CN112068436A - Layered and distributed control method and system for comprehensive energy system of industrial park - Google Patents

Layered and distributed control method and system for comprehensive energy system of industrial park Download PDF

Info

Publication number
CN112068436A
CN112068436A CN202010971600.8A CN202010971600A CN112068436A CN 112068436 A CN112068436 A CN 112068436A CN 202010971600 A CN202010971600 A CN 202010971600A CN 112068436 A CN112068436 A CN 112068436A
Authority
CN
China
Prior art keywords
representing
energy system
cost
control model
power
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010971600.8A
Other languages
Chinese (zh)
Other versions
CN112068436B (en
Inventor
吴奎华
李文升
刘蕊
郑志杰
赵韧
杨扬
王延朔
李�昊
杨波
綦陆杰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202010971600.8A priority Critical patent/CN112068436B/en
Publication of CN112068436A publication Critical patent/CN112068436A/en
Application granted granted Critical
Publication of CN112068436B publication Critical patent/CN112068436B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

The invention discloses a layered and distributed control method and a layered and distributed control system for a comprehensive energy system of an industrial park, wherein the method comprises the following steps: s1, acquiring lower layer data information of the industrial park comprehensive energy system, and establishing a lower layer distribution autonomous coordination control model; s2, acquiring upper layer data information of the industrial park comprehensive energy system, and establishing an upper layer centralized autonomous coordination control model; s3, solving a lower-layer distribution autonomous coordination control model and an upper-layer centralized autonomous coordination control model by using a genetic algorithm to obtain equipment configuration and resource optimization results; and S4, controlling the comprehensive energy system of the industrial park according to the equipment configuration and the resource optimization result. The invention can optimize the resource allocation of the comprehensive energy system and the energy flow and energy equipment in the industrial park.

