Micro-grid optimization scheduling method and device, electronic equipment and storage medium

Abstract

The invention provides a micro-grid optimal scheduling method and device, electronic equipment and a storage medium, the method is applied to a micro-grid, the micro-grid comprises generalized energy storage and a distributed generator, and the method comprises the following steps: pre-constructing a generalized energy storage model of the generalized energy storage, and establishing an optimal scheduling model based on the generalized energy storage model; obtaining historical operation parameters in a plurality of historical operation scenes, and obtaining each historical optimal scheduling strategy based on the historical operation parameters and the optimal scheduling model; obtaining each group of historical charge state sequences based on each historical optimal scheduling strategy; determining a reference charge state sequence in the online scheduling process based on each group of historical charge state sequences, and determining a reference electricity price in the online scheduling process based on each historical electricity price in each historical operation scene; and based on the reference charge state sequence, the reference electricity price and the optimal scheduling model, obtaining an optimal scheduling strategy of the micro-grid in the online scheduling process. Therefore, safe and economical scheduling processing of the micro-grid is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a microgrid optimization scheduling method, device, electronic equipment and storage medium. Background Art

[0002] Microgrid refers to a small power system consisting of distributed power sources, loads, energy storage, power transformation and distribution devices, and the control systems and communication systems of the above power units. Microgrid has become a promising system for absorbing distributed renewable energy.

[0003] With the gradual construction of new power systems, distributed renewable energy has gradually become the main installed capacity and even power generation of microgrids. In the above scenario, microgrids must consider the uncertainty, intermittency and volatility of renewable energy during the dispatch process. However, the uncertainty, intermittency and volatility of renewable energy often come from many aspects and are difficult to predict. Therefore, it is difficult to achieve good economy and safety in the dispatch process of microgrids.

[0004] Therefore, finding a microgrid optimization scheduling method that can achieve good economy and safety has become a current research hotspot. Summary of the invention

[0005] The present invention provides a microgrid optimization scheduling method, device, electronic device and storage medium, which avoid the defect that new energy is difficult to predict by eliminating the need for data prediction processing, thereby achieving safe and economical scheduling of the microgrid.

[0006] The present invention provides a microgrid optimization scheduling method, the method is applied to a microgrid, the microgrid includes at least generalized energy storage and distributed generators, the generalized energy storage is a resource with energy storage function, the method includes: pre-constructing a generalized energy storage model of the generalized energy storage in the microgrid, and establishing an optimization scheduling model of the microgrid based on the generalized energy storage model; obtaining historical operating parameters under multiple historical operating scenarios, and obtaining each historical optimal scheduling strategy under each of the historical operating scenarios based on the historical operating parameters and the optimization scheduling model, wherein the historical operating parameters at least include historical new energy output, historical load, and historical electricity price; based on each of the historical operating scenarios, The method comprises the steps of: determining a reference state of charge sequence in an online scheduling process based on each group of historical state of charge sequences under each of the historical operation scenarios, and determining a reference electricity price in an online scheduling process based on each historical electricity price under each of the historical operation scenarios; and obtaining an optimal scheduling strategy for the microgrid in an online scheduling process based on the reference state of charge sequence, the reference electricity price, and an optimization scheduling model, wherein the optimal scheduling strategy includes at least a charging power control instruction of a generalized energy storage, a discharging power control instruction of a generalized energy storage, and a discharging power control instruction of a distributed generator.

[0007] According to a microgrid optimization scheduling method provided by the present invention, a generalized energy storage model of the generalized energy storage in the microgrid is constructed in the following manner: determining the charging power decision variable of the generalized energy storage, the discharging power decision variable of the generalized energy storage, the charging power boundary, and the discharging power boundary, wherein the charging power and the discharging power of the generalized energy storage are determined according to the difference between the actual response load and the baseline load of the microgrid, and determining the state of charge sequence decision variable of the generalized energy storage, and the state of charge sequence boundary, wherein the state of charge sequence of the generalized energy storage is determined according to the indoor temperature of the microgrid; based on the charging power decision variable of the generalized energy storage, the discharging power decision variable of the generalized energy storage, the charging power boundary, the discharging power boundary, the state of charge sequence decision variable of the generalized energy storage, and the state of charge sequence boundary, the generalized energy storage model is constructed.

[0008] According to a microgrid optimization scheduling method provided by the present invention, the optimization scheduling model of the microgrid is established based on the generalized energy storage model, which specifically includes: determining the incentive cost of the generalized energy storage under the generalized energy storage model; respectively obtaining the power purchase cost of the microgrid from the external power grid, and obtaining the power generation cost of the distributed generator; based on the incentive cost of the generalized energy storage, the power purchase cost, and the power generation cost, establishing the optimization scheduling model of the microgrid.

[0009] According to a microgrid optimization scheduling method provided by the present invention, the incentive cost of the generalized energy storage is determined under the generalized energy storage model, specifically comprising: determining the charging power of the generalized energy storage and the discharging power of the generalized energy storage, as well as the charging cost coefficient of the generalized energy storage and the discharging cost coefficient of the generalized energy storage under the generalized energy storage model; determining the incentive cost of the generalized energy storage based on the charging power of the generalized energy storage, the discharging power of the generalized energy storage, the charging cost coefficient of the generalized energy storage and the discharging cost coefficient of the generalized energy storage.

[0010] According to a microgrid optimization scheduling method provided by the present invention, before determining the reference state of charge sequence in the online scheduling process based on each group of historical state of charge sequences under each of the historical operating scenarios, the method also includes: observing the net load and electricity price of the microgrid in the online scheduling process on the scheduling day at intervals of a preset duration; obtaining the historical net load and historical electricity price of the microgrid under each of the historical operating scenarios; determining weights matching each of the historical operating scenarios based on the net load and the electricity price, as well as the historical net load and historical electricity price of the microgrid under each of the historical operating scenarios; determining the reference state of charge sequence in the online scheduling process based on each group of historical state of charge sequences under each of the historical operating scenarios specifically includes: determining the reference state of charge sequence in the online scheduling process based on the weights matching each of the historical operating scenarios and each group of historical state of charge sequences.

[0011] According to a microgrid optimization scheduling method provided by the present invention, the reference electricity price in the online scheduling process is determined based on the historical electricity prices under the historical operation scenarios, which specifically includes: obtaining weights matching each of the historical operation scenarios; determining the reference electricity price in the online scheduling process based on the weights matching each of the historical operation scenarios and the historical electricity prices under the historical operation scenarios.

[0012] According to a microgrid optimization scheduling method provided by the present invention, before obtaining the optimal scheduling strategy of the microgrid in the online scheduling process based on the reference state of charge sequence, the reference electricity price, and the optimization scheduling model, the method also includes: based on the reference state of charge sequence and the reference electricity price, re-optimizing the optimization scheduling model to obtain an optimized optimization scheduling model; obtaining the optimal scheduling strategy of the microgrid in the online scheduling process based on the reference state of charge sequence, the reference electricity price, and the optimization scheduling model specifically includes: based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model after optimization, obtaining the optimal scheduling strategy of the microgrid in the online scheduling process through an online convex optimization algorithm.

