Micro-grid scheduling method and device and storage medium

Abstract

The invention discloses a micro-grid scheduling method and device and a storage medium. The method comprises the following steps: acquiring environment reference data of a micro-grid; determining a target function based on the scheduling demand of the micro-grid and the photovoltaic consumption mode; wherein the scheduling demand comprises an economical demand and a photovoltaic consumption demand of micro-grid operation; the photovoltaic absorption mode comprises a self-sustaining photovoltaic mode and a non-self-sustaining photovoltaic mode; solving the objective function based on the environment reference data and a preset constraint condition to obtain a scheduling result of the micro-grid; wherein the preset constraint condition comprises one or a combination of a constraint condition of incoming line power, a constraint condition of an energy storage device and a constraint condition of demand. Based on the scheme, the optimization performance of scheduling can be improved, so that the scheduling result can be widely applied to different use scenes, and the applicability of the scheduling result is effectively improved.

Description

A microgrid scheduling method, device, and storage medium Technical Field

[0001] This application relates to the field of power technology, and in particular to a microgrid dispatching method, device and storage medium. Background Technology

[0002] A microgrid is a small-scale power generation and distribution system consisting of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc.

[0003] Currently, when scheduling the operation of microgrids, there is a common problem of having a single scheduling objective. For example, only the economic efficiency of microgrid operation is considered, while other scheduling objectives are not taken into account. In addition, the setting of constraints is too regular and has strong limitations, making it difficult to obtain the globally optimal scheduling result, and thus the scheduling result cannot be applied to different application scenarios. Summary of the Invention

[0004] This application provides a microgrid scheduling method, device, and storage medium, which can improve the optimization performance of scheduling and enable the scheduling results to be widely applied to different application scenarios, effectively improving the applicability of the scheduling results.

[0005] The technical solution of this application embodiment is implemented as follows:

[0006] In a first aspect, embodiments of this application provide a microgrid scheduling method, the method comprising:

[0007] Obtain environmental reference data for the microgrid;

[0008] The objective function is determined based on the microgrid's dispatch requirements and photovoltaic (PV) integration modes; whereby the dispatch requirements include the economic requirements for microgrid operation and the PV integration requirements; and the PV integration modes include self-sustaining PV modes and non-self-sustaining PV modes.

[0009] The objective function is solved based on environmental reference data and preset constraints to obtain the dispatch results of the microgrid. The preset constraints include one or a combination of constraints on incoming power, constraints on energy storage devices, and constraints on demand.

[0010] In this embodiment, when scheduling the microgrid, the dispatching device can first acquire the microgrid's environmental reference data. Simultaneously, based on the current microgrid dispatching requirements for economy and photovoltaic (PV) consumption, and considering the microgrid's PV consumption mode (including self-sufficient and non-self-sufficient PV modes), it determines the objective function for dispatching. This allows for consideration of both economic efficiency and PV consumption objectives. Furthermore, the objective function can be solved using the environmental reference data and preset constraints. During the solution process, the preset constraints can include one or a combination of constraints on incoming power, energy storage devices, and demand, overcoming the limitations and regularity of constraints in current dispatching methods. Through this dispatching method, a globally optimal dispatching result can be obtained, making the result widely applicable to different usage scenarios and effectively improving the applicability of the dispatching result.

[0011] In some embodiments of this application, the objective function is determined based on the microgrid's dispatch requirements and photovoltaic consumption patterns, including:

[0012] The weight information corresponding to each preset calculation item is determined based on the microgrid's dispatch requirements and photovoltaic (PV) consumption mode. Each preset calculation item includes an electricity cost calculation item, a PV consumption amount calculation item, a PV consumption fee calculation item, and a charge / discharge constraint item. The electricity cost calculation item includes an actual electricity consumption cost item and a demand cost item.

[0013] The objective function is determined based on each preset calculation item and its corresponding weight information.

[0014] In this embodiment, when determining the objective function for multiple scheduling objectives, the weights of the electricity cost calculation item, the photovoltaic absorption capacity calculation item, and the photovoltaic absorption fee calculation item are first determined based on the microgrid's scheduling requirements and photovoltaic absorption mode. Then, the objective function is constructed based on these weights, the electricity cost calculation item, the photovoltaic absorption capacity calculation item, the photovoltaic absorption fee calculation item, and the charging and discharging constraint item. Thus, the weights of these scheduling objectives as calculation items in the objective function can be determined according to the demand level of each scheduling objective when scheduling the microgrid, thereby improving the adaptability of the objective function.

[0015] In some embodiments of this application, the environmental reference data includes incoming power constraint values; the scheduling result includes a first scheduling result characterizing the incoming power of the microgrid during the scheduling target time period; the scheduling result of the microgrid is obtained by solving the objective function based on the environmental reference data and preset constraints, including:

[0016] The objective function is solved based on the incoming power constraint value and the incoming power constraint condition to obtain the first scheduling result;

[0017] The incoming power constraints include a first incoming power constraint, a second incoming power constraint, and a third incoming power constraint; the incoming power constraint values ​​include at least one of a first threshold, a second threshold, and the load demand power; the first incoming power constraint represents a constraint that the mains incoming power is less than or equal to the first threshold; the second incoming power constraint represents a constraint that the mains incoming power is greater than or equal to the second threshold; and the third incoming power constraint represents a constraint that the mains incoming power is greater than or equal to the load demand power.

[0018] In this embodiment, the microgrid dispatching device can constrain the incoming power according to the constraints of the incoming power during the process of solving the objective function. This includes constraining the maximum power of the mains incoming power to be less than or equal to a first threshold, the minimum power to be greater than or equal to a second threshold, and constraining the mains incoming power to meet the load demand power. This can make the dispatching result of the incoming power meet the above constraints on the incoming power and improve the adaptability of the dispatching result.

[0019] In some embodiments of this application, the environmental reference data includes photovoltaic power output prediction data and load power prediction data; the method further includes:

[0020] Acquire historical photovoltaic (PV) output data and historical load power data of the microgrid; wherein, historical PV output data represents the PV output data of the microgrid within the dates prior to the target scheduling period; historical load power data represents the load power data of the microgrid within the dates prior to the target scheduling period.

[0021] Based on historical photovoltaic power output data, the photovoltaic power output of the microgrid during the target scheduling period is predicted to obtain photovoltaic power output prediction data.

[0022] Based on historical load power data, the load power of the microgrid during the target scheduling period is predicted to obtain load power prediction data.

[0023] In this embodiment, the photovoltaic output prediction data and load power prediction data in the environmental reference data are predicted based on the historical photovoltaic output data and historical load power data, respectively. In other words, this application can first predict the photovoltaic output data and load power data of the microgrid within the scheduling date, and then perform scheduling based on the predicted photovoltaic output data and load power data to improve the accuracy of scheduling.

[0024] In some embodiments of this application, the method further includes:

[0025] The first power is determined based on the discharge power and charging power of the energy storage device;

[0026] The production capacity is determined based on the first power and photovoltaic output forecast data;

[0027] The load demand power is determined based on load power forecast data and production capacity.

[0028] In this embodiment, when determining the load demand power, a first power can be determined based on the discharge power and charging power of the energy storage device. Then, the production capacity power can be determined using the first power and photovoltaic output prediction data. Thus, the load demand power can be determined using the load power prediction data and production capacity power, which enables accurate calculation of the load demand of the microgrid during the scheduling target period.

[0029] In some embodiments of this application, the constraints on the energy storage device include charging and discharging power constraints, energy constraints, and charging and discharging behavior constraints; the environmental reference data includes charging and discharging power constraints and energy constraints of the energy storage device; the scheduling result includes a second scheduling result characterizing the charging and discharging power of the energy storage device in the microgrid during the scheduling target time period; the scheduling result of the microgrid is obtained by solving the objective function based on the environmental reference data and preset constraints, including:

[0030] The objective function is solved based on the charging and discharging power constraints, charging and discharging power constraints, energy constraints, energy constraints, and charging and discharging behavior constraints to obtain the second scheduling result.

[0031] Among them, the charging and discharging power constraint conditions include charging power constraint conditions and discharging power constraint conditions; the charging and discharging power constraint values ​​include charging power constraint values ​​and discharging power constraint values; the charging power constraint conditions characterize the constraint conditions under which the charging power of the energy storage device is less than or equal to the charging power constraint value; the discharging power constraint conditions characterize the constraint conditions under which the discharging power of the energy storage device is less than or equal to the discharging power constraint value.

