Comprehensive energy system operation optimization method, system and device and storage medium

Abstract

The embodiment of the invention relates to the field of energy storage and new energy power generation, and discloses an integrated energy system operation optimization method, system and device and a storage medium, and the method comprises the steps: building an integrated energy scheduling optimization model with the minimum operation cost of an integrated energy system and the minimum deviation of a day-ahead-day power purchase plan as a target; determining operation constraint conditions of each energy unit and terminal energy consumption equipment in the integrated energy system; formulating day-ahead-real-time scheduling dynamic adjustment optimization logic of the integrated energy system; and based on the mathematical model of each energy unit, the operation constraint condition of each energy unit and the day-ahead-real-time scheduling dynamic adjustment optimization logic, solving the comprehensive energy scheduling model to form a comprehensive energy scheduling optimization scheme. According to the embodiment of the invention, the problems that the prediction precision of uncertain factors is improved by adopting a complex algorithm, resources such as wind and light are wasted, and the optimization scheduling selection scene quantity is insufficient are solved.

Description

TECHNICAL FIELD

[0001] The embodiment of the present disclosure relates to the technical field of energy storage and new energy power generation, in particular to a comprehensive energy system operation optimization method, system, device and storage medium capable of reducing day-ahead-day-in power purchase deviation. BACKGROUND

[0002] The comprehensive energy system connects the power grid and the terminal user, and needs to submit the power purchase plan of the dispatch day to the power grid according to the predicted data of internal wind and light and user load in the dispatch day. However, due to the error between the predicted value and the actual value, the actual power purchase amount of the comprehensive energy system in the dispatch day will deviate from the day-ahead power purchase plan, which will directly affect the operation cost of the system.

[0003] At present, most of the related work is mainly divided into the following categories: using a complex model to improve the accuracy of internal predicted data, reduce the error between the predicted value and the actual value, and thus reduce the deviation; using the wind and light abandonment strategy to reduce the power purchase plan deviation in the dispatch day; using random optimization, robust optimization and other optimization methods to randomly generate typical scenes for dispatch optimization to reduce the deviation or ensure that the system is optimized to achieve the optimal target under the worst case of uncertain factors; using a multi-objective optimization algorithm to optimize multiple targets such as minimum operation cost and minimum power deviation.

[0004] However, the existing technology for reducing the day-ahead-day-in power purchase error of the comprehensive energy system has the following problems: using a complex model to improve the prediction accuracy of wind, light, load and other predicted data has the disadvantages of complex algorithm, long optimization time, high system configuration requirement and the like; using the wind and light abandonment method to reduce the day-ahead-day-in power purchase deviation has the disadvantages of not being able to fully utilize wind and light clean resources and increasing the system operation cost; using the random optimization type optimization method has the disadvantages of insufficient typical scene sample size and simple working condition description; using the robust optimization type optimization method can ensure that the system achieves optimal scheduling under the worst case of uncertain factors, but the probability of the worst case is extremely low, and the optimization scheduling scheme under this working condition is conservative; using the NSGA (Non-dominated Sorting Genetic Algorithm, multi-objective optimization algorithm) type multi-objective optimization algorithm can better solve this type of problem, but there are inconsistencies in the units of different nature targets, subjectivity in the weight ratio of each target, and the decision variables under each target are prone to restriction, resulting in longer system operation time, complex model topology structure and other deficiencies. SUMMARY

[0005] The embodiment of the present disclosure provides a comprehensive energy system operation optimization method, system, device and storage medium to solve or alleviate one or more of the above technical problems in the prior art.

[0006] According to one aspect of the present disclosure, a method for operating an integrated energy system is provided, comprising:

[0007] establishing a mathematical model of each energy unit in a power supply block, a gas supply block, a heating supply block and a cooling supply block in the integrated energy system;

[0008] establishing an integrated energy dispatching optimization model with the objective of minimizing the operation cost of the integrated energy system and the deviation of day-ahead-day-in electricity purchase plan;

[0009] determining the operation constraint conditions of each energy unit and terminal energy-consuming device in the integrated energy system;

[0010] formulating day-ahead-real-time dispatching dynamic adjustment optimization logic of the integrated energy system;

[0011] solving the integrated energy dispatching model based on the mathematical model of each energy unit, the operation constraint conditions of each energy unit and the day-ahead-real-time dispatching dynamic adjustment optimization logic, to form an integrated energy dispatching optimization scheme.

[0012] In one possible implementation, the data model of each energy unit in the power supply block comprises:

[0013] The mathematical model of the gas turbine converting gas into electricity is:

[0014]

[0015] wherein, is the electricity converted by the gas turbine at time t; is the gas consumption of the gas turbine at time t; is the electricity generation efficiency of the gas turbine;

[0016] The mathematical model of the energy storage battery when charging is:

[0017]

[0018] wherein, is the charging power of the energy storage battery at time t; SOC ES is the rated charge capacity of the energy storage battery; SOC ES，t is the charge capacity of the energy storage at time t; R1 is the coefficient of the charge capacity of the energy storage;

[0019] The mathematical model of the energy storage battery when discharging is:

[0020]

[0021] wherein, is the discharging power of the energy storage battery at time t; SOC ES is the rated charge capacity of the energy storage battery; SOCES，t Let R1 be the energy storage charge at time t; R2 is the coefficient of the energy storage charge.

