Direct-current micro-grid dispatching method, device and system

Abstract

The invention relates to a direct-current micro-grid dispatching method, device and system. The method comprises the following steps: acquiring a cost parameter, electricity price information and an initial demand load capacity of the DC micro-grid; inputting the cost parameter, the electricity price information and the initial demand load into a pre-constructed scheduling model; the scheduling model comprises a cost model, a load demand response model and an electricity price optimization model; the cost model is used for optimizing the total cost, the load demand response model is used for constraining the incidence relation between the scheduling load rate and the electricity price, and the load demand response model is used for optimizing the load scheduling rate; based on a preset constraint condition, solving the scheduling model to obtain a load scheduling scheme; the load scheduling scheme comprises a target load scheduling rate and a target scheduling time period; the target load scheduling rate is used for indicating that part of the demand load capacity in the initial demand load capacity is scheduled to the target scheduling period. According to the scheme, the power utilization cost of a user is reduced.

Description

Technical Field

[0001] This disclosure relates to the field of microgrid dispatch optimization technology, and in particular to a DC microgrid dispatch method, device and system. Background Technology

[0002] In related technologies, with the rapid development of renewable energy and the gradual opening of the electricity market, DC microgrids, as an emerging power system architecture, are receiving increasing attention. They can effectively integrate renewable energy sources such as solar and wind power, and improve energy transmission efficiency through direct current. Furthermore, the optimized scheduling of DC microgrids has become crucial to ensure efficient resource utilization and stable power supply. Traditional power dispatching methods are mainly based on load forecasting and generation capacity, employing optimization algorithms such as linear programming and dynamic programming. However, these methods fail to fully consider electricity price fluctuations and changes in user electricity demand, making it difficult to meet actual user needs. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a DC microgrid dispatching method, device and system.

[0004] According to a first aspect of the present disclosure, a DC microgrid dispatching method is provided, comprising:

[0005] Obtain cost parameters, electricity price information, and initial demand load of DC microgrids;

[0006] The cost parameters, electricity price information, and initial demand load are input into a pre-built scheduling model. The scheduling model includes a cost model, a load demand response model, and an electricity price optimization model. The cost model is used to optimize the total cost, the load demand response model is used to constrain the relationship between the scheduling load rate and the electricity price, and the load demand response model is used to optimize the load scheduling rate.

[0007] Based on preset constraints, the scheduling model is solved to obtain a load scheduling scheme; the load scheduling scheme includes a target load scheduling rate and a target scheduling period; the target load scheduling rate is used to indicate that a portion of the initial demand load is scheduled to the target scheduling period.

[0008] According to a second aspect of the present disclosure, a DC microgrid dispatching device is provided, comprising:

[0009] The acquisition unit is used to acquire cost parameters, electricity price information, and initial demand load of the DC microgrid.

[0010] An input unit is used to input the cost parameters, electricity price information, and initial demand load into a pre-built scheduling model; the scheduling model includes a cost model, a load demand response model, and an electricity price optimization model; the cost model is used to optimize the total cost, the load demand response model is used to constrain the correlation between the scheduling load rate and the electricity price, and the load demand response model is used to optimize the load scheduling rate;

[0011] The solution unit is used to solve the scheduling model based on preset constraints to obtain a load scheduling scheme; the load scheduling scheme includes a target load scheduling rate and a target scheduling period; the target load scheduling rate is used to indicate that a portion of the initial demand load is scheduled to the target scheduling period.

[0012] According to a third aspect of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of the first aspects.

[0013] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first aspects.

[0014] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in any one of the first aspects.

