Comprehensive energy system optimization scheduling method and device, electronic equipment and storage medium

Abstract

The invention provides an integrated energy system optimization scheduling method and device, electronic equipment and a storage medium. The method comprises the steps of constructing a heat supply network dynamic model and a power grid model; based on the electric heating load demand response, electric heating load modeling is carried out on the basis of the heat supply network dynamic model and the power grid model, and a comprehensive demand response model considering the electric heating flexible load is constructed; a stepped carbon transaction mechanism is introduced, and a comprehensive energy system optimization scheduling model is constructed based on the comprehensive demand response model with the purpose of minimizing the total operation cost of the system; and solving the model, and generating an output scheduling scheme of the multiple energy supply devices in the integrated energy system under the condition of minimum system total operation cost. According to the integrated energy system optimization scheduling method provided by the invention, efficient utilization and energy-saving operation of energy can be realized while the total operation cost of the system is minimized, the scheduling decision level under the condition of relatively low carbon emission is improved, and the economical efficiency, the reliability and the environmental protection property of the integrated energy system are improved.

Description

Technical Field

[0001] This invention relates to the field of energy dispatching technology, and in particular to a method, apparatus, electronic device and storage medium for optimizing the dispatching of an integrated energy system. Background Technology

[0002] Resource, energy, and environmental pollution issues have become significant constraints on the sustainable development of industrial production. Maximizing the development and utilization of new energy sources, strengthening multi-energy complementary operation, and reducing carbon dioxide emissions are core issues that urgently need to be addressed in the current energy system reform.

[0003] In recent years, Integrated Energy Systems (IES) have attracted widespread attention due to their high energy utilization efficiency and the advantages of multi-energy complementarity. IES can not only strengthen the coupling between energy systems of different properties, but also achieve coordinated and optimized operation of energy over a large area, thereby improving the overall efficiency of energy utilization and sustainable development. By synergistically optimizing the energy structure and dispatching schemes of power grids, gas networks, and heating networks, IES can achieve multi-energy complementarity and cascade utilization, which is a fundamental way to achieve high energy efficiency, high quality, and low emissions in regional production, and has become a current research hotspot.

[0004] Most existing studies mainly analyze the role of improving system scheduling flexibility from the perspective of thermal dynamic characteristics, without further exploring the adjustment potential from the aspects of economic cost and carbon emission reduction, resulting in a rather one-sided approach to system scheduling. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and storage medium for optimizing the scheduling of integrated energy systems, in order to address the issue that the scheduling methods for integrated energy systems in the prior art are rather one-sided.

[0006] In a first aspect, the present invention provides a method for optimizing the scheduling of an integrated energy system, comprising: Construct dynamic models of the heating network and power grid models; Based on the demand response of electric heating load, electric heating load modeling is performed on the basis of the heating network dynamic model and the power grid model to construct a comprehensive demand response model that takes into account the flexible electric heating load. A tiered carbon trading mechanism is introduced into the total system operating cost, with the goal of minimizing the total system operating cost. Based on the comprehensive demand response model, an integrated energy system optimization scheduling model is constructed. Solve the integrated energy system optimization scheduling model to generate a power output scheduling scheme for multiple energy supply devices in the integrated energy system with the minimum total system operating cost.

[0007] In one embodiment, the objective function of the integrated energy system optimization scheduling model is determined in the following manner: Determine the costs of electricity purchase, gas purchase, maintenance, wind curtailment penalties, compensation, and carbon trading; Based on the electricity purchase cost, gas purchase cost, maintenance cost, wind curtailment penalty cost, compensation cost, and carbon trading cost, an objective function is constructed in the integrated energy system optimization scheduling model to minimize the total system operating cost.

[0008] In one embodiment, constructing the heating network dynamic model and the power grid model includes: Obtain a comprehensive energy system dataset; the comprehensive energy system dataset includes, but is not limited to, wind power output, electricity and heat load forecast data, system equipment parameters, parameter baseline values, and system correlation coefficients for a specific time and day in the current region; Based on the integrated energy system dataset, the thermal dynamic characteristics of the district heating system are described by a first-order dynamic model, and the heat network is modeled to obtain the heat network dynamic model. Based on the aforementioned integrated energy system dataset, the steady-state transmission characteristics of the power system are described through a branch power flow model, and the power network is modeled to obtain a power grid model.

[0009] In one embodiment, the step of modeling the electric heating load based on the electric heating load demand response, and constructing a comprehensive demand response model that takes into account the flexible electric heating load, includes: Based on the demand response of electric heating load, a dynamic model of the heating network considering thermal flexibility load is constructed by performing modeling of movable heat load and modeling of heat load that can be reduced, respectively, on the basis of the dynamic model of the heating network. Based on the demand response of electrical and thermal loads, transferable electrical loads, shiftable electrical loads, and reduceable electrical loads are modeled on the basis of the power grid model to construct a power grid model that takes into account the flexible electrical loads. Based on the dynamic model of the heating network that takes into account thermal flexible loads and the power grid model that takes into account electrical flexible loads, a comprehensive demand response model that takes into account electrical and thermal flexible loads is constructed.

[0010] In one embodiment, the integrated energy system optimization scheduling model is subject to multiple constraints; these constraints include, but are not limited to, electric power balance constraints, thermal power balance constraints, cogeneration unit operation constraints, gas boiler operation constraints, wind power generation unit operation constraints, electric energy storage constraints, and thermal energy storage constraints.

[0011] In one embodiment, the integrated energy system optimization scheduling model is solved using the GUROBI solver.

[0012] In one embodiment, the integrated energy system optimization scheduling method further includes: The relevant variables of the heating network dynamic model, the tiered carbon trading mechanism and the integrated energy system optimization scheduling model are initialized to 0. Based on the power grid model, the first system optimization scheduling model to be compared is constructed with the goal of minimizing the total system operating cost. The relevant variables of the tiered carbon trading mechanism and the integrated energy system optimization scheduling model are initialized to 0. Based on the heating network dynamic model and the power grid model, and with the goal of minimizing the total system operating cost, a second system optimization scheduling model to be compared is constructed. The relevant variables of the integrated energy system optimization scheduling model are initialized to 0. Based on the heating network dynamic model, the power grid model and the tiered carbon trading mechanism, a third system optimization scheduling model to be compared is constructed with the goal of minimizing the total system operating cost. The relevant variables of the tiered carbon trading mechanism are initialized to 0. Based on the dynamic model of the heating network, the power grid model, and the integrated energy system optimization scheduling model, a fourth system optimization scheduling model to be compared is constructed with the goal of minimizing the total operating cost of the system. Based on the dynamic model of the heating network, the power grid model, the integrated energy system optimization scheduling model and the tiered carbon trading mechanism, an integrated energy system optimization scheduling model is constructed with the goal of minimizing the total system operating cost. The first, second, third, fourth, and integrated energy system optimization scheduling models are solved respectively to obtain the first minimum total system operating cost corresponding to the first, second, third, fourth, and integrated energy system optimization scheduling models. The first minimum total system operating cost, the second minimum total system operating cost, the third minimum total system operating cost, the fourth minimum total system operating cost, and the fifth minimum total system operating cost are compared and analyzed to verify the feasibility of the system scheduling method of the integrated energy system optimization scheduling model under the condition of the fifth minimum total system operating cost.

[0013] Secondly, the present invention also provides an integrated energy system optimization and scheduling device, comprising: The first modeling module is used to build dynamic models of the heating network and power grid models; The second modeling module is used to model the electric heating load based on the electric heating load demand response, on the basis of the heating network dynamic model and the power grid model, and to construct a comprehensive demand response model that takes into account the electric heating flexible load. The third modeling module is used to introduce a tiered carbon trading mechanism into the total system operating cost, with the goal of minimizing the total system operating cost, and to build an integrated energy system optimization scheduling model based on the integrated demand response model. The scheduling scheme generation module is used to solve the integrated energy system optimization scheduling model and generate a power output scheduling scheme for multiple energy supply devices in the integrated energy system under the condition of minimizing the total system operating cost.

[0014] Thirdly, the present invention provides an electronic device, the electronic device including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the above-described integrated energy system optimization scheduling methods.

[0015] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described integrated energy system optimization scheduling methods.

[0016] The integrated energy system optimization scheduling method, device, electronic equipment, and storage medium provided by this invention combine electric and thermal load demand response. Based on the dynamic model of the heating network and the power grid model, electric and thermal load is modeled, and an integrated demand response model considering flexible electric and thermal loads is constructed. This fully considers the characteristics of both energy supply and demand. Then, a tiered carbon trading mechanism is introduced into the total system operating cost, with the goal of minimizing the total operating cost. Based on the integrated demand response model, an integrated energy system optimization scheduling model is constructed, taking into account both economic costs and carbon emission constraints. Solving the model generates output scheduling schemes for multiple energy supply devices. This can achieve efficient energy utilization and energy-saving operation while minimizing the total system operating cost, improve the scheduling decision-making level under low carbon emission conditions, and enhance the economy, reliability, and environmental protection of the integrated energy system, providing an effective strategy for the optimized operation of integrated energy systems. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1This is one of the flowcharts of the integrated energy system optimization scheduling method provided by the present invention.

[0019] Figure 2 This is the second flowchart of the integrated energy system optimization scheduling method provided by the present invention.

[0020] Figure 3 This is a schematic diagram of the integrated energy system provided in an embodiment of the present invention.

[0021] Figure 4 This is one of the schematic diagrams of the system output scheduling scheme provided by the present invention.

[0022] Figure 5 This is the second schematic diagram of the system output scheduling scheme provided by the present invention.

[0023] Figure 6 This is a schematic diagram of the integrated energy system optimization scheduling device provided by the present invention.

[0024] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0026] The terms "first," "second," etc., used in this invention are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein.

[0027] The following is combined Figures 1-7 The present invention describes the integrated energy system optimization scheduling method, apparatus, electronic device and storage medium provided by the present invention.

[0028] It should be noted that the integrated energy system optimization scheduling method provided in this invention is implemented based on an integrated energy system optimization scheduling device. The integrated energy system optimization scheduling method provided by this invention establishes a complete model system in terms of integrated energy system optimization scheduling technology. It integrates thermal dynamic characteristics, a tiered carbon trading mechanism, and Integrated Demand Response (IDR) to conduct research on peak shaving, valley filling, renewable energy consumption, and carbon emission reduction in integrated energy systems. Specifically, it requires comprehensively considering the coupling of the electric and thermal integrated energy system and the multi-energy flow network structure of the integrated new energy system. By establishing scheduling models for each node unit and integrating the electric and thermal multi-energy flow network, an electric and thermal coupled integrated energy system optimization scheduling model is constructed. This model aims to minimize the total system operating cost and introduces a tiered carbon trading mechanism into the total system operating cost. By solving the objective function of the model, a system scheduling scheme with the minimum total system operating cost is generated.

[0029] This invention describes a method for optimizing the scheduling of an integrated energy system, using an integrated energy system optimization scheduling device as the executing entity.

[0030] Combination Figure 1 , Figure 1 This is one of the flowcharts illustrating the integrated energy system optimization scheduling method provided by the present invention. Figure 2 This is the second flowchart of the integrated energy system optimization scheduling method provided by the present invention.

[0031] like Figure 1 As shown, the integrated energy system optimization scheduling method includes the following steps: Step 101: Construct a dynamic model of the heating network and a power grid model.

[0032] Specifically, firstly, in combination with Figure 3 Explain the structure of the Integrated Energy System (IES). Figure 3 This is a schematic diagram of the integrated energy system provided in an embodiment of the present invention. Figure 3The integrated energy system (IES) comprises a power system, a heating system, and a natural gas system. The power system consists of power sources, a power grid, and electrical loads. Power sources include the upstream power grid, combined heat and power (CHP), batteries, and wind power generation. The heating system consists of heat sources, a heat pipe network, and heat loads. Heat sources include CHP, gas-fired boilers, thermal storage tanks, and heat exchange stations. The natural gas system consists of a gas network and gas sources. The gas sources in the gas network are primarily natural gas, which is mainly consumed by the CHP units. Through optimized scheduling of the IES, multi-energy complementarity and wind power integration can be achieved in the park, enabling energy conservation, green transformation, and upgrading of the park's industries, effectively reducing energy costs and maximizing the economic benefits for new energy power generation enterprises.

[0033] As important components of a comprehensive energy system, the efficient operation of heating networks and power grids plays a crucial role in the overall economy and reliability of the system. By constructing a dynamic model of the heating network, the dynamic characteristics of the heating network can be described; by constructing a power grid model, the steady-state characteristics of the power grid can be described.

[0034] In this embodiment, the construction of the heating network dynamic model and the power grid model specifically includes: Obtain a comprehensive energy system dataset; the comprehensive energy system dataset includes, but is not limited to, wind power output, electricity and heat load forecast data, system equipment parameters, parameter baseline values, and system correlation coefficients for a specific time and day in the current region; Based on the integrated energy system dataset, the thermal dynamic characteristics of the district heating system are described by a first-order dynamic model, and the heat network is modeled to obtain the heat network dynamic model. Based on the aforementioned integrated energy system dataset, the steady-state transmission characteristics of the power system are described through a branch power flow model, and the power network is modeled to obtain a power grid model.

[0035] Specifically, this involves acquiring a comprehensive energy system dataset. This dataset includes, but is not limited to, wind power output on a specific day in the current region (such as the predicted output over a typical 24-hour period during winter), electricity and heat load forecasts, system equipment parameters (i.e., the equipment parameters of multiple devices within the comprehensive energy system), parameter baselines, and system correlation coefficients (i.e., the comprehensive energy system correlation coefficients). The wind power output on a specific day in the current region provides the local renewable energy output; the electricity and heat load forecasts provide the predicted energy consumption data, including natural gas, purchased electricity, and wind power generation; and the system equipment parameters, parameter baselines, and system correlation coefficients provide the analytical data related to the comprehensive energy system.

[0036] Furthermore, based on the integrated energy system dataset, the power network and the thermal network are modeled separately. Specifically, the power network model is performed using the Distflow branch power flow to model the IEEE 33-node power network. The distribution system parameters and load distribution are referenced from the IEEE 33-node network. Second-order cone relaxation is used as the grid constraint, and a first-order dynamic model is used to model the thermal dynamic characteristics network of the 51-node DHS. The preliminary modeling of the electrothermal multi-energy flow network is completed.

[0037] First, the following is a detailed description of the heating network modeling.

[0038] A highly accurate nodal method model is employed, discretizing the heat medium in the pipeline into multiple heat medium micro-elements, which can concisely and accurately reflect the time delay characteristics during heat network transmission. Based on this, the formulas for calculating the transmission delay and heat loss of the heat network pipeline are as follows:

[0039] Where s and t represent the water supply pipe and the return pipe, respectively; , These represent the heat medium temperature at the outlet of the water supply pipe j and the heat medium temperature at the outlet of the return water pipe j at time t, respectively, considering only the transmission delay and not the heat loss, in °C. , These represent the inlet temperatures of the heat transfer medium in water supply pipe j and return pipe j at time t, respectively, taking into account transmission delay and heat loss, in °C. , These represent the correlation coefficient of pipe j (both current and subsequent pipe j include water supply pipe j and return pipe j) and its transmission delay correlation coefficient, respectively. This represents the heat loss coefficient of pipe j; This represents the ambient temperature of the pipeline at time t, in °C. This represents a collection of heating network pipes.

[0040] parameter , , The calculation formula is as follows:

[0041] in, This represents a time interval, measured in seconds (s). This indicates a time interval, expressed in hours (h). This indicates the density of the heat transfer medium, expressed in kg / m³. 3 ; This represents the cross-sectional area of ​​the pipe, in meters (m). 2 ; This represents the length of pipe j, in meters (m). Indicates from The total mass of the heat medium flowing through pipe j up to time t, in kg; This represents the mass flow rate of the heat medium in pipe j, in kg / s. This represents the heat loss coefficient of the pipeline, with units of kW / (m·℃). This indicates the specific heat capacity of the heat transfer medium, expressed in kJ / (kg·℃). This represents the function for rounding up.

[0042] Static heat balance equation at the nodes of the heating network model:

[0043] in, , Let each represent a set of pipes flowing out of node k and into node k, respectively. , , These represent the sets of pipes at confluence nodes, source nodes, and load nodes in the heating network, respectively. , Let represent the temperature of the heat medium at node k in the water supply network and the return network at time t, respectively, in °C; This represents the heat power provided by the heat source at node k at time t, in kW. This represents the heat load power at node k at time t, in kW.

[0044] A first-order dynamic model is used to describe the dynamic characteristics of the terminal heat load of the heating system. The expression of the dynamic model is as follows:

[0045] in, This indicates the building's equivalent heat capacity, expressed in kJ / ℃. This indicates the building's equivalent thermal resistance, expressed in °C / kW. This indicates the outdoor temperature of the building, in °C. This indicates the indoor temperature of a building, expressed in °C. This indicates the heat load power, measured in kW.

[0046] Transforming the expression of the above dynamic model into discrete form, the temperature equation inside the building is as follows:

[0047] in, This represents the indoor temperature of the building at time t+1; This represents the indoor temperature of the building at time t; This represents the outdoor temperature of the building at time t.

[0048] When constructing a dynamic model of a heating network, it is necessary to satisfy the upper and lower limits of the supply / return water temperature constraints, namely:

[0049] in, , , , These represent the upper limit of the supply water temperature, the lower limit of the supply water temperature, the upper limit of the return water temperature, and the lower limit of the return water temperature, respectively. , These represent the supply water temperature and the return water temperature, respectively, in °C.

[0050] Next, the power grid modeling will be described in detail.

[0051] Using second-order cone relaxation as a power grid constraint, a branch power flow model is constructed as follows:

[0052] In the formula, This represents the voltage at node i; Represents a circuit set; , Let each represent a set of numbers representing the head node and tail node of line b, respectively. This represents the resistance value of line b, in Ω. V represents the reactance value of line b, in Ω; V0 represents the node reference voltage value, in kV; This represents the voltage value at node i during time period t, in kV. This represents the active power of line b at time t; This represents the reactive power of line b at time t.

[0053] When constructing a power grid model, it is also necessary to satisfy node voltage constraints, branch transmission power constraints, and power interaction constraints with the upper-level power grid.

[0054] Node voltage constraints, i.e.:

[0055] in, , Let represent the upper limit and lower limit of the voltage at node i, respectively. This represents the voltage at node i, in kV.

[0056] Branch transmission power constraint, namely:

[0057] in, This indicates the upper limit of active power for line b. This indicates the active power of line b, in kW.

[0058] Interacting with the upstream power grid for power constraints, namely:

[0059] in, This represents the upper limit of the power that node i can purchase from the grid during time period t. This represents the amount of electricity purchased by node i from the grid during time period t, in kW.

[0060] This embodiment provides a comprehensive and detailed integrated energy system dataset for model construction. Based on this dataset, a first-order dynamic model is used to accurately characterize the thermal dynamic characteristics of the district heating system, thereby achieving effective modeling of the heating network and obtaining a dynamic model of the heating network that reflects the actual dynamic changes of the heating network. At the same time, the branch power flow model is used to describe the steady-state transmission characteristics of the power system, thereby completing the power network modeling and obtaining an accurate power grid model, which can provide reliable model support for the optimized scheduling of the integrated energy system.

[0061] Step 102: Based on the demand response of electric heating load, perform electric heating load modeling on the basis of the heating network dynamic model and the power grid model, and construct a comprehensive demand response model that takes into account the flexible electric heating load.

[0062] Specifically, based on the modeling of the electric heating network, considering the demand response of electric heating load, the load is divided into transferable electric load, transferable electric load, reduceable electric load, transferable heat load, and reduceable heat load, and each is modeled separately to construct an electric heating multi-energy flow IDR model that takes into account the flexible electric heating load.

[0063] In this embodiment, the construction of an IDR network model considering the flexible electrothermal load specifically includes: Based on the demand response of electric heating load, a dynamic model of the heating network considering thermal flexibility load is constructed by performing modeling of movable heat load and modeling of heat load that can be reduced, respectively, on the basis of the dynamic model of the heating network. Based on the demand response of electrical and thermal loads, transferable electrical loads, shiftable electrical loads, and reduceable electrical loads are modeled on the basis of the power grid model to construct a power grid model that takes into account the flexible electrical loads. Based on the dynamic model of the heating network that takes into account thermal flexible loads and the power grid model that takes into account electrical flexible loads, a comprehensive demand response model that takes into account electrical and thermal flexible loads is constructed.

[0064] Specifically, since IDR can schedule flexible electrical and thermal loads, it can fully utilize the adjustment capabilities of demand-side resources, achieve peak shaving and valley filling, optimize load curves, and improve the flexibility and economy of IES operation. Therefore, electrical and thermal loads are divided into transferable electrical loads, shiftable electrical loads, reduceable electrical loads, shiftable thermal loads, and reduceable thermal loads, and each is modeled separately. The specific modeling process is described below.

[0065] Considering the demand response of electric heating load, modeling of shiftable heat load and modeling of reduceable heat load are carried out on the basis of the dynamic model of the heating network, that is, filling the skeleton of the dynamic model of the heating network with the relevant variables of each heat load.

[0066] Transferable loads refer to loads that can be flexibly transferred across different time periods throughout the entire dispatch cycle, while simultaneously meeting total energy demand. Considering the specific nature of heat load energy demand, transferable heat loads are not considered. For transferable electrical loads, the transferable interval is assumed to be [t]. TL1 ,t TL2 To prevent frequent equipment start-ups and shutdowns, the minimum continuous operating time and transfer power constraints should meet the following requirements:

[0067]

[0068] in, Indicates the minimum continuous running time; A binary variable representing the transfer status of transferable electrical loads, in kW; , These represent the upper and lower limits of the transferred electrical power, respectively, in kW; This represents the transferred electrical power at time t.

[0069] A transferable electrical load has the characteristic of shifting the entire electrical load within a specified interval. Assume the transferable interval within a scheduling cycle is [t]. SL1 ,t SL2 The duration is t. s Then the starting time period set L can be shifted. s As shown below:

[0070]

[0071] Where T represents the scheduling period; This indicates that the electrical load can be shifted at time t.

[0072] Electricity load can be reduced by cutting off some of the user's load during peak hours and providing compensation to meet user demand. Its constraints are:

[0073] in, This represents the maximum load that can be reduced at time t, taken as 10% of the load during that period, in kW. The load value can be reduced at time t.

[0074] The translation range of the transferable heat load is [ t h,SL1 ,t h,SL2 To ensure the continuity of the transferable heat load, the following conditions must be met:

[0075]

[0076] Among them, t start t last These represent the start time and duration of the transferable heat load, respectively. For the transferable heat load state binary variable, The heat load can be shifted at time t.

[0077] Let the temperature that does not affect the user's thermal perception and comfort be [ T h,min ,T h,max Within this temperature range, the heat load can be reduced as follows:

[0078] in, , These represent the upper limit and lower limit of the heat load power that can be reduced, respectively. This indicates the load reduction value at time t, in kW; c w The specific heat capacity of water is expressed as 4.2 × 10³ J / (kg·℃); m w This indicates the mass of water injected into the user-side heating network during each time interval, expressed in kg. This indicates the temperature of the heat transfer medium, expressed in °C. This indicates the time interval per hour.

[0079] Using the above modeling methods, a dynamic model of the heating network considering thermal flexible loads and a power grid model considering electrical flexible loads were constructed. Based on these two models, a comprehensive demand response model considering electrical and thermal flexible loads was further constructed.

[0080] This embodiment is based on the demand response of electric heating load. It models different types of flexible electric heating loads on the basis of heating network and power grid models respectively. First, it constructs a dynamic model of heating network considering flexible heating load and a power grid model considering flexible electric load. Then, it integrates the two to construct a comprehensive demand response model considering flexible electric heating load. This fully leverages the adjustment capability of demand-side resources, realizes peak shaving and valley filling, optimizes the load curve, and improves the flexibility and economy of IES operation.

[0081] Step 103: Introduce a tiered carbon trading mechanism into the total system operating cost, and construct an integrated energy system optimization scheduling model based on the integrated demand response model with the goal of minimizing the total system operating cost.

[0082] Specifically, considering the cost of carbon emissions, this invention incorporates carbon emissions trading into the optimized scheduling of the integrated energy system, which is a key step in achieving low carbon emissions. In the carbon emissions allocation method adopted in this paper, the sources of carbon emissions in the system are upstream electricity purchases, CHP (Consumer Power Purchase), and gas turbines.

[0083] in, , , , These represent carbon emission allowances for IES, upstream power purchases, CHP units, and gas-fired boilers, respectively, in kg. , These represent the carbon emission allowances per unit of electricity consumption for coal-fired power units and per unit of natural gas consumption for natural gas-fired power units, respectively. We can take 0.581 kg / (kW). The value can be taken as 0.385 kg / (kW); The power purchased at time t is expressed in kW. This represents the electrical power of the CHP unit at grid node i, in kW. This represents the heat output power of the CHP unit at node i of the heating network, in kW. The value represents the thermal power of the gas-fired boiler at node i, in kW; T represents the scheduling duration.

[0084] The actual carbon emissions of IES are calculated as follows:

[0085] in, Represents the set of power grid nodes; , , These represent the carbon emission coefficients of the power grid, CHP units, and gas-fired boilers, respectively, in kg / kW. This represents the actual carbon emissions of the IES, in kg. The power purchased at time t is expressed in kW. This represents the electrical power of the CHP unit at grid node i, in kW. This represents the heat output power of the CHP unit at node i of the heating network, in kW. This represents the thermal power of the gas-fired boiler at node i, in kW.

[0086] Carbon emission trading volume is the difference between carbon emission allowances and actual carbon emissions, specifically described as:

[0087] in, For IES carbon emissions trading volume; This represents the carbon emission allowance of the IES; This indicates the actual carbon emissions of the IES, expressed in kg.

[0088] To further control carbon emissions, a tiered carbon trading mechanism is introduced into the total operating cost of the system, enabling the integrated energy system to minimize carbon emissions during scheduling. The tiered carbon trading mechanism divides different purchase ranges based on carbon emissions. When the difference between carbon emissions and carbon allowances exceeds a purchase range, the purchase price for that range is higher. When carbon emissions are lower than carbon allowances, a subsidy is provided as compensation. The introduction of a compensation coefficient can further enhance emission reduction efforts. The tiered carbon trading model with a compensation coefficient is shown in the following formula:

[0089] Where δ represents the carbon trading base price, for example, 0.22 yuan / kg; L represents the length of the carbon emission range, for example, 2000 kg; z represents the carbon trading price increase rate, for example, 25%; and μ represents the compensation coefficient, for example, 0.25. E represents the carbon trading cost of the system at time t, in yuan. IES,t Expressed as the carbon emission trading amount of IES, in kg.

[0090] The integrated energy system for electricity and heat, taking into account thermal dynamic inertia, comprehensively considers thermal inertia, electricity and heat load demand response, and tiered carbon trading mechanism. Through source-load coordinated control, it aims to reduce the economic cost and carbon emissions of the integrated energy system. Taking into account the carbon trading mechanism, with the goal of minimizing the total operating cost, an optimized scheduling model for the integrated energy system is constructed, constrained by energy balance and equipment operation limits.

[0091] The objective function of the integrated energy system optimization scheduling model is:

[0092] in, , , , , , , These represent the total system operating cost, gas purchase cost, maintenance cost, wind curtailment penalty cost, compensation cost, and carbon trading cost, respectively, all in yuan.

[0093] Gas purchase cost refers to the expense incurred in purchasing electricity within an integrated energy system. It is calculated using the following formula:

[0094] in, This represents the electricity purchase price at time t, expressed in yuan / kW. This represents the purchased power at node i at time t, in kW; T represents the set of nodes in the electrical network; T represents the scheduling duration.

[0095] Gas purchase cost refers to the expense incurred in purchasing natural gas and other gaseous energy sources within an integrated energy system. It is calculated using the following formula:

[0096] in, This indicates the price of natural gas, expressed in yuan / (kW∙h). , These represent the power generation efficiency of the CHP unit and the heat production efficiency of the gas-fired boiler at node i, respectively. Indicates the scheduling time interval, in hours (h). , These represent the electrical power of the CHP unit and the thermal power of the gas-fired boiler at node i, respectively, in kW.

[0097] Maintenance costs are the expenses incurred for maintaining, servicing, and repairing equipment and systems to ensure their normal operation. Integrated energy systems involve numerous pieces of equipment, such as power generation equipment, heating equipment, and pipelines. As equipment is used, wear and tear and aging occur, requiring regular maintenance and inspection. Maintenance costs are calculated using the following formula:

[0098] in, This represents the output power of the device at time t, in kW. The value represents the equipment maintenance cost coefficient, expressed in yuan / kW; T represents the scheduling duration.

[0099] The cost of wind curtailment penalties is primarily related to the grid integration of wind power. When the electricity generated by wind power cannot be fully connected to the grid and effectively utilized due to various reasons (such as insufficient grid capacity or difficulties in peak shaving), wind curtailment occurs. To encourage the effective use of wind power and reduce curtailment, penalties may be imposed on such behavior, requiring the payment of corresponding fees; this is the cost of wind curtailment penalties. It is calculated using the following formula:

[0100] in, This represents the wind curtailment penalty coefficient, in yuan / kW; This indicates the predicted wind power output, in kW. This indicates the actual output of wind power, expressed in kW.

[0101] Compensation costs typically refer to the expenses incurred in compensating for electrical heating loads. They are calculated using the following formula:

[0102] in, This represents the cost coefficient for transferable electrical load compensation, expressed in yuan / kW. , The compensation cost coefficient for movable electrical loads and movable thermal loads is expressed in yuan / kW. , These represent the compensation cost coefficients for reducing electrical load and reducing heat load, respectively, in yuan / kW; , , , , These represent the transferable electrical load, transferable electrical power, power reduction, heat transfer, and heat reduction at time t, respectively, in kW.

[0103] The constraints in the constructed integrated energy system optimization scheduling model include, but are not limited to, electric power balance constraints, thermal power balance constraints, cogeneration unit operation constraints, gas boiler operation constraints, wind power generation unit operation constraints, electric energy storage constraints, and thermal energy storage constraints.

[0104] The power balance constraint condition is as follows:

[0105] in, This represents the electrical power of the CHP unit at grid node i, in kW. This indicates the actual output of wind power, measured in kW. This represents the purchased power at node i of the power grid at time t, in kW; This represents the discharge power of the battery at grid node i at time t, in kW; This represents the charging power of the battery at grid node i at time t, in kW; This represents the electrical load required by the user after the grid node i responds at time t, in kW.

[0106] The thermal power balance constraint condition is as follows:

[0107] in, This represents the heat output power of the CHP unit at node i of the heating network, in kW. This represents the thermal power of the gas-fired boiler at node i of the heating network, in kW. This represents the amount of heat released by the thermal storage tank at node i of the heating network at time t, in kW. This represents the amount of heat stored in the thermal storage tank at node i of the heating network at time t, in kW. Let t be the heat load required by the user after the response of node i in the heating network at time t, in kW.

[0108] CHP unit operating constraints, namely:

[0109] in, This represents the power generation efficiency of the CHP unit at node i; This represents the heat production efficiency of the k-th CHP unit at node i. This represents the natural gas power consumed by the k-th CHP unit at node i at time t, in kW; and Let represent the output electrical power and output thermal power of the k-th CHP unit at node i at time t, respectively; and Let represent the minimum and maximum output power of the k-th CHP unit, respectively; and Let represent the minimum and maximum output thermal power of the k-th CHP unit, respectively.

[0110] Operating constraints for gas-fired boilers, namely:

[0111] in, This represents the thermal power of the gas-fired boiler at node i, in kW. This indicates the heat production efficiency of a gas-fired boiler. For the gas boiler at node i The natural gas power input at any given time, in kW; and These represent the maximum and minimum output thermal power of the gas-fired boiler, respectively, in kW.

[0112] The operating constraints of wind power generators are as follows:

[0113] in, This indicates the actual output of wind power, measured in kW. This indicates the predicted wind power output, expressed in kW.

[0114] The constraints of electrical energy storage are as follows:

[0115] in, This represents the storage capacity of the electrical energy storage device at time t; This represents the storage capacity of the electrical energy storage device at time t-1; This represents the charging power of the energy storage device at time t; This represents the discharge power of the energy storage device at time t; Indicates charging efficiency; Indicates discharge efficiency; , These represent the upper and lower limits of the capacity of electrical energy storage, respectively. , These represent the upper limit and lower limit of the charging power for energy storage, respectively. , These represent the upper and lower limits of the discharge power of the electrical energy storage, respectively. , These represent the energy storage capacity at the beginning and end of the time periods, respectively.

[0116] The operating constraints of thermal energy storage are similar to those of electrical energy storage, so the thermal energy storage constraint model will not be elaborated further here.

[0117] Based on the 24-hour wind power forecast and combined with the forecast data of regional electricity and heat load, a multi-energy flow network platform for an integrated energy system is established. The distribution characteristics of the multi-energy flow are analyzed, and a basic framework for optimized scheduling of "source-grid-load-storage" is constructed from both the supply and demand sides. With the goal of minimizing the total operating cost within the region, and considering the operational constraints of the entire energy network system, the established integrated energy system optimized scheduling model, which takes into account thermal dynamic inertia, can real-time correct the day-ahead energy allocation and scheduling scheme, ultimately improving the system's economic operation, wind power absorption, and energy-saving operation.

[0118] Step 104: Solve the integrated energy system optimization scheduling model to generate a power output scheduling scheme for multiple energy supply devices in the integrated energy system under the condition of minimum total system operating cost.

[0119] Specifically, the optimization problem of the integrated energy system scheduling model constructed above belongs to the Mixed Integer Linear Programming (MILP) problem. MILP problems are very common in practice, especially in the field of integrated energy system optimization, because many decision variables in energy systems (such as the on / off states of equipment) are usually integer or binary variables, while there are also some continuous variables (such as energy flow), involving linear objective functions and a series of linear inequalities or equality constraints.

[0120] To address this issue, the MATLAB platform and its related tools can be utilized. MATLAB is a widely used high-performance numerical computation and visualization environment, offering a rich set of toolboxes to solve problems in specific domains. YALMIP is one such open-source toolbox for modeling and solving optimization problems such as linear programming and quadratic programming. It provides a high-level modeling language that allows users to express models in a natural mathematical way and call various external optimization solvers through interfaces. Therefore, modeling can be performed based on the YALMIP toolbox. The process of building a MILP model using YALMIP in MATLAB includes defining decision variables, establishing objective functions and constraints, and setting optimization parameters.

[0121] Regarding the choice of solver, the GUROBI solver can be used. GUROBI is a widely used linear programming solver that provides efficient algorithms for solving large-scale linear, integer, and quadratic programming problems, which is particularly crucial in energy system planning and scheduling. By calling the GUROBI solver through YALMIP, the advantages of both can be fully utilized to efficiently solve the constructed integrated energy system optimal scheduling model, thereby generating the optimal output scheduling scheme for multiple energy supply devices in the integrated energy system under the condition of minimum total system operating cost.

[0122] In one embodiment, a scheduling period of 24 hours and a scheduling interval of 1 hour are selected, combined with... Figures 4-5 , Figure 4 This is one of the schematic diagrams of the system output scheduling scheme provided by the present invention. Figure 5 This is the second schematic diagram of the system output scheduling scheme provided by this invention. Taking into account the system's thermal dynamics, IDR, and tiered carbon trading mechanism, the collaboratively optimized scheduling of equipment power output is as follows: Figure 3As shown. The electrical load is mainly provided by CHP turbines and wind power. Due to the power limit of CHP turbines, the remaining electrical load is discharged by batteries to maintain stable system operation. During off-peak hours (22:00-6:00 the next day), wind power output is high, and the wind power can be fully absorbed. The surplus energy is stored in batteries, and the system does not need to purchase additional electricity, effectively improving the system's energy supply method while reducing the system's energy purchase cost. From 7:00 to 21:00, the CHP turbines have high output and undertake most of the electrical load supply task, which is then handled by... Figure 4 It can be seen that the main heat load is supplied by the CHP unit and the gas boiler. The heat storage tank is charged with heat from 20:00 to 24:00 and released heat from 0:00 to 1:00, which improves the system flexibility.

[0123] The integrated energy system optimization scheduling method provided by this invention combines electric and thermal load demand response. Based on the dynamic model of the heating network and the power grid model, it models the electric and thermal load, constructing an integrated demand response model that considers flexible electric and thermal loads. This fully considers the characteristics of both energy supply and demand. Then, a tiered carbon trading mechanism is introduced into the total system operating cost, with the goal of minimizing the total operating cost. Based on the integrated demand response model, an integrated energy system optimization scheduling model is constructed, balancing economic costs and carbon emission constraints. Solving the model generates output scheduling schemes for multiple energy-supplying devices. This method can achieve efficient energy utilization and energy-saving operation while minimizing the total system operating cost, improving the scheduling decision-making level under low carbon emission conditions, and enhancing the economy, reliability, and environmental friendliness of the integrated energy system. It provides an effective strategy for the optimized operation of integrated energy systems.

[0124] Furthermore, to verify the synergistic optimization scheduling effect considering thermal dynamics, the tiered carbon trading mechanism, and IDR, five scenarios were established for comparative analysis, with a scheduling cycle of 24 hours and a scheduling interval of 1 hour. These scenarios include: Scenario 1: Without considering the thermal inertia of the heating network and buildings, and without considering the traditional optimization scheduling of the tiered carbon trading mechanism and IDR, it can be understood that the relevant variables of the heating network dynamic model, the tiered carbon trading mechanism and the integrated energy system optimization scheduling model are initialized to 0. Based on the power grid model, with the goal of minimizing the total system operating cost, the first system optimization scheduling model to be compared is constructed. Scenario 2: Considering the thermal inertia of the heating network and buildings, but not the economic optimization scheduling of the tiered carbon trading mechanism and IDR, it can be understood as initializing the relevant variables of the tiered carbon trading mechanism and the integrated energy system optimization scheduling model to 0, and constructing a second system optimization scheduling model to be compared based on the heating network dynamic model and the power grid model, with the goal of minimizing the total system operating cost. Scenario 3: Considering the thermal inertia of heating networks and buildings, as well as the tiered carbon trading mechanism, but not the economic optimization scheduling of IDR, it can be understood as initializing the relevant variables of the integrated energy system optimization scheduling model to 0, and constructing a third system optimization scheduling model to be compared based on the dynamic model of the heating network, the power grid model and the tiered carbon trading mechanism, with the goal of minimizing the total operating cost of the system. Scenario 4: Considering the thermal inertia of the heating network and buildings, as well as IDR, but not the economic optimization scheduling of the tiered carbon trading mechanism, it can be understood as initializing the relevant variables of the tiered carbon trading mechanism to 0, and constructing the fourth system optimization scheduling model to be compared based on the dynamic model of the heating network, the power grid model and the integrated energy system optimization scheduling model, with the goal of minimizing the total operating cost of the system. Scenario 5: Taking into account the thermal inertia of heating networks and buildings, tiered carbon trading mechanisms, and IDR, it can be understood as constructing an integrated energy system optimization scheduling model based on the dynamic model of the heating network, the power grid model, the integrated energy system optimization scheduling model, and the tiered carbon trading mechanism, with the goal of minimizing the total operating cost of the system.

[0125] Using the GUROBI solver, the first system optimization scheduling model to be compared and constructed in scenario 1, the second system optimization scheduling model to be compared and constructed in scenario 2, the third system optimization scheduling model to be compared and constructed in scenario 3, the fourth system optimization scheduling model to be compared and constructed in scenario 4, and the comprehensive energy system optimization scheduling model to be constructed in scenario 5 are solved respectively. The first minimum total system operating cost solved in scenario 1, the second minimum total system operating cost solved in scenario 2, the third minimum total system operating cost solved in scenario 3, the fourth minimum total system operating cost solved in scenario 4, and the fifth minimum total system operating cost solved in scenario 5 are obtained.

[0126] Then, the minimum total system operating cost, total carbon emissions (which can be estimated based on carbon trading costs), and wind power utilization (which can be estimated based on wind curtailment penalty costs) obtained in each scenario are compared and analyzed, and the following conclusions are drawn: (1) Considering the thermal dynamic characteristics of the heating network and buildings can significantly improve the system flexibility. By flexibly adjusting the heat load, the wind curtailment phenomenon in the system can be reduced and the wind power utilization rate can be significantly improved, thereby effectively reducing the carbon emissions of the system. (2) The interaction between the thermal dynamic characteristics of DHS and the reward and punishment tiered carbon trading mechanism further guides the system to reduce carbon emissions below the carbon quota, which has certain advantages in reducing IES carbon emissions. The system can reduce carbon emissions by absorbing wind power and then sell excess carbon emission rights to achieve optimal scheduling with low emissions and economy. (3) On the basis of the tiered carbon trading mechanism, adding comprehensive demand response for electricity and heat can effectively alleviate the energy supply pressure during peak energy consumption periods, and fully tap the low emission potential of the system while obtaining economic benefits.

[0127] As shown in Table 1, considering the system's thermal dynamics, the combination of IDR and tiered carbon trading mechanisms can significantly reduce the total operating cost of the system and improve energy efficiency, which has certain advantages in reducing IES carbon emissions, further demonstrating the feasibility and effectiveness of the proposed method.

[0128] Table 1 System operating costs for each scenario

[0129] The integrated energy system optimization scheduling device provided by the present invention is described below. The integrated energy system optimization scheduling device described below and the integrated energy system optimization scheduling method described above can be referred to in correspondence.

[0130] Combination Figure 6 , Figure 6 This is a schematic diagram of the integrated energy system optimization scheduling device provided by the present invention.

[0131] like Figure 6 As shown, the integrated energy system optimization and scheduling device includes: The first modeling module 610 is used to construct the dynamic model of the heating network and the power grid model; The second modeling module 620 is used to perform electric heating load modeling based on the electric heating load demand response, on the basis of the heating network dynamic model and the power grid model, and to construct a comprehensive demand response model that takes into account the electric heating flexible load. The third modeling module 630 is used to introduce a tiered carbon trading mechanism into the total system operating cost, with the goal of minimizing the total system operating cost, and to build an integrated energy system optimization scheduling model based on the integrated demand response model. The scheduling scheme generation module 640 is used to solve the integrated energy system optimization scheduling model and generate a power output scheduling scheme for multiple energy supply devices in the integrated energy system under the condition of minimum total system operating cost.

[0132] The integrated energy system optimization scheduling device provided by this invention combines electric and thermal load demand response. Based on the dynamic model of the heating network and the power grid model, it models the electric and thermal load and constructs an integrated demand response model that takes into account the characteristics of both energy supply and demand. Then, it introduces a tiered carbon trading mechanism into the total system operating cost and aims to minimize the total operating cost. Based on the integrated demand response model, it constructs an integrated energy system optimization scheduling model that balances economic costs and carbon emission constraints. The model solves to generate output scheduling schemes for multiple energy supply devices. It can achieve efficient energy utilization and energy-saving operation while minimizing the total system operating cost, improve the scheduling decision-making level under low carbon emission conditions, and enhance the economy, reliability, and environmental protection of the integrated energy system. It provides an effective strategy for the optimized operation of integrated energy systems.

[0133] Furthermore, the first modeling module 610 is also used for: Obtain a comprehensive energy system dataset; the comprehensive energy system dataset includes, but is not limited to, wind power output, electricity and heat load forecast data, system equipment parameters, parameter baseline values, and system correlation coefficients for a specific time and day in the current region; Based on the integrated energy system dataset, the thermal dynamic characteristics of the district heating system are described by a first-order dynamic model, and the heat network is modeled to obtain the heat network dynamic model. Based on the aforementioned integrated energy system dataset, the steady-state transmission characteristics of the power system are described through a branch power flow model, and the power network is modeled to obtain a power grid model.

[0134] Furthermore, the second modeling module 620 is also used for: Based on the demand response of electric heating load, a dynamic model of the heating network considering thermal flexibility load is constructed by performing modeling of movable heat load and modeling of heat load that can be reduced, respectively, on the basis of the dynamic model of the heating network. Based on the demand response of electrical and thermal loads, transferable electrical loads, shiftable electrical loads, and reduceable electrical loads are modeled on the basis of the power grid model to construct a power grid model that takes into account the flexible electrical loads. Based on the dynamic model of the heating network that takes into account thermal flexible loads and the power grid model that takes into account electrical flexible loads, a comprehensive demand response model that takes into account electrical and thermal flexible loads is constructed.

[0135] Furthermore, the third modeling module 630 is also used for: Determine the costs of electricity purchase, gas purchase, maintenance, wind curtailment penalties, compensation, and carbon trading; Based on the electricity purchase cost, gas purchase cost, maintenance cost, wind curtailment penalty cost, compensation cost, and carbon trading cost, an objective function is constructed in the integrated energy system optimization scheduling model to minimize the total system operating cost.

[0136] Furthermore, the integrated energy system optimization and scheduling device is also used for: The relevant variables of the heating network dynamic model, the tiered carbon trading mechanism and the integrated energy system optimization scheduling model are initialized to 0. Based on the power grid model, the first system optimization scheduling model to be compared is constructed with the goal of minimizing the total system operating cost. The relevant variables of the tiered carbon trading mechanism and the integrated energy system optimization scheduling model are initialized to 0. Based on the heating network dynamic model and the power grid model, and with the goal of minimizing the total system operating cost, a second system optimization scheduling model to be compared is constructed. The relevant variables of the integrated energy system optimization scheduling model are initialized to 0. Based on the heating network dynamic model, the power grid model and the tiered carbon trading mechanism, a third system optimization scheduling model to be compared is constructed with the goal of minimizing the total system operating cost. The relevant variables of the tiered carbon trading mechanism are initialized to 0. Based on the dynamic model of the heating network, the power grid model, and the integrated energy system optimization scheduling model, a fourth system optimization scheduling model to be compared is constructed with the goal of minimizing the total operating cost of the system. Based on the dynamic model of the heating network, the power grid model, the integrated energy system optimization scheduling model and the tiered carbon trading mechanism, an integrated energy system optimization scheduling model is constructed with the goal of minimizing the total system operating cost. The first, second, third, fourth, and integrated energy system optimization scheduling models are solved respectively to obtain the first minimum total system operating cost corresponding to the first, second, third, fourth, and integrated energy system optimization scheduling models. The first minimum total system operating cost, the second minimum total system operating cost, the third minimum total system operating cost, the fourth minimum total system operating cost, and the fifth minimum total system operating cost are compared and analyzed to verify the feasibility of the system scheduling method of the integrated energy system optimization scheduling model under the condition of the fifth minimum total system operating cost.

[0137] It should be noted that the integrated energy system optimization scheduling device provided by the present invention can execute the integrated energy system optimization scheduling method described in any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0138] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 7 As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other through the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute an integrated energy system optimization scheduling method. This method includes: constructing a dynamic model of the heating network and a power grid model; based on the demand response of the heating and heat loads, modeling the heating and heat loads on the basis of the dynamic model of the heating network and the power grid model, and constructing an integrated demand response model that takes into account the flexible heating and heat loads; introducing a tiered carbon trading mechanism into the total system operating cost, and constructing an integrated energy system optimization scheduling model based on the integrated demand response model with the goal of minimizing the total system operating cost; solving the integrated energy system optimization scheduling model to generate a power output scheduling scheme for multiple energy supply devices in the integrated energy system under the condition of minimizing the total system operating cost.

[0139] Furthermore, the logical instructions in the aforementioned memory 730 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, essentially, 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 described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0140] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the integrated energy system optimization scheduling method provided in the above embodiments. The method includes: constructing a dynamic model of a heating network and a power grid model; based on the demand response of electric and heat loads, performing electric and heat load modeling on the basis of the dynamic model of the heating network and the power grid model, and constructing an integrated demand response model that takes into account the flexible electric and heat loads; introducing a tiered carbon trading mechanism into the total system operating cost, and constructing an integrated energy system optimization scheduling model based on the integrated demand response model with the goal of minimizing the total system operating cost; solving the integrated energy system optimization scheduling model to generate a power output scheduling scheme for multiple energy supply devices in the integrated energy system under the condition of minimizing the total system operating cost.

[0141] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the integrated energy system optimization scheduling method provided in the above embodiments. The method includes: constructing a dynamic model of a heating network and a power grid model; based on the demand response of electric and heat loads, performing electric and heat load modeling on the basis of the dynamic model of the heating network and the power grid model, and constructing an integrated demand response model that takes into account the flexible electric and heat loads; introducing a tiered carbon trading mechanism into the total system operating cost, and constructing an integrated energy system optimization scheduling model based on the integrated demand response model with the goal of minimizing the total system operating cost; solving the integrated energy system optimization scheduling model to generate a power output scheduling scheme for multiple energy supply devices in the integrated energy system under the condition of minimizing the total system operating cost.

[0142] The embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0143] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the scheduling of a comprehensive energy system, characterized in that, The integrated energy system optimization scheduling method includes: Construct dynamic models of the heating network and power grid models; Based on the demand response of electric heating load, electric heating load modeling is performed on the basis of the heating network dynamic model and the power grid model to construct a comprehensive demand response model that takes into account the flexible electric heating load. A tiered carbon trading mechanism is introduced into the total system operating cost, with the goal of minimizing the total system operating cost. Based on the comprehensive demand response model, an integrated energy system optimization scheduling model is constructed. Solve the integrated energy system optimization scheduling model to generate a power output scheduling scheme for multiple energy supply devices in the integrated energy system with the minimum total system operating cost.

2. The integrated energy system optimization scheduling method according to claim 1, characterized in that, The objective function of the integrated energy system optimization scheduling model is determined in the following way: Determine the costs of electricity purchase, gas purchase, maintenance, wind curtailment penalties, compensation, and carbon trading; Based on the electricity purchase cost, gas purchase cost, maintenance cost, wind curtailment penalty cost, compensation cost, and carbon trading cost, an objective function is constructed in the integrated energy system optimization scheduling model to minimize the total system operating cost.

3. The integrated energy system optimization scheduling method according to claim 1, characterized in that, The construction of the heating network dynamic model and the power grid model includes: Obtain a comprehensive energy system dataset; the comprehensive energy system dataset includes, but is not limited to, wind power output, electricity and heat load forecast data, system equipment parameters, parameter baseline values, and system correlation coefficients for a specific time and day in the current region; Based on the integrated energy system dataset, the thermal dynamic characteristics of the district heating system are described by a first-order dynamic model, and the heat network is modeled to obtain the heat network dynamic model. Based on the aforementioned integrated energy system dataset, the steady-state transmission characteristics of the power system are described through a branch power flow model, and the power network is modeled to obtain a power grid model.

4. The integrated energy system optimization scheduling method according to claim 3, characterized in that, The method based on electric heating load demand response involves modeling the electric heating load on the basis of the heating network dynamic model and the power grid model, and constructing a comprehensive demand response model that takes into account the flexible electric heating load, including: Based on the demand response of electric heating load, a dynamic model of the heating network considering thermal flexibility load is constructed by performing modeling of movable heat load and modeling of heat load that can be reduced, respectively, on the basis of the dynamic model of the heating network. Based on the demand response of electrical and thermal loads, transferable electrical loads, shiftable electrical loads, and reduceable electrical loads are modeled on the basis of the power grid model to construct a power grid model that takes into account the flexible electrical loads. Based on the dynamic model of the heating network that takes into account thermal flexible loads and the power grid model that takes into account electrical flexible loads, a comprehensive demand response model that takes into account electrical and thermal flexible loads is constructed.

5. The integrated energy system optimization scheduling method according to claim 1, characterized in that, The integrated energy system optimization scheduling model is subject to multiple constraints, including but not limited to electric power balance constraints, thermal power balance constraints, cogeneration unit operation constraints, gas boiler operation constraints, wind power generation unit operation constraints, electric energy storage constraints, and thermal energy storage constraints.

6. The integrated energy system optimization scheduling method according to claim 1, characterized in that, The integrated energy system optimization scheduling model is solved using the GUROBI solver.

7. The integrated energy system optimization scheduling method according to any one of claims 1-6, characterized in that, The integrated energy system optimization scheduling method also includes: The relevant variables of the heating network dynamic model, the tiered carbon trading mechanism and the integrated energy system optimization scheduling model are initialized to 0. Based on the power grid model, the first system optimization scheduling model to be compared is constructed with the goal of minimizing the total system operating cost. The relevant variables of the tiered carbon trading mechanism and the integrated energy system optimization scheduling model are initialized to 0. Based on the heating network dynamic model and the power grid model, and with the goal of minimizing the total system operating cost, a second system optimization scheduling model to be compared is constructed. The relevant variables of the integrated energy system optimization scheduling model are initialized to 0. Based on the heating network dynamic model, the power grid model and the tiered carbon trading mechanism, a third system optimization scheduling model to be compared is constructed with the goal of minimizing the total system operating cost. The relevant variables of the tiered carbon trading mechanism are initialized to 0. Based on the dynamic model of the heating network, the power grid model, and the integrated energy system optimization scheduling model, a fourth system optimization scheduling model to be compared is constructed with the goal of minimizing the total operating cost of the system. Based on the dynamic model of the heating network, the power grid model, the integrated energy system optimization scheduling model and the tiered carbon trading mechanism, an integrated energy system optimization scheduling model is constructed with the goal of minimizing the total system operating cost. The first, second, third, fourth, and integrated energy system optimization scheduling models are solved respectively to obtain the first minimum total system operating cost corresponding to the first, second, third, fourth, and integrated energy system optimization scheduling models. The first minimum total system operating cost, the second minimum total system operating cost, the third minimum total system operating cost, the fourth minimum total system operating cost, and the fifth minimum total system operating cost are compared and analyzed to verify the feasibility of the system scheduling method of the integrated energy system optimization scheduling model under the condition of the fifth minimum total system operating cost.

8. A comprehensive energy system optimization and scheduling device, characterized in that, include: The first modeling module is used to build dynamic models of the heating network and power grid models; The second modeling module is used to model the electric heating load based on the electric heating load demand response, on the basis of the heating network dynamic model and the power grid model, and to construct a comprehensive demand response model that takes into account the electric heating flexible load. The third modeling module is used to introduce a tiered carbon trading mechanism into the total system operating cost, with the goal of minimizing the total system operating cost, and to build an integrated energy system optimization scheduling model based on the integrated demand response model. The scheduling scheme generation module is used to solve the integrated energy system optimization scheduling model and generate a power output scheduling scheme for multiple energy supply devices in the integrated energy system under the condition of minimizing the total system operating cost.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the integrated energy system optimization scheduling method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, wherein a computer program is stored on the non-transitory computer-readable storage medium, characterized in that, When the computer program is executed by the processor, it implements the steps of the integrated energy system optimization scheduling method as described in any one of claims 1 to 7.

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