Multi-energy system energy balance optimization method, device, equipment, medium and product

Abstract

The invention discloses a multi-energy system energy balance optimization method and device, equipment, a medium and a product, and relates to the technical field of energy scheduling, and the method comprises the steps: building a dynamic constraint single-target optimization model of a multi-energy system, constraint conditions of the dynamic constraint single-target optimization model comprise operation constraints and power balance constraints of devices in the energy supply and storage unit model of the multi-energy system; the dynamic constraint single-target optimization model comprises uncertainty parameters of wind power and photovoltaic output; the constraint violation degree is adopted to describe each constraint condition; and taking the constraint violation degree as a robust performance evaluation index of the dynamic constraint single-target optimization model, and performing robust performance evolution solving on the dynamic constraint single-target optimization model by adopting a differential evolution algorithm to obtain a combined state of the energy supply and energy storage unit model in a corresponding time period of the dynamic constraint single-target optimization model. According to the invention, the robustness of energy balance optimization of the multi-energy system can be improved.

Description

Technical Field

[0001] The present application relates to the field of energy scheduling technology, and in particular to a method, device, equipment, medium and product for optimizing energy balance in a multi-energy system. Background Art

[0002] Multi-energy systems incorporate heterogeneous energy forms such as electricity, heat, and natural gas. Compared to traditional, independently operated energy systems, multi-energy systems offer the ability to collaboratively optimize all energy supply and storage units, including various energy production, conversion, and storage units. Multi-energy conversion units, such as electricity-to-gas, electric boilers, and gas turbines, enable efficient conversion between multiple energy sources, while multi-energy storage units, such as electricity, heat, and gas storage, enable energy storage. However, the high uncertainty of wind and photovoltaic outputs leads to time-varying parameters in the objective functions and constraints of multi-energy system energy balance optimization problems. These time-varying objective functions and constraints result in poor robustness of the resulting operational solutions and insufficient adaptability to time-varying environments, posing significant challenges to energy balance in multi-energy systems. Currently, integer programming methods and intelligent algorithms are the primary approaches used to solve energy balance optimization models for multi-energy systems. However, robustness of the operational optimization solutions is difficult to guarantee in the actual operation of multi-energy systems. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, equipment, medium and product for energy balance optimization of a multi-energy system, which can improve the robustness of energy balance optimization of a multi-energy system.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for optimizing energy balance in a multi-energy system, comprising:

[0006] Establishing a dynamic constrained single-objective optimization model for the multi-energy system, wherein the constraints of the dynamic constrained single-objective optimization model include the operation constraints and power balance constraints of each device in the energy supply and storage unit model of the multi-energy system; the dynamic constrained single-objective optimization model includes uncertainty parameters of wind power and photovoltaic output;

[0007] The constraint violation degree is used to describe each of the constraint conditions;

[0008] The degree of constraint violation is used as the robust performance evaluation index of the dynamic constrained single-objective optimization model. The differential evolution algorithm is used to perform robust performance evolutionary solution on the dynamic constrained single-objective optimization model to obtain the combined state of the energy supply and energy storage unit models in the corresponding time period of the dynamic constrained single-objective optimization model.

[0009] Optionally, the energy supply and storage unit model includes a thermal power unit, a cogeneration unit, a natural gas source, an electric boiler, a power-to-gas unit, a gas unit, an electricity storage system, a heat storage system and a gas storage system.

[0010] Optionally, the dynamic constrained single-objective optimization model is expressed as:

[0011] minC MES (t)=minJ(x,υ(t)),x∈R D

[0012]

[0013] Among them, C MES (t) is the overall goal of energy balance optimization in period t, J(x,υ(t)) is the overall goal of energy balance in period t, x represents the D-dimensional decision vector, the D-dimensional decision vector is the combined state of the energy supply and energy storage unit model, υ(t) is the uncertainty parameter of wind power and photovoltaic output, st is the constraint condition, g i (x,υ(t)) is the i-th constraint, g i (x,υ(t)) is the operation constraint, h i (x,υ(t)) is the i-th constraint, h i (x,υ(t)) is a power balance constraint, the operation constraint is an inequality constraint, the power balance constraint is an equality constraint, L is the number of inequality constraints, and RL is the number of equality constraints.