[0032] The power constraint values ​​include a fourth threshold and a fifth threshold; the power constraint conditions of the energy storage device characterize the constraint conditions that the power of the energy storage device is greater than or equal to the fourth threshold and less than or equal to the fifth threshold.

[0033] The charging and discharging behavior constraints characterize the constraints that prevent the energy storage device from charging in the first time period and discharging in the second time period.

[0034] In this embodiment, during the process of solving the objective function, the charging power and discharging power of the energy storage device can be constrained according to the charging and discharging power constraints of the energy storage device. This includes constraining the charging power of the energy storage device to be less than or equal to the charging power constraint value, and constraining the discharging power of the energy storage device to be less than or equal to the discharging power constraint value. The energy capacity of the energy storage device can also be constrained according to the energy capacity constraint condition. At the same time, the energy storage device can be constrained to charge within a fixed time period. This allows the scheduling results of the charging power and discharging power of the energy storage device to meet the above-mentioned constraints on the energy storage device, thereby improving the adaptability of the scheduling results.

[0035] In some embodiments of this application, the environmental reference data includes time-of-use electricity price data; the method further includes:

[0036] The time period in the time-of-use electricity price data that meets the peak electricity price conditions is determined as the first time period;

[0037] The time period in the time-of-use electricity price data that meets the off-peak electricity price criteria is designated as the second time period;

[0038] The charging and discharging behavior constraints are determined based on the first time period and the second time period.

[0039] In this embodiment, the first time period and the second time period can be determined using time-of-use electricity price data in the environmental reference data, and charging and discharging behavior constraints can be determined based on the first time period and the second time period. Thus, during the scheduling process, the charging and discharging behavior of the energy storage device can be constrained based on the charging and discharging behavior constraints, so that the energy storage device does not charge during peak electricity price periods and does not discharge during off-peak electricity price periods, effectively improving the economic efficiency of microgrid operation.

[0040] In some embodiments of this application, the environmental reference data includes the historical maximum demand and the demand at the current operating time; the demand constraints include a first demand constraint and a second demand constraint; the scheduling result includes a third scheduling result characterizing the microgrid's demand during the target scheduling period; the scheduling result of the microgrid is obtained by solving the objective function based on the environmental reference data and preset constraints, including:

[0041] The objective function is solved based on the historical maximum demand, the first demand constraint, the demand at the current running time, and the second demand constraint to obtain the third scheduling result;

[0042] The first demand constraint condition represents the constraint condition that the demand is greater than or equal to the historical maximum demand value; the second demand constraint condition represents the constraint condition that the demand is greater than or equal to the demand at the current running time.

[0043] In this embodiment, during the process of solving the objective function, the demand can be constrained according to the demand constraint conditions, including constraining the demand to be greater than or equal to the maximum value among the historical demand power, and the demand to be greater than or equal to the current running time. This enables the demand scheduling result to meet the above-mentioned demand constraint conditions, thereby improving the adaptability of the scheduling result.

[0044] In some embodiments of this application, the method further includes:

[0045] The average value of the incoming power within the preset sliding time window is determined based on the incoming power within the current operating time.

[0046] The demand for the current operating time is determined based on the average incoming power.

[0047] In this embodiment, when determining the demand for the current running time, the average value of the incoming power can be determined first using a preset sliding time window and the demand within the current running time. Then, the demand for the current running time can be determined from the average value of the incoming power, thus achieving accurate calculation of the demand for the current running time.

[0048] Secondly, embodiments of this application provide a microgrid scheduling device, including an acquisition unit and a determination unit;

[0049] Acquisition unit, used to acquire environmental reference data of the microgrid;

[0050] The determination unit is used to determine the objective function based on the microgrid's dispatch requirements and photovoltaic (PV) consumption modes; wherein, the dispatch requirements include the economic requirements for microgrid operation and the PV consumption requirements; the PV consumption modes include self-sustaining PV modes and non-self-sustaining PV modes; and to solve the objective function based on environmental reference data and preset constraints to obtain the microgrid's dispatch results; wherein, the preset constraints include one or a combination of constraints on incoming power, constraints on energy storage devices, and constraints on demand.

[0051] In this embodiment, when scheduling the microgrid, the microgrid scheduling device can first acquire the microgrid's environmental reference data. Simultaneously, based on the current microgrid scheduling requirements for economic efficiency and photovoltaic (PV) consumption, as well as the microgrid's PV consumption mode (including self-sufficient and non-self-sufficient PV modes), the objective function of the scheduling is determined. This allows for consideration of both economic efficiency and PV consumption objectives during scheduling. Furthermore, the objective function can be solved using the environmental reference data and preset constraints. During the solution process, the preset constraints can include one or a combination of constraints on incoming power, energy storage devices, and demand, overcoming the limitations and regularity of constraints in current scheduling methods. Through this scheduling method, a globally optimal scheduling result can be obtained, making the scheduling result widely applicable to different usage scenarios and effectively improving the applicability of the scheduling result.

[0052] Thirdly, embodiments of this application provide a microgrid scheduling device, including a processor and a memory storing processor-executable instructions; when the instructions are executed by the processor, the above-described scheduling method is implemented.

[0053] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described scheduling method. Attached Figure Description

[0054] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application.

[0055] Figure 1 is a schematic diagram of the implementation process of the scheduling method proposed in this application.

[0056] Figure 2 is a schematic diagram of the time-of-use electricity price data proposed in an embodiment of this application;

[0057] Figure 3 is a schematic diagram of the implementation process of the scheduling method proposed in the embodiments of this application;

[0058] Figure 4 is a schematic diagram of the scheduling results proposed in the embodiments of this application;

[0059] Figure 5 is a schematic diagram of the composition structure of the microgrid dispatching device proposed in the embodiment of this application;

[0060] Figure 6 is a schematic diagram of the composition structure of the microgrid dispatching device proposed in the embodiment of this application. Detailed Implementation

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining the relevant application and not for limiting the application. Furthermore, it should be noted that, for ease of description, only the parts related to the relevant application are shown in the accompanying drawings.

[0062] As an emerging form of energy management, microgrids have developed rapidly in recent years. To ensure the safe, stable and economical operation of microgrid systems, it is crucial to provide reasonable energy dispatch strategies through multi-mode energy photovoltaic and energy storage systems.

[0063] Currently, when scheduling the operation of microgrids, the scheduling objective is singular, and the optimization objective only considers economic efficiency without taking into account photovoltaic (PV) consumption or PV consumption modes. Typically, a fixed demand is required, making it impossible to flexibly balance demand-based electricity costs and consumption-based electricity costs. Furthermore, the constraints are set too regularly, with strong limitations, making them unusable and unable to predict the operation and scheduling of microgrids on a future day, thus failing to achieve global optimization.

[0064] To address the current problems in microgrid scheduling, this application proposes a microgrid scheduling method, apparatus, and storage medium. The microgrid scheduling apparatus can acquire environmental reference data of the microgrid; determine an objective function based on the microgrid's scheduling requirements and photovoltaic (PV) consumption modes; wherein the scheduling requirements include the economic requirements of microgrid operation and PV consumption requirements; and the PV consumption modes include self-sustaining PV modes and non-self-sustaining PV modes; solve the objective function based on the environmental reference data and preset constraints to obtain the microgrid scheduling result; wherein the preset constraints include one or a combination of constraints on incoming power, constraints on energy storage devices, and constraints on demand, thereby considering both economic efficiency and PV consumption scheduling objectives, overcoming the shortcomings of rule-based and limited constraints, and obtaining the globally optimal scheduling result for the specified scheduling date. This allows the scheduling result to be widely applied to different usage scenarios, effectively improving the applicability of the scheduling result.

[0065] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0066] One embodiment of this application provides a microgrid scheduling method. As shown in FIG1, the microgrid scheduling method may include the following steps:

[0067] Step 101: Obtain environmental reference data for the microgrid.

[0068] In the embodiments of this application, the microgrid scheduling device can acquire environmental reference data of the microgrid.

[0069] In the embodiments of this application, the microgrid scheduling device can be any electronic device with communication and storage functions, such as a computer, laptop computer, or other electronic device.

[0070] In embodiments of this application, a microgrid may include energy storage devices, which may include batteries. Batteries may be assembled from one or more battery cells; a battery may be a single battery cell. A single battery cell is a basic unit capable of converting chemical energy into electrical energy, and can be used to manufacture battery modules or battery packs for supplying power to electrical devices. A battery may also be a single physical module comprising one or more battery cells to provide higher voltage and capacity. When there are multiple battery cells, they are connected in series, parallel, or in a mixed configuration via a busbar.

