Joint scheduling method and device for multiple energy storage systems

Abstract

The invention relates to the technical field of energy system optimization and control, and discloses a multi-energy storage system joint scheduling method and device, and the method comprises the steps: building an energy flow model; establishing a fused salt heat storage quantity balance equation, a battery electricity storage quantity balance equation and various constraint conditions; and constructing an optimization model with maximization of the comprehensive benefit of the system as a target, solving the optimization model based on an energy flow model, a fused salt heat storage quantity balance equation, a battery electricity storage quantity balance equation and various constraint conditions, and generating a fused salt power generation distribution strategy, a battery charging and discharging strategy and a fused salt heat supply output strategy per time period. According to the method, through dynamic energy flow path selection and differential cost modeling, continuous satisfaction of the thermal load and the electric load is realized, the overall economic benefit of the system is maximized, and the intelligence and flexibility of system operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system optimization and control, and in particular to a method and device for joint scheduling of multiple energy storage systems. Background Art

[0002] In recent years, with the acceleration of the global energy transition, the proportion of installed renewable energy capacity has continued to rise. While the large-scale integration of renewable energy sources such as wind power and photovoltaics has injected strong momentum into the decarbonization of the energy mix, it has also posed unprecedented challenges to power and regional energy systems. Renewable energy generation is characterized by significant intermittency and volatility, which directly leads to increased system load volatility and significantly increased energy supply uncertainty, posing a severe challenge to the stable operation and efficient regulation of the system.

[0003] To effectively address these challenges and improve system flexibility and cost-effectiveness, a growing number of regional energy systems are incorporating various types of energy storage facilities. Molten salt thermal storage systems and battery energy storage systems are widely used. In existing technologies, thermal and electrical storage systems typically utilize independent optimization and control. Molten salt thermal storage systems primarily balance thermal loads or directly supply power to loads through steam turbine generation. Battery energy storage systems, on the other hand, focus on regulating grid power loads, smoothing power fluctuations, and enabling energy arbitrage.

[0004] However, this independent scheduling approach has exposed numerous drawbacks. First, the energy flow path is single, failing to fully utilize the flexibility of the molten salt thermal storage system's thermoelectric conversion capacity. The electricity generated by the molten salt power generation lacks a dynamic allocation mechanism, making it difficult to flexibly adjust power supply strategies or charge battery storage based on electricity price fluctuations. Second, the cost model is incomplete. During the battery energy storage scheduling process, existing technologies generally ignore the actual cost differences between different sources of electricity and assume that all battery charging energy comes from electricity purchased from the grid, which greatly limits the optimization space for the overall system benefit. Third, resource utilization efficiency is low. When the molten salt heat storage is near full load or when favorable conditions such as low fuel costs and high electricity prices exist, existing scheduling methods cannot flexibly decide whether to use molten salt power generation to charge the battery, making it difficult to achieve optimal energy resource allocation. Fourth, the system lacks intelligence. Existing energy storage scheduling systems are mostly static and lack the ability to dynamically adjust energy flow paths and charging and discharging strategies based on market price fluctuations and load forecasts. This makes it difficult to meet the higher requirements of system flexibility and cost-effectiveness in the new energy era. Summary of the Invention

[0005] In view of this, the present invention provides a method and apparatus for joint scheduling of multiple energy storage systems to solve the problem of how to achieve joint scheduling of multiple energy storage systems.

[0006] In a first aspect, the present invention provides a method for joint scheduling of multiple energy storage systems, wherein the multiple energy storage systems include a molten salt heat storage system and a battery energy storage system. The method includes: establishing an energy flow model, the energy flow model is used to describe the molten salt power generation energy flow path and the battery energy storage system charging energy path, the molten salt power generation energy flow path includes direct power supply, direct power sales or battery energy storage, and the battery energy storage system charging energy path includes charging from molten salt power generation or power purchase from the power grid; establishing a molten salt heat storage balance equation, a battery storage capacity balance equation and multiple constraints; constructing an optimization model with the goal of maximizing the comprehensive benefits of the system, and solving the optimization model based on the energy flow model, the molten salt heat storage balance equation, the battery storage capacity balance equation and multiple constraints to generate a molten salt power generation allocation strategy, a battery charging and discharging strategy and a molten salt heat supply output strategy for each time period.

