CN112001523A - Comprehensive energy multi-scale optimization scheduling control method and system considering multiple energy storages - Google Patents
Comprehensive energy multi-scale optimization scheduling control method and system considering multiple energy storages Download PDFInfo
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Abstract
The application discloses a comprehensive energy multi-scale optimization scheduling control method and system considering various energy storages, comprising the following steps: day-ahead optimization scheduling: establishing a day-ahead scheduling model of the comprehensive energy system; solving a day-ahead scheduling model of the comprehensive energy system to obtain a day-ahead scheduling scheme of the stored and released energy corresponding to the optimal output of the unit; rolling and scheduling in days: according to the energy storage and release day-ahead scheduling scheme, a rolling scheduling model is built, the rolling scheduling model is solved, and the optimal scheduling scheme of a rolling scheduling interval is obtained; the equipment operation combination and the output of the current moment in the optimal scheduling scheme of the rolling scheduling interval are issued to a control system of the comprehensive energy system; and (3) real-time optimization control: the control system of the comprehensive energy system updates the control reference value by taking the result issued by the dynamic scheduling as a given value; and carrying out closed-loop optimization and equipment protection control.
Description
Technical Field
The application relates to the technical field of operation optimization of an integrated energy system, in particular to a multi-scale optimization scheduling control method and system of integrated energy considering multiple energy storages.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The comprehensive intelligent energy is a novel regional energy system which takes comprehensive energy supply as a core, integrates and applies equipment such as distributed gas power generation, photovoltaic, distributed wind power, heat pump, refrigerator and energy storage, is close to user management, promotes management mode by advanced information technology and intelligent technology, and carries out organic coordination and optimization on links such as distribution, conversion, storage and consumption of various energy sources. The system breaks through the traditional mode of independent planning, independent design and independent operation of different energy varieties, provides an integrated solution for regional comprehensive energy, and has the advantages of high comprehensive energy utilization efficiency, capability of promoting on-site consumption of renewable energy, capability of meeting diversified energy utilization requirements and the like.
The application of energy storage in synthesizing wisdom energy system has contained technologies such as electric power storage, cold-storage, heat accumulation, and energy memory's application can realize the peak clipping according to the change of energy supply price and fill the millet, improves system operation economic nature and security, has increased the flexibility of system operation, has also increased the complexity that system optimization was regulated and control simultaneously.
The optimal scheduling control of the comprehensive energy is the key for realizing the safe, stable, efficient and intelligent operation of the comprehensive energy system, but is also the key system-level problem which restricts the popularization and landing of the comprehensive intelligent energy system at present. The difference of the various energy sources in the regulation response time also determines that the regulation and control of the comprehensive energy source system has the characteristic of multiple scales.
In the process of implementing the present application, the inventors found that the following technical problems exist in the prior art:
the optimization scheduling of the comprehensive energy system is generally divided into day-ahead scheduling and day-in-day real-time scheduling, the day-ahead scheduling generates a scheduling scheme according to a day-ahead prediction result, and the problems of prediction error, large scheduling interval and the like are difficult to be used for system operation control; the real-time scheduling in the day usually focuses on the optimization at a certain time point or a small time interval, the global property of system optimization is lacked, and particularly for a comprehensive energy system containing various energy storages, the optimization of the system is often difficult to ensure; in addition, the optimization scheduling and the operation control in the current practical application are lack of necessary connection, so that the economy of the comprehensive energy system cannot be ensured. At present, no comprehensive system and method can well solve the problem of automatic optimization scheduling control of comprehensive intelligent energy. Disclosure of Invention
Aiming at the complexity of operation optimization of a comprehensive intelligent energy system comprising multiple energy storage devices, the application provides a comprehensive energy multi-scale optimization scheduling control method and system considering multiple energy storage devices; the method organically integrates day-ahead optimized scheduling, day-in rolling optimized scheduling and real-time optimized control strategies; the system can automatically optimize and operate according to the condition changes such as load demand, equipment output, energy price and the like, so that the operation of the comprehensive energy system is more economic and efficient.
