CN114004476B - Multi-time scale optimal scheduling method for comprehensive energy system - Google Patents

Multi-time scale optimal scheduling method for comprehensive energy system Download PDF

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CN114004476B
CN114004476B CN202111247565.6A CN202111247565A CN114004476B CN 114004476 B CN114004476 B CN 114004476B CN 202111247565 A CN202111247565 A CN 202111247565A CN 114004476 B CN114004476 B CN 114004476B
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power
energy
load
energy storage
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CN114004476A (en
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王俐英
董厚琦
林嘉琳
许保光
曾鸣
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North China Electric Power University
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North China Electric Power University
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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
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • 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
    • G06F30/25—Design optimisation, verification or simulation using particle-based methods
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces

Abstract

The invention discloses a multi-time scale optimal scheduling method of a comprehensive energy system, which comprises the following steps: based on the predicted values of wind power output, photovoltaic output and load before the day, a day-ahead optimization model is established with the aim of maximum daily operation income of an operation scheduling module and minimum energy consumption cost of the load module, and the price of electricity selling and the price of demand response subsidy are adjusted to determine a day-ahead scheduling plan; based on a day-ahead scheduling plan, taking the minimum output adjustment cost of the energy conversion equipment and the energy storage equipment as a target, establishing a day-ahead optimization model, and determining a day-ahead scheduling plan; based on the daily scheduling plan, with the minimum output adjustment cost of the energy storage equipment as a target, establishing a real-time optimization model, and determining the real-time scheduling plan; and solving the optimal solution of the real-time optimization model before, in the day by utilizing the particle swarm optimization algorithm, and determining the scheduling plan of the comprehensive energy system. According to the scheme, based on master-slave game behaviors between operators and users, the output of each device is continuously optimized and adjusted through multiple time scales, and the system operation economic benefit can be improved while the energy consumption is improved.

Description

Multi-time scale optimal scheduling method for comprehensive energy system
Technical Field
The invention relates to the field of energy system optimization operation, in particular to a multi-time scale optimization scheduling method of a comprehensive energy system.
Background
Along with the continuous development of the economy and society and the increasingly prominent problem of environmental pollution, the traditional energy production and consumption modes are greatly transformed, and the energy industry carries important demands on improving energy efficiency, guaranteeing energy safety, promoting new energy consumption, reducing pollutant emission and the like. The traditional energy system construction is mainly characterized by longitudinal extension of a single system, and physical interconnection and information interaction between different energy systems are less. However, the access of high-proportion new energy and the access of multi-element interactive load are required to change the construction path and development mode of the traditional energy system, and the comprehensive energy system is constructed. The construction of the comprehensive energy system takes 'transverse multi-energy complementation and longitudinal energy load coordination' as the principle, and takes the safety, economy and cleanliness of the energy system into consideration, integrates various energy resources such as electric power, natural gas and the like in an area, and meets various energy requirements of users, so that the optimization scheduling of the comprehensive energy system becomes a research hot spot in the energy field.
Because of uncertainty and fluctuation of wind power, photovoltaic output and load prediction, larger errors are generated in the prediction curves of wind power, photovoltaic and load curves, and prediction accuracy is gradually improved along with approximation of time scales, an energy optimization scheduling method considering multiple time scales is needed, the coupling performance of energy scheduling of different time scales can be improved, flexible scheduling of resources on the user demand side is fully considered, and the economic benefit of system operation is improved while energy consumption is improved.
Disclosure of Invention
Therefore, the invention considers the master-slave game behavior between the system operator and the user, and the user participates in the energy optimization scheduling through a demand response excitation mechanism; the uncertainty of wind and light renewable energy sources and the user load demand response quantity is considered, a multi-time scale optimization scheduling model of the comprehensive energy system is established, the deviation between an operation plan and a prediction result is continuously corrected, and the economic operation benefit of the system can be improved while the renewable energy source consumption is improved.
The invention provides a multi-time scale optimization scheduling method of a comprehensive energy system, which comprises an energy supply module, an operation scheduling module and a load module, wherein equipment in the system comprises energy production equipment, energy conversion equipment and energy storage equipment. The energy supply module is suitable for transmitting energy and energy price information to the operation scheduling module, the operation scheduling module is suitable for purchasing energy from the energy supply module, selling energy to the load module and providing a demand response subsidy price, and the load module is suitable for translating or reducing the demand response load according to the demand response subsidy price and uploading the demand response load to the operation scheduling module so that the operation scheduling module can adjust the output of each device and the energy purchasing amount according to the demand response load.
In the method, firstly, a day-ahead optimization model is built by taking the maximum daily operation income of an operation scheduling module and the minimum energy consumption cost of a load module as targets based on wind-electricity output, photovoltaic output and load predicted values in day-ahead 1-hour energy production equipment, a demand response subsidy price and a demand response load are adjusted, and a day-ahead scheduling plan is determined. And then, based on a day-ahead scheduling plan, updating 15-minute-level wind power output, photovoltaic power output and load predicted values in the day, and establishing a day-ahead optimization model by taking the minimum output adjustment cost of the energy conversion equipment and the energy storage equipment as an optimization target to determine the day-ahead scheduling plan. And then, based on the daily scheduling plan, updating real-time 5-minute-level wind power output, photovoltaic output and load predicted values, and establishing a real-time optimization model by taking the minimum energy storage equipment output adjustment cost as an optimization target to determine the real-time scheduling plan. And finally, solving the optimal solution of the real-time optimization model before, in the day by utilizing a particle swarm optimization algorithm, and determining an optimal scheduling plan of the comprehensive energy system.
Optionally, in the above method, the energy production device includes a wind power generator and a photovoltaic power generator, and an output model of the wind power generator is:
In the method, in the process of the invention, For the output power of the wind driven generator at the moment t, v t is the wind speed at the moment t, and v ci、vco、vr is the cut-in wind speed, the cut-out wind speed and the rated wind speed respectively,/>The rated output power of the wind driven generator is obtained.
The output model of the photovoltaic generator is as follows:
In the method, in the process of the invention, Output power of photovoltaic generator at t moment,/>For rated output power of the photovoltaic generator, G r、Tr is rated illumination radiance and rated temperature respectively, G t、Tt is actual illumination radiance and actual temperature at time t respectively, and τ is a temperature coefficient.
The energy conversion equipment comprises a gas turbine, a waste heat boiler, a gas boiler, a heat pump, an electric refrigerator and an absorption refrigerator, wherein the output model of the gas turbine is as follows:
In the method, in the process of the invention, For the electric power output by the gas turbine at the t moment, phi g is the heat value of natural gas,/>Output electric power efficiency for gas turbine,/>Natural gas power input to the gas turbine;
The output model of the waste heat boiler is as follows:
In the method, in the process of the invention, For the heat power output by the waste heat boiler at the moment t,/>The heat efficiency of the waste heat boiler;
the output model of the gas boiler is as follows:
In the method, in the process of the invention, For the heat power output by the gas boiler at the time t,/>Efficiency of heat power output for gas boiler,/>The natural gas power input to the gas boiler at the moment t;
the output model of the heat pump is as follows:
In the method, in the process of the invention, For the heat power output by the heat pump at the moment t,/>For the electric power consumed by the heat pump at time t,/>The electric heat transfer efficiency of the heat pump;
The output model of the electric refrigerator is as follows:
In the method, in the process of the invention, For the cold power output by the electric refrigerator at the moment t,/>For the electric power input by the electric refrigerator at the moment t,/>The electric refrigerating efficiency of the electric refrigerator is achieved;
The output model of the absorption refrigerator is as follows:
In the method, in the process of the invention, For the cold power output by the absorption refrigerator at the moment t,/>Heat power input for absorption refrigerator at time t,/>Is the heat-to-cold efficiency of the absorption refrigerator.
The model of the energy storage device is as follows:
In the method, in the process of the invention, For the energy storage states of the energy storage device x at the t and t+1 moments, epsilon x is the self-loss rate of the energy storage device x,/>Respectively the energy storage power and the energy release power of the energy storage device x at the t moment,/>The energy storage and energy release efficiencies of the energy storage device x are respectively shown, E x is the rated capacity of the energy storage device x, deltaT is the scheduling duration, x represents the class of the energy storage device, wherein ees represents electric energy storage, tes represents thermal energy storage, and cs represents cold energy storage.