[0014] Optionally, the constraint violation degree is used to describe each constraint condition, which is expressed as:

[0015]

[0016] Among them, Z i (x,υ(t)) is the constraint violation degree of the multi-energy energy balance composed of the i-th constraint condition in time period t.

[0017] Optionally, the combined state of the energy supply and energy storage unit models is the combined state of the target parameters in the multi-energy system, and the target parameters include wind power output, photovoltaic output, coal feed rate of thermal power units, coal feed rate of thermal power units, natural gas source output, electric boiler input power, power-to-gas input power, gas unit air intake, charging power of the power storage system, discharging power of the power storage system, heat storage power of the heat storage system, heat release power of the heat storage system, air intake of the gas storage system, and air output of the gas storage system.

[0018] Optionally, the degree of constraint violation is used as a robust performance evaluation index of the dynamic constrained single-objective optimization model, and a differential evolution algorithm is used to perform robust performance evolutionary solution on the dynamic constrained single-objective optimization model to obtain the combined state of the energy supply and energy storage unit models in the corresponding time period of the dynamic constrained single-objective optimization model, specifically including:

[0019] In the population selection stage of the differential evolution algorithm, the combined state population of the multi-energy supply and energy storage unit model with a preliminary scale of NU in the current period and the combined state population of the multi-energy supply and energy storage unit with a parent scale of NP are merged to obtain a merged combined state population;

[0020] Calculating the total robust constraint violation degree of each individual in the combined state population after merging at the initial moment of the current period, wherein the total robust constraint violation degree is determined according to the constraint violation degree of each constraint condition;

[0021] If the number of individuals that meet the set range of total robust constraint violation is less than NP, then the total robust constraint violation degrees are sorted in ascending order, and the first NP individuals after sorting are selected to form the offspring population;

[0022] If the number of individuals that meet the set range of total robust constraint violation degree is greater than or equal to NP, then the total robust constraint violation degree is sorted in descending order, and the first NP individuals after sorting are selected to form the offspring population;

[0023] After obtaining a progeny population consisting of NP individuals, if there are individuals in the progeny population with a combination state of multiple energy supply and energy storage units with the same survival time, then for multiple individuals in the combination state of multiple energy supply and energy storage units with the same survival time, The total robust constraint violation degree of individuals at the time is sorted in ascending order, and the first NP individuals after sorting are selected to form the offspring population; t0 is the initial time of the current period, It is the maximum survival time of individuals in a population with a combination of multiple energy supply and storage units.

[0024] In a second aspect, the present application provides a multi-energy system energy balance optimization device, wherein the multi-energy system energy balance optimization device applies any of the multi-energy system energy balance optimization methods described above, and the multi-energy system energy balance optimization device comprises:

[0025] A dynamic constrained single-objective optimization model establishment module is used to establish a dynamic constrained single-objective optimization model for the multi-energy system, wherein the constraints of the dynamic constrained single-objective optimization model include the operation constraints and power balance constraints of each device in the energy supply and storage unit model of the multi-energy system; the dynamic constrained single-objective optimization model includes uncertainty parameters of wind power and photovoltaic output;

[0026] A constraint description module, configured to describe each constraint using a constraint violation degree;

[0027] A solution module is used to use the degree of constraint violation as the robust performance evaluation index of the dynamic constraint single-objective optimization model, and adopt a differential evolution algorithm to perform robust performance evolutionary solution on the dynamic constraint single-objective optimization model to obtain the combined state of the energy supply and energy storage unit model in the corresponding time period of the dynamic constraint single-objective optimization model. In a second aspect, the present application provides an energy balance optimization device for a multi-energy system, comprising:

[0028] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the multi-energy system energy balance optimization methods described above.

[0029] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the multi-energy system energy balance optimization methods described above.

[0030] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any one of the multi-energy system energy balance optimization methods described above.