[0015] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: By acquiring the cost parameters, electricity price information, and initial demand load of the DC microgrid; inputting the cost parameters, electricity price information, and initial demand load into a pre-constructed scheduling model; the scheduling model includes a cost model, a load demand response model, and an electricity price optimization model; the cost model is used to optimize the total cost, the load demand response model is used to constrain the correlation between the scheduling load rate and the electricity price, and the load demand response model is used to optimize the load scheduling rate; based on preset constraints, the scheduling model is solved to obtain a load scheduling scheme; the load scheduling scheme includes a target load scheduling rate and a target scheduling period; the target load scheduling rate is used to indicate that a portion of the initial demand load is scheduled to the target scheduling period. By comprehensively solving the cost model, the load demand response model, and the electricity price optimization model, a DC microgrid load scheduling scheme that considers both the overall operating cost of the DC microgrid and electricity price fluctuations and user electricity demand can be obtained, thereby reducing user electricity costs and improving user experience.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0018] Figure 1 This is a flowchart illustrating a DC microgrid scheduling method according to an exemplary embodiment.

[0019] Figure 2 This is a block diagram illustrating a DC microgrid dispatching device according to an exemplary embodiment.

[0020] Figure 3 This is a block diagram illustrating an apparatus for a DC microgrid dispatching method according to an exemplary embodiment. Detailed Implementation

[0021] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0022] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. The singular forms “a” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0023] It should be understood that although the terms first, second, third, etc., may be used to describe various information in embodiments of this disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of embodiments of this disclosure, and similarly, second information may also be referred to as first information. Depending on the context, the words “if” and “suppose” as used herein may be interpreted as “when”, “when”, or “in response to a determination”.

[0024] Furthermore, various forms of processes shown in the embodiments of this disclosure can be used to reorder, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0025] In related technologies, with the rapid development of renewable energy and the gradual opening of the electricity market, DC microgrids, as an emerging power system architecture, are receiving increasing attention. They can effectively integrate renewable energy sources such as solar and wind power, and improve energy transmission efficiency through direct current. Furthermore, the optimized scheduling of DC microgrids has become crucial to ensure efficient resource utilization and stable power supply. Traditional power dispatching methods are mainly based on load forecasting and generation capacity, employing optimization algorithms such as linear programming and dynamic programming. However, these methods fail to fully consider electricity price fluctuations and changes in user electricity demand, making it difficult to meet actual user needs.

[0026] To address the aforementioned issues, this disclosure provides a DC microgrid dispatching method, apparatus, and system. The method involves acquiring the cost parameters, electricity price information, and initial demand load of the DC microgrid; inputting these parameters into a pre-constructed dispatching model; the dispatching model includes a cost model, a load demand response model, and an electricity price optimization model; the cost model optimizes the total cost, the load demand response model constrains the relationship between the dispatched load rate and the electricity price, and the load demand response model optimizes the load dispatch rate; based on preset constraints, the dispatching model is solved to obtain a load dispatching scheme; the load dispatching scheme includes a target load dispatch rate and a target dispatching period; the target load dispatch rate indicates that a portion of the initial demand load will be dispatched to the target dispatching period. By comprehensively solving the cost model, load demand response model, and electricity price optimization model, a DC microgrid load dispatching scheme that considers both the overall operating cost of the DC microgrid and electricity price fluctuations and user electricity demand can be obtained, thereby reducing user electricity costs and improving user experience.

[0027] Figure 1 This is a flowchart illustrating a DC microgrid dispatching method according to an exemplary embodiment, such as... Figure 1 As shown, it should be noted that the DC microgrid dispatching method of this application embodiment is applied in a DC microgrid dispatching device. Figure 1 As shown, the method may include the following steps:

[0028] Step 101: Obtain the cost parameters, electricity price information, and initial demand load of the DC microgrid.

[0029] In one embodiment, cost parameters may include the unit price of power interaction between the microgrid and the upper-level grid during the target period, the power interaction between the microgrid and the upper-level grid, the output power of the photovoltaic power generation system, the unit price of operation and maintenance cost per unit power of the photovoltaic power generation system, the power of the energy storage system, and the unit price of operation and maintenance cost per unit power of the energy storage system.