Description

Layered and distributed control method and system for comprehensive energy system of industrial park
Technical Field
The invention relates to a layered and distributed control method and system for an integrated energy system of an industrial park, belonging to the technical field of integrated energy system control.
Background
The energy is an important material basis for the development of national economy, and is in an extremely important strategic position in the national economy, and people can not leave the energy in production and life. With the development of economy and society, the problem of energy shortage is more and more severe, the conventional fossil energy is increasingly in short supply, and meanwhile, a series of environmental pollution problems caused by overuse of the fossil energy seriously threatens the survival and development of human beings.
Conventional power systems typically employ a top-down centralized coordinated control strategy. The comprehensive energy system is a complex system with a plurality of energy flows such as cold/heat/electricity/gas and the like coupled with each other, renewable energy sources and loads in the system have volatility, and a multi-benefit subject exists in the system, so that the regulation and control of energy supply and demand balance become quite complex.
The integrated energy system often faces the challenge of multi-energy coupling and multi-benefit agent, the traditional centralized coordination control strategy is not completely suitable for the optimization management of the integrated energy system, and is difficult to meet the requirement of the integrated energy system on cooperative complementation, and the existing control method for the integrated energy system is less and has defects, so that the existing actual requirement cannot be met.
Disclosure of Invention
In order to solve the problems, the invention provides a layered and distributed control method and a layered and distributed control system for an integrated energy system of an industrial park, which can optimize the resource allocation of the integrated energy system and the energy flow and energy equipment in the industrial park.
The technical scheme adopted for solving the technical problems is as follows:
in a first aspect, an embodiment of the present invention provides a method for controlling a hierarchical and distributed integrated energy system of an industrial park, including the following steps:
s1, acquiring lower layer data information of the industrial park comprehensive energy system, and establishing a lower layer distribution autonomous coordination control model;
s2, acquiring upper layer data information of the industrial park comprehensive energy system, and establishing an upper layer centralized autonomous coordination control model;
s3, solving a lower-layer distribution autonomous coordination control model and an upper-layer centralized autonomous coordination control model by using a genetic algorithm to obtain equipment configuration and resource optimization results;
and S4, controlling the comprehensive energy system of the industrial park according to the equipment configuration and the resource optimization result.
As a possible implementation manner of this embodiment, in step S1, the lower layer distributed autonomous coordination control model includes a lower layer objective function and a constraint condition of the lower layer objective function.
As a possible implementation manner of this embodiment, the lower layer objective function includes:
(ii) minimization of energy purchasing cost
The objective function for the least energy purchase cost is expressed as:
f1＝CE+CH+CC+Cgas
wherein f is1Representing the total operating costs of the individual users, CE、CH、CCAnd CgasRespectively representing the power supply cost, the heat supply cost, the cold supply cost and the natural gas cost;
the objective function of the electricity purchase cost is expressed as follows:
Figure BDA0002684210960000021
in the formula (I), the compound is shown in the specification,
Figure BDA0002684210960000022
represents the time-of-use electricity price of the user at the time t,
Figure BDA0002684210960000023
representing the purchased electric power of the user at the time t;
the objective function for the cost of purchasing heat is expressed as follows:
Figure BDA0002684210960000024
in the formula (I), the compound is shown in the specification,
Figure BDA0002684210960000025
representing the time-shared heating price of the user at time t, QHThe heat purchasing power of the user;
the objective function for the cost of purchasing cold is expressed as follows:
Figure BDA0002684210960000031
in the formula (I), the compound is shown in the specification,
Figure BDA0002684210960000032
representing the time-shared cooling price, Q, of the user at time tCThe cold purchasing power for the user;
the objective function for the cost of purchasing natural gas is expressed as follows:
Figure BDA0002684210960000033
in the formula (I), the compound is shown in the specification,
Figure BDA0002684210960000034
for micro-burning at user sideElectric power, eta, of the machine at time tMTIn order to achieve the efficiency of a micro-combustion engine,
Figure BDA0002684210960000035
is the heat production power of the gas boiler at the time t etaGFBEfficiency of a gas boiler;
minimization of environmental penalty cost
According to the type and the emission of pollutants, the comprehensive environmental penalty cost is expressed as:
Figure BDA0002684210960000036
Figure BDA0002684210960000037
wherein j represents the type of contaminant, βjIs the penalty charge (yuan/kg) of the corresponding pollutant of the jth pollutant, VjIs the total annual emission of the jth pollutant, alphai,jThe discharge coefficient (g/(kW. h)) of the j pollutant generated by the ith power generation unit in the integrated energy system is alphaGB,jIs the discharge coefficient, alpha, of the jth pollutant generated by a boiler in an integrated energy systemgridThe discharge coefficient of the jth pollutant; eG,i、EGB,i、EgridThe annual generated energy of the distributed power generation unit, the annual heat productivity of the gas boiler and the outsourcing electric quantity are respectively;
cost for operating and maintaining equipment
The operation and maintenance costs of the internal equipment of the comprehensive energy system are shown as follows:
Figure BDA0002684210960000038
CM,i＝cpP+cEE
in the formula, CM,iIs the operation and maintenance cost (ten thousand yuan/year) of the ith equipment in the comprehensive energy system, cpIs provided withPower operation and maintenance cost coefficient of backup i, cEIs the energy cost coefficient of device i;
charge loss of load
The load loss cost of the user is expressed by the following formula:
Figure BDA0002684210960000041
in the formula, j values of 0, 1 and 2 respectively represent an electric load, a cold load and a heat load; rIEA,jCost per unit loss for class j loads, EENS,jA power supply shortage expectation value for the j-th load;
load satisfaction degree
The load satisfaction is represented by the following formula:
Figure BDA0002684210960000042
in the formula, WSRepresenting the number of actual supplied ordinary loads, WIRepresenting the number of important loads to be supplied, WTRepresenting the maximum required value for supplying a normal load.
As a possible implementation manner of this embodiment, the constraint condition of the lower layer objective function is expressed as follows:
Figure BDA0002684210960000043
in the formula, PminRepresenting minimum allowable electric power for operation of the plant, PmaxRepresenting the maximum allowable electric power, Q, of the plantminRepresenting minimum allowable thermal power, Q, for the plantmaxRepresenting the maximum allowable thermal power, W, of the plantminRepresenting the minimum operational allowable capacity, W, of the plantmaxRepresenting the maximum operational allowed capacity of the device.
As a possible implementation manner of this embodiment, in step S2, the upper-level centralized autonomous coordinated control model includes an upper-level objective function and a constraint condition of the upper-level objective function.
As a possible implementation manner of this embodiment, the upper layer objective function is:
the profit-benefit maximization objective is summarized as:
F1＝EE+EH+EC-Cgrid-Cgas-CDR
in the formula, F1Representing profit profits of the upper-level integrated energy provider, EE、EH、EC、CgasSales, heat and cold gains representing the comprehensive energy provider, CgasRepresenting the gas purchase cost of the energy supplier, CDRRepresents an integrated energy provider demand response compensation expenditure.
As a possible implementation manner of this embodiment, the constraint conditions of the lower layer objective function are:
Pgrid≤Pline,max
wherein P isgridPower value, P, for purchasing electricity from integrated energy supplier to external electric network via interconnection lineline,maxThe maximum allowable value of the call wire power flow.
As a possible implementation manner of this embodiment, in step S3, the process of solving the lower distributed autonomous coordination control model and the upper concentrated autonomous coordination control model by using the genetic algorithm includes the following steps:
s31, determining an initial value;
s32, determining genetic algorithm parameters, carrying out binary coding, and generating an initial population;
