CN113919612A - Power determination method of energy system and electronic equipment - Google Patents

Power determination method of energy system and electronic equipment Download PDF

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Publication number
CN113919612A
CN113919612A CN202010657668.9A CN202010657668A CN113919612A CN 113919612 A CN113919612 A CN 113919612A CN 202010657668 A CN202010657668 A CN 202010657668A CN 113919612 A CN113919612 A CN 113919612A
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energy
energy system
load
target power
determining
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祁晓敏
熊煌
李振杰
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China Energy Intelligence New Technology Industry Development Co ltd
Electric Power Planning and Engineering Institute Co Ltd
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China Energy Intelligence New Technology Industry Development Co ltd
Electric Power Planning and Engineering Institute Co Ltd
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Priority to CN202010657668.9A priority Critical patent/CN113919612A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention provides a power determination method of an energy system and electronic equipment, wherein the method comprises the following steps: predicting energy production amount of the target power source and energy consumption amount of the load; the target power supply is a power supply outside the energy system; determining an operating power of the energy system based on the energy production amount of the target power source and the energy consumption amount of the load. The embodiment of the invention can reduce the operation cost of the energy system.

Description

Power determination method of energy system and electronic equipment
Technical Field
The present invention relates to the field of power technologies, and in particular, to a power determination method for an energy system and an electronic device.
Background
The 'source-load-storage' interactive operation oriented to the power system refers to the fact that power sources, loads and stored energy are mutually complemented through sources and sources, stored load interaction, source-load interaction and the like, the dynamic power balance capability of the power system is improved economically, efficiently and safely, and the method is essentially an operation mode and technology for achieving the maximum utilization of energy resources.
In the process of determining power, the existing energy system does not take uncertain consideration on a power supply and a load, so that energy resources are not fully utilized, and the operating cost of the energy system is high.
Disclosure of Invention
The embodiment of the invention provides a power determination method of an energy system and electronic equipment, and aims to solve the problem that the operation cost of the energy system is high.
The embodiment of the invention provides a power determination method of an energy system, which is applied to electronic equipment and comprises the following steps:
predicting energy production amount of the target power source and energy consumption amount of the load; the target power supply is a power supply outside the energy system;
determining an operating power of the energy system based on the energy production amount of the target power source and the energy consumption amount of the load.
Optionally, the predicting the energy production amount of the target power source and the energy consumption amount of the load includes:
acquiring a first historical value of energy production of the target power source and a second historical value of energy consumption of the load;
performing randomness analysis on the first historical numerical value and the second historical numerical value by using a nonparametric kernel density estimation method to generate random probability distribution information;
processing the random probability distribution information by using a probability scene sampling method to generate a first operation scene;
performing clustering reduction on the first operation scene by using a synchronous back substitution reduction method to generate a second operation scene;
predicting an energy production amount of the target power source and an energy consumption amount of the load based on the second operation scenario.
Optionally, the energy system comprises at least one of:
a gas turbine unit for converting gas into electric energy, heat energy or cold energy;
a fuel cell device for converting chemical energy into electric energy;
an electric boiler device for converting electric energy into heat energy;
the electric refrigeration air-conditioning device is used for converting electric energy into cold energy;
an electrical energy storage device for storing electrical energy;
the ice cold accumulation device is used for storing cold energy;
and the heat energy storage device is used for storing heat energy.
Optionally, in a case that the energy system includes at least two devices, the determining the operating power of the energy system includes:
optimizing and solving the cost calculation model by using a sequential quadratic programming algorithm; the cost calculation model is used for calculating the cost of the energy system;
and determining the operation power of each device of the energy system based on the result of the optimization solution.
Optionally, the performing optimization solution on the cost calculation model by using a sequential quadratic programming algorithm includes:
determining network parameters of the energy system and optimization parameters of the algorithm;
determining power boundaries for devices of the energy system;
and based on the network parameters, the optimization parameters and the power boundary, carrying out optimization solution on the cost calculation model by using a sequential quadratic programming algorithm.
An embodiment of the present invention further provides an electronic device, including:
a prediction module for predicting energy production amount of the target power supply and energy consumption amount of the load; the target power supply is a power supply outside the energy system;
a determination module to determine an operating power of the energy system based on the energy production amount of the target power source and the energy consumption amount of the load.