Optionally, in the method, the day-ahead schedule includes a schedule of energy production devices, energy conversion devices, power output of energy storage devices, and power purchased from the grid, and user translatable loads; the daily scheduling plan is the scheduling quantity of the power output of a heat pump, an absorption refrigerator, an electric refrigerator and energy storage equipment, the electric quantity purchased from a power grid and the load which can be cut down by a user; the real-time dispatch plan is the output of the energy storage device.
Optionally, in the method, the day-ahead optimization model includes an upper layer leader model and a lower layer follower model based on master-slave games, the leader model is used for determining an optimal demand subsidy price and electricity selling price, and adjusting the output and the energy purchase amount of each device according to the demand response load determined by the follower model, the follower model is used for determining the demand response load according to the demand subsidy price determined by the leader model, and an objective function of the leader model is a daily operation income maximization objective function of the operation scheduling module:
In the method, in the process of the invention, For the benefit of selling energy,/>For the purchase energy cost, C ope is the operation and maintenance cost of various units,/>For demand response costs, C p is the pollutant emission cost, where,
In the method, in the process of the invention,Electricity selling price, heat selling price and cold selling price at t moment respectively,/>And/>Fuzzy parameters of electric load, thermal load and cold load at time t respectively,/>The electric load transfer-in quantity and transfer-out quantity of the user at the time t are respectively/>For the electricity purchase price of the power grid at the moment t,/>For the amount of electricity purchased from the grid at time t,/>To purchase the price of natural gas,/>For the natural gas amount purchased at the time t, c gb、cgt、cwhb、chp、cef、cac、cwt、cpv、cees and c tes are respectively the unit power operation and maintenance cost of a gas boiler, a gas turbine, a waste heat boiler, a heat pump, an electric refrigerator and an absorption refrigerator, distributed wind power, distributed photovoltaic, electric energy storage equipment and thermal energy storage equipment,/>Subsidize price for demand response at time t,And/>Pollutant emission costs for gas boilers, gas turbines and power grids, respectively,/>The output power of the gas boiler and the output power of the gas turbine at the moment t are respectively.
The objective function of the lower follower model is the minimum energy cost objective function of the load module:
In the method, in the process of the invention, For the purchase energy cost of the user,/>Response cost for user's demand,/>For user satisfaction cost,/>Subsidies obtained for the user to participate in the demand response, wherein,
In the method, in the process of the invention,For the preferential electrical load of the user at time t,/>For the electric load transfer quantity of a user at the time t, a and b are the user demand response cost coefficients, and c is the electric load deviation preference electric load penalty coefficient.
Constraint conditions of the day-ahead optimization model comprise a first power balance constraint, a device output constraint, a first device climbing constraint, a tie line constraint, an energy storage device constraint, a price constraint and a demand response constraint, wherein the first power balance constraint is as follows:
In the method, in the process of the invention, For/>The likelihood of an event in (a), α p is the confidence level,/>Fuzzy parameters of wind power output and photovoltaic output of users at time t respectively,/>The consumption of natural gas at the time t;
the equipment output constraint is as follows:
the first equipment climbing constraint is:
In the method, in the process of the invention, Output of the device m at time t and t+Deltat 1 respectively,/>For maximum capacity of device m,/>For the ramp rate of device m, Δt 1 =1 hour;
tie constraint is:
In the method, in the process of the invention, For the electricity purchase price of the power grid at the moment t,/>The minimum and maximum interactive power between the comprehensive energy system and the power distribution network is achieved;
The energy storage device is constrained as follows:
In the method, in the process of the invention, Maximum power of charge/discharge of electricity storage equipment at t moment,/>Maximum power for heat storage/release for a heat storage device,/>For the lower limit and the upper limit of the energy storage state of the energy storage device at the t-th moment,/>The running states of the energy storage equipment at the t moment are all 0-1 variables;
The price constraint is as follows:
In the method, in the process of the invention, Is the selling price of the heat supply network,/>The highest demand response is subsidized and limited;
The demand response constraints are:
In the method, in the process of the invention, The electric load transfer-in quantity and transfer-out quantity of the user at the time t are respectively/>Is the fuzzy parameter of the electric load at the time t/(The maximum transfer load duty cycle at time t for the user.
Optionally, in the above method, the first power balance constraint is a fuzzy opportunity constraint including wind power output, photovoltaic output and predicted values of each load, and clear equivalent constraint conditions of the fuzzy opportunity constraint are:
In the method, in the process of the invention, Membership parameters of predicted values of electric, thermal and cold loads respectively,/>The membership parameters are respectively a wind power output predicted value and a photovoltaic output predicted value.
Optionally, in the method, the daily optimization model is used for determining the adjustment amount of the direct control load and the electric quantity purchased from the power grid according to the demand response subsidy price determined in the day-ahead stage, and the objective function of the daily optimization model is the minimum objective function of the output adjustment cost of the energy conversion equipment and the energy storage equipment:
In the method, in the process of the invention, For demand response cost generated by directly controlling load for day-period call, ΔC equ is day-period equipment output change cost, ΔC buy is day-period electricity purchase cost change, mu x、μn is unit punishment cost of power change of energy storage equipment and energy conversion equipment respectively, i represents electric, heat and cold load category,/>Respectively the charge and discharge energy power change of the energy storage equipment at the t-th moment in the daily period,/>For the output change at the time t of the nth conversion equipment in the daily stage,For the change of electricity purchasing quantity at the time t of the day stage,/>Subsidy price for demand response at time t,/>For the demand response of the user at the time t,/>And (5) purchasing electricity from the power grid at the moment t.
Constraint conditions of the daily optimization model comprise tie line constraint, equipment output constraint, energy storage equipment constraint, price constraint and demand response constraint, and also comprise second equipment climbing constraint and second power balance constraint, wherein the second power balance constraint is as follows:
the second equipment climbing constraint is:
In the method, in the process of the invention, And/>Predicted values of wind power, photovoltaic, electric load, thermal load and cold load in the daytime respectively,/>Output change values of heat pump, electric refrigerator and absorption refrigerator in the daytime respectively,/> Charging and discharging power change values of electric energy storage, thermal energy storage and cold energy storage in the daytime respectively,/>For the electric load demand of the user at the time t,/>For the maximum direct control load duty cycle of the user at time t,/>Output of the device m at times t and t+Deltat 2,/>, respectivelyFor the ramp rate of device m, Δt 2 =15 minutes.
Optionally, in the above method, the objective function of the real-time optimization model is an objective function with minimum cost for adjusting the output of the energy storage device:
Wherein, delta C e′qu is the equipment output change cost, delta C b′uy is the electricity purchasing cost change, mu x、μn is the unit punishment cost of the power change of the energy storage equipment and the energy conversion equipment, i represents the load types of electricity, heat, cold and the like, Charging and discharging energy power change of energy storage equipment at t time in real-time stage respectively,/>For the output change of the nth energy conversion equipment at the nth moment in the real-time stage,/>The electricity purchasing quantity at the t-th moment in the real-time stage is changed.
Constraint conditions of the real-time optimization model comprise a first power balance constraint, a second power balance constraint, a tie line constraint, a device output constraint, an energy storage device constraint, a price constraint and a demand response constraint, and further comprise a third device climbing constraint:
In the method, in the process of the invention, Output of the device m at times t and t+Deltat 3,/>, respectivelyFor the ramp rate of device m, Δt 3 =5 minutes.