[0013] The present invention also provides a microgrid optimization scheduling device, which is applied to a microgrid, wherein the microgrid includes at least generalized energy storage and distributed generators, wherein the generalized energy storage is a resource with energy storage function, and the device includes: a construction module, which is used to pre-construct a generalized energy storage model of the generalized energy storage in the microgrid, and establish an optimization scheduling model of the microgrid based on the generalized energy storage model; a determination module, which is used to obtain historical operating parameters under multiple historical operating scenarios, and obtain each historical optimal scheduling strategy under each of the historical operating scenarios based on the historical operating parameters and the optimization scheduling model, wherein the historical operating parameters include at least historical new energy output, historical load, and historical electricity price; An acquisition module, configured to obtain, based on each of the historical optimal scheduling strategies, each group of historical state of charge sequences corresponding to each of the historical optimal scheduling strategies; A processing module, configured to determine a reference state of charge sequence in an online scheduling process based on each group of historical state of charge sequences under each of the historical operation scenarios, and to determine a reference electricity price in an online scheduling process based on each historical electricity price under each of the historical operation scenarios; A scheduling module is used to obtain an optimal scheduling strategy for the microgrid during online scheduling based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model, wherein the optimal scheduling strategy at least includes a charging power control instruction for generalized energy storage, a discharging power control instruction for generalized energy storage, and a discharging power control instruction for distributed generators.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the microgrid optimization scheduling method described in any one of the above methods is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the microgrid optimization scheduling method as described in any one of the above is implemented.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the microgrid optimization scheduling method as described above is implemented.

[0017] The present invention provides a microgrid optimization scheduling method, device, electronic device and storage medium, the method is applied to a microgrid, the microgrid includes at least generalized energy storage and distributed generators, the generalized energy storage is a resource with energy storage function, the method includes: pre-constructing a generalized energy storage model of generalized energy storage in the microgrid, and establishing an optimization scheduling model of the microgrid based on the generalized energy storage model; obtaining historical operating parameters under multiple historical operating scenarios, and obtaining each historical optimal scheduling strategy under each historical operating scenario based on the historical operating parameters and the optimization scheduling model; obtaining each group of historical state of charge sequences corresponding to each historical optimal scheduling strategy based on each historical optimal scheduling strategy; determining a reference state of charge sequence in the online scheduling process based on each group of historical state of charge sequences under each historical operating scenario, and determining a reference electricity price in the online scheduling process based on each historical electricity price under each historical operating scenario; obtaining the optimal scheduling strategy of the microgrid in the online scheduling process based on the reference state of charge sequence, the reference electricity price and the optimization scheduling model. By avoiding the defect that new energy is difficult to predict without data prediction processing, safe and economical scheduling processing of the microgrid is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of the microgrid optimization scheduling method provided by the present invention.

[0020] Figure 2 It is a schematic diagram of a flow chart of constructing a generalized energy storage model for generalized energy storage in a microgrid provided by the present invention.

[0021] Figure 3 It is a flow chart of determining a reference state of charge sequence in an online scheduling process based on each group of historical state of charge sequences under each historical operation scenario provided by the present invention.

[0022] Figure 4 It is a flow chart of determining a reference electricity price in an online scheduling process based on various historical electricity prices under various historical operation scenarios provided by the present invention.

[0023] Figure 5 It is a flow chart of obtaining the optimal dispatching strategy of a microgrid in an online dispatching process based on a reference state of charge sequence, a reference electricity price, and an optimized dispatching model provided by the present invention.

[0024] Figure 6It is a structural schematic diagram of the microgrid optimization scheduling device provided by the present invention.

[0025] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The microgrid optimization scheduling method provided by the present invention covers flexible resources such as battery energy storage, temperature-controlled loads and electric vehicles when constructing a generalized energy storage model, and takes into account various uncertainties such as new energy, loads and electricity prices. By referring to the generation and update of the state of charge sequence and the dynamic average electricity price (corresponding to the reference electricity price), it provides a global perspective for adaptive online optimization, thereby providing strong performance guarantees for the economic and safe operation of the microgrid.

[0028] The microgrid optimization scheduling method provided by the present invention can be applied to the scheduling of a grid-connected microgrid containing a high proportion of renewable energy, that is, applied to a microgrid, wherein the microgrid can at least include generalized energy storage and distributed generators, and generalized energy storage is a resource with energy storage function. Among them, generalized energy storage can include actual energy storage, such as battery energy storage, pumped storage, compressed air energy storage, etc.; and virtual energy storage, such as temperature control loads, electric vehicles, etc.

[0029] Figure 1 It is a flow chart of the microgrid optimization scheduling method provided by the present invention.

[0030] The following will be combined Figure 1 The process of the microgrid optimization scheduling method provided by the present invention is described.

[0031] In an exemplary embodiment of the present invention, Figure 1 It can be seen that the microgrid optimization scheduling method may include steps 110 to 150, and each step will be introduced below.

[0032] In step 110, a generalized energy storage model of the generalized energy storage in the microgrid is pre-constructed, and an optimized scheduling model of the microgrid is established based on the generalized energy storage model.

[0033] In one embodiment, a generalized energy storage model of generalized energy storage in a microgrid can be pre-constructed, and an optimized scheduling model of the microgrid can be established based on the generalized energy storage model. It should be noted that the construction of the generalized energy storage model of generalized energy storage in a microgrid can be completed in an offline stage, that is, it is completed without actually performing scheduling optimization (corresponding to the online stage).

[0034] Among them, generalized energy storage refers to flexible resources with "storage-like" characteristics in a broad sense, mainly including actual energy storage, such as battery energy storage, pumped storage, compressed air energy storage, etc.; and virtual energy storage, such as temperature control loads, electric vehicles, etc. Many flexible resources in the power grid have strong heterogeneity, and they can be established into a unified generalized energy storage model to facilitate subsequent optimization and scheduling modeling.

[0035] In step 120, historical operating parameters under multiple historical operating scenarios are obtained, and each historical optimal scheduling strategy under each historical operating scenario is obtained based on the historical operating parameters and the optimization scheduling model.

[0036] In another embodiment, historical operating parameters of the microgrid under multiple historical operating scenarios can also be obtained, wherein the historical operating parameters can at least include historical new energy output, historical load, and historical electricity price. It can be understood that based on the historical operating parameters and the optimization scheduling model, each historical optimal scheduling strategy under each historical operating scenario can be obtained.

[0037] In another example, under different historical renewable energy outputs, and / or historical loads, and / or historical electricity prices, the corresponding historical operating scenarios are different.