[0030] In one embodiment, the electricity price information may include the electricity price before the change and the electricity price after the change within the target time period. It is understood that the electricity price may change at some point within the target time period.

[0031] In one embodiment, the initial demand load can be the load corresponding to the total electrical energy currently required by the user.

[0032] Step 102: Input the cost parameters, electricity price information and initial demand load into the pre-built scheduling model.

[0033] The scheduling model includes a cost model, a load demand response model, and an electricity price optimization model. The cost model is used to optimize the total cost, the load demand response model is used to constrain the relationship between the scheduling load rate and the electricity price, and the load demand response model is used to optimize the load scheduling rate.

[0034] It is understandable that by finding the optimal solution for the scheduling model, which consists of a cost model, a load demand response model, and an electricity price optimization model, an optimal solution can be obtained that satisfies both cost savings and takes into account electricity price fluctuations and users' actual electricity demand, thereby reducing users' electricity costs.

[0035] Step 103: Based on the preset constraints, solve the scheduling model to obtain the load scheduling scheme.

[0036] The load scheduling scheme includes a target load scheduling rate and a target scheduling period; the target load scheduling rate is used to indicate that a portion of the initial demand load will be scheduled to the target scheduling period.

[0037] In one embodiment, a particle swarm optimization algorithm can be used to solve the scheduling model based on preset constraints to obtain a load scheduling scheme.

[0038] As an example, in the particle swarm optimization algorithm of this embodiment, each particle has a fitness value determined by a specific objective function, and the fitness value is used to evaluate the quality of the solution. In particle swarm optimization, the position and velocity of particles are continuously updated to optimize the population. The two key steps in the particle swarm optimization algorithm—velocity update and position update—are as follows:

[0039] v i (t+1)=ωv i(t)+c1r1(t)(p i (t)-x i (t))+c2r2(t)(p g (t)-x i (t))

[0040] x i (t+1)=x i (t)+ν i (t+1)i=1,2,…,D

[0041] Where ω is the inertia weight coefficient; c1 and c2 are learning factors, representing the particle's own cognition and social cognition, respectively; r1 and r2 represent independent random variables between [0,1] that follow a uniform distribution; p i p represents the historical best position of particle i; g This represents the optimal position of all particles in the entire population. The right side of the update rate expression consists of three parts: the first part is the inertial part, representing the tendency of particles to maintain their previous velocity; the second part is the cognitive part, reflecting the particle's memory of its own historical experience, representing the tendency of particles to approach their own historical best position; the third part is the social part, reflecting the collective historical experience of cooperation and knowledge sharing among particles, representing the tendency of particles to approach the collective or neighborhood historical best position.

[0042] Furthermore, the inertia weight coefficient is an important parameter in the particle swarm optimization algorithm, controlling the influence of the change in the previous iteration on the change in the current iteration. This invention employs a nonlinear dynamic adaptive method, which accelerates the search speed of the particle swarm optimization algorithm in the early stages of iteration.

[0043]

[0044] Where w represents the inertia weighting factor; w max w min Let w represent the maximum and minimum values ​​of w, respectively, and let f represent the fitness function of a particle. avg f represents the mean of the current population fitness function; max This represents the maximum value of the current population fitness function.

[0045] To ensure that the particle swarm optimization algorithm does not find unreasonable solutions during the search process, constraints are set to ensure that the particle velocity is not too high or too low, prevent particles from overshooting the optimal solution, avoid getting trapped in local optima, and control the search range and speed of the algorithm.

[0046] In demand response models and electricity price optimization models, it is necessary to identify the factors affecting the models, namely electricity load and grid price. Sensitivity analysis is used to determine the impact of each parameter on the overall price of the DC microgrid. By adjusting the electricity load and grid price, overall changes are observed to identify which parameters have a greater impact on the results. Based on the sensitivity analysis results, the goal of optimal load scheduling following electricity price can be achieved.