Optionally, in the above method, the step of determining the optimal scheduling plan of the integrated energy system by solving an optimal solution of the real-time optimization model before, during and during the day by using a particle swarm optimization algorithm includes:
Step 1: setting population scale, maximum iteration times, acceleration factors and inertia weight coefficients, and randomly initializing day-ahead stage demand response subsidy price and electricity selling price;
Step 2: establishing a follower model, and determining a translatable load transfer-in value and a translatable load transfer-out value in the day-ahead stage by taking the minimum energy cost of a load module as an objective function f2 based on the initialized demand response subsidy price and electricity selling price;
Step 3: establishing a leader model, and optimizing the output of each unit based on the translatable load transfer value by maximizing the daily gain of an operation scheduling module into a fitness function f 1;
Step 4: calculating the daily gain of the operation scheduling module, and updating the daily gain value of the daily gain of the operation scheduling module, the daily-period demand response subsidy price, the electricity selling price, the translatable load transfer value and the output of each unit;
Step 5: judging whether the iteration times are greater than the maximum iteration times, if the iteration times are not up to the maximum iteration times, repeating the iteration processes of the steps 2-4 until the conditions are finally met, and outputting the optimal output force, the demand response subsidy price, the electricity selling price, the optimal value of the translatable load transfer value and the daily optimal benefit of the operation scheduling module of each unit;
Step 6: based on the scheduling result of the day-ahead stage, updating 15-minute-level predicted values of the output power of the wind generating set, the output power of the photovoltaic generating set, the electric load, the thermal load and the cold load in the day-ahead stage;
step 7: based on the updating of the predicted value, the minimum output adjustment cost of the equipment in the day stage is taken as an optimization target, the output of the heat pump, the absorption refrigerator, the electric refrigerator and the energy storage equipment of the equipment and the electric power purchased from the power grid are updated, and the calling quantity of the direct control load of a user is determined;
Step 8: based on the scheduling result of the daytime stage, updating 5-minute-level predicted values of the output power of the wind generating set, the output power of the photovoltaic generating set, the electric load, the thermal load and the cold load in the real-time stage;
Step 9: based on the updating of the predicted value, the minimum output adjustment cost of the energy storage equipment in the real-time stage is used as an optimization target, and the output of the three energy storage equipment, namely the electric energy storage equipment, the thermal energy storage equipment and the cold energy storage equipment is updated.
According to another aspect of the present invention, there is provided a computing device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing a comprehensive energy system multi-time scale optimized scheduling method according to the present invention.
According to yet another aspect of the present invention, there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform the integrated energy system multi-time scale optimized scheduling method according to the present invention.
According to the invention, the master-slave game model based on the demand response excitation mechanism between the system operator and the comprehensive energy user is established, the demand response subsidy price and the demand response quantity are optimized, the output and the load adjustment quantity of each device are continuously optimized through multiple time scales based on the demand response quantity, and the economic benefit of system operation is improved while the energy consumption is improved.
Drawings
To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which set forth the various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to fall within the scope of the claimed subject matter. The above, as well as additional objects, features, and advantages of the present disclosure will become more apparent from the following detailed description when read in conjunction with the accompanying drawings. Like reference numerals generally refer to like parts or elements throughout the present disclosure.
FIG. 1 illustrates a block diagram of a computing device 100 according to an exemplary embodiment of the invention;
FIG. 2 illustrates an integrated energy system architecture diagram according to one embodiment of the invention;
FIG. 3 illustrates a flow diagram of an integrated energy system multi-time scale optimized scheduling method 200 according to one embodiment of the invention;
FIG. 4 illustrates a campus integrated energy system multi-time scale optimized dispatch model framework flowchart in accordance with one embodiment of the invention;
figure 5 illustrates a solution flow diagram for a campus integrated energy system multi-time scale optimized dispatch model according to one embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The comprehensive energy system integrates various energy sources such as natural gas, electric energy, heat energy and the like in a certain area, realizes coordinated planning and optimized operation among energy subsystems, and realizes collaborative management, interactive response and complementary mutual aid, so that the energy utilization efficiency can be effectively improved and the sustainable development of energy can be promoted while the diversified energy utilization requirements in the system are met. The optimized scheduling method is an important means for realizing safe, economical and environment-friendly operation of the energy system, and has important significance in relieving unbalance of energy supply and demand, realizing energy conservation and emission reduction, guaranteeing national energy safety and the like by constructing the optimized scheduling method suitable for the regional comprehensive energy system.
FIG. 1 illustrates a block diagram of a computing device 100 according to one embodiment of the invention. As shown in FIG. 1, in a basic configuration 102, a computing device 100 typically includes a system memory 106 and one or more processors 104. The memory bus 108 may be used for communication between the processor 104 and the system memory 106.
Depending on the desired configuration, the processor 104 may be any type of processing including, but not limited to: a microprocessor (μp), a microcontroller (μc), a digital information processor (DSP), or any combination thereof. The processor 104 may include one or more levels of caches, such as a first level cache 110 and a second level cache 112, a processor core 114, and registers 116. The example processor core 114 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 118 may be used with the processor 104, or in some implementations, the memory controller 118 may be an internal part of the processor 104.
Depending on the desired configuration, system memory 106 may be any type of memory including, but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. Physical memory in a computing device is often referred to as volatile memory, RAM, and data in disk needs to be loaded into physical memory in order to be read by processor 104. The system memory 106 may include an operating system 120, one or more applications 122, and program data 124. The application 122 is actually a plurality of program instructions for instructing the processor 104 to perform a corresponding operation. In some implementations, the application 122 may be arranged to execute instructions on an operating system by the one or more processors 104 using the program data 124 in some implementations. Operating system 120 may be, for example, linux, windows or the like, which includes program instructions for handling basic system services and performing hardware-dependent tasks. The application 122 includes program instructions for implementing various functions desired by the user, and the application 122 may be, for example, a browser, instant messaging software, a software development tool (e.g., integrated development environment IDE, compiler, etc.), or the like, but is not limited thereto. When an application 122 is installed into computing device 100, a driver module may be added to operating system 120.
When the computing device 100 starts up running, the processor 104 reads the program instructions of the operating system 120 from the memory 106 and executes them. Applications 122 run on top of operating system 120, utilizing interfaces provided by operating system 120 and underlying hardware to implement various user-desired functions. When a user launches the application 122, the application 122 is loaded into the memory 106, and the processor 104 reads and executes the program instructions of the application 122 from the memory 106.
Computing device 100 also includes storage device 132, storage device 132 including removable storage 136 and non-removable storage 138, both removable storage 136 and non-removable storage 138 being connected to storage interface bus 134.
Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to basic configuration 102 via bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices such as a display or speakers via one or more a/V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 158. An example communication device 146 may include a network controller 160, which may be arranged to facilitate communication with one or more other computing devices 162 via one or more communication ports 164 over a network communication link.
The network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media in a modulated data signal, such as a carrier wave or other transport mechanism. A "modulated data signal" may be a signal that has one or more of its data set or changed in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or special purpose network, and wireless media such as acoustic, radio Frequency (RF), microwave, infrared (IR) or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
Computing device 100 also includes a storage interface bus 134 that is coupled to bus/interface controller 130. The storage interface bus 134 is coupled to the storage device 132, and the storage device 132 is adapted to store data. An example storage device 132 may include removable storage 136 (e.g., CD, DVD, U disk, removable hard disk, etc.) and non-removable storage 138 (e.g., hard disk drive HDD, etc.). In computing device 100 according to the present invention, application 122 includes a plurality of program instructions to perform method 300.
The main benefit bodies of the comprehensive energy system mainly comprise three types of energy suppliers such as natural gas, electric power and the like, comprehensive energy system operators and comprehensive energy users. The integrated energy provider consists of an external power grid and a natural gas provider, and mainly transmits electric power and natural gas price signals to a park integrated energy system operator and supplies electric power and natural gas energy. The park comprehensive energy system operator is both a dispatch center and an operator of the park comprehensive energy system, and obtains price-difference benefits by purchasing energy from multiple energy suppliers and selling the energy to users. Meanwhile, the park comprehensive energy system operator improves the income of the park comprehensive energy system operator by optimizing the dispatching strategy of the park comprehensive energy system and implementing the incentive type demand response based on the demand response subsidy price to the user. The comprehensive energy users in the park refer to users with cold, heat and electric load demands and certain load reducibility, and the users can respond to the subsidy price signal translation and reduce the electric load demands according to the demands provided by the park comprehensive energy system operators by taking the lowest energy cost per se as a target on the assumption of user uniformity. Figure 2 illustrates a campus integrated energy system architecture diagram, according to one embodiment of the invention. Common parks have industry parks, business parks and integrated parks of producing the city, and the user of different parks constitutes and load characteristic is different. As shown in fig. 2, the campus integrated energy system in the embodiment of the present invention includes an energy supply module, an operation scheduling module, and a load module. The equipment in the system comprises energy production equipment, energy conversion equipment and energy storage equipment, wherein the energy production equipment comprises a wind driven generator, a photovoltaic generator and the like, the energy conversion equipment comprises a gas turbine, a waste heat boiler, a gas boiler, a heat pump, an electric refrigerator, an absorption refrigerator and the like, and the energy storage equipment comprises electric energy storage, thermal energy storage and cold energy storage equipment. In order to implement optimal scheduling on the integrated energy system, an output model of each device needs to be determined, and in one embodiment of the present invention, the output model of the wind driven generator is an output model of the wind driven generator, which is:
/>
In the method, in the process of the invention, For the output power of the wind driven generator at the moment t, v t is the wind speed at the moment t, and v ci、vco、vr is the cut-in wind speed, the cut-out wind speed and the rated wind speed respectively,/>The rated output power of the wind driven generator is obtained.
