Zero-carbon community energy system optimization operation method considering user energy consumption characteristics

Abstract

The invention relates to the technical field of energy scheduling, in particular to a zero-carbon community energy system optimization operation method considering user energy consumption characteristics. The method specifically comprises the following steps: generating a zero-carbon community energy system operation scene: establishing an output model of each energy device, obtaining the reserve condition and output characteristic information of each energy device in a community, analyzing the daily load characteristics of each type of users in the community, and correspondingly generating various energy load typical scenes; establishing a cost model of each energy device; establishing a community energy system optimization operation model by taking the lowest comprehensive operation cost of the community energy system as an objective function and taking the constraint of the community energy system as a constraint condition; and solving the community energy system optimization operation model to obtain a scheduling instruction, and sending the scheduling instruction to each corresponding energy device to enable each energy device to execute the scheduling instruction. The method can consider the energy consumption characteristics of the users, balance the economic benefits and the carbon reduction effect, and well exert the resource integration capability of the comprehensive energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy dispatching, and in particular to a method for optimizing the operation of a zero-carbon community energy system taking into account energy consumption characteristics of users. Background Art

[0002] In recent years, communities have become an important basic unit for urban development. They bring together multiple energy types such as electricity, cold, heat, and gas, and can accommodate distributed equipment such as photovoltaics, wind power, and electric vehicles. They are gradually forming a comprehensive energy system that integrates source, grid, load, and storage, and is multi-energy complementary. Under this development trend, it is of great research value to fully understand the output characteristics of various energy equipment in the community, evaluate and analyze the diverse energy needs of community users, and maximize the efficiency of community energy utilization.

[0003] At the same time, improving the balance of community energy supply and demand, building a reasonable integrated energy system operation mechanism, exploring the potential of coordinated scheduling of different types of energy equipment, promoting low-carbon operation of the energy system, and improving the economic benefits of the system are the current hot topics. Existing studies rarely consider the impact of different types of user energy characteristics on the operation methods of integrated energy systems, and it is difficult to intuitively reflect the coordinated scheduling of energy equipment under different load demand changes. In addition, the existing integrated energy system scheduling methods rarely consider the economic, stable and environmentally friendly operation requirements, as well as balance economic benefits and system carbon emission intensity, and cannot fully exert the resource integration and self-sufficiency capabilities of the integrated energy system.

[0004] In summary, an optimized operation method for an integrated energy system that considers the energy consumption characteristics of different types of users while taking into account the carbon emission intensity of the system needs to be proposed urgently. Summary of the invention

[0005] To this end, the technical problem to be solved by the present invention is to overcome the problem that the existing integrated energy system scheduling method does not take into account the energy consumption characteristics of different types of users while taking into account the carbon emission intensity of the system.

[0006] In order to solve the above technical problems, the present invention provides a method for optimizing the operation of a zero-carbon community energy system taking into account the energy consumption characteristics of users, which specifically includes the following steps:

[0007] Generate zero-carbon community energy system operation scenarios, which include establishing output models of various energy devices, obtaining the reserve status and output characteristics of zero-carbon community energy devices, and generating typical scenarios of various energy loads in the community according to the daily load characteristic curves of different types of users in the zero-carbon community; among them, the zero-carbon community energy system includes multiple energy sources and energy equipment, including electricity, heat, cold, and gas, and energy equipment including distributed power sources, energy storage equipment, energy use equipment, and energy conversion equipment. Different types of users include industrial users, commercial users, and residential users;

[0008] According to the output model of each energy device, the reserve status of the energy device and the output characteristic information, set the cost coefficient and the upper and lower limit parameters of each energy device; according to the cost coefficient and the upper and lower limit parameters of each energy device, establish the cost model of each energy device; according to the output model of each energy device, the output characteristic information of the zero-carbon community energy equipment, the daily load characteristic curves of different types of users in the zero-carbon community and the typical scenarios of each energy load in the corresponding community, design the zero-carbon community energy system constraints;

[0009] According to the cost model of each energy device, the objective function is to minimize the comprehensive operating cost of the zero-carbon community energy system, and the constraints of the zero-carbon community energy system are used as constraints to establish an optimal operation model of the zero-carbon community energy system;

[0010] Solving the zero-carbon community energy system optimization operation model to obtain the dispatching instructions of each energy device, sending the dispatching instructions to the corresponding energy devices, so that each energy device executes the dispatching instructions.

[0011] Preferably, the distributed power sources include photovoltaic and wind power; the energy storage equipment includes power storage system, heat storage system, cold storage system; the energy-consuming equipment includes electric vehicles and flexible loads; the energy conversion equipment includes solar thermal system, gas turbine, gas boiler, electric refrigerator.

[0012] Preferably, establishing the output model of the distributed power source includes:

[0013] Generate wind power and photovoltaic typical output models based on their historical output levels and corresponding weather data;

[0014] Photovoltaic output model, the expression is as follows:

[0015] P pv =r pv ·A pv ·η pv

[0016] Among them, P pv Represents photovoltaic output, r pv Indicates light intensity, A pv represents the photovoltaic panel area, η pv Represents photovoltaic photoelectric conversion efficiency;

[0017] Wind power output model, the expression is as follows:

[0018]

[0019] Among them, P wt Represents wind power output, P wr Indicates the rated power of the fan, vw Indicates the actual wind speed, v r Indicates rated wind speed, v ci represents the cut-in wind speed, v co Indicates the cut-out wind speed.

[0020] Preferably, the cost models of each energy equipment include: photovoltaic cost model, wind power cost model, energy storage equipment cost model, flexible load cost model, electric vehicle cost model, solar thermal system cost model, gas turbine cost model, gas boiler cost model, electric refrigerator cost model, and grid electricity purchase cost model.