[0071] In embodiments of this application, the microgrid may include photovoltaic devices that can be used for photovoltaic power generation.

[0072] In embodiments of this application, the microgrid may include an energy storage device that can be used for adaptive charging and discharging.

[0073] In the embodiments of this application, the environmental reference data may include at least one of the following: photovoltaic output forecast data, load power forecast data, incoming power constraint value, time-of-use electricity price data, demand electricity price data, photovoltaic consumption electricity price data under non-self-owned photovoltaic mode, charging efficiency data of energy storage device, discharging efficiency data of energy storage device, and historical maximum demand value.

[0074] In some embodiments of this application, when the microgrid dispatching device acquires photovoltaic power output prediction data and load power prediction data, it can first acquire historical photovoltaic power output data and historical load power data of the microgrid. The historical photovoltaic power output data represents the photovoltaic power output data of the microgrid within a date prior to the target dispatching period; the historical load power data represents the load power data of the microgrid within a date prior to the target dispatching period. Then, based on the historical photovoltaic power output data, the photovoltaic power output of the microgrid within the target dispatching period is predicted to obtain photovoltaic power output prediction data; and based on the historical load power data, the load power of the microgrid within the target dispatching period is predicted to obtain load power prediction data.

[0075] In the embodiments of this application, the scheduling target time period represents the target time period for scheduling the microgrid; the scheduling target time period can be a time period after the current time; for example, if the current time is xx year xx month 1, and the microgrid needs to be scheduled on xx year xx month 2, then the scheduling target time period is xx year xx month 2.

[0076] In the embodiments of this application, photovoltaic power output prediction data can be presented in the form of a graph, that is, photovoltaic power output prediction data can be a photovoltaic power output prediction graph, which can reflect the trend of photovoltaic power output changing over time.

[0077] In the embodiments of this application, the load power prediction data can also be presented in the form of a curve, that is, the load power prediction data can be a load power prediction curve, which can reflect the trend of the load power of the microgrid changing over time.

[0078] In some embodiments of this application, the incoming power constraint value represents the maximum value of the mains incoming power on a date prior to the scheduling target time period.

[0079] In some embodiments of this application, the time-of-use electricity price data can be the time-of-use electricity price of the area where the microgrid is located, that is, the electricity cost of the area where the microgrid is located during each time period of 24 hours a day; for example, assuming that the time-of-use electricity price data corresponding to the microgrid is as shown in Figure 2, for example, in the time-of-use electricity price data, the electricity price during the peak period is 1.4 yuan.

[0080] In some embodiments of this application, the demand electricity price data is usually a fixed value, such as x yuan per kilowatt; the demand is calculated based on the maximum power required during the electricity consumption period, which can be the maximum value of the average power over a specific measurement period, such as 15 minutes.

[0081] In some embodiments of this application, the photovoltaic (PV) consumption mode of a microgrid can include two types: self-owned PV and non-self-owned PV. When the PV consumption mode of a microgrid is self-owned PV, it means that the microgrid can meet the energy supply of PV on its own, and the consumed PV is free electricity. Non-self-owned PV means that the consumed PV needs to pay electricity fees to the PV owner. Therefore, if the PV consumption mode of a microgrid is non-self-owned PV, it is necessary to obtain PV consumption electricity fee data, that is, data on the electricity fees paid to the PV owner when consuming PV.

[0082] In some embodiments of this application, the charging efficiency data and discharging efficiency data of the energy storage device can be represented by coefficients; for example, the charging efficiency data of the energy storage device is represented by coefficient μc; and the discharging efficiency data of the energy storage device is represented by coefficient μd.

[0083] In some embodiments of this application, the historical maximum demand can be the maximum demand of the microgrid within a historical period, i.e., within the period before the scheduling date. For example, the historical maximum demand can be represented as P. de_hist .

[0084] Step 102: Determine the objective function based on the microgrid's dispatch requirements and photovoltaic (PV) consumption modes; wherein, the dispatch requirements include the economic requirements for microgrid operation and the PV consumption requirements; and the PV consumption modes include self-sustaining PV modes and non-self-sustaining PV modes.

[0085] In the embodiments of this application, after obtaining the environmental reference data of the microgrid, the microgrid dispatching device can determine the objective function based on the dispatching requirements of the microgrid and the photovoltaic consumption mode; wherein, the dispatching requirements include the economic requirements for microgrid operation and the photovoltaic consumption requirements; the photovoltaic consumption mode includes the self-sustaining photovoltaic mode and the non-self-sustaining photovoltaic mode.

[0086] In the embodiments of this application, economic demand can be understood as whether economic efficiency is given priority in scheduling; photovoltaic consumption demand can be understood as whether photovoltaic consumption is given priority in scheduling.

[0087] In some embodiments of this application, when the microgrid dispatching device determines the objective function based on the microgrid's dispatching requirements and photovoltaic (PV) consumption mode, it can determine the weight information corresponding to each preset calculation item based on the microgrid's dispatching requirements and PV consumption mode. Each preset calculation item includes an electricity cost calculation item, a PV consumption amount calculation item, a PV consumption fee calculation item, and a charge / discharge constraint item. The electricity cost calculation item includes an actual electricity consumption fee item and a demand electricity fee item. Thus, the objective function is determined based on each preset calculation item and its corresponding weight information.

[0088] In the embodiments of this application, the electricity cost calculation term in the objective function can correspond to economic demand; the photovoltaic power consumption calculation term can correspond to photovoltaic power consumption demand.

[0089] In the embodiments of this application, if the economic demand is higher than the photovoltaic consumption demand, the weight information corresponding to the electricity cost calculation item is greater than the weight information corresponding to the photovoltaic consumption calculation item.

[0090] In the embodiments of this application, the photovoltaic grid connection fee calculation item depends on the photovoltaic grid connection mode; when the photovoltaic grid connection mode is a self-owned photovoltaic mode, since there is no need to pay grid connection fees to the photovoltaic agent, there is no need to consider the photovoltaic grid connection fee, and the weight corresponding to the photovoltaic grid connection fee calculation item can be 0; however, if the photovoltaic grid connection mode is a non-self-owned photovoltaic mode, the weight corresponding to the photovoltaic grid connection fee calculation item is not 0.

[0091] In some embodiments of this application, the actual electricity consumption fee can be calculated based on the incoming power, decision time interval, and time-of-use electricity price data; the demand fee can be calculated based on the first balance coefficient, the demand electricity price per unit, and the demand; the photovoltaic absorption capacity calculation item can be calculated based on the photovoltaic predicted output data and absorption capacity parameters; and the photovoltaic absorption cost calculation item can be calculated based on the photovoltaic predicted output data, absorption capacity parameters, decision time interval, and photovoltaic absorption cost under non-self-owned photovoltaic mode.

[0092] In some embodiments of this application, the charge / discharge constraint term can be calculated based on the second balance coefficient and the first power; wherein, the first power can be calculated based on the discharge power and charging power of the energy storage device, and the first power can be obtained by subtracting the discharge power and charging power of the energy storage device.

[0093] In some embodiments of this application, during the entire scheduling process, the scheduling data corresponding to each time node can be determined based on a defined decision time interval, thereby constructing a scheduling result based on the scheduling data corresponding to each time node; the various calculation terms in the objective function can also be calculated based on the decision time interval.

[0094] For example, when scheduling the operation of a microgrid, the time to be scheduled can be divided into L time nodes, and the time interval between each time node is Δ, so the decision time interval can be determined as Δ. For example, when scheduling the operation of a microgrid within 1 hour, this 1 hour can be divided into L1 time nodes, and the time interval between each two time nodes is Δ1, L1×Δ1=1 hour.

[0095] For example, given a fixed decision time interval, the objective function can be expressed as the following formula:

[0096]

[0097] Where w1 represents the weight corresponding to the electricity cost calculation item; w2 represents the weight corresponding to the photovoltaic power consumption calculation item; w3 represents the weight corresponding to the photovoltaic power consumption fee calculation item; and L represents the time node. p represents the incoming power at the i-th time node, Δ represents the decision time interval, and p i P represents the time-of-use electricity price corresponding to the i-th time point; α represents the first balance coefficient; P de P represents demand, and pd represents the unit price of electricity demand; i pv This represents the photovoltaic power output at the i-th time point, which can be obtained from the photovoltaic power output forecast data; a iThis parameter represents the grid connection capacity, and this coefficient can be used to reflect the ability of photovoltaic grid connection to absorb power; ps represents the photovoltaic grid connection cost under non-self-owned photovoltaic mode; β represents the second balance coefficient. This indicates the first power.

