CN118034874A - Method, device, equipment and storage medium for optimizing operation of CCHP system - Google Patents

Method, device, equipment and storage medium for optimizing operation of CCHP system Download PDF

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Publication number
CN118034874A
CN118034874A CN202410051300.6A CN202410051300A CN118034874A CN 118034874 A CN118034874 A CN 118034874A CN 202410051300 A CN202410051300 A CN 202410051300A CN 118034874 A CN118034874 A CN 118034874A
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optimized
equipment
function
cchp system
model
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周洲
杨成峰
扈卫卫
孟金波
夏庆生
杨婧
翟江
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Tbea International Engineering Co ltd
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Tbea International Engineering Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects

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  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention provides an operation optimization method, device, equipment and storage medium of a CCHP system, wherein the method comprises the following steps: initializing: constructing an equipment model; determining a first operation cost function; iterative steps: according to the equipment model, carrying out installed capacity optimization simulation calculation to obtain an optimized installed capacity; wherein, the objective function of the simulation calculation includes: a first operation cost function; according to the equipment model and the optimized installed capacity, performing operation and maintenance cost optimization simulation calculation to obtain an optimized operation and maintenance cost function and an optimized scheduling scheme; and (3) checking: determining whether the iteration times after the completion of the iteration step exceeds a preset iteration times threshold; the method comprises the following steps: if the iteration frequency threshold is not exceeded, taking the optimized operation cost function as a new first operation cost function, and returning to the iteration step until the iteration frequency threshold is exceeded; determining: and determining a target installed capacity and a target optimal scheduling scheme. The invention can realize high-precision optimization of the CCHP system to be optimized.

Description

Method, device, equipment and storage medium for optimizing operation of CCHP system
Technical Field
The embodiment of the invention relates to the technical field of operation optimization of integrated energy systems, in particular to an operation optimization method, device, equipment and storage medium of a CCHP system.
Background
The CCHP (Combined Cooling HEATING AND Power) system is also called a Combined Cooling, heating and Power system, and the distributed Combined Cooling, heating and Power system is a solution for comprehensive cascade utilization of energy, and the total energy utilization rate can reach 75% -90%. The CCHP system takes all resources which can generate electricity or heat, such as water energy, biological energy, wind energy, solar energy, geothermal energy, natural gas, garbage energy or industrial waste heat, and the like, as primary energy, and combines a power generation system and a heat supply and cold supply system to form a comprehensive energy supply mode which is distributed near a user in a small-scale and punctiform manner. Thereby meeting the requirements of users on heat, electricity, cold and other energy sources. The CCHP system not only can enable a user to form an energy supply system, but also can run in a grid-connected mode with a large power grid, and the system has relative independence, flexibility and safety. The CCHP system can independently operate and can also operate in parallel, and the user requirements of different power loads can be met.
The existing CCHP system operation optimization method is often realized from a single installed capacity dimension or a single equipment scheduling dimension (equipment scheduling, namely controlling the output of each equipment at different time to ensure that the CCHP system is integrally up to an optimization index, for example, low energy consumption and high new energy consumption rate), and the optimization schemes obtained from the single dimension have the conditions of mutual interference and even contradiction, so that the optimization precision of the CCHP system to be optimized by the existing method is low.
Disclosure of Invention
The embodiment of the invention provides a running optimization method, a device, equipment and a storage medium of a CCHP (hybrid automatic repeat request) system, which are used for solving the problems that the running optimization method of the existing CCHP system is always low in optimization precision of the CCHP system to be optimized due to mutual interference and even contradiction of the optimization schemes obtained from the single dimension from a single installed capacity dimension or a single equipment scheduling dimension (equipment scheduling, namely controlling the output of each equipment at different time to ensure that the CCHP system is integrally optimized, and optimizing indexes, such as low energy consumption and high new energy consumption rate).
In order to solve the technical problems, the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides an operation optimization method for a CCHP system, including:
Initializing: constructing an equipment model of each equipment in the CCHP system to be optimized; and determining a first operation and maintenance cost function of the CCHP system to be optimized according to the equipment model;
Iterative steps: according to the equipment model, carrying out installed capacity optimization simulation calculation on the CCHP system to be optimized to obtain the optimized installed capacity of each piece of equipment; the objective function of the installed capacity optimization simulation calculation comprises the following steps: the first operation cost function; according to the equipment model and the optimized installed capacity, performing operation and maintenance cost optimization simulation calculation on the CCHP system to be optimized to obtain an optimized operation and maintenance cost function of the CCHP system to be optimized, and obtaining an optimized scheduling scheme of each equipment corresponding to the optimized operation and maintenance cost function;
and (3) checking: determining whether the iteration times after the iteration steps are finished exceed a preset iteration time threshold;
the method comprises the following steps: if the iteration number is not exceeded, taking the optimized operation cost function as a new first operation cost function, and returning to the iteration step until the iteration number threshold is exceeded;
Determining: and determining the current optimal installed capacity as a target installed capacity, and determining the current optimal scheduling scheme as a target optimal scheduling scheme.
Alternatively, the process may be carried out in a single-stage,
The installed capacity optimizing simulation calculated objective function further comprises at least one of the following: a carbon emission reduction rate function, a renewable energy permeability function, a renewable energy utilization rate function, and a primary energy conservation rate function.
Alternatively, the process may be carried out in a single-stage,
The expression of the objective function maxf calculated by the installed capacity optimization simulation is as follows:
Wherein η ROI is a return on investment function determined from the first operation cost function; omega 1 is the weight value of the return on investment function; η CER is the carbon emission reduction rate function; omega 2 is the weight value of the carbon emission reduction rate function; η CER is the renewable energy permeability function; omega 3 is the weight value of the renewable energy permeability function; η RER is the renewable energy utilization function; omega 4 is the weight value of the renewable energy utilization function; η PESR is the primary energy conservation rate function; omega 5 is the weight value of the primary energy saving rate function.
Alternatively, the process may be carried out in a single-stage,
Adopting an ant colony algorithm to perform the optimization simulation calculation of the installed capacity; and/or the number of the groups of groups,
And carrying out the operation and maintenance cost optimization simulation calculation by adopting the ant colony algorithm.
Alternatively, the process may be carried out in a single-stage,
The ant colony algorithm comprises: a pheromone updating algorithm;
the expression of the pheromone updating algorithm is as follows:
τj(t+1)＝(1-ρ)τj+L;τj(t+1)＝(1-ρ)τj-L；
Wherein L is an objective function value calculated by the optimal ant selection path; ρ is an information volatilization factor; t is the current iteration times; ρ 0 is a constant; ρ 1 is a constant and ρ 1＞ρ0;tmax is the iteration number threshold.