[0021] Preferably, the photovoltaic cost model is expressed as follows:

[0022] C pv,t =a pv P pv,t

[0023] In the formula, C pv,t is the photovoltaic operation and maintenance cost at time t; a pv is the photovoltaic operation and maintenance cost coefficient; P pv,t is the photovoltaic output power at time t;

[0024] The wind power cost model is expressed as follows:

[0025] C wt,t =a wt P wt,t

[0026] In the formula, C wt,t is the wind power operation and maintenance cost at time t, a wt P is the wind power operation and maintenance cost coefficient; wt,t is the wind power output at time t;

[0027] The energy storage equipment cost model is expressed as follows:

[0028]

[0029] The energy storage equipment cost model includes electricity storage, heat storage, and cold storage systems; where C eps,t is the charging and discharging cost of the energy storage device at time t; a eps,ch 、a eps,dch are the cost coefficients for storing and releasing energy of energy storage equipment respectively; are the power of energy storage and energy release of the energy storage device at time t; η eps,ch , η eps,dch They are the efficiency of storing and releasing energy of energy storage devices;

[0030] The flexible load cost model is expressed as follows:

[0031]

[0032] In the formula, C l,t is the incentive cost of flexible load at time t; a l is the load incentive cost coefficient; P l,t , They are the actual power after load regulation and the original expected power at time t respectively;

[0033] The electric vehicle cost model is expressed as follows:

[0034]

[0035] In the formula, C ev,t is the charging and discharging cost of electric vehicles at time t; a ev,ch 、a ev,dch are the cost coefficients for charging and discharging electric vehicles, respectively; are the charging and discharging power of the electric vehicle at time t; η ev,ch , η ev,dch are the charging and discharging efficiency of electric vehicles respectively;

[0036] The cost model of the solar thermal system is expressed as follows:

[0037] C tes,t =a tes,p P tes,t +a tes,h H tes,t

[0038] In the formula, C tes,t is the operation and maintenance cost of the CSP system at time t; a tes,p 、a tes,h are the cost coefficients of electric power and thermal power of the solar thermal system respectively; P tes,t , H tes,t are the electric power and thermal power of the CSP system at time t respectively;

[0039] The gas turbine cost model and gas boiler cost model are expressed as follows:

[0040] C gtw,t =a gtw P gtw,t

[0041] C gbw,t =a gbw H gbw,t

[0042]

[0043] In the formula, C gtw,t , C gbw,tare the fuel costs of gas turbine and gas boiler at time t respectively; are the carbon emission cost of gas turbine at time t and the carbon emission cost of gas boiler at time t respectively; a gtw 、a gbw are the fuel cost coefficients of gas turbine and gas boiler respectively; P gtw,t , H gbw,t are the gas turbine power and gas boiler power at time t respectively; is the carbon price; are the carbon emission factors of gas turbines and gas boilers respectively;

[0044] The cost model of the electric refrigerator is expressed as follows:

[0045] Q col,t =b col P col,t

[0046] C col,t =a col Q col,t

[0047] In the formula, Q col,t b is the cooling power of the electric refrigerator at time t; col is the electric cooling conversion coefficient; P col,t C is the power consumption of the refrigerator at time t; col,t is the operation and maintenance cost of the electric refrigerator at time t; a col is the operation and maintenance cost coefficient of the electric refrigerator;

[0048] The power grid purchase cost model is expressed as follows:

[0049] C b,t =r e P b,t

[0050]

[0051] In the formula, C b,t , are the interaction cost of electricity purchased from the power grid at time t and the carbon emission cost of electricity purchased from the power grid at time t; r e is the time-of-use electricity price; P b,t is the power purchased at time t; is the carbon emission factor for purchased electricity.

[0052] Preferably, the objective function expression is as follows:

[0053] minC=C ec +C st +C en

[0054] The objective function includes economic objectives C ec 、Stability target C st 、Environmental protection target C en ;

[0055] Economic Objective C ec , the expression is as follows:

[0056]

[0057] Among them, C pv,t is the photovoltaic operation and maintenance cost at time t, C wt,t is the wind power operation and maintenance cost at time t, C eps,t is the charging and discharging cost of the energy storage device at time t, C ev,t is the charging and discharging cost of electric vehicles at time t, C l,t is the incentive cost of flexible load at time t, C tes,t is the operation and maintenance cost of the CSP system at time t, C gtw,t , C gbw,t are the fuel cost of gas turbine at time t and the fuel cost of gas boiler at time t, respectively. col,t is the operation and maintenance cost of the electric refrigerator at time t;

[0058] Stability Target C st , the expression is as follows:

[0059]

[0060] Among them, C b,t represents the interactive cost of electricity purchase from the power grid at time t;

[0061] Environmental Target C en , the expression is as follows:

[0062]

[0063] in, They respectively represent the carbon emission cost of the gas turbine at time t, the carbon emission cost of the gas boiler at time t, and the carbon emission cost of purchasing electricity from the power grid at time t.

[0064] Preferably, the zero-carbon community energy system constraints include: photovoltaic and wind power processing constraints; energy storage equipment power and capacity constraints; flexible load regulation constraints; electric vehicle power and capacity constraints; solar thermal system output constraints; gas boiler, gas turbine, and electric refrigerator output constraints; electricity purchase power constraints; and energy balance power constraints.

[0065] Preferably, the photovoltaic and wind power output constraints are specifically designed as follows:

[0066] 0≤P pv,t ≤P pv,max

[0067] 0≤P wt,t ≤P wt,max

[0068] Where P pv,max , P wt,max are the upper limits of photovoltaic and wind power output respectively; P pv,t is the photovoltaic output power at time t, P wt,t is the wind power output at time t;

[0069] The power and capacity constraints of the energy storage equipment are specifically designed as follows:

[0070]

[0071] E eps,min ≤E eps,t ≤E eps,max

[0072] In the formula, are the power of energy storage device storing energy at time t and the power of energy release device releasing energy at time t respectively; They are the maximum storage and charging and maximum released energy powers of the energy storage device respectively; are the charging and discharging states of the energy storage device at time t; η eps,ch , η eps,dch E are the efficiency of energy storage and energy release of energy storage equipment; eps,t is the capacity of energy storage equipment at time t; E eps,max 、E eps,min They are the maximum and minimum capacity states of the energy storage device respectively;

[0073] The flexible load adjustment constraint is specifically designed as follows:

[0074]

[0075] Where P l,t , They are the actual power after load regulation and the original expected power at time t respectively; They are the maximum and minimum power demands at time t respectively;

