Multi-time scale scheduling method and device of integrated energy system

Abstract

The invention provides a multi-time-scale scheduling method and device for an integrated energy system, and the method comprises the steps: S1, constructing a long-time-scale scheduling model of the integrated energy system, and obtaining a first scheduling plan of long-time energy storage equipment; s2, correcting the first scheduling plan by using the predicted power of the renewable energy power station to obtain a second scheduling plan of the long-time energy storage equipment; s3, optimizing the flexibility of the integrated energy system under a short time scale, and obtaining a flexibility interval of the integrated energy system and a robust operation interval of each device; and S4, scheduling the integrated energy system based on the real-time power of the renewable energy power station by taking the flexibility interval in the integrated energy system and the robust operation interval of each device as constraint conditions. According to the method, under limited prediction information, renewable energy sources can be fully utilized, the electricity-heat conversion efficiency is improved, and good dispatching economy is achieved while the operation reliability of the system is guaranteed.

Description

Technical Field

[0001] This application relates to the field of power planning technology, specifically to a multi-timescale scheduling method and device for an integrated energy system. Background Technology

[0002] The overuse of fossil fuels has led to climate change and serious environmental problems. In response, China has clearly set the goals of achieving carbon peaking by 2030 and carbon neutrality by 2060, and is pursuing energy transformation by continuously and vigorously developing renewable energy sources such as wind and solar power while gradually reducing the capacity of traditional thermal power units.

[0003] The integrated energy system for industrial parks is a promising power system paradigm capable of integrating a high proportion of renewable energy and adapting to various demand-side load scenarios. It boasts high efficiency in several ways: First, through combined heat and power (CHP), waste heat generated during power generation can be reused; second, the conversion between electricity and heat energy achieves complementary advantages, as renewable energy can be stored at a lower cost through thermal energy storage due to its low unit cost; third, the combination of batteries and hydrogen storage for both short and long-term use effectively enables the absorption of renewable energy across multiple time scales. During the electricity-to-hydrogen conversion process, the electrolyzer and fuel cell generate heat, resulting in higher overall energy efficiency.

[0004] Currently, there is considerable research on the real-time scheduling problem of integrated energy systems, employing different scheduling strategies by modeling the uncertainties of renewable energy. Among these, stochastic optimization and robust optimization are the most mainstream strategies. Stochastic optimization simulates uncertainty through multiple sampling scenarios and minimizes the mathematical expectation of cost; while robust optimization minimizes the cost under the worst-case scenario. Although it ensures operational reliability, it is often overly conservative, leading to poor scheduling economics. However, given that current technology can only achieve short-term forecasting of renewable energy, these two methods are mainly applied to day-ahead-scale scheduling plans and are not suitable for long-term scheduling problems, failing to consider the seasonal energy scheduling of renewable energy by long-term energy storage. Therefore, the synergistic cooperation between long-term and short-term energy storage is difficult to study within the framework of stochastic and robust optimization.

[0005] In contrast, a multi-timescale framework offers an effective solution. It first provides a coarse scheduling plan as a rough reference on a long-term timescale, and then refines the reference plan in real-time on a short-term timescale based on observations or short-term forecasts. This dual-timescale scheduling framework has already been practically applied in power systems. For example, one related technology proposes a dual-timescale stochastic dynamic programming method for controlling distributed energy storage devices, which includes a day-ahead coarse scheduling phase and an hourly real-time correction phase. Another related technology proposes a dual-timescale energy scheduling method for large-scale power systems. It employs stochastic dynamic programming on the long-term timescale to ensure robust feasibility of rolling optimization on the short-term timescale. Multi-timescale frameworks can handle scheduling planning problems on weekly or quarterly timescales, covering the complete operating cycle of long-term energy storage, and fully considering the coordination between long-term and short-term energy storage. However, for long-term scheduling with seasonal or even annual cycles, the lack of long-term weather forecasts makes it difficult to predict even the general trend of renewable energy power. How to provide a relatively reliable long-term coarse plan with limited forecast information remains an unsolved problem.

[0006] Furthermore, integrated energy systems involve the coupling of electrical and thermal energy. Combined heat and power (CHP) units can provide heat while generating electricity, and long-term energy storage using hydrogen as a carrier also has thermal effects during charging and discharging. If such energy can be utilized in real-time scheduling, the unit output can be effectively reduced, thereby improving operational economy. This is a key area worthy of research in the scheduling of integrated energy systems.

[0007] In summary, it is necessary to propose a multi-timescale scheduling method and platform for integrated energy systems in industrial parks that considers electricity-heat-hydrogen energy storage. Under limited forecast information, this method can achieve coordinated operation between traditional generating units and long-term and short-term energy storage, fully utilize renewable energy, improve electrothermal conversion efficiency, and ensure the operational reliability of the integrated energy system while also achieving good scheduling economy. Summary of the Invention

[0008] At least one embodiment of this application provides a scheduling device and apparatus for an integrated energy system, which enables the coordinated operation of traditional generating units and long- and short-term energy storage under limited forecast information, makes full use of renewable energy, improves electrothermal conversion efficiency, and ensures the operational reliability of the integrated energy system while also having good scheduling economy.

[0009] According to one aspect of this application, at least one embodiment provides a multi-timescale scheduling method for an integrated energy system, the integrated energy system being connected to an external power grid, and the internal equipment of the integrated energy system including heat generation and power generation equipment, renewable energy power plants, long-term energy storage equipment, electrical loads, and thermal loads; the method includes the following steps:

[0010] S1, Construct a long-term scheduling model for the integrated energy system to obtain the first scheduling plan for the long-term energy storage device;

[0011] S2, using the predicted power of the renewable energy power plant, the first scheduling plan is revised to obtain the second scheduling plan for the long-term energy storage device;

[0012] S3, optimize the flexibility of the integrated energy system in a short time scale to obtain the flexibility range of the integrated energy system and the robust operating range of each device;

[0013] S4. Using the flexibility range and robust operating range of each device in the integrated energy system as constraints, the integrated energy system is scheduled based on the real-time power of the renewable energy power station.

[0014] Optionally, the internal equipment of the integrated energy system may also include at least one of the following: heat generation equipment, electrothermal conversion equipment, short-term energy storage equipment, and thermal storage equipment.

[0015] Optionally, the method may further include the following steps before step S1:

[0016] S0, construct a power model for each device in the integrated energy system, the power model including an electrical power model and a thermal power model; based on the power models of each device in the integrated energy system, construct a power balance model for the integrated energy system, the power balance model including an electrical power balance model and a thermal power balance model.

[0017] Optionally, step S1 includes:

[0018] Using minimizing the operating cost of the integrated energy system over a long time scale as the objective function and the equipment power model of each device in the integrated energy system as the constraint, a long-term scheduling model of the integrated energy system is constructed.

[0019] Based on the long-term scheduling model and the historical power of the renewable energy power plant at at least one historical long-term scale, the optimal scheduling strategy of the integrated energy system at each historical long-term scale is obtained.

[0020] Based on the optimal scheduling strategy of the integrated energy system under various historical time scales, the first scheduling plan of the long-term energy storage device under various historical time scales is obtained.

[0021] Optionally, step S2 includes:

[0022] Obtain a reference power vector for renewable energy power plants at the current long time scale, the reference power vector including the historical power of each short time scale that has been dispatched at the current long time scale and / or the predicted power of each short time scale that has not yet been dispatched.

[0023] The weights corresponding to each historical time scale are determined based on the Euclidean distance between the reference power vector and the historical power vectors of the same period at each historical time scale.

[0024] Based on the weights corresponding to each historical time scale, the first scheduling plan of the long-term energy storage element under each historical time scale is weighted and summed to obtain the second scheduling plan of the long-term energy storage device.

[0025] Optionally, step S3 includes:

[0026] Based on the power balance model of the integrated energy system, the power expression of the renewable energy power station is obtained, and based on the power expression of the renewable energy power station, the flexibility of the integrated energy system under a dispatch cycle is obtained. The flexibility is characterized by the difference between the upper and lower limits of the power of the renewable energy power station, and the dispatch cycle includes multiple short time scales.