[0038] In step 130, based on each historical optimal scheduling strategy, each group of historical state of charge sequences corresponding to each historical optimal scheduling strategy is obtained.

[0039] In step 140, a reference state of charge sequence in the online scheduling process is determined based on each group of historical state of charge sequences under each historical operation scenario, and a reference electricity price in the online scheduling process is determined based on each historical electricity price under each historical operation scenario.

[0040] In step 150, based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model, the optimal scheduling strategy of the microgrid in the online scheduling process is obtained, wherein the optimal scheduling strategy at least includes the charging power control instruction of the generalized energy storage, the discharging power control instruction of the generalized energy storage, and the discharging power control instruction of the distributed generator.

[0041] In another embodiment, each group of historical state of charge (also known as SOC) sequences corresponding to each historical optimal scheduling strategy can be obtained based on each historical optimal scheduling strategy. Compared with prediction, historical data is easier to obtain. In the application process, by solving the optimal scheduling strategy under historical scenarios, valuable experience can be provided for real-time scheduling.

[0042] In another embodiment, the reference state of charge sequence in the online scheduling process can be determined based on each group of historical state of charge sequences under each historical operation scenario, and the reference electricity price in the online scheduling process can be determined based on each historical electricity price under each historical operation scenario. Since the reference state of charge sequence and the reference electricity price in the online scheduling process are not predicted but obtained based on historical data, it can effectively alleviate the problem that the energy scheduling of the grid-connected microgrid faces multiple uncertainties and the uncertainty is difficult to predict.

[0043] Furthermore, based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model, the optimal scheduling strategy of the microgrid during the online scheduling process can be obtained, that is, the charging power control instructions of the generalized energy storage, the discharging power control instructions of the generalized energy storage, and the discharging power control instructions of the distributed generators are obtained. Therefore, the microgrid can be scheduled based on the charging power control instructions of the generalized energy storage, the discharging power control instructions of the generalized energy storage, and the discharging power control instructions of the distributed generators, so as to ensure the optimal economic operation without relying on prediction data, thereby realizing safe and economical scheduling of the microgrid.

[0044] The microgrid optimization scheduling method provided by the present invention is applied to a microgrid, wherein the microgrid includes at least generalized energy storage and distributed generators, and the generalized energy storage is a resource with energy storage function. The method includes: pre-constructing a generalized energy storage model of generalized energy storage in the microgrid, and establishing an optimization scheduling model of the microgrid based on the generalized energy storage model; obtaining historical operating parameters under multiple historical operating scenarios, and obtaining each historical optimal scheduling strategy under each historical operating scenario based on the historical operating parameters and the optimization scheduling model; obtaining each group of historical state of charge sequences corresponding to each historical optimal scheduling strategy based on each historical optimal scheduling strategy; determining a reference state of charge sequence in the online scheduling process based on each group of historical state of charge sequences under each historical operating scenario, and determining a reference electricity price in the online scheduling process based on each historical electricity price under each historical operating scenario; obtaining the optimal scheduling strategy of the microgrid in the online scheduling process based on the reference state of charge sequence, the reference electricity price and the optimization scheduling model. By avoiding the defect that new energy is difficult to predict without data prediction processing, safe and economical scheduling processing of the microgrid is achieved.

[0045] Figure 2It is a schematic diagram of a flow chart of constructing a generalized energy storage model for generalized energy storage in a microgrid provided by the present invention.

[0046] The following will be combined Figure 2 The process of constructing a generalized energy storage model for generalized energy storage in a microgrid provided by the present invention is described.

[0047] In an exemplary embodiment of the present invention, Figure 2 It can be seen that constructing a generalized energy storage model for generalized energy storage in a microgrid may include steps 210 to 230, and each step will be introduced below.

[0048] In step 210 , a charging power decision variable of the generalized energy storage, a discharging power decision variable of the generalized energy storage, a charging power boundary, and a discharging power boundary are determined.

[0049] For the sake of convenience, the present invention takes generalized energy storage including battery energy storage, temperature control load and electric vehicles as examples of three typical flexibility resources, and proposes a generalized energy storage homogeneity modeling method that takes into account decision-dependent uncertainty. Other types of generalized energy storage, such as pumped storage, thermal energy storage, hydrogen energy storage, mobile energy storage, etc., can be further modified and supplemented on the basis of the present invention.

[0050] In one embodiment, the charging power decision variable of the generalized energy storage can be determined as 、Discharge power decision variables of generalized energy storage , charging power boundary, and discharging power boundary. Among them, the charging power and discharging power of generalized energy storage can be based on the actual response of the microgrid after the load With baseline load The difference is determined.

[0051] In one embodiment, the charging and discharging power of the generalized energy storage can be equivalent to the actual response after load With baseline load The difference is as shown in the following formula (1). > , it represents charging behavior; otherwise, it represents discharging behavior.

[0052] (1) in, means and The abbreviation of .

[0053] In step 220 , the state of charge sequence decision variables and the state of charge sequence boundary of the generalized energy storage are determined.

[0054] In another embodiment, the state of charge sequence decision variables of the generalized energy storage can also be determined , and the state of charge sequence boundary. Among them, the state of charge sequence of the generalized energy storage can be determined according to the indoor temperature of the microgrid.

[0055] In one example, for a temperature control load, SoC is defined as a percentage relative to the indoor temperature, as shown in the following formula. is the indoor temperature, and are the maximum and minimum indoor temperatures respectively; and the SoC of an electric vehicle is the SoC of the power battery. Among them, the charge state sequence decision variable It can be expressed as formula (2): …… (2) In step 230, a generalized energy storage model of the generalized energy storage is constructed based on the charging power decision variables of the generalized energy storage, the discharging power decision variables of the generalized energy storage, the charging power boundary, the discharging power boundary, the state of charge sequence decision variables of the generalized energy storage, and the state of charge sequence boundary.

[0056] In another embodiment, the charging power decision variable of the generalized energy storage can be 、Discharge power decision variables of generalized energy storage , charging power boundary, discharging power boundary, state of charge sequence decision variables of generalized energy storage , and the state of charge sequence boundary, the generalized energy storage model of generalized energy storage is constructed. Among them, the generalized energy storage model of generalized energy storage can be expressed by the following formulas (3)-(7): …… (3) …… (4) …… (5) …… (6) …… (7) Wherein, the subscript t represents the scheduling time; represents the time-varying upper charging power boundary of generalized energy storage (corresponding to the charging power boundary); represents the time-varying upper discharge power boundary of generalized energy storage (corresponding to the discharge power boundary); and The upper and lower SoC boundaries of generalized energy storage time-varying (corresponding to the state of charge sequence boundaries) are defined; and Defined the generalized energy storage charging and discharging efficiency; is the self-discharge rate of generalized energy storage; is the capacity of generalized energy storage; It is a newly added correction item related to the baseline energy consumption; is the scheduling time interval; T is the total scheduling time period.