[0007] This invention aims to achieve continuous satisfaction of thermal and electrical loads through dynamic energy flow path selection and differentiated cost modeling in the context of the joint operation of a molten salt thermal storage system and a battery energy storage system, maximize the overall economic benefits of the system, and enhance the intelligence and flexibility of system operation.

[0008] In an optional embodiment, the multiple constraints include: electric load constraint conditions and thermal load constraint conditions, wherein the load constraint conditions are: the sum of the power directly supplied by the molten salt and the battery discharge power is greater than or equal to the total electric load demand at the current moment; the thermal load constraint conditions are: the heating power of the molten salt heat storage system at the current moment is greater than or equal to the thermal load demand at the current moment.

[0009] In an optional embodiment, the multiple constraints also include: a molten salt heat storage constraint condition and a battery storage constraint condition, wherein the molten salt heat storage constraint condition is: the molten salt heat storage at the current moment is greater than or equal to 0, and the molten salt heat storage at the current moment is less than or equal to the upper capacity limit of the molten salt heat storage system; the battery storage constraint condition is: the battery storage at the current moment is greater than or equal to 0, and the battery storage at the current moment is less than or equal to the upper capacity limit of the battery energy storage system.

[0010] In an optional embodiment, the multiple constraints also include: a minimum output constraint for molten salt power generation and a minimum output constraint for molten salt heating, wherein the minimum output constraint for molten salt power generation is: the turbine power generation power of the molten salt heat storage system at the current moment is greater than or equal to the minimum stable operating power lower limit of the turbine; the minimum output constraint for molten salt heating is: the external worker power of the molten salt heat storage system at the current moment is greater than or equal to the molten salt heat storage system.

[0011] In an optional embodiment, when energy resources are limited, priority is given to satisfying the continuous supply of heat loads, and secondly to satisfying the supply of electricity loads.

[0012] In an optional embodiment, when charging, the battery energy storage system gives priority to molten salt power generation or grid-purchased power, whichever has the lower unit energy cost; when the molten salt heat storage reaches a preset high threshold, molten salt power generation is prioritized to supplement battery charging; when the battery energy storage capacity is insufficient and the molten salt power generation capacity is limited, mixed charging of molten salt power generation and grid-purchased power is allowed at the same time.

[0013] In an optional embodiment, the expression of the optimization model with the goal of maximizing the comprehensive benefits of the system is:

[0014]

[0015] Among them, psell(t) is the power selling price of the power grid; Pdirect(t) is the direct power supply part of molten salt power generation; Pbattery_discharge(t) is the battery discharge power supply part; pheat(t) is the unit price of heat energy sales; Qheat(t) is the heating power; csalt(t) is the unit molten salt power generation cost; Pturbine(t) is the total power of molten salt power generation; pbuy(t) is the power purchase price of the power grid; Pgrid_charge(t) is the power charged by the battery from the grid; Cfixed(t) is the fixed operating cost per unit time; Δt is the time step; n is the total number of scheduling periods.

[0016] In a second aspect, the present invention provides a multi-energy storage system joint scheduling device, which includes: a first establishment module for establishing an energy flow model, which is used to describe the molten salt power generation energy flow path and the battery energy storage system charging energy path. The molten salt power generation energy flow path includes direct power supply, direct power sales or battery energy storage, and the battery energy storage system charging energy path includes charging from molten salt power generation or power purchase from the power grid; a second establishment module for establishing a molten salt heat storage balance equation, a battery storage capacity balance equation and multiple constraints; a solution module for constructing an optimization model with the goal of maximizing the comprehensive benefits of the system. Based on the energy flow model, the molten salt heat storage balance equation, the battery storage capacity balance equation and multiple constraints, the optimization model is solved to generate a molten salt power generation allocation strategy, a battery charging and discharging strategy and a molten salt heat supply output strategy for each time period.