s33, calculating a fitness value;
s34, selecting, crossing and mutating genetic algorithms to generate a new generation of population;
s35, judging whether the number of times of terminating iteration is reached; if the iteration times are reached, terminating the evolution and outputting a population optimal result; otherwise, steps S33) and S34 are repeated until the number of termination iterations is reached.
In a second aspect, an embodiment of the present invention provides an integrated energy system hierarchical distributed control system for an industrial park, including:
the lower layer control model establishing module is used for acquiring lower layer data information of the industrial park comprehensive energy system and establishing a lower layer distribution autonomous coordination control model;
the upper control model establishing module is used for acquiring upper data information of the industrial park comprehensive energy system and establishing an upper concentrated autonomous coordination control model;
the model solving module is used for solving a lower-layer distribution autonomous coordination control model and an upper-layer centralized autonomous coordination control model by utilizing a genetic algorithm to obtain an equipment configuration and resource optimization result;
and the control module is used for controlling the comprehensive energy system of the industrial park according to the equipment configuration and the resource optimization result.
In a third aspect, an embodiment of the present invention provides a computer device, including a processor, a memory and a bus, where the memory stores machine-readable instructions executable by the processor, and when the apparatus is operated, the processor and the memory communicate with each other through the bus, and the processor executes the machine-readable instructions to perform, when executed, the steps of the method for hierarchically and distributively controlling an integrated energy system of an industrial park as any of the above.
The technical scheme of the embodiment of the invention has the following beneficial effects:
according to the invention, by utilizing the lower-layer distributed autonomous coordination control model and the upper-layer centralized autonomous coordination control model, data is easy to obtain and is closer to the actual situation, and the resource allocation of the comprehensive energy system and the energy flow and energy equipment in the industrial park can be optimized.
The traditional power system usually adopts a top-down centralized coordination control strategy, and the traditional centralized coordination control strategy is not completely suitable for the optimal management of the comprehensive energy system. The invention provides a hierarchical distributed control method of an integrated energy system of an industrial park, which is distributed and autonomous and centralized in coordination.
Description of the drawings:
FIG. 1 is a flow diagram illustrating a method for hierarchical distributed control of an integrated energy system for an industrial park in accordance with an exemplary embodiment;
FIG. 2 is a schematic diagram of an industrial park according to an exemplary embodiment;
FIG. 3 is a schematic illustration of a time-of-use electricity rate shown in accordance with an exemplary embodiment;
FIG. 4 is a schematic illustration of the results of optimization control of the factory floor 1, shown in accordance with an exemplary embodiment;
FIG. 5 is a schematic illustration of the factory floor 2 and optimization control results shown in accordance with an exemplary embodiment;
FIG. 6 is a schematic illustration of the power resource optimization control results of the factory floor 3, shown in accordance with an exemplary embodiment;
FIG. 7 is a schematic illustration of the thermal resource optimization control results of the factory floor 3, shown in accordance with an exemplary embodiment;
FIG. 8 is a schematic diagram illustrating an integrated energy provider scenario 1 power resource optimization control result according to an exemplary embodiment;
FIG. 9 is a schematic diagram illustrating an integrated energy provider scenario 1 thermodynamic resource optimization control result according to an exemplary embodiment;
FIG. 10 is a diagram illustrating an integrated energy provider scenario 1 cold resource optimization control result according to an exemplary embodiment;
FIG. 11 is a schematic diagram illustrating scenario 2 integrated energy provider electric power optimization scheduling results in accordance with an exemplary embodiment;
FIG. 12 is a diagram illustrating scenario 3 integrated energy provider electric power optimization scheduling results, according to an exemplary embodiment.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
in order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
FIG. 1 is a flow diagram illustrating a method for hierarchical distributed control of an integrated energy system for an industrial park in accordance with an exemplary embodiment. As shown in fig. 1, an embodiment of the present invention provides a method for controlling a hierarchical and distributed integrated energy system of an industrial park, which is characterized by including the following steps:
s1, acquiring lower layer data information of the industrial park comprehensive energy system, and establishing a lower layer distribution autonomous coordination control model;
s2, acquiring upper layer data information of the industrial park comprehensive energy system, and establishing an upper layer centralized autonomous coordination control model;
s3, solving a lower-layer distribution autonomous coordination control model and an upper-layer centralized autonomous coordination control model by using a genetic algorithm to obtain equipment configuration and resource optimization results;
and S4, controlling the comprehensive energy system of the industrial park according to the equipment configuration and the resource optimization result.
In step S1, the lower layer distributed autonomous coordinated control model includes a lower layer objective function and a constraint condition of the lower layer objective function.
(1) Objective function
For users in the integrated energy system, there may be various interest appeal in different scenarios. The analysis and summary of the control targets related to the comprehensive energy system at present can be mainly classified into the following 5 types: the energy purchasing cost is minimized, the operation and maintenance cost is minimized, the environment punishment cost is minimized, the energy utilization efficiency is maximized, the user satisfaction degree is maximized, the energy supply reliability is maximized, and the peak-valley difference variance is minimized. This will be described below.
(ii) minimization of energy purchasing cost
Some factory users in the garden can have the natural gas demand, the electric power demand, the heating power demand, with cold demand etc. and need purchase corresponding energy, consequently can have the minimum appeal of purchasing the ability expense. The objective function for minimizing the energy purchase cost can be expressed as:
f1＝CE+CH+CC+Cgas
wherein f is1Representing the total operating costs of the individual users, CE、CH、CCAnd CgasRespectively representing the electricity, heat, cold and gas purchasing costs of each user.
The electricity purchase cost can be further expressed as follows:
Figure BDA0002684210960000081
in the formula, CEIn order to provide the price of the power supply,
Figure BDA0002684210960000082
represents the time-of-use electricity price of the user at the time t,
Figure BDA0002684210960000083
representing the purchased electric power of the user at the moment t.
The cost of purchasing heat can be further expressed as follows:
Figure BDA0002684210960000091
in the formula, CHIn order to provide the heat at a price,
Figure BDA0002684210960000092
representing the time-shared heating price of the user at time t, QHThe heat purchasing power of the user.
The cost of purchasing cold can be further expressed as follows:
Figure BDA0002684210960000093
in the formula, CCIn order to be a cold supply price,
Figure BDA0002684210960000094
representing the time-shared cooling price, Q, of the user at time tCThe cold purchasing power of the user.
The cost of purchasing natural gas can be further expressed as follows:
Figure BDA0002684210960000095
in the formula, CgasIn order to be the price of the natural gas,
Figure BDA0002684210960000096
the generated power of the user side micro-combustion engine at the time t, etaMTIn order to achieve the efficiency of a micro-combustion engine,
Figure BDA0002684210960000097
is the heat production power of the gas boiler at the time t etaGFBIs the efficiency of a gas boiler.
Minimization of environmental penalty cost
Some diesel generators, gas turbines and gas boilers in the integrated energy system can generate CO in the process of energy supply2、SO2Greenhouse gases and harmful gases such as nitrogen oxides. According to the type and the emission of pollutants, the comprehensive environmental penalty cost can be expressed as:
Figure BDA0002684210960000098
Figure BDA0002684210960000099