[0057] Among them, in formula (7) Indicates the SoC at the start time; Indicates the SoC at the end time.

[0058] It can be seen that compared with the traditional battery energy storage model, the generalized energy storage model proposed in the present invention introduces new constraints and correction terms, and most of the parameters of the model are time-varying. Therefore, the present invention can provide an explanation and transformation of the core parameters for temperature control loads and electric vehicles: Charge and discharge power: The charging and discharging power of generalized energy storage can be equivalent to the actual response load With baseline load The difference is shown in the above formula (1). If > , it represents charging behavior; otherwise, it represents discharging behavior.

[0059] State of Charge: For temperature control load, SoC is defined as the percentage relative to the indoor temperature, as shown in the following formula. is the indoor temperature, and are the maximum and minimum indoor temperatures respectively; and the SoC of an electric vehicle is the SoC of the power battery. Among them, the charge state sequence decision variable It can be expressed as formula (2).

[0060] Charge and discharge power boundary: The charge and discharge power boundary is limited by the load power boundary Baseline energy usage , and presents a time-varying characteristic, as shown in the following formula (8): (8) SOC Boundary: For temperature control loads, the SoC boundary depends on the temperature range that users tolerate at different times, as shown in the following formula; for electric vehicles, the SoC boundary depends on the remaining power range that meets users' travel needs at different times. Among them, it can be referred to as shown in formula (9): (9) Through the above definition, the actual load physical model and the generalized energy storage model can also be converted according to the conversion relationship shown in Table 1.

[0061] Table 1 Conversion table between actual load physical model and generalized energy storage model It can be understood that the generalized energy storage model can be shown as in the above formulas (3)-(7).

[0062] In another exemplary embodiment of the present invention, the above-mentioned embodiment is continued as an example for explanation, wherein establishing an optimization scheduling model of a microgrid based on a generalized energy storage model may include the following steps: Under the generalized energy storage model, determine the incentive cost of generalized energy storage; The power purchase cost of the microgrid from the external power grid and the power generation cost of the distributed generator are obtained respectively; Based on the incentive cost, electricity purchase cost, and power generation cost of generalized energy storage, an optimal scheduling model for microgrids is established.

[0063] In one embodiment, a 5-minute granularity daily scheduling model can be considered, and the index of the time series is Among them, the goal of microgrid scheduling is to minimize the operating cost of the microgrid, that is, to minimize the optimization scheduling model. .in, represents the incentive cost of generalized energy storage; represents the cost of purchasing electricity from the external power grid; Indicates the power generation cost of the distributed generator. Wherein, the above parameters and the subscript t in the present invention represent the corresponding scheduling time.

[0064] In one embodiment, the incentive cost of generalized energy storage can be determined under the generalized energy storage model. ; and respectively obtain the power purchase cost of the microgrid from the external power grid , and obtain the generation cost of distributed generators ; Further, based on the incentive cost of generalized energy storage , Power purchase cost and the cost of electricity generation The optimal dispatching model of the microgrid is established based on the sum of

[0065] In another exemplary embodiment of the present invention, the above-mentioned embodiment is continued to be used as an example for description, wherein, under the generalized energy storage model, determining the incentive cost of generalized energy storage can be implemented in the following manner: Under the generalized energy storage model, determining the charging power of the generalized energy storage and the discharging power of the generalized energy storage, as well as determining the charging cost coefficient of the generalized energy storage and the discharging cost coefficient of the generalized energy storage; The incentive cost of generalized energy storage is determined based on the charging power of generalized energy storage, the discharging power of generalized energy storage, the charging cost coefficient of generalized energy storage and the discharging cost coefficient of generalized energy storage.

[0066] In one embodiment, the discharge power of the generalized energy storage and the charging power of the generalized energy storage can be determined under the generalized energy storage model. , and determine the discharge cost coefficient of generalized energy storage and the charging cost coefficient of generalized energy storage It should be noted that the subscript j of the parameter represents the jth generalized energy storage. Furthermore, a first product is obtained based on the charging power of the generalized energy storage and the charging cost coefficient of the generalized energy storage, and a second product coefficient is obtained based on the discharge power of the generalized energy storage and the discharge cost of the generalized energy storage, and the incentive cost of the generalized energy storage is determined based on the sum of the first product and the second product.

[0067] In another embodiment, the incentive cost of generalized energy storage can be expressed by formula (10): (10) In another embodiment, the cost of purchasing electricity from the external power grid for the microgrid can be expressed by formula (11): (11) In another embodiment, the power generation cost of the distributed generator can be expressed by formula (12), wherein the subscript k in formula (12) represents the kth distributed generator: (12) Among them, the constraints of the aforementioned model include the output power constraint of distributed generators, the ramp power constraint, the output power constraint of generalized energy storage, the expression relationship of the SoC of generalized energy storage, the upper and lower bound constraints of the SoC of generalized energy storage, the energy balance constraint of SoC and the DistFlow flow constraint (applicable to radial microgrids). The two opportunity constraints on generalized energy storage can adopt different confidence levels according to reliability preferences.

[0068] for: , the model constraints are as follows: (13) (14) (15) (16) (17) (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) In the above model, , and They are respectively a collection of nodes of generalized energy storage, distributed generators and microgrids. is the cost coefficient of generalized energy storage j. is the discharge and charge power of the generalized energy storage j. is the unit scheduling interval. It's the price of electricity. It is the power purchased from the grid. is the output power of the distributed generator. is the cost coefficient of the distributed generator. The ramp limit of the distributed generator is given by (Ramp-up power limit of distributed generators) and (Down-ramp power limit of distributed generators) is given. The underscore and overscore indicate minimum and maximum. is the probability level of the chance constraint. , , and Defines self-discharge rate, charge / discharge efficiency, capacity, and baseline energy usage. , is the index of the microgrid branches and nodes, is the set of branches connected to node m, is the set of branches between nodes m and l, , are the active and reactive powers flowing on branch b, , They are respectively the active and reactive power flowing through the side branches. The generalized energy storage can be set to have a fixed power factor. , is the load power and renewable energy generation power. is the voltage at node m. is the voltage of the substation bus. , are the resistance and reactance of branch b.

[0069] It should be noted that the superscript DG in the parameter represents a distributed generator; the superscript GES in the parameter represents a generalized energy storage; the subscript t in the parameter represents the scheduling time; the subscript k in the parameter represents the number of the distributed generator; the subscript j in the parameter represents the number of the generalized energy storage; the subscript m in the parameter represents the node number of the microgrid; and the subscript b in the parameter represents the branch number of the microgrid.