[0022] The mathematical model for electric vehicle charging is as follows:

[0023]

[0024] In the formula, The charging power of electric vehicles that are charging within the jurisdiction of the integrated energy system at time t; Let t be the number of electric vehicles that are charging within the jurisdiction of the integrated energy system at time t; The charging power of the nth electric vehicle at time t;

[0025] The mathematical model for electric vehicle discharge is as follows:

[0026]

[0027] In the formula, Let t be the discharge power of the electric vehicle that has discharge behavior within the jurisdiction of the integrated energy system at time t; Let t be the number of electric vehicles that are discharging within the jurisdiction of the integrated energy system at time t; Let be the discharge power of the nth electric vehicle at time t;

[0028] The data models for each energy unit of the gas supply section include:

[0029] The mathematical model for P2G devices is:

[0030]

[0031] In the formula, Let t be the output power of the P2G device at time t; η represents the power consumption of the P2G device at time t. P2G The gas production capacity of the P2G equipment;

[0032] The mathematical model of the gas storage device is as follows:

[0033]

[0034] In the formula, Let be the output power of the gas storage device at time t; Let be the charging power of the gas storage device at time t; Let be the gas release power of the gas storage device at time t;

[0035] The data models for each energy unit of the heating system include:

[0036] The heating output power model of the gas turbine at time t is as follows:

[0037]

[0038] In the formula, Let be the heating output power of the gas turbine at time t; Let be the gas consumption of the gas turbine at time t. The heating efficiency of the gas turbine;

[0039] The mathematical model for the boiler's output power at time t is:

[0040]

[0041] In the formula, Let be the output power of the boiler at time t; Let η be the gas consumption of the boiler at time t; Boiler The heating efficiency of the boiler;

[0042] The data models for each energy unit of the cooling system include:

[0043] The mathematical model for the output power of the electric chiller at time t is:

[0044]

[0045] In the formula, Let be the output power of the electric chiller at time t; Let η be the amount of electricity consumed by the electric chiller at time t; ER The refrigeration efficiency of the electric chiller;

[0046] The mathematical model for the output power of an absorption chiller at time t is:

[0047]

[0048] In the formula, The output power of the absorption chiller at time t; η is the amount of gas consumed by the absorption chiller at time t; AR This refers to the refrigeration efficiency of an absorption chiller.

[0049] In one possible implementation, the mathematical model for optimizing the operating cost objective of the integrated energy system is as follows:

[0050]

[0051] In the formula, These represent the electricity price and gas price purchased by the integrated energy system from the main power grid and main gas grid at time t, respectively. These represent the amount of electricity and gas purchased by the integrated energy system from the main power grid and main gas grid at time t, respectively.

[0052] The mathematical model for optimizing the day-to-day electricity purchase deviation target is as follows:

[0053]

[0054] In the formula, P is the dynamic electricity purchase deviation penalty coefficient. t The deviation of electricity purchases before and within the day prior to time t;

[0055] Dynamic electricity purchase deviation penalty coefficient The mathematical model is as follows:

[0056]

[0057] In the formula, k is the penalty benchmark electricity price;

[0058] The mathematical model for the overall optimization objective of the integrated energy system is as follows:

[0059] min f = f1 + f2.

[0060] In one possible implementation, the terminal energy-consuming equipment includes electrical equipment, gas-consuming equipment, cooling equipment, and heating equipment, and the load power balance constraint of the electrical equipment, gas-consuming equipment, cooling equipment, and heating equipment is:

[0061]

[0062] In the formula, These represent the loads of terminal electrical equipment, gas-consuming equipment, cooling equipment, and heating equipment at time t, respectively. Let t be the gas purchase capacity of the integrated energy system from the gas grid; These represent the power purchased and sold by the smart energy system from the power grid at time t.

[0063] In one possible implementation, determining the operational constraints of each energy unit within the integrated energy system includes:

[0064] Determine the operating constraints for gas turbines, boilers, electric chillers, absorption chillers, and P2G equipment:

[0065]

[0066] In the formula, These are the maximum operating power of the gas turbine, boiler, electric chiller, absorption chiller, and P2G equipment, respectively; r CGT，d r CGT，u These represent the upward and downward ramp rates of the gas turbine, respectively; r boiler，d r boiler，u These are the downward and upward ramp rates of the boiler, respectively.

[0067] Determine the operational constraints of energy storage batteries, energy storage systems, and gas storage devices:

[0068]

[0069] In the formula, These represent the upper limits for charging and discharging energy storage batteries, respectively; r ed r edc These are the charging and discharging ramp rates of the energy storage system, respectively. These represent the upper limits for filling and releasing gas into the gas storage device, respectively; r gd r gdc These refer to the gas charging and degassing ramp rates of the gas storage device, respectively.

[0070] Determine the operating constraints of electric vehicles:

[0071]

[0072] In the formula, These represent the maximum charging and discharging power of the nth electric vehicle within the jurisdiction of the integrated energy system; Let t be the maximum number of electric vehicles under the jurisdiction of the integrated energy system.

[0073] In one possible implementation, the day-ahead-real-time scheduling dynamic adjustment and optimization logic of the integrated energy system includes:

[0074] Before the dispatch date, historical data is acquired, including historical output data of wind and solar power equipment, historical load data, and weather data for the dispatch date.

[0075] Based on the historical data, output the predicted values ​​of wind power and photovoltaic equipment output and load forecast;

[0076] Based on the predicted output values ​​and load values ​​of the wind and solar power equipment, calculate the daily power purchase plan for dispatch.

[0077] During the scheduling day, real-time data is acquired, including actual output data of wind and solar power equipment within the integrated energy system, actual load data on the user side, and real-time electricity price.

[0078] Calculate the day-to-day power purchase deviation penalty coefficient, day-to-day power purchase deviation penalty cost, and system operating cost based on real-time data;

[0079] The actual power output of each energy unit within the integrated energy system is adjusted in real time based on the day-to-day power purchase deviation penalty coefficient, the day-to-day power purchase deviation penalty cost, and the system operating cost.

[0080] In one possible implementation, based on the error between the actual output data and the predicted output value of the wind and solar power equipment, and the error between the actual load data and the predicted load value of the user-side load, the difference between the real-time power purchase and sale and the planned power purchase and sale of the integrated energy system to the large power grid, the power purchase deviation penalty coefficient, and the power purchase deviation penalty cost are calculated.