[0098] In the embodiments of this application, the charging and discharging constraint terms in the objective function can also be calculated in absolute value or constrained by a higher-order norm, which is not limited in this application.

[0099] In some embodiments of this application, the demand charge item can also be a demand increment charge; for example, the demand increment charge can be calculated as (P de -P de_hist )·pd, where P de_hist This represents the historical maximum demand; using the cost of the demand increment as a calculation item in the objective function can effectively reduce the overall maximum demand of the microgrid dispatch.

[0100] In the embodiments of this application, since the objective function considers both economic needs and photovoltaic consumption needs, as well as different photovoltaic consumption modes, the impact of these different needs on the scheduling results can be balanced by changing the weight information.

[0101] For example, the objective function shown in formula (1) above can have the following different scenarios when determining the weight information based on the microgrid's dispatch demand and photovoltaic (PV) consumption mode: The first scenario is that the economic demand is higher than the PV consumption demand, and the PV consumption mode is a self-sustaining PV mode. In this case, w1 can be 1, w2 is less than 1, and w3 is 0. The second scenario is that the PV consumption demand is higher than the economic demand, and the PV consumption mode is a self-sustaining PV mode. In this case, w2 can be 1, w1 can be less than 1, and w3 is 0. The third scenario is that the economic demand is higher than the PV consumption demand, and the PV consumption mode is a non-self-sustaining PV mode. In the case of a non-self-owned photovoltaic (PV) mode, w1 can be 1, w2 is less than 1, and w3 can be 1. In the fourth case, the demand for PV grid connection exceeds the economic demand, and the PV grid connection mode is a non-self-owned PV mode. In this case, w2 can be 1, w1 is less than 1, and w3 can be 1. In addition, when the PV grid connection mode is a non-self-owned PV mode, w3 can be 1 or other values, such as a value less than 1. This can depend on the economic demand for PV grid connection under the non-self-owned PV mode in actual application. This application does not limit the specific values ​​of the weight information.

[0102] Step 103: Solve the objective function based on environmental reference data and preset constraints to obtain the microgrid scheduling results; wherein, the preset constraints include one or a combination of the following: constraints on incoming power, constraints on energy storage devices, and constraints on demand.

[0103] In the embodiments of this application, after determining the objective function based on the microgrid's scheduling requirements and photovoltaic consumption mode, the microgrid's scheduling device can solve the objective function based on environmental reference data and preset constraints to obtain the microgrid's scheduling result during the scheduling target period. The preset constraints include one or a combination of constraints on incoming power, constraints on energy storage devices, and constraints on demand.

[0104] In the embodiments of this application, when scheduling a microgrid, the operation of the microgrid within a certain future scheduling target time period is scheduled to obtain the scheduling result corresponding to the scheduling target time period. That is, the microgrid scheduling device can predict and optimize the scheduling mode of the microgrid within the scheduling target time period by acquiring relevant data of the microgrid from the current time to the historical time period, thereby enabling the microgrid to improve its performance in terms of economy and photovoltaic consumption when operating based on the scheduling result.

[0105] In some embodiments of this application, the duration of the target scheduling period is not limited. For example, the target scheduling period can be one day. Assuming the current date is A year B month 1, the microgrid on A year B month 2 can be scheduled to obtain the scheduling result of A year B month 2.

[0106] In some embodiments of this application, the environmental reference data may include incoming power constraint values; the scheduling result includes a first scheduling result characterizing the incoming power of the microgrid during the scheduling target period; when the microgrid scheduling device solves the objective function based on the environmental reference data and preset constraint conditions to obtain the microgrid scheduling result, it may solve the objective function based on the incoming power constraint values ​​and the incoming power constraint conditions to obtain the first scheduling result.

[0107] In embodiments of this application, the input power constraint conditions may include a first input power constraint condition, a second input power constraint condition, and a third input power constraint condition; the input power constraint value may include at least one of a first threshold, a second threshold, and the load demand power; the first input power constraint condition represents a constraint condition where the mains input power is less than or equal to the first threshold; the second input power constraint condition represents a constraint condition where the mains input power is greater than or equal to the second threshold; and the third input power constraint condition represents a constraint condition where the mains input power is greater than or equal to the load demand power.

[0108] In some embodiments of this application, when determining the load demand power, the microgrid dispatching device can determine the first power based on the discharge power and charging power of the energy storage device; then determine the production capacity power based on the first power and photovoltaic output prediction data; and thus determine the load demand power based on the load power prediction data and production capacity power.

[0109] For example, for each time node in the target scheduling period, i.e., for all i = 1, 2, ..., L, the first incoming line power constraint can be expressed as: The first threshold can be understood as a constraint on the maximum value of the mains power input, meaning that the mains power input at each time point must be less than or equal to the first threshold; the second power constraint can be... The second threshold can be 0 or any other value; this application does not limit this. The third incoming power constraint can be... This represents the load power at the i-th time node. This means that the load demand power is obtained by subtracting the photovoltaic output power and the first power from the load power.

[0110] In some embodiments of this application, a power conservation constraint can also be obtained based on the third incoming line power constraint. This power conservation constraint can be used to ensure that the mains incoming line power equals the load demand power; for example, the power conservation constraint can be expressed as... Among them, a i As a parameter of absorption capacity, it can be used to reflect the ability of photovoltaics to absorb power. In other words, the dispatching device of the microgrid can also use the absorption capacity parameter, photovoltaic output prediction data and first power to determine the production capacity power, and use this production capacity power and load power to obtain the load demand power, and then adjust the constraint relationship to make the incoming power equal to the load demand power, thereby obtaining the power conservation constraint condition.

[0111] In some embodiments of this application, the constraints of the energy storage device may include the charging and discharging power constraints, the energy constraints, and the charging and discharging behavior constraints of the energy storage device; the environmental reference data may include the charging and discharging power constraints and the energy constraints of the energy storage device; the scheduling result may include a second scheduling result characterizing the charging power and discharging power of the energy storage device in the microgrid during the scheduling target period; when the microgrid scheduling device solves the objective function based on the environmental reference data and preset constraints to obtain the microgrid scheduling result, it may solve the objective function based on the charging and discharging power constraints, the charging and discharging power constraints, the energy constraints, the energy constraints, and the charging and discharging behavior constraints to obtain the second scheduling result.

[0112] In the embodiments of this application, the charge and discharge power constraint conditions include charging power constraint conditions and discharging power constraint conditions; the charge and discharge power constraint values ​​include charging power constraint values ​​and discharging power constraint values; the charging power constraint condition characterizes the constraint condition that the charging power of the energy storage device is less than or equal to the charging power constraint value; the discharging power constraint condition characterizes the constraint condition that the discharging power of the energy storage device is less than or equal to the discharging power constraint value.

[0113] In the embodiments of this application, the power constraint value includes a fourth threshold and a fifth threshold; the power constraint condition of the energy storage device characterizes the constraint condition that the power of the energy storage device is greater than or equal to the fourth threshold and less than or equal to the fifth threshold.

[0114] In the embodiments of this application, the charging and discharging behavior constraint condition characterizes the constraint condition that the energy storage device does not charge during a first time period and does not discharge during a second time period.

[0115] For example, for each time node in the target scheduling period, i.e., for all i = 1, 2, ..., L, the charging power constraint of the energy storage device can be expressed as follows: This represents the charging power of the energy storage device at the i-th time point. This represents the charging power constraint value, i.e., the maximum allowable charging power of the energy storage device; the discharging power constraint condition of the energy storage device can be expressed as... This represents the discharge power constraint value, which is the maximum allowable discharge power of the energy storage device.

[0116] For example, the State of Charge (SOC) of the energy storage device at each time point should satisfy a condition greater than or equal to the minimum SOC limit and less than or equal to the maximum SOC limit; that is, for all i = 1, 2, ..., L, the energy storage device's state of charge constraint can be expressed as the following formula:

[0117] socmin≤soc≤socmax (2)

[0118] Where soc represents the energy storage device’s power, socmin represents the fourth threshold, and scomax represents the fifth threshold.

[0119] In some embodiments of this application, the energy storage device's capacity can be calculated based on the initial energy storage device's capacity, charging power, charging efficiency, discharging power, discharging efficiency, and the energy storage device's capacity; for example, the energy storage device's capacity can be calculated using the following formula:

[0120]

[0121] Where soc_init represents the initial battery level. Let μc represent the charging power of the energy storage device at the j-th time point, and μc represent the charging efficiency. μc represents the discharge power of the energy storage device at the j-th time node, μd represents the discharge efficiency, and capacity represents the capacity of the energy storage device; when μc and μd are 1, it represents the scheduling under lossless conditions.