[0070] Due to the uncertainty and randomness of the SoC and power boundary of generalized energy storage, the chance constraints become difficult to handle. Therefore, the two chance constraints (corresponding to formulas 16 and 18) can be restated deterministically to make them easy to handle, as shown in formulas (25)-(28): … (25) … (26) … (27) … (28) Among them, the normalized inverse cumulative distribution function It can be obtained by Monte Carlo sampling of any type of distribution (e.g., normal distribution, beta distribution). and are the mean and standard deviation of the distribution, respectively. After the deterministic restatement, the dispatch model of the microgrid becomes a convex optimization model, which can be efficiently solved by commercial solvers. However, the optimal solution obtained is for a specific renewable energy and electricity price scenario, which cannot adapt to the real-time changes of uncertainty. It should be noted that for the parameters with the superscript GES, the subscript d in the parameters indicates the discharge process; the subscript c in the parameters indicates the charging process.

[0071] In order to further introduce the microgrid optimization scheduling method provided by the present invention, the following will be combined with Figure 3 Provide explanation.

[0072] Figure 3 It is a flow chart of determining a reference state of charge sequence in an online scheduling process based on each group of historical state of charge sequences under each historical operation scenario provided by the present invention.

[0073] In an exemplary embodiment of the present invention, Figure 3 It can be seen that based on each group of historical state of charge sequences under each historical operation scenario, determining the reference state of charge sequence in the online scheduling process may include steps 310 to 340, and each step will be introduced below.

[0074] In step 310, the net load and electricity price of the microgrid during the online scheduling process on the scheduling day are observed at intervals of a preset duration.

[0075] In step 320, the historical net load and historical electricity price of the microgrid under each historical operation scenario are obtained.

[0076] In step 330, based on the net load and the electricity price, as well as the historical net load and the historical electricity price of the microgrid under each historical operation scenario, a weight matching each historical operation scenario is determined.

[0077] In step 340 , a reference state of charge sequence in the online scheduling process is determined based on the weights matching each historical operation scenario and each group of historical state of charge sequences.

[0078] In one embodiment, the net load and electricity price of the microgrid during the online scheduling process on the scheduling day can be observed at intervals of a preset duration, such as 5 minutes. Further, the historical net load and historical electricity price of the microgrid under each historical operation scenario are obtained, and based on the net load and the electricity price, as well as the historical net load and historical electricity price of the microgrid under each historical operation scenario, the weights matching each historical operation scenario are determined. During the application process, the reference state of charge sequence in the online scheduling process can be determined based on the weights matching each historical operation scenario and each group of historical state of charge sequences.

[0079] It should be noted that due to the lack of forecast data, when generating real-time control actions for microgrids, decisions are inevitably prone to "short-sightedness". This is because only the current system status information can be observed when making decisions, and the energy storage system, as an energy storage system, has time coupling characteristics. The role of the reference SoC (corresponding to the reference state of charge sequence in the online scheduling process) is to provide a long-term perspective for the operation of generalized energy storage, so as to achieve economic and safe operation of the system in the long run. The idea of ​​data-driven reference update is to dynamically assign weights to the offline optimal SoC scheduling sequence based on the correlation between the current scenario (corresponding to the online scheduling process under the scheduling day) and the historical scenario (corresponding to the historical operation scenario). Specifically, if the uncertainty observed in the operation is closer to a historical scenario in the past, then the optimal SoC sequence of the historical scenario should be assigned a larger weight in the reference calculation. This method ensures that the reference SoC is constantly adjusted to reflect the most valuable historical scheduling experience.

[0080] In order to further introduce the process of determining the reference state of charge sequence in the online scheduling process of the present invention, it will be described in conjunction with the following embodiments.

[0081] In one embodiment, a set of net load (actual load minus renewable energy power) and electricity price historical scenarios may be used as an example for illustration, wherein the historical operation scenario may be expressed as the following formula (29): … (29) in, is the net load, is the number of historical scenarios. For each historical operation scenario, the optimal dispatch model of the microgrid for a single day (corresponding to the optimization dispatch model) can be solved and obtained An offline optimal SoC scheduling sequence for generalized energy storage: .

[0082] Furthermore, we can define the vector and It represents the net load and electricity price observed from the start of the dispatching day to the current time t in real-time operation, that is, the net load and electricity price of the microgrid during the online dispatching process on the corresponding dispatching day.

[0083] in, and It can be expressed as follows (30): . (30) The vector corresponding to the sth historical operation scenario can be defined as follows (31): (31) therefore (corresponding to the net load of the microgrid during the online dispatch process on the dispatch day) and The similarity between the historical net load of the microgrid under the historical operation scenario s can be expressed by the Euclidean distance Then, the reference SoC can be calculated and continuously updated by taking the weighted average of the offline optimal SoC sequence.

[0084] Can make is the reference SoC of time period t (corresponding to the reference state of charge sequence in the online scheduling process), then its update rule is as shown in formula (32): (32) in, is the weight of historical scenario s (corresponding to the weight matching each historical operation scenario), which reflects the similarity between the current scheduling day and the sth historical scenario. It can be calculated using the Nadaraya-Waston kernel regression technique and can be expressed as formula (33): (33) Among them, and As an input vector, it can be expressed as the following formula (34): (34) The above formula can be defined as the Gaussian kernel regression function; is the bandwidth parameter. Obviously: , and scenarios similar to the current scheduling day are assigned greater weights. It can be regarded as an offline probability distribution, and the reference SoC can be regarded as the conditional expectation of S offline optimal scheduling sequences on the current scheduling day. Using the latest data, the reference is updated every five minutes, which is a continuous process over time. Through this embodiment, it is possible to obtain a reference state of charge sequence in the online scheduling process that matches the actual situation more reliably without predicting data.

[0085] It can be understood that the aforementioned formula (34) provides a process for determining weights matching each historical operation scenario based on the net load and electricity price, as well as the historical net load and historical electricity price of the microgrid under each historical operation scenario.

[0086] Figure 4 It is a flow chart of determining a reference electricity price in an online scheduling process based on various historical electricity prices under various historical operation scenarios provided by the present invention.

[0087] The following will be combined Figure 4 The process of determining the reference electricity price in the online scheduling process based on various historical electricity prices under various historical operation scenarios is explained.

[0088] In an exemplary embodiment of the present invention, Figure 4 It can be seen that based on each historical electricity price under each historical operation scenario, determining the reference electricity price in the online scheduling process may include step 410 and step 420, and each step will be introduced below.

[0089] In step 410, a weight matching each historical operation scenario is obtained; In step 420, a reference electricity price in the online scheduling process is determined based on the weights matching each historical operation scenario and each historical electricity price under each historical operation scenario.

[0090] Based on a concept similar to the aforementioned determination of a reference state of charge sequence in an online scheduling process, a reference electricity price in an online scheduling process may be determined.