In a first aspect, the application provides a comprehensive energy multi-scale optimization scheduling control method considering multiple energy storages;
the comprehensive energy multi-scale optimization scheduling control method considering multiple kinds of energy storage comprises the following steps:
day-ahead optimization scheduling: establishing a day-ahead scheduling model of the comprehensive energy system; solving a day-ahead scheduling model of the comprehensive energy system to obtain an energy storage and release day-ahead scheduling scheme corresponding to the optimal output of the unit;
rolling and scheduling in days: according to the energy storage and release day-ahead scheduling scheme, a rolling scheduling model is built, the rolling scheduling model is solved, and the optimal scheduling scheme of a rolling scheduling interval is obtained; the equipment operation combination and the output of the current moment in the optimal scheduling scheme of the rolling scheduling interval are issued to a control system of the comprehensive energy system;
and (3) real-time optimization control: the control system of the comprehensive energy system updates the control reference value by taking the result issued by the dynamic scheduling as a given value; and carrying out closed-loop optimization and equipment protection control.
In a second aspect, the application provides a comprehensive energy multi-scale optimization scheduling control system considering multiple energy storages;
the comprehensive energy multi-scale optimization scheduling control system considering various energy storages comprises:
a day-ahead optimization scheduling module configured to: establishing a day-ahead scheduling model of the comprehensive energy system; solving a day-ahead scheduling model of the comprehensive energy system to obtain an energy storage and release day-ahead scheduling scheme corresponding to the optimal output of the unit;
a rolling-in-day scheduling module configured to: according to the energy storage and release day-ahead scheduling scheme, a rolling scheduling model is built, the rolling scheduling model is solved, and the optimal scheduling scheme of a rolling scheduling interval is obtained; the equipment operation combination and the output of the current moment in the optimal scheduling scheme of the rolling scheduling interval are issued to a control system of the comprehensive energy system;
a real-time optimization control module configured to: the control system of the comprehensive energy system updates the control reference value by taking the result issued by the dynamic scheduling as a given value; and carrying out closed-loop optimization and equipment protection control.
In a third aspect, the present application further provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first aspect.
In a fourth aspect, the present application also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
In a fifth aspect, the present application also provides a computer program (product) comprising a computer program for implementing the method of any of the preceding first aspects when run on one or more processors.
Compared with the prior art, the beneficial effects of this application are:
the application is applicable to the operation optimization of the comprehensive intelligent energy system containing various energy storage devices. The comprehensive intelligent energy multi-scale optimization scheduling control method organically integrating day-ahead optimization scheduling, day-in rolling optimization scheduling and real-time optimization control strategies can effectively reduce the workload of operators, enables the system to automatically optimize and run along with the change of environmental conditions, effectively reduces the running cost and improves the economy of a comprehensive energy system.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of a method of the first embodiment;
FIG. 2 is a schematic time length diagram of the multi-scale optimization regulation of the first embodiment;
FIG. 3 is a block diagram of an example park integrated energy system of the first embodiment;
FIG. 4 is a flowchart of an evolutionary algorithm incorporating a "maximum energy storage utilization" heuristic rule according to the first embodiment;
FIG. 5 is a cold load, hot water load and electrical load prediction plot for the first embodiment;
FIG. 6 is a predicted photovoltaic and solar hot water output curve for the first embodiment;
FIG. 7 is a schematic peak-to-valley electricity rate diagram of the first embodiment;
FIG. 8(a) is the result of the day-ahead scheduling scheme solution (cooling) of the first embodiment;
FIG. 8(b) shows the solution (heating) of the day-ahead scheduling scheme of the first embodiment;
FIG. 8(c) shows the results of the day-ahead scheduling scheme solution (power supply) of the first embodiment;
FIG. 9 is an actual load demand curve for the first embodiment;
fig. 10 is a diagram of an example of the rolling scheduling result in day according to the first embodiment (t is 02:10, time interval is 10 minutes, and scheduling interval is 2 hours).