Optionally, the prediction module includes:
an acquisition unit configured to acquire a first history value of an energy production amount of the target power source and a second history value of an energy consumption amount of the load;
the first generation unit is used for carrying out randomness analysis on the first historical numerical value and the second historical numerical value by using a non-parametric kernel density estimation method to generate random probability distribution information;
the second generation unit is used for processing the random probability distribution information by utilizing a probability scene sampling method to generate a first operation scene;
a third generating unit, configured to perform cluster reduction on the first operating scene by using a synchronous back substitution reduction method to generate a second operating scene;
a prediction unit configured to predict an energy production amount of the target power source and an energy consumption amount of the load based on the second operation scenario.
Optionally, in a case where the energy system includes at least two devices, the determining module includes:
the optimization unit is used for optimizing and solving the cost calculation model by using a sequential quadratic programming algorithm; the cost calculation model is used for calculating the cost of the energy system;
and the determining unit is used for determining the operating power of each device of the energy system based on the result of the optimization solution.
The embodiment of the present invention further provides an electronic device, which includes a processor, a memory, and a computer program stored on the memory and capable of running on the processor, and when the computer program is executed by the processor, the steps of the method for determining power of an energy system according to the embodiment of the present invention are implemented.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the power determining method for an energy system provided by an embodiment of the present invention.
In the embodiment of the invention, the energy production amount of the target power supply and the energy consumption amount of the load are predicted; the target power supply is a power supply outside the energy system; determining an operating power of the energy system based on the energy production amount of the target power source and the energy consumption amount of the load. In the process of determining the operating power of the energy system, because the uncertainty of the target power supply and the uncertainty of the load are considered at the same time, the utilization of energy resources is more sufficient and reasonable, the waste of the energy resources is reduced, and the operating cost of the energy resources is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart of a power determination method of an energy system according to an embodiment of the present invention;
fig. 2 is a flowchart of another power determination method for an energy system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an energy system provided by an embodiment of the invention;
fig. 4 is a flowchart of another power determination method for an energy system according to an embodiment of the present invention;
fig. 5 is a flowchart of an optimization solution of an energy system according to an embodiment of the present invention;
FIG. 6 is a block diagram of an electronic device according to an embodiment of the present invention;
fig. 7 is a block diagram of another electronic device provided in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "comprises," "comprising," or any other variation thereof, in the description and claims of this application, 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. Furthermore, the use of "and/or" in the specification and claims means that at least one of the connected objects, such as a and/or B, means that three cases, a alone, B alone, and both a and B, exist.
In the embodiments of the present invention, words such as "exemplary" or "for example" are used to mean serving as examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "e.g.," an embodiment of the present invention is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
Referring to fig. 1, fig. 1 is a flowchart of a power determination method of an energy system according to an embodiment of the present invention, where the method is applied to an electronic device, and as shown in fig. 1, the method includes the following steps:
step 101, predicting energy production amount of a target power supply and energy consumption amount of a load; the target power source is a power source outside the energy system.
In some embodiments, the target power source may also be referred to as an uncertain power source, which refers to a power source whose energy production is uncertain, which may refer to renewable energy sources such as wind energy and solar photovoltaic energy, which are uncertain because their energy production is largely influenced by the weather environment. The prediction of the energy production amount of the target power source may be, but is not limited to, prediction of the total production amount of the energy of the target power source, prediction of the electric energy production amount, the thermal energy production amount, or the cold energy production amount of the target power source, or prediction of the energy production amount of another form.
The energy consumption of the load may refer to the amount of electric energy, thermal energy, or cold energy consumed by a user of the electric power system. The users of the power system may refer to home users or industrial users, facing different seasons or different climates, or during peak and peak periods of power usage, etc., the energy consumption amount of the load is uncertain. The energy consumption amount of the load may be a total energy consumption amount of the load, may be an electric energy consumption amount, a thermal energy consumption amount, or a cold energy consumption amount of the load, or may be another energy consumption amount, and is not limited thereto.
The operating power of the energy system is controllable, the energy production amount, the energy conversion amount or the energy storage amount of the energy system is controllable, and the target energy is energy outside the energy system.
In this step, the prediction method of the energy production amount of the target power source and the energy consumption amount of the load is not limited, and the prediction may be performed by analyzing a first historical value of the energy production amount of the target power source and a second historical value of the energy consumption amount of the load, the prediction may be performed by analyzing a probability distribution of the first historical value and the second historical value, and the prediction may be performed by using a prediction result of an electric power system of another manufacturer or by using another method, and the embodiment of the present invention is not limited. The predicted time period is not limited, and for example, the energy production amount of the target power source and the energy consumption amount of the load on the next day may be predicted, or the energy production amount of the target power source and the energy consumption amount of the load on the next hour may be predicted.