wherein j represents the type of contaminant, βjIs the penalty charge (yuan/kg) of the corresponding pollutant of the jth pollutant, VjIs the total annual emission of the jth pollutant, alphai,jThe discharge coefficient (g/(kW. h)) of the j pollutant generated by the ith power generation unit in the integrated energy system is alphaGB,jIs the discharge coefficient, alpha, of the jth pollutant generated by a boiler in an integrated energy systemgridIs the emission coefficient of the jth pollutant. EG,i、EGB,i、EgridThe annual generated energy of the distributed power generation unit, the annual heat productivity of the gas boiler and the outsourcing power quantity are respectively.
Cost for operating and maintaining equipment
The operation and maintenance costs of the internal equipment of the comprehensive energy system are shown as follows:
Figure BDA0002684210960000101
CM,i＝cpP+cEE
in the formula, CM,iIs the operation and maintenance cost (ten thousand yuan/year) of the ith equipment in the comprehensive energy system, cpIs the power operation and maintenance cost coefficient of the device i, cEIs the energy cost factor of device i.
Charge loss of load
The load loss possibly existing in the factory-area user of the comprehensive energy system is as follows: the loss due to insufficient supply of heat load, insufficient supply of cold load, and insufficient supply of electric load. The description of the load loss cost to the user can be expressed by the following equation:
Figure BDA0002684210960000102
in the formula, j values of 0, 1 and 2 represent an electric load, a cold load and a heat load, respectively. RIEA,jCost per unit loss for class j loads, EENS,jAnd the expected value of the power supply shortage of the j-th type load is provided.
Load satisfaction degree
The load of the user can be divided into an important load and a general load. Wherein, the allowable portion of the important common load is generally required not to be satisfied, wherein the load satisfaction degree can be represented by the following formula:
Figure BDA0002684210960000103
in the formula, WSRepresenting the number of actual supplied ordinary loads, WIRepresenting the number of important loads to be supplied, WTRepresenting the maximum required value for supplying a normal load.
(2) Constraint conditions
The constraint conditions in the coordination control model of the comprehensive energy system mainly comprise two parts: power balance constraints of all universal buses, maximum and minimum operation constraints among all devices and the like. The maximum and minimum operational constraints among the devices can be expressed as follows:
Figure BDA0002684210960000111
in the formula, PminRepresenting minimum allowable electric power for operation of the plant, PmaxRepresenting the maximum allowable electric power, Q, of the plantminRepresenting minimum allowable thermal power, Q, for the plantmaxRepresenting the maximum allowable thermal power, W, of the plantminRepresenting the minimum operational allowable capacity, W, of the plantmaxRepresenting the maximum operational allowed capacity of the device.
In step S2, the upper-level centralized autonomous coordinated control model includes an upper-level objective function and a constraint condition of the upper-level objective function.
Under the normal operation state, the control objective of the upper-layer park control system is to ensure the cold/heat/electricity supply and demand balance in the park, realize the optimal control of self equipment and energy flow, achieve the minimum self operation cost, supply and sell energy for factory users and realize the objective of self income maximization.
The specific situation can be divided into the following 3 stages:
the first situation is as follows: when the power of the gateway of the park is more limited when the load of a user is increased, the upper-layer scheduling needs to carry out peak clipping on the output of a park generator, the charging and discharging of directly-adjusted energy storage and the reasonable economic scheduling of the generator, and the power utilization behavior of the user is not limited (namely, the user is not required to carry out demand response);
case two: the load of a user is increased greatly, the increase of the load can not be stabilized by a park generator and a direct regulation energy storage, the power of a gateway is out of limit, the safety and the stability of a power grid in a park are influenced, and the load fluctuation can be stabilized by an adjustable user. And the upper-layer park dispatching sends an instruction to the lower-layer adjustable users, the interactive users reasonably adjust the self load, participate in peak shaving according to the instruction, the power balance is achieved, and the stability of the power grid is realized.
Case three: the load of a user is greatly increased, the increase of the load of a park generator and a directly-adjusted energy storage cannot be stabilized, the power of a gateway is out of limit, the safety and the stability of a power grid in a park are affected, and the load fluctuation can be stabilized simply through adjustable users. And the upper-layer park dispatching sends an instruction to the lower-layer interruptible users, the interactive users reasonably interrupt the loads of the interactive users, and participate in peak shaving according to the instruction, so that the power balance is achieved, and the stability of the power grid is realized. The out-of-limit value at this time can be clipped by user interaction, and the calculated value of the interaction amount can be calculated by the following formula:
ΔPg＝PL-Pline.max-PCCHP-PWT-PPT-PES.D
ΔPgthe total out-of-limit load of the representative tie line, which cannot be met by power generation and outsourcing in the campus, needs to be met through demand response interaction.
The cost of implementing a DR program by an integrated energy provider issuing demand response demands can be summarized as follows:
Figure BDA0002684210960000121
in the formula, CDRRepresenting the DR project Total interaction Compensation, Δ CDR,iRepresenting the interactive compensation amount of the ith participant in the DR project.
(1) Objective function
The objective function of the integrated energy provider of the industrial park at the upper level is similar to that of the users of the factory at the lower level, and the objective function is as follows: the self direct-regulating equipment is reasonably scheduled and controlled, and the goals of maximizing economic benefits and profits and the like on the basis of guaranteeing energy supply balance in a park and preventing the power of a tie line from exceeding the limit are achieved. Its objective function can be similarly summarized as follows:
the profit and benefit maximization objective
Industrial park integrated energy providers obtain revenue for the supply of power, cooling, heating and other ancillary services to some plant users in the park and are responsible for ensuring the balance of the various supplies of energy in the park. The profit-benefit maximization goal can be summarized as follows:
F1＝EE+EH+EC-Cgrid-Cgas-CDR
in the formula, F1Representing profit profits of the upper-level integrated energy provider, EE、EH、EC、CgasSales, heat and cold gains representing the comprehensive energy provider, CgasRepresenting the gas purchase cost of the energy supplier, CDRRepresents an integrated energy provider demand response compensation expenditure.
(2) Constraint conditions
The upper industrial park comprehensive energy provider is responsible for guaranteeing the supply and demand balance of various energy sources in the park, and the constraint of the upper industrial park comprehensive energy provider is to be constrained by the peak power of a park gateway and a superior power grid tie line in order to guarantee the friendly interaction of the industrial park and the superior power grid.
Pgrid≤Pline,max
Wherein P isgridPower value, P, for purchasing electricity from integrated energy supplier to external electric network via interconnection lineline,maxThe maximum allowable value of the call wire power flow.
In step S3, the process of solving the lower distributed autonomous coordination control model and the upper centralized autonomous coordination control model by using the genetic algorithm includes the following steps:
s31, determining an initial value;
s32, determining genetic algorithm parameters, carrying out binary coding, and generating an initial population;
s33, calculating a fitness value;
s34, selecting, crossing and mutating genetic algorithms to generate a new generation of population;
s35, judging whether the number of times of terminating iteration is reached; if the iteration times are reached, terminating the evolution and outputting a population optimal result; otherwise, steps S33) and S34 are repeated until the number of termination iterations is reached.
The embodiment of the invention provides a layered and distributed control system of a comprehensive energy system of an industrial park, which comprises:
the lower layer control model establishing module is used for acquiring lower layer data information of the industrial park comprehensive energy system and establishing a lower layer distribution autonomous coordination control model;
the upper control model establishing module is used for acquiring upper data information of the industrial park comprehensive energy system and establishing an upper concentrated autonomous coordination control model;
the model solving module is used for solving a lower-layer distribution autonomous coordination control model and an upper-layer centralized autonomous coordination control model by utilizing a genetic algorithm to obtain an equipment configuration and resource optimization result;