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. 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 application 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 example embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Consider the comprehensive wisdom energy system characteristic of multiple energy memory: the energy supply system is provided with one or more energy storage devices of a gas internal combustion engine, a gas boiler or an electric boiler, a water chilling unit, an air source heat pump, a sewage source heat pump, distributed photovoltaic, distributed wind power, solar hot water, cold accumulation, heat accumulation and electric power storage, and can automatically optimize and switch operation modes according to system operation states, user side load conditions, environment and energy price information and the like in different periods such as cold supply seasons, hot supply seasons, transition seasons and the like, optimize corresponding equipment combination and load distribution, meet the requirements of various energy sources such as energy side cold, heat, electricity, water and the like, and maximize economic, energy efficiency and environmental protection benefits.
The multi-scale regulation and control framework mainly comprises three levels, namely day-ahead scheduling, day-in rolling scheduling and real-time closed-loop control. The day-ahead scheduling optimization time scale is hour-level (1 hour), the day-in rolling scheduling optimization time scale is minute-level (1 minute to 10 minutes), and the real-time closed-loop control optimization time scale is second-level (0.5 second to 1 second). FIG. 2 is a time length diagram of multi-scale optimal regulation.
Example one
The embodiment provides a comprehensive energy multi-scale optimization scheduling control method considering various energy storages;
as shown in fig. 1, the comprehensive energy multi-scale optimal scheduling control method considering multiple energy storages includes:
day-ahead optimization scheduling: establishing a day-ahead scheduling model of the comprehensive energy system; solving a day-ahead scheduling model of the comprehensive energy system to obtain an energy storage and release day-ahead scheduling scheme corresponding to the optimal output of the unit;
rolling and scheduling in days: according to the energy storage and release day-ahead scheduling scheme, a rolling scheduling model is built, the rolling scheduling model is solved, and the optimal scheduling scheme of a rolling scheduling interval is obtained; the equipment operation combination and the output of the current moment in the optimal scheduling scheme of the rolling scheduling interval are issued to a control system of the comprehensive energy system;
and (3) real-time optimization control: the control system of the comprehensive energy system updates the control reference value by taking the result issued by the dynamic scheduling as a given value; and carrying out closed-loop optimization and equipment protection control.
As one or more embodiments, the schedule optimizing step further includes:
and acquiring a day-ahead load prediction curve and a renewable energy output prediction curve.
Further, acquiring a day-ahead load prediction curve and an output prediction curve; the method comprises the following specific steps:
forecasting the time-by-time demands of various loads in the day to obtain a day-ahead load forecasting curve;
and predicting the hourly output of the various energy sources to obtain an output prediction curve.
Illustratively, the forecasting of the time-by-time demands of multiple loads in the day ahead to obtain a load forecasting curve in the day ahead specifically includes:
and forecasting the day-ahead cold, hot and electric loads according to factors such as seasons, weather and survival rate to obtain a day-ahead load forecasting curve.
Illustratively, the predicting the hourly output of the multiple energy sources to obtain the output prediction curve includes the following specific steps:
and predicting the time-by-time output of uncertain renewable energy sources such as distributed photovoltaic, distributed wind power, solar hot water and the like according to factors such as seasons, weather and the like to obtain a corresponding output prediction curve.
As one or more embodiments, the establishing a day-ahead dispatch model of the integrated energy system; the method comprises the following specific steps:
and establishing a day-ahead scheduling model of the comprehensive energy system based on the energy flow model of the energy conversion equipment and the energy flow model of the energy storage equipment.
Illustratively, establishing a power flow model for representing the performance, energy efficiency and energy conversion of each controllable device or subsystem in the system; and establishing a day-ahead scheduling model of the comprehensive energy system according to the configuration of the energy supply system, the parameters of the energy storage equipment and the like.
Illustratively, the energy flow model of the energy conversion device is specifically represented as:
Qout(t)＝ηeq·Qin(t)，
wherein Q isout(t),Qin(t) representing the device during a period tOutput, input energy, ηeqIndicating the conversion efficiency of the device. For electric boilers, for example, Qout(t),Qin(t),ηeqRespectively representing the heat supply, the electricity consumption and the heat efficiency of the time period t; for the water chilling unit, the output cold quantity, the consumed electricity quantity and the COP refrigeration coefficient in the time period t are respectively represented.
Illustratively, the energy flow model of the energy storage device is specifically represented as:
Qst(t)＝Qst(t-1)·(1-μloss)+Qst_in(t)·ηin-Qst_out(t)/ηout
wherein Q isst(t) represents the energy storage capacity of the energy storage device at time t, μlossRepresenting the energy loss rate, Q, of the energy storage devicest_in(t)，Qst_out(t) represents the energy storage input and energy release output energy of the energy storage device in the time period from t-1 moment to t moment respectively, etainRepresenting the energy storage efficiency, eta, of the energy storage deviceoutIndicating the energy discharge efficiency of the energy storage device.
Further, the day-ahead scheduling model of the integrated energy system comprises an objective function and a constraint condition;
and the objective function is the lowest total operating cost of the comprehensive energy system.
Constraints, including: energy balance constraint, equipment output constraint, energy storage capacity constraint, gateway constraint, system process network constraint and load response constraint.
Illustratively, the objective function of the day-ahead scheduling model is represented as:
min f＝Cgrid+Cfuel+Com+Cenv，
wherein f represents the total cost of system operation, CgridRepresents the cost of purchasing electricity from outside, CfuelRepresents the fuel cost, ComRepresents the equipment operation and maintenance cost, CenvRepresenting an environmental cost.
As one or more embodiments, the day-ahead scheduling model of the comprehensive energy system is solved to obtain an energy storage and release day-ahead scheduling scheme corresponding to the optimal output of the unit; the method comprises the following specific steps:
and (3) solving a day-ahead scheduling model of the comprehensive energy system by fusing an evolutionary algorithm of a heuristic rule of 'maximum energy storage utilization' to obtain an energy storage and release day-ahead scheduling scheme corresponding to the optimal output of the unit.
Exemplarily, as shown in fig. 4, a day-ahead scheduling model of the integrated energy system is solved by combining an evolutionary algorithm of a heuristic rule of "maximum energy storage utilization", so as to obtain a day-ahead scheduling scheme of the stored and released energy corresponding to the optimal output of the unit; the method comprises the following specific steps:
s401: determining decision variables and algorithm parameters according to a day-ahead scheduling model of the comprehensive energy system, wherein the decision variables comprise start-stop states of all devices at all times in a scheduling interval, load output, energy storage and release states of all energy storage devices and energy storage and release, and the algorithm parameters comprise selection probability, variation probability and iteration times;
s402: coding a scheduling scheme consisting of all decision variables by adopting a decimal system;
s403: generating a feasible solution according to a 'maximum energy storage utilization' rule;
s404: generating an initial population, wherein half of individuals are generated by local variation of a feasible solution generated by S403, and the other half of individuals are randomly generated within a range meeting the value of the output constraint variable of the equipment;
s405: calculating the fitness of each individual in the population, wherein the fitness function adopts the sum of an objective function and a constraint unsatisfied penalty term;
s406: performing selection, crossover or mutation operations, and storing the optimal solution;
s407: updating partial individuals of the population according to heuristic rules;
s408: judging whether a termination condition is reached, and if so, outputting an optimal solution; if not, return to S405.
Further, in S403, a feasible solution is generated according to a "maximum energy storage utilization" rule; the method comprises the following specific steps: taking a charge-discharge strategy every day as an example, charging and discharging can be calculated in a segmented mode for multiple times every day;
s4031: determining the maximum energy release upper limit of the time period t
And minimum energy release lower limit
S4032: calculating the maximum economic energy storage of the energy storage equipment on the same day to obtain an energy storage and release scheme:
s4033: calculating the energy storage and release (positive energy release and negative energy storage) in each time period:
peak segment T ∈ Tp：
Flat segment T ∈ Tl：
Valley section T is belonged to Tv：
Wherein the content of the first and second substances,
the time period t corresponds to the energy demand of the energy source,
the maximum energy release power, the maximum energy storage power and the maximum energy storage capacity which are allowed by the energy storage device are respectively the sum of the maximum output of other energy supply equipment of the corresponding energy variety; t isp、Tl、TvRespectively collecting time periods corresponding to a peak section, a flat section and a valley section of the stepped electricity price;
s4034: and according to the energy storage and release scheme and the energy supply and demand balance principle obtained in the step S4033, distributing the load output of other energy conversion equipment from high energy efficiency to low energy efficiency to generate an economic output scheduling scheme.
Further, the step S407: updating partial individuals of the population according to heuristic rules; the method comprises the following specific steps:
in the iterative process of the evolutionary algorithm, after the population performs selection, intersection and variation operations, individuals are selected from the population by setting an update probability (0.1-0.3), and load output distribution and adjustment are performed on other energy conversion equipment from high energy efficiency to low energy efficiency according to an energy storage and release scheme of the individuals and an energy supply and demand balance principle, so that the individuals are updated and are made to be feasible and optimized.
It should be understood that for the comprehensive intelligent energy system close to the user, the energy storage device is applied to the maximum extent to realize 'peak load elimination', and the outsourcing electric quantity in the peak section of the electricity price is reduced as far as possible, so that the comprehensive intelligent energy system is an important means for achieving energy conservation and expenditure saving. The evolutionary algorithm fused with the heuristic rule of 'maximum energy storage utilization' is based on a genetic algorithm, when a seed group is initialized, a feasible solution is generated according to the heuristic rule of 'maximum energy storage utilization', half of individuals in a first generation group are generated near the feasible solution by adopting local variation, and the other half of individuals are randomly generated in a variable value range meeting the equipment output constraint and the like, so that the individual diversity of the first generation group is ensured, the optimization of the first generation group is also ensured, and the solving speed and the quality of a scheduling scheme are improved.
As one or more embodiments, the building an intra-day rolling scheduling model according to an accumulated energy day-ahead scheduling scheme includes:
correcting a load prediction curve before the day according to the current load at the rolling scheduling time in the day; correcting a renewable energy output prediction curve according to the renewable energy real-time output at the rolling scheduling time in the day;
converting corresponding time into a rolling scheduling time point according to an energy storage device energy storage quantity value, a modified day-ahead load prediction curve and a modified renewable energy output prediction curve in an energy storage and release day-ahead scheduling scheme, and fitting to obtain a rolling scheduling load demand curve and a renewable energy output curve so as to obtain the load demand, the renewable energy output and the energy storage quantity of the scheduling interval end point time of each time period of rolling scheduling; and constructing a rolling scheduling model.
Illustratively, the time interval of the rolling schedule may be selected to be 10 minutes, and the schedule interval may be selected to be 2 hours. Acquiring the energy storage value of the energy storage equipment, the corrected load demand and the renewable energy output value corresponding to the first 1 scheduling point and the last 3 scheduling points in the day-ahead scheduling scheme according to the current time, converting the corresponding time into the time point of rolling scheduling, and fitting the time point into a load demand curve and a renewable energy output curve of the rolling scheduling (Q ═ a)0+a1t+a2t2+a3t3，a0～a3As a fitting coefficient), the load demand of each time interval of the rolling scheduling, the output of the renewable energy source and the energy storage amount at the end point moment of the scheduling interval can be obtained
Further, the objective function of the rolling scheduling model is: and the total running cost of the comprehensive energy system in the dispatching interval is lowest.
Further, the constraints of the rolling scheduling model include: energy balance constraint, equipment output constraint, energy storage capacity constraint, gateway constraint, system flow network constraint, load response constraint, unit output climbing constraint and scheduling interval terminal time energy storage constraint of the day-ahead scheduling model.
Further, the solving process of the rolling scheduling model is the same as the solving process of the day-ahead scheduling model of the integrated energy system.
As one or more embodiments, in the real-time optimization control step, the closed-loop optimization and the equipment protection control are performed by using a PID or an advanced control method.
Example (c): taking an actual park comprehensive energy system as an example, the system comprises three water chilling units, two electric boilers, a set of solar water heating system, a sewage source heat pump, a roof photovoltaic system, a cold and hot double storage tank and two sets of storage batteries, and supplies cold, heat, electricity and domestic hot water for the whole park, as shown in fig. 4. The roof photovoltaic system is self-operated, and the insufficient part is supplemented by power purchased from the power grid; the cold and hot double storage tanks store cold water in summer and hot water in winter and are used for supplying cold and hot loads of an air conditioner; the solar water heating system is provided with a water storage tank and is used for supplying domestic hot water; the water chilling unit is used for air conditioner cold load supply and cold water energy storage; the electric boiler can be used for air conditioning heat load supply, hot water energy storage and domestic hot water supply at the same time; the sewage source heat pump can be operated under the double working conditions of refrigeration and heating and is used for air conditioner heating in winter and air conditioner cooling in summer. Figure 3 is a block diagram of an example park energy system. The summer working condition is taken as an example below.
(1) The cold load, electrical load and hot water load prediction curves are shown in FIG. 5;
(2) the output prediction curves of photovoltaic and solar hot water are shown in fig. 6.
(3) Day-ahead scheduling model
An objective function:
min f＝Cgrid+Cfuel+Com+Cenv
for this example Cfuel＝0,C env0. Constraint conditions are as follows:
electric balance:
cold balance:
heat balance:
and (3) equipment output constraint:
X′it+Xit≤1,i∈Mcs+Mes
energy storage capacity constraint:
and (4) gateway constraint:
and (3) load response constraint:
wherein the content of the first and second substances,
electricity price and outsourcing electricity quantity corresponding to the time period t respectively, and for the energy conversion equipment, XitFor the start-stop state of device i at time t, for the energy storage device, Xit、X′itRespectively representing an energy release state and an energy storage state;
respectively is an equipment start-stop maintenance cost coefficient and a power operation maintenance cost coefficient;
synthesizing the electricity, cold and heat power supplied to the user by the energy system for the time period t;
the power required by the user side for electricity, cold and heat is t;
power is lost for the energy of electricity, cold and heat in the system;
the capacity of the transformer gateway of the external power grid is obtained.
(4) The peak-to-valley electricity rate policy is shown in fig. 7, and the generated day-ahead scheduling scheme is shown in fig. 8(a) -8 (c);
(5) the actual load demand curve is shown in FIG. 9
(6) Intraday rolling scheduling model
The electric balance, cold balance, heat balance, equipment output constraint, energy storage capacity constraint, gateway constraint and load response constraint expression in the target function and constraint condition are the same as those in the day-ahead scheduling model, and the constraint condition also comprises:
and (3) equipment output climbing restraint:
energy storage restraint:
the rolling-over-the-day scheduling scheme is shown in fig. 10.
Example two
The embodiment provides a comprehensive energy multi-scale optimization scheduling control system considering various energy storages;
the comprehensive energy multi-scale optimization scheduling control system considering various energy storages comprises:
a day-ahead optimization scheduling module configured to: establishing a day-ahead scheduling model of the comprehensive energy system; solving a day-ahead scheduling model of the comprehensive energy system to obtain an energy storage and release day-ahead scheduling scheme corresponding to the optimal output of the unit;
a rolling-in-day scheduling module configured to: according to the energy storage and release day-ahead scheduling scheme, a rolling scheduling model is built, the rolling scheduling model is solved, and the optimal scheduling scheme of a rolling scheduling interval is obtained; the equipment operation combination and the output of the current moment in the optimal scheduling scheme of the rolling scheduling interval are issued to a control system of the comprehensive energy system;
a real-time optimization control module configured to: the control system of the comprehensive energy system updates the control reference value by taking the result issued by the dynamic scheduling as a given value; and carrying out closed-loop optimization and equipment protection control.
It should be noted here that the day-ahead optimization scheduling module, the day-inside rolling scheduling module, and the real-time optimization control module correspond to the day-ahead optimization scheduling step, the day-inside rolling scheduling step, and the real-time optimization control step in the first embodiment, and the modules are the same as the corresponding steps in the implementation example and application scenarios, but are not limited to the contents disclosed in 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.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical functional division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first embodiment.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The fourth embodiment also provides a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. The comprehensive energy multi-scale optimization scheduling control method considering various energy storages is characterized by comprising the following steps:
day-ahead optimization scheduling: establishing a day-ahead scheduling model of the comprehensive energy system; solving a day-ahead scheduling model of the comprehensive energy system to obtain an energy storage and release day-ahead scheduling scheme corresponding to the optimal output of the unit;
rolling and scheduling in days: according to the energy storage and release day-ahead scheduling scheme, a rolling scheduling model is built, the rolling scheduling model is solved, and the optimal scheduling scheme of a rolling scheduling interval is obtained; the equipment operation combination and the output of the current moment in the optimal scheduling scheme of the rolling scheduling interval are issued to a control system of the comprehensive energy system;
and (3) real-time optimization control: the control system of the comprehensive energy system updates the control reference value by taking the result issued by the dynamic scheduling as a given value; and carrying out closed-loop optimization and equipment protection control.