Alternatively, the process may be carried out in a single-stage,
The construction of the equipment model of each equipment in the CCHP system to be optimized comprises the following steps:
acquiring state parameters of a CCHP system to be optimized and equipment parameters of each piece of equipment in a preset first time period;
constructing the equipment model according to the state parameters and the equipment parameters;
Wherein the status parameter comprises at least one of: the energy consumption type, the energy price corresponding to each energy consumption type, the real-time cold load, the real-time heat load, the real-time electric load and the meteorological data of the deployment place of the CCHP system to be optimized;
The device parameters include at least one of: device type, device rating parameters, output power.
Alternatively, the process may be carried out in a single-stage,
The equipment model is a steady-state model;
the equipment model comprises at least one of the following models:
An electrolytic tank model, a photovoltaic generator set model, a hydrogen fuel cell model, a diesel generator model, an air source heat pump model, an absorption heat pump model, a hydrogen storage tank model and a heat storage tank model.
In a second aspect, an embodiment of the present invention provides an operation optimization apparatus for a CCHP system, including:
An initialization module, configured to initialize: constructing an equipment model of each equipment in the CCHP system to be optimized; and determining a first operation and maintenance cost function of the CCHP system to be optimized according to the equipment model;
The iteration module is used for iterating the steps of: according to the equipment model, carrying out installed capacity optimization simulation calculation on the CCHP system to be optimized to obtain the optimized installed capacity of each piece of equipment; the objective function of the installed capacity optimization simulation calculation comprises the following steps: the first operation cost function; according to the equipment model and the optimized installed capacity, performing operation and maintenance cost optimization simulation calculation on the CCHP system to be optimized to obtain an optimized operation and maintenance cost function of the CCHP system to be optimized, and obtaining an optimized scheduling scheme of each equipment corresponding to the optimized operation and maintenance cost function;
the verification module is used for verifying: determining whether the iteration times after the iteration steps are finished exceed a preset iteration time threshold;
The execution module is used for executing the steps of: if the iteration number is not exceeded, taking the optimized operation cost function as a new first operation cost function, and returning to the iteration step until the iteration number threshold is exceeded;
A determining module, configured to determine: and determining the current optimal installed capacity as a target installed capacity, and determining the current optimal scheduling scheme as a target optimal scheduling scheme.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a memory, and a program or an instruction stored on the memory and executable on the processor, where the program or the instruction implements the steps in the method for optimizing operation of the CCHP system according to any one of the first aspects when executed by the processor.
In a fourth aspect, an embodiment of the present invention provides a readable storage medium having stored thereon a program or instructions which, when executed by a processor, implement the steps in the method for optimizing operation of a CCHP system according to any of the first aspects.
In the embodiment of the invention, the initialization step is carried out: constructing an equipment model of each equipment in the CCHP system to be optimized; and determining a first operation and maintenance cost function of the CCHP system to be optimized according to the equipment model; iterative steps: according to the equipment model, carrying out installed capacity optimization simulation calculation on the CCHP system to be optimized to obtain the optimized installed capacity of each equipment; the objective function of the installed capacity optimization simulation calculation comprises the following steps: a first operation cost function; according to the equipment model and the optimized installed capacity, carrying out operation and maintenance cost optimization simulation calculation on the CCHP system to be optimized to obtain an optimized operation and maintenance cost function of the CCHP system to be optimized, and obtaining an optimized scheduling scheme of each equipment corresponding to the optimized operation and maintenance cost function; and (3) checking: determining whether the iteration times after the completion of the iteration step exceeds a preset iteration times threshold; the method comprises the following steps: if the iteration frequency threshold is not exceeded, taking the optimized operation cost function as a new first operation cost function, and returning to the iteration step until the iteration frequency threshold is exceeded; determining: the method and the device for optimizing the CCHP system to be optimized can achieve operation optimization of the CCHP system to be optimized in two dimensions of the installed capacity dimension and the equipment scheduling dimension, obtain the matched optimal installed capacity (namely the target installed capacity) and the optimal scheduling scheme (namely the target optimal scheduling scheme), and achieve high-precision optimization of the CCHP system to be optimized.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a flow chart of a method for optimizing operation of a CCHP system in accordance with an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a CCHP system;
Fig. 3 is a schematic diagram of an execution flow of the ant colony algorithm;
FIG. 4 is a functional block diagram of an operation optimization device of the CCHP system of the embodiment of the present invention;
fig. 5 is a functional block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "first," "second," and the like, herein, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or otherwise described herein, and that the "first" and "second" distinguishing between objects generally are not limited in number to the extent that the first object may, for example, be one or more. Furthermore, the "or" in the present application means at least one of the connected objects. For example, "a or B" encompasses three schemes, scheme one: including a and excluding B; scheme II: including B and excluding a; scheme III: both a and B. The character "/" generally indicates that the context-dependent object is an "or" relationship.
In the technical solutions of the present disclosure, terms "connected," "coupled," or "connected" are not limited to physical or mechanical connections, but may include electrical connections.
The embodiment of the invention provides an operation optimization method of a combined cooling, heating and power (CCHP) system, which is shown in fig. 1, and fig. 1 is a flow chart diagram of the operation optimization method of the CCHP system, comprising the following steps:
initializing step 11: constructing an equipment model of each equipment in the CCHP system to be optimized; and determining a first operation and maintenance cost function of the CCHP system to be optimized according to the equipment model;
In the embodiment of the invention, the physical information of the specific characterization of the equipment model is determined by the action (output form) of the corresponding equipment in the CCHP system. For example: the CCHP system to be optimized comprises: a photovoltaic generator set. And the photovoltaic generator set is used as a main power supply in the CCHP system, so that the photovoltaic generator set model represents the power generation power of the photovoltaic generator set. For example: the CCHP system to be optimized comprises: an electrolytic cell. The role of the electrolyzer in the CCHP system is to produce hydrogen and heat, and the electrolyzer model characterizes the electrolyzer's hydrogen and heat production power. For example: the CCHP system to be optimized comprises: a hydrogen storage tank. The role of the hydrogen storage tank in the CCHP system is to store hydrogen energy, and the hydrogen storage tank model characterizes the remaining energy in the hydrogen storage tank. It should be noted that, the CCHP system to be optimized specifically includes which devices, and the roles of the devices in the CCHP system to be optimized, there are differences in different CCHP systems to be optimized, which cannot be exhaustive and should not be considered as unclear.
In the embodiment of the invention, the first operation cost function is a system operation cost function calculated according to the equipment model.
Referring to fig. 2, fig. 2 is a schematic block diagram of a CCHP system, and based on an equipment model of each equipment constructed by the CCHP system, cost terms include an operation energy cost and an annual average equipment maintenance management cost, where the operation energy cost includes a cost of outsourcing diesel oil and water, the annual average equipment maintenance management cost includes a maintenance cost of each equipment, and an expression of a first operation cost function AOC is determined as follows:
AOC＝Cf+Cm
Wherein, C f is the annual average operation energy cost, unit cell; and C m is annual average equipment maintenance and management cost, and unit element.
Wherein T is the number of annual operating hours; the price of diesel oil at the time t is per ton/yuan; /(I) The consumption of diesel oil at the time t is per ton; /(I)The water price at time t is per ton/yuan; /(I)The water consumption is t time, and the unit ton.