and the control module is used for controlling the comprehensive energy system of the industrial park according to the equipment configuration and the resource optimization result.
Calculation example:
the example is constructed on the basis of the site situation of the demonstration project of the comprehensive energy system of an industrial park, and the park has 13 lower-layer user factories and 1 upper-layer comprehensive energy provider (1 group of backpressure CHP units without heat storage devices and electric energy storage) in total, and the structure is shown in figure 2.
3 important users in the park are selected for calculation and analysis, and the configuration conditions are shown in table 1. As used hereinThe local time of use electricity price details of (1) are shown in fig. 3. The daily load, photovoltaic output prediction curve and the optimization control result of the plant areas 1-3 are respectively shown in fig. 4-7. The natural gas price is 3.45 yuan/m according to the local actual price3And the foldable unit heat value price is 0.349 yuan/(kW.h).
Table 1: configuration of three important users and comprehensive energy provider
Figure BDA0002684210960000141
Table 2: condition of equipment parameters
Figure BDA0002684210960000142
(1) Lower layer distributed autonomous control
The proposed lower-layer optimization control model considers a single objective function with minimized energy purchasing cost and calculates the 3 plant areas. The specific parameters of the apparatus involved in the calculation are shown in table 2. The optimized scheduling results of the factories 1-3 are shown in FIGS. 8-10, respectively. And each plant area produces under respective optimized scheduling to achieve the aim of minimum operating cost. The total operating cost of the plant 1 is 13884.72 dollars, the total operating cost of the plant 2 is 22928.57 dollars, and the total operating cost of the plant 3 is 3791.938 dollars.
By analyzing the result, the following results can be obtained:
1) the storage battery is charged at the low valley price and discharged at the high peak price, so that the peak value of the power grid is reduced while the user saves the self running electricity fee, and the win-win situation between the user and the power grid is achieved.
2) The cold load is provided by the base load host and the ice storage system. The ice storage system has a similar "peak clipping and valley filling" effect as the storage battery. During the low-price period of electricity, the dual-working-condition main machine works in an ice making mode to store ice, and the cold load is provided by the base load main machine. And in the peak period, single ice melting refrigeration is carried out, so that the electric charge is saved for the user. The ice cold storage system realizes the energy conversion and complementation of electricity and cold.
3) The heat load is provided by the micro-combustion engine and the gas boiler together. The micro-combustion engine is only started at the flat moment and the peak moment of the electricity price due to the limitation of the electricity price and the gas price, so that heat is supplied to users and electricity is generated at the same time, and the part with insufficient electric quantity is provided by energy providers in a park. During off-peak periods of electricity prices, the full heat load is provided to the user by the gas boiler. Under the operation mode, the micro-combustion engine realizes the energy conversion and complementation of gas-electricity and gas-heat.
(2) Upper layer centralized coordination control
The comprehensive energy provider obtains certain preference during gas purchase due to the fact that the quantity of used fuel gas is large, and the gas purchase price is 2.66 yuan/m foldable unit heat value price 0.269 yuan/(kWh & h).
The campus complex energy providers give different subsidies of electricity prices to the electricity demand response customers in the campus, as shown in table 3, respectively.
TABLE 3 electric demand response Compensation Table
Figure BDA0002684210960000151
Case 1: economic operation of integrated energy provider equipment
The optimal scheduling policy obtained by the calculation and analysis of the optimal control policy of the campus management system is shown in fig. 8 to 10. At the moment, the CCHP and the electric energy storage of the user are cooperatively controlled without responding to the demands of the lower-layer user factory,
various power balances can be achieved on the campus. In this case, the total operating cost of the integrated energy provider is 452474.8 yuan, and the net profit of the integrated energy provider is 180848.7 yuan.
Case 2: electric energy storage to smooth out load fluctuations
If the user needs to increase the electric load of 3.5MW in the park at 14 hours on the second day according to the forecast of the day, the heat consumption and the cold consumption are kept unchanged, and the energy consumption at the rest time is kept unchanged. The optimized scheduling strategy for case 2 is shown in fig. 11. At the moment, various power balances in the garden can be achieved only by cooperatively controlling the CCHP and the electric energy storage, mainly stabilizing load fluctuation through the electric energy storage and not needing to participate peak clipping through a lower-layer user factory. In this case, the net profit of the integrated energy provider is 462261.5 yuan.
Case 3: power demand response user participation response
If the user needs to increase the electric load of 4.5MW in the 14-hour park on the second day according to the forecast of the day, the heat consumption and the cold consumption are kept unchanged, and the energy consumption is kept unchanged at the rest of time.
The calculated threshold value is 470.9815kWo, in order to ensure energy supply and demand balance, a power demand response item needs to be issued, and part of plant users are guided to participate in the power demand response item. Power DR users of 470.9815kW need to be interrupted to meet the power balance. In this case, the total compensation charge of the campus energy complex provider to the electricity demand response user is 16059.4722 yuan, and the net profit of the complex energy provider is 446347.634 yuan. The optimized scheduling results are shown in fig. 12.
The analysis of each scene of the upper layer centralized coordination optimization scheduling can be known as follows:
the cold and heat loads in the campus are supplied entirely by the upper integrated energy provider side CCHP. CCHP adopts the working mode of 'deciding electricity with heat', supplies heat, cooling and power for the garden simultaneously, and the part with insufficient electric quantity is acquired by the garden comprehensive energy provider to an external power grid through a connecting line.
Because of the restriction of the upper limit of the load power of the gate of the park, the direct power regulation and energy storage of the park do not completely follow the principle of 'valley reserve peak power'. When the power of the gate of the park exceeds the maximum allowable value under the peak clipping requirement of the electric energy storage, the discharge peak clipping is carried out, and the power constraint of the tie line is ensured. In some peak electricity price periods, when the power of the gateway is not beyond the limit, if the electricity energy storage has residual electricity, the electricity energy storage discharges to the outside, and the effect of reducing the electricity purchasing cost of the energy provider to an external power grid is achieved.
3) Because the compensation electricity price is relatively high, the upper control system requires the user to interactively adjust and interrupt the load when the power of the gate is out of limit under the peak clipping requirement caused by the increase of the load of the user, and the electricity utilization behavior of the user is not limited during normal operation.
Through control strategy analysis under three different scenes, compared with a traditional centralized scheduling method taking a power grid side target as a core, the hierarchical distributed coordination control method provided by the invention can better guarantee the benefits of lower-layer users, improve the enthusiasm of the users for participating in interaction, guarantee the benefits of park energy providers to the maximum extent on the premise of guaranteeing safe and stable operation of the park, and reduce the difficulty of upper-layer park energy providers in coordination control of various resources in the park.
Embodiments of the present invention further provide a computer device, including a processor, a memory and a bus, where the memory stores machine-readable instructions executable by the processor, and when the apparatus is operated, the processor and the memory communicate with each other through the bus, and the processor executes the machine-readable instructions to perform the steps of the method for controlling the hierarchical and distributed energy system of the industrial park according to any of the above mentioned embodiments.
Specifically, the memory and the processor can be general-purpose memory and processor, and are not particularly limited herein, and the processor can execute the integrated energy system hierarchical distributed control method of the industrial park when executing the computer program stored in the memory.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of modules is merely a division of logical functions, and an actual implementation may have another division, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.
Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments provided in the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules are integrated into one module.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. A layered and distributed control method for an integrated energy system of an industrial park is characterized by comprising the following steps:
s1, acquiring lower layer data information of the industrial park comprehensive energy system, and establishing a lower layer distribution autonomous coordination control model;
s2, acquiring upper layer data information of the industrial park comprehensive energy system, and establishing an upper layer centralized autonomous coordination control model;
s3, solving a lower-layer distribution autonomous coordination control model and an upper-layer centralized autonomous coordination control model by using a genetic algorithm to obtain equipment configuration and resource optimization results;
and S4, controlling the comprehensive energy system of the industrial park according to the equipment configuration and the resource optimization result.
2. The method according to claim 1, wherein the lower layer distributed autonomous coordinated control model includes a lower layer objective function and a constraint condition of the lower layer objective function in step S1.
3. The method of claim 2, wherein the lower level objective function comprises:
the objective function with the minimum energy purchase cost is expressed as follows:
f1＝CE+CH+CC+Cgas
wherein f is1Representing the total operating costs of the individual users, CE、CH、CCAnd CgasRespectively representing the power supply cost, the heat supply cost, the cold supply cost and the natural gas cost;
the objective function of the electricity purchase cost is expressed as follows:
Figure FDA0002684210950000011
in the formula (I), the compound is shown in the specification,
Figure FDA0002684210950000012
represents the time-of-use electricity price of the user at the time t,
Figure FDA0002684210950000013
representing the purchased electric power of the user at the time t;
the objective function for the cost of purchasing heat is expressed as follows:
Figure FDA0002684210950000021
in the formula (I), the compound is shown in the specification,
Figure FDA0002684210950000022
representing the time-shared heating price of the user at time t, QHThe heat purchasing power of the user;
the objective function for the cost of purchasing cold is expressed as follows:
Figure FDA0002684210950000023
in the formula (I), the compound is shown in the specification,
Figure FDA0002684210950000024
representing the time-shared cooling price, Q, of the user at time tCThe cold purchasing power for the user;
the objective function for the cost of purchasing natural gas is expressed as follows:
Figure FDA0002684210950000025
in the formula (I), the compound is shown in the specification,
Figure FDA0002684210950000026
the generated power of the user side micro-combustion engine at the time t, etaMTIn order to achieve the efficiency of a micro-combustion engine,
Figure FDA0002684210950000027
is the heat production power of the gas boiler at the time t etaGFBEfficiency of a gas boiler;
secondly, according to the type and the emission of pollutants, the comprehensive environment punishment cost is expressed as follows:
Figure FDA0002684210950000028
Figure FDA0002684210950000029
wherein j represents the type of contaminant, βjIs the penalty charge of the corresponding pollutant of the j-th pollutant, VjIs the total annual emission of the jth pollutant, alphai,jThe discharge coefficient alpha of the j pollutant generated by the ith power generation unit in the integrated energy systemGB,jIs the discharge coefficient, alpha, of the jth pollutant generated by a boiler in an integrated energy systemgridThe discharge coefficient of the jth pollutant; eG,i、EGB,i、EgridThe annual generated energy of the distributed power generation unit, the annual heat productivity of the gas boiler and the outsourcing electric quantity are respectively;
the operation and maintenance cost of the internal equipment of the comprehensive energy system is shown as follows:
Figure FDA00026842109500000210
CM,i＝cpP+cEE
in the formula, CM,iIs the operation and maintenance cost of the ith equipment in the integrated energy system, cpIs the power operation and maintenance cost coefficient of the device i, cEIs the energy cost coefficient of device i;
the load loss cost of the user is expressed by the following formula:
Figure FDA0002684210950000031
in the formula, j values of 0, 1 and 2 respectively represent an electric load, a cold load and a heat load; rIEA,jCost per unit loss for class j loads, EENS,jA power supply shortage expectation value for the j-th load;
the load satisfaction is represented by the following formula:
Figure FDA0002684210950000032
in the formula, WSRepresenting the number of actual supplied ordinary loads, WIRepresenting the number of important loads to be supplied, WTRepresenting the maximum required value for supplying a normal load.
4. The method of claim 3, wherein the constraints of the lower level objective function are expressed as follows:
Figure FDA0002684210950000033
in the formula, PminRepresenting minimum allowable electric power for operation of the plant, PmaxRepresenting the maximum allowable electric power, Q, of the plantminRepresenting minimum allowable thermal power, Q, for the plantmaxRepresenting the maximum allowable thermal power, W, of the plantminRepresenting the minimum operational allowable capacity, W, of the plantmaxRepresenting the maximum operational allowed capacity of the device.
5. The method of claim 1, wherein the upper level centralized autonomous coordinated control model comprises an upper level objective function and constraints of the upper level objective function.
6. The method of claim 5, wherein the upper level objective function is:
the profit-benefit maximization objective is summarized as:
F1＝EE+EH+EC-Cgrid-Cgas-CDR
in the formula, F1Representing profit profits of the upper-level integrated energy provider, EE、EH、EC、CgasSales, heat and cold gains representing the comprehensive energy provider, CgasRepresenting the gas purchase cost of the energy supplier, CDRRepresents an integrated energy provider demand response compensation expenditure.
7. The method of claim 6, wherein the constraints of the lower layer objective function are:
Pgrid≤Pline,max
wherein P isgridFor comprehensive energy providers through junctorPower value, P, for purchasing electricity to external power gridline,maxThe maximum allowable value of the call wire power flow.
8. The method of hierarchical distributed control of an integrated energy system of an industrial park according to claim 1, wherein the process of solving the lower distributed autonomous coordination control model and the upper centralized autonomous coordination control model using a genetic algorithm comprises the steps of, at step S3:
s31, determining an initial value;
s32, determining genetic algorithm parameters, carrying out binary coding, and generating an initial population;
s33, calculating a fitness value;
s34, selecting, crossing and mutating genetic algorithms to generate a new generation of population;
s35, judging whether the number of times of terminating iteration is reached; if the iteration times are reached, terminating the evolution and outputting a population optimal result; otherwise, steps S33 and S34 are repeated until the number of termination iterations is reached.
9. A hierarchical distributed control system of a comprehensive energy system of an industrial park is characterized by comprising:
the lower layer control model establishing module is used for acquiring lower layer data information of the industrial park comprehensive energy system and establishing a lower layer distribution autonomous coordination control model;
the upper control model establishing module is used for acquiring upper data information of the industrial park comprehensive energy system and establishing an upper concentrated autonomous coordination control model;
the model solving module is used for solving a lower-layer distribution autonomous coordination control model and an upper-layer centralized autonomous coordination control model by utilizing a genetic algorithm to obtain an equipment configuration and resource optimization result;
and the control module is used for controlling the comprehensive energy system of the industrial park according to the equipment configuration and the resource optimization result.
10. A computer apparatus comprising a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating over the bus when the apparatus is operated, the processor executing the machine readable instructions to perform the steps of the method of hierarchical distributed control of an integrated energy system for an industrial park as claimed in any one of claims 1 to 8.
CN202010971600.8A 2020-09-16 2020-09-16 Layered and distributed control method and system for comprehensive energy system of industrial park Active CN112068436B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010971600.8A CN112068436B (en) 2020-09-16 2020-09-16 Layered and distributed control method and system for comprehensive energy system of industrial park