And 102, determining the operation power of the energy system based on the energy production amount of the target power source and the energy consumption amount of the load.
For example, when the predicted value of the energy production amount of the target power source is higher than the predicted value of the energy consumption amount of the load, the energy system may store the surplus energy to avoid the energy waste. When the predicted value of the energy production amount of the target power source is lower than the predicted value of the energy consumption amount of the load, the energy system may supplement the supply of energy in an optimum cost manner. The internal structure of the energy system is not limited in the embodiment of the present invention, for example, the energy system may include a plurality of energy storage devices for storing electricity, storing heat or storing cold, a plurality of energy supply devices for supplying electricity, supplying heat and supplying cold, and a conversion device for different energy sources, and the embodiment of the present invention is not limited in this embodiment.
The determining the operating power of the energy system may be determining the operating power of the energy system on the next day, and may be determining the operating power of the energy system on the next hour, which is not limited in this embodiment of the present invention.
The energy system fully transfers an internal energy device according to the energy production of the target power supply and the prediction result of the energy consumption of the load, is flexible in regulation and control and interactive in coordination, and improves the dynamic power balance capability of the power system more economically, efficiently and safely through various interactive forms such as source-source complementation, storage-load interaction and source-load interaction, so that the operation mode of maximum utilization of energy resources is realized.
In the embodiment of the invention, the energy production amount of the target power supply and the energy consumption amount of the load are predicted; the target power supply is a power supply outside the energy system; determining an operating power of the energy system based on the energy production amount of the target power source and the energy consumption amount of the load. In the process of determining the operating power of the energy system, because the uncertainty of the target power supply and the uncertainty of the load are considered at the same time, the utilization of energy resources is more sufficient and reasonable, the waste of the energy resources is reduced, and the operating cost of the energy resources is reduced.
Referring to fig. 2, fig. 2 is a flowchart of another power determination method for an energy system according to an embodiment of the present invention, as shown in fig. 2, including the following steps:
step 201, obtaining a first historical value of the energy production amount of the target power supply and a second historical value of the energy consumption amount of the load.
The first historical value and the second historical value may be obtained from the power system of the plant, or may be obtained from the power systems of other plants, which is not limited herein. The historical value of the energy production amount of the target power source and the historical value of the energy consumption amount of the load may be on a daily basis or on an hourly basis, and are not limited thereto.
Step 202, performing randomness analysis on the first historical numerical value and the second historical numerical value by using a non-parametric kernel density estimation method to generate random probability distribution information.
For example, by analyzing and processing the first historical values and the second historical values in this step, a probability distribution of the energy production amount of the target power source and the energy consumption amount of the load per hour on the next day can be obtained, and particularly, a value of the maximum probability of the energy production amount of the target power source and the energy consumption amount of the load per hour on the next day is concerned. Other forms of random probability distribution information may be generated in this step, which is not limited.
And 203, processing the random probability distribution information by using a probability scene sampling method to generate a first operation scene.
The first operation scenario may be described by a graph of numerical values of the energy production amount of the target power source and the energy consumption amount of the load at the maximum probability per hour of the next day, or may be described in other scenario manners.
And 204, performing cluster reduction on the first operation scene by using a synchronous back substitution reduction method to generate a second operation scene.
And integrating and classifying the similar or similar first operation scenes, and performing cluster reduction to generate a second operation scene.
And step 205, predicting the energy production amount of the target power supply and the energy consumption amount of the load based on the second operation scene.
In this step, the energy production amount of the target power source and the energy consumption amount of the load per hour in the next day may be predicted, or the energy production amount of the target power source and the energy consumption amount of the load per day in the next week may be predicted, which is not limited.
And step 206, determining the operation power of the energy system based on the energy production amount of the target power source and the energy consumption amount of the load.
In the embodiment, the randomness of the renewable energy and the load is analyzed by adopting a non-parameter kernel density estimation method, so that the random probability distribution of the output of the renewable energy and the load is obtained. And generating random operation data of renewable energy and load output by adopting a probability scene sampling method, and performing clustering reduction on each operation scene by utilizing a synchronous back-substitution reduction method to obtain clustering scenes and probabilities of the renewable energy and the load output.
According to the embodiment, the accuracy of prediction of the energy production amount of the target power supply and the energy consumption amount of the load is improved, so that the prediction result is close to the actual numerical values of the energy production amount of the target power supply and the energy consumption amount of the load at the next moment as much as possible, the running power of the energy system at the next moment is determined more accurately, the matching degree is higher, the energy resource is utilized more fully and more reasonably, the waste of the energy resource is reduced, and the running cost of the energy resource is reduced.