[0081] In one possible implementation, the real-time adjustment of the actual power output of each energy unit within the integrated energy system based on the day-to-day power purchase deviation penalty coefficient, the day-to-day power purchase deviation penalty cost, and the system operating cost includes:

[0082] The decision is made by comparing the penalty cost for electricity purchase deviation before and during the dispatch time with the electricity purchase cost at the dispatch time:

[0083] When the penalty cost for the day-to-day power purchase deviation is greater than the power purchase cost at the dispatch time, the penalty cost for the day-to-day power purchase deviation will be reduced in the order of energy storage batteries, electric vehicles, and gas storage equipment.

[0084] The day-to-day power purchase deviation penalty cost includes the deviation penalty cost for actual power purchase exceeding the planned power purchase and for actual power purchase falling short of the planned power purchase;

[0085] When the actual purchased power exceeds the planned power, the system purchases power according to the planned power, and the difference between the actual purchased power and the planned power is stored in the order of energy storage batteries, electric vehicles, and gas storage equipment.

[0086] When the actual purchased power is less than the planned purchased power, the difference between the actual purchased power and the planned purchased power will be released in the order of energy storage batteries, electric vehicles, and gas storage equipment.

[0087] If there is remaining capacity in energy storage batteries, electric vehicles, and gas storage equipment after deducting the cost of electricity purchase deviation within one day before the reduction date, then charging and discharging will be carried out according to the reduced electricity purchase cost.

[0088] When the penalty cost for power purchase deviation before and during the dispatch time is less than the power purchase cost at the dispatch time, the integrated energy system purchases power according to the planned power purchase capacity.

[0089] According to one aspect of this disclosure, a comprehensive energy system operation optimization system is provided, comprising:

[0090] The first establishment unit is used to establish mathematical models of each energy unit within the power supply, gas supply, heating, and cooling sectors of the integrated energy system.

[0091] The second establishment unit is used to establish an integrated energy dispatch optimization model with the goal of minimizing the operating cost of the integrated energy system and the deviation of the day-ahead to day-intraday power purchase plan;

[0092] A determining unit is used to determine the operational constraints of each energy unit and terminal energy-consuming equipment within the integrated energy system;

[0093] The formulation unit is used to formulate the day-ahead-real-time scheduling dynamic adjustment and optimization logic of the integrated energy system.

[0094] The solution unit is used to solve the integrated energy scheduling model based on the mathematical model of each energy unit, the operating constraints of each energy unit, and the day-ahead-real-time scheduling dynamic adjustment and optimization logic, so as to form an integrated energy scheduling optimization scheme.

[0095] According to one aspect of this disclosure, an integrated energy system operation optimization device is provided, comprising:

[0096] Processor and memory;

[0097] The memory is used to store computer programs, and the processor calls the computer programs stored in the memory to execute the integrated energy system operation optimization method described above.

[0098] According to one aspect of this disclosure, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, the processor is able to perform the integrated energy system operation optimization method described in any of the preceding claims.

[0099] The exemplary embodiments of this disclosure have the following beneficial effects: The exemplary embodiments of this disclosure transform the energy unit of the day-to-day power purchase plan deviation caused by the prediction data errors of wind, solar, load, etc., into the economic unit of power purchase deviation penalty cost, keeping the unit consistent with the system operating cost unit. It uses energy storage units such as internal energy storage batteries, electric vehicles, and gas storage equipment to reduce system operating costs and power purchase deviation penalty costs, and uses the CPLEX linear programming toolbox to achieve rapid solution of the model. It avoids problems such as using complex algorithms to improve the prediction accuracy of uncertain factors, causing waste of wind and solar resources, insufficient selection of scenarios for optimization scheduling, and inconsistent units in multi-objective optimization.

[0100] Details of one or more embodiments of this application are set forth in the following drawings and description. Other features and advantages of this application will become apparent from the accompanying drawings. It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not intended to limit this disclosure. Attached Figure Description

[0101] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0102] Figure 1 This is a flowchart of an integrated energy system operation optimization method according to an exemplary embodiment of the present invention;

[0103] Figure 2 This is a schematic diagram of the energy flow within the integrated energy system of this exemplary embodiment;

[0104] Figure 3 This is a flowchart illustrating the operation of the method in this exemplary embodiment;

[0105] Figure 4 This is a schematic diagram illustrating the data processing, transmission, and association of each subsystem in this exemplary embodiment;

[0106] Figure 5 This is a block diagram of an integrated energy system operation optimization system according to an exemplary embodiment of the present invention;

[0107] Figure 6 This is a schematic diagram of the structure of an integrated energy system operation optimization device according to an exemplary embodiment of this invention. Detailed Implementation

[0108] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0109] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware units or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0110] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0111] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0112] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or device that includes a series of steps or sub-modules is not necessarily limited to those steps or sub-modules that are explicitly listed, but may include other steps or sub-modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0113] Figure 1 This is a flowchart of an integrated energy system operation optimization method according to an exemplary embodiment of the present invention, such as... Figure 1 As shown, an exemplary embodiment of this disclosure provides a method for optimizing the operation of an integrated energy system, including:

[0114] S1 establishes mathematical models of each energy unit within the integrated energy system's power supply, gas supply, heating, and cooling sectors.

[0115] S2 establishes an integrated energy dispatch optimization model with the objective of minimizing the operating cost of the integrated energy system and the deviation of the day-ahead to day-intraday power purchase plan;

[0116] S3 determines the operational constraints of each energy unit and terminal energy-consuming equipment within the integrated energy system;

[0117] S4 formulates the day-ahead-real-time scheduling dynamic adjustment and optimization logic for the integrated energy system;

[0118] S5 solves the comprehensive energy dispatch model based on the mathematical models of each energy unit, the operating constraints of each energy unit, and the day-ahead-real-time scheduling dynamic adjustment and optimization logic, thereby forming a comprehensive energy dispatch optimization scheme.

