Method and device for determining power dispatching strategy and electronic equipment

Abstract

The invention discloses a method and device for determining a power dispatching strategy and electronic equipment. The method comprises the following steps: acquiring a power dispatching request of a target building; in response to the power dispatching request, calling a target function corresponding to the target building and a first constraint condition for constraining the target function according to the building identifier; the target function is controlled to take the scheduling target as a target, the multiple initial sets are updated, a target set is obtained, the initial sets comprise initial values corresponding to the multiple variable items respectively, and the target set comprises target values corresponding to the multiple variable items respectively; and determining a power dispatching strategy corresponding to the target building according to the target set. According to the method and the device, the technical problem that the power utilization cost is increased due to the fact that the determined power dispatching strategy is difficult to deal with power fluctuation caused by the energy storage equipment and the photovoltaic equipment in the related technology is solved.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more specifically, to a method, apparatus, and electronic device for determining a power dispatching strategy. Background Technology

[0002] Determining the power dispatch strategy for photovoltaic-storage-direct-drive-flexible (PV-SSD) buildings is crucial for optimizing energy allocation, improving system efficiency, ensuring power supply reliability, and promoting the consumption of renewable energy. Currently, the focus is primarily on determining the power dispatch strategy for the external power grid of traditional buildings. However, this approach struggles to address the power fluctuations caused by energy storage and photovoltaic devices when dealing with PV-SSD building clusters, leading to increased electricity costs.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and electronic device for determining power dispatch strategies, to at least solve the technical problem in the related art where the determined power dispatch strategies are difficult to cope with power fluctuations caused by energy storage devices and photovoltaic devices, leading to increased electricity costs.

[0005] According to one aspect of the present invention, a method for determining a power dispatch strategy is provided, comprising: acquiring a power dispatch request for a target building, wherein the target building includes multiple target devices, the power dispatch request carrying a building identifier corresponding to the target building and a dispatch target, the multiple target devices including: new energy devices and energy storage devices; responding to the power dispatch request, based on the building identifier, retrieving an objective function corresponding to the target building, and a first constraint condition constraining the objective function, wherein the objective function includes: multiple variable terms, an electricity cost function term, a carbon emission cost function term, and a temperature cost function term, the electricity cost function term including an electricity purchase cost term, a new energy device cost term, and an energy storage device cost term, the first constraint condition including a power supply and demand balance constraint of the target building, a capacity constraint and an output power constraint of the energy storage device; controlling the objective function to update multiple initial sets with the dispatch target as the objective, to obtain a target set, wherein the initial sets include initial values ​​corresponding to the multiple variable terms respectively, and the target set includes target values ​​corresponding to the multiple variable terms respectively; and determining a power dispatch strategy corresponding to the target building based on the target set.

[0006] Optionally, in response to the power dispatch request, before retrieving the objective function corresponding to the target building based on the building identifier and the first constraint condition for constraining the objective function, the method further includes: obtaining the equipment models corresponding to the plurality of target devices respectively; determining the equipment operating parameters corresponding to the plurality of target devices respectively based on the plurality of equipment models; and determining the objective function corresponding to the target building and the first constraint condition for constraining the objective function based on the plurality of equipment operating parameters.

[0007] Optionally, controlling the objective function to update multiple initial sets with the scheduling target as the objective to obtain a target set includes: controlling the objective function to update multiple initial values ​​included in the multiple initial sets according to the initial update parameters, obtaining multiple first update sets, and updating multiple update values ​​included in the multiple first update sets according to the initial update parameters, until multiple Nth update sets are obtained, where N is a positive integer greater than 1; and determining the target set from the multiple Nth update sets according to the predicted total cost corresponding to each of the multiple Nth update sets.

[0008] Optionally, based on the initial update parameters, the initial values ​​included in the plurality of initial sets are updated to obtain a plurality of first update sets, and the update values ​​included in the plurality of first update sets are updated according to the initial update parameters until a plurality of Nth update sets are obtained. This includes: determining a total change value during the process of updating the values ​​in the update sets, wherein the total change value is determined based on the set change values ​​corresponding to the plurality of Mth update sets, and the corresponding set change values ​​are used to represent the change value between the corresponding Mth update set and the corresponding (M-1)th update set, where M is a positive integer greater than 2 and less than N; determining an update parameter threshold when the total change value is less than a change threshold; determining a change update parameter based on the update parameter threshold and the initial update parameters; and updating the values ​​in the Mth update set according to the change update parameter until the plurality of Nth update sets are obtained.

[0009] Optionally, before retrieving the objective function corresponding to the target building based on the building identifier, and before applying the first constraint condition to the objective function, the method further includes: if the multiple target devices include new energy devices, determining the environmental parameters of the target building within multiple predetermined time periods; determining the target power function corresponding to the new energy device based on the device parameters corresponding to the new energy device and the multiple environmental parameters, wherein the target power function is used to determine the output power of the new energy device within the multiple predetermined time periods; and determining the cost item of the new energy device based on the target power function and the unit time operating cost corresponding to the new energy device.

[0010] Optionally, in response to the power dispatch request, before retrieving the objective function corresponding to the target building based on the building identifier and the first constraint condition constraining the objective function, the method further includes: determining the electricity price cost and carbon emission cost values ​​corresponding to the target building in multiple predetermined time periods; determining the power purchase function of the target building, wherein the power purchase function is used to determine the power purchase corresponding to the target building in the multiple predetermined time periods; determining a power purchase cost item based on multiple electricity price costs and the power purchase function; determining a carbon emission cost function item based on multiple carbon emission cost values ​​and the power purchase function; and determining the objective function based on the power purchase cost item and the carbon emission cost function item.

[0011] Optionally, after determining the power dispatch strategy corresponding to the target building based on the target set, the method further includes: determining the actual total cost corresponding to the power dispatch strategy; and updating the power dispatch strategy to obtain an updated dispatch strategy if the actual total cost is greater than the cost threshold corresponding to the dispatch target, so as to dispatch the power of the target building according to the updated dispatch strategy.