Optionally, the energy system 300 comprises at least one of:
a gas turbine unit 301 for converting gas into electric energy, heat energy or cold energy;
a fuel cell device 302 for converting chemical energy into electrical energy;
an electric boiler device 303 for converting electric energy into heat energy;
an electric refrigeration air conditioning unit 304 for converting electric energy into cold energy;
an electrical energy storage device 305 for storing electrical energy;
the ice cold storage device 306 is used for storing cold energy;
a thermal energy storage device 307 for storing thermal energy.
Fig. 3 is a schematic diagram of an energy system according to an embodiment of the present invention, where the energy system 300 shown in fig. 3 includes all of a gas turbine device 301, a fuel cell device 302, an electric boiler device 303, an electric refrigerating and air conditioning device 304, an electric energy storage device 305, an ice thermal storage device 306, and a thermal energy storage device 307, and the energy system shown in fig. 3 is an implementation manner thereof.
In this embodiment, the gas turbine device 301, the fuel cell device 302, the electric boiler device 303, the electric refrigeration air-conditioning device 304, the electric energy storage device 305, the ice cold storage device 306 and the heat energy storage device 307 of the energy system 300 may operate independently without interfering with each other, thereby achieving decoupling of multiple energy sources and improving the accuracy of adjustment and control of the energy system.
The gas turbine model of the gas turbine apparatus 301 is:
Figure BDA0002577334200000081
wherein the content of the first and second substances,
Figure BDA0002577334200000082
exhaust waste heat quantity of the gas turbine in a period t;
Figure BDA0002577334200000083
electric power output by the gas turbine for a period t; etaeGenerating efficiency for the gas turbine; etalIs the gas turbine heat dissipation loss coefficient;
Figure BDA0002577334200000084
heating capacity provided for the waste heat of the flue gas of the gas turbine at the time t;
Figure BDA0002577334200000085
refrigerating capacity provided for the waste heat of the flue gas of the gas turbine at the time t; kHE、KCEThe heating and refrigerating coefficients of the bromine refrigerator are respectively, theta is the proportion of the waste heat of the gas turbine for supplying heat,
Figure BDA0002577334200000086
for the total efficiency of operation of the gas turbine during the period t, vfIs the flow rate of natural gas, LfIs the low heat value of natural gas.
The total cost of gas turbine operation of the gas turbine plant 301 is:
Figure BDA0002577334200000091
where Δ t is the time interval between two schedules,
Figure BDA0002577334200000092
at the price of natural gas for a period of t, CMTThe total cost of gas turbine operation.
The fuel cell model of the fuel cell apparatus 302 is:
Figure BDA0002577334200000093
wherein the content of the first and second substances,
Figure BDA0002577334200000094
output electric power of the fuel cell for a period t; etaFCThe power generation efficiency of the fuel cell.
The electric boiler model of the electric boiler device 303 is as follows:
Figure BDA0002577334200000095
wherein the content of the first and second substances,
Figure BDA0002577334200000096
and
Figure BDA0002577334200000097
respectively the heat generating power and the power consumption power of the electric boiler in the time period t,
Figure BDA0002577334200000098
the heat generating efficiency of the electric boiler.
The electric refrigeration air conditioner model of the electric refrigeration air conditioner is as follows:
Figure BDA0002577334200000099
wherein the content of the first and second substances,
Figure BDA00025773342000000910
and
Figure BDA00025773342000000911
respectively the refrigeration power and the power consumption power of the electric refrigeration air conditioner,
Figure BDA00025773342000000912
the refrigerating efficiency of the refrigerator.
The electrical energy storage system model of the electrical energy storage device 305 is:
Figure BDA00025773342000000913
wherein the content of the first and second substances,
Figure BDA0002577334200000101
is the state of charge of the electrical energy storage,
Figure BDA0002577334200000102
and
Figure BDA0002577334200000103
charging power and discharging power which are electric energy storage respectively;
Figure BDA0002577334200000104
and
Figure BDA0002577334200000105
charging efficiency and discharge efficiency, σ, of the electrical energy storage, respectivelyESIs the self-discharge rate of the electrical energy storage system.
The ice storage system model of the ice storage device 306 is as follows:
Figure BDA0002577334200000106
wherein the content of the first and second substances,
Figure BDA0002577334200000107
is the cold accumulation amount of the ice cold accumulation system in the period t,
Figure BDA0002577334200000108
and
Figure BDA0002577334200000109
respectively the ice making and ice melting power of the ice cold storage system,
Figure BDA00025773342000001010
and
Figure BDA00025773342000001011
efficiency of ice making and ice melting, σ, respectively, of ice storage systemisIs the cold energy self-loss rate of the ice cold accumulation system.