[0122] In some embodiments of this application, the environmental reference data may also include time-of-use electricity price data; the microgrid dispatching device may further determine the time period in the time-of-use electricity price data where the electricity price meets the peak electricity price condition as the first time period; determine the time period in the time-of-use electricity price data where the electricity price meets the off-peak electricity price condition as the second time period; and determine the charging and discharging behavior constraints based on the first time period and the second time period.

[0123] In the embodiments of this application, the peak electricity price condition can be used to determine whether a time period is in the peak electricity price period of the time-of-use electricity price, and the off-peak electricity price condition can be used to determine whether a time period is in the off-peak electricity price period of the time-of-use electricity price. This application does not limit the specific time periods of the peak electricity price period and the off-peak electricity price period. For example, the time period when the electricity price is higher than a certain value can be regarded as the peak electricity price period, and the time period when the electricity price is lower than a certain value can be regarded as the off-peak electricity price period.

[0124] In the embodiments of this application, in addition to adding a charging and discharging constraint term to the objective function, adaptive control of the charging and discharging behavior of energy storage devices in the microgrid can also be achieved by adding constraint conditions. That is, energy storage devices are not allowed to charge during peak hours of time-of-use electricity pricing and are not allowed to discharge during off-peak hours of electricity pricing.

[0125] In some embodiments of this application, the environmental reference data may include the historical maximum demand and the demand at the current operating time; the demand constraints include a first demand constraint and a second demand constraint; the scheduling result includes a third scheduling result characterizing the microgrid's demand during the scheduling target period; when the microgrid's scheduling device solves the objective function based on the environmental reference data and preset constraints to obtain the microgrid's scheduling result, it may solve the objective function based on the historical maximum demand, the first demand constraint, the demand at the current operating time, and the second demand constraint to obtain the third scheduling result.

[0126] In the embodiments of this application, the first demand constraint condition represents the constraint condition that the demand is greater than or equal to the historical maximum demand value; the second demand constraint condition represents the constraint condition that the demand is greater than or equal to the demand at the current running time.

[0127] In the embodiments of this application, the current running time represents the current running time of the microgrid. For example, the current date is January 1, 2018. When the above scheduling method is executed on the microgrid on January 2, 2018, which is the next day, the current running time can be the demand on January 1, 2018. For example, the demand can be the maximum value of the average power within every 15 minutes calculated based on a sliding window.

[0128] For example, the constraint on the first demand can be expressed as P de ≥P de_hist , where P de P represents the power demand. de_hist This represents the maximum value in the historical demand power, i.e., the historical maximum demand.

[0129] In some embodiments of this application, when determining the demand for the current operating time, the dispatching device of the microgrid can determine the average value of the incoming power within the preset sliding time window based on the preset sliding time window and the incoming power within the current operating time; and determine the demand for the current operating time based on the average value of the incoming power.

[0130] In the embodiments of this application, the size of the sliding window is not limited when calculating the demand.

[0131] For example, if the preset sliding time window is 15 minutes, and assuming the decision interval is 5 minutes, then within the preset 15-minute sliding time window, the average value of the incoming power at three time points will be calculated, which can be expressed as: The constraint condition for the second demand can then be expressed as:

[0132] In the embodiments of this application, in the process of predicting the scheduling result of the microgrid within the target scheduling period, if the predicted demand power is less than the maximum value among the historical demand power, then there is no need to use the first demand constraint condition, that is, at this time it is not necessary to enforce the first demand constraint condition on the demand power.

[0133] In the embodiments of this application, the scheduling results may include at least the charging power scheduling results and discharging power scheduling results of the energy storage device within the scheduling target time period; in addition, the microgrid scheduling device may also output scheduling results such as absorption capacity parameters, demand and incoming power.

[0134] This application provides a microgrid scheduling method. The microgrid scheduling device can acquire environmental reference data of the microgrid; determine an objective function based on the microgrid's scheduling requirements and photovoltaic (PV) consumption mode; wherein, the scheduling requirements include the economic requirements for microgrid operation and the PV consumption requirements; the PV consumption mode includes self-sustaining PV mode and non-self-sustaining PV mode; solve the objective function based on the environmental reference data and preset constraints to obtain the microgrid scheduling result; wherein, the preset constraints include one or a combination of incoming power constraints, energy storage device constraints, and demand constraints. Therefore, when scheduling a microgrid, the dispatching device can first obtain the microgrid's environmental reference data. Simultaneously, based on the current economic and photovoltaic (PV) consumption requirements of the microgrid, and considering the PV consumption mode (including self-sufficient and non-self-sufficient PV modes), the objective function of the dispatch can be determined. This allows for consideration of both economic efficiency and PV consumption objectives. Furthermore, the objective function can be solved using the environmental reference data and preset constraints. During the solution process, the preset constraints can include one or a combination of constraints on incoming power, energy storage devices, and demand, overcoming the limitations and rigidity of constraints in current dispatching methods. Through this dispatching method, a globally optimal dispatching result can be obtained, making the result widely applicable to different scenarios and effectively improving its applicability.

[0135] Based on the above embodiments, in another embodiment of this application, a scheduling method is provided that can perform adaptive energy scheduling for photovoltaic power generation and energy storage devices in a microgrid system. The optimization objectives include improving economic efficiency and increasing photovoltaic absorption rate, and it supports two business modes, including photovoltaic self-sufficiency and photovoltaic non-self-sufficiency. It can also support three functions: demand control, photovoltaic power scheduling, and energy storage power scheduling. This application establishes a scheduling model by combining photovoltaic and load forecasting technologies. This scheduling model can be a positive definite quadratic programming optimization model. The scheduling model can include objective functions and constraints, thereby stably solving the optimal scheduling strategy through the scheduling model, which is applicable to various scenarios.

[0136] For example, the pricing model of the scheduling method in this application can adopt a two-part electricity pricing method, that is, the electricity fee consists of two parts. One part is the demand electricity fee calculated based on demand, where demand is the maximum value of the average power calculated through a 15-minute sliding window; the other part of the electricity fee is the electricity consumption fee calculated based on the actual electricity used. In addition, the demand electricity fee unit price is generally a fixed value, while the electricity consumption fee is calculated based on the time-of-use electricity price.

[0137] In the embodiments of this application, scheduling and calculation can be completed based on a fixed decision frequency; for example, the scheduling target time period is divided into L time nodes with Δ as the decision time interval, that is, the scheduling duration is L·Δ.

[0138] In some embodiments of this application, the decision variable may include incoming power. Energy storage device discharge power Energy storage device charging power Absorption capacity parameter {a i ,i=1,..,L}、Demand P de The dispatching results output by the microgrid dispatching device may include at least the discharge power and charging power of the energy storage device from the decision variables mentioned above. Other information from the decision variables may also be output as dispatching results.

[0139] In some embodiments of this application, during scheduling, the microgrid scheduling device needs to obtain environmental reference data of the microgrid, which may include photovoltaic power output prediction curves. Load power prediction curve Maximum power of mains incoming line Time-of-use electricity price {p i The following parameters are considered: i = 1, ..., L}, demand electricity price per unit pd, photovoltaic power consumption cost ps under non-self-owned photovoltaic mode, charging efficiency data μc of energy storage device, discharging efficiency data μd of energy storage device, and historical maximum demand P. de_hist .

[0140] In some embodiments of this application, the objective function can be as shown in the aforementioned formula (1). The objective function considers two-part electricity charges, including the electricity charges for electricity drawn from the grid based on time-of-use pricing and the demand charges. If the main scheduling objective in actual application is economic, w1 can be increased accordingly. The objective corresponding to w2 is the total amount of photovoltaic power consumption. If photovoltaic power consumption is the main scheduling objective, w2 can be increased accordingly. The objective corresponding to w3 is the electricity consumption charge payable to the photovoltaic agent for photovoltaic power consumption. w3 is 0 in the self-owned photovoltaic mode and not 0 in the non-self-owned photovoltaic mode. The objective function may also include charge and discharge constraint terms. Alternatively, the charge / discharge constraint term can be calculated using absolute values ​​or a higher-order norm. To balance the constraint effect with computational efficiency, a second-order norm is used here.

