understood that the method may also be applied to a terminal, and may also be applied to a system including a terminal and a server, and is implemented by interaction between the terminal and the server. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network server, cloud communication, middleware service, a domain name service, a security service CDN, a big data and artificial intelligence platform, and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein. In this embodiment, the method includes the steps of:

a real-time scheduling stage: and adjusting the output value of the gas turbine, the output value of the static reactive power compensation device, the output value of the energy storage device and the output value of the renewable energy source based on the medium response speed comprehensive demand response action value by taking the minimum power fluctuation at the interface of the power transmission network and the power distribution network as an optimization target.

The specific scheme of the embodiment can be realized according to the following steps:

1. active and reactive coordinated scheduling framework of multi-time scale comprehensive energy system

The comprehensive energy system scheduling framework provided by the embodiment comprises a day-ahead scheduling stage, an in-day scheduling stage and a real-time scheduling stage. The system is optimized with different objectives on different time scales.

(1) A day-ahead scheduling stage: and obtaining a scene of renewable energy output by using a Monte Carlo sampling method, and performing optimized scheduling on the comprehensive energy system by taking 1h as resolution according to a typical scene and load prediction data. Meanwhile, in the stage, the air load demand response with low response speed and the price type demand response of the electric load participate in the optimized dispatching. The optimization objective is to minimize the operating cost of the system 24 h. In the day-ahead scheduling stage, the action state and the action quantity of the slow-motion equipment in the system on the next day are determined, namely the on-load tap changer and the switchable capacitor bank, and the action quantity of the comprehensive demand response is also determined. The optimization result will also be used as a reference value for the intra-day scheduling phase.

(2) Scheduling stage in day: and predicting a typical scene of 4h renewable output in the future by using a Monte Carlo sampling method and taking 15min as resolution. Since the incentive type demand response in the electric load has a medium-speed response characteristic, which is in accordance with the time scale of the intra-day scheduling, the influence of the incentive type demand response of the electric load is considered in the scheduling stage. And performing optimized scheduling on the comprehensive energy based on a typical scene, aiming at minimizing the operation cost of the system, wherein the optimized variables are the output of the gas turbine, the energy storage device and the static reactive power compensation device. The optimized result is used as a reference value for the real-time scheduling phase.

(3) A real-time scheduling stage: and performing deterministic optimization according to the renewable energy output data and the load data of 1h in the future by taking 5min as resolution. The interruptible load can be reduced at any time according to the demand of the system operator within the effective time of the contract made in advance, so that it has a quick response characteristic, matching with the characteristic of the real-time scheduling stage, and thus the interruptible load is introduced in the real-time stage. The optimization goal in the dispatching mode is to minimize the fluctuation between the exchange power of the transmission and distribution network and the reference value.

2. Multi-time scale active and reactive power coordination optimization model of comprehensive energy system

(1) Objective function

1) Day ahead scheduling phase and day in scheduling phase

In order to ensure the economical efficiency of system operation, the optimization targets of the scheduling stage before the day and the scheduling stage in the day are that the operation cost of the power distribution network is minimum, namely

In the formula, the superscript DA represents the day-ahead scheduling phase, the ID represents the day-inside scheduling phase,

the cost of obtaining electric energy from the power transmission network by the power distribution network in the s scene in the t time interval is calculated;

the power generation cost of the ith gas turbine in the s scene in the t time period is calculated;

is the operating cost of the ith energy storage device in the s scene in the t period,

is the operating cost of the ith air source in the tth time period under the s scene, wherein the gas price is kept unchanged all day,

the subsidy amount paid for participation of the incentive load in the demand response at the corresponding dispatch stage,

a subsidy amount paid for incentivizing participation of the electrical load in the demand response; p is a radical of _s The probability of the s-th scene is shown, N is the number of scenes, M is the number of controllable DGs, N is the number of energy storage devices, and T is the number of time periods.

2) Real-time scheduling phase

In order to reduce the influence of the uncertainty of renewable energy sources on the operation of the power grid, the optimization target of the real-time scheduling stage is the minimum power fluctuation at the interface of the power transmission network and the power distribution network.

In the formula, the superscript RT represents the real-time scheduling phase,

and

is the power exchange value between the transmission network and the distribution network promised in the scheduling stage in the day,

and

is an optimization variable in a real-time scheduling stage and represents a power value on a connecting line of a transmission network and a distribution network in the T-th time period, T is the number of the time periods, alpha _t Representing the weight of the power fluctuation in the t-th period.