[0076] The electric vehicle power and capacity constraints are specifically designed as follows:

[0077]

[0078] E ev,min ≤E ev,t ≤E ev,max

[0079] In the formula, They are respectively the charging and discharging power of the electric vehicle at time t; The maximum charging and discharging power of electric vehicles are the charging and discharging states of the electric vehicle at time t; η ev,ch , η ev,dch E are the charging and discharging efficiency of electric vehicles respectively; ev,t E is the battery capacity of the electric vehicle at time t; ev,max 、E ev,min They are the maximum and minimum capacity states of electric vehicle batteries respectively;

[0080] The output constraint of the solar thermal system is specifically designed as follows:

[0081]

[0082] In the formula, H tes,max is the maximum heating power of the photothermal system; η tes is the power conversion efficiency of the photothermal system; P tes,t , H tes,t are the electric power of the solar thermal system at time t and the thermal power of the solar thermal system at time t respectively;

[0083] The output constraints of the gas boiler, gas turbine and electric refrigerator are specifically designed as follows:

[0084] P gtw,min ≤P gtw,t ≤P gtw,max

[0085] H gbw,min ≤H gbw,t ≤H gbw,max

[0086] Q col,min ≤Q col,t ≤Q col,max

[0087] Where P gtw,t is the gas turbine power at time t, H gbw,t is the power of the gas boiler at time t; Q col,t P is the cooling power of the electric refrigerator at time t; gtw,max , P gtw,min are the upper and lower limits of gas turbine output respectively; H gbw,max , H gbw,min They are the upper and lower limits of gas boiler output respectively; Q col,max , Q col,min They are the upper and lower limits of the output of the electric refrigerator respectively;

[0088] The power purchase power constraint is specifically designed as follows:

[0089] 0≤P b,t≤P b,max

[0090] Where P b,t is the power purchased at time t, P b,max is the maximum power purchase;

[0091] The energy balance power constraint is specifically designed as follows:

[0092]

[0093]

[0094]

[0095] In the formula, C col,t is the operation and maintenance cost of the electric refrigerator at time t, H l,t , C l,t They are the system heating load and cooling load demands at time t respectively.

[0096] Preferably, a zero-carbon community energy system optimization operation device taking into account user energy consumption characteristics comprises:

[0097] An operation scenario generation module is used to generate an operation scenario of a zero-carbon community energy system, wherein the operation scenario of the zero-carbon community energy system includes establishing an output model of each energy device, obtaining the reserve status and output characteristic information of the zero-carbon community energy device, and generating typical scenarios of each energy load in the community according to the daily load characteristic curves of different types of users in the zero-carbon community; wherein the zero-carbon community energy system includes a variety of energy sources and energy equipment, the multiple energy sources include four types of energy: electricity, heat, cold, and gas, the energy equipment includes distributed power sources, energy storage equipment, energy use equipment, and energy conversion equipment, and the different types of users include industrial users, commercial users, and residential users;

[0098] The cost model and system constraint construction module is used to set the cost coefficient and output upper and lower limit parameters of each energy device according to the output model, reserve status and output characteristic information of each energy device; establish the cost model of each energy device according to the cost coefficient and output upper and lower limit parameters of each energy device; design the zero-carbon community energy system constraints according to the output model of each energy device, the output characteristic information of zero-carbon community energy devices, the daily load characteristic curves of different types of users in the zero-carbon community and the typical scenarios of each energy load in the corresponding community;

[0099] The system model building module is used to establish an optimal operation model of the zero-carbon community energy system based on the cost model of each energy device, with the minimum comprehensive operation cost of the zero-carbon community energy system as the objective function and the zero-carbon community energy system constraints as the constraints;

[0100] The scheduling module is used to solve the zero-carbon community energy system optimization operation model to obtain the scheduling instructions of each energy device, and send the scheduling instructions to the corresponding energy devices so that each energy device executes the scheduling instructions.

[0101] Preferably, a computer-readable storage medium for optimizing the operation of a zero-carbon community energy system taking into account user energy consumption characteristics comprises:

[0102] The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for optimizing the operation of a zero-carbon community energy system taking into account user energy consumption characteristics.

[0103] The above technical solution of the present invention has the following beneficial effects compared with the prior art:

[0104] The method of the present invention, by generating a zero-carbon community energy system operation scenario, includes: generating an output model of distributed power sources, obtaining the reserve status and output characteristic information of each energy device in the zero-carbon community, and generating typical scenarios of each energy load in the community according to the daily load characteristic curves of different types of users in the zero-carbon community; according to the zero-carbon community energy system operation scenario, setting the cost coefficient of each energy device, the upper and lower limit parameters of the output, etc., and establishing a cost model for each energy device, in which new energy conversion equipment such as a photothermal system is additionally considered; designing the constraint conditions of the zero-carbon community energy system according to the daily load characteristic curves of different types of users in the zero-carbon community and the typical scenarios of each energy load in the corresponding community; comprehensively considering the operation requirements of the zero-carbon community energy system in terms of economy, stability and environmental protection, and establishing an objective function with the lowest comprehensive operation cost of the community energy system as the goal; according to the cost models, objective functions and zero-carbon community energy system constraints of the above-mentioned energy devices, establishing an optimized operation model of the zero-carbon community energy system, solving the model according to actual application requirements, obtaining scheduling instructions that meet the current actual application requirements, and completing the comprehensive scheduling of various energy sources and energy equipment according to the scheduling instructions. Fully consider the energy consumption characteristics of various types of users in the community, including industrial, commercial and residential users. By providing incentives, flexible loads are encouraged to be adjusted during the day while meeting basic energy needs, so that staff can adjust and dispatch various energy sources and energy equipment in the system according to different energy needs.