[0141] In some embodiments of this application, the demand charge item in the objective function can also be calculated in another way, namely, by calculating the cost of the demand increment (P). de -P de_hist)·pd, by adding the cost of demand increment to the objective function, can effectively reduce the overall maximum demand.

[0142] In some embodiments of this application, the scheduling objective considers both economic efficiency and photovoltaic (PV) consumption, as well as PV consumption modes, including self-sufficient PV mode and non-self-sufficient PV mode. In practical applications, the adaptive control of different scheduling objectives can be achieved by changing the weights of each calculation item in the objective function, so as to obtain the globally optimal scheduling result.

[0143] In some embodiments of this application, when the economic demand is higher than the photovoltaic (PV) grid connection demand and the PV grid connection mode is a self-sufficient PV mode, w1 can be 1, w2 is less than 1, and w3 is 0; when the PV grid connection demand is higher than the economic demand and the PV grid connection mode is a self-sufficient PV mode, w2 can be 1, w1 can be less than 1, and w3 is 0; when the economic demand is higher than the PV grid connection demand and the PV grid connection mode is a non-self-sufficient PV mode, w1 can be 1, w2 is less than 1, and w3 can be 1; when the PV grid connection demand is higher than the economic demand and the PV grid connection mode is a non-self-sufficient PV mode, w2 can be 1, w1 is less than 1, and w3 can be 1; in addition, when the PV grid connection mode is a non-self-sufficient PV mode, the value of w3 can be 1 or other values, such as a value less than 1, depending on the economic demand for PV grid connection fees under the non-self-sufficient PV mode in actual application.

[0144] In some embodiments of this application, different constraints can be set to constrain the solution process of the objective function; wherein, the charging and discharging constraint term in the objective function can also be regarded as a constraint with a constraining effect.

[0145] For example, the preset constraints may include constraints on the incoming power. For each time node in the target scheduling period, i.e., for all i = 1, 2, ..., L, the incoming power of the mains power must satisfy the following conditions: as well as In other words, the incoming power at each time point needs to meet the constraint of the maximum value, and it needs to be greater than or equal to 0, and it also needs to meet all load requirements.

[0146] For example, the preset constraints may include charging and discharging power constraints for the energy storage device, used to constrain the upper limit of the charging and discharging power of the energy storage device; for all time points, i.e., i = 1, 2, ..., L, the charging power of the energy storage device must satisfy... The discharge power must meet the following requirements.

[0147] For example, the preset constraints may also include power conservation constraints, which must be satisfied for all time points, i.e., i = 1, 2, ..., L.

[0148] For example, the preset constraints may also include the energy storage device's power constraints. For all i = 1, 2, ..., L, the energy storage device's power needs to satisfy socmin ≤ soc ≤ socmax; where soc can be calculated as shown in the aforementioned formula (3).

[0149] For example, the preset constraints may also include constraints on demand, whereby the demand needs to satisfy P. de ≥P de_hist This means that the demand needs to be greater than the historical maximum demand; at the same time, the demand also needs to be greater than or equal to the demand at the current operating time, for example, it needs to be greater than the corresponding demand of the microgrid within the day. Assuming the decision interval is 5 minutes, the demand will be obtained by calculating the average power at 3 time points within a 15-minute sliding time window. This constraint method can be expressed as follows: When the decision interval is of other lengths, a sliding time window of appropriate size can also be used to calculate the demand.

[0150] For example, the preset constraints may also include charging and discharging behavior constraints. Although the regularization term of charging and discharging power has been added to the objective function as a charging and discharging constraint term to impose certain constraints, which can adaptively control the charging and discharging behavior to a certain extent, explicit charging and discharging behavior constraints can still be added based on experience. For example, charging is not allowed during peak electricity price periods, discharging is not allowed during off-peak electricity price periods, and charging and discharging behavior constraints for other time periods can constrain the charging and discharging behavior.

[0151] In the embodiments of this application, when solving the objective function based on constraints and input environmental reference data, an open-source solver can be used to solve the above-mentioned constrained scheduling model. For example, the solver can be Gurobi, Solving Constraint Integer Programs (SCIP), etc.

[0152] In the embodiments of this application, the above scheduling method is applicable to the general scenario of microgrids under two-part tariffs, and is not limited to photovoltaic and energy storage devices. It can be applied to other wind power generation devices and other green energy sources. It is only necessary to replace the photovoltaic output data in the model with the power data corresponding to green energy, or the sum of the power generation of different types of green energy.

[0153] For example, as shown in Figure 3, when scheduling the operation of a microgrid on a future day (the target scheduling period), the historical photovoltaic data and historical load data of the microgrid can be predicted first to obtain photovoltaic prediction curves and load prediction curves (step 201). The photovoltaic prediction curves and load prediction curves represent the results of predicting the photovoltaic output and load power of the microgrid on a future day. Then, information such as time-of-use pricing, demand pricing, scheduling requirements, relevant parameters of energy storage devices, and photovoltaic consumption modes can be obtained (step 202). The relevant parameters of energy storage devices can include parameters such as the charging and discharging efficiency or charging and discharging loss of energy storage devices. Scheduling requirements can also be understood as scheduling objectives, i.e., whether to prioritize economic efficiency or photovoltaic consumption. Then, the objective function can be determined according to the scheduling requirements and photovoltaic consumption modes (step 203). Then, the constraints can be determined (step 204), and the objective function can be solved based on the constraints to obtain the scheduling result (step 205). This scheduling result is the scheduling result of the microgrid on a future day.

[0154] For example, when determining the scheduling result of a microgrid on the 30th of a certain month (the target scheduling period), assuming the decision interval is 5 minutes and the scheduling duration is the 30th, i.e., the scheduling duration is 24 hours, the target scheduling period can be divided into 288 time nodes, i.e., L = 288. The microgrid's scheduling device can first predict the photovoltaic output curve of the microgrid on the 30th of a certain month based on the photovoltaic output curve of the microgrid from the 1st to the 29th of a certain month. At the same time, it can predict the load power curve of the microgrid on the 30th of a certain month based on the load power curve of the microgrid from the 1st to the 29th of a certain month. It can also obtain the time-of-use electricity price and demand price of the microgrid's location in that month. The demand price is the demand charge per unit. It can also obtain relevant parameters of the energy storage device, such as the energy storage device's capacity of 230 kWh, maximum charging and discharging power of 100 kW, and charging and discharging efficiency of 0.9. 2. Simultaneously, the scheduling demand is determined with economic efficiency as the priority, and the photovoltaic consumption mode is photovoltaic self-sufficiency. The weights in the objective function can be determined as w1=1, W2=0.01, and w3=0. After determining the objective function and constraints, the objective function can be solved using the constraints and the above information to obtain the scheduling results. The scheduling results can at least include the charging and discharging power results of the energy storage equipment. As shown in Figure 4, curves are plotted based on the scheduling results. Curve 1 is the curve corresponding to the result of discharging power minus charging power of the energy storage equipment, curve 2 is the incoming power, curve 3 is the load power prediction curve, curve 4 is the photovoltaic output prediction curve, and curve 5 is the energy of the energy storage equipment. This scheduling result can greatly reduce the demand of the microgrid in 30 days, from 408.6KW to 343.6KW. In addition, the excess photovoltaic power can be fed into the grid according to the feed-in tariff, improving the consumption rate, and the energy storage charging and discharging behavior is stable.

[0155] In summary, this application collects data such as grid load and photovoltaic power output to minimize the cost of microgrids drawing power from external grids and improve photovoltaic absorption rate as scheduling objectives. It can establish a microgrid energy scheduling model under the constraints of microgrid system power balance, photovoltaic output, energy storage power and SOC status, and schedule the operation of microgrids, effectively improving the optimization performance of microgrid scheduling.

[0156] This application provides a microgrid scheduling method. The microgrid scheduling device can acquire environmental reference data of the microgrid; determine an objective function based on the microgrid's scheduling requirements and photovoltaic (PV) consumption mode; wherein, the scheduling requirements include the economic requirements for microgrid operation and the PV consumption requirements; the PV consumption mode includes self-sustaining PV mode and non-self-sustaining PV mode; solve the objective function based on the environmental reference data and preset constraints to obtain the microgrid scheduling result; wherein, the preset constraints include one or a combination of incoming power constraints, energy storage device constraints, and demand constraints. Therefore, when optimizing the dispatching results of a microgrid, the dispatching device can first obtain the environmental reference data of the microgrid. Simultaneously, based on the current economic and photovoltaic (PV) consumption requirements of the microgrid dispatching, and considering the PV consumption mode of the microgrid (including self-sufficient and non-self-sufficient PV modes), the objective function of the dispatching can be determined. This allows for consideration of both economic efficiency and PV consumption objectives. Furthermore, the objective function can be solved using the environmental reference data and preset constraints. During the solution process, the preset constraints can include one or a combination of constraints on incoming power, energy storage devices, and demand, overcoming the limitations and formalization of constraints in current dispatching methods. Through this dispatching method, the globally optimal dispatching result for the given dispatching date can be obtained, making the dispatching results widely applicable to different usage scenarios and effectively improving the applicability of the dispatching results.