[0012] According to one aspect of the present invention, an apparatus for determining a power dispatch strategy is provided, comprising: an acquisition module, configured to acquire a power dispatch request of a target building, wherein the target building includes a plurality of target devices, and the power dispatch request carries a building identifier corresponding to the target building and a dispatch target, the plurality of target devices including: new energy devices and energy storage devices; and a response module, configured to, in response to the power dispatch request, retrieve an objective function corresponding to the target building based on the building identifier, and a first constraint condition constraining the objective function, wherein the objective function includes: a plurality of variable terms, an electricity cost function term, and a carbon... The system comprises an emission cost function term and a temperature cost function term, wherein the electricity cost function term includes an electricity purchase cost term, a new energy equipment cost term, and an energy storage equipment cost term; the first constraint condition includes the power supply and demand balance constraint of the target building, the capacity constraint and the output power constraint of the energy storage equipment; a control module is used to control the objective function to update multiple initial sets with the scheduling objective as the target, thereby obtaining a target set, wherein the initial sets include the initial values ​​corresponding to the multiple variable terms respectively, and the target set includes the target values ​​corresponding to the multiple variable terms respectively; and a determination module is used to determine the power dispatch strategy corresponding to the target building based on the target set.

[0013] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the method for determining a power dispatch strategy as described above.

[0014] According to one aspect of the present invention, a computer-readable storage medium is provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the method for determining the power dispatch strategy described above.

[0015] In this embodiment of the invention, a power dispatch request for a target building is obtained. The target building includes multiple target devices. The power dispatch request carries a building identifier corresponding to the target building and a dispatch target. The multiple target devices include: new energy devices and energy storage devices. In response to the power dispatch request, based on the building identifier, a target function corresponding to the target building is retrieved, along with a first constraint condition constraining the target function. The target function includes multiple variable terms: an electricity cost function term, a carbon emission cost function term, and a temperature cost function term. The electricity cost function term includes an electricity purchase cost term, a new energy device cost term, and an energy storage device cost term. The first constraint condition includes a power supply and demand balance constraint for the target building and a capacity constraint for the energy storage devices. The system employs a control objective function that updates multiple initial sets to obtain a target set, where each initial set contains initial values ​​for multiple variables, and the target set contains target values ​​for each variable. Based on this target set, a power dispatch strategy corresponding to the target building is determined. This method achieves the goal of determining the power dispatch strategy based on the target building by updating multiple initial sets with the control objective function as the target. Since the objective function includes elements reflecting the electricity cost of the target building, and the first constraint includes power supply and demand balance constraints, reflecting the actual limitations and power demand of the building's new energy equipment under different times and scenarios, the power dispatch strategy determined by iteratively updating multiple initial sets with the dispatch objective as the optimization direction can respond in real time to fluctuations in photovoltaic output, changes in the state of charge of energy storage devices, and changes in power demand within the building, ensuring compliance with the dispatch objective. This solves the technical problem in related technologies where the determined power dispatch strategy is unable to cope with power fluctuations caused by energy storage and photovoltaic devices, leading to increased electricity costs. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of a method for determining a power dispatching strategy according to an embodiment of the present invention;

[0018] Figure 2This is a schematic diagram of the optical-storage-direct-flexible system architecture provided by an optional embodiment of the present invention;

[0019] Figure 3 This is an equivalent circuit diagram of a photovoltaic cell provided by an optional embodiment of the present invention;

[0020] Figure 4 This is a battery structure model diagram provided by an optional embodiment of the present invention;

[0021] Figure 5 This is a flowchart of the particle swarm optimization algorithm provided by an optional embodiment of the present invention;

[0022] Figure 6 This is a structural block diagram of the power dispatching strategy determination device according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 the invention 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 a 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.

[0025] Example 1

[0026] According to an embodiment of the present invention, an embodiment of a method for determining a power dispatching strategy is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] Figure 1 This is a flowchart of a method for determining a power dispatching strategy according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0028] Step S102: Obtain the power dispatch request of the target building. The target building includes multiple target devices. The power dispatch request carries the building identifier corresponding to the target building and the dispatch target. The multiple target devices include: new energy devices and energy storage devices.

[0029] In step S102 of this application, the power dispatch request of the target building is obtained.

[0030] This involves target buildings, which refer to buildings or building complexes with specific energy management and dispatching needs, such as photovoltaic-storage-direct-flexible buildings, which are buildings equipped with new energy equipment such as photovoltaic power generation and wind power generation, as well as energy storage equipment such as battery energy storage and thermal energy storage.

[0031] This involves power dispatch requests, which are requests to determine power dispatch strategies based on the energy demand of a target building and the predicted output of renewable energy equipment.

[0032] This involves target equipment, which refers to equipment that directly affects the power supply of the target building, such as new energy equipment (e.g., solar photovoltaic panels, wind turbines) and energy storage equipment (e.g., battery energy storage systems, thermal energy storage devices).

[0033] This involves building identification, which refers to codes or serial numbers used to identify target buildings.

[0034] This involves scheduling objectives, which refer to the desired results specified in the power dispatch request. These objectives can be any combination of objectives such as cost minimization, carbon emission reduction, power supply and demand balance, or maximizing user comfort.

[0035] In this step, a power dispatch request is received to determine the power dispatch strategy for the target building. The power dispatch request includes the unique identifier of the target building (i.e., building identifier) ​​and the specific dispatch target. Based on the building identifier, the target building can be quickly identified. Based on the dispatch target, a corresponding power dispatch strategy can be formulated to meet or optimize the building's energy use. This is the fundamental step in determining the power dispatch strategy.

[0036] Step S104: In response to the power dispatch request, based on the building identifier, retrieve the objective function corresponding to the target building, as well as the first constraint condition for constraining the objective function. The objective function includes multiple variable terms, an electricity cost function term, a carbon emission cost function term, and a temperature cost function term. The electricity cost function term includes an electricity purchase cost term, a new energy equipment cost term, and an energy storage equipment cost term. The first constraint condition includes the power supply and demand balance constraint of the target building, the capacity constraint of the energy storage equipment, and the output power constraint.