Wherein n is the number of devices (1, 2,3 … n); t is the number of annual operating hours; annual average fixed maintenance cost for class i C fc,i equipment; c om,i is the variable operation cost per unit time of the unit capacity of the i-th type equipment/system; m i is the rated capacity of the class i device.
Iterative step 12: according to the equipment model, carrying out installed capacity optimization simulation calculation on the CCHP system to be optimized to obtain the optimized installed capacity of each equipment; the objective function of the installed capacity optimization simulation calculation comprises the following steps: a first operation cost function; according to the equipment model and the optimized installed capacity, carrying out operation and maintenance cost optimization simulation calculation on the CCHP system to be optimized to obtain an optimized operation and maintenance cost function of the CCHP system to be optimized, and obtaining an optimized scheduling scheme of each equipment corresponding to the optimized operation and maintenance cost function;
In the embodiment of the invention, one iteration specifically comprises two steps. The first step comprises: according to the equipment model, carrying out installed capacity optimization simulation calculation on the CCHP system to be optimized to obtain the optimized installed capacity of each equipment; the objective function of the installed capacity optimization simulation calculation comprises the following steps: a first operation cost function. In the first step, according to the equipment model, the installed capacity of each equipment in the CCHP system is optimized by taking the first operation and maintenance cost as an objective function, and the optimized installed capacity is determined. The second step comprises: and carrying out operation and maintenance cost optimization simulation calculation on the CCHP system to be optimized according to the equipment model and the optimized installed capacity to obtain an optimized operation and maintenance cost function of the CCHP system to be optimized, and obtaining an optimized scheduling scheme of each equipment corresponding to the optimized operation and maintenance cost function. In the second step, the CCHP system to be optimized is assumed to set each device according to the optimized installed capacity, simulation optimization is performed on the overall operation and maintenance cost of the CCHP system to be optimized according to the device model, the optimized operation and maintenance cost (namely, the optimized operation and maintenance cost function) is determined, and an optimized scheduling scheme capable of enabling the CCHP system to achieve the optimized operation and maintenance cost under the optimized installed capacity is determined, namely, the real-time output of each device is scheduled according to the optimized scheduling scheme, and the optimized operation and maintenance cost can be achieved.
And a checking step 13: determining whether the iteration number after the completion of the iteration step 12 exceeds a preset iteration number threshold;
in the embodiment of the present invention, the iteration number is increased once per iteration step 12. The initial iteration number is 0, and after the iteration step 12 is completed for the first time, it is determined in the checking step 13 whether the iteration number 1 (i.e. the iteration number after the completion of the current iteration step 12) exceeds a preset iteration number threshold.
In the embodiment of the invention, the iteration times threshold can be specifically set by a user according to the running optimization requirement of the user on the CCHP system.
Step 14 is executed: if the iteration number is not exceeded, taking the optimized operation cost function as a new first operation cost function, and returning to the iteration step 12 until the iteration number threshold is exceeded;
determining step 15: and determining the current optimal installed capacity as the target installed capacity, and determining the current optimal scheduling scheme as the target optimal scheduling scheme.
In the embodiment of the invention, if the running optimization requirement is not met, the CCHP system is required to be further run and optimized. In this case, the optimized operation cost function is used as a new first operation cost function, and the iteration step 12 is returned to further optimize the installed capacity of each device, further obtain the optimized operation cost function of the CCHP system to be optimized according to the device model and the optimized installed capacity, and obtain the optimized scheduling scheme of each device corresponding to the optimized operation cost function. And performing loop execution between the iteration step 12 and the execution step 14 until the verification result obtained in the verification step 13 exceeds the iteration frequency threshold, finishing the loop, performing a determination step 15, determining the current optimal installed capacity (i.e. the optimal installed capacity determined in the last execution of the iteration step 13) as the target installed capacity (i.e. the optimal installed capacity), and determining the current optimal scheduling scheme (i.e. the optimal scheduling scheme determined in the last execution of the iteration step 13) as the target optimal scheduling scheme (i.e. the optimal installed capacity).
In the embodiment of the invention, the initialization step is carried out: constructing an equipment model of each equipment in the CCHP system to be optimized; and determining a first operation and maintenance cost function of the CCHP system to be optimized according to the equipment model; iterative steps: according to the equipment model, carrying out installed capacity optimization simulation calculation on the CCHP system to be optimized to obtain the optimized installed capacity of each equipment; the objective function of the installed capacity optimization simulation calculation comprises the following steps: a first operation cost function; according to the equipment model and the optimized installed capacity, carrying out operation and maintenance cost optimization simulation calculation on the CCHP system to be optimized to obtain an optimized operation and maintenance cost function of the CCHP system to be optimized, and obtaining an optimized scheduling scheme of each equipment corresponding to the optimized operation and maintenance cost function; and (3) checking: determining whether the iteration times after the completion of the iteration step exceeds a preset iteration times threshold; the method comprises the following steps: if the iteration frequency threshold is not exceeded, taking the optimized operation cost function as a new first operation cost function, and returning to the iteration step until the iteration frequency threshold is exceeded; determining: the method and the device for optimizing the CCHP system to be optimized can achieve operation optimization of the CCHP system to be optimized in two dimensions of the installed capacity dimension and the equipment scheduling dimension, obtain the matched optimal installed capacity (namely the target installed capacity) and the optimal scheduling scheme (namely the target optimal scheduling scheme), and achieve high-precision optimization of the CCHP system to be optimized.
In some embodiments of the invention, the method, optionally,
The installed capacity optimizing simulation calculated objective function further comprises at least one of the following: a carbon emission reduction rate function, a renewable energy permeability function, a renewable energy utilization rate function, and a primary energy conservation rate function.
In an embodiment of the present invention, at least one of the following is combined by a first operation cost function: the carbon emission reduction rate function, the renewable energy permeability function, the renewable energy utilization rate function and the primary energy saving rate function expand the optimization dimension of the installed capacity optimization simulation calculation. Compared with the prior art scheme only considering economy, the embodiment of the invention can further optimize the installed capacity from the dimensions of carbon emission reduction rate, renewable energy permeability, renewable energy utilization rate, primary energy saving rate and the like on the premise of ensuring the economy index based on the actual positioning and the requirements of the project.
In some embodiments of the invention, the method, optionally,
The expression of the objective function maxf calculated by the installed capacity optimization simulation is as follows:
wherein η ROI is a return on investment function determined from the first operation cost function; omega 1 is the weight value of the return on investment function; η CER is a carbon emission reduction rate function; omega 2 is the weight value of the carbon emission reduction rate function; η CER is a renewable energy permeability function; omega 3 is the weight value of the renewable energy permeability function; η RER is a renewable energy utilization function; omega 4 is the weight value of the renewable energy utilization function; η PESR is a primary energy conservation rate function; omega 5 is the weight value of the primary energy saving rate function.