[0157] Based on the above embodiments, in another embodiment of this application, a microgrid scheduling device is provided. As shown in FIG5, the microgrid scheduling device 1 may include an acquisition unit 11 and a determination unit 12.

[0158] The acquisition unit 11 can be used to acquire environmental reference data of the microgrid.

[0159] The determining unit 12 can be used to determine the objective function based on the microgrid's dispatch requirements and photovoltaic (PV) consumption modes; wherein, the dispatch requirements include the economic requirements for microgrid operation and the PV consumption requirements; the PV consumption modes include self-sustaining PV modes and non-self-sustaining PV modes; and to solve the objective function based on environmental reference data and preset constraints to obtain the microgrid's dispatch results; wherein, the preset constraints include one or a combination of constraints on incoming power, constraints on energy storage devices, and constraints on demand.

[0160] In some embodiments, the determining unit 12 can also be used to determine the weight information corresponding to each preset calculation item based on the microgrid's scheduling requirements and photovoltaic consumption mode; each preset calculation item includes an electricity cost calculation item, a photovoltaic consumption amount calculation item, a photovoltaic consumption fee calculation item, and a charge / discharge constraint item; the electricity cost calculation item includes an actual electricity consumption cost item and a demand cost item; and to determine the objective function based on each preset calculation item and its corresponding weight information.

[0161] In some embodiments, the environmental reference data includes incoming power constraint values; the scheduling result includes a first scheduling result characterizing the incoming power of the microgrid during the scheduling target time period; the determining unit 12 can also be used to solve an objective function based on the incoming power constraint values ​​and the constraints of the incoming power to obtain the first scheduling result; wherein, the constraints of the incoming power include a first incoming power constraint, a second incoming power constraint, and a third incoming power constraint; the incoming power constraint values ​​include at least one of a first threshold, a second threshold, and the load demand power; the first incoming power constraint characterizes a constraint that the mains incoming power is less than or equal to the first threshold; the second incoming power constraint characterizes a constraint that the mains incoming power is greater than or equal to the second threshold; and the third incoming power constraint characterizes a constraint that the mains incoming power is greater than or equal to the load demand power.

[0162] In some embodiments, the environmental reference data may include photovoltaic power output prediction data and load power prediction data; the acquisition unit 11 may also be used to acquire historical photovoltaic power output data and historical load power data of the microgrid; wherein, the historical photovoltaic power output data represents the photovoltaic power output data of the microgrid within the dates prior to the scheduling target period; the historical load power data represents the load power data of the microgrid within the dates prior to the scheduling target period; and the photovoltaic power output of the microgrid within the scheduling target period is predicted based on the historical photovoltaic power output data to obtain photovoltaic power output prediction data; and the load power of the microgrid within the scheduling target period is predicted based on the historical load power data to obtain load power prediction data.

[0163] In some embodiments, the determining unit 12 can also be used to determine a first power based on the discharge power and charging power of the energy storage device; determine the production capacity power based on the first power and photovoltaic output prediction data; and determine the load demand power based on load power prediction data and production capacity power.

[0164] In some embodiments, the constraints of the energy storage device include charging and discharging power constraints, energy constraints, and charging and discharging behavior constraints; environmental reference data includes charging and discharging power constraints and energy constraints of the energy storage device; the scheduling result includes a second scheduling result characterizing the charging power and discharging power of the energy storage device in the microgrid during the scheduling target time period; the determining unit 12 can also be used to solve the objective function based on the charging and discharging power constraints, charging and discharging power constraints, energy constraints, energy constraints, and charging and discharging behavior constraints to obtain the second scheduling result; wherein, the charging and discharging power constraints include charging power constraints and discharging power constraints; the charging and discharging power constraints include charging power constraints and discharging power constraints; the charging power constraints characterize the constraint that the charging power of the energy storage device is less than or equal to the charging power constraint value; the discharging power constraints characterize the constraint that the discharging power of the energy storage device is less than or equal to the discharging power constraint value; the energy constraints include a fourth threshold and a fifth threshold; the energy constraints of the energy storage device characterize the constraint that the energy storage device's energy is greater than or equal to the fourth threshold and less than or equal to the fifth threshold; the charging and discharging behavior constraints characterize the constraint that the energy storage device does not charge during the first time period and does not discharge during the second time period.

[0165] In some embodiments, the environmental reference data includes time-of-use electricity price data; the determining unit 12 can also be used to determine the time period in the time-of-use electricity price data where the electricity price meets the peak electricity price condition as a first time period; determine the time period in the time-of-use electricity price data where the electricity price meets the off-peak electricity price condition as a second time period; and determine charging and discharging behavior constraints based on the first time period and the second time period.

[0166] In some embodiments, the environmental reference data includes the historical maximum demand and the demand at the current operating time; the demand constraints include a first demand constraint and a second demand constraint; the scheduling result includes a third scheduling result characterizing the microgrid's demand during the target scheduling period; the determining unit 12 can also be used to solve the objective function based on the historical maximum demand, the first demand constraint, the demand at the current operating time, and the second demand constraint to obtain the third scheduling result; wherein, the first demand constraint characterizes the constraint that the demand is greater than or equal to the historical maximum demand; and the second demand constraint characterizes the constraint that the demand is greater than or equal to the demand at the current operating time.

[0167] In some embodiments, the determining unit 12 can also be used to determine the average value of the incoming power within the preset sliding time window based on the preset sliding time window and the incoming power within the current operating time; and to determine the demand for the current operating time based on the average value of the incoming power.

[0168] In the embodiments of this application, Figure 6 is a schematic diagram of the composition structure of the microgrid scheduling device proposed in the embodiments of this application. As shown in Figure 6, the microgrid scheduling device 1 proposed in the embodiments of this application may further include a processor 13 and a memory 14 storing executable instructions of the processor 13. Furthermore, the microgrid scheduling device 1 may further include a communication interface 15 and a bus 16 for connecting the processor 13, the memory 14 and the communication interface 15.

[0169] In the embodiments of this application, the processor 13 can be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that for different devices, the electronic devices used to implement the above-mentioned processor functions can also be other types, and this application embodiment does not specifically limit them. The microgrid scheduling device 1 may also include a memory 14, which can be connected to the processor 13. The memory 14 is used to store executable program code, which includes computer operation instructions. The memory 14 may include high-speed RAM memory and may also include non-volatile memory, such as at least two disk drives.

[0170] In embodiments of this application, bus 16 is used to connect communication interface 15, processor 13 and memory 14 and the mutual communication between these devices.

[0171] In embodiments of this application, memory 14 is used to store instructions and data.

[0172] Furthermore, in the embodiments of this application, the processor 13 is used to acquire environmental reference data of the microgrid; determine an objective function based on the microgrid's scheduling requirements and photovoltaic (PV) consumption mode; wherein, the scheduling requirements include the economic requirements for microgrid operation and PV consumption requirements; the PV consumption mode includes self-sustaining PV mode and non-self-sustaining PV mode; solve the objective function based on the environmental reference data and preset constraints to obtain the microgrid's scheduling result; wherein, the preset constraints include one or a combination of constraints on incoming power, constraints on energy storage devices, and constraints on demand.

[0173] In practical applications, the aforementioned memory 14 can be volatile memory, such as random-access memory (RAM); or non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); or a combination of the above types of memory, and provide instructions and data to the processor 13.

[0174] Furthermore, in this embodiment, the functional modules 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 module.