The thermal energy storage system model of the thermal energy storage device 307 is:
Figure BDA00025773342000001012
wherein the content of the first and second substances,
Figure BDA00025773342000001013
is the heat storage amount of the thermal energy storage system in the period t,
Figure BDA00025773342000001014
the heat storage power and the heat release power in the period t are respectively,
Figure BDA00025773342000001015
respectively the heat storage and release efficiency, sigma, of the thermal energy storage system during the period tHSIs the heat loss rate of the heat storage tank.
The demand response model is:
Figure BDA00025773342000001016
wherein the content of the first and second substances,
Figure BDA00025773342000001017
in the form of a load shedding cost per unit power,
Figure BDA00025773342000001018
load shedding power for demand response; n is a radical ofECLThe number of loads participating in demand response.
The embodiment of the invention achieves the purposes of fully playing the mutual energy complementary regulation potential of distributed energy, energy storage, demand response and the like and ensuring the optimal rationality of the operation scheme. Under the condition of fully considering the characteristics of multi-energy difference, randomness, coupling and the like, a high-efficiency and reasonable comprehensive energy optimization operation scheme is designed, and the guidance of an optimization result on actual engineering is improved.
The energy system can produce electric energy, heat energy or cold energy, can store the electric energy, the heat energy or the cold energy, and can realize energy conversion among the electric energy, the heat energy and the cold energy. Diversified energy supply and energy storage forms, and multiple energy forms decoupling, the flexibility of energy system's regulation and control is higher. After the operating power of the energy system is determined, in the case that the energy system comprises at least two devices, the energy system dynamically balances the energy production of the target power supply, the energy consumption of the load and the operating capacity of the energy system through power distribution, energy conversion and flexible coordination of the at least two devices inside, so that the power dynamic balance capacity of the power system is improved more economically, efficiently and safely.
Referring to fig. 4, fig. 4 is a flowchart of another power determination method for an energy system according to an embodiment of the present invention, as shown in fig. 4, including the following steps:
step 401, predicting energy production amount of a target power supply and energy consumption amount of a load; the target power source is a power source outside the energy system.
Step 402, under the condition that the energy system comprises at least two devices, optimizing and solving a cost calculation model by using a sequential quadratic programming algorithm; the cost calculation model is used for calculating the cost of the energy system.
And 403, determining the operating power of each device of the energy system based on the result of the optimization solution.
Taking the expected total running cost of the energy system as an objective function of an optimization model, wherein the objective function is as follows:
Figure BDA0002577334200000111
wherein C is the total expected running cost of the system,
Figure BDA0002577334200000112
the running cost formed by the consumption of system fuel, the service life loss of equipment and the like,
Figure BDA0002577334200000121
the maintenance costs for the operation of each piece of equipment,
Figure BDA0002577334200000122
for the purchase of electricity or for the sale of electricity revenue. OmegasIs the probability of occurrence of the s-th random scene, NSNumber of random scenes, TNThe total number of time segments optimized for operation.
The calculation formulas of the costs of fuel consumption, service life loss and the like are as follows, and mainly cover the fuel consumption costs of a gas turbine and a fuel cell, the operation service life loss conversion costs of an electric energy storage system, a heat energy storage system and an ice cold storage system, and the compensation cost of demand response.
Figure BDA0002577334200000123
The electrical energy storage operating cost of the electrical energy storage device 305:
Figure BDA0002577334200000124
the ice storage system cost of the ice storage device 306 is as follows:
Figure BDA0002577334200000125
the thermal energy storage system operating cost of the thermal energy storage device 307:
Figure BDA0002577334200000126
wherein the content of the first and second substances,
Figure BDA0002577334200000127
indicates a state of charge of
Figure BDA0002577334200000128
While using discharge power
Figure BDA0002577334200000129
The equivalent of the discharge translates to a cost function. k is a radical ofisCost per unit power loss for ice storage system, CisThe running cost of the ice cold storage system is low. Tau isHSCost per unit power loss for thermal energy storage system, CHSThe operating cost of the heat energy storage system is reduced.
The operating maintenance cost is calculated as follows:
Figure BDA00025773342000001210
wherein the content of the first and second substances,
Figure BDA0002577334200000131
for the operating maintenance cost factor, P, of the ith planti tThe output power of the ith equipment unit in the time period t.