Return on investment function η ROI:
Wherein, AAP: annual average net profit, unit cells; ITI is the initial total investment cost, unit element;
AAP＝PeQe+PhoQho+PcQc+PhdQhd-AOC
Wherein P e is the unit price of electricity (Yuan/kWh); p ho is a heat-selling unit price (Yuan/GJ); p c is a cold selling unit price (Yuan/GJ); p hd is hydrogen selling unit price (yuan/t); q e is annual average sales (kWh), Q ho is annual average sales (GJ); q c is annual average cold vending (GJ); q hd is annual average hydrogen sales (GJ); the AOC is annual average operation and maintenance cost, namely a first operation and maintenance cost function in the embodiment of the invention; m i is the i-th device/system installed capacity; r i is the initial total investment cost per unit capacity of the i-th type equipment/system.
The first operation cost function AOC for performing the iteration step 12 for the first time is determined by the initialization step 11. In the following explanation with reference to specific examples, referring to fig. 2, fig. 2 is a schematic block diagram of a CCHP system, in which renewable energy sources of solar energy and air energy are used as main input energy sources of the system, hydrogen is produced by electrolysis of water and regenerated by a hydrogen fuel cell, and intermittent and fluctuating photovoltaic power is output as stable electric energy by combining long-term energy storage advantages of a hydrogen storage tank, and waste heat in hydrogen production and power generation processes is recovered by fully utilizing an absorption heat pump technology, so that heating and refrigeration requirements of buildings are met. Based on the equipment model of each equipment constructed by the CCHP system, the cost item comprises operation energy cost and annual average equipment maintenance management cost, wherein the operation energy cost comprises the cost of outsourcing diesel oil and water, the annual average equipment maintenance management cost comprises the maintenance cost of each equipment, and the expression of the first operation and maintenance cost function AOC for executing the iteration step 12 for the first time is as follows:
AOC＝Cf+Cm
Wherein, C f is the annual average operation energy cost, unit cell; and C m is annual average equipment maintenance and management cost, and unit element.
Wherein T is the number of annual operating hours; the price of diesel oil at the time t is per ton/yuan; /(I) The consumption of diesel oil at the time t is per ton; /(I)The water price at time t is per ton/yuan; /(I)The water consumption is t time, and the unit ton.
Wherein n is the number of devices (1, 2,3 … n); t is the number of annual operating hours; annual average fixed maintenance cost for class i C fc,i equipment; c om,i is the variable operation cost per unit time of the unit capacity of the i-th type equipment/system; m i is the rated capacity of the class i device.
The expression of the carbon emission reduction rate function η CER is as follows:
Wherein E CCHP is the annual average carbon emission of the CCHP system, and ton CO 2/year; e SP is the annual average carbon emission of a conventional energy system, and tons of CO 2/year.
The expression of the renewable energy permeability function η CER is as follows:
Wherein, Q er is the annual average energy of renewable energy sources, and the renewable energy sources at least comprise: wind power, photoelectricity and air energy, and the unit is kWh; q et is the average total input energy per system year, in kWh.
The expression of the renewable energy utilization function η RER is as follows:
Wherein Q pv is the actual annual average power generation of the photovoltaic power station, kWh; q pa is the annual average power generation of the photovoltaic power station available, kWh; q ni is annual average power generation amount without light rejection statistics, and refers to a photovoltaic power generation consumption monitoring and statistics management method, and kWh.
Expression of the primary energy conservation rate function η PESR:
Wherein F CCHP is the annual average total energy consumption of the CCHP system, and kWh; f SP is the annual average total energy consumption of the conventional energy system.
It should be noted that, the weight value of the ω 1 return on investment rate function, the weight value of the ω 2 carbon emission reduction rate function, the weight value of the ω 3 renewable energy permeability function, the weight value of the ω 4 renewable energy utilization rate function, and the weight value of the ω 5 primary energy saving rate function are all specifically set by the user according to the user's own operation optimization requirement of the CCHP system. For example, the user needs to increase the priority of the new energy utilization rate in the CCHP system optimization process, and the user can increase ω 4 and adapt to adjust other weight values.
In the embodiment of the invention, the objective function of organically combining the economic index and the low-carbon index can be obtained by integrating the return on investment function, the carbon emission reduction function, the renewable energy permeability function, the renewable energy utilization function and the primary energy saving function and giving the corresponding weight value to each function. Based on the objective function, executing the iteration step 12, and optimizing the installed capacity from low carbon dimensions such as carbon emission reduction rate, renewable energy permeability, renewable energy utilization rate, primary energy saving rate and the like on the premise of ensuring the economic index, so that the economic index and the low carbon index are both considered; after the target installed capacity and the target optimal scheduling scheme which are finally obtained by the embodiment of the invention are applied to the CCHP system to be optimized, the economic index and the low-carbon index of the CCHP system to be optimized are synchronously improved.
In some embodiments of the invention, the method, optionally,
Adopting an ant colony algorithm to perform installed capacity optimization simulation calculation;
alternatively, the process may be carried out in a single-stage,
And carrying out operation and maintenance cost optimization simulation calculation by adopting an ant colony algorithm.
The ant colony algorithm (ANT SYSTEM or Ant Colony System) is a probabilistic algorithm for finding an optimized path. The basic idea of applying the ant colony algorithm to solve the optimization problem is as follows: the walking path of the ants is used for representing the feasible solution of the problem to be optimized, and all paths of the whole ant group form a solution space of the problem to be optimized. The ants with shorter paths release more pheromones, the concentration of the pheromones accumulated on the shorter paths gradually increases along with the advancement of time, and the number of ants selecting the paths is increased. Finally, the whole ant is concentrated on the optimal path under the action of positive feedback, and the optimal solution of the problem to be optimized is correspondingly obtained.
The ant colony algorithm has the characteristics of distributed calculation, information positive feedback and heuristic search, and is essentially a heuristic global optimization algorithm in the evolutionary algorithm.
In some embodiments of the invention, the method, optionally,
The ant colony algorithm includes: a pheromone updating algorithm;
The expression of the pheromone updating algorithm is as follows:
τj(t+1)＝(1-ρ)τj-L;τj(t+1)＝(1-ρ)τj-L；
Wherein L is an objective function value calculated by the optimal ant selection path; ρ is an information volatilization factor; t is the current iteration number; ρ 0 is a constant; ρ 1 is a constant and ρ 1＞ρ0;tmax is an iteration number threshold.
The following description is made with reference to specific examples, and referring to fig. 3, fig. 3 is a schematic diagram of an execution flow of an ant colony algorithm, including the following steps:
Step A1: and constructing a solving space and encoding processing. Coding a solving space of capacity configuration of each device of the system, and randomly initializing a population of a genetic algorithm by adopting a roulette mode;
step A2: individual fitness calculation and genetic iteration (corresponding to the three steps of "population of initialized genetic algorithm", "fitness calculation and genetic iteration", "whether the genetic algorithm iteration condition is satisfied" in fig. 3). And calculating the fitness of each initial population by taking the objective function of the capacity configuration optimization layer as a fitness function, finding out the population with the fitness ordered at the front, generating a new population by selecting, crossing, mutating and the like, and circularly iterating to calculate the fitness, and reserving the population with the highest fitness until the condition that the genetic algorithm stops is met.