[0031] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0032] The present application provides a method, device, equipment, medium and product for energy balance optimization of a multi-energy system, which uses equipment operation constraints and power balance constraints as constraints, and uses the degree of constraint violation to describe each of the constraints. The degree of constraint violation is used as the robust performance evaluation index of the dynamic constraint single-objective optimization model, and a differential evolution algorithm is used to perform robust performance evolutionary solution on the dynamic constraint single-objective optimization model. Since the dynamic constraint single-objective optimization model takes into account the uncertainty parameters of wind power and photovoltaic output, and in the process of solving the dynamic constraint single-objective optimization model corresponding to each time period, the degree of constraint violation is used as the evaluation index of the solution, so that each constraint condition meets the requirements of each time period, thereby improving the robustness of the energy balance optimization of the multi-energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 A flowchart of a multi-energy system energy balance optimization method provided in one embodiment of the present application;

[0035] Figure 2 A schematic diagram of the principle of a multi-energy system energy balance optimization method provided in one embodiment of the present application;

[0036] Figure 3 A schematic diagram of the load demand curves for electricity, heat, and gas energy corresponding to a typical day provided in an embodiment of the present application;

[0037] Figure 4 A schematic diagram of a typical day wind power and photovoltaic output forecast change curve provided in an embodiment of the present application;

[0038] Figure 5 A schematic diagram of the functional modules of a multi-energy system energy balance optimization device provided in one embodiment of the present application;

[0039] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0041] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0042] In an exemplary embodiment, the present application provides a multi-energy system energy balance optimization method, such as Figure 1 As shown, the multi-energy system energy balance optimization method includes steps 101 to 103.

[0043] Step 101: Establish a dynamic constrained single-objective optimization model for the multi-energy system, wherein the constraints of the dynamic constrained single-objective optimization model include the operation constraints and power balance constraints of each device in the energy supply and storage unit model of the multi-energy system; the dynamic constrained single-objective optimization model includes uncertainty parameters of wind power and photovoltaic output.

[0044] Step 102: Use the constraint violation degree to describe each constraint condition.

[0045] Step 103: Taking the constraint violation degree as the robust performance evaluation index of the dynamic constrained single-objective optimization model, a differential evolution algorithm is used to perform robust performance evolutionary solution on the dynamic constrained single-objective optimization model to obtain the combined state of the energy supply and energy storage unit models in the corresponding time period of the dynamic constrained single-objective optimization model.

[0046] In an exemplary embodiment, Figure 2 As shown, before step 101, the multi-energy system energy balance optimization method further includes steps 1 to 4.

[0047] Step 1: Collect target parameters required for energy balance optimization of the multi-energy system, wherein each target parameter determines the combined state of the energy supply and energy storage unit models.

[0048] The target parameters include wind power output P WP (t), photovoltaic output P PV (t), coal supply of thermal power units Coal feed rate for thermal power units Natural gas source output Electric boiler input power Power-to-gas input power Gas unit air intake Charging power of the energy storage system and discharge power Thermal storage power of the thermal storage system and heat release power Air intake of the gas storage system and gas output t represents the time period, i.e. the scheduling period.

[0049] The load demand curves for electricity, heat and gas on a typical day are as follows: Figure 3 The typical day wind power and photovoltaic output forecast value change curve is shown in Figure 4 shown.

[0050] Step 2: Establish a multi-energy system energy supply and storage unit model.

[0051] The energy supply and storage unit model includes thermal power units, cogeneration units, natural gas sources, electric boilers, power-to-gas, gas units, power storage systems, heat storage systems, and gas storage systems. The mathematical models of each unit are as follows.

[0052] The mathematical model of thermal power unit is:

[0053]

[0054] Where: The output power of the thermal power unit during period t; is the efficiency of the thermal power unit, which is determined by the efficiency of the pulverizing unit Boiler unit efficiency Turbine efficiency Generator efficiency and auxiliary engine efficiency It consists of five parts; is the coal supply of the thermal power unit during period t.

[0055] The mathematical model of the thermal power unit is:

[0056]

[0057] Where, and They are the output electrical power and output thermal power of the thermal power unit respectively; and are the power generation efficiency and heat conversion efficiency of the thermal power unit, By milling unit efficiency Boiler unit efficiency Turbine efficiency Generator efficiency and auxiliary engine efficiency It consists of five parts. Also affected by the characteristic coefficient of the thermal power unit operating range the impact of; is the coal supply to the thermal power unit during period t.