[0175] If the integrated unit is implemented as a software functional module and is not 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 this embodiment, 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.) or processor to execute all or part of the steps of the method of this embodiment. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0176] This application provides a microgrid dispatching device for acquiring environmental reference data of the microgrid; determining an objective function based on the microgrid's dispatching requirements and photovoltaic (PV) consumption modes; wherein the dispatching requirements include the economic requirements for microgrid operation and PV consumption requirements; the PV consumption modes include self-sustaining PV modes and non-self-sustaining PV modes; solving the objective function based on the environmental reference data and preset constraints to obtain the microgrid dispatching results; wherein the preset constraints include one or a combination of constraints on incoming power, constraints on energy storage devices, and constraints on demand. Therefore, when scheduling a microgrid, the dispatching device can first obtain the microgrid's environmental reference data. Simultaneously, based on the current economic and photovoltaic (PV) consumption requirements of the microgrid, and considering the PV consumption mode (including self-sufficient and non-self-sufficient PV modes), the objective function of the dispatch can be determined. This allows for consideration of both economic efficiency and PV consumption objectives. Furthermore, the objective function can be solved using the environmental reference data and preset constraints. During the solution process, the preset constraints can include one or a combination of constraints on incoming power, energy storage devices, and demand, overcoming the limitations and formalization of constraints in current dispatching methods. Through this dispatching method, the globally optimal dispatching result for the given date can be obtained, making the dispatching results widely applicable to different scenarios and effectively improving the applicability of the dispatching results.

[0177] Specifically, the program instructions corresponding to the microgrid scheduling method in this embodiment can be stored on storage media such as optical discs, hard disks, and USB flash drives. When the program instructions corresponding to the microgrid scheduling method in the storage media are read or executed by an electronic device, the following steps are included:

[0178] Obtain environmental reference data for the microgrid;

[0179] The objective function is determined based on the microgrid's dispatch requirements and photovoltaic (PV) integration modes; whereby the dispatch requirements include the economic requirements for microgrid operation and the PV integration requirements; and the PV integration modes include self-sustaining PV modes and non-self-sustaining PV modes.

[0180] The objective function is solved based on environmental reference data and preset constraints to obtain the dispatch results of the microgrid. The preset constraints include one or a combination of constraints on incoming power, constraints on energy storage devices, and constraints on demand.

[0181] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0182] This application is described with reference to schematic and / or block diagrams of implementations of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the schematic and / or block diagrams, and combinations thereof, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the schematic and / or block diagrams.

[0183] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0184] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more blocks in a block diagram.

[0185] The above embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention.

Claims

1. A microgrid scheduling method, characterized in that, The method includes: acquiring environmental reference data of the microgrid; determining an objective function based on the dispatch requirements and photovoltaic (PV) consumption modes of the microgrid; wherein the dispatch requirements include the economic requirements for the operation of the microgrid and the PV consumption requirements; the PV consumption modes include self-sustaining PV modes and non-self-sustaining PV modes; solving the objective function based on the environmental reference data and preset constraints to obtain the dispatch results of the microgrid; wherein the preset constraints include one or a combination of constraints on incoming power, constraints on energy storage devices, and constraints on demand.

2. The scheduling method according to claim 1, characterized in that, The step of determining the objective function based on the microgrid's scheduling requirements and photovoltaic (PV) consumption mode includes: determining the weight information corresponding to each preset calculation item based on the microgrid's scheduling requirements and PV consumption mode; each preset calculation item includes an electricity cost calculation item, a PV consumption amount calculation item, a PV consumption expense calculation item, and a charge / discharge constraint item; the electricity cost calculation item includes an actual electricity cost item and a demand electricity cost item; and determining the objective function based on each preset calculation item and its corresponding weight information.

3. The scheduling method according to claim 1 or 2, characterized in that, The environmental reference data includes incoming power constraint values; the scheduling results include a first scheduling result characterizing the incoming power of the microgrid during the target scheduling period. The step of solving the objective function based on the environmental reference data and preset constraints to obtain the microgrid scheduling result includes: solving the objective function based on the incoming power constraint value and the incoming power constraint condition to obtain the first scheduling result; wherein, the incoming power constraint condition includes a first incoming power constraint condition, a second incoming power constraint condition, and a third incoming power constraint condition; the incoming power constraint value includes at least one of a first threshold, a second threshold, and the load demand power; the first incoming power constraint condition represents the constraint condition that the mains incoming power is less than or equal to the first threshold; the second incoming power constraint condition represents the constraint condition that the mains incoming power is greater than or equal to the second threshold; and the third incoming power constraint condition represents the constraint condition that the mains incoming power is greater than or equal to the load demand power.

4. The scheduling method according to claim 3, characterized in that, The environmental reference data includes photovoltaic (PV) output prediction data and load power prediction data; the method further includes: acquiring historical PV output data and historical load power data of the microgrid; wherein, the historical PV output data represents the PV output data of the microgrid within the dates prior to the target scheduling period; the historical load power data represents the load power data of the microgrid within the dates prior to the target scheduling period; the PV output of the microgrid within the target scheduling period is predicted based on the historical PV output data to obtain the PV output prediction data; the load power of the microgrid within the target scheduling period is predicted based on the historical load power data to obtain the load power prediction data.

5. The scheduling method according to claim 4, characterized in that, The method further includes: determining a first power based on the discharge power and charging power of the energy storage device; determining the production capacity power based on the first power and the photovoltaic output prediction data; and determining the load demand power based on the load power prediction data and the production capacity power.

6. The scheduling method according to claim 5, characterized in that, The constraints of the energy storage device include the charging and discharging power constraints, the energy constraints, and the charging and discharging behavior constraints; the environmental reference data includes the charging and discharging power constraints and the energy constraints of the energy storage device; the scheduling result includes a second scheduling result characterizing the charging and discharging power of the energy storage device in the microgrid during the scheduling target time period. The step of solving the objective function based on the environmental reference data and preset constraints to obtain the scheduling result of the microgrid includes: solving the objective function based on the charging and discharging power constraints, the charging and discharging power constraint values, the energy constraint conditions, the energy constraint values, and the charging and discharging behavior constraints to obtain the second scheduling result; wherein, the charging and discharging power constraints include charging power constraints and discharging power constraints; the charging and discharging power constraint values ​​include charging power constraint values ​​and discharging power constraint values; the charging power constraint conditions characterize the constraint that the charging power of the energy storage device is less than or equal to the charging power constraint value; the discharging power constraint conditions characterize the constraint that the discharging power of the energy storage device is less than or equal to the discharging power constraint value; the energy constraint values ​​include a fourth threshold and a fifth threshold; the energy constraint conditions of the energy storage device characterize the constraint that the energy storage device's energy is greater than or equal to the fourth threshold and less than or equal to the fifth threshold; the charging and discharging behavior constraints characterize the constraint that the energy storage device does not charge in a first time period and does not discharge in a second time period.

7. The scheduling method according to claim 6, characterized in that, The environmental reference data includes time-of-use electricity price data; the method further includes: determining the time period in the time-of-use electricity price data where the electricity price meets the peak electricity price condition as the first time period; determining the time period in the time-of-use electricity price data where the electricity price meets the off-peak electricity price condition as the second time period; and determining the charging and discharging behavior constraints based on the first time period and the second time period.

8. The scheduling method according to claim 7, characterized in that, The environmental reference data includes the historical maximum demand and the demand at the current operating time; the demand constraints include a first demand constraint and a second demand constraint; the scheduling result includes a third scheduling result characterizing the microgrid's demand during the target scheduling period; the process of solving the objective function based on the environmental reference data and preset constraints to obtain the microgrid's scheduling result includes: solving the objective function based on the historical maximum demand, the first demand constraint, the demand at the current operating time, and the second demand constraint to obtain the third scheduling result; wherein, the first demand constraint characterizes the constraint that the demand is greater than or equal to the historical maximum demand; the second demand constraint characterizes the constraint that the demand is greater than or equal to the demand at the current operating time.

9. The scheduling method according to claim 8, characterized in that, The method further includes: determining the average value of the incoming power within the preset sliding time window based on the incoming power within the current operating time; and determining the demand for the current operating time based on the average value of the incoming power.

10. A dispatching device for a microgrid, characterized in that, The microgrid dispatching device includes an acquisition unit and a determination unit. The acquisition unit is used to acquire environmental reference data of the microgrid. The determination unit is used to determine an objective function based on the dispatching requirements of the microgrid and the photovoltaic (PV) consumption mode. The dispatching requirements include the economic requirements for the operation of the microgrid and the PV consumption requirements. The PV consumption mode includes a self-sustaining PV mode and a non-self-sustaining PV mode. The device also solves the objective function based on the environmental reference data and preset constraints to obtain the dispatching result of the microgrid. The preset constraints include one or a combination of constraints on incoming power, constraints on energy storage devices, and constraints on demand.

11. A dispatching device for a microgrid, characterized in that, The microgrid scheduling device includes a processor and a memory storing processor-executable instructions; when the instructions are executed by the processor, the method described in any one of claims 1 to 9 is implemented.

12. 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 9.

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