[0047] In some embodiments of this application, prior to step 101, the method further includes the following steps:

[0048] The cost model is constructed using the following formula:

[0049] min C total =C grid +C OM

[0050]

[0051] Among them, C total C represents the total operating cost of the system (in yuan). grid C represents the system's interaction cost with the upstream power grid (in yuan). OM P represents the equipment operation and maintenance cost (in yuan). grid (t) represents the interaction power (kWh) between the microgrid and the upstream grid during time period t. gird (t) represents the unit price (yuan / kWh) of the power exchange between the microgrid and the upstream grid during time period t. A positive value indicates the purchase of electricity from the upstream grid, while a negative value indicates the sale of the user's energy storage to the upstream grid (kW / h). pv (t) represents the output power (kW) of the photovoltaic power generation system during time period t, p PV The unit cost of operation and maintenance for photovoltaic power generation systems (yuan / kWh), P ESS (t) represents the power of the energy storage system, p ESS The unit power operation and maintenance cost of the electric energy storage system (RMB / kWh);

[0052] The preset constraints include:

[0053] P PV (t)+P grid (t)+P ESS (t)=P load (t)

[0054] P grid.min ≤P grid (t)≤P grid.max

[0055] P ESS.min ≤|P ESS (t)|≤P ESS.max

[0056] Among them, P load (t) represents the initial demand load during time period t, P grid.min P represents the lower limit (kW) of power interaction between the DC microgrid and the upstream grid. grid.max P represents the upper limit (kW) of power interaction between the DC microgrid and the upstream grid. ESS.min P represents the lower limit of the power output of an energy storage system (kW). ESS.max This represents the upper limit of the power output (kW) for an electric energy storage system.

[0057] It should be noted that in a DC microgrid, the power distribution of photovoltaic energy depends on the grid connection and the configuration of the energy storage system. During the day, the photovoltaic system and energy storage devices provide energy to the DC microgrid load as much as possible to reduce electricity costs; while at night, the grid supplies power to the energy storage system and user loads. However, the volatility of market prices necessitates more precise demand analysis. Therefore, demand response models and electricity price optimization models are established to adjust prices and discuss the optimal solutions for electricity load under different price conditions. Through comparative analysis, the factors affecting the microgrid are identified, maximizing the overall utilization rate of the microgrid.

[0058] In some embodiments, the demand elasticity coefficient can be defined as the ratio of the percentage change in load demand to the percentage change in electricity price, used to represent the degree to which electricity demand changes with its price, as shown in the following formula:

[0059]

[0060] ΔP load =P′ load -P load

[0061] Δρ=ρ′-ρ

[0062] Where, ε e Let ΔP be the demand elasticity coefficient for electricity and electricity price. load P represents the change in electrical load (kW). load For the original electrical load (kW), P′ load Let ρ be the electricity load (kW) after the price change, Δρ be the change in electricity price (yuan / kWh), ρ be the original electricity price (yuan / kWh), and ρ′ be the electricity price after the change (yuan / kWh).

[0063] In some embodiments of this application, prior to step 101, the method further includes the following steps:

[0064] The load demand response model is constructed using the following formula:

[0065]

[0066] Where, ε e (t,t) represents the self-elasticity coefficient at time t, ε e (s,t) represents the cross-elasticity coefficient of time period s with respect to time period t, P′ load (t) represents the load (kW) in time period t after scheduling the initial demand load, P load (t) represents the initial demand load (kW) for time period t; ε e (t,t) represents the self-elasticity coefficient at time t, ε e (s,t) represents the cross elasticity coefficient of time period s to time period t, ρ(t) is the electricity price before the change in time period t (yuan / kWh), ρ(s) is the electricity price before the change in time period s (yuan / kWh), ρ′(t) is the electricity price after the change in time period t (yuan / kWh), and ρ′(s) is the electricity price after the change in time period s (yuan / kWh).