[0058] The mathematical model of electric boiler is:

[0059]

[0060] Where, is the thermal power output of the electric boiler during period t; is the electric power of the electric boiler during period t; It is the electric-to-heat conversion efficiency of the electric boiler related to the equipment parameters.

[0061] The mathematical model of power-to-gas is:

[0062]

[0063] Where, is the amount of natural gas output from power-to-gas conversion during period t; LHV g is the lower calorific value of natural gas; η WE ,η MV are the water electrolysis conversion efficiency and the methanation reactor conversion efficiency respectively; is the electric power input of the power-to-gas converter during period t.

[0064] The mathematical model of the gas turbine unit is:

[0065]

[0066] Where, is the output of the gas generator set during period t; HHV g is the high calorific value of natural gas; η GT is the conversion efficiency of the gas unit; is the gas intake volume of the gas generator set during period t.

[0067] The mathematical model of the power storage system is:

[0068]

[0069] Where, is the electric energy stored in the storage system during the dispatch period t; are the energy consumed and injected by the storage system during the dispatching period t; κ EST is the loss coefficient of the energy storage system; are the charging and discharging power of the energy storage system respectively; They are the energy storage efficiency and energy release efficiency of the energy storage system respectively.

[0070] The mathematical model of the heat storage system is:

[0071]

[0072] Where, and are the thermal energy contained in the heat storage system during the scheduling periods t and t-1 respectively; are the heat energy consumed and injected by the heat storage system during the scheduling period t; κ HST is the loss coefficient of the heat storage system; are the heat storage and heat release powers of the heat storage system during the scheduling period t respectively; are the thermal energy storage efficiency and thermal energy release efficiency of the thermal storage system respectively.

[0073] The mathematical model of the gas storage system is:

[0074]

[0075] Where, and The natural gas energy stored in the gas storage system during the scheduling periods t and t-1; are the gas energy consumed and injected by the gas storage system during the scheduling period t; κ GST is the loss coefficient of the gas storage system; are the gas intake and gas output of the gas storage system during the scheduling period t respectively.

[0076] Step 3: Establish an energy balance optimization model for the multi-energy system.

[0077] The energy balance of a multi-energy system refers to adjusting the combination state of the multi-energy supply unit and the energy storage unit within a certain time scale so that the output of the multi-energy supply and storage unit can meet the energy demand of electricity, heat and natural gas of the multi-energy load, and can adapt to the fluctuation of wind power and photovoltaic output.

[0078] The energy balance optimization objective function of the multi-energy system, that is, the energy balance optimization model is expressed as:

[0079]

[0080] C MES (t) = C RE (t);

[0081]

[0082] Among them, C MES The overall goal of optimizing the energy balance of a multi-energy system is C MES (t) is the overall goal of energy balance optimization in period t, Δt is the time step, N T is the total scheduling period. If the scheduling period is 24 hours and the step length is 1 hour, then N T =24; if the scheduling period is 24 hours and the step length is 15 minutes, then N T =96;C RE (t) is the penalty function for curtailment of renewable energy during period t; c WP and c PV are the penalty function coefficients for wind power and photovoltaic power respectively; is the day-ahead wind power forecast for period t; is the photovoltaic power forecasted during period t, P WP (t) is the wind power output during period t, P PV (t) is the photovoltaic output during period t. and As a known quantity, the machine learning algorithm can be trained through the training set to obtain the corresponding prediction model. or predictions.

[0083] The equipment in step 101 includes multi-energy system equipment and multi-energy storage equipment. The operation constraints in step 101 include multi-energy system equipment operation constraints and multi-energy storage equipment operation constraints.