Step A3: and initializing ant colony algorithm parameters. And A2, taking the optimal capacity configuration optimization result obtained in the step as an initial value of a pheromone of the ant colony algorithm, and setting initialization parameters such as ant number, heuristic factors, information volatilization factors and the like.
Step A4: ant colony search (corresponding to the "ant colony search, calculate transition probability" step in fig. 3). The ant colony calculates transition probability according to a given heuristic function formula, and updates the ant searching path according to the calculated probability value
Ant colony search: the ant colony calculates transition probability according to a given heuristic function formula, and updates the ant searching path according to the calculated probability value. The heuristic function calculation formula is as follows:
Wherein, The probability of transferring to the capacity configuration interval j for the ant k; τ j is the pheromone amount of the capacity allocation section j; n j is the information expectation heuristic parameter of the capacity configuration interval j; alpha is an information heuristic; beta is a desired heuristic; j k is a set of capacity allocation intervals in which ant k can transfer; s is any capacity configuration interval which can be traversed by ant k; τ s is the pheromone amount of any capacity configuration interval S that ant k can traverse; n s is the information expectation heuristic parameter of any capacity configuration interval S that ant k can traverse.
Step A5: pheromone update (corresponding to the "path calculation and pheromone update" step in fig. 3). The ant week model updated by the pheromone updating algorithm of the embodiment of the invention is that after all ants of the ant colony complete one-time search, the objective function value of each ant under the capacity configuration interval is calculated, the path of the optimal ant is obtained, and the pheromone of the corresponding path is updated.
The expression of the pheromone updating algorithm is as follows:
τj(t+1)＝(1-ρ)τj+L;τj(t+1)＝(1-ρ)τj-L；
Wherein L is an objective function value calculated by the optimal ant selection path; ρ is an information volatilization factor; t is the current iteration number; ρ 0 is a constant; ρ 1 is a constant and ρ 1＞ρ0;tmax is an iteration number threshold.
After each iteration, the pheromones of the optimal path and the worst path are respectively enhanced according to τ j(t+1)＝(1-ρ)τj +L or weakened according to τ j(t+1)＝(1-ρ)τj -L, and the information volatilizing factors are respectively enhanced according toUpdating, and avoiding the problem of trapping in local optimum.
Actual measurement proves that the pheromone updating algorithm adopting the embodiment of the invention can avoid sinking into local optimum and improve optimizing accuracy.
Step A6: and outputting a primary iteration result of the capacity configuration optimization layer. And when the iteration times reach a defined threshold, ending searching and storing the optimal path, and outputting a primary iteration result of the capacity configuration optimization layer.
Step A7: and (5) operating a scheduling optimization layer solution. And (3) taking a group of optimal capacity configuration schemes obtained by the first-stage optimization as input, taking the minimum annual operation and maintenance cost of the system as an optimization target, taking the time-by-time output of equipment on a typical day as an optimization parameter, repeating algorithm steps A2 to A6, and finally outputting a group of optimal time-by-time output data of the equipment on the typical day (namely, carrying out operation and maintenance cost optimization simulation calculation on the CCHP system to be optimized according to the equipment model and the optimizing installed capacity to obtain an optimized operation and maintenance cost function of the CCHP system to be optimized, and obtaining an optimized scheduling scheme of each equipment corresponding to the optimized operation and maintenance cost function).
Step A8: and (5) double-layer iterative optimization. And returning the calculation result (namely, the optimal operation and maintenance cost function) of the optimal annual operation and maintenance cost obtained by the operation and maintenance layer (namely, the scheduling layer correspondingly executes the optimal operation and maintenance cost function) to the capacity configuration layer (namely, the capacity configuration layer correspondingly executes the device model according to the embodiment of the invention to perform the installed capacity optimization simulation calculation on the CCHP system to be optimized to obtain the optimal installed capacity of each device, wherein the objective function of the installed capacity optimization simulation calculation comprises the first operation and maintenance cost function), and then performs a second iteration to obtain a more optimal system capacity configuration scheme. When the number of loop iterations reaches a defined threshold (the number of loop iterations reaches a defined threshold, i.e., corresponds to "whether the iteration condition of the ant colony algorithm is satisfied.
In the example, a genetic algorithm is introduced into an ant colony algorithm, an objective function of a capacity configuration optimization layer is used as an fitness function, fitness of each initial population is calculated, populations with front fitness sequences are found out, a new population is generated through operations such as selection, intersection, mutation and the like, fitness is calculated in a cyclic iteration mode, the population with the highest fitness is reserved until the condition that the genetic algorithm stops is met, the obtained optimization result is used as an initial value of an pheromone of the ant colony algorithm, the algorithm convergence speed is improved, and the phenomenon that the ant colony algorithm falls into a local optimal solution is effectively avoided.
In some embodiments of the invention, the method, optionally,
The method for constructing the equipment model of each equipment in the CCHP system to be optimized comprises the following steps:
Acquiring state parameters of a CCHP system to be optimized and equipment parameters of each equipment in a preset first time period;
constructing an equipment model according to the state parameters and the equipment parameters;
wherein the status parameter comprises at least one of: the energy consumption type, the energy price corresponding to each energy consumption type, the real-time cold load, the real-time heat load, the real-time electric load and the meteorological data of the deployment place of the CCHP system to be optimized;
The device parameters include at least one of: device type, device rating parameters, output power.
In the embodiment of the invention, the state parameters of the CCHP system to be optimized and the equipment parameters of each equipment in a preset first time period are obtained; constructing an equipment model according to the state parameters and the equipment parameters; wherein the status parameter comprises at least one of: the energy consumption type, the energy price corresponding to each energy consumption type, the real-time cold load, the real-time heat load, the real-time electric load and the meteorological data of the deployment place of the CCHP system to be optimized; the device parameters include at least one of: the equipment type, the equipment rated parameters and the output power can ensure that the obtained equipment model truly represents the state of equipment, ensure that the subsequent iteration step 12 based on the equipment model positively optimizes the CCHP system to be optimized, and ensure that the obtained optimized installed capacity, optimized operation and maintenance cost function and optimized scheduling scheme have high accuracy; further, a solid foundation is laid for obtaining a high-accuracy target installed capacity and a target optimal scheduling scheme.
In some embodiments of the invention, the method, optionally,
The equipment model is a steady-state model;
The device model includes at least one of the following:
An electrolytic tank model, a photovoltaic generator set model, a hydrogen fuel cell model, a diesel generator model, an air source heat pump model, an absorption heat pump model, a hydrogen storage tank model and a heat storage tank model.
In the embodiment of the invention, the equipment model is a steady-state model, so that the introduction of an excessively complex equipment model is avoided on the premise of meeting the requirement of high-accuracy operation optimization of the CCHP system to be optimized, the problems of high calculation force requirement, long time consumption and the like caused by the introduction of the excessively complex equipment model are avoided, and the optimization efficiency is improved.