The output model of the photovoltaic generator is as follows:
In the method, in the process of the invention, Output power of photovoltaic generator at t moment,/>For rated output power of the photovoltaic generator, G r、Tr is rated illumination radiance and rated temperature, G t、Tt is actual illumination radiance and actual temperature at time t, τ is a temperature coefficient, and typically 0.0047 ℃ -1 is taken.
The output model of the gas turbine is as follows:
In the method, in the process of the invention, For the electric power output by the gas turbine at the t moment, phi g is the heat value of natural gas,/>Output electric power efficiency for gas turbine,/>Natural gas power input to the gas turbine.
The output model of the waste heat boiler is as follows:
In the method, in the process of the invention, For the heat power output by the waste heat boiler at the moment t,/>Is the heat efficiency of the waste heat boiler,/>Output electric power efficiency for gas turbine,/>Natural gas power input to the gas turbine.
The output model of the gas boiler is as follows:
In the method, in the process of the invention, For the heat power output by the gas boiler at the time t,/>Efficiency of heat power output for gas boiler,/>The natural gas power input to the gas boiler at the moment t;
the output model of the heat pump is as follows:
In the method, in the process of the invention, For the heat power output by the heat pump at the moment t,/>For the electric power consumed by the heat pump at time t,/>Is the electric heat transfer efficiency of the heat pump.
The output model of the electric refrigerator is as follows:
In the method, in the process of the invention, For the cold power output by the electric refrigerator at the moment t,/>The electric power input by the electric refrigerator at the time t,The electric refrigerating machine is the electric refrigerating efficiency.
The output model of the absorption refrigerator is as follows:
In the method, in the process of the invention, For the cold power output by the absorption refrigerator at the moment t,/>Heat power input for absorption refrigerator at time t,/>Is the heat-to-cold efficiency of the absorption refrigerator. /(I)
The model of the energy storage device is
In the method, in the process of the invention,For the energy storage states of the energy storage device x at the t and t+1 moments, epsilon x is the self-loss rate of the energy storage device x,/>Respectively the energy storage power and the energy release power of the energy storage device x at the t moment,/>The energy storage and release efficiencies of the energy storage device x are respectively, E x is the rated capacity of the energy storage device x, deltaT is the scheduling duration, x represents the class of the energy storage device, wherein ees represents the electric energy storage device, tes represents the hot energy storage device, and ces represents the cold energy storage device.
FIG. 3 illustrates a flow diagram of an integrated energy system multi-time scale optimized scheduling method 300 according to one embodiment of the invention. The scheduling results of various schedulable resources of each time scale (1 hour before day, 15 minutes in day and 5 minutes in real time) can be determined by the method. Specifically as shown in table 1:
TABLE 1 Multi-timescale scheduling resources
/>
As shown in the table above, the energy production device, conversion device and energy storage device, and the amount of electricity purchased from the grid, the amount of scheduling of user translatable loads, are determined at a pre-day stage. And determining energy conversion equipment such as a heat pump, an absorption refrigerator, an electric refrigerator and the like, energy storage equipment, electric quantity purchased from a power grid and adjustment quantity of load which can be reduced by a user in a daily period, and determining the adjustment quantity of the energy storage equipment in a real-time period. Wherein translatable or reducible load refers to a load whose load power supply time can be varied in accordance with a schedule.
And the day-ahead stage optimization takes 1h as a time interval, and each unit output plan of 24h in the future is determined. As shown in fig. 3, the method 300 begins with step S310, based on the predicted values of the wind power output, the photovoltaic power output, and the load in the 1-hour-before-day energy production device, the method establishes a day-ahead optimization model with the goal of maximum daily operation yield of the operation scheduling module and minimum energy consumption cost of the load module, adjusts the demand response subsidy price and the demand response load, and determines the day-ahead scheduling plan.
The comprehensive energy user can only passively accept subsidy prices formulated by the comprehensive energy system operators as followers, and the demand response quantity of the user can also affect the benefits of the comprehensive energy system operators in turn. According to one embodiment of the invention, the day-ahead optimization model comprises an upper layer leader model and a lower layer follower model based on master-slave games, a comprehensive energy system operator is used as a leader in a game framework, an optimal demand subsidy price and electricity selling price are determined based on the leader model, a comprehensive energy user is used as a follower, and demand response load is determined according to the demand subsidy price determined by the leader model. The objective function of the leader model is the daily operation income maximum objective function of the operation scheduling module, and simultaneously the economic objective (daily income maximum) and the environmental objective (carbon emission minimum) are considered:
In the method, in the process of the invention, For the benefit of selling energy,/>For the purchase energy cost, C ope is the operation and maintenance cost of various units,/>For demand response costs, C p is the pollutant emission cost, where,
In the method, in the process of the invention,Electricity selling price, heat selling price and cold selling price at t moment respectively,/>And/>Fuzzy parameters of electric load, thermal load and cold load at time t respectively,/>The electric load transfer-in quantity and transfer-out quantity of the user at the time t are respectively/>For the electricity purchase price of the power grid at the moment t,/>For the amount of electricity purchased from the grid at time t,/>Price of natural gas for purchase of natural gas,/>For the natural gas amount purchased at the time t, c gb、cgt、cwhb、chp、cef、cac、cwt、cpv、cees and c tes are respectively the unit power operation and maintenance cost of a gas boiler, a gas turbine, a waste heat boiler, a heat pump, an electric refrigerator and an absorption refrigerator, distributed wind power, distributed photovoltaic, electric energy storage equipment and thermal energy storage equipment,/>Subsidy price for demand response at time t,/> And/>Pollutant emission costs for gas boilers, gas turbines and power grids, respectively,/>The output power of the gas boiler and the output power of the gas turbine at the moment t are respectively.
The objective function of the lower follower model is the minimum energy cost objective function of the load module:
In the method, in the process of the invention, For the purchase energy cost of the user,/>Response cost for user's demand,/>For user satisfaction cost,/>Subsidies obtained for the user to participate in the demand response, wherein,
In the method, in the process of the invention,For the preferential electrical load of the user at time t,/>For the electric load transfer quantity of a user at the time t, a and b are the user demand response cost coefficients, and c is the electric load deviation preference electric load penalty coefficient.
The constraint conditions of the day-ahead optimization model include a first power balance constraint, a device output constraint, a first device climbing constraint, a tie-line constraint, an energy storage device constraint, a price constraint and a demand response constraint. Wherein the first power balance constraint is:
In the method, in the process of the invention, For/>The likelihood of an event in (a), α p is the confidence level,/>Fuzzy parameters of wind power output and photovoltaic output of users at time t respectively,/>Is the consumption of natural gas at time t. The power balance constraint conditions of electricity, heat and cold contain fuzzy variables, so that the power balance constraint can be met only under a certain confidence level.
The equipment constraint comprises start-stop, output and climbing constraint of the unit, wherein the equipment output constraint is as follows:
the first equipment climbing constraint is:
In the method, in the process of the invention, For the output of the device m at time t,/>For the output of the device m at time t+Deltat 1,/>For maximum capacity of device m,/>For the ramp rate of device m, Δt 1 =1 hour.
Tie constraint is:
In the method, in the process of the invention, For the electricity purchase price of the power grid at the moment t,/>The minimum and maximum interactive power between the comprehensive energy system and the power distribution network is achieved.