[0067] It should be noted that user electricity load can be divided into two categories based on whether it has time-shifting characteristics: demand that cannot be shifted to other time periods is called "self-demand elasticity"; demand that can be shifted from peak hours to off-peak hours is called "cross-elasticity demand". For example, the usage time of electrical appliances such as washing machines and water heaters can be changed, that is, it can be shifted, while the power consumption of items such as lighting cannot be shifted, that is, lighting must be used at night.

[0068] In some embodiments of this application, prior to step 101, the method further includes the following steps:

[0069] The electricity price optimization model is constructed using the following objective function f:

[0070]

[0071] Among them, P′ load (i) represents the electrical load for time period i after scheduling the initial demand load, P′ load (i+1) represents the electrical load during the i+1 time period after scheduling the initial demand load;

[0072] The constraints on the objective function f include:

[0073] p′ min ≤p′≤p′ max

[0074]

[0075] Where, p′ max p′ is the upper limit of the real-time electricity price. minThe lower limit of the real-time electricity price is given by r, which represents the allowable limit of the total load change rate after the implementation of the real-time electricity price; ρ′(i) is the electricity price after the change in time period i, and ρ(i) is the electricity price before the change in time period i. load (i) represents the initial demand load for time period i, and n represents the n time periods.

[0076] As prices fluctuate, the power of the regulator changes with the grid connection price, ultimately maximizing the overall power load utilization of the microgrid.

[0077] In some embodiments of this application, the load dispatching scheme further includes user electricity costs, and after step 103, the method further includes the following steps:

[0078] The system outputs the user's electricity cost, target load dispatch rate, and target dispatch period, so that the user can adjust their electricity usage time based on these parameters.

[0079] In some embodiments of this application, demand analysis can be performed on the scheduling of electricity loads under different prices to identify factors affecting the overall benefits of the DC microgrid and to more accurately assess the optimal load scheduling of the microgrid.

[0080] According to the DC microgrid dispatching method proposed in this application, the cost parameters, electricity price information, and initial demand load of the DC microgrid are obtained. These parameters are then input into a pre-constructed dispatching model. The dispatching model includes a cost model, a load demand response model, and an electricity price optimization model. The cost model optimizes the total cost, the load demand response model constrains the relationship between the dispatched load rate and the electricity price, and the load demand response model optimizes the load dispatch rate. Based on preset constraints, the dispatching model is solved to obtain a load dispatching scheme. The load dispatching scheme includes a target load dispatch rate and a target dispatching period. The target load dispatch rate indicates that a portion of the initial demand load will be dispatched to the target dispatching period. By comprehensively solving the cost model, the load demand response model, and the electricity price optimization model, a DC microgrid load dispatching scheme that considers both the overall operating cost of the DC microgrid and electricity price fluctuations and user electricity demand can be obtained, thereby reducing user electricity costs and improving user experience. Furthermore, dispatching optimization based on real-time electricity prices mitigates peak-shaving conflicts in the electricity load.

[0081] Figure 2 This is a block diagram of a DC microgrid dispatching device according to an exemplary embodiment. (Refer to...) Figure 2 The device includes an acquisition unit 201, an input unit 202, and a solution unit 203.

[0082] Among them, the acquisition unit 201 is used to acquire the cost parameters, electricity price information and initial demand load of the DC microgrid;

[0083] Input unit 202 is used to input the cost parameters, electricity price information and initial demand load into a pre-built scheduling model; the scheduling model includes a cost model, a load demand response model and an electricity price optimization model; the cost model is used to optimize the total cost, the load demand response model is used to constrain the correlation between the scheduling load rate and the electricity price, and the load demand response model is used to optimize the load scheduling rate;

[0084] The solution unit 203 is used to solve the scheduling model based on preset constraints to obtain a load scheduling scheme; the load scheduling scheme includes a target load scheduling rate and a target scheduling period; the target load scheduling rate is used to indicate that a portion of the initial demand load is scheduled to the target scheduling period.