[0037] In step S104 of this application, the objective function corresponding to the target building is retrieved, as well as the first constraint condition for constraining the objective function.

[0038] This involves an objective function, which is a function used to determine the total cost of electricity for a building. Based on the total cost of the building determined by the objective function, the target values ​​of multiple variables can be adjusted to determine a power dispatching strategy that meets the dispatching objectives.

[0039] This involves the first constraint condition, which refers to the constraint condition that the determined power dispatch strategy needs to satisfy.

[0040] This involves variables, which refer to various decision variables that affect the optimization objective, such as the output power of photovoltaic cells, the charging and discharging status of energy storage devices, and the usage patterns of building loads.

[0041] This involves an electricity cost function term, which refers to the electricity cost of the target building over a certain period of time.

[0042] This involves a carbon emission cost function term, which refers to the carbon emission cost term incurred by the target building during the electricity purchase process.

[0043] This involves a temperature cost function term, which refers to the cost incurred when the temperature of the target building is maintained at or around the target temperature.

[0044] This includes the electricity purchase cost item, which refers to the cost of purchasing electricity from the power grid.

[0045] This includes the cost of new energy equipment, which refers to the cost incurred by new energy equipment in the process of converting new energy into electrical energy.

[0046] This includes the cost of energy storage equipment, which refers to the charging and discharging costs of energy storage equipment.

[0047] This involves the constraint of power supply and demand balance, which means ensuring that the power demand and supply of a building complex are equal at any given time to avoid overload or waste.

[0048] This involves capacity constraints, which refer to the fact that the capacity of an energy storage device during charging and discharging cannot exceed its maximum or fall below its minimum safe value.

[0049] This involves output power constraints, which means that the charging and discharging power of energy storage devices must be within an allowable range to prevent equipment damage or grid instability.

[0050] In this step, upon receiving a power dispatch request, the objective function for the target building is retrieved based on the building identifier. This objective function is designed to minimize electricity costs, carbon emissions, and temperature costs to optimize overall operational efficiency and sustainability. Simultaneously, a series of constraints, namely the first constraint, are set to ensure the dispatch scheme is feasible in practice and will not overload or exceed the normal operating range of the equipment. Through this step, optimizing the objective function allows for the prediction and control of the target building's total electricity costs, including electricity purchase costs, maintenance costs of renewable energy equipment, and charging and discharging costs of energy storage equipment, thereby minimizing costs. Furthermore, the carbon emission cost and temperature cost terms in the objective function also contribute to reducing the carbon emissions of the building complex and ensuring the comfort of the target building's indoor environment.

[0051] Step S106: The control objective function updates multiple initial sets with the scheduling objective as the target to obtain the target set. The initial set includes the initial values ​​corresponding to multiple variable items, and the target set includes the target values ​​corresponding to multiple variable items.

[0052] In step S106 provided in this application, the target set is obtained.

[0053] This involves an initial set, which refers to a set of initial values ​​for multiple variables that are randomly determined before the determination of variable values ​​that meet the scheduling objective begins.

[0054] This involves the target set, which refers to the set of variable values ​​that meet the scheduling objectives, determined by iterative optimization of the initial set.

[0055] In this step, the control objective function dynamically updates multiple initial values ​​in the initial set, with the scheduling objective as the optimization direction, in order to find the optimal combination of decision variables that makes the objective function optimal. The final result is a target set containing variable values ​​(i.e., target values) that meet the scheduling objective. Through this step, the control objective function continuously updates the initial set, intelligently adjusting energy allocation to optimize resource utilization and improve energy efficiency.

[0056] Step S108: Based on the target set, determine the power dispatch strategy corresponding to the target building.

[0057] In step S108 provided in this application, a power dispatch strategy corresponding to the target building is determined.

[0058] This involves power dispatching strategies, which are power usage plans formulated based on the optimal equipment operating parameters contained in the target set. It includes how to allocate and utilize photovoltaic power, energy storage power, and power purchased from the grid within the building at different times to meet the building's internal load demand, while achieving the best balance between economy, environmental protection, and comfort.

[0059] Through the above steps S102-S110, the power dispatch request of the target building can be obtained. The target building includes multiple target devices. The power dispatch request carries the building identifier corresponding to the target building and the dispatch target. The multiple target devices include: new energy equipment and energy storage equipment. In response to the power dispatch request, based on the building identifier, the objective function corresponding to the target building is retrieved, along with a first constraint condition for constraining the objective function. The objective function includes multiple variable terms: an electricity cost function term, a carbon emission cost function term, and a temperature cost function term. The electricity cost function term includes an electricity purchase cost term, a new energy equipment cost term, and an energy storage equipment cost term. The first constraint condition includes the power supply and demand balance constraint of the target building and the capacity of the energy storage equipment. The system incorporates quantity constraints and output power constraints. The control objective function, with the scheduling objective as the goal, updates multiple initial sets to obtain a target set. The initial sets include initial values ​​for multiple variables, and the target set includes target values ​​for multiple variables. Based on the target set, a power dispatch strategy corresponding to the target building is determined. This method achieves the goal of determining the power dispatch strategy based on the target building by updating multiple initial sets with the control objective function as the scheduling objective. Since the objective function includes elements reflecting the electricity cost of the target building, and the first constraint includes power supply and demand balance constraints, reflecting the actual limitations and power demand of the building's new energy equipment supply under different times and scenarios, the power dispatch strategy determined by iteratively updating multiple initial sets with the scheduling objective as the optimization direction can respond in real time to fluctuations in photovoltaic output, changes in the state of charge of energy storage devices, and changes in power demand within the building, ensuring compliance with the scheduling objective. This solves the technical problem in related technologies where the determined power dispatch strategy is difficult to cope with power fluctuations caused by energy storage and photovoltaic devices, leading to increased electricity costs.

[0060] As an optional embodiment, in response to a power dispatch request, before retrieving the objective function corresponding to the target building based on the building identifier and the first constraint condition for constraining the objective function, the method further includes: obtaining equipment models corresponding to multiple target devices respectively; determining equipment operating parameters corresponding to multiple target devices based on the multiple equipment models; and determining the objective function corresponding to the target building and the first constraint condition for constraining the objective function based on the multiple equipment operating parameters.