The energy storage device is constrained as follows:
In the method, in the process of the invention, Maximum power of charge/discharge of electricity storage equipment at t moment,/>Maximum power for heat storage/release for a heat storage device,/>For the lower limit and the upper limit of the energy storage state of the energy storage device at the t-th moment,/>The running states of the energy storage equipment at the t moment are all 0-1 variables.
The price constraint is as follows:
In the method, in the process of the invention, Is the selling price of the heat supply network,/>The highest demand response is subsidized for a limit price.
The demand response constraints are:
In the method, in the process of the invention, The electric load transfer-in quantity and transfer-out quantity of the user at the time t are respectively/>Is the fuzzy parameter of the electric load at the time t/(The maximum transfer load duty cycle at time t for the user.
The fuzzy parameters of wind power, photovoltaic and various load predictions can be represented by trapezoidal functions or trigonometric functions:
Where μ (P F) is a membership function, P Fi (i=1, 2,3, 4) is a membership of a trapezoidal function, and the relationship with the predicted value q pre before day is expressed as:
PFi＝ωiqpre1,i＝1,2,3,4
Where ω i is a scaling factor whose value is typically determined from historical data for wind power, photovoltaic and various loads. When ω 2＝ω3 =1, i.e. P F2＝PF3＝qpre1, the blurring parameter is a trigonometric function. The invention adopts the trapezoidal fuzzy parameter to represent the fuzzy variable. Thus, the first power balance constraint may be converted into a clear equivalent constraint condition:
In the method, in the process of the invention, Membership parameters of predicted values of electric, thermal and cold loads respectively,/>The membership parameters are respectively a wind power output predicted value and a photovoltaic output predicted value.
In the master-slave gaming model, if a Nash equilibrium solution exists and is unique, the following three conditions must be satisfied: 1) The value space of decision variables of game interactive participants is a non-empty tight convex set; 2) After the market subject serving as a leader gives a policy, the market subject optimal policy serving as a follower exists and is unique; 3) After the market subject as the follower gives the policy, the market subject optimal policy as the leader exists and is unique. Since the decision variable intervals of both the integrated energy system operator and the integrated energy users are bounded, non-empty and closed convex sets, the gaming model satisfies condition 1). When the optimal subsidy price of the leaderAnd real-time electricity selling price/>After the determination, the analysis method can be used for determining the unique equilibrium solution-the demand response load/> -of the user under the scene omega at the moment tThe following are provided:
From the above equation, after the subsidy price at time t is determined, the power demand response amount at time t can be determined. Because the target function of the leader model is a monotonic function and the variables are all bounded values, the master-slave gaming model constructed herein has a unique equilibrium solution.
Due to uncertainty in wind, light renewable energy, and user load and demand response, a daily rolling optimization scheduling model is presented herein to correct errors in the daily scheduling plans. And step S320 is executed, based on the day-ahead scheduling plan, 15-minute wind power output, photovoltaic power output and load predicted values are updated, a day-ahead optimization model is built by taking the minimum output adjustment cost of the energy conversion equipment and the energy storage equipment as an optimization target, and the day-ahead scheduling plan is determined.
Specifically, after the day-ahead scheduling stage, the running states of various energy production and coupling devices can be taken as determined amounts into the day-ahead and real-time scheduling stage, the period is 4h, the step length is 15min, the day-ahead scheduling plan is taken as a base line, and the output of the energy conversion device and the energy storage device is further optimized. In addition, in this stage, according to the demand response subsidy price determined in the day-ahead stage, the direct control load which is informed to the user in advance of 15min-4h is called for by shorter response time in the electricity consumption peak period (11-12 h, 17-18 h). Therefore, compared with the day-ahead stage, the stage takes the lowest running cost of the system as an optimization target, does not consider the purchase energy cost and the equipment start-stop cost, and assumes that the electricity selling price, the heat selling price, the cold selling price and the demand response subsidy price optimized in the day-ahead stage are unchanged, and additionally considers the demand response cost for calling the direct control load and the punishment cost for the power change of the energy storage equipment and the energy conversion equipment.
According to the embodiment of the invention, the objective function of the daily optimization model is the minimum objective function of the output adjustment cost of the energy conversion equipment and the energy storage equipment:
In the method, in the process of the invention, For demand response cost generated by directly controlling load for day-period call, ΔC equ is day-period equipment output change cost, ΔC buy is day-period electricity purchase cost change, mu x、μn is unit punishment cost of power change of energy storage equipment and energy conversion equipment respectively, i represents load types such as electricity, heat, cold and the like, and/ >Respectively the charge and discharge energy power change of the energy storage equipment at the t-th moment in the daily period,/>For the output change at the time t of the nth conversion equipment in the daily stage,For the change of electricity purchasing quantity at the time t of the day stage,/>Subsidy price for demand response at time t,/>For the demand response of the user at the time t,/>And (5) purchasing electricity from the power grid at the moment t.
Besides the tie line constraint, the equipment output constraint, the energy storage equipment constraint, the price constraint and the demand response constraint which are met in the day-ahead stage, the constraint condition of the day-ahead optimization model is changed from 1h to 15min, and the power consumption peak period directly controls the load call, so that the climbing constraint and the electric power balance constraint of various units are changed. Thus, the constraints further include a second equipment hill climbing constraint, a second power balancing constraint, the second power balancing constraint being:
the second equipment climbing constraint is:
In the method, in the process of the invention, And/>Predicted values of wind power, photovoltaic, electric load, thermal load and cold load in the daytime respectively,/>Output change values of heat pump, electric refrigerator and absorption refrigerator in the daytime respectively,/> Charging and discharging power change values of electric energy storage, thermal energy storage and cold energy storage in the daytime respectively,/>For the electric load demand of the user at the time t,/>For the maximum direct control load duty ratio of the user at the time t, the value of the load duty ratio can be set to be 10 percent and the value of the load duty ratio can be set to be/()Output of the device m at times t and t+Deltat 2,/>, respectivelyFor the ramp rate of device m, Δt 2 =15 minutes.
And step S330 is executed, based on the daily scheduling plan, the real-time 5-minute-level wind power output, photovoltaic power output and load predicted values are updated, a real-time optimization model is built by taking the minimum energy storage equipment output adjustment cost as an optimization target, and the real-time scheduling plan is determined.
Specifically, the real-time scheduling plan can be rolled for 5min after optimization according to 15min as a period and 5min as a step length, so as to adjust the output of the energy storage device in real time based on the daily scheduling plan, and correct the deviation between the running plan and the prediction result. The goal of the real-time scheduling phase is to reduce the variation of the power of each device as much as possible while meeting the user's energy demand, as compared to the intra-day phase. Therefore, the power of each energy conversion device is not changed in the daily stage, and only the change cost of the output of the energy storage device is considered in the stage.
In the embodiment of the invention, the objective function of the real-time optimization model is the minimum objective function of the energy storage device output adjustment cost:
Wherein, delta C e′qu is the equipment output change cost, delta C b′uy is the electricity purchasing cost change, mu x、μn is the unit punishment cost of the power change of the energy storage equipment and the energy conversion equipment, i represents the load types of electricity, heat, cold and the like, Charging and discharging energy power change of energy storage equipment at t time in real-time stage respectively,/>For the output change of the nth energy conversion equipment at the nth moment in the real-time stage,/>The electricity purchasing quantity at the t-th moment in the real-time stage is changed.
The constraint conditions of the stage still meet the equipment constraint, the power balance constraint and the like of the stages before and during the day, and the corresponding climbing constraint of each equipment can be changed because the scheduling time scale is changed from 15min to 5min, so that the method further comprises the step of climbing constraint of a third equipment:
In the method, in the process of the invention, Output of the device m at times t and t+Deltat 3,/>, respectivelyFor the ramp rate of device m, Δt 3 =5 minutes.