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010971600.8A CN112068436B (en) 2020-09-16 2020-09-16 Layered and distributed control method and system for comprehensive energy system of industrial park

Publications (2)

Publication Number Publication Date
CN112068436A true CN112068436A (en) 2020-12-11
CN112068436B CN112068436B (en) 2022-05-27

Family

ID=73695971

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010971600.8A Active CN112068436B (en) 2020-09-16 2020-09-16 Layered and distributed control method and system for comprehensive energy system of industrial park

Country Status (1)

Country Link
CN (1) CN112068436B (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113361976A (en) * 2021-07-19 2021-09-07 烟台锐控自动化控制工程有限公司 Park comprehensive energy scheduling method and system based on multi-agent distributed operation
CN113469533A (en) * 2021-07-02 2021-10-01 河海大学 Power distribution network operation evaluation method containing comprehensive energy system
CN114462724A (en) * 2022-04-12 2022-05-10 中建安装集团有限公司 Park aggregator double-layer optimization scheduling method considering multi-energy price incentive comprehensive demand response model
CN115619145A (en) * 2022-10-14 2023-01-17 国网江苏省电力有限公司电力科学研究院 Cooperative control method and device for comprehensive energy system and computer equipment
CN116667342A (en) * 2023-07-27 2023-08-29 南京邮电大学 Agricultural greenhouse temperature control load regulation and control method considering standby capability