The electricity purchase cost and the electricity selling profit are calculated as follows:
Figure BDA0002577334200000132
wherein the content of the first and second substances,
Figure BDA0002577334200000133
the interactive power of the comprehensive energy system and the external power grid in the period of t is represented by purchasing electricity from the comprehensive energy system to the external power grid when taking a positive value and selling electricity from the comprehensive energy system to the external power grid when taking a negative value,
Figure BDA0002577334200000134
and
Figure BDA0002577334200000135
the electricity prices of the comprehensive energy system for purchasing and selling electricity are respectively t time period.
The embodiment of the invention considers the uncertainty of the source load; the complementation of various energy storage forms such as electricity storage, heat storage, cold storage and the like is considered; the method comprises the steps of considering the influence of factors such as source-load-storage coordinated interaction and the like on the optimization process of the energy system, taking the expected operation total cost of the system as a target function of an optimization model, carrying out deep mining and characterization on source-load uncertainty in the energy system by adopting a method combining nonparametric kernel density estimation and probability scene sampling, and solving the optimization model by adopting a solving method based on a sequence quadratic programming algorithm, thereby realizing the optimization design of the operation strategy of the energy system, being beneficial to realizing energy decoupling, and improving the operation flexibility and economy of the system.
For example, when the predicted value of the energy production amount of the target power source is higher than the predicted value of the energy consumption amount of the load, the energy system may store electric energy using the electric energy storage device 305, or store cold energy using the ice thermal storage device 306, or store hot energy using the thermal energy storage device 307; the energy can be directly stored or can be converted and then stored. In the process of the optimization solution, the energy processing mode with the lowest operation cost and the highest energy utilization rate can be calculated in a plurality of energy processing modes.
For example, when the predicted value of the energy production amount of the target power source is lower than the predicted value of the energy consumption amount of the load, the energy system may generate electric energy, thermal energy, or cold energy using gas as a supplementary supply energy using the gas turbine device 301; the fuel cell device 302 may also be utilized to generate electrical energy chemically as a supplemental energy supply; the energy storage device can also be directly utilized to directly supplement and provide energy; the energy can be directly supplied, or the energy can be supplied after being converted. In the process of the optimization solution, the energy processing mode with the lowest operation cost and the highest energy utilization rate can be calculated in a plurality of energy processing modes.
In one embodiment, when the predicted value of the energy production amount of the target power source is higher than the predicted value of the energy consumption amount of the load, the energy system may not store the surplus energy and reduce the operation cost of the energy system if the cost of energy storage is higher than the cost of energy itself in the result of the optimization solution.
The embodiment of the invention comprehensively considers the source charge uncertainty, realizes the complementary conversion and matching of various energy storage forms such as electricity storage, heat storage, cold storage and the like, considers the coordination interaction among all links of source charge storage and energy storage in detail, and fully utilizes the multi-energy storage to realize the decoupling and the regulation flexibility improvement of multiple energy sources.
Referring to fig. 5, fig. 5 is a flowchart of an optimization solution of an energy system according to an embodiment of the present invention, where the optimization solution of a cost calculation model by using a sequential quadratic programming algorithm includes:
step 501, determining network parameters of the energy system and optimization parameters of the algorithm;
step 502, determining power boundaries of devices of the energy system;
and 503, based on the network parameters, the optimization parameters and the power boundary, performing optimization solution on the cost calculation model by using a sequential quadratic programming algorithm.
In this embodiment, the power boundary of each device of the energy system is determined in advance, and the result of the optimization solution is prevented from exceeding the equipment capacity of each device, thereby ensuring the practicability and improving the practicability.
1) And initializing all optimized parameters, including network parameters of the energy system, equipment parameters, relevant parameters of an optimization algorithm and the like.
2) Determining the capacity of the equipment, and determining the boundary conditions of renewable energy, load, energy storage and the like.
3) And (5) starting optimization solution by adopting a sequential quadratic programming algorithm.
4) And if the optimization termination condition is met, stopping optimization and outputting an optimization result. Otherwise, continuing the optimization solution.
The process of the optimization solution may be to obtain a plurality of total operating costs by continuously adjusting the hourly operating power of each device of the energy system, and determine the hourly operating power of each device of the energy system when the lowest cost of the energy system is determined among the plurality of adjustment values. Of course, the operating power of each device of the energy system may be adjusted every 2 hours, which is not limited.