[0061] This embodiment describes the specific steps for determining the objective function corresponding to the target building and the first constraint condition for constraining the objective function.

[0062] This involves equipment models, which are mathematical models used to describe and simulate the characteristics and behavior of target equipment (such as solar photovoltaic panels and batteries). These models are built based on physical laws, experimental data, or historical operating records and can predict the performance of the equipment under different conditions, such as output power, energy storage capacity, and efficiency.

[0063] This involves equipment operating parameters, which refer to the control parameters required for the normal operation of the equipment, such as the optimal operating temperature and light intensity of the photovoltaic panels, the charge / discharge rate of the energy storage equipment, and the state of charge (SOC). These parameters directly determine the output power and operating efficiency of the equipment.

[0064] In this step, firstly, equipment models for each target device are obtained. These models detail the physical behavior and performance characteristics of the target devices, such as the relationship between the power generation efficiency of photovoltaic panels and light intensity, and the charging and discharging characteristics of batteries. Next, based on the equipment models, the operating parameters for each device under current or expected conditions are determined, such as the expected output power of photovoltaic panels and the state of charge (SOC) of batteries. These parameters are crucial for formulating the objective function. Finally, based on the equipment operating parameters, the objective function is constructed. This is a multi-objective optimization problem, incorporating considerations from multiple dimensions, including economics (electricity costs), environmental factors (carbon emissions), and social factors (user comfort). Simultaneously, first constraints are set to ensure that during the optimization process, the operation of the equipment will not exceed its physical limitations, nor will it disrupt power balance or threaten grid security. Through this step, using accurate equipment models and operating parameters, the output of the equipment can be predicted and controlled more precisely, improving the accuracy and efficiency of power energy dispatch.

[0065] As an optional embodiment, the control objective function updates multiple initial sets to obtain a target set, with the scheduling objective as the goal. This includes: the control objective function, with the scheduling objective as the goal, updates multiple initial values ​​included in the multiple initial sets according to the initial update parameters to obtain multiple first update sets, and updates multiple update values ​​included in the multiple first update sets according to the initial update parameters until multiple Nth update sets are obtained, where N is a positive integer greater than 1; and the target set is determined from the multiple Nth update sets based on the predicted total cost corresponding to each of the multiple Nth update sets.

[0066] This embodiment illustrates the specific steps of the control objective function in updating multiple initial sets to obtain the target set, with the scheduling objective as the objective.

[0067] This involves initial update parameters, which are parameters used by optimization algorithms to adjust the values ​​of decision variables during the iteration process. These parameters can affect the search direction and speed of the algorithm, such as adjusting the step size, learning rate, or other parameters that control the behavior of the algorithm.

[0068] This involves the Nth update set, which refers to the set of decision variables after N iterations.

[0069] This involves the prediction of total cost, which refers to the cost obtained by predicting the cost of future power dispatch strategies based on the updated values ​​in the current Nth update set.

[0070] In this step, the system first evaluates the current scheduling strategy by calling the objective function based on the set scheduling objective. Then, the system begins the optimization process based on a set of initial decision variables (i.e., the initial set), adjusting the initial set using initial update parameters to generate a new set of decision variable values ​​(the first update set). The first update set is then updated multiple times using the initial update parameters (or other parameters adjusted during the iteration process), resulting in a new set of decision variable values ​​(the Nth update set), where N represents the iteration number. By continuously iterating and updating the decision variable values ​​(i.e., the update sets), the scheduling strategy is gradually improved to better approximate the scheduling objective. For each update set (the Nth update set), its corresponding total cost (including economic, environmental, and social costs) is predicted, yielding the predicted total cost. Finally, the set with the lowest cost or best meeting the scheduling objective is selected from all update sets as the target set, used to guide actual power dispatching.

[0071] This step involves updating multiple initial sets and continuously adjusting the operating parameters of the target equipment. This enables refined management of new energy equipment (such as photovoltaics), energy storage equipment, and building internal loads, allowing for the determination of the lowest-cost scheduling scheme, reducing building operating costs, and improving economic efficiency.

[0072] As an optional embodiment, based on initial update parameters, multiple initial values ​​included in multiple initial sets are updated to obtain multiple first update sets. Then, based on the initial update parameters, multiple update values ​​included in the multiple first update sets are updated until multiple Nth update sets are obtained. This includes: during the process of updating the values ​​in the update sets, determining the total change value, wherein the total change value is determined based on the set change values ​​corresponding to the multiple Mth update sets, and the corresponding set change value is used to represent the change value between the corresponding Mth update set and the corresponding (M-1)th update set, where M is a positive integer greater than 2 and less than N; if the total change value is less than a change threshold, determining an update parameter threshold; based on the update parameter threshold and the initial update parameters, determining a change update parameter; and updating the values ​​in the Mth update set based on the change update parameter until multiple Nth update sets are obtained.

[0073] This embodiment describes the specific steps for determining the change and update parameters.

[0074] This involves the total change value, which is a comprehensive index determined by the set of changes of multiple Mth update sets. It reflects the degree of change of the overall scheduling strategy or the degree to which it approaches the optimal solution.

[0075] This involves the set change value, which refers to the degree of difference between two consecutive set updates. This helps to measure the extent of improvement or convergence speed of the scheduling strategy during the iteration process.

[0076] This involves a change threshold, which is a set standard value used to determine whether the iterative optimization process has fallen into a local optimum.

[0077] This involves the update parameter threshold, which refers to the threshold used to determine how to adjust the update parameters during the optimization process.

[0078] This involves changing the update parameters. Changing the update parameters refers to the new set of parameters adjusted based on the update parameter threshold and the initial update parameters, which can improve the performance of the optimization algorithm and make it more effectively approach the optimal solution.