[0017] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby execute the multi-energy storage system joint scheduling method of the first aspect or any corresponding embodiment thereof.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the multi-energy storage system joint scheduling method of the first aspect or any corresponding embodiment thereof.

[0019] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the multi-energy storage system joint scheduling method according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 is a flow chart of a method for joint scheduling of multiple energy storage systems according to an embodiment of the present invention;

[0022] Figure 2 is a structural block diagram of a multi-energy storage system joint scheduling device according to an embodiment of the present invention;

[0023] Figure 3 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0025] According to an embodiment of the present invention, an embodiment of a method for jointly dispatching multiple energy storage systems is provided. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system, such as a set of computer-executable instructions, and although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in an order different from that shown.

[0026] In this embodiment, a multi-energy storage system joint scheduling method is provided, wherein the multi-energy storage system includes a molten salt heat storage system and a battery energy storage system. Figure 1 As shown, the method includes:

[0027] Step S1: Establish an energy flow model. The energy flow model is used to describe the energy flow path of molten salt power generation and the charging energy path of the battery energy storage system. The energy flow path of molten salt power generation includes direct power supply, direct power sales or battery energy storage. The charging energy path of the battery energy storage system includes charging from molten salt power generation or power purchase from the grid.

[0028] Specifically, within this new energy system, the molten salt thermal storage system achieves efficient power generation through a unique energy conversion mechanism. Using a low-melting-point salt mixture as the heat storage medium, the system utilizes heat sources such as solar thermal energy and industrial waste heat to heat the molten salt to temperatures exceeding 500°C during the thermal storage phase. During the power generation phase, the high-temperature molten salt flows through a steam generator, converting the heated water into high-temperature, high-pressure steam, which drives the steam turbine and generator, ultimately achieving a stable conversion of thermal energy into electrical energy.

[0029] Specifically, the electricity generated by molten salt power generation can be dynamically selected according to the real-time status of the system and market prices: (1) directly supplied to external power loads; (2) connected to the grid for sale to obtain immediate electricity market benefits; (3) charged to the battery energy storage system, storing energy for release in the future during periods of high electricity prices.

[0030] Optionally, the electricity generated by molten salt power generation has a flexible allocation strategy, which can be dynamically optimized based on the system's operating status and the power market environment. During peak hours, the system prioritizes direct transmission of electricity to external loads to ensure regional power supply stability. When electricity market prices are high, the system releases electricity into the spot market through a grid-connected sales mechanism to generate immediate economic benefits. During low electricity prices or insufficient storage capacity within the system, excess electricity is used to charge the battery energy storage system, creating an economic operating model of "storing energy during low-peak periods and releasing it during peak periods," realizing the temporal and spatial transfer value of electricity.

[0031] Specifically, the battery energy storage system, as a key node in energy regulation, possesses bidirectional interactive features. In charging mode, it can selectively receive clean electricity from molten salt power generation, or purchase electricity and energy storage from the grid during periods of low electricity prices, achieving cost optimization. In the discharge phase, it can flexibly respond to end-user electricity demand or participate in electricity spot market transactions based on power load forecasts and market price fluctuations. This collaborative operation mode of molten salt heat storage and battery energy storage not only effectively mitigates the intermittent nature of renewable energy generation, but also significantly improves the economic efficiency and reliability of the energy system through a multi-dimensional power allocation strategy.

[0032] Optionally, the molten salt power generation power distribution relationship is as follows:

[0033] Pturbine(t)=Pdirect(t)+Psalt_charge(t) (1)

[0034] Among them, Pturbine(t) is the total power generated by the molten salt system through the turbine at time t (unit: kW); Pdirect(t) is the power output directly used to meet the current load demand in the molten salt power generation; Psalt_charge(t) is the power used to charge the battery in the molten salt power generation.