[0027] To avoid using grid power and maximize the flexibility of the integrated energy system during the scheduling cycle, and to ensure that the flexibility is smoothly distributed across various short time scales, an objective function is constructed. This function is constrained by the power models of each device in the integrated energy system, the power ramp-up constraints of each device at a short time scale, and the constraint that the final energy value of the long-term energy storage device during the scheduling cycle is not lower than the final energy value determined by the second scheduling plan. The resulting solution yields the flexibility range of the integrated energy system and the robust operating range of each device. The robust operating range is characterized by the upper and lower limits of the device power.

[0028] Optionally, step S4 includes:

[0029] Based on the power balance model of the integrated energy system, power balance constraints are constructed with the load quantiles of the electrical equipment side, the load quantiles of the heat-consuming equipment side, and the power quantiles of each equipment as variables. Specifically, the load quantiles of the electrical equipment side represent the position of the load on that side between its upper and lower load limits; the load quantiles of the heat-consuming equipment side represent the position of the load on that side between its upper and lower load limits; and the power quantiles of each equipment represent the position of its power between its upper and lower power limits.

[0030] When determining the scheduling strategy for the t-th short time scale under the current scheduling cycle, an objective function is constructed with the goal of minimizing the operating cost of the integrated energy system from the t-th to the (t+R)-th short time scale. The real-time scheduling strategy of the integrated energy system is obtained by solving the constraints of the power balance constraints, the equipment power models of each device, the power ramping constraints of each device in a short time scale, the power of the renewable energy power plant in each short time scale being less than or equal to the observed actual power or predicted power, and the power supply power of the power grid being lower than the preset maximum power supply power.

[0031] Scheduling is performed based on the real-time scheduling strategy of the integrated energy system.

[0032] According to another aspect of this application, at least one embodiment provides a multi-timescale scheduling device for an integrated energy system, the integrated energy system being connected to an external power grid, and the internal equipment of the integrated energy system including heat generation and power generation equipment, renewable energy power plants, long-term energy storage equipment, electrical loads, and thermal loads; the device includes:

[0033] The first scheduling module is used to construct a long-term scheduling model of the integrated energy system and obtain the first scheduling plan of the long-term energy storage device.

[0034] The correction module is used to correct the first scheduling plan using the predicted power of the renewable energy power plant to obtain a second scheduling plan for the long-term energy storage device.

[0035] An optimization module is used to optimize the flexibility of the integrated energy system in a short time scale, thereby obtaining the flexibility range of the integrated energy system and the robust operating range of each device.

[0036] The second scheduling module is used to schedule the integrated energy system based on the real-time power of the renewable energy power plant, using the flexibility range and robust operation range of each device in the integrated energy system as constraints.

[0037] Optionally, the internal equipment of the integrated energy system may also include at least one of the following: heat generation equipment, electrothermal conversion equipment, short-term energy storage equipment, and thermal storage equipment.

[0038] Optional, also includes:

[0039] The model building module is used to build equipment power models for each device in the integrated energy system, the equipment power models including equipment electrical power models and equipment thermal power models; based on the equipment power models of each device in the integrated energy system, the power balance model of the integrated energy system is built, the power balance model including electrical power balance model and thermal power balance model.

[0040] Optionally, the first scheduling module is further configured to:

[0041] Using minimizing the operating cost of the integrated energy system over a long time scale as the objective function and the equipment power model of each device in the integrated energy system as the constraint, a long-term scheduling model of the integrated energy system is constructed.

[0042] Based on the long-term scheduling model and the historical power of the renewable energy power plant at at least one historical long-term scale, the optimal scheduling strategy of the integrated energy system at each historical long-term scale is obtained.

[0043] Based on the optimal scheduling strategy of the integrated energy system under various historical time scales, the first scheduling plan of the long-term energy storage device under various historical time scales is obtained.

[0044] Optionally, the correction module is further configured to:

[0045] Obtain a reference power vector for renewable energy power plants at the current long time scale, the reference power vector including the historical power of each short time scale that has been dispatched at the current long time scale and / or the predicted power of each short time scale that has not yet been dispatched.

[0046] The weights corresponding to each historical time scale are determined based on the Euclidean distance between the reference power vector and the historical power vectors of the same period at each historical time scale.

[0047] Based on the weights corresponding to each historical time scale, the first scheduling plan of the long-term energy storage element under each historical time scale is weighted and summed to obtain the second scheduling plan of the long-term energy storage device.

[0048] Optionally, the optimization module is further configured to:

[0049] Based on the power balance model of the integrated energy system, the power expression of the renewable energy power station is obtained, and based on the power expression of the renewable energy power station, the flexibility of the integrated energy system under a dispatch cycle is obtained. The flexibility is characterized by the difference between the upper and lower limits of the power of the renewable energy power station, and the dispatch cycle includes multiple short time scales.

[0050] To avoid using grid power and maximize the flexibility of the integrated energy system during the scheduling cycle, and to ensure that the flexibility is smoothly distributed across various short time scales, an objective function is constructed. This function is constrained by the power models of each device in the integrated energy system, the power ramp-up constraints of each device at a short time scale, and the constraint that the final energy value of the long-term energy storage device during the scheduling cycle is not lower than the final energy value determined by the second scheduling plan. The resulting solution yields the flexibility range of the integrated energy system and the robust operating range of each device. The robust operating range is characterized by the upper and lower limits of the device power.

[0051] Optionally, the second scheduling module is further configured to:

[0052] Based on the power balance model of the integrated energy system, power balance constraints are constructed with the load quantiles of the electrical equipment side, the load quantiles of the heat-consuming equipment side, and the power quantiles of each equipment as variables. Specifically, the load quantiles of the electrical equipment side represent the position of the load on that side between its upper and lower load limits; the load quantiles of the heat-consuming equipment side represent the position of the load on that side between its upper and lower load limits; and the power quantiles of each equipment represent the position of its power between its upper and lower power limits.

[0053] When determining the scheduling strategy for the t-th short time scale under the current scheduling cycle, an objective function is constructed with the goal of minimizing the operating cost of the integrated energy system from the t-th to the (t+R)-th short time scale. The real-time scheduling strategy of the integrated energy system is obtained by solving the constraints of the power balance constraints, the equipment power models of each device, the power ramping constraints of each device in a short time scale, the power of the renewable energy power plant in each short time scale being less than or equal to the observed actual power or predicted power, and the power supply power of the power grid being lower than the preset maximum power supply power.

[0054] Scheduling is performed based on the real-time scheduling strategy of the integrated energy system.

[0055] According to another aspect of this application, at least one embodiment provides a computer-readable storage medium storing a program that, when executed by a processor, implements the steps of the method described above.

[0056] The integrated energy system scheduling device and apparatus provided in this application establish a dual-timescale operating model for equipment such as cogeneration units, boilers, battery energy storage, hydrogen storage, and thermal storage in the integrated energy system. This constructs an operation scheduling model for the integrated energy system including renewable energy, obtains several typical scheduling scenarios using historical data, and solves for a coarse scheduling plan for long-term energy storage. The long-term energy storage scheduling plan is then revised, and the system flexibility is allocated based on a coarse forecast of day-ahead renewable energy output. A quadratic convex optimization problem is constructed to maximize the system's flexibility response range to renewable energy uncertainties, and to distribute flexibility as smoothly as possible across different time periods. This application also constructs load-side quantiles and equipment operating quantiles on both the electricity and heat sides of the integrated energy system, deriving a quantile-dominated electricity-heat power balance relationship. Furthermore, a rolling-lookahead real-time scheduling problem is constructed, using the system's flexibility range obtained in the day-ahead phase and the robust operating range of each device as constraints to reduce the short-sightedness of the scheduling and exhibiting good optimality. Attached Figure Description

[0057] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0058] Figure 1 This is a schematic diagram of an integrated energy system that can be applied to the embodiments of this application;

[0059] Figure 2 This is a schematic diagram of another integrated energy system that can be applied to the embodiments of this application;

[0060] Figure 3 This is a schematic diagram of yet another integrated energy system to which the embodiments of this application can be applied;

[0061] Figure 4 This is a schematic flowchart of a scheduling method for an integrated energy system according to an embodiment of this application;

[0062] Figure 5 This is a schematic diagram illustrating the calculation results of the annual SOC of a long-term energy storage device under multiple historical scenarios in embodiments of this application;

[0063] Figure 6 This diagram illustrates the constraints of day-ahead scheduling as a long-term scheduling result in an embodiment of this application.