[0105] On the basis of ensuring the economic benefits of the system, it can also reduce the carbon emission intensity of the system, achieve good carbon reduction results, and give full play to the resource integration and self-sufficiency capabilities of the community energy system, making the method of the present invention reasonable and effective in the context of a zero-carbon community, and has practical reference and executability. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0107] Figure 1 It is the overall flow chart of the present invention;

[0108] Figure 2 This is an example diagram of typical photovoltaic and wind power output scenarios of the present invention;

[0109] Figure 3 This is an example diagram of typical scenarios of various types of user loads in the present invention;

[0110] Figure 4 It is a schematic diagram of the industrial load optimization effect of the community energy system of the present invention;

[0111] Figure 5 It is a schematic diagram of the commercial load optimization effect of the community energy system of the present invention;

[0112] Figure 6 This is a schematic diagram of the residential load optimization effect of the community energy system of the present invention;

[0113] Figure 7 It is a schematic diagram of flexible load regulation of the community energy system of the present invention;

[0114] Figure 8 It is a schematic diagram of the heat load optimization effect of the community energy system of the present invention;

[0115] Fig. 9 It is a schematic diagram of the cooling load optimization effect of the community energy system of the present invention. DETAILED DESCRIPTION

[0116] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0117] Example 1

[0118] Reference Figure 1 As shown, it is the overall process of the method for optimizing operation of a zero-carbon community energy system taking into account the energy consumption characteristics of users according to the present invention.

[0119] The community includes three types of users: industrial, commercial, and residential. The community energy system mainly includes four types of energy: electricity, heat, cold, and gas, as well as four types of energy equipment: distributed power sources, energy storage equipment, energy-using equipment, and energy conversion equipment. Among them, distributed power sources include photovoltaics and wind power; energy storage equipment includes power storage systems, heat storage systems, and cold storage systems; energy-using equipment includes electric vehicles and flexible loads; energy conversion equipment mainly includes solar thermal systems, gas turbines, gas boilers, and electric refrigerators.

[0120] A method for optimizing the operation of a zero-carbon community energy system taking into account user energy consumption characteristics The method for optimizing the operation of a community energy system comprises the following steps:

[0121] S1: Generate a zero-carbon community energy system operation scenario, which includes establishing an output model for each energy device, obtaining the reserve status and output characteristic information of the zero-carbon community energy device, and generating typical scenarios for each energy load in the community according to the daily load characteristic curves of different types of users in the zero-carbon community;

[0122] ① Based on the historical statistical data of a photovoltaic power station and wind turbine, the typical output models of wind power and photovoltaic power are generated respectively. Figure 2 As shown, it serves as the basis for setting the upper limit of the output of distributed power sources;

[0123] Photovoltaic output model, the expression is as follows:

[0124] P pv =r pv ·A pv ·η pv

[0125] Among them, P pv Represents photovoltaic output, r pv Indicates the light intensity, A pv represents the photovoltaic panel area, η pv Represents photovoltaic photoelectric conversion efficiency;

[0126] Wind power output model, the expression is as follows:

[0127]

[0128] Among them, P wt Represents wind power output, P wr Indicates the rated power of the fan, v w Indicates the actual wind speed, v r Indicates rated wind speed, v ci represents the cut-in wind speed, v co Indicates the cut-out wind speed.

[0129] ② Obtain and organize the reserve status and output characteristics of the community's adjustable energy equipment, analyze the coupling characteristics of various types of energy equipment in time and space, and lay a feasibility foundation for the formulation of subsequent methods;

[0130] ③Analyze the daily load characteristic curves of industrial, commercial and residential users in the community, and generate typical energy load scenarios for industrial, commercial and residential users according to the daily load characteristic curves. Figure 3 As shown;

[0131] S2: According to the output model of each energy device, the reserve status of the energy device and the output characteristic information, set the cost coefficient and the upper and lower limit parameters of each energy device; according to the cost coefficient and the upper and lower limit parameters of each energy device, establish the cost model of each energy device; according to the output model of each energy device, the output characteristic information of the zero-carbon community energy equipment, the daily load characteristic curves of different types of users in the zero-carbon community and the typical scenarios of each energy load in the corresponding community, design the zero-carbon community energy system constraints;

[0132] Establish cost models for various energy equipment, including: photovoltaic cost model, wind power cost model, energy storage equipment cost model, flexible load cost model, electric vehicle cost model, solar thermal system cost model, gas turbine cost model, gas boiler cost model, electric refrigerator cost model, and grid electricity purchase cost model.

[0133] In this embodiment, the carbon price is set at 0.05 yuan / kWh; the carbon emission factor of gas turbines and gas boilers is set to 0.56 kgCO2 / kWh; the carbon emission factor of power grid purchased electricity is set to 0.5942 kgCO2 / kWh; the electricity price adopts a time-of-use electricity price mechanism, 8:00-11:00 and 17:00-22:00 are peak periods, and the electricity price is set to 1.2544 yuan / kWh, 11:00-17:00 and 22:00-24:00 are normal periods, and the electricity price is set to 0.7503 yuan / kWh, and 0:00-8:00 is a valley period, and the electricity price is set to 0.339 yuan / kWh.

[0134] The photovoltaic cost model is mainly used to model the photovoltaic operation and maintenance costs. The expression is as follows:

[0135] C pv,t =a pv P pv,t

[0136] In the formula, C pv,t is the photovoltaic operation and maintenance cost at time t; a pv is the photovoltaic operation and maintenance cost coefficient; P pv,t is the photovoltaic output power at time t;

[0137] The wind power cost model is mainly used to model the wind power operation and maintenance costs. The expression is as follows:

[0138] C wt,t =a wt P wt,t

[0139] In the formula, C wt,t is the wind power operation and maintenance cost at time t, a wt P is the wind power operation and maintenance cost coefficient; wt,t is the wind power output at time t;

[0140] The energy storage equipment cost model mainly models the operating cost of energy storage and release of energy. The expression is as follows:

[0141]

[0142] The energy storage equipment cost model includes electricity storage, heat storage, and cold storage systems; where C eps,t is the charging and discharging cost of the energy storage device at time t; a eps,ch 、a eps,dch are the cost coefficients for storing and releasing energy of energy storage equipment respectively; are the power of energy storage and energy release of the energy storage device at time t; η eps,ch , η eps,dch They are the efficiency of storing and releasing energy of energy storage devices;

[0143] The flexible load cost model is mainly used to model the flexible load incentive cost. The expression is as follows:

[0144]