[0035] Equation (1) describes the power distribution relationship of molten salt power generation, stipulating that its output is divided into two parts: "direct power supply" and "battery charging." This is one of the core constraints in the energy path selection mechanism. Through this equation, the dynamic switching of molten salt power between different uses can be controlled, achieving the coordinated utilization of system resources.

[0036] Step S2: Establishing the molten salt heat storage balance equation, the battery power storage balance equation and various constraints.

[0037] Specifically, the molten salt heat storage balance equation describes the energy evolution of the molten salt heat storage system during each scheduling cycle: the current heat storage minus the total energy consumed for power generation and heating during that period. The molten salt heat storage balance equation is used to construct the energy balance constraints for the heat storage system and is key to controlling the evolution of the heat storage state in the scheduling model. Specifically, it is as follows:

[0038] Esalt(t+1)=Esalt(t)-(Pturbine(t) / etaturbine+Qheat(t))×Δt (2)

[0039] Among them, Esalt(t) is the molten salt heat storage capacity at time t (unit: kWh), which indicates the thermal energy currently stored in the heat storage system; Esalt(t+1) is the molten salt heat storage capacity at time t+1, which indicates the energy state of the heat storage system after update; Pturbine(t) is the turbine output power (unit: kW) used by the molten salt for power generation at time t; ηturbine is the turbine power generation efficiency, which indicates the conversion efficiency of unit thermal power into electrical power; Qheat(t) is the thermal power of the molten salt used for heating at time t (unit: kW); Δt is the time step (unit: hour) used to convert power into energy.

[0040] Specifically, the battery storage balance equation is used to describe the state changes of the battery energy storage system within each time step, reflecting the coordinated scheduling behavior of the battery energy storage system under different charging paths. It is also the key to building system energy constraints, path allocation and economic scheduling. The details are as follows:

[0041]

[0042] Among them, Ebattery(t) is the battery energy storage at time t (unit: kWh); Ebattery(t+1): the battery energy storage at time t+1 is ηbattery: the battery charge and discharge efficiency (generally 0.9~0.95), taking into account the loss of energy conversion; Psalt_charge(t) is the power of electricity from molten salt power generation to charge the battery (unit: kW); Pgrid_charge(t) is the power of electricity from the grid to charge the battery (unit: kW); Pbattery_discharge(t) is the discharge power of the battery at the current moment (unit: kW); Δt is the time step (unit: hour).

[0043] It can be seen from formula (3) that the update of stored energy mainly consists of the following three points: (1) retaining the power at the previous moment; (2) adding the charging energy from molten salt power generation and power purchase from the grid (considering efficiency); (3) subtracting the discharge energy (considering efficiency).

[0044] Optionally, the multiple constraints include: electric load constraints, thermal load constraints, molten salt heat storage constraints, battery storage constraints, molten salt power generation minimum output constraints, and molten salt heating minimum output constraints.

[0045] Specifically, the electrical load must be met by the combined power of the molten salt system and battery discharge, which is a mandatory constraint to ensure the continuity of power supply to users and the stable operation of the system. The load constraint condition is that the sum of the power directly supplied by the molten salt and the battery discharge power is greater than or equal to the total power load demand at the current moment, as follows:

[0046] Pdirect(t)+Pbattery_discharge(t)≥Lpower(t) (4)

[0047] Where Pdirect(t) is the power directly supplied by the molten salt; Pbattery_discharge(t) is the battery discharge power; and Lpower(t) is the total power load demand of the system at time t (unit: kW).

[0048] Specifically, the thermal output of the molten salt system must meet the heat demand of industry or buildings, which is also one of the operational safety constraints. The heat load constraint condition is: the heating power of the molten salt thermal storage system at the current moment is greater than or equal to the heat load demand at the current moment. The details are as follows:

[0049] Qheat(t)≥Lheat(t) (5)

[0050] Among them, Qheat(t) is the heating power of the molten salt system at time t (unit: kW); Lheat(t) is the system heat load demand (unit: kW).