The fees paid for the system to mobilize the interruptible load at the real-time stage. Since only the scheduling decision of the first time interval is executed in the rolling optimization, the present embodiment proposes to reasonably allocate the weight of the optimization target of each time interval, thereby ensuring the realization of the optimization target in the rolling optimization and better coping with the uncertainty and the volatility of the renewable energy output.

(2) Constraint conditions

1) Flow restraint

According to the characteristics of the power distribution network, the power flow constraint equation of the embodiment can ensure the accuracy of the result on the basis of easy solution.

In the formula, alpha (j) is a branch first node set taking a node j as a tail node; beta (j) is a branch end node set taking the node j as a first node; p is _ij,t And Q _ij,t Respectively active power and reactive power at the head ends of the branches i-j; p is _j,t And Q _j,t Respectively injecting active power and reactive power of a node j; r is _ij +jx _ij Is the impedance of branch i-j; i is _ij,t Is the square of the magnitude of the current flowing through branch i-j; k is a radical of formula _ij,t The transformation ratio of the on-load tap changing transformer at the branch i-j is set;

which is the square of the magnitude of the voltage at node i.

2) Branch capacity constraint

In the formula:

the upper current amplitude limit for branch i-j.

3) Node voltage constraint

In the formula:

and

respectively, an upper limit and a lower limit of the node voltage amplitude.

4) Pipe flow restraint

In the formula: eta _ij,t Is an auxiliary variable and represents the secondary variance of the air pressure at two ends of the pipeline ij, d _ij,+ And d _ij,- Is a variable of 0 to 1, d _ij,+ A value of 1 indicates that the natural gas flows from nodes i to j, d _ij,- 1 denotes the natural gas flow direction from j to i, χ _i, t represents the square value of the air pressure of the node i, k _ij Is the pipe constant, G, of the pipe ij _ij,t Is the natural gas flow rate of conduit ij.

5) Integrated demand response constraints

In the formula: alpha (alpha) ("alpha") _e For the ratio of the price type transferable electric load to the total electric loadFor example, the following examples are given,

and

respectively a minimum value and a maximum value of the price-type transferable electrical load,

is the minimum value of the electrical load, P _t ^OL Is a baseline electrical load, P _t ^CL For the excited type to reduce the electrical load, alpha _g The proportion of the transferable gas load to the total gas load,

and

respectively a minimum value and a maximum value of the transferable gas load,

is the minimum value of the air load,

is a baseline gas load for the gas load,

to reduce the air load, P _t ^IL An interruptible load value that is directly controlled.

6) Renewable energy power generation operation constraints

The support of providing reactive power to a power distribution network by using an inverter of a renewable energy power generation device has recently received more and more attention. When the active power generated by the renewable energy power generation device is smaller than the rated capacity of the inverter, the inverter can be regulated to generate reactive power so as to improve the voltage distribution condition of the power distribution network.

In the formula:

the active power output of the renewable energy sources is predicted,

active output value, Q, for renewable energy sources _RES,i,t Reactive output value of renewable energy, S _i The capacity of the inverter of the renewable energy power generation device. Because the power generation cost of the renewable energy is lower than the power generation cost of the controllable DG and the power price of the power grid, the active output value of the renewable energy under the constraint condition can approach the predicted value of the active output of the renewable energy as much as possible, so that the renewable energy is consumed to the maximum extent.

7) Controllable distributed power generation operation constraints

In the formula: p _CDG,i,t And Q _CDG,i,t Representing active and reactive power output of the controllable distributed power generation; p _CDG,min ，P _CDG,max ，Q _CDG,min And Q _CDG,max Representing the upper and lower limits of the active and reactive power output of the controllable distributed generation, S _CDG,i,t The installed capacity is controllable distributed power generation.

8) On-load tap changer operation constraint

And carrying out accurate linear modeling on the on-load tap changer.

In the formula: k is a radical of ₀ For on-load tap changersStandard transformer ratio of the transformer; Δ k of _ij To adjust the step length; k _ij,t ，

The adjustable gear of the on-load tap changer and the upper limit and the lower limit of the on-load tap changer are respectively.

9) Operation constraint of static var compensator

In the formula:

and

the upper limit and the lower limit of the adjustable reactive power output of the static reactive power compensation device are respectively.

10 Operation constraints of switchable capacitor banks

In the formula: h _i,t The gear can be adjusted; delta Q _c,i,t To adjust the step length; h _max In order to compensate for the maximum adjustable gear of the capacitor bank.