[0064] Figure 7 This is a schematic diagram illustrating the flexibility optimization results of an embodiment of this application;

[0065] Figure 8 This is a schematic diagram of the calculation results of the upper and lower bounds of some devices in an embodiment of this application;

[0066] Figure 9 This is a schematic diagram of the calculation results of the upper and lower bounds of some devices in an embodiment of this application;

[0067] Figure 10 This is an example diagram of a scheduling device for an integrated energy system according to an embodiment of this application;

[0068] Figure 11 This is another structural schematic diagram of the scheduling device of the integrated energy system according to an embodiment of this application. Detailed Implementation

[0069] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0070] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The word "and / or" in the specification and claims indicates at least one of the connected objects. In this article, the upper bound and the upper limit have the same meaning, both referring to the maximum value of a parameter within a certain period of time; similarly, the lower bound and the lower limit have the same meaning, both referring to the minimum value of a parameter within a certain period of time.

[0071] In the various embodiments of this application, it should be understood that the sequence number of each process described below does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0072] The following description provides examples and is not intended to limit the scope, applicability, or configuration set forth in the claims. Changes may be made to the function and arrangement of the elements discussed without departing from the spirit and scope of this disclosure. Various procedures or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with reference to certain examples may be combined in other examples.

[0073] This application's embodiments consider a low-carbon industrial park integrated energy system configured with multiple types of energy storage, including electricity, heat, and hydrogen. The system prioritizes the use of renewable energy sources such as wind and solar power, only drawing electricity from the grid when renewable energy cannot meet energy demand (e.g., during peak load periods or under adverse weather conditions). Considering the volatility of renewable energy at different time scales, electrochemical energy storage and thermal energy storage are used for intraday and interday regulation, while hydrogen storage equipment is a long-term energy storage device to cope with the seasonal fluctuations in wind and solar power output.

[0074] The multi-timescale real-time scheduling method for integrated energy systems considering the synergy of long-term and short-term energy storage proposed in this application first obtains a coarse scheduling plan for long-term energy storage by constructing a long-term scheduling model of the integrated energy system, and then corrects the short-term scheduling plan for long-term energy storage using coarse forecast data of renewable energy. After obtaining the scheduling plan for long-term energy storage for that day, the system flexibility for the short-term timescale is optimized and the robust operating range of the equipment is obtained based on the coarse forecast of renewable energy for that day. In real-time scheduling, the electrothermal coupling relationship and ramp-up constraints are fully considered, and real-time scheduling at the short-term timescale is achieved using real-time renewable energy data. Here, the long-term timescale can specifically be a timescale in units of years, quarters, months, etc., and the short-term timescale can specifically be a timescale in units of one hour, half an hour, 10 minutes, 5 minutes, etc. A long-term timescale includes multiple short-term timescales.

[0075] Figures 1 to 2 This application provides a schematic diagram of the structure of an integrated energy system to which embodiments of this application can be applied. Figure 3 A more specific structure of the integrated energy system according to embodiments of this application is provided. For example... Figure 1 As shown, the integrated energy system is connected to an external power grid and can receive power from the external grid when needed. The internal equipment of the integrated energy system includes heat and power generation equipment, a renewable energy power plant, long-term energy storage equipment, electrical load, and heat load. Optionally, such as... Figure 2 As shown, the internal equipment of the integrated energy system in this application embodiment also includes at least one of the following: heat generation equipment, electrothermal conversion equipment, short-term energy storage equipment, and thermal storage equipment. Figures 1 to 3 In the diagram, blue lines represent the supply of electrical energy, which is connected to relevant equipment through power lines, and arrows indicate the direction of power supply; red lines represent the supply of thermal energy, which is connected through thermal pathways, and arrows indicate the direction of heat supply. It can be seen that each power supply device provides electrical energy to the electrothermal conversion equipment, long- and short-term energy storage devices, and electrical loads, while each heating device provides thermal energy to the thermal storage devices and thermal loads.

[0076] As can be seen, the external power supply equipment of the integrated energy system includes the power grid, and the internal power supply equipment includes at least one of the following: heat generation and power generation equipment, short-term energy storage equipment, and long-term energy storage equipment; the internal electrical equipment includes at least one of the following: electrothermal conversion equipment, short-term energy storage equipment, long-term energy storage equipment, and electrical load. The internal heating equipment of the integrated energy system includes at least one of the following: heat generation and power generation equipment, heat generation equipment, and heat storage equipment; the internal heat consumption equipment of the integrated energy system includes at least one of the following: heat storage equipment and heat load. The electrical load can be further divided into fixed load (fixed electrical load) and deferred load (deferred electrical load). In this document, the various devices within the integrated energy system are sometimes referred to as components.

[0077] Here, heat-generating equipment refers to equipment that independently generates heat energy and provides heat energy to the heat load of a comprehensive energy system, such as... Figure 3 Boilers and other equipment in a system; heat-generating and power-generating elements refer to equipment capable of simultaneously generating heat and electricity and providing heat and electricity to the loads in a comprehensive energy system, such as... Figure 3 The equipment includes combined heat and power (CHP) units. The electrothermal conversion equipment is a device that converts electrical energy into heat energy, such as... Figure 3 Electric heaters, etc. Short-term and long-term energy storage devices are typically categorized by the duration of energy storage. For example, energy storage devices with a storage time longer than a preset time are considered long-term energy storage devices, while those shorter are considered short-term energy storage devices. Short-term energy storage devices are generally power-type and energy-type, primarily including supercapacitor energy storage, flywheel energy storage, and various electrochemical energy storage methods (such as...). Figure 3 The energy storage battery shown can specifically be a lithium battery. The long-term energy storage device can be compressed air energy storage, flow batteries, etc. Figure 3 The hydrogen storage equipment shown is an example. This application's embodiments propose a power balance model and equipment power model for an integrated energy system.

[0078] In this embodiment, a device power model is pre-constructed for each device in the integrated energy system. The device power model includes a device electrical power model and a device thermal power model. Based on the device power models of each device in the integrated energy system, a power balance model of the integrated energy system is constructed. The power balance model includes an electrical power balance model and a thermal power balance model.

[0079] Here, a device power model is constructed for the equipment in the integrated energy system, specifically including one or more of the following:

[0080] A power generation model based on power generation and a heat generation model based on heat generation power are constructed for the heat generation and power generation elements.

[0081] An electrical energy model based on charge and discharge power is constructed for the long-term energy storage element;

[0082] An electrical energy model based on charge and discharge power is constructed for the short-term energy storage element;

[0083] A thermal energy model based on the thermal storage charge and discharge power is constructed for the thermal storage element;

[0084] A heat generation model based on heat generation power is constructed for the heat generation element.

[0085] The following sections will explain each point.

[0086] (1) Model of heat generation and power generation equipment (such as model of combined heat and power unit)

[0087] Heat and power generation equipment (such as combined heat and power units) are facilities that utilize heat engines or power plants to simultaneously generate electricity and heat. In conventional power plants, the residual heat from the power generation process is mainly released into the environment through cooling towers or cooling water. However, in combined heat and power plants, the heat can be recovered and applied to residential, commercial, and industrial users. Taking an extraction-condensing combined heat and power unit as an example, it can recover the waste heat generated during the turbine's operation. Its operating region consists of a polyhedron with several poles. The electrical and thermal power output of the unit can be represented by a convex combination of poles:

[0088]

[0089] In the formula, and These are the power generation and heat production at pole k; π k,t For optimizable parameters, 0 ≤ π k,t ≤1 and satisfy K represents the number of poles. The relationship between power generation and heat production is a linear constraint and can fully describe the operating characteristics of a combined heat and power (CHP) unit over a long timescale. However, for short timescales, the time interval between two adjacent periods is very short, and the ramp-up constraint on the output of the CHP unit cannot be ignored.

[0090]

[0091] Where: RD g and RU g It represents the maximum value of the power generation of the combined heat and power unit per unit time during the downward and upward ramp-up phases; Δt is the time interval on a short time scale.