[0051] Specifically, the energy storage capacity constraint ensures that all energy storage state variables are always within the physical capacity range, avoiding overcharging or over-discharging and ensuring the safety of system operation.

[0052] The molten salt heat storage constraint condition is: the molten salt heat storage capacity at the current moment is greater than or equal to 0, and the molten salt heat storage capacity at the current moment is less than or equal to the upper capacity limit of the molten salt heat storage system. The battery storage capacity constraint condition is: the battery storage capacity at the current moment is greater than or equal to 0, and the battery storage capacity at the current moment is less than or equal to the upper capacity limit of the battery energy storage system. The details are as follows:

[0053]

[0054] Among them, Esalt(t) is the molten salt heat storage capacity at the current moment; Ebattery(t) is the battery storage capacity at the current moment; Esaltmax and Ebatterymax correspond to the upper limits of the capacity of the heat storage system and the electricity storage system, respectively.

[0055] Specifically, the minimum output constraint for molten salt power generation is used to ensure that the steam turbine does not fall below the minimum technical threshold required for stable power generation during operation. This constraint reflects the start-stop stability limits of the physical equipment, preventing mechanical damage or efficiency loss caused by frequent starts and stops. The minimum output constraint for molten salt power generation is: the steam turbine power generation power of the molten salt thermal storage system at the current moment is greater than or equal to the minimum stable operating power limit of the steam turbine. The details are as follows:

[0056] Pturbine(t)≥Pturbinemin (7)

[0057] Among them, Pturbine(t) is the turbine power generation power of the molten salt system at the current moment (unit: kW); Pturbinemin is the minimum stable operating power limit of the turbine (unit: kW).

[0058] Specifically, the molten salt heating minimum output constraint ensures that the molten salt heating system operates above the minimum heat output level allowed by the technology, which is used to avoid heat energy waste or reduced heat exchange efficiency caused by low-load operation. The molten salt heating minimum output constraint condition is: the external worker power of the molten salt heat storage system at the current moment is greater than or equal to the molten salt heat storage system. The details are as follows:

[0059] Qheat(t)≥Qheatmin (7)

[0060] Among them, Qheat(t) is the current external heating power of the molten salt system; Qheatmin is the minimum output power limit of the heating system (unit: kW).

[0061] Step S3: Construct an optimization model with the goal of maximizing the comprehensive benefits of the system. Based on the energy flow model, the molten salt heat storage balance equation, the battery storage balance equation, and various constraints, solve the optimization model to generate the molten salt power generation allocation strategy, battery charging and discharging strategy, and molten salt heat output strategy for each time period.

[0062] Specifically, through the dynamic switching and optimization of multiple energy flow paths, flexible allocation and optimal utilization of energy resources can be achieved under the constraints of multiple factors such as molten salt heat storage, battery storage capacity, load demand, fluctuations in electricity and heat prices, and changes in fuel costs.

[0063] Specifically, a molten salt heat storage balance equation, a battery power storage balance equation and multiple constraints are established. Based on the balance equations and multiple constraints, a variety of energy flow paths can be obtained using the energy flow model. Each energy flow path corresponds to a comprehensive benefit. The energy flow path with the maximized comprehensive benefit will be used as the current molten salt power generation allocation strategy, battery charging and discharging strategy and molten salt heating output strategy.

[0064] Specifically, for the optimization model aimed at maximizing the overall benefits of the system, in order to accurately evaluate the economic feasibility of different energy paths, the actual costs of electricity from different sources are distinguished. The actual costs include: the cost of molten salt power generation is determined by the comprehensive conversion of fuel thermal energy prices and turbine power generation efficiency; the cost of the battery energy storage system purchasing electricity from the grid is calculated based on the real-time electricity price.

[0065] Based on the actual costs of electricity from different sources, a comprehensive revenue model was constructed. Revenue items include: direct electricity sales revenue; electricity sales revenue from battery energy storage discharge; and thermal revenue from meeting heating loads. Cost items include: fuel costs for molten salt power generation; electricity purchase costs for battery charging; and basic system operating expenses.