[0084] The operating constraints of multi-energy system equipment refer to the fact that the operation of multi-energy supply and storage units must meet the upper and lower limits of their own energy input and output, as well as the ramp limit. These constraints include thermal power units, cogeneration units, gas units, electric boilers, power-to-gas, electricity storage systems, heat storage systems, and gas storage systems. The expression is:

[0085]

[0086] Where, are the upper and lower limits of thermal power unit output respectively; R CGF+ 、R CGF- They are the upper and lower limits of the ramp rate of thermal power units respectively; They are the upper and lower limits of the thermal power output of the thermal power unit respectively; They are the upper and lower limits of the output power of the thermal power unit, and are functions of the thermal power; R TG+ 、R TG- are the upper and lower limits of the thermal power unit ramp rate respectively; λ TG is the thermoelectric ratio; is the upper limit of gas unit output; R GT+ 、R GT- They are the upper and lower limits of the ramp rate of the gas generator set respectively; The upper limit of the thermal power output of the electric boiler; The upper limit of the power-to-gas output power; They are the upper and lower limits of the natural gas flow rate output by the gas source.

[0087] The operational constraints of multi-energy storage devices refer to the upper and lower capacity limits and the upper and lower charge and discharge rate limits that maintain the normal operation of the multi-energy storage devices. The charge and discharge rates of the electric storage system and the heat storage system can be described by the charge and discharge power and the charge and discharge heat power, respectively. The charge and discharge rates of the gas storage system can be described by the natural gas intake and storage volume. Considering the actual state of the capacity and charge and discharge rate of the multi-energy storage device, the upper and lower capacity constraints and the upper and lower charge and discharge rate constraints of the multi-energy storage device are expressed as follows:

[0088]

[0089] Where, are the maximum remaining capacities of the electricity storage system, heat storage system, and gas storage system respectively; are the upper and lower limit coefficients of the remaining capacity of the power storage system respectively; are the upper and lower limit coefficients of the remaining capacity of the heat storage system respectively; are the upper and lower limit coefficients of the remaining capacity of the gas storage system respectively; They are electricity storage power, heat storage power, and gas storage capacity; are the upper and lower limit coefficients of the energy storage system charging power respectively; are the upper and lower limit coefficients of the charging power of the thermal storage system respectively; are the upper and lower limit coefficients of the air intake of the gas storage system respectively; They are respectively the electricity storage release power, heat storage release power and gas storage release capacity; are the upper and lower limit coefficients of the energy storage system’s discharge power respectively; are the upper and lower limit coefficients of the energy release power of the heat storage system respectively; are the upper and lower limit coefficients of the natural gas release amount of the gas storage system.

[0090] The power balance of a multi-energy system refers to the balance of electric power, thermal power, and natural gas supply and demand in the system. The power balance constraints of a multi-energy system are:

[0091]

[0092] Where, are electricity, heat and gas loads respectively; τ H is the equivalent delay coefficient from heat source to heat load.

[0093] Step 4: Convert the multi-energy system energy balance optimization model into a dynamic constrained single-objective optimization model.

[0094] The dynamic constrained single-objective optimization model is expressed as:

[0095] minC MES (t)=minJ(x,υ(t)),x∈R D

[0096]

[0097] Among them, C MES (t) is the overall goal of energy balance optimization in period t, J(x,υ(t)) is the overall goal of energy balance in period t, x represents the D-dimensional decision vector, the D-dimensional decision vector is the combined state of the energy supply and energy storage unit model, υ(t) is the uncertainty parameter of wind power and photovoltaic output, and the upper and lower limits of wind power and photovoltaic output uncertainty can be given by the prediction error interval modeling method, st is the constraint condition, g i (x,υ(t)) is the i-th constraint, g i (x,υ(t)) is the operation constraint, h i (x,υ(t)) is the i-th constraint, h i(x,υ(t)) is a power balance constraint, the operation constraint is an inequality constraint, the power balance constraint is an equality constraint, L is the number of inequality constraints, and RL is the number of equality constraints.

[0098] Inequality constraints include operational constraints for thermal power units, cogeneration units, gas-fired units, electric boilers, power-to-gas converters, electricity storage systems, heat storage systems, and gas storage systems. Equality constraints include multi-energy power balance constraints.