[0079] In this step, firstly, the operating states of the devices in the initial set are adjusted based on the initial update parameters to form the first update set. Then, the change value and total change value of the set are determined to determine whether the initial update parameters need to be updated. When the total change value is lower than a preset change threshold, it indicates that further updates may not bring significant improvement. Based on the current initial update parameters and the update parameter threshold, new update parameters are determined, i.e., the update parameters are changed. The current update set is then iterated using the changed update parameters until a predetermined number of iterations (the Nth update set) is reached, thereby obtaining the optimal scheduling strategy.

[0080] This step determines the total change value of the set. If the total change value is less than the change threshold, the update parameters are changed to increase the randomness and diversity of the search process, avoid premature convergence to local optima, and improve the performance and robustness of the algorithm.

[0081] As an optional embodiment, before retrieving the objective function corresponding to the target building based on the building identifier, and before applying the first constraint condition to the objective function, the method further includes: determining the environmental parameters of the target building within multiple predetermined time periods when multiple target devices include new energy devices; determining the target power function corresponding to the new energy devices based on the device parameters corresponding to the new energy devices and the multiple environmental parameters, wherein the target power function is used to determine the output power of the new energy devices within the multiple predetermined time periods; and determining the cost item of the new energy devices based on the target power function and the unit time operating cost corresponding to the new energy devices.

[0082] This embodiment illustrates the specific steps for determining the cost items of new energy equipment.

[0083] This involves environmental parameters, which refer to external conditions that directly affect the power generation performance of new energy equipment (such as photovoltaic cells and wind turbines), such as sunlight intensity, temperature, wind speed, and humidity. These parameters are key to predicting the output power of new energy equipment.

[0084] This involves equipment parameters, which refer to the technical specifications and performance indicators of the new energy equipment itself, including the equipment's maximum power generation, efficiency, operating temperature range, and failure rate. Combining equipment parameters with environmental parameters allows for a more accurate prediction of the equipment's operating status and output power.

[0085] This involves the target power function, which is a function model established based on the equipment parameters and environmental parameters of new energy equipment, used to predict and calculate the expected output power of the equipment under different conditions.

[0086] This involves the cost per unit time, which refers to the cost required to operate (including power generation and maintenance) within a unit of time.

[0087] In this step, the first step is to collect and analyze environmental parameters, which directly affect the power generation of new energy equipment. For example, for photovoltaic cells, light intensity and temperature are key environmental factors determining power generation efficiency; for wind turbines, wind speed is the primary operating condition. By monitoring or predicting these environmental parameters in real time, and combining them with the equipment parameters of the new energy equipment (such as maximum power generation and efficiency), a target power function can be established to predict the expected output power of the equipment over multiple predetermined time periods. The next step is to calculate the cost item of the new energy equipment using the target power function and the unit-time operating cost. The unit-time operating cost reflects the economic costs of operation, maintenance, depreciation, etc., of the equipment within a specific time period, while the target power function reflects the equipment's power generation capacity. Combining the two, the total operating cost of the new energy equipment over multiple predetermined time periods under different environmental conditions can be obtained. This cost item will be integrated into the optimization objective function as a consideration for scheduling strategy optimization. By combining environmental parameters and equipment parameters, the accuracy of predicting the power generation of new energy equipment can be improved, making the scheduling strategy more reasonable.

[0088] As an optional embodiment, in response to a power dispatch request, before retrieving the objective function corresponding to the target building based on the building identifier and the first constraint condition constraining the objective function, the method further includes: determining the electricity price cost and carbon emission cost values ​​corresponding to the target building in multiple predetermined time periods; determining the power purchase function of the target building, wherein the power purchase function is used to determine the power purchase corresponding to the target building in multiple predetermined time periods; determining the power purchase cost item based on the multiple electricity price costs and power purchase functions; determining the carbon emission cost function item based on the multiple carbon emission cost values ​​and power purchase functions; and determining the objective function based on the power purchase cost item and the carbon emission cost function item.

[0089] This embodiment describes the specific steps for determining the objective function.

[0090] This involves electricity cost, which refers to the unit cost of purchasing electricity from the power grid within a certain period of time.

[0091] This includes carbon emission cost, which refers to the cost of generating carbon emissions from the use of purchased electricity within a certain period of time.

[0092] This involves the power purchase function, which is used to predict or determine the amount of electricity a target building needs to purchase from the grid over multiple predetermined time periods. It is based on the building's internal electricity demand and the output capacity of its energy equipment.

[0093] In this step, firstly, the electricity cost and carbon emission cost values ​​for the target building over multiple predetermined time periods are determined. These values ​​directly impact the building's energy costs and environmental impact. Next, a power purchase function is determined, which predicts the target building's electricity demand over each time period. This information is used to calculate the power purchase cost term and the carbon emission cost function term, which are key components of the objective function and represent the economic and environmental goals of the optimization process.

[0094] As an optional embodiment, after determining the power dispatch strategy corresponding to the target building based on the target set, the method further includes: determining the actual total cost corresponding to the power dispatch strategy; if the actual total cost is greater than the cost threshold corresponding to the dispatch target, updating the power dispatch strategy to obtain an updated dispatch strategy, so as to dispatch the power of the target building according to the updated dispatch strategy.

[0095] This embodiment describes the specific steps for determining the update scheduling policy.

[0096] This involves the actual total cost, which refers to the total operating cost of the target building within a specific time period after the implementation of the power dispatch strategy.

[0097] This involves a cost threshold, which refers to the upper limit or benchmark value of costs defined in the scheduling objectives. If the actual total cost exceeds the cost threshold, it means that the power dispatching strategy needs to be adjusted to reduce the overall cost to an acceptable range.

[0098] This involves updating the dispatch strategy, which refers to determining a new power dispatch strategy that can meet the dispatch objectives when the actual total cost exceeds the cost threshold.

[0099] In this step, after establishing a preliminary power dispatch strategy based on the optimization objectives, the actual total cost of implementing this strategy is determined to evaluate its effectiveness and economic efficiency. If the determined actual total cost is higher than a pre-set cost threshold, it means that the dispatch strategy has not fully achieved the cost control objective, and the power dispatch strategy needs to be updated and adjusted until an optimal dispatch scheme is found that makes the actual total cost lower than or equal to the cost threshold; that is, the dispatch strategy is updated. This iterative adjustment process ensures that the final implemented power dispatch strategy can simultaneously achieve the optimization objectives of economic efficiency, environmental protection, and user comfort.