[0066] Based on this, with maximizing total net benefits as the optimization goal, the optimal operation strategy is formulated by comprehensively considering the energy supply and demand balance, market dynamics, and system operation economics. The expression of the optimization model with maximizing the comprehensive benefits of the system is:

[0067]

[0068] Among them, psell(t) is the power selling price of the power grid; Pdirect(t) is the direct power supply part of molten salt power generation; Pbattery_discharge(t) is the battery discharge power supply part; pheat(t) is the unit price of heat energy sales; Qheat(t) is the heating power; csalt(t) is the unit molten salt power generation cost; Pturbine(t) is the total power of molten salt power generation; pbuy(t) is the power purchase price of the power grid; Pgrid_charge(t) is the power charged by the battery from the grid; Cfixed(t) is the fixed operating cost per unit time; Δt is the time step; n is the total number of scheduling periods.

[0069] Optionally, under conditions of limited energy resources, priority is given to ensuring continuous heat load supply, followed by electricity load supply. Through differentiated guarantee mechanisms, system operation risks can be minimized under conditions of limited energy resources. At the same time, overall energy efficiency can be improved through demand-side management and technological optimization, achieving the rational allocation and efficient utilization of limited resources.

[0070] Optionally, when charging, the battery energy storage system gives priority to the one with lower unit energy cost between molten salt power generation and power purchased from the grid; when the molten salt heat storage reaches a preset high threshold, molten salt power generation is given priority to supplement battery charging; when the battery energy storage capacity is insufficient and the molten salt power generation capacity is limited, mixed charging of molten salt power generation and power purchased from the grid is allowed at the same time.

[0071] In some optional implementations, solving the optimization model with the goal of maximizing the overall system benefit includes the following process:

[0072] (1) The scheduling control module uses rolling time window optimization, and each optimization covers the next n hours (n≥6).

[0073] Specifically, the strategy uses the timeline as a benchmark, with each optimization span covering the next n hours (n≥6), building a continuously rolling optimization cycle. As time progresses, each time an optimization cycle is completed, the time window slides forward to incorporate new time intervals while excluding already executed time intervals, ensuring that the optimization plan always fits the real-time operating status of the system. This rolling optimization mechanism can not only effectively address various uncertainties that arise during the operation of the energy system, but also achieve the rational allocation and utilization of resources over a longer time scale, providing a strong guarantee for the stable and economic operation of the energy system.

[0074] (2) Future electricity load curve prediction, heat load curve prediction and electricity price prediction data are introduced into the optimization process.

[0075] Specifically, the future electricity load curve forecast accurately predicts electricity demand trends over different time periods through a comprehensive analysis of historical electricity consumption data, meteorological conditions, economic activity, and other factors. The heat load curve forecast accurately captures fluctuations in heat demand by combining historical district heating data, building heat characteristics, and outdoor temperature fluctuations. The electricity price forecast predicts future price fluctuations based on information such as power market supply and demand, policy guidance, and energy price trends. These interrelated and complementary forecast data provide a solid foundation for the dispatching and control module to develop optimized plans that balance supply and demand, economic efficiency, and system stability.

[0076] (3) Forecast data can be generated based on historical regression analysis, external forecasting systems, or hybrid models.

[0077] Specifically, based on the historical regression analysis method, by exploring the potential patterns and causal relationships in historical data, a mathematical model is established to predict future trends. This method is suitable for scenarios where the data has strong regularity; the external prediction system uses professional third-party data platforms or institutions to obtain its prediction results based on advanced algorithms and extensive data resources, and can quickly adapt to the complex and changing external environment; the hybrid model combines the advantages of multiple prediction methods, combining historical regression analysis with machine learning algorithms, artificial intelligence technology, etc., which can not only utilize the patterns of historical data, but also capture the nonlinear characteristics and sudden changes in the data, thereby effectively improving the accuracy and adaptability of the prediction, and meeting the scheduling control module's requirements for high-quality prediction data.