[0092] Operating costs of a combined heat and power (CHP) unit during time period t This can be expressed as:

[0093]

[0094] In the formula, c0 to c5 are all constants, and the running cost function It is about and A convex function.

[0095] In this article, the duration of a time period or time period t can be a short time scale.

[0096] (2) Model of heat-generating equipment (such as boiler model)

[0097] A boiler can burn natural gas or other fuels to produce high-grade heat, and its heat production model can be expressed as:

[0098]

[0099] In the formula, This represents the boiler's heat output during time period t. and This represents the upper and lower limits of the boiler's heat production. Similarly, this formula can describe the boiler's operating characteristics over long timescales, but for short timescales, a ramp-up constraint on boiler output is also required.

[0100]

[0101] Where: RD b and RU b It represents the maximum value of the boiler's heat output per unit time, both downward and upward.

[0102] (3) Long-term energy storage device model

[0103] Long-term energy storage, exemplified by compressed air energy storage and hydrogen storage, features long full-power charge-discharge times, making it suitable for long-cycle, large-capacity charge-discharge operations and enabling long-cycle, seasonal energy dispatch. In this application, a hydrogen storage device is used as the long-term energy storage device in a comprehensive energy system, achieving the conversion between electrical energy and hydrogen energy through an electrolyzer and a fuel cell. Under current technology, the charge-discharge efficiency of hydrogen storage is relatively low, but the self-discharge effect is negligible. Therefore, the long-term energy storage model in the comprehensive energy system can be expressed as:

[0104]

[0105]

[0106] In the formula: and These represent the charging power and discharging power of long-term energy storage during time period t, respectively; 0-1 variable u t The constraint on long-term storage capacity allows for simultaneous charging and discharging; η c and η d These represent the charge and discharge efficiencies of long-term energy storage, respectively. It is the energy stored during the long-term energy storage period t; It is the rated power for long-term energy storage; It is the rated capacity for long-term energy storage.

[0107] (4) Short-term energy storage device model

[0108] In integrated energy systems, both battery energy storage and thermal energy storage are considered short-term energy storage due to their relatively short full-charge (thermal) duration. Battery energy storage can be used for short-term peak power and ancillary services, providing power energy reserves and participating in frequency control. Because of its high charge-discharge efficiency, the charge-discharge process of battery energy storage can be considered ideal; however, batteries are not suitable as long-term energy storage carriers, as the self-discharge effect of batteries cannot be ignored over long timescales. Its model can be expressed as:

[0109]

[0110]

[0111] In the formula: It represents the charging and discharging power of the battery energy storage during time period t, and can be positive or negative. It is the energy stored in the battery during time period t; ρ B It is the self-discharge coefficient of battery energy storage; It is the rated power of the battery energy storage; It is the rated capacity of the battery energy storage. It is the lower limit of battery energy storage capacity.

[0112] (5) Thermal storage equipment model

[0113] Similarly, the charging and discharging efficiency of thermal storage equipment is also very high, and its charging and discharging process can be considered ideal. However, self-heating loss should also be taken into account:

[0114]

[0115] In the formula: It represents the heat charging and discharging power of the thermal storage equipment during time period t, which can be positive or negative; It is the amount of heat stored in the thermal storage device during time period t; ρ H It is the self-heating coefficient of the thermal storage equipment; This is the rated power of the thermal storage equipment; This is the rated capacity of the thermal storage equipment. It is the lower limit of the heat storage capacity of thermal storage equipment.

[0116] (6) Model of electrothermal conversion equipment (such as electric heater)

[0117] An electrothermal conversion device (such as an electric heater) is a device that converts electrical energy into heat energy and is an important electrothermal coupling device in an integrated energy system. A resistance heater is a typical electrothermal conversion device, with an efficiency that can reach almost 100%. The output model of an electric heater can be represented as:

[0118]

[0119] This represents the power of the electric heater during time period t; Indicates the rated capacity of the electric heater;

[0120] (7) Power Balance Model

[0121] At each stage of operation, the integrated energy system needs to ensure a power balance between electrical and thermal energy sources, as expressed below:

[0122]

[0123] In the formula, It is the power output of renewable energy power plants used during time period t; This refers to the electrical power obtained from the power grid during time period t, which needs to meet the power purchase capacity requirement. Constraints:

[0124]

[0125] and These are the electrical and thermal loads for the corresponding time period; σ c and σ d These are the thermal effect parameters during the electrolysis and combustion of hydrogen processes.

[0126] Please refer to Figure 4 The integrated energy system scheduling method provided in this application includes:

[0127] Step 41: Construct a long-term scheduling model for the integrated energy system to obtain the first scheduling plan for the long-term energy storage device.

[0128] Step 42: Using the predicted power of the renewable energy power plant, revise the first scheduling plan to obtain the second scheduling plan for the long-term energy storage device.

[0129] Step 43: Optimize the flexibility of the integrated energy system in a short time scale to obtain the flexibility range of the integrated energy system and the robust operating range of each device.

[0130] Step 44: Using the flexibility range and robust operating range of each device in the integrated energy system as constraints, the integrated energy system is scheduled based on the real-time power of the renewable energy power station.

[0131] Through the above steps, the embodiments of this application can achieve the coordinated operation of traditional cogeneration units and long-term and short-term energy storage under limited predictive information, make full use of renewable energy, improve electrothermal conversion efficiency, and ensure the operational reliability of the integrated energy system while also having good dispatch economy.

[0132] The following section provides a more detailed explanation of each step of the above method.

[0133] In step 41 above, this embodiment of the application uses minimizing the operating cost of the integrated energy system over a long time scale as the objective function and the equipment power model of each device in the integrated energy system as the constraint condition to construct a long-term scheduling model for the integrated energy system. Based on the long-term scheduling model and the historical power of the renewable energy power plant over at least one historical long time scale, the optimal scheduling strategy of the integrated energy system under each historical long time scale is obtained. Then, based on the optimal scheduling strategy of the integrated energy system under each historical long time scale, the first scheduling plan (i.e., the coarse scheduling plan for long-term energy storage) of the long-term energy storage device under each historical long time scale is obtained.

[0134] Specifically, based on the equipment power model and power balance model of the integrated energy system, a scheduling model for the integrated energy system with access to renewable energy power plants can be constructed (as shown in Formula 20 below). To obtain a coarse scheduling plan for long-term energy storage devices over a long time scale, several historical scenarios with a long time scale (e.g., one year) can be constructed using historical data. By solving the optimal scheduling problem over a long time scale in the corresponding scenario, a coarse scheduling plan for the integrated energy system can be obtained, and the scheduling plan for long-term energy storage devices can be extracted from it.

[0135] For example, suppose there are N s There are 3 historical scenarios, each with a time scale of one year. In the s-th historical scenario, the corresponding known historical renewable energy output power is ξ. s,t Therefore, the optimal scheduling problem over a long timescale in this scenario can be expressed as:

[0136]

[0137] In the formula: T is the total number of time periods in a year on a long-term scale. If one hour is taken as a time interval, then T = 8760; θ is the fixed price of natural gas; γ t The electricity purchase price varies over time. st represents the constraints, including formulas (1)-(2), (5), (7), and (19) above. Solving problem (20) yields the optimal scheduling solution for the corresponding scenario throughout the year. Figure 5 The table shows the calculated State of Charge (SOC, also known as remaining capacity) of long-term energy storage devices under multiple historical scenarios. Different colored curves correspond to the SOC of different historical scenarios. It can be seen that the scheduling results of long-term energy storage devices exhibit similar trends. The optimal scheduling plan for long-term energy storage devices under historical scenario s (i.e., the first scheduling plan) is denoted as... It can reflect historical experience in scheduling over long time scales and will serve as a rough scheduling reference, becoming an important component of multi-time-scale scheduling methods.

[0138] In step 42 above, this embodiment of the application obtains a reference power vector for the renewable energy power plant at the current long-term time scale. The reference power vector includes the historical power of each scheduled short-term time scale and / or the predicted power of each unscheduled short-term time scale at the current long-term time scale. Then, based on the Euclidean distance between the reference power vector and the historical power vectors of the same period at each historical long-term time scale, the weights corresponding to each historical long-term time scale are determined. Then, based on the weights corresponding to each historical long-term time scale, a weighted summation is performed on the first scheduling plan of the long-term energy storage device at each historical long-term time scale to obtain a second scheduling plan for the long-term energy storage device.