11 ) energy storage device operating constraints

In the formula: p is _ch,i,t And P _dis,i,t Respectively the charging and discharging power of the energy storage device; eta _ch And η _dis The charging efficiency and the discharging efficiency of the energy storage device are respectively obtained;

and

the maximum charging power and the maximum discharging power of the energy storage device are respectively; d _ch,i,t And D _dis,i,t The variable is 0-1 and represents the charging and discharging state of the energy storage device so as to ensure that the charging and discharging of the energy storage device cannot occur simultaneously;

the capacity of the energy storage device is limited to 20-80% in consideration of the service life of the stored energy.

It should be noted that, in the day-ahead scheduling phase and the day-in scheduling phase, a dimension, i.e., the scene, is added to all the optimization variables.

3. Convex relaxation of model

In the optimization model provided in this embodiment, the optimization variables include both continuous variables and integer variables, the third formula and the fourth formula of the power flow constraint are non-convex constraints, and the solution of the optimization model is an NP problem, which is difficult to obtain an optimal solution, so that it needs to be processed.

The third formula is the constraint of the on-load tap changer, and the on-load tap changer is modeled by adopting an accurate linearization modeling method based on piecewise linearization, so that the constraint is linearized. And performing convex relaxation on the fourth formula by adopting a second-order cone method, so that the fourth formula is converted into a second-order cone programming problem. After relaxation, the rewriteable values are:

rewriting it into a standard form of a second order cone constraint, i.e.

Through the processing of the steps, the original non-convex optimization problem is converted into a mixed integer second-order cone programming model containing both integer variables and continuous variables. The current mature commercial software can directly solve the second-order cone optimization problem and ensure the calculation efficiency and the optimality of the solution.

4. Analysis of examples

The embodiment performs simulation calculation on an IEEE33 node power grid and Belgian 20 node air grid improvement system.

For example, the example simulation can be modeled by using a Yalmip optimization tool under the MATLAB R2019a compiling environment, and Gurobi is called for solving.

(1) Running cost and running grid loss analysis

The values of resistance and reactance of the power transmission line of the distribution network level comprehensive energy system are close, so that the active and reactive coupling effects are obvious. To verify the effectiveness of the method described in this example in reducing operating costs and operating grid losses, the method was compared with a single active optimization and a single reactive optimization. The single active optimization means that the optimization variables only include the active output of the distributed power supply and the active output of the energy storage device; the single reactive power optimization means that the optimized variables only comprise the reactive power output of the distributed power supply, OLTC gears, switchable capacitor bank switching gears and SVC reactive power output. Table 1 gives the operating costs of the three processes.

TABLE 1 operating cost and operating loss

By comparison, the running cost and running network loss of the active and reactive coordinated optimization are lower than those of the other two methods. This is because the values of the resistance and the reactance in the distribution network are close, and the coupling of the active power and the reactive power is strong, so that the single active and reactive power optimization scheduling is no longer applicable.

(2) Grid voltage analysis

The situation that renewable energy is accessed to multiple points in an integrated energy system is more and more common, and due to the fluctuation and uncertainty of the output of the renewable energy, the voltage situation of a renewable energy access point in a power grid fluctuates greatly and easily exceeds a limit value, so that overvoltage phenomenon may be caused, and even the grid-connected capacity of the renewable energy is limited. The embodiment adopts a multi-time scale scheduling method to cope with the influence of fluctuation and uncertainty of renewable energy output on the access point voltage. For comparison, a scheduling method with a single time scale is selected as a comparison method. The effects of the method described in this example and the comparative method in limiting overvoltage are compared below.

As shown in fig. 2, it can be seen by comparison that the multi-time scale scheduling method is better than the single-time scale optimization method in limiting the overvoltage. This is because the single time scale only optimizes the scheduling of the integrated energy system in the future, and cannot adjust the system operation state according to the updated prediction data, which is in an off-line state. In the off-line process, the balance of system power and relative stability of voltage can be ensured through an automatic adjusting device, but the realization of the optimization target of the system cannot be ensured. The multi-time scale scheduling method has more detailed time scale and can ensure that the latest prediction time sequence value is provided at any moment. Particularly, rolling optimization and feedback correction are adopted in a real-time stage, the effect of online control is achieved, and the influence of the uncertainty of the output of the renewable energy and the randomness on the voltage of the access point is further reduced. However, the multi-time-scale scheduling method has high requirements on communication accuracy, communication instantaneity, computing power and storage space. In a distribution network level comprehensive energy system with huge distributed resources, the scheduling method has higher cost.