[0145] In the formula, C l,t is the incentive cost of flexible load at time t; a l is the load incentive cost coefficient; P l,t , They are the actual power after load regulation and the original expected power at time t respectively;

[0146] The electric vehicle cost model is mainly used to model the charging and discharging costs of electric vehicles. The expression is as follows:

[0147]

[0148] In the formula, C ev,t is the charging and discharging cost of electric vehicles at time t; a ev,ch 、a ev,dch are the cost coefficients for charging and discharging electric vehicles, respectively; are the charging and discharging power of the electric vehicle at time t; η ev,ch , η ev,dch are the charging and discharging efficiency of electric vehicles respectively;

[0149] The cost model of the solar thermal system is mainly used to model the operation and maintenance costs of the solar thermal system. The expression is as follows:

[0150] C tes,t =a tes,p P tes,t +a tes,h H tes,t

[0151] In the formula, Ctes,t is the operation and maintenance cost of the CSP system at time t; a tes,p 、a tes,h are the cost coefficients of electric power and thermal power of the solar thermal system respectively; P tes,t , H tes,t are the electric power and thermal power of the CSP system at time t respectively;

[0152] The gas turbine cost model and gas boiler cost model are mainly used to model fuel cost and carbon emission cost. The expressions are as follows:

[0153] C gtw,t =a gtw P gtw,t

[0154] C gbw,t =a gbw H gbw,t

[0155]

[0156] In the formula, C gtw,t , C gbw,t are the fuel costs of gas turbine and gas boiler at time t respectively; are the carbon emission costs of gas turbine and gas boiler at time t; a gtw 、a gbw are the fuel cost coefficients of gas turbine and gas boiler respectively; P gtw,t , H gbw,t are the power of gas turbine and gas boiler at time t respectively; is the carbon price; are the carbon emission factors of gas turbines and gas boilers respectively;

[0157] The cost model of electric refrigerator is mainly used to model the electric cooling power conversion and operation and maintenance costs. The expression is as follows:

[0158] Q col,t =b col P col,t

[0159] C col,t =a col Q col,t

[0160] In the formula, Q col,t b is the cooling power of the electric refrigerator at time t; col is the electric cooling conversion coefficient; P col,t C is the power consumption of the refrigerator at time t; col,t is the operation and maintenance cost of the electric refrigerator at time t; a col is the operation and maintenance cost coefficient of the electric refrigerator;

[0161] The power grid purchase cost model is mainly used to model the power grid purchase interaction cost and carbon emission cost. The expression is as follows:

[0162] C b,t =r e P b,t

[0163]

[0164] In the formula, C b,t , are the interaction cost of electricity purchased from the power grid at time t and the carbon emission cost of electricity purchased from the power grid at time t; r e is the time-of-use electricity price; P b,t is the power purchased at time t; is the carbon emission factor for purchased electricity.

[0165] S3: Based on the cost model of each energy device, taking the minimum comprehensive operating cost of the zero-carbon community energy system as the objective function and the zero-carbon community energy system constraints as the constraints, an optimal operation model of the zero-carbon community energy system is established;

[0166] ① Considering the economic, stable and environmentally friendly operation requirements of the community energy system, the objective function is to minimize the comprehensive operation cost of the system. The objective function expression is as follows:

[0167] minC=C ec +C st +C en

[0168] Among them, the objective function includes economic objectives C ec 、Stability target C st 、Environmental protection target C en ;

[0169] Economic Objective C ec Including equipment fuel cost, equipment operation and maintenance cost and flexible load incentive, the expression is as follows:

[0170]

[0171] Stability Target C st In order to minimize the power purchased by the community energy system from the main grid at time t and improve the self-sufficiency of the system, the expression is as follows:

[0172]

[0173] Environmental Target C en Including the carbon emission costs of gas turbines, gas boilers and other equipment and the carbon emission costs of power purchases from the power grid, the expression is as follows:

[0174]

[0175] ② Further, taking the community energy system constraints as constraints, an optimal operation model of the community energy system is established;

[0176] Community energy system constraints include: photovoltaic and wind power processing constraints; energy storage equipment power and capacity constraints; flexible load regulation constraints; electric vehicle power and capacity constraints; solar thermal system output constraints; gas boiler, gas turbine, and electric refrigerator output constraints; electricity purchase power constraints; and energy balance power constraints.

[0177] The specific design of photovoltaic and wind power output constraints is as follows:

[0178] 0≤P pv,t ≤P pv,max

[0179] 0≤P wt,t ≤P wt,max

[0180] Where P pv,max , P wt,max are the upper limits of photovoltaic and wind power output respectively; P pv,t is the photovoltaic output power at time t, P wt,t is the wind power output at time t;

[0181] The power and capacity constraints of energy storage equipment are specifically designed as follows:

[0182]

[0183] E eps,min ≤E eps,t ≤E eps,max

[0184] In the formula, are the power of energy storage device storing energy at time t and the power of energy release device releasing energy at time t respectively; They are the maximum storage and charging and maximum released energy powers of the energy storage device respectively; are the charging and discharging states of the energy storage device at time t; η eps,ch , η eps,dch E are the efficiency of energy storage and energy release of energy storage equipment; eps,t is the capacity of energy storage equipment at time t; E eps,max 、E eps,min They are the maximum and minimum capacity states of the energy storage device respectively;

[0185] Flexible load adjustment constraints, the specific design is as follows:

[0186]

[0187] Where Pl,t , They are the actual power after load regulation and the original expected power at time t respectively; They are the maximum and minimum power demands at time t respectively;

[0188] The power and capacity constraints of electric vehicles are specifically designed as follows:

[0189]

[0190]

[0191] E ev,min ≤E ev,t ≤E ev,max

[0192] In the formula, They are respectively the charging and discharging power of the electric vehicle at time t; are the maximum charging and discharging power of electric vehicles respectively; are the charging and discharging states of the electric vehicle at time t; η ev,ch , η ev,dch E are the charging and discharging efficiency of electric vehicles respectively; ev,t E is the battery capacity of the electric vehicle at time t; ev,max 、E ev,min They are the maximum and minimum capacity states of electric vehicle batteries respectively;

[0193] The output constraints of the solar thermal system are specifically designed as follows:

[0194]

[0195] In the formula, H tes,max is the maximum heating power of the photothermal system; η tes is the power conversion efficiency of the photothermal system; P tes,t , H tes,t are the electric power and thermal power of the CSP system at time t respectively;

[0196] The output constraints of gas boilers, gas turbines, and electric refrigerators are specifically designed as follows:

[0197] P gtw,min ≤P gtw,t ≤P gtw,max

[0198] H gbw,min ≤H gbw,t ≤H gbw,max

[0199] Q col,min ≤Q col,t ≤Q col,max

[0200] Where P gtw,t is the gas turbine power at time t, H gbw,t is the power of the gas boiler at time t; Q col,t P is the cooling power of the electric refrigerator at time t; gtw,max , P gtw,min are the upper and lower limits of gas turbine output respectively; H gbw,max , H gbw,min They are the upper and lower limits of gas boiler output respectively; Q col,max , Q col,min They are the upper and lower limits of the output of the electric refrigerator respectively;

[0201] The specific design of the purchased power constraint is as follows:

[0202] 0≤P b,t ≤P b,max

[0203] Where P b,t is the power purchased at time t, P b,max is the maximum power purchase;

[0204] Energy balance power constraint, the specific design is as follows:

[0205]

[0206] In the formula, C col,t H is the operation and maintenance cost of the electric refrigerator at time t; l,t , C l,t They are the system heating load and cooling load demands at time t respectively.

[0207] S4: Solving the zero-carbon community energy system optimization operation model to obtain the dispatching instructions of each energy device, and sending the dispatching instructions to the corresponding energy devices so that each energy device executes the dispatching instructions.

[0208] In the electricity load optimization scheme, the energy equipment dispatching method is adjusted in time according to the load demand changes corresponding to the load characteristics of different types of users, such as Figures 4 to 6 shown.

[0209] In general, wind power and photovoltaic power, as clean energy sources, will support the system load electricity demand at the maximum power possible; the solar thermal system can flexibly adjust its electric power and thermal power output conversion ratio; the gas turbine will continue to operate at a low power generation power when other resources are abundant; the energy storage will be charged during valley and normal periods, and discharged during peak periods to save system operating costs; electric vehicles will be charged during valley periods; and at the same time, the integrated energy system will be self-sufficient as much as possible to reduce the purchase of electricity.

[0210] In addition, by giving certain load regulation incentives, flexible loads can achieve flexible load shifting to reduce the operating cost of the microgrid while meeting the basic power demand of the system at the current moment and ensuring that the total load does not decrease. Figure 7 shown.

[0211] In the heat load optimization scheme, the solar thermal system mainly supports the system heat load demand during the daytime, and the gas boiler is mainly used in the rest of the time; the thermal storage system stores heat power at noon and releases it at night. Figure 8 shown.

[0212] In the cooling load optimization scheme, the cooling power demand of the system is mainly supported by cooling equipment such as electric refrigerators; the cold storage system stores cooling power at night and releases cooling power during the day. Fig. 9 shown.

[0213] Example 2

[0214] Based on Example 1, this example introduces a zero-carbon community energy system optimization operation device taking into account user energy consumption characteristics, including:

[0215] An operation scenario generation module is used to generate an operation scenario of a zero-carbon community energy system, wherein the operation scenario of the zero-carbon community energy system includes establishing an output model of each energy device, obtaining the reserve status and output characteristic information of the zero-carbon community energy device, and generating typical scenarios of each energy load in the community according to the daily load characteristic curves of different types of users in the zero-carbon community; wherein the zero-carbon community energy system includes a variety of energy sources and energy equipment, the multiple energy sources include four types of energy: electricity, heat, cold, and gas, the energy equipment includes distributed power sources, energy storage equipment, energy use equipment, and energy conversion equipment, and the different types of users include industrial users, commercial users, and residential users;

[0216] The cost model and system constraint construction module is used to set the cost coefficient and output upper and lower limit parameters of each energy device according to the output model, reserve status and output characteristic information of each energy device; establish the cost model of each energy device according to the cost coefficient and output upper and lower limit parameters of each energy device; design the zero-carbon community energy system constraints according to the output model of each energy device, the output characteristic information of zero-carbon community energy devices, the daily load characteristic curves of different types of users in the zero-carbon community and the typical scenarios of each energy load in the corresponding community;

[0217] The system model building module is used to establish an optimal operation model of the zero-carbon community energy system based on the cost model of each energy device, with the minimum comprehensive operation cost of the zero-carbon community energy system as the objective function and the zero-carbon community energy system constraints as the constraints;

[0218] The scheduling module is used to solve the zero-carbon community energy system optimization operation model to obtain the scheduling instructions of each energy device, and send the scheduling instructions to the corresponding energy devices so that each energy device executes the scheduling instructions.

[0219] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0220] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0221] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0222] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0223] Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A method for optimizing the operation of a zero-carbon community energy system taking into account the energy consumption characteristics of users, characterized in that: The following steps are involved: Generate a zero-carbon community energy system operation scenario, which includes establishing an output model for each energy device, obtaining the reserve status and output characteristic information of the zero-carbon community energy device, and generating typical scenarios of each energy load in the community according to the daily load characteristic curves of different types of users in the zero-carbon community; wherein, the zero-carbon community energy system includes multiple energy sources and energy equipment, the multiple energy sources include four types of energy: electricity, heat, cold, and gas, the energy equipment includes distributed power sources, energy storage equipment, energy use equipment, and energy conversion equipment, and the different types of users include industrial users, commercial users, and residential users; According to the output model of each energy device, the reserve status of the energy device and the output characteristic information, set the cost coefficient and the upper and lower limit parameters of each energy device; according to the cost coefficient and the upper and lower limit parameters of each energy device, establish the cost model of each energy device; according to the output model of each energy device, the output characteristic information of the zero-carbon community energy equipment, the daily load characteristic curves of different types of users in the zero-carbon community and the typical scenarios of each energy load in the corresponding community, design the zero-carbon community energy system constraints; According to the cost model of each energy device, the objective function is to minimize the comprehensive operating cost of the zero-carbon community energy system, and the constraints of the zero-carbon community energy system are used as constraints to establish an optimal operation model of the zero-carbon community energy system; Solving the zero-carbon community energy system optimization operation model to obtain the dispatching instructions of each energy device, sending the dispatching instructions to the corresponding energy devices, so that each energy device executes the dispatching instructions.