[0139] While coarse-scale dispatch references over long historical periods exhibit similar trends, specific SOC curves still differ, making direct application difficult. Furthermore, actual renewable energy output may deviate from historical data, necessitating appropriate adjustments to the reference curves before application. Assuming a short timescale of 1 hour, a coarse renewable energy power forecast for the 24 hours following the start of a given day can be obtained. (The prediction method can be a well-known method based on historical data, such as using historical power curves and date, weather, and other data to form a feature vector and training a neural network or support vector machine model, or directly using the data from the previous 24 hours as the predicted value for the current 24 hours. This application does not specifically limit this method.) and combines historical observations from the beginning of the year to the present [ξ1,...,ξ] t ], to obtain the reference vector The Nadaraya-Watson kernel regression method can be used to calculate the reference vector and the corresponding curve ξ in historical scenarios. s,[t] The Euclidean distance is calculated, and the weights of each element are determined using a kernel regression function. These weighted values ​​are then used to obtain the corrected long-term energy storage operation reference. Specifically, this can be expressed as:

[0140]

[0141] In the formula: This is the revised value of the long-term energy storage scheduling strategy (i.e., the second scheduling plan for long-term energy storage equipment) obtained through kernel regression, which is derived from the weighted average of historical long-term energy storage equipment scheduling references. ρ s The weights are the data corresponding to the s-th historical scene, expressed through a Gaussian kernel function K. k (x, y) is calculated. Here, x and y are two vectors; N s Indicates the number of historical scenes; ξ represents the optimal scheduling plan for long-term energy storage devices under historical scenario s; s,[t] A vector representing the same historical period of a given historical scene; σ represents the Euclidean distance between two vectors; σ represents the bandwidth, a parameter related to kernel regression.

[0142] Dispatch strategy correction value for long-term energy storage devices It will serve as an important reference in short-term scheduling, and coordinate the cooperation between long-term and short-term energy storage. It is a key parameter in the multi-timescale real-time scheduling method of integrated energy systems.

[0143] In step 43 above, this embodiment of the application obtains the power expression of the renewable energy power station based on the power balance model of the integrated energy system, and obtains the flexibility of the integrated energy system under a scheduling cycle based on the power expression of the renewable energy power station. The flexibility is characterized by the difference between the upper and lower limits of the power of the renewable energy power station, and the scheduling cycle includes multiple short time scales. For example, the scheduling cycle can be one day (one calendar day), and the short time scales can be 1 hour, 10 minutes, 5 minutes, etc. Then, to avoid using the grid's electricity and maximize the flexibility of the integrated energy system under the scheduling cycle, and to make the flexibility smoothly distributed in each short time scale, an objective function is constructed. With the constraints of the equipment power model of each device in the integrated energy system, the power ramping constraint of each device under a short time scale, and the energy final value of the long-term energy storage device not being lower than the energy final value determined by the second scheduling plan in the scheduling cycle, the flexibility range of the integrated energy system and the robust operating range of each device are obtained by solving the problem. The robust operating range is characterized by the upper and lower limits of the equipment power. Figure 6 A long-term scheduling result is provided as an example of a day-ahead scheduling constraint, wherein the end-of-day energy value of the long-term energy storage device in the scheduling period (e.g., each day) is not lower than the end-of-day energy value determined by the long-term scheduling result (the second scheduling plan).

[0144] The flexibility of an integrated energy system is manifested in its ability to cope with external fluctuations. Considering the integrated energy system as a whole, its fluctuations (uncertainties) mainly originate from renewable energy power plants. Based on the power balance model, let... A value of 0 indicates the power output of the renewable energy power plant. The expression is:

[0145]

[0146] The uncertainties of renewable energy power plants are further transferred to the load side through the transmission of electrical and thermal energy. Figures 1-3 The power corresponding to the first node connecting the power generation side and the load side of the electrical energy shown is... And the power corresponding to the second node connecting the heat-generating side and the load side. The expressions are as follows:

[0147]

[0148] The flexibility of the system at every moment is essentially The range that can vary can therefore be quantified as The difference between the upper and lower limits that can be achieved The greater the system's flexibility, the wider the range of output fluctuations from renewable energy power plants it can absorb, and the stronger its ability to cope with uncertainties. Since uncertainty exists at every moment, the allocation of flexibility should aim to distribute it smoothly across different time periods. Therefore, the problem of optimizing the allocation of system flexibility can be expressed as:

[0149]

[0150] In this problem, the decision variables are the upper and lower limits of renewable energy power plant capacity absorption. and And the output power variables x of each system device corresponding to (24)-(26) up and x lw where x represents and The first term of the objective function (27) maximizes system flexibility, and the quadratic term of the second term reduces the variance of flexibility at each moment, making it smoothly distributed across each time period. ε is the weight parameter controlling the quadratic term. It is important to note that the optimization problem here is at a short time scale, where D is the total number of time periods at the short time scale included in a day. If the time interval of the short time scale is 5 minutes, then D = 288. Upper and lower limits of system flexibility. and Each corresponds to a set of independent variables with superscripts "up" and "lw", namely x. up ,x lw They all need to satisfy the above-mentioned basic system constraints (28).

[0151] The system's flexibility needs to be adjusted according to the output trend of renewable energy to make it as consistent as possible with the output characteristics of renewable energy. The calculated flexibility region should be a subset of the uncertainty range of renewable energy output, i.e., satisfying (29), where α is a parameter less than 1, determined by experience or during commissioning, and related to the configuration of the system's unit and energy storage parameters. Equation (30) specifies the relationship between the upper and lower limits of the output power of long-term and short-term energy storage to ensure the feasibility of real-time scheduling. Constraint (31) constrains the final energy value of the long-term energy storage equipment on the day by using the modified long-term energy storage operation reference, ensuring that the final energy value of long-term energy storage on the day is not lower than the reference value, and ensuring the operating trend on a long time scale; in the optimization problem, the constraints on the energy storage at other times of the day for long-term energy storage are relaxed, so that long-term and short-term energy storage can fully cooperate.

[0152] In addition, in order to satisfy the ramp-up constraints on short timescales in real-time scheduling, the following equation must also be satisfied:

[0153]

[0154] Constraints (30)-(32) ensure the feasibility of the real-time scheduling strategy mentioned below and are called "robust feasibility constraints". Solving optimization problems (27)-(32) yields optimized results for system flexibility.

[0155] Figure 7 A schematic diagram of the flexibility optimization results is provided, such as Figure 7 As shown, the yellow area represents the possible range of renewable energy output, and the green area is a subset of the yellow area, representing the range of system flexibility. Figure 8 and Figure 9 A schematic diagram is provided showing the results of calculating the upper and lower bounds of each device. The range of the upper and lower bounds of the flexibility of each device is the robust operating range of the device, which will serve as an important reference for real-time scheduling.

[0156] In step 44 above, this embodiment of the application constructs power balance constraints based on the power balance model of the integrated energy system, with the load quantiles of the electrical equipment side, the load quantiles of the heat equipment side, and the power quantiles of each equipment as variables.

[0157] Here, the load quantile on the equipment side refers to the position of the load on the equipment side between the upper and lower load limits on the equipment side. Specifically, it can be represented by the ratio of a first difference to a second difference. The first difference can be the difference between the load on the equipment side and the lower load limit on the equipment side, and the second difference can be the difference between the upper and lower load limits on the equipment side. The upper and lower load limits on the equipment side can be determined based on the robust operating range of the equipment determined in step 43.

[0158] The load quantile on the heat-using equipment side refers to the position of the load on the heat-using equipment side between the upper and lower load limits on the heat-using equipment side. Specifically, it can be represented by the ratio of a third difference to a fourth difference. The third difference can be the difference between the load on the heat-using equipment side and the lower load limit on the heat-using equipment side, and the fourth difference can be the difference between the upper and lower load limits on the heat-using equipment side. The upper and lower load limits on the heat-using equipment side can be determined based on the robust operating range of the equipment determined in step 43.