[0099] In an exemplary embodiment, in order to ensure that the combined states of the multi-energy supply and energy storage units in the energy balance optimization of the multi-energy system can all meet the constraints, for the i-th constraint in time period t, the constraint violation degree is used to describe each constraint, which is expressed as:

[0100]

[0101] Among them, Z i (x,υ(t)) is the constraint violation degree of the multi-energy energy balance composed of the i-th constraint condition in time period t. Based on this parameter, a robust performance evaluation mechanism for the solution of the multi-energy system energy balance optimization problem in the time domain is established. ε C is the satisfaction threshold of energy balance of multi-energy system.

[0102] In an exemplary embodiment, the degree of constraint violation is used as the robust performance evaluation index of the dynamic constrained single-objective optimization model, and a differential evolution algorithm is used to perform robust performance evolutionary solution on the dynamic constrained single-objective optimization model to obtain the combined state of the energy supply and energy storage unit model in the corresponding time period of the dynamic constrained single-objective optimization model. Specifically, the steps include: solving the energy balance optimization problem of the multi-energy system based on the robust performance evaluation mechanism of the solution of the multi-energy system energy balance optimization problem in the time domain. The specific implementation steps of the differential evolution algorithm can be summarized as: initializing the combined state population of the multi-energy supply and energy storage unit, mutating random individuals in the population, cross-updating the offspring, and generating the next generation population by comparing the fitness values of the parent and offspring individuals, merging the parent population and the offspring population to select the next generation population, and repeating the above process until the preset number of iterations is reached or the stopping criterion of the dynamic parameter environment is met, that is, the stopping standard is reached.

[0103] In the combined state of the offspring multi-energy supply and energy storage units, that is, in the population selection stage of the differential evolution algorithm:

[0104] (1) The combined state population X of the multi-energy supply and storage unit model with the initial scale of NU in the current period (period t) is G+1 (t) and the combined state population P of multiple energy supply and storage units with parent size NP G+1(t) Merge to obtain the combined state population after the merger.

[0105] (2) In order to improve the survival time of individuals in the combination state of multiple energy supply and energy storage units during the evolution process, ensure the robustness of individuals in the combination state of multiple energy supply and energy storage units, and improve the convergence speed of the combination state population of multiple energy supply and energy storage units, an individual selection strategy based on the total robust constraint violation degree is proposed. Specifically, the total robust constraint violation degree of each individual in the combined state population after the current period is calculated at the initial moment, and the total robust constraint violation degree is determined according to the constraint violation degree of each constraint condition. The individual selection strategy calculates the survival time of individuals in the combined population The survival time is used as the robust performance evaluation index of the optimization model, and the total robust constraint violation degree at time t0 is calculated as Z tot ={Z tot,1 ,Z tot,2 ,...,Z tot,i ,...,Z tot,NU+NP}.

[0106] Among them, L TIME is the set of individual survival times, is the survival time of the jth individual, Z tot is the set of total robust constraint violation degrees, Z tot,i is the total robust constraint violation degree of the i-th individual, and i ranges from 1 to NU+NP.

[0107] If Z tot If the number of individuals that meet the set range of total robust constraint violation is less than NP, the total robust constraint violation degrees are sorted in ascending order, and the first NP individuals after sorting are selected to form the offspring population.

[0108] If Z tot If the number of individuals that meet the set range of total robust constraint violation degree is greater than or equal to NP, then the total robust constraint violation degree is sorted in descending order, the first NP individuals after sorting are selected, and then arranged in ascending order to form the offspring population.

[0109] After obtaining the offspring population consisting of NP individuals, for multiple individuals in the combination state of multiple energy supply and energy storage units with the same survival time, The total robust constraint violation degree of the individuals at the time is arranged in ascending order, and the combination state individuals of the multi-energy supply and energy storage units with the same survival time are reselected according to this sequence, that is, the first NP individuals after sorting are selected to form the offspring population; t0 is the initial time of the current period, It is the maximum survival time of individuals in a population with a combination of multiple energy supply and storage units.

[0110] (3) Finally, the combined state population of the offspring multi-energy supply and energy storage units is obtained, which can simultaneously ensure the convergence of the evolution of the combined state population of the multi-energy supply and energy storage units and the robustness of the individuals in the combined state population of the multi-energy supply and energy storage units.