2. According to claim 1, a method for optimizing the operation of a zero-carbon community energy system taking into account the energy consumption characteristics of users is characterized in that: The distributed power sources include photovoltaic and wind power; the energy storage equipment includes power storage system, heat storage system, and cold storage system; the energy-consuming equipment includes electric vehicles and flexible loads; the energy conversion equipment includes solar thermal systems, gas turbines, gas boilers, and electric refrigerators.

3. According to claim 1, a method for optimizing the operation of a zero-carbon community energy system taking into account the energy consumption characteristics of users is characterized in that: The output model of distributed power generation includes: Generate wind power and photovoltaic typical output models based on their historical output levels and corresponding weather data; Photovoltaic output model, the expression is as follows: P pv =r pv ·A pv ·or pv Among them, P pv Represents photovoltaic output, r pv Indicates the light intensity, A pv represents the photovoltaic panel area, η pv Represents photovoltaic photoelectric conversion efficiency; Wind power output model, the expression is as follows: Among them, P wt Represents wind power output, P wr Indicates the rated power of the fan, v w Indicates the actual wind speed, v r Indicates rated wind speed, v ci represents the cut-in wind speed, v co Indicates the cut-out wind speed.

4. According to claim 1, a method for optimizing the operation of a zero-carbon community energy system taking into account the energy consumption characteristics of users is characterized in that: The cost models of each energy equipment include: photovoltaic cost model, wind power cost model, energy storage equipment cost model, flexible load cost model, electric vehicle cost model, solar thermal system cost model, gas turbine cost model, gas boiler cost model, electric refrigerator cost model, and power grid purchase cost model.

5. A method for optimizing the operation of a zero-carbon community energy system taking into account user energy consumption characteristics according to claim 4, characterized in that: The photovoltaic cost model is expressed as follows: C pv,t =a pv P pv,t In the formula, C pv ,t is the photovoltaic operation and maintenance cost at time t; a pv is the photovoltaic operation and maintenance cost coefficient; P pv,t is the photovoltaic output power at time t; The wind power cost model is expressed as follows: C wt,t =a wt P wt,t In the formula, C wt,t is the wind power operation and maintenance cost at time t, a wt P is the wind power operation and maintenance cost coefficient; wt,t is the wind power output at time t; The energy storage equipment cost model is expressed as follows: The energy storage equipment cost model includes electricity storage, heat storage, and cold storage systems; where C eps,t is the charging and discharging cost of the energy storage device at time t; a eps,ch 、a eps,dch are the cost coefficients for storing and releasing energy of energy storage equipment respectively; are the power of energy storage device storing energy at time t and the power of energy release device releasing energy at time t respectively; η eps,ch , η eps,dch They are the efficiency of storing and releasing energy of energy storage devices; The flexible load cost model is expressed as follows: In the formula, C l,t is the incentive cost of flexible load at time t; a l is the load incentive cost coefficient; P l,t , They are the actual power after load regulation and the original expected power at time t respectively; The electric vehicle cost model is expressed as follows: In the formula, C ev,t is the charging and discharging cost of electric vehicles at time t; a ev,ch 、a ev,dch are the cost coefficients for charging and discharging electric vehicles, respectively; are the charging and discharging power of the electric vehicle at time t; η ev,ch , η ev,dch are the charging and discharging efficiency of electric vehicles respectively; The cost model of the solar thermal system is expressed as follows: C tes,t =a tes,p P tes,t +a tes,h H tes,t In the formula, C tes,t is the operation and maintenance cost of the CSP system at time t; a tes,p 、a tes,h are the cost coefficients of electric power and thermal power of the solar thermal system respectively; P tes,t , H tes,t are the electric power and thermal power of the CSP system at time t respectively; The gas turbine cost model and the gas boiler cost model are expressed as follows: C gtw,t =a gtw P gtw,t C gbw,t =a gbw H gbw,t In the formula, C gtw,t , C gbw,t are the fuel costs of gas turbine and gas boiler at time t respectively; are the carbon emission cost of gas turbine at time t and the carbon emission cost of gas boiler at time t respectively; a gtw 、a gbw are the fuel cost coefficients of gas turbine and gas boiler respectively; P gtw,t , H gbw,t They are respectively the gas turbine power at time t and the gas boiler power at time t; is the carbon price; are the carbon emission factors of gas turbines and gas boilers respectively; The cost model of the electric refrigerator is expressed as follows: Q col,t =b col P col,t C col,t =a col Q col,t In the formula, Q col,t b is the cooling power of the electric refrigerator at time t; col is the electric cooling conversion coefficient; P col,t C is the power consumption of the refrigerator at time t; col,t is the operation and maintenance cost of the electric refrigerator at time t; a col is the operation and maintenance cost coefficient of the electric refrigerator; The power grid purchase cost model is expressed as follows: C b,t =r e P b,t In the formula, C b,t , are the interaction cost of electricity purchased from the power grid at time t and the carbon emission cost of electricity purchased from the power grid at time t; r e is the time-of-use electricity price; P b,t is the power purchased at time t; is the carbon emission factor for purchased electricity.

6. The method for optimizing the operation of a zero-carbon community energy system taking into account the energy consumption characteristics of users according to claim 1, characterized in that: The objective function expression is as follows: minC=C ec +C st +C en The objective function includes economic objectives C ec 、Stability target C st 、Environmental protection target C en ; Economic Objective C ec , the expression is as follows: Among them, C pv,t is the photovoltaic operation and maintenance cost at time t, C wt,t is the wind power operation and maintenance cost at time t, C eps,t is the charging and discharging cost of the energy storage device at time t, C ev,t is the charging and discharging cost of electric vehicles at time t, C l,t is the incentive cost of flexible load at time t, C tes,t is the operation and maintenance cost of the CSP system at time t, C gtw,t , C gbw,t are the fuel cost of gas turbine at time t and the fuel cost of gas boiler at time t, respectively. col,t is the operation and maintenance cost of the electric refrigerator at time t; Stability Target C st , the expression is as follows: Among them, C b,t represents the interactive cost of electricity purchase from the power grid at time t; Environmental Target C en , the expression is as follows: in, They respectively represent the carbon emission cost of the gas turbine at time t, the carbon emission cost of the gas boiler at time t, and the carbon emission cost of purchasing electricity from the power grid at time t.