[0085] In some embodiments of this application, the apparatus further includes a construction model for constructing the cost model using the following formula:

[0086] min C total =C grid +C OM

[0087]

[0088] Among them, C total C represents the total operating cost of the system. grid For the system's interaction cost with the upper-level power grid, C OM For equipment operation and maintenance costs, P grid (t) represents the interaction power between the microgrid and the upstream grid during time period t, p gird (t) represents the unit price of power interaction between the microgrid and the upstream grid during time period t, P pv (t) represents the output power of the photovoltaic power generation system during time period t, p PV P represents the unit cost of operation and maintenance for a photovoltaic power generation system. ESS (t) represents the power of the energy storage system, p ESS The unit power operation and maintenance cost of the energy storage system;

[0089] The preset constraints include:

[0090] P PV (t)+P grid (t)+P ESS (t)=P load (t)

[0091] P grid.min ≤P grid (t)≤Pgrid.max

[0092] P ESS.min ≤|P ESS (t)|≤P ESS.max

[0093] Among them, P load (t) represents the initial demand load during time period t, P grid.min P represents the lower limit of power interaction between the DC microgrid and the upstream grid. grid.max P represents the upper limit of power interaction between the DC microgrid and the upstream grid. ESS.min P is the lower limit of the power of the energy storage system. ESS.max This represents the upper limit of the power of the energy storage system.

[0094] In some embodiments of this application, the model is also used to construct the load demand response model using the following formula:

[0095]

[0096]

[0097] Where, ε e (t,t) represents the self-elasticity coefficient at time t, ε e (s,t) represents the cross-elasticity coefficient of time period s with respect to time period t, P′ load (t) represents the electrical load during time period t after scheduling the initial demand load, P load (t) represents the initial demand load during time period t; ε e (t,t) represents the self-elasticity coefficient at time t, ε e (s,t) represents the cross elasticity coefficient of time period s to time period t, ρ(t) is the electricity price before the change in time period t, ρ(s) is the electricity price before the change in time period s, ρ′(t) is the electricity price after the change in time period t, and ρ′(s) is the electricity price after the change in time period s.

[0098] In some embodiments of this application, the model is also used to construct the electricity price optimization model through the following objective function f:

[0099]

[0100] Among them, P′ load (i) represents the electrical load for time period i after scheduling the initial demand load, P′ load (i+1) represents the electrical load during the i+1 time period after scheduling the initial demand load;

[0101] The constraints on the objective function f include:

[0102] p′min ≤p′≤p′ max

[0103]

[0104] Where, p′ max p′ is the upper limit of the real-time electricity price. min The lower limit of the real-time electricity price is given by r, which represents the allowable limit of the total load change rate after the implementation of the real-time electricity price; ρ′(i) is the electricity price after the change in time period i, and ρ(i) is the electricity price before the change in time period i. load (i) represents the initial demand load for time period i, and n represents the n time periods.

[0105] In some embodiments of this application, the load dispatching scheme further includes user electricity costs, and the device further includes:

[0106] The output unit is used to output the user's electricity cost, target load dispatch rate, and target dispatch period, so that the user can adjust the electricity consumption time according to the user's electricity cost, target load dispatch rate, and target dispatch period.

[0107] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0108] According to the DC microgrid dispatching device proposed in this application, the cost parameters, electricity price information, and initial demand load of the DC microgrid are obtained. These parameters are then input into a pre-constructed dispatching model. The dispatching model includes a cost model, a load demand response model, and an electricity price optimization model. The cost model optimizes the total cost, the load demand response model constrains the relationship between the dispatched load rate and the electricity price, and the load demand response model optimizes the load dispatch rate. Based on preset constraints, the dispatching model is solved to obtain a load dispatching scheme. The load dispatching scheme includes a target load dispatch rate and a target dispatching period. The target load dispatch rate indicates that a portion of the initial demand load will be dispatched to the target dispatching period. By comprehensively solving the cost model, the load demand response model, and the electricity price optimization model, a DC microgrid load dispatching scheme that considers both the overall operating cost of the DC microgrid and electricity price fluctuations and user electricity demand can be obtained, thereby reducing user electricity costs and improving user experience.