[0078] (4) When the predicted fluctuation of electricity prices exceeds a set threshold (e.g., 5%), the optimization strategy update is automatically triggered.

[0079] Specifically, when the predicted fluctuation in electricity prices exceeds a set threshold (e.g., 5%), the system automatically triggers an optimization strategy update. This threshold was determined through extensive data analysis and simulation testing, taking into account the energy system's operating costs, revenue targets, and risk tolerance. Once the update is triggered, the dispatch control module will quickly reassess the current system status and, based on the latest electricity price forecast data, optimize and adjust the energy dispatch strategy to mitigate the cost risks associated with price fluctuations while seizing favorable opportunities in price fluctuations to maximize economic benefits.

[0080] (5) The rolling optimization strategy includes readjusting the molten salt power generation power, battery charging and discharging strategy, and heating power output.

[0081] Specifically, in terms of molten salt power generation, the power generation power is readjusted according to the optimization results. By controlling the heating and heat storage process of the molten salt, the power generation is reasonably adjusted to ensure that the power generation matches the power demand and electricity price changes; for the battery charging and discharging strategy, the high and low electricity prices, the power load demand and the charging and discharging characteristics of the battery itself are comprehensively considered. Charging and energy storage are carried out during the period of low electricity prices, and discharge is carried out during the peak period of electricity prices or when the power demand is tight, so as to realize the efficient utilization of battery resources; in terms of heating power output, according to the heat load curve prediction and optimization plan, the operating parameters of the heating equipment are dynamically adjusted to ensure the quality of heating while reducing the heating cost, so as to realize the coordinated optimization and efficient operation of the energy system in electricity and heat supply.

[0082] This embodiment also provides a multi-energy storage system joint scheduling device, which is used to implement the above-mentioned embodiments and preferred implementations. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0083] This embodiment provides a multi-energy storage system joint scheduling device, such as Figure 2 As shown, the device includes:

[0084] The first establishment module is used to establish an energy flow model. The energy flow model is used to describe the flow path of molten salt power generation energy and the charging energy path of the battery energy storage system. The molten salt power generation energy flow path includes direct power supply, direct power sales, or battery energy storage. The battery energy storage system charging energy path includes charging from molten salt power generation or purchasing electricity from the grid.

[0085] The second establishment module is used to establish the molten salt heat storage balance equation, the battery power storage balance equation and various constraints;

[0086] The solution module is used to build an optimization model with the goal of maximizing the overall benefits of the system. Based on the energy flow model, the molten salt heat storage balance equation, the battery storage balance equation, and various constraints, the optimization model is solved to generate the molten salt power generation allocation strategy, battery charging and discharging strategy, and molten salt heat supply output strategy for each time period.

[0087] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0088] In this embodiment, the multi-energy storage system joint scheduling device is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0089] The embodiment of the present invention also provides a computer device having the above Figure 2 The multi-energy storage system joint dispatching device shown.

[0090] See also Figure 3 , Figure 3 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 3 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0091] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0092] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0093] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0094] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0095] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 can be connected via a bus or other means. Figure 3 The bus connection is taken as an example.

[0096] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0097] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0098] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0099] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for joint scheduling of multiple energy storage systems, characterized in that: The multi-energy storage system includes a molten salt heat storage system and a battery energy storage system, and the method includes: Establish an energy flow model to describe the energy flow path of molten salt power generation and the energy flow path of the battery energy storage system. The molten salt power generation energy flow path includes direct power supply, direct power sales, or battery energy storage. The battery energy storage system charging energy path includes charging from molten salt power generation or power purchase from the grid. Establish the molten salt heat storage balance equation, battery power storage balance equation and various constraints; An optimization model is constructed with the goal of maximizing the overall benefits of the system. Based on the energy flow model, the molten salt heat storage balance equation, the battery storage balance equation, and various constraints, the optimization model is solved to generate a molten salt power generation allocation strategy, a battery charging and discharging strategy, and a molten salt heat supply output strategy for each time period.