(3) Power fluctuation analysis

With the continuous access of renewable energy, the uncertainty and the volatility of the renewable energy not only can affect the distribution network level comprehensive energy system, but also can even affect the operation of a power transmission network. In the embodiment, equipment with corresponding response speed is arranged under each time scale so as to balance the power balance of the comprehensive energy system and reduce the influence of the fluctuation of renewable energy on the operation of the power transmission network. The state of the slow-motion equipment and the action quantity of comprehensive demand response, such as gears of an on-load tap changer and a switchable capacitor bank, are determined by scheduling in the day, the power reference value of a power transmission and distribution network interface is determined in the in-day stage, and the output values of a gas turbine, a static reactive power compensation device, an energy storage device and a renewable energy source are adjusted in the real-time stage according to updated data. In order to show the superiority of the method, the selected comparison method only carries out optimal scheduling on the comprehensive energy system at the day ahead and does not consider the comprehensive demand response capability of the user side, and the effects of the method and the comparison method of the embodiment on limiting the power fluctuation between the power transmission and distribution networks are compared.

As shown in fig. 3, the first is the power fluctuation situation of the method of the present embodiment, and the second is the power fluctuation situation of the comparative method, by comparing with the comparative method, the method of the present embodiment can better stabilize the exchange power between the transmission network and the distribution network, and reduce the power fluctuation. The method not only coordinates the devices with different response speeds under each time scale to ensure the power balance of the system, but also can select a random optimization method based on a scene method according to the characteristic of inaccurate prediction in the day-ahead stage and the day-in stage, and fully considers the uncertainty of the prediction result by performing optimal scheduling on the typical scene, so that the scheduling result can adapt to each typical scene. And in the real-time stage, a deterministic optimization method is adopted according to the characteristic that the prediction result is accurate, so that the operation time can be reduced, and the scheduling effect can be ensured. The comparison method can not fully play the power fluctuation balancing capability of various devices because various devices are coordinated under a single time scale, and the comprehensive demand response in the system is not called up, so that the power fluctuation of the power transmission and distribution network exchange power is large. Therefore, compared with the comparison method, the method of the embodiment can be more suitable for the fluctuation of the output of the renewable energy source due to the fact that the equipment and the optimization method of the corresponding response speed are selected according to different time scales.

The superiority of the method of the embodiment for limiting power fluctuation is analyzed from the aspect of real-time phase distributed generation and SVC output. In order to ensure the economic operation of the comprehensive energy system, the wind power active output is directly injected into the power grid in the real-time stage without regulating the active output, so that only controllable distributed power generation is used for counteracting the active power fluctuation in the real-time stage, wind power with reactive power fluctuation, controllable distributed power generation and SVC are used for counteracting the reactive power fluctuation, the active and reactive power output and the wind power of the controllable distributed power generation in the real-time scheduling stage in one day are shown in figure 4.

As can be seen from the figure, at the instant when the active power of the controllable distributed power generation is 0 in the real-time scheduling stage, the corresponding active fluctuation is large because a part of the active regulation capability of the controllable distributed power generation is lost, when the actual exchange power is lower than the reference value, the controllable distributed power generation cannot be regulated, so that the exchange power is lower than the reference value, but when the actual exchange power is higher than the reference value, the controllable distributed power generation can increase the active output, so that the exchange power approaches the reference value; because the reactive power of the distributed power generation and the SVC is always converted and does not reach the critical value, the fluctuation of the reactive power can be well counteracted.

The embodiment provides a multi-time scale scheduling framework according to the response speed of various devices and the difference of prediction accuracy under different time scales. And secondly, establishing a comprehensive demand response model containing transferable interruptible electric load and transferable interruptible gas load, and performing source load cooperative scheduling to further reduce the influence of the renewable energy fluctuation on the system operation. And finally, according to the response characteristics of various comprehensive demand responses, the comprehensive demand response items with low response speed, medium response speed and high response speed are respectively applied to a day-ahead scheduling stage, a day-in scheduling stage and a real-time scheduling stage, so that the coordination of various comprehensive demand responses in the comprehensive energy system on the type and time is realized, and the response capability of a user side is fully developed. And finally, simulating a verification result.