[0159] The power quantile of a device refers to the position of the device's power between its upper and lower power limits. Specifically, it can be expressed as the ratio of the fifth difference to the sixth difference, where the fifth difference is the difference between the device's power and its lower power limit, and the sixth difference is the difference between the device's upper and lower power limits. The device's upper and lower power limits can be determined based on the robust operating range of the device determined in step 43.

[0160] by Figures 1 to 3Taking the system shown as an example, the electrical equipment includes various electrical devices on the load side, such as long-term and short-term energy storage devices, electrical loads, and electrothermal conversion devices. The heat-consuming equipment includes various heat-consuming devices on the load side, such as thermal storage devices and heat loads. The load quantile on the electrical equipment side can be expressed as the ratio of a first difference to a second difference, where the first difference is the sum of the power generation of all power generation devices on the generation side minus... The difference obtained, the second difference is and The difference. Among them, They represent The upper and lower limits. The load quantile on the heat-using equipment side can be expressed as the ratio of the third difference to the fourth difference, where the third difference is the sum of the heating power of each heating device on the heating side minus The resulting difference, the fourth difference is and The difference. Among them, They represent The upper and lower limits of the power quantiles. The power quantiles of each device can be expressed as the ratio of the fifth difference to the sixth difference, where the fifth difference is the difference between the power of the device and the lower limit of the power of the device, and the sixth difference is the difference between the upper and lower limits of the power of the device.

[0161] When determining the scheduling strategy for the t-th short-term timescale under the current scheduling cycle, an objective function is constructed with the goal of minimizing the operating cost of the integrated energy system from the t-th to the (t+R)-th short-term timescales. The real-time scheduling strategy of the integrated energy system is obtained by solving for the following constraints: power balance constraints, equipment power models of each device, power ramping constraints of each device in a short-term timescale, the power of the renewable energy power plant being less than or equal to the observed actual or predicted power in each short-term timescale, and the grid's power supply being lower than the preset maximum power supply. Then, scheduling is performed based on the real-time scheduling strategy of the integrated energy system. Here, R can be set arbitrarily or based on the maximum number of short-term timescales required for each device in the integrated energy system to complete power ramping up and down.

[0162] For integrated energy systems with renewable energy integration, accurate renewable energy output forecasts are often difficult to obtain. The day-ahead flexibility allocation optimization described above only utilizes a rough day-ahead forecast of renewable energy. However, in real-time operation, the forecast error of renewable energy in the near future can significantly impact the current scheduling strategy, making scheduling based solely on rough forecasts unreliable. Therefore, this application's embodiments utilize the system's flexibility range and the robust operating range of each device obtained in the day-ahead phase as constraints to guide the real-time scheduling of the system.

[0163] First, the load-side quantiles for both the electrical and thermal sides are introduced as follows:

[0164]

[0165]

[0166] Power quantiles of the equipment:

[0167]

[0168] In the formula For the corresponding load-side equipment, such as batteries, hydrogen storage, thermal storage, and electric heaters, the superscripts are B, Hy, H, and He, respectively. Substituting equations (33)-(35) into the power balance equations (17) and (18), we can obtain the power balance constraint with quantiles as independent variables:

[0169]

[0170] In the formula: It represents the degree of equipment flexibility at that moment.

[0171] The real-time scheduling problem at time t within a day can be expressed as the following look-ahead scheduling problem:

[0172]

[0173]

[0174]

[0175] At this point, the decision variables are the power outputs on the generation side. and And the quantiles on the load side. The objective function (38) minimizes the scheduling cost for the current and future R time periods. Where R is the maximum number of time periods during which the cogeneration unit and boiler ramp-up may be affected. For example, if the short-time interval is 5 minutes, and the cogeneration unit itself needs 15 minutes to ramp up from minimum power to maximum power, then the corresponding case is R=3. Decision variables on the generation side Electricity Purchase The corresponding constraints need to be met, and the load side requires that each quantile be strictly within the interval [0,1], so that the scheduling strategy of the corresponding equipment must be within the robust operating range. ξ in equation (40) τ This represents the observed (true) value of renewable energy output at the current time t, while inaccurate predicted values ​​can be used for the remaining times. Solving the real-time scheduling problem (38)-(40) yields the real-time scheduling strategy for the integrated energy system.

[0176] As can be seen from the above, this application's embodiments establish a dual-timescale operating model for cogeneration units, boilers, battery energy storage, hydrogen storage, and thermal storage within an integrated energy system, thus constructing an operation and scheduling model for an integrated energy system incorporating renewable energy. Several typical scheduling scenarios are obtained through historical data, and a coarse scheduling plan for long-term energy storage is derived. A method for revising the long-term energy storage scheduling plan is proposed, and a revised long-term energy storage operating reference is obtained by weighting using the Nadaraya-Watson kernel regression method. A method for allocating system flexibility using renewable energy forecast data is proposed. System flexibility is allocated based on the coarse forecast of day-ahead renewable energy output, constructing a quadratic convex optimization problem to maximize the system's flexibility response range to renewable energy uncertainties, and ensuring that flexibility is distributed smoothly across different time periods. Load-side quantiles and component operating quantiles on both the electricity and heat sides of the integrated energy system are constructed, deriving the quantile-dominated electricity and heat power balance relationship; furthermore, a rolling-lookahead real-time scheduling problem is constructed, using the system's flexibility range obtained in the day-ahead phase and the robust operating range of each device as constraints to reduce the shortsightedness of the scheduling and achieve good optimality.

[0177] The feasibility of the real-time scheduling strategy in the embodiments of this application will be demonstrated below.

[0178] Given variables This constraint essentially strengthens the constraints on the real-time scheduling method proposed in the embodiments of this application. If the scheduling strategy is feasible under this constraint, then the proposed scheduling strategy must be feasible. It is demonstrated below that the real-time scheduling strategy is feasible given this constraint.

[0179] The feasibility of a real-time scheduling strategy needs to satisfy three types of constraints. The first type is a linear equation or inequality without time-period coupling, which can be uniformly expressed in the form of (41), and its feasibility is easy to prove:

[0180]

[0181] The second type involves ramp constraints that are coupled over time periods, and its feasibility is proven as follows:

[0182]

[0183] Therefore:

[0184]

[0185] Similarly:

[0186]

[0187] The feasibility of ramp-up constraints for combined heat and power (CHP) units and boilers has been proven. The third type of constraint is the time-coupling constraint for energy storage, and the general form of time-coupling for energy storage is given:

[0188]

[0189] It is not difficult to see:

[0190]

[0191] Therefore:

[0192]

[0193] Therefore, the SOC of energy storage obtained by scheduling according to the quantile strategy can always satisfy the upper and lower bounds of the energy storage capacity. The feasibility of real-time scheduling has been fully proven.

[0194] In addition, simulation test results also show that when the penetration rate of renewable energy sources such as wind and solar power in the hardware configuration of the integrated energy system is high, the scheduling method of this application embodiment has a better energy saving and emission reduction effect.

[0195] This application embodiment also provides a multi-timescale scheduling device for an integrated energy system. The integrated energy system is connected to an external power grid, and its internal equipment includes heat generation and power generation equipment, a renewable energy power plant, long-term energy storage equipment, electrical load, and heat load. Figure 10 As shown, the multi-timescale scheduling device of the integrated energy system includes:

[0196] The first scheduling module 91 is used to construct a long-term scheduling model of the integrated energy system and obtain a first scheduling plan for the long-term energy storage device.

[0197] The correction module 92 is used to correct the first scheduling plan using the predicted power of the renewable energy power plant to obtain a second scheduling plan for the long-term energy storage device;

[0198] Optimization module 93 is used to optimize the flexibility of the integrated energy system in a short time scale, and obtain the flexibility range of the integrated energy system and the robust operating range of each device.

[0199] The second scheduling module 94 is used to schedule the integrated energy system based on the real-time power of the renewable energy power plant, using the flexibility range and the robust operation range of each device in the integrated energy system as constraints.

[0200] Through the above modules, the embodiments of this application can achieve the coordinated operation of traditional units and long-term and short-term energy storage, make full use of renewable energy, improve electrothermal conversion efficiency, and ensure the operational reliability of the integrated energy system while also having good dispatch economy.

[0201] Optionally, the internal equipment of the integrated energy system may also include at least one of the following: heat generation equipment, electrothermal conversion equipment, short-term energy storage equipment, and thermal storage equipment.