[0111] Through calculation, the impact of different renewable energy output uncertainties on the energy balance during the operation of the multi-energy system on a typical day is obtained. The uncertainty of renewable energy output refers to the prediction error of renewable energy output. The energy balance optimization results of the multi-energy system considering different renewable energy output uncertainties are shown in Table 1.

[0112] Table 1 Energy balance optimization results of multi-energy system considering the uncertainty of different renewable energy output

[0113]

[0114]

[0115] The results show that the energy balance optimization method of the multi-energy system in this application can improve the robust performance of the individual combination states of multi-energy supply and storage units in different scheduling periods, and effectively promote the consumption of new energy.

[0116] This application establishes a multi-energy system energy balance optimization model with the renewable energy curtailment penalty function in the multi-energy system as the target, and with equipment operation constraints and energy balance constraints as constraints. It proposes a robust performance evaluation mechanism for the operation optimization solution, and uses a robust evolutionary solution algorithm to solve the optimization model to improve the robustness of the optimization combination solution.

[0117] Based on the same inventive concept, the present application also provides a multi-energy system energy balance optimization device for implementing the multi-energy system energy balance optimization method mentioned above. The implementation solution provided by this device is similar to the implementation solution described in the above method. Therefore, the specific limitations of one or more multi-energy system energy balance optimization device embodiments provided below can be referred to the limitations of the multi-energy system energy balance optimization method above, and will not be repeated here.

[0118] In an exemplary embodiment, Figure 5 As shown, a multi-energy system energy balance optimization device is provided, comprising:

[0119] A dynamic constrained single-objective optimization model establishment module is used to establish a dynamic constrained single-objective optimization model of the multi-energy system. The constraints of the dynamic constrained single-objective optimization model include the operating constraints and power balance constraints of each device in the energy supply and storage unit model of the multi-energy system; the dynamic constrained single-objective optimization model includes the uncertainty parameters of wind power and photovoltaic output.

[0120] The constraint condition description module is used to describe each constraint condition by using the constraint violation degree.

[0121] A solution module is used to use the degree of constraint violation as the robust performance evaluation index of the dynamic constraint single-objective optimization model, adopt a differential evolution algorithm to perform robust performance evolutionary solution on the dynamic constraint single-objective optimization model, and obtain the combined state of the energy supply and energy storage unit model in the corresponding time period of the dynamic constraint single-objective optimization model.

[0122] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store energy balance optimization data of a multi-energy system. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing energy balance of a multi-energy system is implemented.

[0123] Those skilled in the art will understand that Figure 6 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0124] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0125] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0127] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0128] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, data processing logic of programmable logic devices, and the like.

[0129] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0130] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A multi-energy system energy balance optimization method, characterized in that: The multi-energy system energy balance optimization method comprises: Establishing a dynamic constrained single-objective optimization model for the multi-energy system, wherein the constraints of the dynamic constrained single-objective optimization model include the operation constraints and power balance constraints of each device in the energy supply and storage unit model of the multi-energy system; the dynamic constrained single-objective optimization model includes uncertainty parameters of wind power and photovoltaic output; The constraint violation degree is used to describe each of the constraint conditions; The degree of constraint violation is used as the robust performance evaluation index of the dynamic constrained single-objective optimization model. The differential evolution algorithm is used to perform robust performance evolutionary solution on the dynamic constrained single-objective optimization model to obtain the combined state of the energy supply and energy storage unit models in the corresponding time period of the dynamic constrained single-objective optimization model.

2. The multi-energy system energy balance optimization method according to claim 1, characterized in that: The energy supply and storage unit model includes a thermal power unit, a cogeneration unit, a natural gas source, an electric boiler, a power-to-gas unit, a gas unit, an electricity storage system, a heat storage system and a gas storage system.

3. The multi-energy system energy balance optimization method according to claim 1, characterized in that: The dynamic constrained single-objective optimization model is expressed as: Among them, C MES (t) is the overall goal of energy balance optimization in period t, J(x,υ(t)) is the overall goal of energy balance in period t, x represents the D-dimensional decision vector, the D-dimensional decision vector is the combined state of the energy supply and energy storage unit model, υ(t) is the uncertainty parameter of wind power and photovoltaic output, st is the constraint condition, g i (x,υ(t)) is the i-th constraint, g i (x,υ(t)) is the operation constraint, h i (x,υ(t)) is the i-th constraint, h i (x,υ(t)) is a power balance constraint, the operation constraint is an inequality constraint, the power balance constraint is an equality constraint, L is the number of inequality constraints, and RL is the number of equality constraints.