7. The method for optimizing the operation of a zero-carbon community energy system taking into account the energy consumption characteristics of users according to claim 1, characterized in that: The zero-carbon community energy system constraints include: photovoltaic and wind power processing constraints; energy storage equipment power and capacity constraints; flexible load regulation constraints; electric vehicle power and capacity constraints; solar thermal system output constraints; gas boiler, gas turbine, and electric refrigerator output constraints; electricity purchase power constraints; and energy balance power constraints.

8. A method for optimizing the operation of a zero-carbon community energy system taking into account user energy consumption characteristics according to claim 7, characterized in that: The photovoltaic and wind power output constraints are specifically designed as follows: 0≤P pv,t ≤P pv,max 0≤P wt,t ≤P wt,max Where P pv,max , P wt,max are the upper limit of photovoltaic power output at time t and the upper limit of wind power output at time t respectively; P pv,t is the photovoltaic output power at time t, P wt,t is the wind power output at time t; The power and capacity constraints of the energy storage equipment are specifically designed as follows: AND eps,min ≤E eps,t ≤E eps,max In the formula, are the power of energy storage device storing energy at time t and the power of energy release device releasing energy at time t respectively; They are the maximum charging power of the energy storage device and the maximum releasing power of the energy storage device respectively; are the charging state of the energy storage device at time t and the discharging state of the energy storage device at time t respectively; η eps,ch , η eps,dch E are the efficiency of energy storage and energy release of energy storage equipment; eps,t is the capacity of energy storage equipment at time t; E eps,max 、E eps,min They are respectively the maximum and minimum capacity states of the energy storage device; The flexible load adjustment constraint is specifically designed as follows: Where P l,t , They are the actual power after load regulation and the original expected power at time t respectively; They are the maximum and minimum power demands at time t respectively; The electric vehicle power and capacity constraints are specifically designed as follows: AND ev,min ≤E ev,t ≤E ev,max In the formula, They are respectively the charging power of the electric vehicle at time t and the discharging power of the electric vehicle at time t; They are the maximum charging power of electric vehicles and the maximum discharging power of electric vehicles respectively; are the charging and discharging states of the electric vehicle at time t; η ev,ch , η ev,dch E are the charging and discharging efficiency of electric vehicles respectively; ev,t E is the battery capacity of the electric vehicle at time t; ev,max 、E ev,min They are the maximum and minimum capacity states of electric vehicle batteries respectively; The output constraint of the solar thermal system is specifically designed as follows: In the formula, H tes,max is the maximum heating power of the photothermal system; η tes is the power conversion efficiency of the photothermal system; P tes,t , H tes,t are the electric power and thermal power of the CSP system at time t respectively; The output constraints of the gas boiler, gas turbine and electric refrigerator are specifically designed as follows: P gtw,min ≤P gtw,t ≤P gtw,max H gbw,min ≤H gbw,t ≤H gbw,max Q col,min ≤Q col,t ≤Q col,max Where P gtw,t is the gas turbine power at time t, H gbw,t is the power of the gas boiler at time t; Q col,t P is the cooling power of the electric refrigerator at time t gtw,max , P gtw,min They are the upper and lower limits of gas turbine output respectively; H gbw,max , H gbw,min They are the upper and lower limits of gas boiler output respectively; Q col,max , Q col,min They are the upper and lower limits of the output of the electric refrigerator respectively; The power purchase power constraint is specifically designed as follows: 0≤P b,t ≤P b,max Where P b,t is the power purchased at time t, P b,max is the maximum power purchase; The energy balance power constraint is specifically designed as follows: In the formula, C col,t H is the operation and maintenance cost of the electric refrigerator at time t; l,t , C l,t They are the system heating load and cooling load demands at time t respectively.

9. A zero-carbon community energy system optimization operation device taking into account user energy consumption characteristics, characterized in that: include: An operation scenario generation module is used to generate an operation scenario of a zero-carbon community energy system, wherein the operation scenario of the zero-carbon community energy system includes establishing an output model of each energy device, obtaining the reserve status and output characteristic information of the zero-carbon community energy device, and generating typical scenarios of each energy load in the community according to the daily load characteristic curves of different types of users in the zero-carbon community; wherein the zero-carbon community energy system includes a variety of energy sources and energy equipment, the multiple energy sources include four types of energy: electricity, heat, cold, and gas, the energy equipment includes distributed power sources, energy storage equipment, energy use equipment, and energy conversion equipment, and the different types of users include industrial users, commercial users, and residential users; The cost model and system constraint construction module is used to set the cost coefficient and output upper and lower limit parameters of each energy device according to the output model, reserve status and output characteristic information of each energy device; establish the cost model of each energy device according to the cost coefficient and output upper and lower limit parameters of each energy device; design the zero-carbon community energy system constraints according to the output model of each energy device, the output characteristic information of zero-carbon community energy devices, the daily load characteristic curves of different types of users in the zero-carbon community and the typical scenarios of each energy load in the corresponding community; The system model building module is used to establish an optimal operation model of the zero-carbon community energy system based on the cost model of each energy device, with the minimum comprehensive operation cost of the zero-carbon community energy system as the objective function and the zero-carbon community energy system constraints as the constraints; The scheduling module is used to solve the zero-carbon community energy system optimization operation model to obtain the scheduling instructions of each energy device, and send the scheduling instructions to the corresponding energy devices so that each energy device executes the scheduling instructions.

10. A computer-readable storage medium for optimizing the operation of a zero-carbon community energy system taking into account user energy consumption characteristics, characterized in that: include: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for optimizing operation of a zero-carbon community energy system taking into account user energy consumption characteristics as described in any one of claims 1 to 8.

Images