[0202] Optionally, the device further includes:

[0203] The model building module is used to build equipment power models for each device in the integrated energy system, the equipment power models including equipment electrical power models and equipment thermal power models; based on the equipment power models of each device in the integrated energy system, the power balance model of the integrated energy system is built, the power balance model including electrical power balance model and thermal power balance model.

[0204] Optionally, the first scheduling module is further configured to:

[0205] Using minimizing the operating cost of the integrated energy system over a long time scale as the objective function and the equipment power model of each device in the integrated energy system as the constraint, a long-term scheduling model of the integrated energy system is constructed.

[0206] Based on the long-term scheduling model and the historical power of the renewable energy power plant at at least one historical long-term scale, the optimal scheduling strategy of the integrated energy system at each historical long-term scale is obtained.

[0207] Based on the optimal scheduling strategy of the integrated energy system under various historical time scales, the first scheduling plan of the long-term energy storage device under various historical time scales is obtained.

[0208] Optionally, the correction module is further configured to:

[0209] Obtain a reference power vector for renewable energy power plants at the current long time scale, the reference power vector including the historical power of each short time scale that has been dispatched at the current long time scale and / or the predicted power of each short time scale that has not yet been dispatched.

[0210] The weights corresponding to each historical time scale are determined based on the Euclidean distance between the reference power vector and the historical power vectors of the same period at each historical time scale.

[0211] Based on the weights corresponding to each historical time scale, the first scheduling plan of the long-term energy storage element under each historical time scale is weighted and summed to obtain the second scheduling plan of the long-term energy storage device.

[0212] Optionally, the optimization module is further configured to:

[0213] Based on the power balance model of the integrated energy system, the power expression of the renewable energy power station is obtained, and based on the power expression of the renewable energy power station, the flexibility of the integrated energy system under a dispatch cycle is obtained. The flexibility is characterized by the difference between the upper and lower limits of the power of the renewable energy power station, and the dispatch cycle includes multiple short time scales.

[0214] To avoid using grid power and maximize the flexibility of the integrated energy system during the scheduling cycle, and to ensure that the flexibility is smoothly distributed across various short time scales, an objective function is constructed. This function is constrained by the power models of each device in the integrated energy system, the power ramp-up constraints of each device at a short time scale, and the constraint that the final energy value of the long-term energy storage device during the scheduling cycle is not lower than the final energy value determined by the second scheduling plan. The resulting solution yields the flexibility range of the integrated energy system and the robust operating range of each device. The robust operating range is characterized by the upper and lower limits of the device power.

[0215] Optionally, the second scheduling module is further configured to:

[0216] Based on the power balance model of the integrated energy system, power balance constraints are constructed with the load quantiles of the electrical equipment side, the load quantiles of the heat-consuming equipment side, and the power quantiles of each equipment as variables. Specifically, the load quantiles of the electrical equipment side represent the position of the load on that side between its upper and lower load limits; the load quantiles of the heat-consuming equipment side represent the position of the load on that side between its upper and lower load limits; and the power quantiles of each equipment represent the position of its power between its upper and lower power limits.

[0217] When determining the scheduling strategy for the t-th short time scale under the current scheduling cycle, an objective function is constructed with the goal of minimizing the operating cost of the integrated energy system from the t-th to the (t+R)-th short time scale. The real-time scheduling strategy of the integrated energy system is obtained by solving the constraints of the power balance constraints, the equipment power models of each device, the power ramping constraints of each device in a short time scale, the power of the renewable energy power plant in each short time scale being less than or equal to the observed actual power or predicted power, and the power supply power of the power grid being lower than the preset maximum power supply power.

[0218] Scheduling is performed based on the real-time scheduling strategy of the integrated energy system.

[0219] It should be noted that the systems provided in the above embodiments are devices corresponding to the scheduling method of the above integrated energy system. The implementation methods in the above embodiments are all applicable to the embodiments of this device and can achieve the same technical effect. The device provided in this application can implement all the method steps implemented in the above method embodiments and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiments and the beneficial effects will not be described in detail.

[0220] Please refer to Figure 11 The present application provides a schematic diagram of the structure of another integrated energy system scheduling device, which includes: a processor 1001, a transceiver 1002, a memory 1003, a user interface 1004, and a bus interface.

[0221] In this embodiment of the application, the device further includes a program stored on memory 1003 and executable on processor 1001.

[0222] The transceiver 1002 is used to send and receive data under the control of the processor;

[0223] The processor 1001 is configured to read the computer program in the memory and perform the following operations:

[0224] S1, Construct a long-term scheduling model for the integrated energy system to obtain the first scheduling plan for the long-term energy storage device;

[0225] S2, using the predicted power of the renewable energy power plant, the first scheduling plan is revised to obtain the second scheduling plan for the long-term energy storage device;

[0226] S3, optimize the flexibility of the integrated energy system in a short time scale to obtain the flexibility range of the integrated energy system and the robust operating range of each device;

[0227] S4. Using the flexibility range and robust operating range of each device in the integrated energy system as constraints, the integrated energy system is scheduled based on the real-time power of the renewable energy power station.

[0228] Understandably, in this embodiment of the application, when the computer program is executed by the processor 1001, it can implement the various processes of the above-described integrated energy system scheduling method embodiment and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0229] exist Figure 11In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 1001 and memory represented by memory 1003 together. The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 1002 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. For different user equipment, the user interface 1004 can also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0230] The processor 1001 is responsible for managing the bus architecture and general processing, and the memory 1003 can store the data used by the processor 1001 when performing operations.

[0231] It should be noted that the device in this embodiment corresponds to the scheduling method of the aforementioned integrated energy system. The implementation methods in the above embodiments are all applicable to the embodiments of this device and can achieve the same technical effect. In this device, the transceiver 1002 and the memory 1003, as well as the transceiver 1002 and the processor 1001, can be connected via a bus interface. The functions of the processor 1001 can also be implemented by the transceiver 1002, and vice versa. It should be noted that the device provided in this application embodiment can implement all the method steps implemented in the above method embodiments and achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiments will not be described in detail here.

[0232] In some embodiments of this application, a computer-readable storage medium is also provided, on which a program is stored, which, when executed by a processor, performs the following steps:

[0233] S1, Construct a long-term scheduling model for the integrated energy system to obtain the first scheduling plan for the long-term energy storage device;

[0234] S2, using the predicted power of the renewable energy power plant, the first scheduling plan is revised to obtain the second scheduling plan for the long-term energy storage device;

[0235] S3, optimize the flexibility of the integrated energy system in a short time scale to obtain the flexibility range of the integrated energy system and the robust operating range of each device;

[0236] S4. Using the flexibility range and robust operating range of each device in the integrated energy system as constraints, the integrated energy system is scheduled based on the real-time power of the renewable energy power station.

[0237] When executed by the processor, this program can implement all the methods of scheduling the above-mentioned integrated energy system and achieve the same technical effect. To avoid repetition, it will not be described in detail here.

[0238] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0239] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0240] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0241] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0242] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0243] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0244] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-timescale scheduling method for an integrated energy system, characterized in that, The integrated energy system is connected to an external power grid. The internal equipment of the integrated energy system includes heat generation and power generation equipment, a renewable energy power plant, long-term energy storage equipment, electrical load, and heat load. The method includes the following steps: S1, Construct a long-term scheduling model for the integrated energy system to obtain the first scheduling plan for the long-term energy storage device; S2, using the predicted power of the renewable energy power plant, the first scheduling plan is revised to obtain the second scheduling plan for the long-term energy storage device; S3, optimize the flexibility of the integrated energy system in a short time scale to obtain the flexibility range of the integrated energy system and the robust operating range of each device; S4. Using the flexibility range and robust operating range of each device in the integrated energy system as constraints, the integrated energy system is scheduled based on the real-time power of the renewable energy power station.

2. The method as described in claim 1, characterized in that, The internal equipment of the integrated energy system also includes at least one of the following: heat generation equipment, electrothermal conversion equipment, short-term energy storage equipment, and thermal storage equipment.