[0100] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0101] An optional embodiment of this invention provides a method for low-carbon economic dispatch of controllable flexible loads in a photovoltaic-storage-direct current-flexible system, which comprehensively considers multiple objectives such as daily economic efficiency, carbon emissions, and user comfort costs. It effectively utilizes photovoltaic power generation and configures related energy storage devices, increasing the proportion of distributed energy in the energy supply of photovoltaic-storage-direct current-flexible building complexes, promoting the consumption of new energy sources, optimizing the energy structure, and solving the problem of some building loads' dependence on traditional energy sources.

[0102] The following details the specific steps of the controllable flexible load low-carbon economic dispatch method for photovoltaic-storage-direct-drive-flexible systems provided by the optional embodiments of the present invention.

[0103] S1. Obtain the power dispatch request for the target building.

[0104] The optimized scheduling system uses the output of distributed energy resources and loads at a 1-hour scale predicted recently to schedule flexible loads and energy storage equipment in the photovoltaic-storage-direct-drive-flexible building complex over the next 24 hours.

[0105] S2. In response to a power dispatch request, based on the building identifier, retrieve the objective function corresponding to the target building, as well as the first constraint condition for constraining the objective function.

[0106] Before proceeding, the objective function and the first constraint condition need to be determined. The following details the steps involved in this process:

[0107] A1. Obtain the device models corresponding to multiple target devices.

[0108] Figure 2 This is a schematic diagram of the optical-storage-direct-flexible system architecture provided by an optional embodiment of the present invention, such as... Figure 2 As shown, the photovoltaic-storage-direct-flex system includes multiple photovoltaic devices, energy storage devices, and flexible load devices. A mathematical model (same as the device model) is established for the photovoltaic cells, energy storage devices, and flexible loads (same as the target devices) of the photovoltaic-storage-direct-flex system (same as the target building).

[0109] Figure 3 This is an equivalent circuit diagram of a photovoltaic cell provided by an optional embodiment of the present invention, such as... Figure 3 As shown, an equivalent circuit model of a photovoltaic cell is constructed. Based on this model, a series resistor (R) is added to account for issues such as cell aging. s ) and a parallel shunt resistor (R) sh ).

[0110] Based on the above, the output current (I) and voltage (U) characteristics of a photovoltaic cell can be derived as follows:

[0111]

[0112] Among them, I ph Photocurrent; I d I represents the current flowing through the diode when there is no light. sh I is the current flowing through the parallel shunt resistor; D ν is the diode current in saturation; q is the charge of an electron; n is the diode's ideality factor; k is Boltzmann's constant; T is the absolute temperature of the photovoltaic cell.

[0113] Construct an equivalent circuit model for the energy storage device: Figure 4 This is a battery structure model diagram provided by an optional embodiment of the present invention, such as... Figure 4 As shown, the optional embodiment of the present invention adopts a structural model of a lithium iron phosphate battery. When the battery is charged and discharged, the remaining capacity is represented by the state of charge (SOC), and its basic relationship with the remaining capacity and battery capacitance is as follows:

[0114]

[0115] Among them, Q r Q represents the remaining capacity of the battery. n This refers to the rated capacity of the battery.

[0116] Battery capacity can be expressed in terms of charge and discharge current as follows:

[0117]

[0118] in, Battery current I b Integral over time, SOC (t) This indicates the capacity of the battery after charging and discharging for time t.

[0119] To construct an external characteristic model of the flexible load, we first construct an external characteristic model of the intelligent lighting. Intelligent lighting adjusts its brightness according to the brightness level, thus affecting the power output. The power P(t) of the intelligent lighting is:

[0120] P(t=η i L(t)P n

[0121] Where, η i The indicator shows the lighting level, i = 1 to 4, corresponding to 0%, 10%, 40%, and 100% respectively; L(t) represents the lighting duration; and Pn represents the rated power of the smart lighting.

[0122] Secondly, construct the external characteristic model of the charging pile:

[0123]

[0124] in, The charging pile's power is represented by Qt, which is the electric vehicle's charge at time t; η is the electric charge at time t. cp Δt represents the charging efficiency; Δt represents the charging time interval.

[0125] By modeling the external characteristics of the above equipment, we can obtain the relationship between certain quantities and their output power, laying the groundwork for subsequent day-to-day multi-timescale scheduling plans.

[0126] A2. Determine the objective function.

[0127] The optimized scheduling uses the output of distributed energy resources and loads at a 1-hour scale predicted in the previous day to schedule flexible loads and energy storage equipment in the photovoltaic-storage-direct-drive-flexible building complex over the next 24 hours.

[0128] The day-ahead optimization scheduling objective function (same as the objective function above) aims to minimize the overall cost of the optical-storage-direct-drive-flexible system within the day-ahead optimization scheduling period. The day-ahead optimization scheduling objective function is as follows:

[0129] C1=w1(C grid +C gf +C cn +C soc )+w2C c +w3C shd

[0130] Where C1 is the weighted total cost; C grid The cost of purchasing electricity from the grid (same as the electricity purchase cost item above); C gf C is the photovoltaic operation and maintenance cost (same as the above-mentioned new energy equipment cost item); cn Energy storage operation and maintenance costs (same as the energy storage equipment cost item above); C soc Cost of electricity; C c For carbon emissions (same as the carbon emission cost function term above); C shd w1 is the comfort cost (same as the temperature cost function term above); w2 is the economic weight; w3 is the carbon emission weight; w4 is the comfort weight.

[0131] Among them, the cost of purchasing electricity from the grid (C) grid for:

[0132]

[0133] Among them, c grid,t P represents the electricity purchase price during time period t (same as the electricity cost mentioned above); grid,t Let t be the power purchased during time period t (same as the power purchased function above).

[0134] Among them, the operation and maintenance cost C of the photovoltaic system gf for:

[0135]

[0136] Among them, C gf The photovoltaic system operation and maintenance cost (same as the above-mentioned unit time operation cost); P gf,t Let be the output power of the photovoltaic system during time period t (same as the target power function mentioned above).