FIG. 4 illustrates a multi-time scale optimized scheduling model framework diagram for an integrated energy system according to one embodiment of the invention. As shown in fig. 4, the integrated energy provider consists of an external grid and natural gas provider, mainly communicating and supplying power and natural gas energy to the campus integrated energy system operator. The park comprehensive energy system operator is both a dispatch center and an operator of the park comprehensive energy system, and obtains price-difference benefits by purchasing energy from multiple energy suppliers and selling the energy to users. Meanwhile, the park comprehensive energy system operator improves the income of the park comprehensive energy system operator by optimizing the dispatching strategy of the park comprehensive energy system and implementing the incentive type demand response based on the demand response subsidy price to the user. The comprehensive energy users in the park refer to users with cold, heat and electric load demands and certain load reducibility, and the users can respond to the subsidy price signal translation and reduce the electric load demands according to the demands provided by the park comprehensive energy system operators by taking the lowest energy cost per se as a target on the assumption of user uniformity. The park comprehensive energy system operator provides a demand response subsidy price for the user, the user adjusts the reduction amount of the electric load according to the demand response subsidy price and uploads the reduction amount to the park comprehensive energy system operator side, and the park comprehensive energy system operator readjusts the output of each device of the park comprehensive energy system and the energy purchased from the multi-energy provider based on the demand response amount of the user.
And in the day-ahead stage, based on wind power, photovoltaic output and load predicted values in the day-ahead stage, determining output of each unit by taking the maximum daily operation income of a system operator and the minimum energy consumption cost of a user as optimization targets, and purchasing electric quantity, demand response subsidy price, electricity selling price and demand response load of the power grid. And in the daytime, determining the output of the energy conversion equipment and the energy storage equipment, the electric quantity purchased from the power grid and the call quantity of the direct control load based on the scheduling plan in the daytime and taking the minimum output adjustment cost of the energy conversion equipment and the energy storage equipment as an optimization target. And in a real-time stage, based on the daily scheduling plan, determining the output of the energy conversion equipment and the energy storage equipment and the electric quantity purchased from the power grid by taking the minimum output adjustment cost of the energy storage equipment as an optimization target.
And finally, executing step S340, and solving the optimal solution of the real-time optimization model before, in the day by utilizing the particle swarm optimization algorithm to determine the optimal scheduling plan of the comprehensive energy system.
The particle swarm optimization algorithm has the characteristics of simple and easy realization of principle, higher convergence speed, fewer parameters to be adjusted and the like, has certain memory and evolutionary property, can completely save the local optimal solutions and the global optimal solutions of all particles in the iterative process, and can update individual historical optima and population optima according to operators. The invention adopts the particle swarm algorithm to better simulate the game interaction process between the park comprehensive energy system operator and the users, and simultaneously, the collaborative optimization of individuals and groups is helpful for quickly finding out the equilibrium solution of the game. Therefore, the optimal solution of the model can be solved by adopting a method of combining a particle swarm optimization algorithm and CPLEX12.8 optimization software in a YALMIP tool box under the MATLAB R2017b platform.
For a leader comprehensive energy system operator, a particle swarm optimization algorithm is adopted, the profit maximization of the park comprehensive energy system operator is taken as an fitness function, and the optimal demand response subsidy price in the iterative process is solvedAnd optimal electricity price/>And CPLEX is adopted to solve the optimal output of each unit of the park comprehensive energy system operator. For the follower comprehensive energy source user, the demand response subsidy price/>, is combinedAnd electricity selling price/>And (3) directly solving a demand response strategy, and ensuring the calculation efficiency of the solution. Meanwhile, the adaptive variation is added for increasing the capability of the particle swarm algorithm to jump out of the local optimal solution. FIG. 5 shows a schematic solution flow diagram of a multi-time scale optimized scheduling model of an integrated energy system according to one embodiment of the invention. As shown in fig. 5, the step of solving the model by using the particle swarm optimization algorithm includes:
(1) Firstly, setting population scale, iteration times, acceleration factors, inertia weight coefficients and the like, and randomly initializing day-ahead stage demand response patch prices And value of electricity selling price/>
(2) Establishing a follower optimization model, calling a follower optimization program, and determining the value of translatable load in the day-ahead stageAnd/>
(3) Establishing a leader optimization model based on initializationBy CPLEX software, the daily gain of the maximized park comprehensive energy system operator is taken as an objective function f1, and the output of each unit is optimized;
(4) Calculating the fitness value of each particle: daily gain of park comprehensive energy system operator, and updating based on updated speed and position And the fitness function f1, updating the local optimum of each particle and the global optimum of the particle swarm. /(I)
(5) Judging whether the iteration times are greater than the maximum iteration times, if the iteration times are not reached, repeating the iteration processes of the steps (2) - (4) until the conditions are finally met to output the optimal output force of each unit,Is the most optimal value of the energy system and the most daily income of the park comprehensive energy system operators.
(6) And updating predicted values of 15 minutes of the output power of the wind generating set, the output power of the photovoltaic generating set, the electric load, the thermal load and the cold load in the daytime based on the scheduling result in the daytime.
(7) Based on the updating of the predicted value, the minimum output adjustment cost of the equipment in the day stage is taken as an optimization target, the output of the heat pump, the absorption refrigerator, the electric refrigerator and the energy storage equipment of the equipment and the electric power purchased from the power grid are updated, and the calling quantity of the direct control load of a user is determined.
(8) Based on the scheduling result of the daytime stage, the predicted values of 5 minutes of the output power of the wind generating set, the output power of the photovoltaic generating set, the electric load, the thermal load and the cold load are updated in the real-time stage.
(9) Based on the updating of the predicted value, the output of the three types of energy storage equipment, namely the electric energy storage equipment, the thermal energy storage equipment and the cold energy storage equipment, is updated by taking the minimum output adjustment cost of the energy storage equipment at the real-time stage as an optimization target, and the scheduling plan of the real-time energy storage equipment is determined.
According to the invention, the master-slave game model between the system operator and the comprehensive energy users based on the demand response excitation mechanism is established, so that the demand response subsidy price and the demand response quantity are optimized, the demand side load can be flexibly scheduled, and the running economy of the comprehensive energy system is improved. By means of the time scale optimization scheduling schemes, on the premise of guaranteeing energy balance, instability of wind power and photovoltaic output and uncertainty of load prediction are effectively treated, and the capacity of absorbing renewable energy sources is improved.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is defined by the appended claims.