In the following explanation with reference to specific examples, referring to fig. 2, fig. 2 is a schematic block diagram of a CCHP system, in which renewable energy sources of solar energy and air energy are used as main input energy sources of the system, hydrogen is produced by electrolysis of water and regenerated by a hydrogen fuel cell, and intermittent and fluctuating photovoltaic power is output as stable electric energy by combining long-term energy storage advantages of a hydrogen storage tank, and waste heat in hydrogen production and power generation processes is recovered by fully utilizing an absorption heat pump technology, so that heating and refrigeration requirements of buildings are met. Device models of the devices constructed based on the CCHP system:
Photovoltaic generator set model:
the photovoltaic generator is a dominant power supply of the CCHP system, the photovoltaic generator set model represents the power generation of the photovoltaic generator set, and the expression of the photovoltaic generator set model is as follows:
Ppv(t)＝G(t)Apv(t)ηpv
wherein P pv (t) is the output power of the photovoltaic system at the moment t, and the unit kW; g (t) is the solar radiation quantity (kW/m 2) at the moment t, and the unit kW; a pv is the area of the photovoltaic module, and the unit m 2;ηpv is the photovoltaic power generation efficiency in percent.
Electrolytic cell model:
the PEM electrolyzer with high hydrogen production purity and better matching property with renewable energy sources is adopted, the electrolyzer model represents the hydrogen production and heat production power of the electrolyzer, and the expression of the electrolyzer model is as follows:
Wherein P pem,h2 (t) is hydrogen production power of the electrolytic cell at the time t, and the unit Nm 3/kWh;Hpem,h (t) is waste heat output power of the electrolytic cell at the time t and the unit KW; η pem,h2(t),ηpem,h (t) is the hydrogen production efficiency of the electrolyzer at the moment t and the heat production efficiency of the electrolyzer at the moment t respectively in sequence, and the units are all; p pem,in (t) is the electric power input by the electrolyzer at the moment t, and the electric power is unit kW.
Hydrogen fuel cell model:
The hydrogen fuel cell model characterizes the power generation and heat production of the hydrogen fuel cell, and the expression of the hydrogen fuel cell model is as follows:
Wherein P pfc,e (t) is the power generated by the fuel cell at the moment t, and the unit kW; h pfc,h (t) is the waste heat output power of the fuel cell at the moment t, and the unit kW; η pfc,e(t),ηpfc,h (t) is the hydrogen production efficiency of the fuel cell at the moment t and the heat production efficiency of the fuel cell at the moment t in sequence respectively, and the units are all; and p pfc,in (t) is the electric power input by the electrolyzer at the moment t and is in units of kW.
Diesel generator model:
the diesel generator is used as an emergency standby power supply of the system, the diesel generator model represents the electric power of the diesel generator, and the expression of the diesel generator model is as follows:
Poe(t)＝ηoe(t)Vo(t)Co(t)
wherein P oe (t) is the power generated by the diesel generator at the moment t, and the unit kW; η oe (t) is the power generation efficiency of the diesel generator at the moment t; v o (t) is the fuel consumption of the diesel generator at the moment t, and the unit is L/s; c o (t) is the lower calorific value of the diesel oil at time t, and the unit is Kcal/L.
Air source heat pump model:
The air source heat pump is used as equipment for bearing basic heating and refrigerating loads, the air source heat pump model represents the output power of the air source heat pump, and the expression of the air source heat pump model is as follows:
h ahsp,h(t)、Hahsp,c (t) is sequentially respectively the heating working condition output power of the air source heat pump at the moment t and the refrigerating working condition output power of the air source heat pump at the moment t, and the units are kW; COP ahsp,h(t)、COPahsp,c (t) is the COP of the heating condition of the air source heat pump at time t and the COP of the cooling condition of the air source heat pump at time t (COP, which is totally called Coefficient of Performance, and the chinese meaning is the system running efficiency, which is also called coefficient of performance) in sequence.
Absorption heat pump model:
The lithium bromide absorption heat pump driven by a low-temperature heat source is used for recovering waste heat generated in the operation process of a hydrogen production system and a hydrogen fuel cell system, so that the requirements of daily hot water and part of winter heating load are met, the absorption heat pump model represents the output power of the absorption heat pump, and the expression of the absorption heat pump model is as follows:
h aht,h(t)、Haht,w (t) is sequentially the output power of the heating working condition of the absorption heat pump at the moment t and the output power of the hot water working condition of the absorption heat pump at the moment t, and the units are kW; COP aht,h(t)、COPaht,w (t) is the COP of the heating condition of the absorption heat pump at time t and the COP of the hot water condition of the absorption heat pump at time t (COP, which is totally Coefficient of Performance, and the chinese meaning is the system running efficiency, which is also called coefficient of performance).
Hydrogen storage tank model:
The high-pressure hydrogen storage tank with high charging and discharging speed and low manufacturing cost and suitable for small-scale hydrogen storage is adopted, the residual energy in the hydrogen storage tank is represented by a hydrogen storage tank model, and the expression of the hydrogen storage tank model is as follows:
EHS(t)＝EHS(t-1)+[PHS,in(t)ηHS,in(t)-PHS,out(t)ηHS,out(t)]Δt)
E HS(t)、EHS (t-1) is the residual energy in the hydrogen storage tank at the moment t and the residual energy in the hydrogen storage tank at the moment t-1 in sequence, and the units are kWh; η HS,in(t)、ηHS,out (t) is the hydrogen storage power of the hydrogen storage tank at the time t and the hydrogen discharge power of the hydrogen storage tank at the time t respectively in sequence; η HS,in(t)、ηHS,out (t) is the hydrogen storage efficiency of the hydrogen storage tank at the time t and the hydrogen release efficiency of the hydrogen storage tank at the time t respectively in sequence; Δt is the calculated time period, i.e., the number of operating hours of the hydrogen storage and release amount.
And (3) a heat storage tank model:
The water heat storage tank is adopted, the heat storage tank model represents the residual energy in the heat storage tank, and the expression of the heat storage tank model is as follows:
EWS(t)＝EWS(t-1)+[PWS,in(t)ηWS,in(t)-PWS,out(t)ηWS,out(t)]Δt)
E WS(t)、EWS (t-1) is the residual energy in the heat storage tank at the time t and the residual energy in the heat storage tank at the time t-1 in sequence, and the units are kWh; η ws,in(t)、Pws,in (t) is the heat storage power of the heat storage tank at the moment t and the heat release power of the heat storage tank at the moment t respectively in sequence; η ws,in(t)、ηws,in (t) is the heat storage efficiency of the heat storage tank at the moment t and the heat release efficiency of the heat storage tank at the moment t respectively in sequence; Δt is the calculated time period, i.e. the number of operating hours of the heat storage and release.