[0109] Figure 3This is a block diagram illustrating an apparatus for a DC microgrid dispatching method according to an exemplary embodiment. For example, apparatus 300 may be an electronic device, such as a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0110] Reference Figure 3 The device 300 may include one or more of the following components: a processing component 302, a memory 304, a power component 306, a multimedia component 308, an audio component 310, an input / output (I / O) interface 312, a sensor component 314, and a communication component 316.

[0111] Processing component 302 typically controls the overall operation of device 300, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 302 may include one or more processors 320 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 302 may include one or more modules to facilitate interaction between processing component 302 and other components. For example, processing component 302 may include a multimedia module to facilitate interaction between multimedia component 308 and processing component 302.

[0112] Memory 304 is configured to store various types of data to support the operation of device 300. Examples of this data include instructions for any application or method operating on device 300, contact data, phonebook data, messages, pictures, videos, etc. Memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0113] The power supply component 306 provides power to the various components of the device 300. The power supply component 306 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 300.

[0114] Multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 308 includes a front-facing camera and / or a rear-facing camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0115] Audio component 310 is configured to output and / or input audio signals. For example, audio component 310 includes a microphone (MIC) configured to receive external audio signals when device 300 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 304 or transmitted via communication component 316. In some embodiments, audio component 310 also includes a speaker for outputting audio signals.

[0116] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0117] Sensor assembly 314 includes one or more sensors for providing status assessments of various aspects of device 300. For example, sensor assembly 314 may detect the on / off state of device 300, the relative positioning of components such as the display and keypad of device 300, changes in the position of device 300 or a component of device 300, the presence or absence of user contact with device 300, the orientation or acceleration / deceleration of device 300, and temperature changes of device 300. Sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 314 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0118] Communication component 316 is configured to facilitate wired or wireless communication between device 300 and other devices. Device 300 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 316 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 316 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0119] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0120] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, which can be executed by a processor 320 of the device 300 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0121] In an exemplary embodiment, a computer program product is also provided, including a computer program that implements the above-described method when executed by the processor 320 of the device 300.

[0122] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0123] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A DC microgrid dispatching method, characterized in that, include: Obtain cost parameters, electricity price information, and initial demand load of DC microgrids; The cost parameters, electricity price information, and initial demand load are input into a pre-built scheduling model. The scheduling model includes a cost model, a load demand response model, and an electricity price optimization model. The cost model is used to optimize the total cost, the load demand response model is used to constrain the relationship between the scheduling load rate and the electricity price, and the load demand response model is used to optimize the load scheduling rate. Based on preset constraints, the scheduling model is solved to obtain a load scheduling scheme; the load scheduling scheme includes a target load scheduling rate and a target scheduling period; the target load scheduling rate is used to indicate that a portion of the initial demand load is scheduled to the target scheduling period.

2. The DC microgrid dispatching method according to claim 1, characterized in that, Before obtaining the cost parameters, electricity price information, and initial demand load of the DC microgrid, the following steps are also included: The cost model is constructed using the following formula: my C total =C grid +C OM Among them, C total C represents the total operating cost of the system. grid For the system's interaction cost with the upper-level power grid, C OM For equipment operation and maintenance costs, P grid (t) represents the interaction power between the microgrid and the upstream grid during time period t, p gird (t) represents the unit price of power interaction between the microgrid and the upstream grid during time period t, P pv (t) represents the output power of the photovoltaic power generation system during time period t, p PV P represents the unit cost of operation and maintenance for a photovoltaic power generation system. ESS (t) represents the power of the energy storage system, p ESS The unit power operation and maintenance cost of the energy storage system; The preset constraints include: P PV (t)+P grid (t)+P ESS (t)=P load (t) P grid.min ≤P grid (t)≤P grid.max P ESS.min ≤|P ESS (t)|≤P ESS.max Among them, P load (t) represents the initial demand load during time period t, P grid.min P represents the lower limit of power interaction between the DC microgrid and the upstream grid. grid.max P represents the upper limit of power interaction between the DC microgrid and the upstream grid. ESS.min P is the lower limit of the power of the energy storage system. ESS.max This represents the upper limit of the power of the energy storage system.