2. The multi-energy storage system joint scheduling method according to claim 1, characterized in that: The multiple constraints include: electrical load constraints, thermal load constraints, where: The load constraint condition is that the sum of the power directly supplied by the molten salt and the battery discharge power is greater than or equal to the total power load demand at the current moment; The heat load constraint condition is: the heating power of the molten salt heat storage system at the current moment is greater than or equal to the heat load demand at the current moment.

3. The multi-energy storage system joint scheduling method according to claim 2, characterized in that: The multiple constraints also include: molten salt heat storage constraint, battery storage capacity constraint, where: The molten salt heat storage constraint condition is: the molten salt heat storage capacity at the current moment is greater than or equal to 0, and the molten salt heat storage capacity at the current moment is less than or equal to the upper limit of the molten salt heat storage system; The battery storage capacity constraint condition is: the battery storage capacity at the current moment is greater than or equal to 0, and the battery storage capacity at the current moment is less than or equal to the capacity upper limit of the battery energy storage system.

4. The multi-energy storage system joint scheduling method according to claim 2, characterized in that: The multiple constraints also include: minimum output constraint of molten salt power generation and minimum output constraint of molten salt heating, wherein: The minimum output constraint of molten salt power generation is: the turbine power generation power of the molten salt thermal storage system at the current moment is greater than or equal to the minimum stable operating power lower limit of the turbine; The minimum output constraint of molten salt heating is: the external worker power of the molten salt heat storage system at the current moment is greater than or equal to the molten salt heat storage system.

5. The multi-energy storage system joint scheduling method according to claim 1, characterized in that: Also includes: When energy resources are limited, priority is given to meeting the continuous supply of heat load, followed by meeting the supply of electricity load.

6. The multi-energy storage system joint scheduling method according to claim 1, characterized in that: Also includes: When charging, the battery energy storage system prioritizes the use of molten salt power generation or grid-purchased power, whichever has the lower unit energy cost; When the molten salt heat storage reaches the preset high threshold, molten salt power generation is prioritized to supplement battery charging; When the battery energy storage capacity is insufficient and the molten salt power generation capacity is limited, mixed charging of molten salt power generation energy and grid-purchased electricity is allowed.

7. The multi-energy storage system joint scheduling method according to claim 1, characterized in that: The expression of the optimization model with the goal of maximizing the comprehensive benefits of the system is: Among them, psell(t) is the power selling price of the power grid; Pdirect(t) is the direct power supply part of molten salt power generation; Pbattery_discharge(t) is the battery discharge power supply part; pheat(t) is the unit price of heat energy sales; Qheat(t) is the heating power; csalt(t) is the unit molten salt power generation cost; Pturbine(t) is the total power of molten salt power generation; pbuy(t) is the power purchase price of the power grid; Pgrid_charge(t) is the power charged by the battery from the grid; Cfixed(t) is the fixed operating cost per unit time; Δt is the time step; n is the total number of scheduling periods.

8. A multi-energy storage system joint dispatching device, characterized in that: The device comprises: A first establishment module is used to establish an energy flow model, wherein the energy flow model is used to describe the molten salt power generation energy flow path and the battery energy storage system charging energy path. The molten salt power generation energy flow path includes direct power supply, direct power sales, or battery energy storage. The battery energy storage system charging energy path includes charging from molten salt power generation or power purchase from the grid; The second establishment module is used to establish the molten salt heat storage balance equation, the battery power storage balance equation and various constraints; A solution module is used to construct an optimization model with the goal of maximizing the overall benefits of the system. Based on the energy flow model, the molten salt heat storage balance equation, the battery storage balance equation, and various constraints, the optimization model is solved to generate a molten salt power generation allocation strategy, a battery charging and discharging strategy, and a molten salt heat supply output strategy for each time period.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the multi-energy storage system joint scheduling method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the multi-energy storage system joint scheduling method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the multi-energy storage system joint scheduling method according to any one of claims 1 to 7.

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