In some embodiments of the present invention, optionally, the constraint condition of the installed capacity optimization simulation calculation includes: capacity out-of-limit constraints, heat storage/hydrogen upper limit constraints, and supply-demand balance constraints.
Capacity out-of-limit constraints:
Upper limit constraint of heat storage/hydrogen:
Wherein, The residual energy of the heat storage/hydrogen system at the time t is kW; /(I)The maximum stored energy for the heat storage/hydrogen system, kW.
Supply and demand balance constraint:
Wherein: the power consumption is average annual power consumption, kW; /(I) The annual average power consumption of the hydrogen production system is kW; /(I)Annual power consumption, kW, of the hydrogen storage system; /(I)Annual power consumption, kW, of the air source heat pump system; /(I)The electric power consumed by the annual waste heat utilization system is kW; /(I)Annual forecast power generation capacity, kW, for a photovoltaic power plant; /(I)Annual average power production, kW, for a hydrogen fuel cell; The heat required by annual average heating is kW; /(I) The heat required by annual average domestic hot water is kW; /(I)The annual average waste heat recovery heating capacity of the hydrogen production system is kW; /(I)The annual average waste heat recovery heating capacity for hydrogen fuel cell power generation is kW; /(I)The annual average heating capacity of the air source heat pump system is kW; /(I)The annual average heating capacity of the waste heat utilization system is kW; /(I)The cold quantity required by annual average refrigeration is kW; /(I)Is the annual average refrigerating capacity of the air source heat pump system, kW.
In some embodiments of the invention, optionally, the shipping dimension cost optimization simulation calculation optimizes the objective function with the minimum annual operation dimension cost of the system:
AOC＝Cf+Cm
Wherein: the output power of the i-th device at the time t is obtained; d n is the number of days of the nth typical day;
in some embodiments of the invention, optionally, the shipping dimension cost optimization simulation calculated constraints include:
1. operating state constraints:
δi(t)∈{0，1}
Wherein: delta i (t) is the running state of the ith system equipment at the moment t, 0 represents shutdown, and 1 represents startup.
2. Output power constraint:
Wherein: the real-time power of the ith system equipment at the time t is obtained; /(I) Rated output power for the ith system equipment; /(I)The lowest limited output power for the ith system device.
3. Electric energy balance constraint:
4. Thermal energy balance constraint:
5. Cold energy balance constraint:
6. Device performance decay constraints:
Wherein: output power of the j-th device in t period under the typical day d of the nth year; /(I) Rated output power for class j devices; σ j is the annual average performance decay rate for class j devices.
The meaning of each parameter in the electric energy balance constraint, the heat energy balance constraint and the cold energy balance constraint corresponds to the supply and demand balance constraint, and the difference is that the superscript a in the supply and demand balance constraint represents the year, and the superscript t in the electric energy balance constraint, the heat energy balance constraint and the cold energy balance constraint represents the instantaneous quantity at the moment t.
Referring to fig. 4, fig. 4 is a schematic block diagram of an operation optimization device of a CCHP system according to an embodiment of the present invention, and an operation optimization device 40 of a CCHP system includes:
an initialization module 41, configured to initialize: constructing an equipment model of each equipment in the CCHP system to be optimized; and determining a first operation and maintenance cost function of the CCHP system to be optimized according to the equipment model;
An iteration module 42 for iterating the steps of: according to the equipment model, carrying out installed capacity optimization simulation calculation on the CCHP system to be optimized to obtain the optimized installed capacity of each piece of equipment; the objective function of the installed capacity optimization simulation calculation comprises the following steps: the first operation cost function; according to the equipment model and the optimized installed capacity, performing operation and maintenance cost optimization simulation calculation on the CCHP system to be optimized to obtain an optimized operation and maintenance cost function of the CCHP system to be optimized, and obtaining an optimized scheduling scheme of each equipment corresponding to the optimized operation and maintenance cost function;
A verification module 43, configured to verify: determining whether the iteration times after the iteration steps are finished exceed a preset iteration time threshold;
An execution module 44 for executing the steps of: if the iteration number is not exceeded, taking the optimized operation cost function as a new first operation cost function, and returning to the iteration step until the iteration number threshold is exceeded;
a determining module 45, configured to determine: and determining the current optimal installed capacity as a target installed capacity, and determining the current optimal scheduling scheme as a target optimal scheduling scheme.
In some embodiments of the invention, the method, optionally,
The installed capacity optimizing simulation calculated objective function further comprises at least one of the following: a carbon emission reduction rate function, a renewable energy permeability function, a renewable energy utilization rate function, and a primary energy conservation rate function.
In some embodiments of the present invention, optionally, the expression of the objective function maxf calculated by the installed capacity optimization simulation is as follows:
Wherein η ROI is a return on investment function determined from the first operation cost function; omega 1 is the weight value of the return on investment function; η CER is the carbon emission reduction rate function; omega 2 is the weight value of the carbon emission reduction rate function; η CER is the renewable energy permeability function; omega 3 is the weight value of the renewable energy permeability function; η RER is the renewable energy utilization function; omega 4 is the weight value of the renewable energy utilization function; η PESR is the primary energy conservation rate function; omega 5 is the weight value of the primary energy saving rate function.
In some embodiments of the invention, the method, optionally,
The iteration module 42 is further configured to perform the installed capacity optimization simulation calculation by using an ant colony algorithm; and/or the number of the groups of groups,
The iteration module 42 is further configured to perform the operation and maintenance cost optimization simulation calculation by using the ant colony algorithm.
In some embodiments of the invention, optionally, the ant colony algorithm includes: a pheromone updating algorithm;
the expression of the pheromone updating algorithm is as follows:
τj(t+1)＝(1-ρ)τj+L;τj(t+1)＝(1-ρ)τj-L；
Wherein L is an objective function value calculated by the optimal ant selection path; ρ is an information volatilization factor; t is the current iteration times; ρ 0 is a constant; ρ 1 is a constant and ρ 1＞ρ0;tmax is the iteration number threshold.
In some embodiments of the invention, the method, optionally,
The initialization module 41 is further configured to obtain a state parameter of the CCHP system to be optimized and an equipment parameter of each of the equipments in a preset first period of time;
the initialization module 41 is further configured to construct the device model according to the state parameter and the device parameter;
Wherein the status parameter comprises at least one of: the energy consumption type, the energy price corresponding to each energy consumption type, the real-time cold load, the real-time heat load, the real-time electric load and the meteorological data of the deployment place of the CCHP system to be optimized;
The device parameters include at least one of: device type, device rating parameters, output power.
In some embodiments of the invention, the method, optionally,
The equipment model is a steady-state model;
the equipment model comprises at least one of the following models:
An electrolytic tank model, a photovoltaic generator set model, a hydrogen fuel cell model, a diesel generator model, an air source heat pump model, an absorption heat pump model, a hydrogen storage tank model and a heat storage tank model.
The method and the device for optimizing the operation of the CCHP system provided by the embodiment of the application can realize each process realized by the method embodiments of fig. 1 to 3 and achieve the same technical effect, and are not repeated here for avoiding repetition.