[0119] This embodiment provides a comprehensive energy system operation optimization method that can reduce day-ahead and intraday power purchase deviations, specifically including the following:

[0120] The integrated energy system comprises power supply, gas supply, heating, and cooling sectors. The power supply sector includes energy units such as wind power, photovoltaics, energy storage batteries, gas turbines, and electric vehicles; the gas supply sector includes energy units such as P2G (power-to-gas) equipment and gas storage devices; the heating sector includes energy units such as steam turbines and boilers; and the cooling sector includes energy units such as electric chillers and absorption chillers. Figure 3 This is a flowchart illustrating the method operation of this exemplary embodiment, as follows: Figure 3 As shown, the integrated energy system operation optimization method includes the following steps:

[0121] Establish mathematical models for each energy unit within the power supply, gas supply, heating, and cooling sectors of the integrated energy system;

[0122] A comprehensive energy dispatch model is proposed, aiming to minimize system operating costs and the deviation of day-ahead to day-intra-day power purchase plans while meeting user-side electricity, gas, cooling, and heating load demands. Specifically, regarding optimizing the day-ahead to day-intra-day power purchase plan deviation, a dynamic power purchase deviation penalty coefficient is proposed. The energy objective of minimizing the day-ahead to day-intra-day power purchase plan deviation is transformed into the economic objective of minimizing the penalty cost of power purchase deviation by multiplying the unit power purchase deviation by the penalty coefficient.

[0123] Determine the operational constraints of each energy unit within the integrated energy system.

[0124] Establish a day-ahead-real-time scheduling dynamic adjustment and optimization logic for the integrated energy system.

[0125] Input the above data, model, and logic into the CPLEX linear programming toolbox to solve the relevant objective function.

[0126] This embodiment proposes a method to transform the energy unit of the day-to-day power purchase plan deviation caused by errors in forecast data such as wind, solar, and load into an economic unit of power purchase deviation penalty cost, keeping the unit consistent with the system operating cost unit. This method utilizes energy storage units such as internal energy storage batteries, electric vehicles, and gas storage equipment to reduce system operating costs and power purchase deviation penalty costs, and uses the CPLEX linear programming toolbox to achieve rapid solution of the model.

[0127] The mathematical models of each energy unit within the power supply, gas supply, heating, and cooling sectors of the integrated energy system are as follows:

[0128] Specifically, the data model of each energy unit of the power supply board includes:

[0129] Figure 2 This is a schematic diagram of the energy flow within the integrated energy system of this exemplary embodiment, as shown below. Figure 2 As shown: Gas turbines can convert gas energy into electrical and thermal energy. The mathematical model for a gas turbine converting gas into electricity is as follows:

[0130]

[0131] In the formula, Let t be the amount of electricity converted by the gas turbine; Let t be the gas consumption of the gas turbine; For the power generation efficiency of gas turbines;

[0132] As an energy storage device, an energy storage battery has two states of output power: charging and discharging (charge is represented by -, discharge by +). The charging and discharging power at any given moment is related to its state of charge (SOC). When the energy storage battery is charging, its output is:

[0133]

[0134] In the formula, The charging power of the energy storage battery at time t; SOC ES The rated charge capacity of the energy storage battery; SOC ES，t Let be the energy storage charge at time t; R1 is a coefficient for the energy storage charge; to ensure the lifespan of the energy storage device, the energy storage charge should be maintained between 20% and 80%; for example, when R1 is 0.8, the mathematical model for electric vehicle charging is as follows:

[0135]

[0136] The mathematical model for the discharge of an energy storage battery is as follows:

[0137]

[0138] In the formula, Let t be the discharge power of the energy storage battery; SOC ES The rated charge capacity of the energy storage battery; SOC ES，t Let R1 be the energy storage charge at time t; R2 is the coefficient of the energy storage charge.

[0139] For example, when R1 is 0.2, the mathematical model for charging an electric vehicle is as follows:

[0140]

[0141] Electric vehicles, acting as mobile energy storage units, can contribute electrical energy from their batteries to the integrated energy system after participating in reverse grid power supply (vehicle-to-grid, V2G) (similar to energy storage batteries, charging takes negative values, discharging takes positive values). When an electric vehicle is charging, its output power is:

[0142]

[0143] In the formula, The charging power of electric vehicles that are charging within the jurisdiction of the integrated energy system at time t; Let t be the number of electric vehicles that are charging within the jurisdiction of the integrated energy system at time t; The charging power of the nth electric vehicle at time t;

[0144] Mathematical model of electric vehicle discharge:

[0145]

[0146] In the formula, Let t be the discharge power of the electric vehicle that has discharge behavior within the jurisdiction of the integrated energy system at time t; Let t be the number of electric vehicles that are discharging within the jurisdiction of the integrated energy system at time t; Let be the discharge power of the nth electric vehicle at time t;

[0147] like Figure 2 As shown, the P2G equipment can convert electrical energy into hydrogen through a water electrolysis device to meet gas demand; it can also react hydrogen with carbon dioxide to produce gases such as methane, as needed. The data models of each energy unit in the gas supply module include:

[0148] Mathematical model of P2G device:

[0149]

[0150] In the formula, Let t be the output power of the P2G device at time t; η represents the power consumption of the P2G device at time t. P2G The gas production capacity of the P2G equipment;

[0151] The gas storage device is similar to an energy storage battery, capable of bidirectional operation of charging and degassing (similar to an energy storage battery, charging is represented by a negative value, and degassing by a positive value). Its output power can be expressed as:

[0152]

[0153] In the formula, Let be the output power of the gas storage device at time t; Let be the charging power of the gas storage device at time t; Let be the gas release power of the gas storage device at time t;

[0154] The data models for each energy unit of the heating system include:

[0155] Model of the heating output power of a gas turbine at time t:

[0156]

[0157] In the formula, Let be the heating output power of the gas turbine at time t; Let be the gas consumption of the gas turbine at time t. The heating efficiency of the gas turbine;

[0158] Mathematical model of boiler output power at time t:

[0159]

[0160] In the formula, Let be the output power of the boiler at time t; Let η be the gas consumption of the boiler at time t; Boiler The heating efficiency of the boiler;

[0161] The data models for each energy unit of the cooling system include:

[0162] Mathematical model of the output power of the electric chiller at time t:

[0163]

[0164] In the formula, Let be the output power of the electric chiller at time t; Let η be the amount of electricity consumed by the electric chiller at time t; ER The refrigeration efficiency of the electric chiller;

[0165] Mathematical model of the output power of an absorption chiller at time t:

[0166]

[0167] In the formula, The output power of the absorption chiller at time t; η is the amount of gas consumed by the absorption chiller at time t; AR This refers to the refrigeration efficiency of an absorption chiller.