[0091] In one example, the weights matching each historical operation scenario can be obtained. ; Then, based on the weights matching each historical operation scenario and the historical electricity prices under each historical operation scenario, the reference electricity price in the online dispatching process is determined. The reference electricity price in the online dispatching process can be expressed by formula (35): (35) in, It is the dynamic average electricity price (corresponding to the reference electricity price during online dispatch). is the average electricity price of historical scenario s. Similarly, the dynamic average electricity price can also be regarded as the expected average of the average electricity prices of S historical scenarios on the current scheduling day.

[0092] Figure 5 It is a flow chart of obtaining the optimal dispatching strategy of a microgrid in an online dispatching process based on a reference state of charge sequence, a reference electricity price, and an optimized dispatching model provided by the present invention.

[0093] The following will be combined Figure 5 The process of obtaining the optimal dispatching strategy of the microgrid during online dispatching based on the reference state of charge sequence, reference electricity price, and optimized dispatching model is explained.

[0094] In an exemplary embodiment of the present invention, Figure 5 It can be seen that based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model, obtaining the optimal scheduling strategy of the microgrid during the online scheduling process may include steps 510 to 520, and each step will be introduced below.

[0095] In step 510, based on the reference state of charge sequence and the reference electricity price, the optimization scheduling model is re-optimized to obtain an optimized optimization scheduling model.

[0096] Based on the reference SoC, the generation of real-time control actions is usually based on minimizing the immediate cost based on tracking the reference SoC. Usually this operation strategy tends to discharge the generalized energy storage based on the reference SoC (when the electricity price is higher than This strategy, while incurring immediate costs, is certainly not optimal in the long run. A better approach is to discharge when the electricity price is above the “average” and charge when the electricity price is below the “average”. (Formula 10) has been modified: (36) In other words, based on the reference electricity price right A correction was made, that is, formula (10) was corrected to formula (36).

[0097] Furthermore, in order to allow generalized energy storage to better arbitrage through the peak-valley difference of electricity prices, the optimization problem of online decision-making in time period t (corresponding to ) can be expressed as the following formula (37): (37) That is, based on the reference state of charge sequence and reference electricity price, the optimization scheduling model is re-optimized to obtain the optimized optimization scheduling model (corresponding to Formula 37).

[0098] The second part of the objective function (Equation 37) represents the penalty term for deviation from the reference, where is the weight coefficient. The energy balance constraint of SoC needs to be removed from the constraints, because the above optimization problem is a local online optimization problem in a single period and cannot handle the cross-period coupling constraints throughout the day. However, by reasonably tracking the reference SoC, generalized energy storage can still achieve a sustainable dispatchable energy state.

[0099] In step 520, based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model, an optimal scheduling strategy of the microgrid in the online scheduling process is obtained through an online convex optimization algorithm.

[0100] In one embodiment, based on the reference state of charge sequence, the reference electricity price, and the optimized dispatch model, an online convex optimization algorithm can be used to obtain the optimal dispatch strategy of the microgrid during the online dispatch process. Through this embodiment, safe and economical dispatch processing of the microgrid can be achieved.

[0101] According to the foregoing description, the microgrid optimization scheduling method provided by the present invention is applied to a microgrid, and the microgrid includes at least generalized energy storage and distributed generators, and the generalized energy storage is a resource with energy storage function. The method includes: pre-constructing a generalized energy storage model of generalized energy storage in the microgrid, and establishing an optimization scheduling model of the microgrid based on the generalized energy storage model; obtaining historical operating parameters under multiple historical operating scenarios, and obtaining each historical optimal scheduling strategy under each historical operating scenario based on the historical operating parameters and the optimization scheduling model; based on each historical optimal scheduling strategy, obtaining each group of historical state of charge sequences corresponding to each historical optimal scheduling strategy; based on each group of historical state of charge sequences under each historical operating scenario, determining the reference state of charge sequence in the online scheduling process, and based on each historical electricity price under each historical operating scenario, determining the reference electricity price in the online scheduling process; based on the reference state of charge sequence, the reference electricity price and the optimization scheduling model, obtaining the optimal scheduling strategy of the microgrid in the online scheduling process. By avoiding the defect that new energy is difficult to predict without data prediction processing, safe and economical scheduling processing of the microgrid is achieved.

[0102] The microgrid optimization scheduling device provided by the present invention is described below. The microgrid optimization scheduling device described below and the microgrid optimization scheduling method described above can be referenced to each other.

[0103] Figure 6 It is a structural schematic diagram of the microgrid optimization scheduling device provided by the present invention.

[0104] The following will be combined Figure 6 The structure of the microgrid optimization scheduling device provided by the present invention is described.

[0105] In an exemplary embodiment of the present invention, the microgrid optimization scheduling device can be applied to a microgrid, wherein the microgrid includes at least generalized energy storage and distributed generators, wherein the generalized energy storage is a resource with energy storage function, combined with Figure 6 It can be known that the device may include a construction module 610, a determination module 620, an acquisition module 630, a processing module 640, and a scheduling module 650, and each module will be introduced below.

[0106] The construction module 610 may be configured to pre-construct a generalized energy storage model of the generalized energy storage in the microgrid, and establish an optimized scheduling model of the microgrid based on the generalized energy storage model; The determination module 620 may be configured to obtain historical operation parameters under a plurality of historical operation scenarios, and obtain each historical optimal dispatching strategy under each of the historical operation scenarios based on the historical operation parameters and the optimization dispatching model, wherein the historical operation parameters at least include historical new energy output, historical load, and historical electricity price; The acquisition module 630 may be configured to obtain, based on each of the historical optimal scheduling strategies, each group of historical state of charge sequences corresponding to each of the historical optimal scheduling strategies; The processing module 640 may be configured to determine a reference state of charge sequence in the online scheduling process based on each group of historical state of charge sequences under each of the historical operation scenarios, and determine a reference electricity price in the online scheduling process based on each historical electricity price under each of the historical operation scenarios; The scheduling module 650 can be configured to obtain the optimal scheduling strategy of the microgrid during the online scheduling process based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model, wherein the optimal scheduling strategy includes at least the charging power control instruction of the generalized energy storage, the discharging power control instruction of the generalized energy storage, and the discharging power control instruction of the distributed generator.

[0107] In an exemplary embodiment of the present invention, the construction module 610 may construct a generalized energy storage model of the generalized energy storage in the microgrid in the following manner: Determining a charging power decision variable of the generalized energy storage, a discharging power decision variable of the generalized energy storage, a charging power boundary, and a discharging power boundary, wherein the charging power and the discharging power of the generalized energy storage are determined according to the difference between the actual response load of the microgrid and the baseline load, and Determine the state of charge sequence decision variables and the state of charge sequence boundary of the generalized energy storage, wherein the state of charge sequence of the generalized energy storage is determined according to the indoor temperature of the microgrid; Based on the charging power decision variable of the generalized energy storage, the discharging power decision variable of the generalized energy storage, the charging power boundary, the discharging power boundary, the state of charge sequence decision variable of the generalized energy storage, and the state of charge sequence boundary, a generalized energy storage model of the generalized energy storage is constructed.