[0137] Among them, the operation and maintenance cost C of the energy storage system cn for:

[0138]

[0139] Among them, C e E represents the unit capacity operation and maintenance cost of energy storage equipment. ba c is the rated capacity of the energy storage device; cn P represents the unit price for the operation and maintenance of energy storage equipment. cn,t Let t be the output power of the energy storage device during time period t.

[0140] Among them, carbon emissions C c for:

[0141]

[0142] Among them, c c,t Let t be the value of the carbon emission factor during time period t.

[0143] Among them, the comfort cost C shd for:

[0144]

[0145] Where a is the indoor thermal comfort coefficient; b is a constant coefficient; K(t) is the number of people in the room during time period t; T(t) is the indoor temperature during time period t; and Tset is the user's desired temperature.

[0146] A3. Determine the first constraint condition.

[0147] Power balance (similar to the power supply and demand balance constraints of the target building mentioned above) is the most basic and important constraint in the photovoltaic-storage-DC-flexible system. The energy storage system plays an important role in the photovoltaic-storage-DC-flexible system, not only absorbing 100% of the new energy, but also supporting the power grid. Therefore, energy storage equipment also has many complex constraints, including upper and lower limits of energy storage equipment capacity (similar to the capacity constraints of energy storage equipment mentioned above) and upper and lower limits of charging and discharging power (similar to the output power constraints mentioned above). Due to the uncertainty of distributed energy output, the sum of charging and discharging of the energy storage equipment after one day must be greater than 40% of the rated capacity.

[0148] S3. The control objective function takes the scheduling objective as the goal, updates multiple initial sets, and obtains the objective set.

[0149] The Particle Swarm Optimization (PSO) algorithm is swarm-based, moving individuals within the swarm to better regions based on their fitness to the environment. However, it does not use evolutionary operators on individuals; instead, it treats each individual as a volumeless particle in a D-dimensional search space, flying at a certain speed that is dynamically adjusted based on its own flight experience and that of its companions.

[0150] Figure 5 This is a flowchart of the particle swarm optimization algorithm provided by an optional embodiment of the present invention, such as... Figure 5 As shown, the value x of the i-th particle is... i Represented as:

[0151] x i =(x i1 ,x i2 , ..., x id )

[0152] The best position it has ever experienced, i.e., the best fitness value p. i , denoted as:

[0153] p i =(p i1 ,p i2 …p id )

[0154] The velocity v of particle i i for:

[0155] v i =(v i1 ,v i2 ,…,v id )

[0156] For each generation, its (d+1)th dimension (1≤d+1≤D) changes according to the following equation:

[0157] v id+1 =wv id +c1rand()(p id -x id )+c2rand()(p gd -x id )

[0158] x id+1 =x id +v id+1

[0159] Where w is the inertia weight, c1 and c2 are acceleration constants, and rand() is a random value that varies in the range [0,1].

[0160] Furthermore, the velocity V of a particle is limited by a maximum velocity Vmax. If the current acceleration of a particle causes its velocity vid in a certain dimension to exceed the maximum velocity Vmax,d in that dimension, then the velocity in that dimension is limited to the maximum velocity Vmax,d.

[0161] S4. Based on the target set, determine the power dispatch strategy corresponding to the target building.

[0162] The above optional implementation methods can achieve at least the following beneficial effects:

[0163] (1) By using day-ahead optimization scheduling technology, the relationship between photovoltaic power generation, energy storage and electricity load in buildings can be better matched, thereby reducing dependence on the power grid, improving the comprehensive utilization efficiency of energy and reducing the overall energy consumption of building complexes;

[0164] (2) The photovoltaic-storage-direct-flexible building complex can independently supply power when the power grid has problems by using energy storage equipment and flexible energy consumption regulation, so as to ensure the stable supply of important equipment and basic power demand in the building and reduce the impact of power grid failure on the building's use function.

[0165] (3) Based on the guidance of dynamic carbon emissions, building groups can use energy more rationally, prioritize the use of low-carbon or zero-carbon energy, reduce dependence on traditional high-carbon energy, and thus reduce building carbon emissions.

[0166] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0167] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0168] Example 2

[0169] According to an embodiment of the present invention, an apparatus for implementing the above-described method for determining power dispatch strategies is also provided. Figure 6 This is a structural block diagram of a power dispatching strategy determination device according to an embodiment of the present invention, such as... Figure 6 As shown, the device includes: an acquisition module 602, a response module 604, a control module 606, and a determination module 608. The device will be described in detail below.

[0170] Acquisition module 602 is used to acquire power dispatch requests from target buildings. The target buildings include multiple target devices. The power dispatch request carries a building identifier corresponding to the target building and the dispatch target. The multiple target devices include: new energy equipment and energy storage equipment. Response module 604, connected to acquisition module 602, is used to respond to the power dispatch request by retrieving the objective function corresponding to the target building based on the building identifier, and setting a first constraint condition for the objective function. The objective function includes multiple variable terms: an electricity cost function term, a carbon emission cost function term, and a temperature cost function term. The items include electricity purchase cost, new energy equipment cost, and energy storage equipment cost. The first constraint includes the power supply and demand balance constraint of the target building, the capacity constraint and output power constraint of the energy storage equipment. The control module 606, connected to the response module 604, is used to control the objective function to update multiple initial sets with the scheduling target as the objective, and obtain the target set. The initial set includes the initial values ​​corresponding to multiple variable items, and the target set includes the target values ​​corresponding to multiple variable items. The determination module 608, connected to the control module 606, is used to determine the power dispatch strategy corresponding to the target building based on the target set.

[0171] It should be noted that the above-mentioned acquisition module 602, response module 604, control module 606 and determination module 608 correspond to steps S102 to S108 in the method for determining the implementation of power dispatch strategy. The multiple modules and the corresponding steps are the same in terms of implementation instances and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0172] Example 3

[0173] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute instructions to implement the method for determining the power dispatch strategy described above.

[0174] Example 4

[0175] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the power dispatching strategy determination method described above.