An embodiment of the present invention provides an electronic device 50, referring to fig. 5, and fig. 5 is a schematic block diagram of the electronic device 50 according to an embodiment of the present invention, including a processor 51, a memory 52, and a program or an instruction stored in the memory 52 and capable of running on the processor 51, where the program or the instruction implements steps in the method for optimizing operation of any CCHP system according to the present invention when executed by the processor.
The embodiment of the present invention provides a readable storage medium, on which a program or an instruction is stored, where the program or the instruction, when executed by a processor, implements each process of the embodiment of the method for optimizing the operation of the CCHP system according to any one of the above embodiments, and can achieve the same technical effect, and in order to avoid repetition, a description is omitted herein.
The readable storage medium is, for example, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk or an optical disk. In some examples, the readable storage medium may be a non-transitory readable storage medium.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.

Claims (10)

1. An operation optimization method of a combined cooling, heating and power (CCHP) system is characterized by comprising the following steps:
Initializing: constructing an equipment model of each equipment in the CCHP system to be optimized; and determining a first operation and maintenance cost function of the CCHP system to be optimized according to the equipment model;
Iterative steps: according to the equipment model, carrying out installed capacity optimization simulation calculation on the CCHP system to be optimized to obtain the optimized installed capacity of each piece of equipment; the objective function of the installed capacity optimization simulation calculation comprises the following steps: the first operation cost function; according to the equipment model and the optimized installed capacity, performing operation and maintenance cost optimization simulation calculation on the CCHP system to be optimized to obtain an optimized operation and maintenance cost function of the CCHP system to be optimized, and obtaining an optimized scheduling scheme of each equipment corresponding to the optimized operation and maintenance cost function;
and (3) checking: determining whether the iteration times after the iteration steps are finished exceed a preset iteration time threshold;
the method comprises the following steps: if the iteration number is not exceeded, taking the optimized operation cost function as a new first operation cost function, and returning to the iteration step until the iteration number threshold is exceeded;
Determining: and determining the current optimal installed capacity as a target installed capacity, and determining the current optimal scheduling scheme as a target optimal scheduling scheme.
2. The method for optimizing the operation of a CCHP system according to claim 1, wherein:
The installed capacity optimizing simulation calculated objective function further comprises at least one of the following: a carbon emission reduction rate function, a renewable energy permeability function, a renewable energy utilization rate function, and a primary energy conservation rate function.
3. The method for optimizing the operation of a CCHP system according to claim 2, wherein:
The expression of the objective function maxf calculated by the installed capacity optimization simulation is as follows:
Wherein η ROI is a return on investment function determined from the first operation cost function; omega 1 is the weight value of the return on investment function; η CER is the carbon emission reduction rate function; omega 2 is the weight value of the carbon emission reduction rate function; η CER is the renewable energy permeability function; omega 3 is the weight value of the renewable energy permeability function; η RER is the renewable energy utilization function; omega 4 is the weight value of the renewable energy utilization function; η PESR is the primary energy conservation rate function; omega 5 is the weight value of the primary energy saving rate function.
4. The method for optimizing the operation of a CCHP system according to claim 1, wherein:
adopting an ant colony algorithm to perform the optimization simulation calculation of the installed capacity; and/or the number of the groups of groups,
And carrying out the operation and maintenance cost optimization simulation calculation by adopting the ant colony algorithm.
5. The method for optimizing the operation of a CCHP system according to claim 4, wherein:
the ant colony algorithm comprises: a pheromone updating algorithm;
the expression of the pheromone updating algorithm is as follows:
τj(t+1)＝(1-ρ)τj+L;τj(t+1)＝(1-ρ)τj-L；
Wherein L is an objective function value calculated by the optimal ant selection path; ρ is an information volatilization factor; t is the current iteration times; ρ 0 is a constant; ρ 1 is a constant and ρ 1>ρ0;tmax Is that is the iteration number threshold.
6. The method for optimizing the operation of a CCHP system according to claim 1, wherein:
The construction of the equipment model of each equipment in the CCHP system to be optimized comprises the following steps:
acquiring state parameters of a CCHP system to be optimized and equipment parameters of each piece of equipment in a preset first time period;
constructing the equipment model according to the state parameters and the equipment parameters;
Wherein the status parameter comprises at least one of: the energy consumption type, the energy price corresponding to each energy consumption type, the real-time cold load, the real-time heat load, the real-time electric load and the meteorological data of the deployment place of the CCHP system to be optimized;
The device parameters include at least one of: device type, device rating parameters, output power.
7. The method for optimizing the operation of a CCHP system according to claim 1 or 6, wherein:
The equipment model is a steady-state model;
the equipment model comprises at least one of the following models:
An electrolytic tank model, a photovoltaic generator set model, a hydrogen fuel cell model, a diesel generator model, an air source heat pump model, an absorption heat pump model, a hydrogen storage tank model and a heat storage tank model.
8. An operation optimizing device of a CCHP system, comprising:
An initialization module, configured to initialize: constructing an equipment model of each equipment in the CCHP system to be optimized; and determining a first operation and maintenance cost function of the CCHP system to be optimized according to the equipment model;
The iteration module is used for iterating the steps of: according to the equipment model, carrying out installed capacity optimization simulation calculation on the CCHP system to be optimized to obtain the optimized installed capacity of each piece of equipment; the objective function of the installed capacity optimization simulation calculation comprises the following steps: the first operation cost function; according to the equipment model and the optimized installed capacity, performing operation and maintenance cost optimization simulation calculation on the CCHP system to be optimized to obtain an optimized operation and maintenance cost function of the CCHP system to be optimized, and obtaining an optimized scheduling scheme of each equipment corresponding to the optimized operation and maintenance cost function;
the verification module is used for verifying: determining whether the iteration times after the iteration steps are finished exceed a preset iteration time threshold;
The execution module is used for executing the steps of: if the iteration number is not exceeded, taking the optimized operation cost function as a new first operation cost function, and returning to the iteration step until the iteration number threshold is exceeded;
A determining module, configured to determine: and determining the current optimal installed capacity as a target installed capacity, and determining the current optimal scheduling scheme as a target optimal scheduling scheme.
9. An electronic device, characterized in that: comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, which program or instruction when executed by the processor implements the steps in the method of optimizing the operation of a CCHP system according to any of claims 1 to 7.
10. A readable storage medium, characterized by: the readable storage medium stores thereon a program or instructions that when executed by a processor implement the steps in the method of optimizing operation of the CCHP system according to any one of claims 1 to 7.
CN202410051300.6A 2024-01-12 2024-01-12 Method, device, equipment and storage medium for optimizing operation of CCHP system Pending CN118034874A (en)

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CN118195178A (en) * 2024-05-17 2024-06-14 山东大学 Hydrogen energy full-link equipment combination selection method and system

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CN118195178A (en) * 2024-05-17 2024-06-14 山东大学 Hydrogen energy full-link equipment combination selection method and system

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