3. The method as described in claim 1 or 2, characterized in that, Before S1, the following also applies: S0, construct a power model for each device in the integrated energy system, the power model including an electrical power model and a thermal power model; based on the power models of each device in the integrated energy system, construct a power balance model for the integrated energy system, the power balance model including an electrical power balance model and a thermal power balance model.

4. The method as described in claim 3, characterized in that, S1 includes: Using minimizing the operating cost of the integrated energy system over a long time scale as the objective function and the equipment power model of each device in the integrated energy system as the constraint, a long-term scheduling model of the integrated energy system is constructed. Based on the long-term scheduling model and the historical power of the renewable energy power plant at at least one historical long-term scale, the optimal scheduling strategy of the integrated energy system at each historical long-term scale is obtained. Based on the optimal scheduling strategy of the integrated energy system under various historical time scales, the first scheduling plan of the long-term energy storage device under various historical time scales is obtained.

5. The method as described in claim 3, characterized in that, S2 includes: Obtain a reference power vector for renewable energy power plants at the current long time scale, the reference power vector including the historical power of each short time scale that has been dispatched at the current long time scale and / or the predicted power of each short time scale that has not yet been dispatched. The weights corresponding to each historical time scale are determined based on the Euclidean distance between the reference power vector and the historical power vectors of the same period at each historical time scale. Based on the weights corresponding to each historical time scale, the first scheduling plan of the long-term energy storage element under each historical time scale is weighted and summed to obtain the second scheduling plan of the long-term energy storage device.

6. The method as described in claim 3, characterized in that, S3 includes: Based on the power balance model of the integrated energy system, the power expression of the renewable energy power station is obtained, and based on the power expression of the renewable energy power station, the flexibility of the integrated energy system under a dispatch cycle is obtained. The flexibility is characterized by the difference between the upper and lower limits of the power of the renewable energy power station, and the dispatch cycle includes multiple short time scales. To avoid using grid power and maximize the flexibility of the integrated energy system during the scheduling cycle, and to ensure that the flexibility is smoothly distributed across various short time scales, an objective function is constructed. This function is constrained by the power models of each device in the integrated energy system, the power ramp-up constraints of each device at a short time scale, and the constraint that the final energy value of the long-term energy storage device during the scheduling cycle is not lower than the final energy value determined by the second scheduling plan. The resulting solution yields the flexibility range of the integrated energy system and the robust operating range of each device. The robust operating range is characterized by the upper and lower limits of the device power.

7. The method as described in claim 3, characterized in that, S4 includes: Based on the power balance model of the integrated energy system, power balance constraints are constructed with the load quantiles of the electrical equipment side, the load quantiles of the heat-consuming equipment side, and the power quantiles of each equipment as variables. Specifically, the load quantiles of the electrical equipment side represent the position of the load on that side between its upper and lower load limits; the load quantiles of the heat-consuming equipment side represent the position of the load on that side between its upper and lower load limits; and the power quantiles of each equipment represent the position of its power between its upper and lower power limits. When determining the scheduling strategy for the t-th short time scale under the current scheduling cycle, an objective function is constructed with the goal of minimizing the operating cost of the integrated energy system from the t-th to the (t+R)-th short time scale. The real-time scheduling strategy of the integrated energy system is obtained by solving the constraints of the power balance constraints, the equipment power models of each device, the power ramping constraints of each device in a short time scale, the power of the renewable energy power plant in each short time scale being less than or equal to the observed actual power or predicted power, and the power supply power of the power grid being lower than the preset maximum power supply power. Scheduling is performed based on the real-time scheduling strategy of the integrated energy system.

8. A multi-timescale scheduling device for an integrated energy system, characterized in that, The integrated energy system is connected to an external power grid. The internal equipment of the integrated energy system includes heat generation and power generation equipment, a renewable energy power station, long-term energy storage equipment, electrical load, and heat load. The device includes: The first scheduling module is used to construct a long-term scheduling model of the integrated energy system and obtain the first scheduling plan of the long-term energy storage device. The correction module is used to correct the first scheduling plan using the predicted power of the renewable energy power plant to obtain a second scheduling plan for the long-term energy storage device. An optimization module is used to optimize the flexibility of the integrated energy system in a short time scale, thereby obtaining the flexibility range of the integrated energy system and the robust operating range of each device. The second scheduling module is used to schedule the integrated energy system based on the real-time power of the renewable energy power plant, using the flexibility range and robust operation range of each device in the integrated energy system as constraints.

9. The apparatus as claimed in claim 8, characterized in that, The internal equipment of the integrated energy system also includes at least one of the following: heat generation equipment, electrothermal conversion equipment, short-term energy storage equipment, and thermal storage equipment.

10. The apparatus as claimed in claim 8 or 9, characterized in that, Also includes: The model building module is used to build equipment power models for each device in the integrated energy system, the equipment power models including equipment electrical power models and equipment thermal power models; based on the equipment power models of each device in the integrated energy system, the power balance model of the integrated energy system is built, the power balance model including electrical power balance model and thermal power balance model.

11. The apparatus as claimed in claim 10, characterized in that, The first scheduling module is further configured to: Using minimizing the operating cost of the integrated energy system over a long time scale as the objective function and the equipment power model of each device in the integrated energy system as the constraint, a long-term scheduling model of the integrated energy system is constructed. Based on the long-term scheduling model and the historical power of the renewable energy power plant at at least one historical long-term scale, the optimal scheduling strategy of the integrated energy system at each historical long-term scale is obtained. Based on the optimal scheduling strategy of the integrated energy system under various historical time scales, the first scheduling plan of the long-term energy storage device under various historical time scales is obtained.

12. The apparatus as claimed in claim 10, characterized in that, The correction module is also used for: Obtain a reference power vector for renewable energy power plants at the current long time scale, the reference power vector including the historical power of each short time scale that has been dispatched at the current long time scale and / or the predicted power of each short time scale that has not yet been dispatched. The weights corresponding to each historical time scale are determined based on the Euclidean distance between the reference power vector and the historical power vectors of the same period at each historical time scale. Based on the weights corresponding to each historical time scale, the first scheduling plan of the long-term energy storage element under each historical time scale is weighted and summed to obtain the second scheduling plan of the long-term energy storage device.

13. The apparatus as claimed in claim 10, characterized in that, The optimization module is also used for: Based on the power balance model of the integrated energy system, the power expression of the renewable energy power station is obtained, and based on the power expression of the renewable energy power station, the flexibility of the integrated energy system under a dispatch cycle is obtained. The flexibility is characterized by the difference between the upper and lower limits of the power of the renewable energy power station, and the dispatch cycle includes multiple short time scales. To avoid using grid power and maximize the flexibility of the integrated energy system during the scheduling cycle, and to ensure that the flexibility is smoothly distributed across various short time scales, an objective function is constructed. This function is constrained by the power models of each device in the integrated energy system, the power ramp-up constraints of each device at a short time scale, and the constraint that the final energy value of the long-term energy storage device during the scheduling cycle is not lower than the final energy value determined by the second scheduling plan. The resulting solution yields the flexibility range of the integrated energy system and the robust operating range of each device. The robust operating range is characterized by the upper and lower limits of the device power.

14. The apparatus as claimed in claim 10, characterized in that, The second scheduling module is also used for: Based on the power balance model of the integrated energy system, power balance constraints are constructed with the load quantiles of the electrical equipment side, the load quantiles of the heat-consuming equipment side, and the power quantiles of each equipment as variables. Specifically, the load quantiles of the electrical equipment side represent the position of the load on that side between its upper and lower load limits; the load quantiles of the heat-consuming equipment side represent the position of the load on that side between its upper and lower load limits; and the power quantiles of each equipment represent the position of its power between its upper and lower power limits. When determining the scheduling strategy for the t-th short time scale under the current scheduling cycle, an objective function is constructed with the goal of minimizing the operating cost of the integrated energy system from the t-th to the (t+R)-th short time scale. The real-time scheduling strategy of the integrated energy system is obtained by solving the constraints of the power balance constraints, the equipment power models of each device, the power ramping constraints of each device in a short time scale, the power of the renewable energy power plant in each short time scale being less than or equal to the observed actual power or predicted power, and the power supply power of the power grid being lower than the preset maximum power supply power. Scheduling is performed based on the real-time scheduling strategy of the integrated energy system.

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