[0108] In an exemplary embodiment of the present invention, the construction module 610 may implement the establishment of the optimization scheduling model of the microgrid based on the generalized energy storage model in the following manner: Under the generalized energy storage model, determining the incentive cost of the generalized energy storage; Respectively obtaining the electricity purchase cost of the microgrid from the external power grid and the power generation cost of the distributed generator; An optimization scheduling model for the microgrid is established based on the incentive cost of the generalized energy storage, the electricity purchase cost, and the power generation cost.

[0109] In an exemplary embodiment of the present invention, the construction module 610 may determine the incentive cost of the generalized energy storage under the generalized energy storage model in the following manner: Under the generalized energy storage model, determining the charging power of the generalized energy storage and the discharging power of the generalized energy storage, and determining the charging cost coefficient of the generalized energy storage and the discharging cost coefficient of the generalized energy storage; The incentive cost of the generalized energy storage is determined based on the charging power of the generalized energy storage, the discharging power of the generalized energy storage, the charging cost coefficient of the generalized energy storage, and the discharging cost coefficient of the generalized energy storage.

[0110] In an exemplary embodiment of the present invention, the processing module 640 may also be configured to: At each preset time interval, the net load and electricity price of the microgrid during the online scheduling process on the scheduling day are observed; Obtaining the historical net load and historical electricity price of the microgrid under each of the historical operation scenarios; Based on the net load and the electricity price, and the historical net load and the historical electricity price of the microgrid under each of the historical operation scenarios, determining a weight matching each of the historical operation scenarios; The processing module 640 may also determine the reference state of charge sequence in the online scheduling process based on each group of historical state of charge sequences under each of the historical operation scenarios in the following manner: Based on the weights matching each of the historical operating scenarios and each group of historical state of charge sequences, a reference state of charge sequence in the online scheduling process is determined.

[0111] In an exemplary embodiment of the present invention, the processing module 640 may determine the reference electricity price in the online scheduling process based on each historical electricity price under each of the historical operation scenarios in the following manner: Obtaining weights matching each of the historical operation scenarios; Based on the weights matching the historical operation scenarios and the historical electricity prices under the historical operation scenarios, a reference electricity price in the online scheduling process is determined.

[0112] In an exemplary embodiment of the present invention, the scheduling module 650 may also be configured to: Based on the reference state of charge sequence and the reference electricity price, re-optimizing the optimization scheduling model to obtain an optimized optimization scheduling model; The scheduling module 650 may also adopt the following method to obtain the optimal scheduling strategy of the microgrid in the online scheduling process based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model: Based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model after optimization, an optimal scheduling strategy of the microgrid in the online scheduling process is obtained through an online convex optimization algorithm.

[0113] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7As shown, the electronic device may include: a processor 710, a communications interface 720, a memory 730 and a communication bus 740, wherein the processor 710, the communications interface 720 and the memory 730 communicate with each other through the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the microgrid optimization scheduling method, the method being applied to a microgrid, the microgrid at least including generalized energy storage and distributed generators, the generalized energy storage being a resource with energy storage function, the method comprising: pre-constructing a generalized energy storage model of the generalized energy storage in the microgrid, and establishing an optimization scheduling model of the microgrid based on the generalized energy storage model; obtaining historical operating parameters under multiple historical operating scenarios, and obtaining each historical optimal scheduling strategy under each of the historical operating scenarios based on the historical operating parameters and the optimization scheduling model, wherein the historical operating parameters at least include historical new energy output, historical load, and historical electricity price; based on each of the historical optimal scheduling strategies, obtaining each group of historical state of charge sequences corresponding to each of the historical optimal scheduling strategies; based on each group of historical state of charge sequences under each of the historical operating scenarios, determining a reference state of charge sequence in the online scheduling process, and based on each historical electricity price under each of the historical operating scenarios, determining a reference electricity price in the online scheduling process; based on the reference state of charge sequence, the reference electricity price, and the optimization scheduling model, obtaining an optimal scheduling strategy for the microgrid in the online scheduling process, wherein the optimal scheduling strategy at least includes a charging power control instruction for generalized energy storage, a discharging power control instruction for generalized energy storage, and a discharging power control instruction for distributed generators.

[0114] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0115] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the microgrid optimization scheduling method provided by the above-mentioned methods. The method is applied to a microgrid, and the microgrid at least includes generalized energy storage and distributed generators. The generalized energy storage is a resource with energy storage function. The method includes: pre-constructing a generalized energy storage model of the generalized energy storage in the microgrid, and establishing an optimization scheduling model of the microgrid based on the generalized energy storage model; obtaining historical operating parameters under multiple historical operating scenarios, and obtaining each historical optimal scheduling strategy under each of the historical operating scenarios based on the historical operating parameters and the optimization scheduling model. , wherein the historical operating parameters at least include historical new energy output, historical load, and historical electricity price; based on each of the historical optimal scheduling strategies, each group of historical state of charge sequences corresponding to each of the historical optimal scheduling strategies is obtained; based on each group of historical state of charge sequences under each of the historical operating scenarios, a reference state of charge sequence in the online scheduling process is determined, and based on each historical electricity price under each of the historical operating scenarios, a reference electricity price in the online scheduling process is determined; based on the reference state of charge sequence, the reference electricity price, and the optimization scheduling model, an optimal scheduling strategy for the microgrid in the online scheduling process is obtained, wherein the optimal scheduling strategy at least includes a charging power control instruction of a generalized energy storage, a discharging power control instruction of a generalized energy storage, and a discharging power control instruction of a distributed generator.

[0116] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the processor executes the microgrid optimization scheduling method provided by the above methods, the method is applied to a microgrid, the microgrid includes at least generalized energy storage and distributed generators, the generalized energy storage is a resource with energy storage function, the method includes: pre-constructing a generalized energy storage model of the generalized energy storage in the microgrid, and establishing an optimization scheduling model of the microgrid based on the generalized energy storage model; obtaining historical operating parameters under multiple historical operating scenarios, and obtaining each historical optimal scheduling strategy under each of the historical operating scenarios based on the historical operating parameters and the optimization scheduling model, wherein the historical operating parameters are at least Including historical renewable energy output, historical load, and historical electricity price; based on each of the historical optimal dispatching strategies, each group of historical state of charge sequences corresponding to each of the historical optimal dispatching strategies is obtained; based on each group of historical state of charge sequences under each of the historical operating scenarios, a reference state of charge sequence in the online dispatching process is determined, and based on each historical electricity price under each of the historical operating scenarios, a reference electricity price in the online dispatching process is determined; based on the reference state of charge sequence, the reference electricity price, and the optimized dispatching model, an optimal dispatching strategy for the microgrid in the online dispatching process is obtained, wherein the optimal dispatching strategy at least includes a charging power control instruction for generalized energy storage, a discharging power control instruction for generalized energy storage, and a discharging power control instruction for distributed generators.