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106950840A (en) * 2017-05-11 2017-07-14 山东理工大学 Towards the integrated energy system layered distribution type control method for coordinating of power network peak clipping
US20170329291A1 (en) * 2016-05-10 2017-11-16 Conectric, Llc Method and system for minimizing time-variant energy demand and consumption of built environment
CN110262433A (en) * 2019-07-08 2019-09-20 山东理工大学 The layered distribution type coordinated control timing method of industrial park integrated energy system
CN111553544A (en) * 2020-05-19 2020-08-18 国网浙江省电力有限公司杭州供电公司 Industrial park distributed comprehensive demand response method based on consistency algorithm
CN111552181A (en) * 2020-05-06 2020-08-18 国网江苏省电力有限公司无锡供电分公司 Park level demand response resource allocation method under comprehensive energy service mode

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170329291A1 (en) * 2016-05-10 2017-11-16 Conectric, Llc Method and system for minimizing time-variant energy demand and consumption of built environment
CN106950840A (en) * 2017-05-11 2017-07-14 山东理工大学 Towards the integrated energy system layered distribution type control method for coordinating of power network peak clipping
CN110262433A (en) * 2019-07-08 2019-09-20 山东理工大学 The layered distribution type coordinated control timing method of industrial park integrated energy system
CN111552181A (en) * 2020-05-06 2020-08-18 国网江苏省电力有限公司无锡供电分公司 Park level demand response resource allocation method under comprehensive energy service mode
CN111553544A (en) * 2020-05-19 2020-08-18 国网浙江省电力有限公司杭州供电公司 Industrial park distributed comprehensive demand response method based on consistency algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
YANG SHENQUAN.ETAL: "Considering the wire erecting model of transmission grid planning", 《ADVANCED MATERIALS RESEARCH》, vol. 986, 18 July 2014 (2014-07-18), pages 263 - 266 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113469533A (en) * 2021-07-02 2021-10-01 河海大学 Power distribution network operation evaluation method containing comprehensive energy system
CN113361976A (en) * 2021-07-19 2021-09-07 烟台锐控自动化控制工程有限公司 Park comprehensive energy scheduling method and system based on multi-agent distributed operation
CN114462724A (en) * 2022-04-12 2022-05-10 中建安装集团有限公司 Park aggregator double-layer optimization scheduling method considering multi-energy price incentive comprehensive demand response model
CN114462724B (en) * 2022-04-12 2022-07-01 中建安装集团有限公司 Park aggregator double-layer optimization scheduling method considering multi-energy price incentive comprehensive demand response model
CN115619145A (en) * 2022-10-14 2023-01-17 国网江苏省电力有限公司电力科学研究院 Cooperative control method and device for comprehensive energy system and computer equipment
CN115619145B (en) * 2022-10-14 2024-03-19 国网江苏省电力有限公司电力科学研究院 Cooperative control method and device for comprehensive energy system and computer equipment
CN116667342A (en) * 2023-07-27 2023-08-29 南京邮电大学 Agricultural greenhouse temperature control load regulation and control method considering standby capability
CN116667342B (en) * 2023-07-27 2023-10-03 南京邮电大学 Agricultural greenhouse temperature control load regulation and control method considering standby capability

Also Published As

Publication number Publication date
CN112068436B (en) 2022-05-27

Similar Documents

Publication Publication Date Title
CN112068436B (en) Layered and distributed control method and system for comprehensive energy system of industrial park
Yang et al. Bidding strategy of a virtual power plant considering carbon-electricity trading
Ju et al. A two-stage optimal coordinated scheduling strategy for micro energy grid integrating intermittent renewable energy sources considering multi-energy flexible conversion
CN109523065B (en) Micro energy network optimization scheduling method based on improved quantum particle swarm algorithm
Wang et al. Decentralized coordinated operation model of VPP and P2H systems based on stochastic-bargaining game considering multiple uncertainties and carbon cost
Nosratabadi et al. Simultaneous planning of energy carriers by employing efficient storages within main and auxiliary energy hubs via a comprehensive MILP modeling in distribution network
Zhang et al. Optimal scheduling strategy of virtual power plant with power-to-gas in dual energy markets
CN111668878A (en) Optimal configuration method and system for renewable micro-energy network
CN112270433B (en) Micro-grid optimization method considering renewable energy uncertainty and user satisfaction
Nosratabadi et al. Optimal planning of multi‐energy microgrid with different energy storages and demand responsive loads utilizing a technical‐economic‐environmental programming
Huang et al. Incomplete information oriented optimal scheduling of multi-energy hub systems with thermal energy storage
CN116231655A (en) Virtual power plant double-layer optimized scheduling method considering source-load multi-type standby
Wu et al. Dynamic pricing and energy management of hydrogen-based integrated energy service provider considering integrated demand response with a bi-level approach
CN115829142A (en) Industrial enterprise comprehensive energy system optimization planning method
CN115689166A (en) Method and system for aggregated utilization of regional distributed energy resources
Han et al. Multi-stage distributionally robust optimization for hybrid energy storage in regional integrated energy system considering robustness and nonanticipativity
CN112016825A (en) Centralized transaction optimization decision method of regional comprehensive energy system
Hayati et al. A Two-Stage Stochastic Optimization Scheduling Approach for Integrating Renewable Energy Sources and Deferrable Demand in the Spinning Reserve Market
Yu et al. A bi-level scheduling strategy for integrated energy systems considering integrated demand response and energy storage co-optimization
Li et al. Robust bidding strategy for multi-energy virtual power plant in peak-regulation ancillary service market considering uncertainties
Zhang et al. Multi-level distributed demand response study for a multi-park integrated energy system
Su et al. Optimal economic operation of microgrids considering combined heat and power unit, reserve unit, and demand-side management using developed adolescent identity search algorithm
CN112001613A (en) Benefit distribution strategy based on multi-micro-energy-source network system cooperative game decision mechanism
CN116957139A (en) Multi-comprehensive-energy microgrid optimal operation method and system considering carbon transaction among microgrids
CN111126675A (en) Multi-energy complementary microgrid system optimization method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant