Scheduling Method for Park Integrated Energy System Considering Coupling Characteristics of Supply and Demand Sides

Abstract

The present invention relates to the field of power systems, and specifically discloses a scheduling method for a park integrated energy system considering the coupling characteristics of both supply and demand sides. The method includes: constructing a supply-demand bilateral energy coupling model considering the integrated demand response incentive mechanism according to the energy supply-demand balance relationship of the park integrated energy system and the coupling characteristics of the equipment between the supply and demand bilateral energy hubs; based on the probability distribution function of the user's energy consumption load, using the Latin hypercube sampling method and the synchronous back substitution method to obtain multiple scenarios of the energy consumption load and the probability of each scenario; based on the probability of each scenario, on the premise that the daily operating income of the integrated energy service provider is not lower than the critical value, taking the maximum uncertainty of the predicted values of wind power output and photovoltaic power output as the upper-layer objective and the minimum daily operating cost of users as the lower-layer objective, constructing a two-layer optimal scheduling model for the park integrated energy system and solving it to obtain the scheduling strategy of the park integrated energy system. The present invention can improve the accuracy of scheduling decisions.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular, to a scheduling method for a park integrated energy system considering the coupling characteristics of both supply and demand sides. Background Art

[0002] With the attention to environmental and energy issues, how to build a low-carbon, clean and sustainable energy supply system is crucial for promoting the high-quality development of energy.

[0003] The Park Integrated Energy System (PIES) is a typical energy Internet based on multi-energy coupled production and with the coordinated operation of multiple energy networks as the scheduling core. While meeting the diverse energy consumption needs of users, it promotes the consumption of renewable energy and improves energy utilization efficiency, which is of great significance for vigorously developing low-carbon power and enhancing the consumption capacity of renewable energy. However, the current research on the optimal scheduling of the park integrated energy system is not yet perfect. Summary of the Invention

[0004] Therefore, the present invention provides a scheduling method for a park integrated energy system considering the coupling characteristics of both supply and demand sides, aiming to solve or at least alleviate the above problems.

[0005] According to one aspect of the present invention, there is provided a scheduling method for a comprehensive energy system in a park considering the coupling characteristics of both supply and demand sides. The energy hub of the comprehensive energy system in the park includes a supply-side energy hub and a demand-side energy hub. The supply-side energy hub includes energy production equipment and energy storage equipment, and the demand-side energy hub includes energy conversion equipment. The method includes: constructing a supply-demand bilateral energy coupling model considering the comprehensive demand response incentive mechanism based on the energy supply-demand balance relationship of the comprehensive energy system in the park and the coupling characteristics of the equipment between the supply-side energy hub and the demand-side energy hub; generating scenarios using the Latin hypercube sampling method and reducing scenarios using the synchronous back substitution method based on the probability distribution function of the energy consumption load of comprehensive energy users to obtain multiple scenarios of the energy consumption load and the probability of each scenario; constructing an energy consumption scheduling model for comprehensive energy users with the goal of minimizing the daily operating cost of comprehensive energy users based on the obtained multiple scenarios and the probability of each scenario; constructing a robust scheduling model for comprehensive energy service providers under risk aversion strategies with the goal of maximizing the uncertainty of the predicted wind power output value and the predicted photovoltaic power output value based on the information gap decision theory; constructing a two-layer optimal scheduling model for the comprehensive energy system in the park with the energy consumption scheduling model as the lower layer model and the robust scheduling model as the upper layer model, and solving it based on the supply-demand bilateral energy coupling model to obtain the scheduling strategy of the comprehensive energy system in the park. The scheduling strategy includes the load curtailment amount, load transfer amount, and output of energy conversion equipment at each moment for comprehensive energy users, and the energy selling price, comprehensive demand response incentive subsidy price, output of energy production equipment, and output of energy storage equipment at each moment for comprehensive energy service providers.

[0006] According to another aspect of the present invention, there is provided a computing device, including: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by at least one processor, and the program instructions include instructions for executing the scheduling method for the comprehensive energy system in the park considering the coupling characteristics of both supply and demand sides according to the present invention.

[0007] According to another aspect of the present invention, there is provided a readable storage medium storing program instructions, which when read and executed by a computing device, causes the computing device to execute the scheduling method for the comprehensive energy system in the park considering the coupling characteristics of both supply and demand sides according to the present invention.

[0008] In summary, the present invention proposes a two-layer optimization scheduling model for an integrated energy system based on an IDR incentive mechanism under bilateral uncertainty and coupling characteristics. Among them, on the basis of considering the coupling characteristics of the internal equipment of PIES, a supply-demand bilateral coupling characteristic model is proposed, which fully taps the interactive potential of both sides of supply and demand; the uncertainty of multi-type load forecasts such as electricity, heat, and cold is characterized by multi-scenario technology, and the uncertainty of new energy output such as wind power and photovoltaics is characterized by IGDT theory, which fully considers the risk tolerance of integrated energy service providers in the scheduling process; a comprehensive demand response incentive mechanism including electricity, heat, cold and other loads is set from the two aspects of energy price and incentive subsidy price, which enriches both the incentive means and the types of loads participating in the response. Therefore, the present invention can improve the accuracy of scheduling decisions, thereby ensuring the low-carbon, efficient and economical operation of the park's integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] To achieve the above and related purposes, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings, which indicate various ways in which the principles disclosed herein can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The above and other purposes, features and advantages of the present disclosure will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

[0010] Figure 1 A structural block diagram of a computing device 100 according to an embodiment of the present invention is shown;

[0011] Figure 2 A flow chart of a method 200 for scheduling a park integrated energy system taking into account the coupling characteristics of supply and demand sides according to an embodiment of the present invention is shown;

[0012] Figure 3 A schematic diagram of an integrated energy system operation architecture for a park considering integrated demand response according to an embodiment of the present invention is shown;

[0013] Figure 4 A schematic diagram showing the energy coupling relationship between supply and demand sides considering a comprehensive demand response incentive mechanism according to an embodiment of the present invention is shown;

[0014] Figure 5 A schematic diagram showing comprehensive demand response behaviors under different excitation signals according to an embodiment of the present invention is shown;

[0015] Figure 6 A schematic diagram of a comprehensive demand response incentive process according to an embodiment of the present invention is shown;

[0016] Figure 7Shows the framework diagram of the two - layer optimal scheduling model of the campus integrated energy system according to an embodiment of the present invention;

[0017] Figure 8 Shows the schematic diagram of the solution process of the two - layer optimal scheduling model of the campus integrated energy system according to an embodiment of the present invention;

[0018] Figure 9 Shows the schematic diagram of the predicted curves of the day - ahead electricity, heat, and cooling loads and the predicted curves of the day - ahead wind power and photovoltaic power outputs according to an embodiment of the present invention;

[0019] Figure 10 Shows the schematic diagram of the iterative results of the daily operation revenue of the integrated energy service provider, the revenue composition of the integrated energy service provider, and the cost composition of the integrated energy users according to an embodiment of the present invention;

[0020] Figure 11 Shows the schematic diagram of the probability of the typical scenario at t = 12 according to an embodiment of the present invention;

[0021] Figure 12 Shows the schematic diagram of the typical scenarios of the electricity load, heat load, and cooling load according to an embodiment of the present invention;

[0022] Figure 13 Shows the schematic diagram of the regulation schemes of each device under the risk - neutral strategy without considering the uncertainty of wind power and photovoltaic power outputs according to an embodiment of the present invention;

[0023] Figure 14 Shows the schematic diagram of the Pareto frontiers under different robustness levels according to an embodiment of the present invention;

[0024] Figure 15 Shows the schematic diagram of the scheduling results of gas turbines and waste heat boilers under the risk - neutral strategy and the risk - aversion strategy according to an embodiment of the present invention;

[0025] Figure 16 Shows the schematic diagram of the scheduling results of the energy purchase quantity and the integrated demand response quantity under the risk - neutral strategy and the risk - aversion strategy according to an embodiment of the present invention;

[0026] Figure 17 Shows the schematic diagram of the integrated demand response incentive strategy of the integrated energy service provider and the response results of the integrated energy users according to an embodiment of the present invention. Detailed implementation manners

[0027] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0028] In view of the fact that the research on the optimal scheduling of the current campus integrated energy system is not yet perfect, the present invention provides a scheduling method for a campus integrated energy system considering the coupling characteristics of both supply and demand sides, wherein the method can be executed in a computing device. Figure 1 A block diagram of the physical components (i.e., hardware) of a computing device 100 is shown. In a basic configuration, the computing device 100 includes at least one processing unit 102 and a system memory 104. According to one aspect, depending on the configuration and type of the computing device, the processing unit 102 can be implemented as a processor. The system memory 104 includes, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, the system memory 104 includes an operating system 105 and program modules 106, and the program modules 106 include a scheduling module 120, and the scheduling module 120 is configured to execute the scheduling method 200 of the campus integrated energy system considering the coupling characteristics of both supply and demand sides of the present invention.

[0029] According to one aspect, the operating system 105 is suitable for controlling the operation of the computing device 100, for example. In addition, the examples are practiced in conjunction with a graphics library, other operating systems, or any other application programs, and are not limited to any specific application or system. In Figure 1 the basic configuration is shown by those components within the dashed line 108. According to one aspect, the computing device 100 has additional features or functions. For example, according to one aspect, the computing device 100 includes additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or magnetic tapes. Such additional storage is Figure 1 shown by a removable storage 109 and a non-removable storage 110 in

[0030] As stated above, according to one aspect, program modules are stored in the system memory 104. According to one aspect, the program modules can include one or more application programs, and the present invention does not limit the types of application programs. For example, the application programs can include: email and contact applications, word processing applications, spreadsheet applications, database applications, slide show applications, painting or computer-aided applications, web browser applications, etc.

[0031] According to one aspect, examples may be practiced in a circuit including discrete electronic components, a packaged or integrated electronic chip containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic components or a microprocessor. For example, examples may be practiced via a system on a chip (SOC) in which each or many of the components shown in Figure 1 may be integrated on a single integrated circuit. According to one aspect, such an SOC device may include one or more processing units, graphics units, communication units, system virtualization units, and various application functions, all of which are integrated (or "burned") onto a chip substrate as a single integrated circuit. When operating via an SOC, the functions described herein may be operated via dedicated logic integrated with other components of computing device 100 on a single integrated circuit (chip). Embodiments of the invention may also be practiced using other technologies capable of performing logical operations (such as AND, OR, and NOT), including but not limited to mechanical, optical, fluidic, and quantum technologies. Additionally, embodiments of the invention may be practiced within a general purpose computer or in any other circuit or system.

[0032] According to one aspect, computing device 100 may also have one or more input devices 112, such as a keyboard, mouse, pen, voice input device, touch input device, etc. An output device 114, such as a display, speaker, printer, etc., may also be included. The foregoing devices are examples and other devices may also be used. Computing device 100 may include one or more communication connections 116 that allow communication with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver, and / or transceiver circuits; universal serial bus (USB), parallel, and / or serial ports.

[0033] As used herein, the term computer-readable medium includes computer storage media. Computer storage media can include volatile and nonvolatile, removable and nonremovable media implemented in any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules). System memory 104, removable storage 109, and nonremovable storage 110 are all examples of computer storage media (i.e., memory storage). Computer storage media can include random access memory (RAM), read only memory (ROM), electrically erasable read only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other article of manufacture that can be used to store information and that can be accessed by a computing device 100. According to one aspect, any such computer storage media can be part of a computing device 100. Computer storage media does not include carrier waves or other propagated data signals.

[0034] According to one aspect, communication media is embodied by computer-readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transmission mechanism) and includes any information delivery media. According to one aspect, the term "modulated data signal" describes a signal having one or more sets of characteristics or a signal that has been altered in such a manner as to encode information in the signal. By way of example and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0035] Figure 2 A flowchart of a scheduling method 200 for a campus integrated energy system considering the coupling characteristics of both supply and demand sides according to an embodiment of the present invention is shown. The method 200 is adapted to be executed in a computing device (e.g., Figure 1 the computing device 100 shown).

[0036] Among them, the Energy Hub (EH) is an interface platform between the source, grid, and load in the energy system, which includes the mutual conversion, distribution, and storage of various forms of energy, thereby realizing the optimal allocation of energy resources and providing technical support for the coordinated operation of multiple energies. Under the basic framework of EH, a large number of energy conversion devices make the coupling of different types of energy in the energy supply, transmission, and demand links stronger. Based on this, according to the different installation positions and compositions of the devices, the EH of PIES in this invention is divided into the supply-side energy hub and the demand-side energy hub, which are regulated by the Integrated Energy Service Provider (IESP) and the integrated energy user respectively. Specifically, the supply-side energy hub includes energy production devices and energy storage devices, and the demand-side energy hub includes energy conversion devices.

[0037] In addition, IDR is the derivation and extension of traditional power DR on the demand side in the integrated energy network. IDR is the extension and expansion of traditional power demand response technology under the multi-energy coupling characteristics of the energy Internet. Users can adjust their load demands through "multi-dimensional interactions" such as load conversion, transfer, or reduction, and alternative energy use and multi-energy complementarity. Therefore, the effective implementation and regulation of IDR in the integrated energy system can not only improve the energy economy on the user side but also enhance the economy of the system, thereby promoting the consumption of renewable energy and reducing carbon emissions.

[0038] Based on this, this invention constructs an operation architecture of PIES considering Integrated Demand Response (IDR) with EH as the basic framework, as Figure 3 shown.

[0039] From the perspective of energy flow, the IDR strategy of this invention mainly includes two processes: energy supply and demand. On the energy supply side, the IESP purchases various energies from the upper-level energy trading market and integrates its own distributed wind power, photovoltaic, and other resources. Through devices such as transformers, combined heat and power units, and gas boilers, the above resources are exchanged or converted into energy forms that users can utilize and transmitted to users to meet the diverse load demands of users. On the energy demand side, users use their configured energy coupling devices such as heat pumps, electric chillers, and absorption chillers, and adopt reasonable and beneficial energy use methods to consume the electric energy and heat energy transmitted from the energy supply side to meet their own electricity, heat, cooling, and other demands.

[0040] From the perspective of information flow, IESP uses the energy management system to control the output plans of each unit inside, and on the other hand, to interact with the adjustable load on the user side, to convey the demand response targets of electricity, heat and other energy and the corresponding energy prices and incentive subsidy prices to users, to encourage users to participate in IDR and ensure the balance of energy supply and demand. It should be noted that the comprehensive energy users participating in IDR are mainly industrial users with energy management systems, diversified energy needs and large loads, and they participate without affecting their energy satisfaction.

[0041] The above is the operation architecture of the park comprehensive energy system of the present invention. Next, each device in the energy hub of the park comprehensive energy system is modeled.

[0042] (1) Energy production equipment: mainly includes distributed wind turbine (WT), distributed photovoltaic generator (PV), gas turbine (GT), waste heat boiler (WHB), gas boiler (GB) and other equipment or units. Below, they are explained one by one.

[0043] 1) Distributed wind turbines (for ease of description, they will be referred to as wind turbines in the following descriptions): They are one of the basic renewable resources that convert wind energy into electrical energy. The uncertainty of wind speed will lead to intermittency and uncertainty in wind power output. The specific model is as follows.

[0044]

[0045] In the formula, Q wt,t represents the output of distributed wind turbines at time t, v t represents the wind speed at time t, v ci 、v co 、v r Respectively represent the cut-in, cut-out and rated speed of wind speed, Indicates the rated output power of distributed wind turbines.

[0046] 2) Distributed photovoltaic units (for ease of description, they will be referred to as photovoltaic units in the subsequent descriptions): they are another basic renewable resource that converts solar energy into electrical energy. Due to the uncertainty of solar radiation, the output power of distributed photovoltaic units is also highly random. The specific model is as follows.

[0047]

[0048] In the formula, Q pv,t represents the output power of the distributed photovoltaic unit at time t, represents the rated output power of the photovoltaic unit, G r , T r respectively represent the rated irradiance and the rated temperature, G t , T t respectively represent the actual irradiance and the actual temperature at time t, τ is the temperature power coefficient, usually taken as 0.0047 °C -1 .

[0049] 3) Gas turbine: It is the most commonly used generator set in the energy hub and is a typical gas-to-electricity device that outputs electric power by inputting natural gas. The specific model is as follows.

[0050]

[0051] In the formula, represents the electric power output by the gas turbine at time t, φ g represents the calorific value of natural gas, represents the electrical efficiency of the gas turbine (i.e., the efficiency of outputting electric power), represents the natural gas power input to the gas turbine at time t.

[0052] 4) Waste heat boiler: It is a device that outputs thermal power by recovering and utilizing the remaining waste heat of the gas turbine and jointly forms a combined heat and power (CHP) device with the gas turbine. The specific model is as follows.

[0053]

[0054] In the formula, represents the thermal power output by the waste heat boiler at time t, represents the thermal efficiency of the waste heat boiler (i.e., the efficiency of outputting thermal power).

[0055] 5) Gas boiler: It is a commonly used heat source device that outputs thermal power by inputting natural gas. The specific model is as follows.

[0056]

[0057] In the formula, represents the thermal power output by the gas boiler at time t, represents the thermal efficiency of the gas boiler, represents the natural gas power input to the gas boiler at time t.

[0058] (2) Energy conversion equipment: mainly includes heat pump (HP), electric refrigerator (EF) and absorption chiller (AC). Such equipment is used for the conversion between different energy forms, such as electricity to heat, electricity to cold, heat to cold, etc., and only involves conversion efficiency. Below, each of them will be described one by one.

[0059] 1) Heat pump: It is a typical electricity-to-heat equipment, and the specific model is as follows.

[0060]

[0061] In the formula, represents the heat power output by the heat pump at time t, represents the electric power consumed by the heat pump at time t, represents the electricity-to-heat efficiency of the heat pump.

[0062] 2) Electric refrigerator: It is a typical electricity-to-cold equipment, and the specific model is as follows.

[0063]

[0064] In the formula, represents the cooling power output by the electric refrigerator at time t, represents the electric power input to the electric refrigerator at time t, represents the electricity-to-cold efficiency of the electric refrigerator.

[0065] 3) Absorption chiller: It is a typical heat-to-cold equipment, and the specific model is as follows.

[0066]

[0067] In the formula, represents the cooling power output by the absorption chiller at time t, represents the heat power input to the absorption chiller at time t, represents the heat-to-cold efficiency of the absorption chiller.

[0068] (3) Energy storage equipment: It is a key equipment in the supply-side energy hub, including electrical energy storage equipment and thermal energy storage equipment, and the specific model is as follows.

[0069]

[0070]

[0071] In the formula, respectively represent the state of charge of the electrical energy storage equipment at times (t + 1) and t, ε ess 、ε hssThey represent the self-loss rates of the electric energy storage device (or electric energy storage unit) and the thermal energy storage device (or thermal energy storage unit), They represent the charging power and discharging power of the energy storage device at time t, Respectively represent the charging efficiency and discharging efficiency of the energy storage device, E ess Indicates the rated capacity of the electrical energy storage device. represent the heat storage capacity of the thermal energy storage equipment at time (t+1) and time t, respectively. They represent the heat storage power and heat release power of the thermal energy storage device at time t, They respectively represent the heat storage efficiency and heat release efficiency of the thermal energy storage equipment, and Δt represents the scheduling duration, that is, the interval between two adjacent moments.

[0072] It should be noted here that the above-mentioned device models are applicable to the scenarios involved below and regardless of whether comprehensive demand response is implemented.

[0073] At this point, the operation architecture of the park integrated energy system and the construction of each equipment model in the energy hub of the park integrated energy system have been completed. Next, on this basis, the scheduling method 200 of the park integrated energy system taking into account the coupling characteristics of the supply and demand sides of the present invention is described. Figure 2 As shown, the method 200 begins at 210 .

[0074] In 210, according to the energy supply and demand balance relationship of the park's integrated energy system, based on the coupling characteristics of the equipment between the supply-side energy hub and the demand-side energy hub, a supply-demand dual-side energy coupling model taking into account the comprehensive demand response incentive mechanism is constructed.

[0075] Among them, according to one embodiment of the present invention, the model of the supply-side energy hub can be expressed as the following formula.

[0076] D=EQ+S+R (11)

[0077]

[0078]

[0079]

[0080] In the formula, D represents the energy demand matrix, E represents the efficiency matrix of energy production equipment, Q represents the external energy supply matrix, S represents the energy storage equipment output matrix, and R represents the renewable energy output matrix. They represent the electricity demand and heat demand of comprehensive energy users after the implementation of comprehensive demand response at time t, respectively represent the electricity and natural gas quantities purchased by the integrated energy service provider from the superior (i.e., external) power grid and natural gas grid after implementing the integrated demand response at time t, respectively represent the discharge power and charging power of the electrical energy storage unit after implementing the integrated demand response at time t, respectively represent the heat release power and heat storage power of the thermal energy storage unit after implementing the integrated demand response at time t, respectively represent the outputs of the wind turbine and photovoltaic unit after implementing the integrated demand response at time t, η e represents the transformer efficiency, respectively represent the distribution coefficients of natural gas in the gas turbine and gas boiler gas usage, respectively represent the efficiencies of the gas turbine, gas boiler, and waste heat boiler, φ g represents the calorific value of natural gas.

[0081] Regarding the demand-side energy hub (i.e., the energy hub on the energy demand side), in some embodiments, its model can be specifically expressed as the following formula.

[0082] L = CD - ΔL (15)

[0083]

[0084]

[0085] In the formula, L represents the user load matrix after implementing the integrated demand response, C represents the efficiency matrix of the energy conversion equipment, and ΔL represents the matrix of the load reduction and transfer amounts, respectively represent the electrical load, thermal load, and cooling load of the integrated energy user after participating in the integrated demand response at time t, respectively represent the direct electricity demand of the integrated energy user after participating in the integrated demand response, and the electricity demand distribution coefficients of the heat pump and electric chiller at any time in it, respectively represent the direct heat demand of the integrated energy user after participating in the integrated demand response and the heat demand distribution coefficients of the absorption chiller at any time in it, represents the electric-to-heat conversion efficiency of the heat pump, represents the electric-to-cooling conversion efficiency of the electric chiller, represents the heat-to-cooling conversion efficiency of the absorption chiller, respectively represent the reducible electrical load, reducible thermal load, and reducible cooling load of the integrated energy user after participating in the integrated demand response at time t, respectively represent the incoming and outgoing amounts of the electrical load of the integrated energy user after participating in the integrated demand response at time t.

[0086] Based on this, the supply-demand bilateral energy coupling model considering the integrated demand response incentive mechanism can be expressed as the following formula.

[0087] L = C(EQ + S + R) - ΔL (18)

[0088] To better understand the constructed supply-demand bilateral energy coupling model considering the integrated demand response incentive mechanism, a schematic diagram of the supply-demand bilateral energy coupling relationship under the consideration of the integrated demand response incentive mechanism is also given, as Figure 4 .

[0089] Regarding the integrated demand response incentive mechanism, an explanation is given here. The IDR incentive strategy proposed in the present invention is mainly applied between the IESP and users (i.e., integrated energy users). It is assumed that both the IESP and users are equipped with energy management systems, and users can interact with the IESP through smart meters and the Internet. As the integration center of energy and information, the IESP is the link between the upper-level energy supply side and the energy consumption side. Based on distributed power sources and equipment such as gas boilers and gas turbines, it undertakes the integrated energy supply market upwards and coordinates and interacts with users downwards to achieve economic and efficient energy supply. As the main body of energy consumption, users are based on energy coupling devices (i.e., energy conversion devices) such as heat pumps, electric refrigerators, and absorption refrigerators, receive electricity, heat and other energies from the IESP, and convert them into loads in the forms of electricity, heat, and cold to meet their own production energy consumption needs.

[0090] Among them, for different incentive methods of IDR, in this embodiment, different incentive signals are set from two aspects: the energy market price level and the incentive subsidy price to guide users to participate in IDR. Figure 5 The schematic diagram of the integrated demand response behavior under different incentive signals according to an embodiment of the present invention is shown. Specifically, under different IDR incentive signals, users participate in different types of IDR through specific IDR forms, so as to achieve the purpose of completing the IDR target.

[0091] Based on this, an IDR incentive process as Figure 6 shown can be designed, specifically as follows.

[0092] (1) The IESP releases the initial IDR plan and incentive price: The IESP obtains data such as the wholesale electricity price and natural gas price, the original electricity consumption, heat consumption, and cooling consumption of users at each time period from the upper-level power grid and natural gas grid through the Internet and the energy management system respectively. At the same time, it issues the electricity and heat demand response quantities, as well as the initial incentives and electricity and heat selling prices to the users.

[0093] (2) User feedback on the IDR implementation plan: After the user obtains the initial incentive subsidy price and the time-of-use prices for electricity, heat, and cooling through the energy management system, the user decides whether to participate in this IDR considering their own energy consumption habits, energy consumption plan, maximum energy adjustment amount, and acceptance of the current incentive subsidy price and energy prices. If the user confirms participation, they will feedback the new energy consumption amount or the energy adjustment plan for each time period to the IESP.

[0094] (3) Determine the optimal energy prices and incentive subsidy prices: Through continuous iteration and interaction between the IESP and the user's supply and demand sides based on their own optimization goals for information such as the energy consumption amount, IDR amount, IESP's energy prices and incentive subsidies for each time period, the optimal selling energy prices and incentive subsidy prices for each time period, as well as the user's optimal energy consumption plan and response plan, are obtained.

[0095] (4) IDR revenue settlement: According to the user's actual energy consumption situation in each time period, the IESP charges the energy consumption fees according to the optimized time-of-use energy prices. For the user's response to the IDR plan released by the IESP in each time period, the IESP gives subsidies to the user according to the optimized incentive subsidy prices. Finally, the user can obtain corresponding IDR subsidy revenues while paying the energy consumption costs, and the IESP can ensure the maximization of its own selling energy revenues and the user's energy consumption demands while paying the IDR costs.

[0096] It can be seen that in this embodiment, the integrated demand response incentive mechanism includes an energy price mechanism and an integrated demand response incentive subsidy price mechanism. Specifically, the energy price mechanism includes electricity prices and heat prices, and the integrated energy demand incentive subsidy price mechanism includes electricity load reduction incentive subsidy prices, heat load reduction incentive subsidy prices, and cooling load reduction incentive subsidy prices.

[0097] So far, the construction of the supply-demand bilateral energy coupling model considering the integrated demand response incentive mechanism has been completed, giving full play to the coupling characteristics of various energy devices in the park's integrated energy system between the supply and demand sides of energy.

[0098] Furthermore, considering that the uncertainties in the forecasting of various types of loads such as cooling, heat, and electricity on the demand side in the PIES will pose challenges to the output of various PIES devices, this embodiment uses the multi-scenario technology to generate possible scenarios for simulating the uncertainties in load forecasting. The multi-scenario technology is a method for describing random processes. In this embodiment, it is used to convert the load forecasting results containing uncertainty errors into a set of deterministic scenarios, so that subsequent scheduling includes considerations of different error levels. Among them, the uncertainty modeling in this embodiment mainly includes two parts: scenario generation and scenario reduction, which will be specifically described in step 220 below.

[0099] In 220, based on the probability distribution function of the energy consumption load of the integrated energy user, the Latin hypercube sampling method is used for scenario generation and the synchronous back substitution method is used for scenario reduction to obtain multiple scenarios of the energy consumption load and the probability of each scenario.

[0100] Among them, the prediction errors of the energy consumption loads of integrated energy users mostly follow a normal distribution. Therefore, in some embodiments, it can be set that the electric load, heat load, and cold load of the user at time t all follow a normal distribution with their predicted values as the mean and a certain proportion of the predicted values as the standard deviation. Based on this, in some embodiments, the probability distribution function of the energy consumption load of the integrated energy user can be expressed as the following formula.

[0101]

[0102] In the formula, represents the probability that the load of the d-type load of the integrated energy user at time t is , represents the predicted value of the d-type load of the integrated energy user at time t, and σ d,t represents the standard deviation of the d-type load of the integrated energy user at time t. Regarding the standard deviation σ d,t The proportional coefficient of the load prediction output can be set to γ d,t , that is Among them, the value of d is e, h, c, representing the electric load, heat load, and cold load respectively. Further, in some embodiments, it can be set that the proportional coefficients of the standard deviations of each type of load to their predicted values are equal, such as γ e,t =γ h,t =γ c,t =10%, that is, the proportion of the standard deviation of the electric load to the predicted value of the electric load, the proportion of the standard deviation of the heat load to the predicted value of the heat load, and the proportion of the standard deviation of the cold load to the predicted value of the cold load are all 10%. Of course, this is only an example, and the present invention is not limited thereto. In addition, regarding the predicted values of the electric load, heat load, and cold load at each moment, in some embodiments, the data of a typical day can be used, or the average data of several typical days can be used, and the present invention is not limited thereto.

[0103] Next, the Latin hypercube sampling method is used for scenario generation, which can specifically include the following steps.

[0104] Based on the probability distribution function of the energy consumption load, the probability distribution of the energy consumption load of the integrated energy user is equally divided into a preset number of probability intervals. Then, within each probability interval, the electric load, heat load, and cooling load at a certain moment are obtained through the probability distribution function of the energy consumption load to obtain a scenario, and the product of the probability of the electric load, the probability of the heat load, and the probability of the cooling load at this moment is used as the probability of this scenario, obtaining a preset number of scenarios and the probabilities of each scenario. Among them, when obtaining the electric load, heat load, and cooling load at a certain moment through the probability distribution function of the energy consumption load to obtain a scenario, a random number S i can be selected as the sampling point within each probability interval, and then the probability distribution function of the energy consumption load is inversely transformed to obtain the sample value of the sampling point Here, it should be noted that a scenario is composed of an electric load, a heat load, and a cooling load.

[0105] Among them, considering that the generation of a large number of scenarios will increase the computational burden of the solution, this implementation further adopts the synchronous back substitution method for scenario reduction. The reduced set of typical scenarios can well reflect the probability distribution of the original scenario set, and its main steps are as follows.

[0106] Step 1: For each obtained scenario, based on the probability of this scenario and the Euclidean distance between it and other scenarios, the probability distance between this scenario and other scenarios is obtained, and the other scenario corresponding to the smallest probability distance is determined as the nearest scenario to this scenario.

[0107] Among them, according to an embodiment of the present invention, for each scenario, the minimum probability distance in the probability distances between it and other scenarios can be obtained through the following formula (that is, the probability distance between its nearest scenario and it).

[0108] D i =min(λ i d(i,j)), i, j = 1, 2,..., n so (20)

[0109] In the formula, D i represents the probability distance between the nearest scenario of scenario i and it, λ i represents the probability of scenario i, d(i,j) represents the Euclidean distance between scenario i and scenario j, and n so represents the number of initial scenarios.

[0110] Step 2: For each scenario, calculate the product of its probability and the probability distance between its nearest scenario and it obtained above, and determine the scenario with the smallest product as the scenario to be reduced. Among them, for the convenience of description, the minimum product in this step of this embodiment is called the reduction distance. Specifically, the reduction distance can be obtained through the following formula.

[0111] D min = min(λ i D i ), i = 1, 2, ..., n so (21)

[0112] In the formula, D min represents the reduction distance.

[0113] Step 3: Delete the scene to be reduced, and accumulate the probability of the deleted scene to the nearest scene of the deleted scene. That is to say, after deleting the determined scene to be reduced, accumulate the probability of the scene to be reduced to its nearest scene, so as to ensure that the sum of probabilities is 1. Specifically, if the determined scene to be reduced is i and the nearest scene of i is j, then after deleting scene i, the probability of scene j can be obtained by the following formula.

[0114] λ′ j = λ i + λ j (22)

[0115] In the formula, λ′ j represents the probability of scene j after deleting scene i, and λ j represents the probability of scene j before deleting scene i. Here, it should be noted that the left side of this formula uses λ′ j to represent the probability of scene j only to indicate that its value has changed before and after deleting scene i.

[0116] Step 4: Decrease the number of scenes by one, and detect whether the current number of scenes (i.e., n so - 1) reaches the preset value.

[0117] Step 5: If it does not reach the preset value, repeat the steps of obtaining the probability distance, determining the nearest scene, determining and deleting the scene to be reduced, accumulating the probability, decreasing the number of scenes by one, and detecting the number of scenes (i.e., the above Steps 1 to 4) until the current number of scenes reaches the preset value, and obtain the preset number of scenes and the probabilities of each scene. Regarding the probabilities of each scene, it should be further explained here that the probability of each scene is the product of the probability of the electrical load (the probability corresponding to the electrical load), the probability of the thermal load (the probability corresponding to the thermal load), and the probability of the cooling load (the probability corresponding to the cooling load) in this scene, representing the probability of this scene occurring, and can be specifically expressed as the following formula.

[0118] λ w = θ e θ h θ c (23)

[0119] In the formula, λ w represents the probability of scene ω, and θ e, θ h , θ c respectively represent the probabilities corresponding to the electrical load, heat load, and cooling load in the scenario ω.

[0120] So far, the construction of the energy coupling model for both the supply and demand sides and the generation of the energy consumption load scenarios of integrated energy users have been completed. Next, a two-layer optimal scheduling model for the park integrated energy system is constructed. Here, first, an explanation of the framework of this model is given. It specifically includes two stages: pricing decision-making and quantitative decision-making. The two iterate until equilibrium is reached. The specific framework can be seen in Figure 7 .

[0121] Pricing stage: The upper-layer IESP, based on the supply-demand relationship and price information of the superior power grid and natural gas suppliers, takes the maximization of its own energy sales economic benefits as the optimization goal, formulates the electricity sales price, heat sales price, and incentive subsidy prices for electricity, heat, and cooling load reduction for the lower-layer users, and while completing the IDR target, minimizes its own IDR implementation cost. In this stage, the IESP uses the IDR incentive strategy to formulate the energy sales price and the subsidy price to encourage users to reduce their energy consumption load, and uses the master-slave game to simulate the process of users and the IESP jointly deciding the energy sales price and incentive subsidy, realizing the restriction of users on the IESP.

[0122] Quantitative stage: The lower-layer users take the minimization of their own energy consumption cost as the optimization goal. Respectively, based on the price information and supply-demand gap information conveyed by the upper layer, they determine the optimal output, load reduction, and load transfer plan based on the coupling equipment on the demand side, and convey it to the upper-layer IESP. The IESP, based on the principle of maximizing its own benefits according to the information uploaded by the users, announces the re-formulated energy sales price and incentive price to the users and accepts user supervision. It can be seen from this that the optimal decision of the lower layer is a function of the decision variables of the upper layer. Through the decision-making of the quantitative and pricing stages by the upper and lower layers, the optimization results are continuously iteratively updated until the game equilibrium is reached, and each subject achieves the maximization of benefits.

[0123] Next, a specific explanation of the construction of the two-layer optimal scheduling model for the park integrated energy system is given. In 230, based on the obtained multiple scenarios and the probabilities of each scenario, an energy consumption scheduling model for integrated energy users is constructed with the goal of minimizing the daily operating cost of integrated energy users.

[0124] Among them, while obtaining incentive subsidies, the integrated energy user will lose a certain degree of energy consumption satisfaction. Therefore, when constructing an energy consumption scheduling model, the energy consumption satisfaction can be taken into account to ensure the accuracy and reliability of the constructed energy consumption scheduling model. Further, in some embodiments, the energy consumption satisfaction of the integrated energy user can be characterized by the satisfaction cost of the integrated energy user participating in the integrated demand response. The lower this value is, the higher the user's satisfaction is. Based on this, the following method can be used to construct an energy consumption scheduling model for the integrated energy user with the goal of minimizing the daily operating cost of the integrated energy user based on the obtained multiple scenarios and the probabilities of each scenario.

[0125] According to the energy consumption cost of the integrated energy user after participating in the integrated demand response in each scenario, the response cost generated by the integrated energy user adjusting the energy consumption plan due to participating in the integrated demand response, the subsidy income obtained by the integrated energy user participating in the integrated demand response, the satisfaction cost of the integrated energy user participating in the integrated demand response, and the operation and maintenance cost of the energy conversion equipment, determine the daily operating cost of the integrated energy user, and construct an energy consumption scheduling model with the minimum of this daily operating cost as the first objective function.

[0126] Specifically, in some embodiments, the first objective function can be expressed as the following formula.

[0127]

[0128] In the formula, C IEU represents the daily operating cost of the integrated energy user, λ w represents the probability of scenario ω, Ω represents the total number of scenarios, represents the energy consumption cost of the integrated energy user after participating in the integrated demand response in scenario ω, represents the response cost generated by the integrated energy user adjusting the energy consumption plan due to participating in the integrated demand response in scenario ω, represents the satisfaction cost of the integrated energy user participating in the integrated demand response in scenario ω, represents the operation and maintenance cost of the energy conversion equipment on the user side (i.e., the demand side) in scenario ω, represents the incentive subsidy income obtained by the integrated energy user participating in the integrated demand response in scenario ω.

[0129] Among them, and can be obtained through the following formula.

[0130]

[0131] In the formula, represents the energy sold income of the integrated energy service provider after implementing the integrated demand response in scenario ω, respectively represent the prices of electric energy and heat energy sold by the integrated energy service provider after implementing integrated demand response at time t, respectively represent the electricity demand and heat demand of the integrated energy user after implementing integrated demand response in scenario ω at time t, respectively represent the reducible electric load, reducible heat load and reducible cooling load of the integrated energy user after participating in integrated demand response in scenario ω at time t, respectively represent the electric load, heat load and cooling load of the integrated energy user before participating in integrated demand response in scenario ω at time t, respectively represent the incoming and outgoing amounts of electric load of the integrated energy user after participating in integrated demand response in scenario ω at time t, respectively represent the outputs of the heat pump, electric chiller and absorption chiller after implementing integrated demand response in scenario ω at time t, c hp 、c ef 、c ac respectively represent the unit power operation and maintenance costs of the heat pump, electric chiller and absorption chiller, represents the cost of the integrated energy service provider implementing integrated demand response in scenario ω, a, b, c1, c2, c3 all represent the cost coefficients of the integrated energy user participating in integrated demand response, T represents the total duration of implementing integrated demand response. Specifically, in some embodiments, its value can be 24.

[0132] Furthermore, according to an embodiment of the present invention, it can be obtained by the following formula.

[0133]

[0134] In addition, the energy scheduling model of the integrated energy user further includes constraint conditions corresponding to the first objective function, which will be hereinafter referred to as the first constraint conditions for the sake of convenience of description. Specifically, the first constraint conditions include the output constraints and ramp rate constraints of the devices in the demand-side energy hub, the reducible load constraints and the transferable load constraints, which will be described one by one below.

[0135] 1) Output constraints and ramp rate constraints of the devices in the demand-side energy hub: mainly include the output constraints and ramp rate constraints of the heat pump, electric chiller and absorption chiller, as follows.

[0136]

[0137]

[0138] In the formula, respectively represent the maximum outputs of the heat pump, electric chiller and absorption chiller, respectively represent the outputs of the heat pump, electric chiller, and absorption chiller after implementing the integrated demand response in scenario ω at time (t + 1). respectively represent the maximum ramps of the heat pump, electric chiller, and absorption chiller. Additionally, regarding the outputs of the heat pump, electric chiller, and absorption chiller, it should be noted here that for the heat pump, it refers to the output heat power, and for the electric chiller and absorption chiller, it refers to the output cooling power.

[0139] 2) Curtailable load constraint: The amount of load that the user can curtail should satisfy the maximum curtailable load constraint to ensure that the user's production plan is not affected after curtailment. Specifically, it is as follows.

[0140]

[0141] In the formula, respectively represent the maximum values of the curtailable electric load, curtailable heat load, and curtailable cooling load of the integrated energy user.

[0142] 3) Shiftable load constraint: The amount of load shifted by the user should satisfy its maximum and minimum value constraints, and it is necessary to ensure that the increased load is equal to the curtailed load. That is, the user shifts the load during peak hours to off-peak hours as needed, but the total electricity consumption remains unchanged. Specifically, it is as follows.

[0143]

[0144] In the formula, represents the maximum value of the shiftable electric load.

[0145] So far, the construction of the energy consumption scheduling model for the integrated energy user has been completed. Next, the scheduling model for the integrated energy service provider is constructed. Among them, considering that the uncertainties of wind power output and photovoltaic output on the energy supply side in PIES will pose challenges to the outputs of various devices in PIES, in this embodiment, the Information Gap Decision Theory (IGDT) is used to model the uncertainties of wind power output and photovoltaic output.

[0146] Based on this, in 240, based on the Information Gap Decision Theory, with the maximum uncertainty of the wind power output prediction value and the photovoltaic output prediction value as the goal, a robust scheduling model for the integrated energy service provider under the risk aversion strategy is constructed. Among them, in some embodiments, the maximum uncertainty of the uncertain parameters can be sought on the premise that the maximization of the daily operating profit of IESP is not lower than the acceptable critical value to complete the construction of the robust scheduling model. In this way, even in the face of the most severe degree of uncertainty, the acceptable value of the daily operating profit of IESP can still be guaranteed, and then the optimal control result under the risk aversion strategy can be obtained.

[0147] For the robust model under the risk avoidance strategy, the most important thing is to set the target decision variables to hedge the adverse effects of uncertain parameters on the decision. That is to say, when establishing the robust model based on IGDT to ensure the realization of the minimum requirements of IESP, for the IESP with the goal of maximizing revenue, the lower the critical value, the weaker the robustness. Therefore, in this embodiment, in order to make the daily operating profit of IESP not less than the preset target value, the uncertainty of uncertain parameters such as wind power and photovoltaic output is maximized.

[0148] In this way, in some embodiments, the uncertainty of the wind power output prediction value can be maximized as the second objective function, the uncertainty of the photovoltaic output prediction value can be maximized as the third objective function, and the difference between the daily operating revenue of the integrated energy service provider under the risk-neutral strategy and the daily operating revenue of the integrated energy service provider under the risk avoidance strategy does not exceed the target deviation value as the constraint condition, to construct the robust scheduling model of the integrated energy service provider under the risk avoidance strategy.

[0149] It can be seen that the daily operating revenue of the integrated energy service provider under the risk-neutral strategy is required for the construction of the robust scheduling model. Based on this, a deterministic scheduling model of the integrated energy service provider under the risk-neutral strategy (Risk Neutral Strategy, RNS) is first constructed. According to an embodiment of the present invention, with the goal of maximizing the daily operating revenue of the integrated energy service provider under the risk-neutral strategy, a deterministic scheduling model of the integrated energy service provider under the risk-neutral strategy is constructed. Further, it can be constructed in the following way.

[0150] According to the cost of implementing integrated demand response in each scenario, as well as the energy sales revenue, energy procurement cost, equipment operation and maintenance cost, and carbon emission cost of the integrated energy service provider after implementing integrated demand response, the daily operating revenue of the integrated energy service provider is determined, and a deterministic scheduling model is constructed with the maximum of this daily operating revenue as the fourth objective function.

[0151] Specifically, in some embodiments, the fourth objective function can be expressed as the following formula.

[0152]

[0153] In the formula, represents the daily operating revenue of the integrated energy service provider under the risk-neutral strategy, represents the energy sales revenue of the integrated energy service provider after implementing integrated demand response in scenario ω, represents the energy procurement cost of the integrated energy service provider after implementing integrated demand response in scenario ω, Denote the equipment operation and maintenance cost of the integrated energy service provider after implementing integrated demand response under scenario ω. Denote the carbon emission cost of the integrated energy service provider after implementing integrated demand response under scenario ω. Denote the cost of the integrated energy service provider implementing integrated demand response under scenario ω.

[0154] Among them, and can be obtained through the following formula.

[0155]

[0156] In the formula, respectively denote the prices of the integrated energy service provider selling electric energy and heat energy after implementing integrated demand response at time t. respectively denote the electricity demand and heat demand of the integrated energy user after implementing integrated demand response under scenario ω at time t. respectively denote the prices of the integrated energy service provider purchasing electric energy and natural gas after implementing integrated demand response at time t. respectively denote the electricity quantity and natural gas quantity purchased by the integrated energy service provider from the external power grid and natural gas grid after implementing integrated demand response under scenario ω at time t. respectively denote the outputs of the gas turbine, waste heat boiler, gas boiler, wind turbine and photovoltaic unit after implementing integrated demand response under scenario ω at time t. respectively denote the capacities of the electric energy storage unit and heat energy storage unit after implementing integrated demand response under scenario ω at time t, c gt 、c whb 、c gb 、c wt 、c pv 、c ess 、c hss respectively denote the unit power operation and maintenance costs of the gas turbine, waste heat boiler, gas boiler, wind turbine, photovoltaic unit, electric energy storage unit and heat energy storage unit. respectively denote the electric load reduction incentive subsidy price, heat load reduction incentive subsidy price and cold load reduction incentive subsidy price issued by the integrated energy service provider to users after implementing integrated demand response at time t. respectively denote the reducible electric load quantity, reducible heat load quantity and reducible cold load quantity of the integrated energy user after participating in integrated demand response under scenario ω at time t. Denote the unit carbon emission price. respectively denote the unit power carbon emission intensities of the gas turbine, waste heat boiler, gas boiler and external distribution network.

[0157] Furthermore, according to an embodiment of the present invention, It can be obtained by the following formula.

[0158]

[0159] In addition, the deterministic scheduling model of the integrated energy service provider under the risk-neutral strategy also includes the constraint conditions corresponding to the fourth objective function. For the sake of convenience of description, the following will refer to it as the fourth constraint condition. Specifically, the fourth constraint condition includes energy price constraint, incentive subsidy price constraint, energy supply-demand balance constraint, output constraint and ramp constraint of the equipment in the supply-side energy hub, and grid tie-line constraint. The following will explain them one by one.

[0160] 1) Energy price constraint: To prevent users from directly trading with energy suppliers, it is necessary to ensure that the average price at which users purchase energy from the IESP is not higher than the average price at which users directly purchase energy from energy suppliers. Specifically as follows.

[0161]

[0162] In the formula, represents the price at which the integrated energy service provider purchases thermal energy after implementing integrated demand response at time t.

[0163] 2) Incentive subsidy price constraint: The incentive subsidy price issued by the IESP to users should be greater than the incentive subsidy price for users to participate in the grid demand response and less than the incentive subsidy price obtained when declaring the response target from the energy market. Specifically as follows.

[0164]

[0165] In the formula, respectively represent the incentive subsidy price for electricity load reduction, heat load reduction and cooling load reduction for integrated energy users to participate in the grid demand response, respectively represent the incentive subsidy price for electricity load reduction, heat load reduction and cooling load reduction obtained by integrated energy users when declaring the response target from the energy market.

[0166] 3) Energy supply-demand balance constraint: The IESP makes the energy supply and demand balanced by regulating the energy production equipment, energy conversion equipment, energy storage equipment, and the shiftable load and curtailable load on the user side. Specifically as follows.

[0167]

[0168] Among them, respectively represent the electricity load, heat load and cooling load of the integrated energy user before participating in the integrated demand response at time t under scenario ω, respectively represent the reducible electricity load, reducible heat load, and reducible cooling load of the integrated energy user after participating in the integrated demand response under scenario ω at time t, respectively represent the incoming and outgoing electricity load of the integrated energy user after participating in the integrated demand response under scenario ω at time t, η e represents the transformer efficiency, represents the electricity quantity purchased by the integrated energy service provider from the external power grid after implementing the integrated demand response under scenario ω at time t, respectively represent the outputs of the gas turbine, wind turbine, and photovoltaic unit after implementing the integrated demand response under scenario ω at time t, respectively represent the discharge power and charging power of the electrical energy storage unit after implementing the integrated demand response under scenario ω at time t, respectively represent the electrical powers input to the heat pump and electric chiller after implementing the integrated demand response under scenario ω at time t, respectively represent the outputs of the waste heat boiler, gas boiler, and heat pump after implementing the integrated demand response under scenario ω at time t, respectively represent the heat release power and heat storage power of the thermal energy storage unit after implementing the integrated demand response under scenario ω at time t, represents the thermal power input to the absorption chiller after implementing the integrated demand response under scenario ω at time t, respectively represent the cooling powers output by the electric chiller and absorption chiller after implementing the integrated demand response under scenario ω at time t.

[0169] 4) Output constraints and ramping constraints of equipment in the supply-side energy hub: mainly include the output constraints and ramping constraints of the waste heat boiler, gas boiler, and gas turbine, the output constraints of the electrical energy storage unit and thermal energy storage unit, and the output constraints of renewable energy, as follows.

[0170]

[0171]

[0172]

[0173]

[0174] In the formula, respectively represent the maximum outputs of the waste heat boiler, gas boiler, and gas turbine, respectively represent the outputs of the waste heat boiler, gas boiler, and gas turbine after implementing the integrated demand response under scenario ω at time (t + 1), respectively represent the maximum rampings of the waste heat boiler, gas boiler, and gas turbine, respectively represent the minimum and maximum capacities of the electrical energy storage unit, represent the minimum and maximum capacities of the thermal energy storage unit respectively, represent the charge and discharge states of the electrical energy storage unit at time t under scenario ω respectively, with values of 0 or 1, Taking 1 means that the electrical energy storage unit is in the charging state at time t under scenario ω, and taking 0 means that the electrical energy storage unit is not in the charging state at time t under scenario ω, Taking 1 means that the electrical energy storage unit is in the discharging state at time t under scenario ω, and taking 0 means that the electrical energy storage unit is not in the discharging state at time t under scenario ω, represent the heat storage state and heat release state of the thermal energy storage unit at time t under scenario ω respectively, with values of 0 or 1, represent the capacities of the electrical energy storage unit after implementing integrated demand response at the initial time (i.e., t = 1) and the end time (i.e., t = 24) under scenario ω respectively, represent the capacities of the thermal energy storage unit after implementing integrated demand response at the initial time and the end time under scenario ω respectively, represent the predicted output values of the wind turbine and photovoltaic unit at time t respectively.

[0175] 5) Grid connection line constraint: It means that the power of PIES purchasing and selling electricity to the grid cannot exceed the maximum capacity of the transmission and distribution line, specifically as follows.

[0176]

[0177] In the formula, represents the maximum transmission power of the connection line (i.e., the transmission and distribution line).

[0178] So far, the construction of the deterministic scheduling model of the integrated energy service provider under the risk-neutral strategy has been completed. Next, the construction of the robust scheduling model of the integrated energy service provider under the risk-averse strategy will be continued to be described.

[0179] Among them, according to an embodiment of the present invention, the second objective function and the third objective function can be respectively expressed as the following formulas (43) and (44).

[0180] maxε wt (43)

[0181] maxε pv (44)

[0182] In the formula, ε wt represents the uncertainty of the predicted output value of the wind turbine, and ε pv represents the uncertainty of the predicted output value of the photovoltaic unit.

[0183] Further, regarding the constraint that the difference between the daily operating revenue of the integrated energy service provider under the risk-neutral strategy and the daily operating revenue of the integrated energy service provider under the risk-averse strategy does not exceed the target deviation value, in some embodiments, it can be expressed as the following formula.

[0184]

[0185] In the formula, represents the daily operating revenue of the integrated energy service provider under the risk-averse strategy, represents the daily operating revenue of the integrated energy service provider under the risk-neutral strategy, and β represents the target deviation factor of the robust model. Among them, and are calculated in the same way. For specific details, see the above relevant description and will not be elaborated here.

[0186] In addition, according to an embodiment of the present invention, the robust scheduling model of the integrated energy service provider under the risk-averse strategy, in addition to including the constraint that the difference between the daily operating revenue of the integrated energy service provider under the risk-neutral strategy and the daily operating revenue of the integrated energy service provider under the risk-averse strategy does not exceed the target deviation value, also includes energy price constraints, incentive subsidy price constraints, energy supply-demand balance constraints, output constraints and ramp constraints of equipment in the supply-side energy hub, and grid tie-line constraints. Therefore, the constraint conditions of the robust scheduling model of the integrated energy service provider under the risk-averse strategy can be expressed as the following formula.

[0187]

[0188] In the formula, respectively represent the output of the wind turbine and photovoltaic unit after implementing the integrated demand response in scenario ω at time t considering the degree of uncertainty under the risk-averse strategy, respectively represent the output of the wind turbine and photovoltaic unit after implementing the integrated demand response in scenario ω at time t without considering the degree of uncertainty under the risk-neutral strategy.

[0189] So far, the construction of the robust scheduling model has been completed. Next, enter 250. Take the obtained energy scheduling model as the lower-layer model and the robust scheduling model as the upper-layer model to construct a two-layer optimal scheduling model of the park integrated energy system, and solve it based on the energy coupling model on both the supply and demand sides to obtain the scheduling strategy of the park integrated energy system.

[0190] Among them, before explaining the solution of the entire two-layer optimal scheduling model, first explain the solution of the upper-layer robust scheduling model. Considering that the constructed robust scheduling model is a multi-objective model, in this embodiment, the ε-constraint method (Epsilon constraint method) is used to solve it, as follows.

[0191] First, introduce the constraint parameter ε, and use ε to transform the third objective function into a constraint condition of the robust scheduling model. Thus, the robust scheduling model is transformed into the following formula:

[0192] max ε wt (47)

[0193]

[0194] where the value range of ε is from the minimum value to the maximum value of ε pv .

[0195] Then, by adjusting the value of the constraint parameter ε multiple times, obtain the Pareto optimal solution set (Pareto optimal solution set) of the robust scheduling model. Specifically, by changing the value of ε, obtain different objective function values (ε wt and ε pv ), and these values together constitute the Pareto optimal solution set of the multi-objective problem. Each solution in the Pareto optimal solution set (i.e., a group of ε wt , ε pv ) corresponds to a scheduling strategy of the integrated energy service provider (i.e., the energy selling price, the integrated demand response incentive subsidy price, the output of the energy production equipment, and the output of the energy storage equipment at each moment of the integrated energy service provider). In addition, for the solution of the conversion model, it can be completed by calling CPLEX in the MATLAB environment. Of course, this is only an example, and the present invention is not limited thereto.

[0196] Finally, use the fuzzy decision-making method to obtain the optimal scheduling strategy from the scheduling strategies corresponding to all solutions in the Pareto optimal solution set, and obtain the scheduling strategy of the integrated energy service provider. According to an embodiment of the present invention, the fuzzy decision-making method can be used to compromise and obtain the optimal scheduling strategy from the Pareto front in the following manner.

[0197] First, respectively obtain the membership function values of the scheduling strategies corresponding to each solution in the Pareto optimal solution set under the second objective function and the third objective function. That is, for the scheduling strategies corresponding to each solution in the Pareto optimal solution set, respectively obtain their membership function values under the second objective function and the third objective function. Among them, in some embodiments, the following fuzzy membership function can be used to obtain the membership function values of the scheduling strategies corresponding to each solution in the Pareto optimal solution set under the second objective function and the third objective function:

[0198]

[0199] In the formula, represents the membership function value of the scheduling strategy corresponding to the m-th solution under the objective function k, Denote the function value of the objective function \(k\) under the scheduling strategy corresponding to the \(m\)-th solution. Denote the minimum and maximum values of the objective function \(k\) respectively. The value of \(k\) is 1 or 2. The objective function 1 corresponds to the second objective function \(\varepsilon\). wt The objective function 2 corresponds to the third objective function \(\varepsilon\). pv where \(m = 1, 2, \cdots, N_0\), and \(N_0\) is the number of solutions in the Pareto optimal solution set.

[0200] Next, for the scheduling strategy corresponding to each solution, obtain the smaller of its membership function values under the two objective functions, and take it as the final membership function value of this scheduling strategy, which can be specifically expressed as the following formula.

[0201]

[0202] In the formula, \(\mu\) m denotes the final membership function value of the scheduling strategy corresponding to the \(m\)-th solution. denote the membership function values of the scheduling strategy corresponding to the \(m\)-th solution under the objective function 1 (i.e., the second objective function) and the objective function 2 (i.e., the third objective function) respectively.

[0203] Finally, determine the scheduling strategy with the largest final membership function value as the optimal scheduling strategy. That is, obtain the largest value from the final membership function values of the scheduling strategies corresponding to all solutions, and determine the scheduling strategy corresponding to it as the optimal scheduling strategy, which can be specifically expressed as the following formula:

[0204]

[0205] In the formula, \(\mu\) max denotes the largest value among the final membership function values of all scheduling strategies.

[0206] For easy understanding, the following gives an example. For instance, if the final membership function value of the scheduling strategy corresponding to the \(N_0\)-th solution in the Pareto optimal solution set is the largest, then the scheduling strategy corresponding to the \(N_0\)-th solution is the optimal compromise solution, that is, the optimal scheduling strategy. Obtaining the optimal scheduling strategy gives the scheduling strategy when the daily operating income of the integrated energy service provider is the largest.

[0207] The dispatching strategies of the integrated energy service provider specifically include the energy selling prices at each moment of the integrated energy service provider, the incentive subsidy prices for integrated demand response, the output of energy production equipment, and the output of energy storage equipment. Further, the energy selling prices include the electricity selling price and the heat selling price. The incentive subsidy prices for integrated demand response include the incentive subsidy price for electricity load curtailment, the incentive subsidy price for heat load curtailment, and the incentive subsidy price for cooling load curtailment. The output of energy production equipment includes the output of gas turbines, gas boilers, waste heat boilers, distributed wind turbine units, and distributed photovoltaic units. The output of energy storage equipment includes the output of electric energy storage units and heat energy storage units.

[0208] Among them, the above-mentioned use of the ε-constraint method to solve the constructed robust dispatching model and convert the third objective function into a constraint condition is only an example. In other embodiments, the second objective function can also be converted into a constraint condition, and the present invention is not limited thereto.

[0209] The above is the solution process of the robust dispatching model. Next, the solution of the entire two-layer optimal dispatching model will be described. Among them, an improved particle swarm optimization algorithm can be used for solution. Based on this, according to an embodiment of the present invention, a method combining an improved particle swarm optimization algorithm and the CPLEX optimization software in the YALMIP toolbox under the MATLAB platform is used to solve the optimal solution of the model. Specifically, for the leader IESP, the particle swarm optimization algorithm is used, and the highest daily operating income of the IESP is used as the fitness function to solve the optimal IDR subsidy price and the optimal electricity selling price the heat selling price and the optimal output of energy production equipment and energy storage equipment at each moment. At the same time, CPLEX is used to solve the optimal IDR target curve; for the follower users, combined with the IDR subsidy price, the electricity selling price, and the heat selling price, IDR strategies such as load conversion (i.e., the output of energy conversion equipment), load curtailment, and load transfer are solved. In addition, to increase the ability of the particle swarm algorithm to jump out of the local optimal solution, an adaptive mutation is added in this example. Next, combined with Figure 8 the solution process of the two-layer optimal dispatching model of the campus integrated energy system will be described.

[0210] Step 1: Initialize the population size, the number of iterations, the acceleration factor, the velocity and position of each particle. Among them, a particle is a decision variable. Further, a particle is a decision variable of the robust dispatching model, and the position of the particle is the value of the decision variable. Specifically, the decision variables include the energy selling price variables, the incentive subsidy price variables for integrated demand response, the output variables of energy production equipment, and the output variables of energy storage equipment at each moment.

[0211] Step 2: Input the positions of each particle into the energy consumption scheduling model for solution to obtain the scheduling strategy of the integrated energy user, specifically including the amount of load that can be curtailed, the amount of load that can be shifted, and the output of the energy conversion equipment at each moment. Further, the amount of load that can be curtailed includes the amount of electrical load that can be curtailed, the amount of thermal load that can be curtailed, and the amount of cooling load that can be curtailed. The amount of load that can be shifted includes the amount of electrical load that can be shifted. The output of the energy conversion equipment includes the output of heat pumps, electric chillers, and absorption chillers. Among them, for the energy consumption scheduling model, it can be solved by the CPLEX solver. In addition, regarding the positions of each particle input into the energy consumption scheduling model, it should be noted that in some embodiments, it may only include the selling energy price at each moment and the integrated demand response incentive subsidy price at each moment.

[0212] Step 3: Input the amount of load that can be curtailed, the amount of load that can be shifted, and the output of the energy conversion equipment of the integrated energy user at each moment into the robust scheduling model for solution to obtain the best individual position of each particle and the best group position of the particle swarm (composed of the best individual positions of all particles). Among them, the process of solving the robust scheduling model using the ε - constraint method and the fuzzy decision - making method has been shown above and will not be elaborated here.

[0213] Step 4: Detect whether the daily operating revenue of the integrated energy service provider at the current best group position under the risk - aversion strategy is greater than the daily operating revenue of the integrated energy service provider at the previous best group position under the risk - aversion strategy. Specifically, that is, under the risk - aversion strategy (i.e., considering the degree of uncertainty), for the integrated energy service provider, detect whether the daily operating revenue at the currently obtained best group position is greater than the daily operating revenue at the previously obtained best group position. Among them, regarding the daily operating revenue of the integrated energy service provider at the previously obtained best group position in the first detection process, according to an embodiment of the present invention, it can be set to 0. Of course, this is only an example and the present invention is not limited thereto. In addition, as pointed out above, when using the particle swarm optimization algorithm for solution, the highest daily operating revenue of the IESP is used as the fitness function. Therefore, in other words, this step is to detect whether the fitness of the current (or this iteration) is greater than the fitness of the previous iteration.

[0214] Step 5: If not, that is, the daily operating revenue of the current optimal group position of the integrated energy service provider under the risk aversion strategy is not greater than the daily operating revenue of the previous optimal group position of the integrated energy service provider under the risk aversion strategy, then update the velocities and positions of each particle based on the acceleration factor, and repeat the above steps of solving the energy consumption scheduling model, solving the robust scheduling model, and detecting the daily operating revenue (i.e., the steps except Step 1) until the daily operating revenue of the current optimal group position of the integrated energy service provider under the risk aversion strategy is greater than the daily operating revenue of the previous optimal group position of the integrated energy service provider under the risk aversion strategy. Update the positions of each particle to their optimal individual positions, and detect whether the initialized number of iterations has been reached.

[0215] Step 6: If the initialized number of iterations has not been reached, then increment the current number of iterations by one, update the velocities and positions of each particle based on the acceleration factor, and repeat the above steps of solving the energy consumption scheduling model, solving the robust scheduling model, and detecting the daily operating revenue (i.e., the steps except Step 1) until the initialized number of iterations is reached. Take the current optimal group position as the scheduling strategy of the integrated energy service provider, and take the load curtailment amounts, load transfer amounts, and output powers of energy conversion devices at each moment of the integrated energy users obtained last time as the scheduling strategy of the integrated energy users, so as to obtain the scheduling strategy of the campus integrated energy system. That is, the scheduling strategy of the campus integrated energy system consists of the scheduling strategy of the integrated energy service provider and the scheduling strategy of the integrated energy users.

[0216] Here, the scheduling strategies of the integrated energy service provider and the integrated energy users are further explained. The scheduling strategy of the integrated energy service provider specifically includes: the electricity selling price and heat selling price at each moment, the electricity load curtailment subsidy price, heat load curtailment subsidy price, and cold load curtailment subsidy price at each moment, the output powers of gas turbines, gas boilers, waste heat boilers, distributed wind turbines, and distributed photovoltaic units at each moment, and the output powers of electric energy storage units and thermal energy storage units at each moment. The scheduling strategy of the integrated energy users specifically includes: the load curtailment amounts of electricity, heat, and cold, and the load transfer amount of electricity at each moment, and the output powers of heat pumps, electric refrigerators, and absorption refrigerators at each moment.

[0217] The above is the scheduling method of the campus integrated energy system considering the coupling characteristics of both supply and demand sides of the present invention. Further, in order to verify its effectiveness and practicality, the present invention also selects the actual engineering data of a typical industrial park, and gives an example with a 24-hour scheduling period and a 1-hour step length, as follows.

[0218] 1. Basic parameters

[0219] Figure 9Shows the predicted curves of the current-day electricity, heat, and cooling loads, as well as the predicted curves of the current-day wind power and PV power outputs. Table 1 shows the parameters of the wind power and PV equipment, Table 2 shows the parameters of the energy storage equipment in PIES, Table 3 shows the parameters of the energy production equipment and coupling equipment in PIES, Table 4 shows the division of the electricity and heat usage periods set by IESP, Table 5 shows the price of purchasing electricity from the power grid by IESP, the purchase price of natural gas is 0.25 yuan / kWh, and the calorific value of natural gas is 9.7 kWh / m 3 . Additionally, set the user response cost coefficients a = 0.001, b = 2, c1 = 0.2, c2 = 0.11, c3 = 0.1 for this park, with the unit being yuan / kWh. The maximum transferable load ratio is 20% of the load at that moment, and the maximum load curtailment ratio is 10% of the load at that moment.

[0220] Table 1

[0221]

[0222] Table 2

[0223]

[0224] Table 3

[0225]

[0226] Table 4

[0227] Equipment Power grid Heat supply network Peak period 8:00-11:00，16:00-20:00 20:00 - 7:00 of the next day Normal period 6:00-8:00，11:00-16:00，20:00-22:00 7:00-10:00，18:00-20:00 Valley period 22:00 - 6:00 of the next day 10:00-18:00

[0228] Table 5

[0229]

[0230] 2. Dispatch results of the park integrated energy system

[0231] (1) Dispatch results under the risk-neutral strategy

[0232] After considering the uncertainties in the predicted electricity load, heat load, and cooling load based on the multi-scenario technology, through the game between IESP and users, the daily operating revenue of IESP is obtained. As Figure 10 shown, after 118 iterations, the results converge. The daily operating revenue of IESP is 31,240.00 yuan, the total cost of users is 36,620.64 yuan, and Figure 10Also shown are various types of revenues and cost components. It can be seen that the energy consumption cost and operation and maintenance cost of users account for a relatively high proportion. Secondly, the response cost and satisfaction cost generated by participating in IDR by adjusting the energy consumption plan are considered. The revenue obtained from participating in IDR is higher than the response cost and satisfaction cost generated, and the energy sales revenue of IESP is much greater than the incentive subsidy cost paid to users for implementing IDR. Therefore, implementing IDR can increase the IDR revenue of users, thereby reducing the total energy consumption cost of users, and at the same time ensuring the energy sales revenue of IESP.

[0233] Furthermore, Figure 11 The typical scenario probabilities at t = 12 are shown, and the scenario probabilities are 0.20, 0.24, 0.27, 0.12, and 0.16 respectively. Figure 12 The typical scenarios of electric load, heat load, and cold load are shown. Additionally, Figure 13 The specific equipment regulation scheme of IESP under the RNS without considering the uncertainty of wind power and photovoltaic power output is shown.

[0234] From Figure 13 it can be seen that the power demand of the system is mainly met by units such as wind power, photovoltaic power, and gas turbines. During the period from 1:00 to 6:00, the electric load demand of users is relatively low, mainly met by the output power of wind power and gas turbine equipment. At the same time, it can also meet the charging demand of electric energy storage equipment and the refrigeration demand of electric refrigerators. During the period from 7:00 to 15:00, the photovoltaic power output begins to increase, the wind power output begins to decrease, while the electricity consumption demand and cooling demand of users gradually increase, and their demand is mainly met by the output power of photovoltaic power generation and gas turbines. During the period from 16:00 to 18:00, the electric load demand of users is the highest, but the output power of wind power and photovoltaic power is the lowest. At this time, IESP purchases the largest amount of electricity from the power grid, and the electric energy storage equipment begins to discharge, thus ensuring the balance of power supply and demand. During the period from 19:00 to 24:00, the photovoltaic power output is 0, the wind power output gradually increases, and the electric load demand gradually decreases. Therefore, the output power of IESP production equipment and the electricity purchased from the power grid can not only meet the electricity load demand of users, but also bear the electricity demand of heat pump and electric refrigerator equipment. At the same time, due to the relatively low electricity purchase price at this stage, the electricity purchased by IESP from the power grid remains at a relatively high level. It should be noted that the output state of the electric energy storage equipment is highly correlated with the electricity load level of users. For example, during the peak electricity consumption period, the electric energy storage equipment is in the discharging state, and during the low electricity consumption period, the electric energy storage equipment is in the charging state, which not only improves the economy of IESP, but also plays a role in reducing the peak-valley difference.

[0235] From Figure 13It can also be seen that the heat load demand of users and the heat demand of absorption chillers are mainly met by equipment such as waste heat boilers and heat pumps. During the period from 1:00 to 6:00, both the heat and cooling demands of users are at a relatively high level. At this time, the heat demand is mainly met by the output power of waste heat boilers and heat pumps, and the cooling demand is mainly met by electric chillers. During this stage, the IESP utilizes the differences in the peak and valley periods of electric load and cooling and heat loads to achieve electric-to-heat and electric-to-cool conversion. During the period from 7:00 to 19:00, the heat load demand of users decreases and the cooling load demand increases. Therefore, the cooling load demand is simultaneously met by the output power of electric chillers and absorption chillers, and the heat load demand is mainly met by the output power of waste heat boilers. Therefore, during this stage, the thermal power output by the IESP production equipment not only meets the heat load demand of users themselves, but also meets part of the cooling load demand, reducing the power consumption demand of electric chiller equipment. During the period from 20:00 to 24:00, the heat demand of users begins to increase, and the cooling load demand remains at a relatively high level. At this time, the equipment for producing thermal power by the IESP is mainly used to meet the direct heat demand of users, reducing the output of cooling power. Therefore, the cooling demand during this stage is mainly met by electric chillers. It can be seen that by utilizing the complementary characteristics of various electric, heat, and cooling loads within a certain period in the park and the various energy production and conversion coupling units in the PIES, energy cascade utilization and load substitution can be achieved, thereby improving the comprehensive energy utilization efficiency.

[0236] (2) Scheduling results under risk aversion strategies

[0237] In the IGDT robust model under risk aversion strategies, the optimization objective is to ensure that the daily operating profit of the IESP is not lower than the acceptable critical operating profit under the premise of maximizing the robustness level, that is, under the worst-case scenarios of wind power and photovoltaic power generation, rather than simply maximizing the daily operating profit of the IESP. Although this conservative decision will sacrifice some of its own operating profit, it can enable the IESP to avoid the adverse risks brought about by the deviation of wind power and photovoltaic power generation from the predicted values. Figure 14 It is the Pareto front under different robustness levels obtained by solving the robust model after iterative convergence under RNS. Among them, the IESP is allowed to select the optimal strategy under five uncertainties of wind power and photovoltaic power generation.

[0238] From Figure 14It can be seen that for each robust parameter, there are multiple points representing the Pareto front, and when the uncertainty of PV output increases, the uncertainty of wind power output decreases. With the increase of the robustness parameter, the uncertainties of wind power and PV also increase. For example, when β = 0.1, the minimum and maximum values of the uncertainty degree of wind power output are 0.01 and 0.081 respectively, and the minimum and maximum values of the uncertainty degree of PV output are 0.05 and 0.29 respectively. At this time, 0.066 and 0.1 are respectively selected as the best compromise solutions for the uncertainty degrees of wind power and PV outputs. When β = 0.5, the minimum and maximum values of the uncertainty degree of wind power output are 0.343 and 0.55 respectively, and the minimum and maximum values of the uncertainty degree of PV output are 0.05 and 0.3 respectively. At this time, 0.2 and 0.348 are respectively selected as the best compromise solutions. This means that the greater the risk that the IESP can bear, the greater the tolerance for the prediction errors of wind power and PV outputs.

[0239] Furthermore, Figure 15 shows the scheduling results of gas turbines and waste heat boilers under RNS and RAS. Figure 16 shows the scheduling results of the energy purchase volume and the integrated demand response volume under RNS and RAS.

[0240] It can be seen from the figure that during the low electricity consumption period from 22:00 to 6:00 the next day, the wind power output is the highest and the selling price of electricity of the power grid is the lowest. At this time, the superior power grid, the wind turbines and gas turbine units of the IESP itself bear the main electricity demand. Under the risk aversion strategy, in order to avoid the risks caused by the unstable wind power output, the IESP is more inclined to increase the electricity and natural gas purchased from the superior energy network, thus increasing the carbon emission cost and energy purchase cost of the system. During the peak electricity consumption periods from 8:00 to 11:00 and from 16:00 to 20:00, as the wind power output decreases, the PV power generation gradually increases. At this time, the superior power grid, the wind turbine units, PV units and gas turbine units of the IESP itself bear the main electricity demand. Among them, during the periods from 10:00 to 12:00 and from 15:00 to 18:00, the electricity purchase volume and gas purchase volume of the IESP under the risk aversion strategy are the highest, and the gas turbine units meet 16.54% of the power load demand. During the peak heat consumption period, the output thermal power of the waste heat boiler under the risk aversion strategy is more than that under RNS. The main reason for this difference is that the heat load of users is mainly met by two devices, namely the waste heat boiler and the electric heat pump, and the peak and valley periods of the heat load and the electric load are opposite. Therefore, during the low heat consumption and high electricity consumption periods, the IESP under the risk aversion strategy mainly uses the waste heat boiler equipment to meet the heat load demand and reduces the heat supply of the electric heat pump, so as to avoid the risk of insufficient power supply, while the IESP under RNS cuts less on the output of the heat pump in order to seek more efficient energy supply.

[0241] In addition, IESP achieves the balance of energy supply and demand by calling more IDR loads. Generally speaking, due to the instability of the user's shiftable load, IESP has the least regulation amount of the shiftable load under the risk aversion strategy, while the regulation amount of the relatively stable curtailable load is relatively large. Specifically, in the time periods of 1:00-9:00, 13:00-15:00, and 19:00-22:00, IESP mainly guides users to use more electricity, transfers the production plan during peak periods to low and normal periods, thereby alleviating the power supply pressure. At this time, the amount of electricity load transferred under the risk aversion strategy is less than that under RNS, and the amount of electricity load reduction is more than that in the RNS scenario.

[0242] In summary, compared with RNS, the production plan and IDR regulation plan of IESP under the risk aversion strategy are more conservative. It mainly responds to the comprehensive uncertainty brought by the power source and load through gas turbine units with stable output, waste heat boiler units, the superior power grid, and regulating the IDR load on the user side. It can be seen that IESP sacrifices certain benefits in order to avoid certain risks. For decision-makers, it is particularly important to formulate reasonable production plans and regulation plans under different risk awareness or risk tolerance levels.

[0243] (3) Optimization results of the IDR incentive strategy

[0244] Figure 17 Shows the IDR incentive strategy of IESP and the response results of users, or the user response results under the IDR incentive mechanisms such as the time-of-use energy price and incentive subsidy price signal of IESP.

[0245] As can be seen from the figure, the electricity selling price released by IESP is consistent with the trend of the incentive subsidy price, and the electricity load curve after response is smoother than that before response. During the time periods of 1:00-6:00 and 23:00-24:00, the electricity load is at a low level. At this time, both the electricity selling price of IESP and the incentive subsidy price for the load that can be curtailed are at a relatively low level. The average electricity selling price and the average incentive subsidy are 0.38￥ / kW h and 2￥ / kW h respectively. And at this time, users increase part of the electricity load through the form of load transfer and curtail unnecessary electricity load, but the amount of curtailment is small, and the overall electricity load of users shows a growing form. Therefore, during this time period, users not only utilize the lower electricity price to increase the electricity load to complete the production plan, but also obtain a certain amount of incentive subsidy for the load that can be curtailed by curtailing a small part of the unnecessary load, obtaining part of the IDR incentive subsidy income while reducing the energy consumption cost. During the time periods of 7:00-8:00, 12:00-16:00 and 21:00-22:00, the average electricity selling price and the average incentive subsidy of IESP are 0.68￥ / kW h and 2.29￥ / kW h respectively. The electricity load of users is at a stable level, but both the subsidy price for the load that can be curtailed and the amount of the load that can be curtailed are at a relatively high level. The load that can be transferred mainly focuses on the incoming load, and the overall electricity load of users shows a decreasing state. During the above stages, the heat and cold load of users is at a relatively high level. Therefore, in order to meet the heat and cold demand during this stage, users can only continuously curtail the electricity load. During the two peak electricity consumption stages of 9:00-11:00 and 17:00-20:00, the average electricity selling price and the average incentive subsidy of IESP are 1.13￥ / kW h and 2.55￥ / kW h respectively. The curtailment amount of the electricity load is at the highest level, and the proportion of the transferred load amount reaches about 16% at the highest. The electricity consumption of heat pumps and electric chillers also decreases significantly. It can be seen that the high-level electricity price and incentive subsidy price play a role in guiding users to reduce the electricity load. At this time, the IDR strategy of users mainly includes the curtailment and transfer of the electricity load and the reduction of the load conversion amount such as electricity-to-heat and electricity-to-cooling.

[0246] The peak-valley flat periods of the heat load are exactly opposite to those of the electricity load. After the response, the heat load decreases significantly compared to before the response. During the periods of 1:00 - 6:00 and 20:00 - 24:00, the heat consumption load is at a relatively high level. At this time, the heat selling price of the IESP and the average incentive subsidy for the load that can be curtailed are about 0.67 ¥ / kW h and 2.5 ¥ / kW h respectively. The amount of heat load that can be curtailed accounts for about 10% of the original heat consumption load. It can be seen from this that the higher the incentive subsidy price, the higher the amount of heat load that can be curtailed. Relatively speaking, the periods of 7:00 - 10:00 and 18:00 - 19:00 are the normal heat consumption periods. At this time, the heat selling price of the IESP and the average incentive subsidy for the load that can be curtailed are about 0.38 ¥ / kW h and 2.3 ¥ / kW h respectively. However, the amount of heat load that can be curtailed in this stage is still about 10% of the original heat load. The reason for the above situation is that when the heat supply of the IESP is relatively small in this stage, the user's electricity load and cooling load are both at a relatively high level. Therefore, the heat pump cannot output more heat power by consuming more electric power, and part of the heating load needs to be converted into cooling power by the absorption chiller to meet part of the user's cooling load demand. The period of 11:00 - 17:00 is the low heat consumption stage. At this time, the heat selling price of the IESP and the average incentive subsidy for the load that can be curtailed are about 0.18 ¥ / kW h and 2.18 ¥ / kW h respectively. The amount of heat load that can be curtailed in this stage is still about 10% of the original heat load. The reason is that almost all of the cooling load in this stage is supplied by the absorption chiller and the heat load output by the heat pump is seriously insufficient, resulting in the heat supply of the IESP not only needing to meet the user's original heat consumption load but also the supply of the cooling load. Therefore, in order to meet all the cooling, heating and electricity load demands of the user, the heat load still needs to curtail some non-essential loads to ensure the balance of energy supply and demand.

[0247] The user's cooling load is completely supplied by the user himself through energy coupling devices such as electric chillers and absorption chillers without the need to purchase from the outside. Moreover, the total cooling load of this user and the cooling load demand in most periods are higher than the heat load. At this time, the user only needs to respond to the incentive subsidy for the load that can be curtailed by the IESP to reduce the load in the corresponding period. Therefore, the average incentive subsidy for the load that can be curtailed by the IESP is about 2.33 ¥ / kW h, and the amount of cooling load curtailed by the user at each moment has reached about 10% of the original load. It can be seen from this that under the high subsidy incentive, the user can actively respond to the IDR instruction to curtail some non-essential cooling loads.

[0248] Therefore, implementing comprehensive incentive measures such as time-of-use energy selling prices and incentive subsidy prices at the same time can better guide users to better achieve the comprehensive demand response goal through rich response behavior methods such as load transfer, load curtailment and load conversion, and can also effectively ensure the economy of integrated energy service providers and users.

[0249] In summary, the present invention constructs a two-layer game scheduling optimization model for a campus integrated energy system considering the coupling characteristics and uncertainties of both supply and demand sides under a comprehensive demand response incentive mechanism. Specifically, first, a comprehensive demand response incentive mechanism is designed from three dimensions: incentive objectives, incentive signals, and incentive processes, and an energy coupling model for both supply and demand sides considering the comprehensive demand response incentive mechanism is constructed; second, the uncertainties of multi-type load forecasts such as electricity, heat, and cooling are characterized through multi-scenario technology, and the uncertainties of new energy outputs such as wind power and photovoltaic power are characterized through the IGDT theory; finally, on the premise that the maximum daily operating revenue of the integrated energy service provider is not lower than an acceptable critical value, with the maximum uncertainty of wind power output forecast value and photovoltaic power output forecast value as the upper-layer objective and the lowest total user cost as the lower-layer objective, a two-layer optimal scheduling model for the campus integrated energy system considering the coupling and uncertainties of both supply and demand sides is constructed, and a solution method combining the particle swarm optimization algorithm and the analytical method is used to solve it through the CPLEX optimization software to obtain the scheduling strategy of the campus integrated energy system. It can be seen that the two-layer optimal scheduling model of the campus integrated energy system constructed by the present invention not only considers the impacts of the uncertainties of renewable energy outputs on the source side and the uncertainties of energy consumption on the demand side on the operation of the PIES, but also considers the coupling characteristics of the internal devices of the PIES and the response characteristics of the user energy consumption load. Therefore, the scheduling method of the campus integrated energy system of the present invention can improve the accuracy of scheduling decisions, and thus ensure the efficient, low-carbon, and economic operation of the campus integrated energy system.

[0250] The various technologies described herein can be implemented in combination with hardware or software, or a combination thereof. Thus, the method and device of the present invention, or certain aspects or parts of the method and device of the present invention, can take the form of program code (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, a USB flash drive, a floppy disk, a CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into a machine such as a computer and executed by the machine, the machine becomes a device for practicing the present invention.

[0251] In the case where the program code is executed on a programmable computer, the computing device generally includes a processor, a processor-readable storage medium (including volatile and non-volatile memories and / or storage elements), at least one input device, and at least one output device. Among them, the memory is configured to store the program code; the processor is configured to execute the scheduling method of the campus integrated energy system considering the coupling characteristics of both supply and demand sides of the present invention according to the instructions in the program code stored in the memory.

[0252] In the specification provided herein, a large number of specific details are set forth. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail so as not to obscure an understanding of this description.

[0253] It should be understood that, for the purpose of streamlining this disclosure and aiding in the understanding of one or more of the various inventive aspects, in the foregoing description of exemplary embodiments of the invention, the various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0254] In addition, unless otherwise specified, the use of ordinal numbers such as "first", "second", "third", etc. to describe ordinary objects merely indicates different instances of similar objects and is not intended to imply that the objects so described must have a given order in terms of time, space, ranking, or in any other manner.

[0255] Although the invention has been described in terms of a limited number of embodiments, those skilled in the art within the technology will appreciate that other embodiments can be contemplated within the scope of the invention as thus described. In addition, it should be noted that the language used in this specification has been principally selected for readability and instructional purposes and not for the purpose of explaining or limiting the subject matter of the invention. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. For the scope of the invention, the disclosure of the invention is illustrative, not restrictive, and the scope of the invention is defined by the appended claims.

Claims

1. A scheduling method for a park integrated energy system considering the coupling characteristics of both supply and demand sides. The energy hub of the park integrated energy system includes a supply-side energy hub and a demand-side energy hub. The supply-side energy hub includes energy production equipment and energy storage equipment, and the demand-side energy hub includes energy conversion equipment. The method includes: Based on the energy supply-demand balance relationship of the park integrated energy system and the coupling characteristics of the equipment between the supply-side energy hub and the demand-side energy hub, construct a supply-demand bilateral energy coupling model considering the integrated demand response incentive mechanism; Based on the probability distribution function of the energy consumption load of integrated energy users, use the Latin hypercube sampling method for scenario generation and the synchronous back substitution method for scenario reduction to obtain multiple scenarios of the energy consumption load and the probability of each scenario; Based on the multiple scenarios and the probability of each scenario, construct an energy consumption scheduling model for integrated energy users with the goal of minimizing the daily operating cost of integrated energy users; Based on the information gap decision theory, with the goal of maximizing the uncertainty of the predicted wind power output and the predicted photovoltaic power output, construct a robust scheduling model for integrated energy service providers under a risk aversion strategy; Take the energy consumption scheduling model as the lower-level model and the robust scheduling model as the upper-level model to construct a two-layer optimal scheduling model for the park integrated energy system, and solve it based on the supply-demand bilateral energy coupling model to obtain the scheduling strategy of the park integrated energy system. The scheduling strategy includes the load curtailment amount, load transfer amount, and output of energy conversion equipment at each moment for integrated energy users, and the energy selling price, integrated demand response incentive subsidy price, output of energy production equipment, and output of energy storage equipment at each moment for integrated energy service providers.

2. The method according to claim 1, wherein The supply-demand bilateral energy coupling model includes: L = C(EQ + S + R) - ΔL Wherein, L represents the user load matrix after implementing the integrated demand response, C represents the efficiency matrix of energy conversion equipment, E represents the efficiency matrix of energy production equipment, Q represents the external energy supply matrix, S represents the energy storage equipment output matrix, R represents the renewable energy output matrix, and ΔL represents the matrix of load curtailment amount and transfer amount.

3. The method according to claim 1 or 2, wherein The constructing an energy consumption scheduling model for integrated energy users with the goal of minimizing the daily operating cost of integrated energy users based on the multiple scenarios and the probability of each scenario includes: Determine the daily operating cost of integrated energy users according to the energy consumption cost of integrated energy users after participating in the integrated demand response under each scenario, the response cost generated by integrated energy users due to adjusting the energy consumption plan by participating in the integrated demand response, the subsidy income obtained by integrated energy users for participating in the integrated demand response, the satisfaction cost of integrated energy users for participating in the integrated demand response, and the operation and maintenance cost of energy conversion equipment, and construct the energy consumption scheduling model with the minimum daily operating cost as the first objective function.

4. The method according to claim 3, wherein, The satisfaction cost of integrated energy users for participating in the integrated demand response includes: Among them, represents the satisfaction cost of the integrated energy user participating in the integrated demand response under scenario ω, respectively represent the electricity load, heat load, and cooling load of the integrated energy user before participating in the integrated demand response at time t under scenario ω, respectively represent the reducible electricity load, reducible heat load, and reducible cooling load of the integrated energy user after participating in the integrated demand response at time t under scenario ω, respectively represent the electricity load transfer-in and electricity load transfer-out of the integrated energy user after participating in the integrated demand response at time t under scenario ω. c1, c2, and c3 all represent the cost coefficients after the integrated energy user participates in the integrated demand response, and T represents the total duration of implementing the integrated demand response.

5. The method according to claim 1 or 2, wherein, The constructing a robust scheduling model for integrated energy service providers under a risk aversion strategy based on the information gap decision theory with the goal of maximizing the uncertainty of the predicted wind power output and the predicted photovoltaic power output includes: Taking the maximum uncertainty of the wind power output prediction value as the second objective function, the maximum uncertainty of the photovoltaic power output prediction value as the third objective function, and the difference between the daily operating revenue of the integrated energy service provider under the risk-neutral strategy and the daily operating revenue of the integrated energy service provider under the risk-averse strategy not exceeding the target deviation value as the constraint condition, the robust scheduling model is constructed.

6. The method according to claim 5, wherein, The constraint conditions of the robust scheduling model further include the energy supply-demand balance constraint, and the energy supply-demand balance constraint includes: Among them, respectively represent the electric load, heat load, and cooling load of the integrated energy user before participating in the integrated demand response under scenario ω at time t. respectively represent the reducible electric load, reducible heat load, and reducible cooling load of the integrated energy user after participating in the integrated demand response under scenario ω at time t. respectively represent the electric load transfer-in and transfer-out of the integrated energy user after participating in the integrated demand response under scenario ω at time t, η e represents the transformer efficiency. represents the electricity quantity purchased by the integrated energy service provider from the external power grid after implementing the integrated demand response under scenario ω at time t. respectively represent the outputs of the gas turbine, wind turbine, and photovoltaic unit after implementing the integrated demand response under scenario ω at time t. respectively represent the discharge power and charge power of the electric energy storage unit after implementing the integrated demand response under scenario ω at time t. respectively represent the electric powers input to the heat pump and electric chiller after implementing the integrated demand response under scenario ω at time t. respectively represent the outputs of the waste heat boiler, gas boiler, and heat pump after implementing the integrated demand response under scenario ω at time t. respectively represent the heat release power and heat storage power of the heat energy storage unit after implementing the integrated demand response under scenario ω at time t. represents the heat power input to the absorption chiller after implementing the integrated demand response under scenario ω at time t. respectively represent the cooling powers output by the electric chiller and absorption chiller after implementing the integrated demand response under scenario ω at time t.

7. The method according to claim 1 or 2, wherein Based on the probability distribution function of the energy consumption load of the integrated energy user, the Latin hypercube sampling method is used for scenario generation and the synchronous back substitution method is used for scenario reduction to obtain multiple scenarios of the energy consumption load and the probability of each scenario, including: Based on the probability distribution function of the energy consumption load, the probability distribution of the energy consumption load of the integrated energy user is equally divided into a preset number of probability intervals; In each probability interval, the electric load, heat load, and cooling load at a certain moment are obtained through the probability distribution function of the energy consumption load to obtain a scenario, and the product of the electric load probability, heat load probability, and cooling load probability at this moment is used as the probability of this scenario, obtaining a preset number of scenarios and the probability of each scenario; For each obtained scenario, based on the probability of this scenario and the Euclidean distance between it and other scenarios, the probability distance between this scenario and other scenarios is obtained, and the other scenario corresponding to the minimum probability distance is determined as the nearest scenario to this scenario; For each scenario, calculate the product of its probability and the probability distance between it and its nearest scenario obtained above, and determine the scenario with the smallest product as the scenario to be reduced; Delete the scenario to be reduced, and accumulate the probability of the deleted scenario to the nearest scenario of the deleted scenario; Decrease the number of scenarios by one, and detect whether the current number of scenarios reaches the preset value; If it does not reach the preset value, repeat the above steps of obtaining the probability distance, determining the nearest scenario, determining and deleting the scenario to be reduced, accumulating the probability, decreasing the number of scenarios by one, and detecting the number of scenarios until the current number of scenarios reaches the preset value, obtaining a preset number of scenarios and the probability of each scenario.

8. The method according to claim 1 or 2, wherein An improved particle swarm optimization algorithm is used to solve the double-layer optimal scheduling model, and the use of the improved particle swarm optimization algorithm to solve the double-layer optimal scheduling model includes: Initialize the population size, number of iterations, acceleration factor, the velocity and position of each particle; Input the position of each particle into the energy consumption scheduling model for solution to obtain the reducible load, transferable load, and the output of the energy conversion device at each moment of the integrated energy user; Input the obtained reducible load, transferable load, and the output of the energy conversion device at each moment of the integrated energy user into the robust scheduling model for solution to obtain the best individual position of each particle and the best group position of the particle swarm; Detect whether the daily operating revenue of the integrated energy service provider at the current best group position under the risk-averse strategy is greater than the daily operating revenue of the integrated energy service provider at the previous best group position under the risk-averse strategy; Otherwise, update the velocities and positions of all particles based on the acceleration factor, and repeatedly execute the above steps of solving the energy consumption scheduling model, solving the robust scheduling model, and detecting the daily operating revenue until the daily operating revenue of the current best swarm position under the risk aversion strategy of the integrated energy service provider is greater than the daily operating revenue of the last best swarm position under the risk aversion strategy of the integrated energy service provider. Update the positions of all particles to their best individual positions, and detect whether the initialized number of iterations has been reached; If the initialized number of iterations has not been reached, increment the current number of iterations by one, update the velocities and positions of all particles based on the acceleration factor, and repeatedly execute the above steps of solving the energy consumption scheduling model, solving the robust scheduling model, and detecting the daily operating revenue until the initialized number of iterations is reached. Take the current best swarm position and the curtailable load, shiftable load, and output of the energy conversion device at each moment of the integrated energy users obtained last time as the scheduling strategy of the park integrated energy system.

9. A computing device, comprising: At least one processor; And A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, and the program instructions include instructions for executing the method according to any one of claims 1-8.

10. A readable storage medium storing program instructions, which when read and executed by a computing device cause the computing device to execute the method according to any one of claims 1-8.

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