2. The method of claim 1, wherein said rolling-over-the-day scheduling step further comprises:
and acquiring a day-ahead load prediction curve and a renewable energy output prediction curve.
3. The method of claim 2, wherein said obtaining a pre-day load prediction curve and a contribution prediction curve; the method comprises the following specific steps:
forecasting the time-by-time demands of various loads in the day to obtain a day-ahead load forecasting curve;
and predicting the hourly output of the various energy sources to obtain an output prediction curve.
4. The method of claim 1, wherein the building of a day-ahead dispatch model of the integrated energy system; the method comprises the following specific steps:
and establishing a day-ahead scheduling model of the comprehensive energy system based on the energy flow model of the energy conversion equipment and the energy flow model of the energy storage equipment.
5. The method of claim 1, wherein the day-ahead scheduling model of the integrated energy system includes an objective function and constraints;
the objective function is the lowest total operating cost of the comprehensive energy system;
constraints, including: energy balance constraint, equipment output constraint, energy storage capacity constraint, gateway constraint, system process network constraint and load response constraint.
6. The method as claimed in claim 1, wherein the day-ahead scheduling model of the integrated energy system is solved to obtain a stored and released energy day-ahead scheduling scheme corresponding to the optimal output of the unit; the method comprises the following specific steps:
and (3) solving a day-ahead scheduling model of the comprehensive energy system by fusing an evolutionary algorithm of a heuristic rule of 'maximum energy storage utilization' to obtain an energy storage and release day-ahead scheduling scheme corresponding to the optimal output of the unit.
7. The method of claim 1, wherein constructing a rolling scheduling model according to the accumulated energy day-ahead scheduling scheme comprises:
correcting a load prediction curve before the day according to the current load at the rolling scheduling time in the day; correcting a renewable energy output prediction curve according to the renewable energy real-time output at the rolling scheduling time in the day;
converting corresponding time into a rolling scheduling time point according to an energy storage device energy storage quantity value, a modified day-ahead load prediction curve and a modified renewable energy output prediction curve in an energy storage and release day-ahead scheduling scheme, and fitting to obtain a rolling scheduling load demand curve and a renewable energy output curve so as to obtain the load demand, the renewable energy output and the energy storage quantity of the scheduling interval end point time of each time period of rolling scheduling; constructing a rolling scheduling model;
alternatively, the first and second electrodes may be,
the objective function of the rolling scheduling model is as follows: the total running cost of the comprehensive energy system in the dispatching interval is lowest; the constraints of the rolling scheduling model comprise: energy balance constraint, equipment output constraint, energy storage capacity constraint, gateway constraint, system flow network constraint, load response constraint, unit output climbing constraint and scheduling interval terminal time energy storage constraint of the day-ahead scheduling model.
8. The comprehensive energy multi-scale optimization scheduling control system considering various stored energy is characterized by comprising the following components:
a day-ahead optimization scheduling module configured to: establishing a day-ahead scheduling model of the comprehensive energy system; solving a day-ahead scheduling model of the comprehensive energy system to obtain an energy storage and release day-ahead scheduling scheme corresponding to the optimal output of the unit;
a rolling-in-day scheduling module configured to: according to the energy storage and release day-ahead scheduling scheme, a rolling scheduling model is built, the rolling scheduling model is solved, and the optimal scheduling scheme of a rolling scheduling interval is obtained; the equipment operation combination and the output of the current moment in the optimal scheduling scheme of the rolling scheduling interval are issued to a control system of the comprehensive energy system;
a real-time optimization control module configured to: the control system of the comprehensive energy system updates the control reference value by taking the result issued by the dynamic scheduling as a given value; and carrying out closed-loop optimization and equipment protection control.
9. An electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs being stored in the memory, the processor executing the one or more computer programs stored in the memory when the electronic device is running, to cause the electronic device to perform the method of any of the preceding claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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