[0168] Specifically, the mathematical model for optimizing the operating cost target of the integrated energy system is as follows:

[0169]

[0170] In the formula, These represent the electricity price and gas price purchased by the integrated energy system from the main power grid and main gas grid at time t, respectively. These represent the amount of electricity and gas purchased by the integrated energy system from the main power grid and main gas grid at time t, respectively.

[0171] The mathematical model for optimizing the day-to-day electricity purchase deviation target is as follows:

[0172]

[0173] In the formula, P is the dynamic electricity purchase deviation penalty coefficient. t The day-to-day (day-to-day) electricity purchase deviation is defined as the deviation at time t. To reduce this deviation, a dynamic electricity purchase deviation penalty coefficient is introduced. To minimize day-to-day electricity purchase deviations, the penalty factor is defined as increasing faster as the deviation increases; this is the dynamic electricity purchase deviation penalty factor. The mathematical model is as follows:

[0174]

[0175] In the formula, k is the penalty benchmark electricity price; it is worth noting that the dynamic electricity purchase deviation penalty coefficient in this embodiment... The calculation function can be replaced; other similar functions are also acceptable, and it is not limited to the calculation function proposed in this embodiment.

[0176] The mathematical model for the overall optimization objective of the integrated energy system is as follows:

[0177] min f = f1 + f2.

[0178] This embodiment transforms the energy unit of the day-to-day electricity purchase deviation of the integrated energy system into an economic unit of dynamic deviation penalty cost. This is combined with minimizing system operating costs to form an optimized scheduling objective. By utilizing internal energy storage devices, it optimizes the overall cost of the integrated energy system from two levels: reducing penalty costs and operating costs. Compared to existing mainstream methods, this avoids shortcomings such as using complex algorithms to improve the prediction accuracy of uncertain factors, causing waste of resources like wind and solar power, insufficient selection of optimization scenarios, and inconsistent optimization units across multiple objectives.

[0179] Specifically, the terminal energy-consuming equipment includes electrical equipment, gas-consuming equipment, cooling equipment, and heating equipment, and the load power balance constraint for the electrical equipment, gas-consuming equipment, cooling equipment, and heating equipment is as follows:

[0180]

[0181] In the formula, These represent the loads of terminal electrical equipment, gas-consuming equipment, cooling equipment, and heating equipment at time t, respectively. Let t be the gas purchase capacity of the integrated energy system from the gas grid; These represent the power purchased and sold by the smart energy system from the power grid at time t.

[0182] Specifically, the operational constraints for each energy unit within the integrated energy system include:

[0183] Determine the operating constraints for gas turbines, boilers, electric chillers, absorption chillers, and P2G equipment:

[0184]

[0185] In the formula, These are the maximum operating power of the gas turbine, boiler, electric chiller, absorption chiller, and P2G equipment, respectively; r CGT，d r CGT，u These represent the upward and downward ramp rates of the gas turbine, respectively; r boiler，d r boiler，u These are the downward and upward ramp rates of the boiler, respectively.

[0186] Determine the operational constraints of energy storage batteries, energy storage systems, and gas storage devices:

[0187]

[0188] In the formula, These represent the upper limits for charging and discharging energy storage batteries, respectively; r ed r edc These are the charging and discharging ramp rates of the energy storage system, respectively. These represent the upper limits for filling and releasing gas into the gas storage device, respectively; r gd r gdc These refer to the gas charging and degassing ramp rates of the gas storage device, respectively.

[0189] Determine the operating constraints of electric vehicles:

[0190]

[0191] In the formula, These represent the maximum charging and discharging power of the nth electric vehicle within the jurisdiction of the integrated energy system; Let t be the maximum number of electric vehicles under the jurisdiction of the integrated energy system.

[0192] Specifically, the day-ahead-real-time scheduling dynamic adjustment and optimization logic of the integrated energy system includes:

[0193] Before the dispatch date, historical data is acquired, including historical output data of wind and solar power equipment, historical load data, and weather data for the dispatch date.

[0194] Based on the historical data, output the predicted values ​​of wind power and photovoltaic equipment output and load forecast;

[0195] Based on the predicted output values ​​and load values ​​of the wind and solar power equipment, calculate the daily power purchase plan for dispatch.

[0196] During the scheduling day, real-time data is acquired, including actual output data of wind and solar power equipment within the integrated energy system, actual load data on the user side, and real-time electricity price.

[0197] Calculate the day-to-day power purchase deviation penalty coefficient, day-to-day power purchase deviation penalty cost, and system operating cost based on real-time data;

[0198] The actual power output of each energy unit within the integrated energy system is adjusted in real time based on the day-to-day power purchase deviation penalty coefficient, the day-to-day power purchase deviation penalty cost, and the system operating cost.

[0199] Specifically, based on the error between the actual output data and the predicted output value of wind and solar power equipment, and the error between the actual load data and the predicted load value on the user side, the difference between the real-time power purchase and sale and the planned power purchase and sale of the integrated energy system to the power grid, the power purchase deviation penalty coefficient, and the power purchase deviation penalty cost are calculated.

[0200] Specifically, the real-time adjustment of the actual power output of each energy unit within the integrated energy system based on the day-to-day power purchase deviation penalty coefficient, the day-to-day power purchase deviation penalty cost, and the system operating cost includes:

[0201] The decision is made by comparing the penalty cost for electricity purchase deviation before and during the dispatch time with the electricity purchase cost at the dispatch time:

[0202] When the penalty cost for the day-to-day power purchase deviation is greater than the power purchase cost at the dispatch time, the penalty cost for the day-to-day power purchase deviation will be reduced in the order of energy storage batteries, electric vehicles, and gas storage equipment.

[0203] The day-to-day power purchase deviation penalty cost includes the deviation penalty cost for actual power purchase exceeding the planned power purchase and for actual power purchase falling short of the planned power purchase;

[0204] When the actual purchased power exceeds the planned power, the system purchases power according to the planned power, and the difference between the actual purchased power and the planned power is stored in the order of energy storage batteries, electric vehicles, and gas storage equipment.

[0205] When the actual purchased power is less than the planned purchased power, the difference between the actual purchased power and the planned purchased power will be released in the order of energy storage batteries, electric vehicles, and gas storage equipment.

[0206] If there is remaining capacity in energy storage batteries, electric vehicles, and gas storage equipment after deducting the cost of electricity purchase deviation within one day before the reduction date, then charging and discharging will be carried out according to the reduced electricity purchase cost.

[0207] When the penalty cost for power purchase deviation before and during the dispatch time is less than the power purchase cost at the dispatch time, the integrated energy system purchases power according to the planned power purchase capacity.

[0208] Figure 4 This is a schematic diagram illustrating the data processing, transmission, and association of each subsystem in this exemplary embodiment; as shown... Figure 4 As shown, in this embodiment, the integrated energy system includes a data prediction subsystem, a computing subsystem, and an optimization scheduling subsystem. Based on the received dispatched daily electricity price data, dispatched daily electricity, gas, cooling, and heating load data, and the day-to-day power error data for load dispatch, the integrated energy system adjusts the power output of each internal energy unit to achieve the optimal overall target of the integrated energy system. The overall logic is as follows:

[0209] 1. Before the dispatch date, the integrated energy system's internal data forecasting subsystem outputs wind power and photovoltaic output forecasts and load forecasts based on information such as historical wind power and load data, and weather data for the dispatch date; the calculation subsystem calculates the day-ahead power purchase plan based on the relevant forecasts; and submits the day-ahead power purchase plan to the main power grid.

[0210] 2. During the dispatch day, the calculation subsystem calculates data such as the day-ahead-day power purchase deviation penalty coefficient, day-ahead-day power purchase deviation penalty cost, and system operating cost based on real-time information such as actual wind and solar power output data, actual user load data, and real-time electricity prices. The optimization dispatch subsystem adjusts the actual power output of each energy conversion unit, energy storage battery, electric vehicle, gas storage, and other energy storage units in real time based on relevant data.

[0211] 2.1 The calculation of the power purchase deviation penalty cost is as follows: The calculation subsystem calculates the difference between the real-time power purchase and sale power and the planned power purchase and sale power of the system facing the large power grid, the power purchase deviation penalty coefficient, and the power purchase deviation penalty cost based on the error between the real-time wind and solar power output and the predicted output, and the error between the real-time load and the predicted load on the user side.

[0212] 2.2 The optimization logic of the scheduling subsystem is as follows: A decision is made by comparing the penalty cost for power purchase deviation before and during the day prior to the scheduling time with the power purchase cost at that time.

[0213] 1) If the penalty cost for power purchase deviation is greater than the power purchase cost at that moment, the penalty cost will be reduced in the following order: energy storage battery, electric vehicle, gas storage equipment (when storing electricity in gas storage equipment, the electrical energy should first be converted into hydrogen through P2G equipment before storage; when releasing electricity, a gas boiler will be used to burn hydrogen and natural gas to supply power). Specifically, the penalty cost for power purchase deviation can be divided into two cases: the actual power purchase is greater than the planned power purchase and the actual power purchase is less than the planned power purchase. When the actual power purchase is greater than the planned power purchase, the system will still purchase electricity according to the planned power purchase, and the excess electricity will be stored in the order of energy storage battery, electric vehicle, and gas storage equipment. Conversely, when the actual power purchase is less than the planned power purchase, the missing electricity will be released in the order of energy storage battery, electric vehicle, and gas storage equipment.

[0214] 2) If the penalty cost for power purchase deviation is less than the power purchase cost at that time, the system will still purchase power according to the day-ahead power purchase plan;

[0215] 3) In 1), if the energy storage battery, electric vehicle, and gas storage equipment still have remaining capacity after deducting the penalty cost for the power purchase deviation at that time, then charging and discharging shall be carried out according to the reduction of power purchase cost.

[0216] The above model and optimization logic are input into the system optimization module, and solved using the CPLEX linear optimization toolbox. It is worth noting that the CPLEX model calculation toolbox in this embodiment can be replaced, and is not limited to CPLEX.

[0217] Figure 5 This is a block diagram of an integrated energy system operation optimization system according to an exemplary embodiment of the present invention, such as... Figure 5 As shown, an exemplary embodiment of this disclosure provides a comprehensive energy system operation optimization system, including:

[0218] The first establishment unit 10 is used to establish mathematical models of each energy unit within the power supply, gas supply, heating and cooling sectors of the integrated energy system.

[0219] The second establishment unit 20 is used to establish an integrated energy dispatch optimization model with the goal of minimizing the operating cost of the integrated energy system and the deviation of the day-ahead to day-intraday power purchase plan;

[0220] The determining unit 30 is used to determine the operating constraints of each energy unit and terminal energy-consuming equipment within the integrated energy system;

[0221] The formulation unit 40 is used to formulate the day-ahead-real-time scheduling dynamic adjustment and optimization logic of the integrated energy system.

[0222] The solution unit 50 is used to solve the comprehensive energy scheduling model based on the mathematical model of each energy unit, the operating constraints of each energy unit, and the day-ahead-real-time scheduling dynamic adjustment and optimization logic, so as to form a comprehensive energy scheduling optimization scheme.

[0223] Figure 6 This is a schematic diagram of the structure of an integrated energy system operation optimization device according to an exemplary embodiment of this invention. Figure 6 As shown, corresponding to the integrated energy system operation optimization method provided above, the present invention also provides an integrated energy system operation optimization device. Since the embodiment of this device is similar to the embodiment of the method described above, the description is relatively simple. For relevant details, please refer to the description in the method embodiment section above. The device described below is merely illustrative. This device may include: a processor 1, a memory 2, a communication bus (i.e., the aforementioned device bus), and a lookup engine. The processor 1 and the memory 2 communicate with each other through the communication bus and communicate with external systems through a communication interface. The processor 1 can call logical instructions in the memory 2 to execute the integrated energy system operation optimization method.

[0224] Furthermore, the logical instructions in the aforementioned memory 2 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: memory chips, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0225] On the other hand, embodiments of the present invention also provide a processor-readable storage medium storing a computer program 3, which, when executed by a processor 1, is implemented to perform the integrated energy system operation optimization method provided in the above embodiments.

[0226] The processor-readable storage medium can be any available medium or data storage device that the processor 1 can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0227] The above are merely preferred embodiments of this disclosure. The scope of protection of this disclosure is not limited to the above embodiments. All technical solutions falling within the scope of this disclosure are protected. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of this disclosure should be considered within the scope of protection of this disclosure.

Claims

1. A method for integrated energy system operation optimization, characterized in that, The method comprises the following steps: establishing mathematical models of each energy unit in a power supply block, a gas supply block, a heat supply block and a cold supply block in a comprehensive energy system; establishing a comprehensive energy scheduling optimization model with the minimum operation cost and the minimum deviation of day-ahead-day-in electricity purchase plan of the comprehensive energy system as the target; determining operation constraints of each energy unit and terminal energy consumption equipment in the comprehensive energy system; formulating day-ahead-real-time scheduling dynamic adjustment optimization logic of the comprehensive energy system; solving the comprehensive energy scheduling model based on the mathematical models of each energy unit, the operation constraints of each energy unit and the day-ahead-real-time scheduling dynamic adjustment optimization logic to form a comprehensive energy scheduling optimization scheme.

2. The method according to claim 1, wherein: the mathematical model of each energy unit in the power supply block comprises: the mathematical model of the gas turbine converting gas into electricity is: wherein is the amount of electricity converted by the gas turbine at time t; is the amount of gas consumed by the gas turbine at time t; is the electricity production efficiency of the gas turbine; the mathematical model of the energy storage battery when charging is: In the formula, is the charging power of the energy storage battery at time t; SOC ES is the rated charge capacity of the energy storage battery; SOC ES，t is the energy storage charge at time t; R1 is a coefficient of the energy storage charge; the mathematical model of the energy storage battery when discharging is: In the formula, is the discharge power of the energy storage battery at time t; SOC ES is the rated charge capacity of the energy storage battery; SOC ES，t is the energy storage charge at time t; R2 is a coefficient of the energy storage charge; the mathematical model of the electric vehicle when charging is: In the formula, is the charging power of the electric vehicle with charging behavior within the jurisdiction of the integrated energy system at time t; is the number of electric vehicles with charging behavior within the jurisdiction of the integrated energy system at time t; is the charging power of the nth electric vehicle at time t; the mathematical model of the electric vehicle when discharging is: In the formula, is the discharging power of the electric vehicle at time t within the jurisdiction of the integrated energy system; is the number of electric vehicles with discharging behavior at time t within the jurisdiction of the integrated energy system; is the discharging power of the nth electric vehicle at time t. the mathematical model of each energy unit in the gas supply block comprises: the mathematical model of the P2G device is: wherein P2G device's output power at time t; P2G device's power consumption at time t; η P2G P2G device's power generation; the mathematical model of the gas storage device is: In the formula, is the output power of the gas storage device at time t; is the charging power of the gas storage device at time t; is the discharging power of the gas storage device at time t; the mathematical model of each energy unit in the heat supply block comprises: the heating output power model of the gas turbine at time t is: In the formula, Q is the heating output power of the gas turbine at time t; Q is the heating output power of the gas turbine at time t; Q is the heating output power of the gas turbine at time t; the output power mathematical model of the boiler at time t is: In the formula, is the output power of the boiler at time t; is the gas consumption of the boiler at time t; η Boiler is the heating efficiency of the boiler; the mathematical model of each energy unit in the cold supply block comprises: the output power mathematical model of the electric refrigerator at time t is: In the formula, is the output power of the electric refrigerator at time t; is the amount of electricity consumed by the electric refrigerator at time t; η ER is the refrigeration efficiency of the electric refrigerator; the output power mathematical model of the absorption refrigerator at time t is: In the formula, Output power of the absorption chiller at time t; Gas consumption of the absorption chiller at time t; η AR Cooling efficiency of the absorption chiller.

3. The method according to claim 2, wherein: the mathematical model of the operation cost target of the comprehensive energy system is: In the formula, respectively, the electricity purchase price and the gas purchase price of the integrated energy system from the main power grid and the main gas grid at time t; respectively, the electricity purchase quantity and the gas purchase quantity of the integrated energy system from the main power grid and the main gas grid at time t; the mathematical model of the day-ahead-day-in electricity purchase deviation target is: In the formula, is a dynamic electricity purchase deviation penalty coefficient; P t is the day-ahead-day-ahead electricity purchase deviation at time t; Dynamic electricity purchase deviation penalty coefficient The mathematical model is: wherein k is a penalty benchmark electricity price; the mathematical model of the overall optimization target of the comprehensive energy system is: min f = f1 + f2.

4. The integrated energy system operation optimization method of claim 3, wherein, the terminal energy consumption equipment comprises electricity-using equipment, gas-using equipment, cold-using equipment and heat-using equipment, and the load power balance constraints of the electricity-using equipment, the gas-using equipment, the cold-using equipment and the heat-using equipment are: In the formula, respectively, terminal electricity-using equipment, gas-using equipment, cold-using equipment and heat-using equipment load at time t; is the gas purchasing power of the integrated energy system from the gas network at time t; respectively, electricity purchasing and selling power of the intelligent energy system from the power grid at time t.

5. The integrated energy system operation optimization method of claim 4, wherein, determining the operation constraints of each energy unit in the comprehensive energy system comprises: determining the operation constraints of the gas turbine, the boiler, the electric refrigerator, the absorption refrigerator and the P2G device; wherein respectively the maximum operating power of the gas turbine, the boiler, the electric chiller, the absorption chiller, the P2G plant; r CGT，d , r CGT，u respectively the up- and down ramping rate of the gas turbine; r boiler，d , T boiler，u respectively the down- and up-ramping rate of the boiler; determining the operation constraints of the energy storage battery, the energy storage system and the gas storage device; In the formula, respectively, the upper limit of charging and discharging of the energy storage battery; T ed , T edc respectively, the charging and discharging ramp rate of the energy storage system; respectively, the upper limit of charging and discharging of the gas storage device; r gd , r gdc respectively, the charging and discharging ramp rate of the gas storage device; determining the operation constraints of the electric vehicle; In the formula, respectively, the maximum power of the nth electric vehicle charging and discharging in the jurisdiction of the integrated energy system; is the maximum number of electric vehicles in the jurisdiction of the integrated energy system at time t. 6.The method of Claim 1-5, wherein, the day-ahead-real-time scheduling dynamic adjustment optimization logic of the comprehensive energy system comprises: scheduling day-ahead, obtaining historical data, wherein the historical data comprises wind power photovoltaic device output historical data, load historical data and scheduling day weather data; outputting wind power photovoltaic device output prediction values and load prediction values according to the historical data; calculating a scheduling day electricity purchase plan according to the wind power photovoltaic device output prediction values and the load prediction values; scheduling day-in, obtaining real-time data, wherein the real-time data comprises wind power photovoltaic device output actual data, user side load actual data and real-time electricity price in the comprehensive energy system. According to the real-time data, a day-ahead-day-ahead electricity purchase deviation penalty coefficient, a day-ahead-day-ahead electricity purchase deviation penalty cost and a system operation cost are calculated. According to the day-ahead-day-ahead electricity purchase deviation penalty coefficient, the day-ahead-day-ahead electricity purchase deviation penalty cost and the system operation cost, the actual power output of each energy unit in the integrated energy system is adjusted in real time.

7. The integrated energy system operation optimization method of claim 6, wherein: According to the error between the actual output data of the wind power and photovoltaic equipment and the predicted value of the wind power and photovoltaic equipment output, and the error between the actual data of the user side load and the predicted value of the load, the difference between the real-time purchase and sale power and the planned purchase and sale power of the integrated energy system facing the large power grid, the electricity purchase deviation penalty coefficient and the electricity purchase deviation penalty cost are calculated. 8.The integrated energy system operation optimization method of claim 7, wherein: The actual power output of each energy unit in the integrated energy system is adjusted in real time according to the day-ahead-day-ahead electricity purchase deviation penalty coefficient, the day-ahead-day-ahead electricity purchase deviation penalty cost and the system operation cost, which includes: According to the comparison between the day-ahead-day-ahead electricity purchase deviation penalty cost at the dispatch time and the purchase cost at the dispatch time, it is determined: When the day-ahead-day-ahead electricity purchase deviation penalty cost at the dispatch time is greater than the purchase cost at the dispatch time, the day-ahead-day-ahead electricity purchase deviation penalty cost is reduced in the order of the energy storage battery, the electric vehicle and the gas storage device; The day-ahead-day-ahead electricity purchase deviation penalty cost includes the deviation penalty cost of the actual purchase power being greater than the planned purchase power and the actual purchase power being less than the planned purchase power; When the actual purchase power is greater than the planned purchase power, the system purchases electricity according to the planned purchase power, and the difference between the actual purchase power and the planned purchase power is stored in the energy storage battery, the electric vehicle and the gas storage device in turn; When the actual purchase power is less than the planned purchase power, the difference between the actual purchase power and the planned purchase power is released in the energy storage battery, the electric vehicle and the gas storage device in turn; When the energy storage battery, the electric vehicle and the gas storage device have remaining capacity after reducing the day-ahead-day-ahead electricity purchase deviation penalty cost, they are charged and discharged to reduce the purchase cost; When the day-ahead-day-ahead electricity purchase deviation penalty cost at the dispatch time is less than the purchase cost at the dispatch time, the integrated energy system purchases electricity according to the planned purchase power.

9. An integrated energy system operation optimization system, characterized by, It includes: A first establishing unit is configured to establish mathematical models of each energy unit in power supply, gas supply, heat supply and cold supply blocks in the integrated energy system; A second establishing unit is configured to establish an integrated energy dispatching optimization model with the operation cost of the integrated energy system and the minimum day-ahead-day-ahead electricity purchase plan deviation as targets; A determining unit is configured to determine operation constraint conditions of each energy unit and terminal energy consumption device in the integrated energy system; A formulating unit is configured to formulate day-ahead-real-time dispatching dynamic adjustment optimization logic of the integrated energy system; A solving unit is configured to solve the integrated energy dispatching model based on the mathematical models of each energy unit, the operation constraint conditions of each energy unit and the day-ahead-real-time dispatching dynamic adjustment optimization logic, and form an integrated energy dispatching optimization scheme.

10. An integrated energy system operation optimization device, characterized by, It includes: A processor and a memory; The memory is configured to store a computer program, and the processor is configured to invoke the computer program stored in the memory to execute the integrated energy system operation optimization method in any one of claims 1 to 8.

11. A computer readable storage medium characterized by, The computer readable storage medium stores a computer program, and when the computer program is executed by the processor, the processor is capable of executing the integrated energy system operation optimization method in any one of claims 1 to 8.

Images