[0176] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0177] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0178] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0179] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0180] Furthermore, the functional units in the various embodiments of the present invention 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. The integrated unit can be implemented in hardware or as a software functional unit.

[0181] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0182] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining a power dispatching strategy, characterized in that, include: Obtain a power dispatch request for a target building, wherein the target building includes multiple target devices, and the power dispatch request carries a building identifier corresponding to the target building and a dispatch target, wherein the multiple target devices include: new energy devices and energy storage devices; In response to the power dispatch request, based on the building identifier, the objective function corresponding to the target building is retrieved, along with a first constraint condition constraining the objective function. The objective function includes multiple variable terms, an electricity cost function term, a carbon emission cost function term, and a temperature cost function term. The electricity cost function term includes an electricity purchase cost term, a new energy equipment cost term, and an energy storage equipment cost term. The first constraint condition includes the power supply and demand balance constraint of the target building, the capacity constraint of the energy storage equipment, and the output power constraint. The objective function is controlled to update multiple initial sets with the scheduling target as the objective, to obtain a target set, wherein the initial sets include the initial values ​​corresponding to the multiple variable items respectively, and the target set includes the target values ​​corresponding to the multiple variable items respectively; Based on the target set, a power dispatching strategy corresponding to the target building is determined.

2. The method according to claim 1, characterized in that, Before responding to the power dispatch request and retrieving the objective function corresponding to the target building based on the building identifier, and before applying the first constraint condition to the objective function, the method further includes: Obtain the device models corresponding to the multiple target devices; Based on multiple device models, determine the device operating parameters corresponding to the multiple target devices respectively; Based on multiple equipment operating parameters, an objective function corresponding to the target building is determined, along with a first constraint condition for constraining the objective function.

3. The method according to claim 1, characterized in that, The control objective function updates multiple initial sets with the scheduling objective as the target, resulting in a target set, including: The objective function is controlled to take the scheduling target as the target, and according to the initial update parameters, it updates the multiple initial values ​​included in the multiple initial sets respectively to obtain multiple first update sets, and updates the multiple update values ​​included in the multiple first update sets according to the initial update parameters, until multiple Nth update sets are obtained, where N is a positive integer greater than 1; Based on the total predicted cost corresponding to the plurality of Nth update sets, the target set is determined from the plurality of Nth update sets.

4. The method according to claim 3, characterized in that, The process involves updating multiple initial values ​​included in the multiple initial sets according to the initial update parameters to obtain multiple first update sets, and then updating multiple update values ​​included in the multiple first update sets according to the initial update parameters until multiple Nth update sets are obtained, including: During the process of updating the values ​​in the update set, the total change value is determined, wherein the total change value is determined based on the set change values ​​corresponding to the multiple Mth update sets respectively, and the corresponding set change value is used to represent the change value between the corresponding Mth update set and the corresponding (M-1)th update set, where M is a positive integer greater than 2 and less than N; If the total change is less than the change threshold, determine the update parameter threshold; Based on the update parameter threshold and the initial update parameters, the change update parameters are determined; The values ​​in the Mth update set are updated according to the change and update parameters until the plurality of Nth update sets are obtained.

5. The method according to claim 1, characterized in that, Before retrieving the objective function corresponding to the target building based on the building identifier, and before applying the first constraint condition to the objective function, the method further includes: When the multiple target devices include new energy devices, determine the environmental parameters of the target building within multiple predetermined time periods; Based on the equipment parameters and multiple environmental parameters corresponding to the new energy equipment, a target power function corresponding to the new energy equipment is determined, wherein the target power function is used to determine the output power of the new energy equipment in the multiple predetermined time periods respectively; Based on the target power function and the unit time operating cost of the new energy equipment, the cost item of the new energy equipment is determined.

6. The method according to claim 1, characterized in that, Before responding to the power dispatch request and retrieving the objective function corresponding to the target building based on the building identifier, and before applying the first constraint condition to the objective function, the method further includes: Determine the electricity cost and carbon emission cost values ​​corresponding to the target building in multiple predetermined time periods; Determine the power purchase function of the target building, wherein the power purchase function is used to determine the power purchase corresponding to the target building in the multiple predetermined time periods respectively; The electricity purchase cost item is determined based on multiple electricity price costs and the aforementioned electricity purchase power function; The carbon emission cost function term is determined based on multiple carbon emission cost values ​​and the power purchase function. The objective function is determined based on the electricity purchase cost item and the carbon emission cost function item.

7. The method according to any one of claims 1 to 6, characterized in that, After determining the power dispatch strategy corresponding to the target building based on the target set, the method further includes: Determine the actual total cost corresponding to the power dispatch strategy; If the actual total cost is greater than the cost threshold corresponding to the scheduling target, the power scheduling strategy is updated to obtain an updated scheduling strategy, and the power of the target building is scheduled according to the updated scheduling strategy.

8. A device for determining a power dispatching strategy, characterized in that, include: The acquisition module is used to acquire the power dispatch request of the target building, wherein the target building includes multiple target devices, and the power dispatch request carries the building identifier corresponding to the target building and the dispatch target. The multiple target devices include: new energy devices and energy storage devices. The response module is used to respond to the power dispatch request, retrieve the objective function corresponding to the target building based on the building identifier, and the first constraint condition constraining the objective function. The objective function includes multiple variable terms, an electricity cost function term, a carbon emission cost function term, and a temperature cost function term. The electricity cost function term includes an electricity purchase cost term, a new energy equipment cost term, and an energy storage equipment cost term. The first constraint condition includes the power supply and demand balance constraint of the target building, the capacity constraint of the energy storage equipment, and the output power constraint. The control module is used to control the objective function to update multiple initial sets with the scheduling objective as the target, to obtain a target set, wherein the initial sets include the initial values ​​corresponding to the multiple variable items respectively, and the target set includes the target values ​​corresponding to the multiple variable items respectively; The determination module is used to determine the power dispatching strategy corresponding to the target building based on the target set.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method for determining the power dispatch strategy as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method for determining the power dispatch strategy as described in any one of claims 1 to 7.

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