Claims (5)

1. A multi-time scale optimized scheduling method of an integrated energy system adapted to be executed in a computing device, the integrated energy system comprising an energy supply module, an operation scheduling module and a load module, the devices of the integrated energy system comprising an energy production device, an energy conversion device and an energy storage device, the energy supply module being adapted to transmit energy and energy price information to the operation scheduling module, the operation scheduling module being adapted to purchase energy from the energy supply module and sell energy to the load module and provide a demand response subsidy price, the load module being adapted to translate or cut down a demand response load according to the demand response subsidy price and upload to the operation scheduling module for the operation scheduling module to adjust each device output and energy purchase amount according to the demand response load, the method comprising:
Based on wind power output, photovoltaic output and load forecast values in the 1-hour-before-day energy production equipment, a day-before optimization model is established with the aim of maximum daily operation income of an operation scheduling module and minimum energy consumption cost of a load module, a demand response subsidy price and a demand response load are adjusted, and a day-before scheduling plan is determined, wherein the day-before scheduling plan comprises the output of the energy production equipment, the energy conversion equipment and energy storage equipment, the electric quantity purchased from a power grid and the scheduling quantity of translatable load of a user;
Based on the day-ahead scheduling plan, updating 15-minute-level wind power output, photovoltaic power output and load prediction values in the day, taking the minimum output adjustment cost of the energy conversion equipment and the energy storage equipment as an optimization target, establishing a day-ahead optimization model, and determining a day-ahead scheduling plan, wherein the day-ahead scheduling plan is the output of a heat pump, an absorption refrigerator, an electric refrigerator and the energy storage equipment, the electric quantity purchased from a power grid and the scheduling quantity capable of reducing the load for a user;
based on the intra-day scheduling plan, updating real-time 5-minute-level wind power output, photovoltaic output and load predicted values, taking the minimum energy storage equipment output adjustment cost as an optimization target, establishing a real-time optimization model, and determining a real-time scheduling plan, wherein the real-time scheduling plan is the energy storage equipment output; and
Solving an optimal solution of a real-time optimization model before, in the day by utilizing a particle swarm optimization algorithm, and determining an optimal scheduling plan of the comprehensive energy system;
The day-ahead optimization model comprises an upper layer leader model and a lower layer follower model based on master-slave games, wherein the upper layer leader model is used for determining optimal demand subsidy prices and electricity selling prices and adjusting output and energy purchase quantity of each device according to demand response loads determined by the lower layer follower model, the lower layer follower model is used for determining demand response loads according to the demand subsidy prices determined by the upper layer leader model, and an objective function of the upper layer leader model is a daily operation income maximum objective function of an operation scheduling module:
In the method, in the process of the invention, For the benefit of selling energy,/>For the purchase energy cost, C ope is the operation and maintenance cost of various units,/>For demand response costs, C p is the pollutant emission cost, where,
In the method, in the process of the invention,Electricity selling price, heat selling price and cold selling price at t moment respectively,/>And/>Fuzzy parameters of electric load, thermal load and cold load at time t respectively,/>The electric load transfer-in quantity and transfer-out quantity of the user at the time t are respectively/>For the electricity purchase price of the power grid at the moment t,/>For the amount of electricity purchased from the grid at time t,/>To purchase the price of natural gas,/>For the natural gas amount purchased at the time t, c gb、cgt、cwhb、chp、cef、cac、cwt、cpv、cees and c tes are respectively the unit power operation and maintenance cost of a gas boiler, a gas turbine, a waste heat boiler, a heat pump, an electric refrigerator and an absorption refrigerator, distributed wind power, distributed photovoltaic, electric energy storage equipment and thermal energy storage equipment,/>Subsidy price for demand response at time t,/>AndPollutant emission costs for gas boilers, gas turbines and power grids, respectively,/>The output power of the gas boiler and the output power of the gas turbine at the moment t are respectively;
the objective function of the lower layer follower model is the minimum objective function of the energy consumption cost of the load module:
In the method, in the process of the invention, For the purchase energy cost of the user,/>Response cost for user's demand,/>For user satisfaction cost,/>Subsidies obtained for the user to participate in the demand response, wherein,
In the method, in the process of the invention,For the preferential electrical load of the user at time t,/>For the electric load transfer quantity of a user at the time t, a and b are the user demand response cost coefficients, and c is the penalty coefficient of the electric load deviating from the preferential electric load;
constraint conditions of the day-ahead optimization model comprise a first power balance constraint, a device output constraint, a first device climbing constraint, a tie line constraint, an energy storage device constraint, a price constraint and a demand response constraint, wherein the first power balance constraint is as follows:
In the method, in the process of the invention, For/>The likelihood of an event in (a), α p is the confidence level,/>Fuzzy parameters of wind power output and photovoltaic output of users at time t respectively,/>For the consumption of natural gas at time t,/>The output power of the gas boiler and the output power of the gas turbine at the moment t are respectively;
The equipment output constraint is as follows:
the first equipment climbing constraint is as follows:
In the method, in the process of the invention, Output of the device m at time t and t+Deltat 1 respectively,/>For the maximum capacity of the device m,For the ramp rate of device m, Δt 1 =1 hour;
the tie constraint is as follows:
In the method, in the process of the invention, For the electricity purchase price of the power grid at the moment t,/>The minimum and maximum interactive power between the comprehensive energy system and the power distribution network is achieved;
the energy storage device constraints are:
In the method, in the process of the invention, Maximum power of charge/discharge of electricity storage equipment at t moment,/>For maximum power of heat storage/release of the heat storage device,Respectively the lower limit and the upper limit of the energy storage state of the energy storage device at the t moment,/> The running states of the energy storage equipment at the t moment are 0-1 variables respectively;
the price constraint is as follows:
In the method, in the process of the invention, Is the selling price of the heat supply network,/>The highest demand response is subsidized and limited;
the demand response constraint is:
In the method, in the process of the invention, For the maximum transfer load duty cycle of the user at time t,/>The method comprises the steps of respectively obtaining the electric load transfer-in quantity and the transfer-out quantity of a user at the time t;
The daily optimization model is used for determining the calling quantity of the direct control load and the electric quantity purchased from the power grid according to the demand response subsidy price determined in the day-ahead stage, and the objective function of the daily optimization model is the minimum objective function of the output adjustment cost of the energy conversion equipment and the energy storage equipment:
In the method, in the process of the invention, For demand response cost generated by directly controlling load for day-period call, ΔC equ is day-period equipment output change cost, ΔC buy is day-period electricity purchase cost change, mu x、μn is unit punishment cost of power change of energy storage equipment and energy conversion equipment respectively, i represents electric, heat and cold load category,/>Respectively the charge and discharge energy power change of the energy storage equipment at the t-th moment in the daily period,/>For the output change of the nth conversion equipment at the nth moment in the day period,/>For the change of electricity purchasing quantity at the time t of the day stage,/>Subsidy price for demand response at time t,/>For the demand response of the user at the time t,/>The electricity purchasing price of the power grid is at the time t;
Constraint conditions of the daily optimization model comprise the tie line constraint, the equipment output constraint, the energy storage equipment constraint, the price constraint and the demand response constraint, and also comprise a second equipment climbing constraint and a second power balance constraint, wherein the second power balance constraint is as follows:
the second equipment climbing constraint is:
In the method, in the process of the invention, And/>Predicted values of wind power, photovoltaic, electric load, thermal load and cold load in the daytime respectively,/>Output change values of heat pump, electric refrigerator and absorption refrigerator in the daytime respectively,/> Charging and discharging power change values of electric energy storage, thermal energy storage and cold energy storage in the daytime respectively,/>For the electric load demand of the user at the time t,/>For the maximum direct control load duty cycle of the user at time t,/> Output of the device m at times t and t+Deltat 2,/>, respectivelyFor the ramp rate of device m, Δt 2 = 15 minutes;
The objective function of the real-time optimization model is the minimum objective function of the energy storage device output adjustment cost:
Wherein, deltaC 'equ is the equipment output change cost, deltaC' buy is the electricity purchasing cost change, mu x、μn is the unit penalty cost of the power change of the energy storage equipment and the energy conversion equipment respectively, i represents the electric, heat and cold load types, Charging and discharging energy power change of energy storage equipment at t time in real-time stage respectively,/>For the output change of the nth energy conversion equipment at the nth moment in the real-time stage,/>The electricity purchasing quantity change at the t-th moment in the real-time stage;
Constraint conditions of the real-time optimization model comprise the first power balance constraint, the second power balance constraint, the equipment output constraint, the tie line constraint, the energy storage equipment constraint, the price constraint and the demand response constraint, and further comprise a third equipment climbing constraint:
In the method, in the process of the invention, Output of the device m at times t and t+Deltat 3,/>, respectivelyFor the ramp rate of device m, Δt 3 = 5 minutes;
The step of determining the optimal scheduling plan of the comprehensive energy system by utilizing the particle swarm optimization algorithm to solve the optimal solution of the real-time optimization model before, in the day comprises the following steps:
Step 1: setting population scale, maximum iteration times, acceleration factors and inertia weight coefficients, and randomly initializing day-ahead stage demand response subsidy price and electricity selling price;
Step 2: establishing a follower model, and determining a translatable load transfer-in value and a translatable load transfer-out value in the day-ahead stage by taking the minimum energy cost of a load module as an objective function f2 based on the initialized demand response subsidy price and electricity selling price;
Step 3: establishing a leader model, and optimizing the output of each unit based on the translatable load transfer value by maximizing the daily gain of an operation scheduling module into a fitness function f 1;
Step 4: calculating the daily gain of the operation scheduling module, and updating the daily gain value of the daily gain of the operation scheduling module, the daily-period demand response subsidy price, the electricity selling price, the translatable load transfer value and the output of each unit;
Step 5: judging whether the iteration times are greater than the maximum iteration times, if the iteration times are not up to the maximum iteration times, repeating the iteration processes of the steps 2-4 until the conditions are finally met, and outputting the optimal output force, the demand response subsidy price, the electricity selling price, the optimal value of the translatable load transfer value and the daily optimal benefit of the operation scheduling module of each unit;
Step 6: based on the scheduling result of the day-ahead stage, updating 15-minute-level predicted values of the output power of the wind generating set, the output power of the photovoltaic generating set, the electric load, the thermal load and the cold load in the day-ahead stage;
step 7: based on the updating of the predicted value, the minimum output adjustment cost of the equipment in the day stage is taken as an optimization target, the output of the heat pump, the absorption refrigerator, the electric refrigerator and the energy storage equipment of the equipment and the electric power purchased from the power grid are updated, and the calling quantity of the direct control load of a user is determined;
Step 8: based on the scheduling result of the daytime stage, updating 5-minute-level predicted values of the output power of the wind generating set, the output power of the photovoltaic generating set, the electric load, the thermal load and the cold load in the real-time stage;
Step 9: based on the updating of the predicted value, the minimum output adjustment cost of the energy storage equipment in the real-time stage is used as an optimization target, and the output of the three energy storage equipment, namely the electric energy storage equipment, the thermal energy storage equipment and the cold energy storage equipment is updated.