4. The multi-energy system energy balance optimization method according to claim 3, characterized in that: The constraint violation degree is used to describe each constraint condition, which is expressed as: Among them, Z i (x,υ(t)) is the constraint violation degree of the multi-energy energy balance composed of the i-th constraint condition in time period t.

5. The multi-energy system energy balance optimization method according to claim 1, characterized in that: The combined state of the energy supply and energy storage unit models is the combined state of the target parameters in the multi-energy system, and the target parameters include wind power output, photovoltaic output, coal feed rate of thermal power units, coal feed rate of thermal power units, natural gas source output, electric boiler input power, power-to-gas input power, gas unit air intake, charging power of the electricity storage system, discharging power of the electricity storage system, heat storage power of the heat storage system, heat release power of the heat storage system, air intake of the gas storage system, and air output of the gas storage system.

6. The multi-energy system energy balance optimization method according to claim 1, characterized in that: Taking the degree of constraint violation as the robust performance evaluation index of the dynamic constrained single-objective optimization model, the differential evolution algorithm is used to perform robust performance evolutionary solution on the dynamic constrained single-objective optimization model to obtain the combined state of the energy supply and energy storage unit models in the corresponding period of the dynamic constrained single-objective optimization model, specifically including: In the population selection stage of the differential evolution algorithm, the combined state population of the multi-energy supply and energy storage unit model with a preliminary scale of NU in the current period and the combined state population of the multi-energy supply and energy storage unit with a parent scale of NP are merged to obtain a merged combined state population; Calculating the total robust constraint violation degree of each individual in the combined state population after merging at the initial moment of the current period, wherein the total robust constraint violation degree is determined according to the constraint violation degree of each constraint condition; If the number of individuals that meet the set range of total robust constraint violation is less than NP, then the total robust constraint violation degrees are sorted in ascending order, and the first NP individuals after sorting are selected to form the offspring population; If the number of individuals that meet the set range of total robust constraint violation degree is greater than or equal to NP, then the total robust constraint violation degree is sorted in descending order, and the first NP individuals after sorting are selected to form the offspring population; After obtaining a progeny population consisting of NP individuals, if there are individuals in the progeny population with a combination state of multiple energy supply and energy storage units with the same survival time, then for multiple individuals in the combination state of multiple energy supply and energy storage units with the same survival time, The total robust constraint violation degree of individuals at the time is sorted in ascending order, and the first NP individuals after sorting are selected to form the offspring population; t0 is the initial time of the current period, It is the maximum survival time of individuals in a population with a combination of multiple energy supply and storage units.

7. A multi-energy system energy balance optimization device, characterized in that: The multi-energy system energy balance optimization device applies the multi-energy system energy balance optimization method according to any one of claims 1 to 6, and the multi-energy system energy balance optimization device comprises: A dynamic constrained single-objective optimization model establishment module is used to establish a dynamic constrained single-objective optimization model for the multi-energy system, wherein the constraints of the dynamic constrained single-objective optimization model include the operation constraints and power balance constraints of each device in the energy supply and storage unit model of the multi-energy system; the dynamic constrained single-objective optimization model includes uncertainty parameters of wind power and photovoltaic output; A constraint description module, configured to describe each constraint using a constraint violation degree; A solution module is used to use the degree of constraint violation as the robust performance evaluation index of the dynamic constraint single-objective optimization model, adopt a differential evolution algorithm to perform robust performance evolutionary solution on the dynamic constraint single-objective optimization model, and obtain the combined state of the energy supply and energy storage unit model in the corresponding time period of the dynamic constraint single-objective optimization model.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-energy system energy balance optimization method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-energy system energy balance optimization method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the multi-energy system energy balance optimization method according to any one of claims 1 to 6 is implemented.

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