The termination condition of the optimization solution may be to limit the number of solutions, such as debugging different devices for 1000 times, or debugging 5000 different values of the operating power per hour; the termination condition of the optimization solution may also be a time for limiting the solution, such as 20 minutes or 2 hours; the termination condition of the optimization solution may be other conditions, which is not limited to this. And selecting the running power of each device when the cost of the energy system is the lowest and the energy utilization rate is the highest in all schemes of the optimization solution. The energy resource is more fully and reasonably utilized, the waste of the energy resource is reduced, and the operating cost of the energy resource is reduced.
Referring to fig. 6, fig. 6 is a structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 6, the electronic device 600 includes:
a prediction module 601, configured to predict energy production and energy consumption of the load of the target power source; the target power supply is a power supply outside the energy system;
a determining module 602, configured to determine an operating power of the energy system based on the energy production amount of the target power source and the energy consumption amount of the load.
Optionally, the prediction module includes:
an acquisition unit configured to acquire a first history value of an energy production amount of the target power source and a second history value of an energy consumption amount of the load;
the first generation unit is used for carrying out randomness analysis on the first historical numerical value and the second historical numerical value by using a non-parametric kernel density estimation method to generate random probability distribution information;
the second generation unit is used for processing the random probability distribution information by utilizing a probability scene sampling method to generate a first operation scene;
a third generating unit, configured to perform cluster reduction on the first operating scene by using a synchronous back substitution reduction method to generate a second operating scene;
a prediction unit configured to predict an energy production amount of the target power source and an energy consumption amount of the load based on the second operation scenario.
Optionally, the energy system comprises at least one of:
a gas turbine unit for converting gas into electric energy, heat energy or cold energy;
a fuel cell device for converting chemical energy into electric energy;
an electric boiler device for converting electric energy into heat energy;
the electric refrigeration air-conditioning device is used for converting electric energy into cold energy;
an electrical energy storage device for storing electrical energy;
the ice cold accumulation device is used for storing cold energy;
and the heat energy storage device is used for storing heat energy.
Optionally, in a case where the energy system includes at least two devices, the determining module includes:
the optimization unit is used for optimizing and solving the cost calculation model by using a sequential quadratic programming algorithm; the cost calculation model is used for calculating the cost of the energy system;
and the determining unit is used for determining the operating power of each device of the energy system based on the result of the optimization solution.
Optionally, the optimizing unit includes:
a first determining subunit, configured to determine network parameters of the energy system and optimization parameters of the algorithm;
a second determining subunit for determining power boundaries of devices of the energy system;
and the optimization subunit is used for optimizing and solving the cost calculation model by using a sequential quadratic programming algorithm based on the network parameters, the optimization parameters and the power boundary.
The electronic device provided by the embodiment of the present invention can implement each process implemented by the electronic device in the method embodiment of the present invention, and can achieve the same beneficial effects, and for avoiding repetition, details are not described here.
Referring to fig. 7, fig. 7 is a block diagram of another electronic device according to an embodiment of the present invention, and as shown in fig. 7, an electronic device 700 includes a processor 701, a memory 702, and a computer program stored in the memory 702 and executable on the processor.
Wherein the computer program when executed by the processor 701 implements the steps of:
predicting energy production amount of the target power source and energy consumption amount of the load; the target power supply is a power supply outside the energy system;
determining an operating power of the energy system based on the energy production amount of the target power source and the energy consumption amount of the load.
Optionally, the predicting the energy production amount of the target power source and the energy consumption amount of the load includes:
acquiring a first historical value of energy production of the target power source and a second historical value of energy consumption of the load;
performing randomness analysis on the first historical numerical value and the second historical numerical value by using a nonparametric kernel density estimation method to generate random probability distribution information;
processing the random probability distribution information by using a probability scene sampling method to generate a first operation scene;
performing clustering reduction on the first operation scene by using a synchronous back substitution reduction method to generate a second operation scene;
predicting an energy production amount of the target power source and an energy consumption amount of the load based on the second operation scenario.
Optionally, the energy system comprises at least one of:
a gas turbine unit for converting gas into electric energy, heat energy or cold energy;
a fuel cell device for converting chemical energy into electric energy;
an electric boiler device for converting electric energy into heat energy;
the electric refrigeration air-conditioning device is used for converting electric energy into cold energy;
an electrical energy storage device for storing electrical energy;
the ice cold accumulation device is used for storing cold energy;
and the heat energy storage device is used for storing heat energy.
Optionally, in a case that the energy system includes at least two devices, the determining the operating power of the energy system includes:
optimizing and solving the cost calculation model by using a sequential quadratic programming algorithm; the cost calculation model is used for calculating the cost of the energy system;
and determining the operation power of each device of the energy system based on the result of the optimization solution.