Example two

The embodiment provides an integrated energy system multi-scale dispatching system considering integrated demand response.

a day-ahead scheduling module configured to: the method comprises the steps of obtaining the state of slow-motion equipment on the next day and the action value of the slow response speed comprehensive demand response by taking the minimum running cost of the power distribution network as an optimization target;

an intra-day scheduling module configured to: the method comprises the steps of obtaining exchange power between power transmission and distribution networks and a medium response speed comprehensive demand response action value based on a slow response speed comprehensive demand response action value by taking the minimum running cost of the power distribution network as an optimization target;

a real-time scheduling module configured to: and adjusting the output value of the gas turbine, the output value of the static reactive power compensation device, the output value of the energy storage device and the output value of the renewable energy source based on the medium response speed comprehensive demand response action value by taking the minimum power fluctuation at the interface of the power transmission network and the power distribution network as an optimization target.

It should be noted here that the day-ahead scheduling module, the day-inside scheduling module, and the real-time scheduling module are the same as the example and the application scenario realized by the steps in the first embodiment, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer executable instructions.

EXAMPLE III

The present embodiment provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the integrated energy system multi-scale scheduling method considering integrated demand response as described in the first embodiment above.

Example four

The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the comprehensive energy system multi-scale scheduling method considering the comprehensive demand response according to the first embodiment.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a computer to implement the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The comprehensive energy system multi-scale scheduling method considering comprehensive demand response is characterized by comprising the following steps of:

2. The method for multi-scale scheduling of an integrated energy system considering integrated demand response of claim 1, wherein the status of the slow dynamic devices on the following day includes on-load tap changer steps and switchable capacitor bank steps.

3. The method of multi-scale scheduling of an integrated energy system taking into account integrated demand response of claim 1, wherein the optimization objectives of the pre-day scheduling phase and the intra-day scheduling phase are:

the power generation cost of the ith gas turbine in the s scene in the t period is shown;

the subsidy amount paid for the participation of the incentive load in the demand response at the corresponding dispatch stage,

a subsidy amount paid for incentivizing participation of the electrical load in the demand response; p is a radical of formula _s The probability of the s-th scene, N is the number of scenes, M is the number of controllable DGs, N is the number of energy storage devices, and T is the number of time periods.

4. The method for multi-scale dispatching of an integrated energy system considering integrated demand response of claim 1, wherein the optimization objectives of the real-time dispatching stage are:

in the formula, the superscript RT represents the real-time scheduling phase,

and

and

is an optimized variable in the real-time scheduling stage and represents the power value on the connecting line of the transmission network and the distribution network in the T-th time period, T is the number of the time periods, alpha _t A weight value representing the power fluctuation of the t-th period,

the fees paid for the system to mobilize the interruptible load at the real-time stage.

5. The method of multi-scale scheduling of an integrated energy system considering integrated demand response of claim 1, wherein the day-ahead scheduling stage comprises: the method comprises the following steps of load flow restraint, branch capacity restraint, node voltage restraint, pipeline flow restraint, comprehensive demand response restraint, renewable energy power generation operation restraint, controllable distributed power generation operation restraint, on-load tap changer operation restraint, static reactive power compensation device operation restraint, switchable capacitor bank operation restraint and energy storage device operation restraint; the constraint conditions of the in-day scheduling stage and the real-time scheduling stage comprise: the method comprises the following steps of power flow constraint, branch capacity constraint, node voltage constraint, pipeline flow constraint, comprehensive demand response constraint, renewable energy power generation operation constraint, controllable distributed power generation operation constraint, static reactive power compensation device operation constraint and energy storage device operation constraint.

6. The method of multi-scale dispatching of an integrated energy system taking into account integrated demand response of claim 5, wherein the integrated demand response constraints are:

in the formula: alpha is alpha _e To be a proportion of the total electrical load of the price type transferable electrical load,

and

is the minimum value of the electrical load, P _t ^OL Is a baseline electrical load, P _t ^CL For the excited type, with a reduction in electrical load _g The proportion of transferable gas load to the total gas load,

and

respectively a minimum value and a maximum value of the transferable gas load,

is the minimum value of gas load, H _t ^OL Is a baseline gas load, H _t ^CL To reduce the air load, P _t ^IL Is an interruptible load value of direct control.

7. The comprehensive energy system multi-scale scheduling method considering comprehensive demand response of claim 5, wherein the on-load tap changer operation constraint is linearized by using a precise linearization modeling method based on piecewise linearization.

8. An integrated energy system multiscale scheduling system that considers an integrated demand response, comprising:

9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for multi-scale scheduling of an integrated energy system taking account of an integrated demand response as set forth in any one of claims 1 to 7.

10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps in the method for integrated energy system multi-scale scheduling considering integrated demand response according to any one of claims 1 to 7.