[0117] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0118] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A microgrid optimization scheduling method, characterized in that: The method is applied to a microgrid, wherein the microgrid includes at least generalized energy storage and a distributed generator, wherein the generalized energy storage is a resource having an energy storage function, and the method includes: Pre-constructing a generalized energy storage model of the generalized energy storage in the microgrid, and establishing an optimized scheduling model of the microgrid based on the generalized energy storage model; Acquire historical operating parameters under multiple historical operating scenarios, and obtain each historical optimal dispatching strategy under each of the historical operating scenarios based on the historical operating parameters and the optimization dispatching model, wherein the historical operating parameters at least include historical new energy output, historical load, and historical electricity price; Based on each of the historical optimal scheduling strategies, obtaining each group of historical state of charge sequences corresponding to each of the historical optimal scheduling strategies; Determining a reference state of charge sequence in the online scheduling process based on each group of historical state of charge sequences under each of the historical operation scenarios, and determining a reference electricity price in the online scheduling process based on each historical electricity price under each of the historical operation scenarios; Based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model, an optimal scheduling strategy for the microgrid during the online scheduling process is obtained, wherein the optimal scheduling strategy at least includes a charging power control instruction for generalized energy storage, a discharging power control instruction for generalized energy storage, and a discharging power control instruction for distributed generators.

2. The microgrid optimization scheduling method according to claim 1, characterized in that: The generalized energy storage model of the generalized energy storage in the microgrid is constructed in the following manner: Determining a charging power decision variable of the generalized energy storage, a discharging power decision variable of the generalized energy storage, a charging power boundary, and a discharging power boundary, wherein the charging power and the discharging power of the generalized energy storage are determined according to the difference between the actual response load of the microgrid and the baseline load, and Determine the state of charge sequence decision variables and the state of charge sequence boundary of the generalized energy storage, wherein the state of charge sequence of the generalized energy storage is determined according to the indoor temperature of the microgrid; Based on the charging power decision variable of the generalized energy storage, the discharging power decision variable of the generalized energy storage, the charging power boundary, the discharging power boundary, the state of charge sequence decision variable of the generalized energy storage, and the state of charge sequence boundary, a generalized energy storage model of the generalized energy storage is constructed.

3. The microgrid optimization scheduling method according to claim 1 or 2, characterized in that: The optimization scheduling model of the microgrid is established based on the generalized energy storage model, specifically including: Under the generalized energy storage model, determining the incentive cost of the generalized energy storage; Respectively obtaining the electricity purchase cost of the microgrid from the external power grid and the power generation cost of the distributed generator; An optimization scheduling model for the microgrid is established based on the incentive cost of the generalized energy storage, the electricity purchase cost, and the power generation cost.

4. The microgrid optimization scheduling method according to claim 3 is characterized in that: Determining the incentive cost of the generalized energy storage under the generalized energy storage model specifically includes: Under the generalized energy storage model, determining the charging power of the generalized energy storage and the discharging power of the generalized energy storage, and determining the charging cost coefficient of the generalized energy storage and the discharging cost coefficient of the generalized energy storage; The incentive cost of the generalized energy storage is determined based on the charging power of the generalized energy storage, the discharging power of the generalized energy storage, the charging cost coefficient of the generalized energy storage, and the discharging cost coefficient of the generalized energy storage.

5. The microgrid optimization scheduling method according to claim 1, characterized in that: Before determining the reference state of charge sequence in the online scheduling process based on each group of historical state of charge sequences under each of the historical operation scenarios, the method further includes: At each preset time interval, the net load and electricity price of the microgrid during the online scheduling process on the scheduling day are observed; Obtaining the historical net load and historical electricity price of the microgrid under each of the historical operation scenarios; Based on the net load and the electricity price, and the historical net load and the historical electricity price of the microgrid under each of the historical operation scenarios, determining a weight matching each of the historical operation scenarios; The determining of a reference state of charge sequence in the online scheduling process based on each group of historical state of charge sequences under each of the historical operation scenarios specifically includes: Based on the weights matching each of the historical operating scenarios and each group of historical state of charge sequences, a reference state of charge sequence in the online scheduling process is determined.

6. The microgrid optimization scheduling method according to claim 5, characterized in that: The determining of the reference electricity price in the online scheduling process based on the historical electricity prices in the historical operation scenarios specifically includes: Obtaining weights matching each of the historical operation scenarios; Based on the weights matching the historical operation scenarios and the historical electricity prices under the historical operation scenarios, a reference electricity price in the online scheduling process is determined.

7. The microgrid optimization scheduling method according to claim 1, characterized in that: Before obtaining the optimal dispatching strategy of the microgrid in the online dispatching process based on the reference state of charge sequence, the reference electricity price, and the optimized dispatching model, the method further includes: Based on the reference state of charge sequence and the reference electricity price, re-optimizing the optimization scheduling model to obtain an optimized optimization scheduling model; The optimal dispatching strategy of the microgrid in the online dispatching process is obtained based on the reference state of charge sequence, the reference electricity price, and the optimized dispatching model, specifically including: Based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model after optimization, an optimal scheduling strategy of the microgrid in the online scheduling process is obtained through an online convex optimization algorithm.

8. A microgrid optimization dispatching device, characterized in that: The device is applied to a microgrid, the microgrid at least comprising generalized energy storage and a distributed generator, the generalized energy storage being a resource having an energy storage function, and the device comprising: A construction module, used to pre-construct a generalized energy storage model of the generalized energy storage in the microgrid, and establish an optimized scheduling model of the microgrid based on the generalized energy storage model; A determination module, used to obtain historical operating parameters under multiple historical operating scenarios, and obtain each historical optimal scheduling strategy under each of the historical operating scenarios based on the historical operating parameters and the optimization scheduling model, wherein the historical operating parameters at least include historical new energy output, historical load, and historical electricity price; An acquisition module, configured to obtain, based on each of the historical optimal scheduling strategies, each group of historical state of charge sequences corresponding to each of the historical optimal scheduling strategies; A processing module, configured to determine a reference state of charge sequence in an online scheduling process based on each group of historical state of charge sequences under each of the historical operation scenarios, and to determine a reference electricity price in an online scheduling process based on each historical electricity price under each of the historical operation scenarios; A scheduling module is used to obtain an optimal scheduling strategy for the microgrid during online scheduling based on the reference state of charge sequence, the reference electricity price, and the optimized scheduling model, wherein the optimal scheduling strategy at least includes a charging power control instruction for generalized energy storage, a discharging power control instruction for generalized energy storage, and a discharging power control instruction for distributed generators.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the microgrid optimization scheduling method as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the microgrid optimization scheduling method according to any one of claims 1 to 7 is implemented.

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