Optionally, the performing optimization solution on the cost calculation model by using a sequential quadratic programming algorithm includes:
determining network parameters of the energy system and optimization parameters of the algorithm;
determining power boundaries for devices of the energy system;
and based on the network parameters, the optimization parameters and the power boundary, carrying out optimization solution on the cost calculation model by using a sequential quadratic programming algorithm.
The electronic device provided by the embodiment of the present invention can implement each process implemented by the electronic device in the method embodiment of the present invention, and can achieve the same beneficial effects, and for avoiding repetition, details are not described here.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the power determining method for an energy system provided by an embodiment of the present invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. A power determination method of an energy system, which is applied to an electronic device, is characterized by comprising the following steps:
predicting energy production amount of the target power source and energy consumption amount of the load; the target power supply is a power supply outside the energy system;
determining an operating power of the energy system based on the energy production amount of the target power source and the energy consumption amount of the load.
2. The power determination method of the energy system according to claim 1, wherein the predicting the energy production amount of the target power source and the energy consumption amount of the load comprises:
acquiring a first historical value of energy production of the target power source and a second historical value of energy consumption of the load;
performing randomness analysis on the first historical numerical value and the second historical numerical value by using a nonparametric kernel density estimation method to generate random probability distribution information;
processing the random probability distribution information by using a probability scene sampling method to generate a first operation scene;
performing clustering reduction on the first operation scene by using a synchronous back substitution reduction method to generate a second operation scene;
predicting an energy production amount of the target power source and an energy consumption amount of the load based on the second operation scenario.
3. The method of power determination of an energy system according to claim 1, characterized in that the energy system comprises at least one of:
a gas turbine unit for converting gas into electric energy, heat energy or cold energy;
a fuel cell device for converting chemical energy into electric energy;
an electric boiler device for converting electric energy into heat energy;
the electric refrigeration air-conditioning device is used for converting electric energy into cold energy;
an electrical energy storage device for storing electrical energy;
the ice cold accumulation device is used for storing cold energy;
and the heat energy storage device is used for storing heat energy.
4. The method according to claim 3, wherein in the case where the energy system includes at least two devices, the determining the operating power of the energy system comprises:
optimizing and solving the cost calculation model by using a sequential quadratic programming algorithm; the cost calculation model is used for calculating the cost of the energy system;
and determining the operation power of each device of the energy system based on the result of the optimization solution.
5. The method for determining power of an energy system according to claim 4, wherein the optimal solution of the cost calculation model by using a sequential quadratic programming algorithm comprises:
determining network parameters of the energy system and optimization parameters of the algorithm;
determining power boundaries for devices of the energy system;
and based on the network parameters, the optimization parameters and the power boundary, carrying out optimization solution on the cost calculation model by using a sequential quadratic programming algorithm.
6. An electronic device, comprising:
a prediction module for predicting energy production amount of the target power supply and energy consumption amount of the load; the target power supply is a power supply outside the energy system;
a determination module to determine an operating power of the energy system based on the energy production amount of the target power source and the energy consumption amount of the load.
7. The electronic device of claim 6, wherein the prediction module comprises:
an acquisition unit configured to acquire a first history value of an energy production amount of the target power source and a second history value of an energy consumption amount of the load;
the first generation unit is used for carrying out randomness analysis on the first historical numerical value and the second historical numerical value by using a non-parametric kernel density estimation method to generate random probability distribution information;
the second generation unit is used for processing the random probability distribution information by utilizing a probability scene sampling method to generate a first operation scene;
a third generating unit, configured to perform cluster reduction on the first operating scene by using a synchronous back substitution reduction method to generate a second operating scene;
a prediction unit configured to predict an energy production amount of the target power source and an energy consumption amount of the load based on the second operation scenario.
8. The electronic device of claim 6, wherein, in the case where the energy system includes at least two devices, the determining module comprises:
the optimization unit is used for optimizing and solving the cost calculation model by using a sequential quadratic programming algorithm; the cost calculation model is used for calculating the cost of the energy system;
and the determining unit is used for determining the operating power of each device of the energy system based on the result of the optimization solution.
9. An electronic device, characterized in that it comprises a processor, a memory and a computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, carries out the steps of the method for power determination of an energy system according to any one of claims 1 to 5.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for power determination of an energy system according to any one of claims 1 to 5.
CN202010657668.9A 2020-07-09 2020-07-09 Power determination method of energy system and electronic equipment Pending CN113919612A (en)

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