2. The method of claim 1, wherein the energy production facility comprises at least a wind generator and a photovoltaic generator, the wind generator having an output model of:
In the method, in the process of the invention, For the output power of the wind driven generator at the moment t, v t is the wind speed at the moment t, and v ci、vco、vr is the cut-in wind speed, the cut-out wind speed and the rated wind speed respectively,/>Rated output power of the wind driven generator;
the output model of the photovoltaic generator is as follows:
In the method, in the process of the invention, Output power of photovoltaic generator at t moment,/>For rated output power of the photovoltaic generator, G r、Tr is rated illumination radiance and rated temperature respectively, G t、Tt is actual illumination radiance and actual temperature at time t respectively, and τ is a temperature coefficient;
the energy conversion equipment at least comprises a gas turbine, a waste heat boiler, a gas boiler, a heat pump, an electric refrigerator and an absorption refrigerator, wherein the output model of the gas turbine is as follows:
In the method, in the process of the invention, For the electric power output by the gas turbine at the t moment, phi g is the heat value of natural gas,/>Output electric power efficiency for gas turbine,/>Natural gas power input to the gas turbine;
the output model of the waste heat boiler is as follows:
In the method, in the process of the invention, For the heat power output by the waste heat boiler at the moment t,/>Is the heat efficiency of the waste heat boiler,/>Output electric power efficiency for gas turbine,/>Natural gas power input to the gas turbine;
the output model of the gas boiler is as follows:
In the method, in the process of the invention, For the heat power output by the gas boiler at the time t,/>Efficiency of heat power output for gas boiler,/>The natural gas power input to the gas boiler at the moment t;
The output model of the heat pump is as follows:
In the method, in the process of the invention, For the heat power output by the heat pump at the moment t,/>For the electric power consumed by the heat pump at time t,/>The electric heat transfer efficiency of the heat pump;
the output model of the electric refrigerator is as follows:
In the method, in the process of the invention, For the cold power output by the electric refrigerator at the moment t,/>For the electric power input by the electric refrigerator at the moment t,/>The electric refrigerating efficiency of the electric refrigerator is achieved;
the output model of the absorption refrigerator is as follows:
In the method, in the process of the invention, For the cold power output by the absorption refrigerator at the moment t,/>Heat power input for absorption refrigerator at time t,/>The heat-to-cold efficiency of the absorption refrigerator is improved;
the model of the energy storage equipment is that
In the method, in the process of the invention,For the energy storage states of the energy storage device x at the t and t+1 moments, epsilon x is the self-loss rate of the energy storage device x,/>Respectively the energy storage power and the energy release power of the energy storage device x at the t moment,/>The energy storage and energy release efficiencies of the energy storage device x are respectively shown, E x is the rated capacity of the energy storage device x, deltaT is the scheduling duration, x represents the class of the energy storage device, wherein ees represents electric energy storage, tes represents thermal energy storage, and cs represents cold energy storage.
3. The method of claim 1, wherein the first power balance constraint is a fuzzy opportunity constraint comprising wind power output, photovoltaic output, and load predictions, and the clear equivalent constraint of the fuzzy opportunity constraint is:
In the method, in the process of the invention, Membership parameters of predicted values of electric, thermal and cold loads respectively,The membership parameters are respectively a wind power output predicted value and a photovoltaic output predicted value.
4. A computing device, comprising:
one or more processors;
A memory; and
One or more devices comprising instructions for performing the method of any of claims 1-3.
5. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform the method of any of claims 1-3.
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Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114545878B (en) * 2022-02-22 2023-09-01 山东大学 Comprehensive energy system optimal scheduling method and system
CN114676979B (en) * 2022-03-04 2022-11-25 南方科技大学 Energy scheduling method and device, computer equipment and storage medium
CN114493048A (en) * 2022-04-06 2022-05-13 国网江西省电力有限公司经济技术研究院 Demand response mechanism-based optimization scheduling method and device for comprehensive energy system
CN114844124B (en) * 2022-06-20 2024-04-19 中国电力工程顾问集团中南电力设计院有限公司 Operation control method of comprehensive energy system based on target optimization
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CN115375032A (en) * 2022-09-01 2022-11-22 东南大学溧阳研究院 Multi-time scale optimization scheduling method for regional power grid with distributed energy storage
CN116581825A (en) * 2023-05-06 2023-08-11 西华大学 Future scheduling optimization method of building comprehensive energy system based on new energy consumption
CN116544935B (en) * 2023-07-06 2023-12-19 中国电力科学研究院有限公司 Optimization control method and device for motor loads participating in demand response
CN116613750B (en) * 2023-07-18 2023-10-13 山东大学 Integrated scheduling method, system, terminal equipment and medium for power system
CN117217496B (en) * 2023-11-07 2024-03-12 天津电力工程监理有限公司 Regional comprehensive energy system control method and device considering master-slave game
CN117892884A (en) * 2024-03-08 2024-04-16 国网北京市电力公司 Comprehensive energy system optimization design method, device, equipment and medium

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108629470A (en) * 2017-03-17 2018-10-09 华北电力大学 System capacity management of providing multiple forms of energy to complement each other based on non-cooperative game is run with optimization
CN108876040A (en) * 2018-06-21 2018-11-23 广州供电局有限公司 The multiclass energy of garden energy internet operators is fixed a price and energy management method
WO2019196375A1 (en) * 2018-04-13 2019-10-17 华南理工大学 Demand side response-based microgrid optimal unit and time-of-use electricity price optimization method
CN110417006A (en) * 2019-07-24 2019-11-05 三峡大学 Consider the integrated energy system Multiple Time Scales energy dispatching method of multipotency collaboration optimization
CN111969614A (en) * 2020-08-26 2020-11-20 华北电力大学（保定） Master-slave game-based optimal scheduling method for multi-park comprehensive energy system
CN113139711A (en) * 2021-03-03 2021-07-20 国网江西省电力有限公司供电服务管理中心 Wisdom energy data center management system based on data integration

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108629470A (en) * 2017-03-17 2018-10-09 华北电力大学 System capacity management of providing multiple forms of energy to complement each other based on non-cooperative game is run with optimization
WO2019196375A1 (en) * 2018-04-13 2019-10-17 华南理工大学 Demand side response-based microgrid optimal unit and time-of-use electricity price optimization method
CN108876040A (en) * 2018-06-21 2018-11-23 广州供电局有限公司 The multiclass energy of garden energy internet operators is fixed a price and energy management method
CN110417006A (en) * 2019-07-24 2019-11-05 三峡大学 Consider the integrated energy system Multiple Time Scales energy dispatching method of multipotency collaboration optimization
CN111969614A (en) * 2020-08-26 2020-11-20 华北电力大学（保定） Master-slave game-based optimal scheduling method for multi-park comprehensive energy system
CN113139711A (en) * 2021-03-03 2021-07-20 国网江西省电力有限公司供电服务管理中心 Wisdom energy data center management system based on data integration

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Optimal dispatch model based on coordination between "generation - grid - load - energy storage" and demand-side resource;Zeng Ming等;《Electric Power Automation Equipment》;20161130;第36卷(第02期);第102-111页，全文 *
促进新能源消纳的电力市场机制及政策优化模型研究;夏天;《中国博士学位论文全文数据库工程科技Ⅱ辑》;20210615(第06期);第C042-22页，全文 *
考虑需求响应激励机制的园区综合能源系统博弈优化调度;王俐英等;《控制与决策》;20231130;第38卷(第11期);第3192-3200页，全文 *

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