3. The DC microgrid dispatching method according to claim 1, characterized in that, Before obtaining the cost parameters, electricity price information, and initial demand load of the DC microgrid, the following steps are also included: The load demand response model is constructed using the following formula: Where, ε e (t,t) represents the self-elasticity coefficient at time t, ε e (s,t) represents the cross-elasticity coefficient of time period s with respect to time period t, P′ load (t) represents the electrical load during time period t after scheduling the initial demand load, P load (t) represents the initial demand load during time period t; ε e (t,t) represents the self-elasticity coefficient at time t, ε e (s,t) represents the cross elasticity coefficient of time period s to time period t, ρ(t) is the electricity price before the change in time period t, ρ(s) is the electricity price before the change in time period s, ρ′(t) is the electricity price after the change in time period t, and ρ′(s) is the electricity price after the change in time period s.

4. The DC microgrid dispatching method according to claim 1, characterized in that, Before obtaining the cost parameters, electricity price information, and initial demand load of the DC microgrid, the following steps are also included: The electricity price optimization model is constructed using the following objective function f: Among them, P l ′oad(i) represents the electrical load in time period i after scheduling the initial demand load, P l ′oad(i+1) is the electrical load in time period i+1 after scheduling the initial demand load; The constraints on the objective function f include: p′ min ≤p′≤p′ max Where, p′ max p′ is the upper limit of the real-time electricity price. min The lower limit of the real-time electricity price is given by r, which represents the allowable limit of the total load change rate after the implementation of the real-time electricity price. ρ′(i) is the electricity price after the change in time period i, and ρ(i) is the electricity price before the change in time period i. P load (i) represents the initial demand load for time period i, and n represents the n time periods.

5. The DC microgrid dispatching method according to claim 1, characterized in that, The load dispatching scheme also includes user electricity costs; After solving the scheduling model based on preset constraints to obtain the target load scheduling rate and target scheduling period, the method further includes: The system outputs the user's electricity cost, target load dispatch rate, and target dispatch period, so that the user can adjust their electricity usage time based on these parameters.

6. A DC microgrid dispatching device, characterized in that, include: The acquisition unit is used to acquire cost parameters, electricity price information, and initial demand load of the DC microgrid. An input unit is used to input the cost parameters, electricity price information, and initial demand load into a pre-built scheduling model; the scheduling model includes a cost model, a load demand response model, and an electricity price optimization model; the cost model is used to optimize the total cost, the load demand response model is used to constrain the relationship between the scheduling load rate and the electricity price, and the load demand response model is used to optimize the load scheduling rate; The solution unit is used to solve the scheduling model based on preset constraints to obtain a load scheduling scheme; the load scheduling scheme includes a target load scheduling rate and a target scheduling period; the target load scheduling rate is used to indicate that a portion of the initial demand load is scheduled to the target scheduling period.

7. The DC microgrid dispatching device according to claim 6, characterized in that, The load dispatching scheme also includes user electricity costs, and the device also includes: The output unit is used to output the user's electricity cost, target load dispatch rate, and target dispatch period, so that the user can adjust the electricity consumption time according to the user's electricity cost, target load dispatch rate, and target dispatch period.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method as described in any one of claims 1 to 5.

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