Open microgrid multi-target resource planning method and device considering cloud energy storage

Abstract

The invention relates to the field of energy resource planning, in particular to an open micro-grid multi-target resource planning method and device considering cloud energy storage. The method mainly comprises the following steps: establishing an open micro-grid topological structure considering cloud energy storage according to a connection relationship among an upper-layer energy system, an open micro-grid and an energy supply and demand system among users; based on an open micro-grid topological structure and energy and information interaction among a user, the open micro-grid and a cloud energy storage data center, establishing an open micro-grid business mode when the user uses cloud energy storage through the open micro-grid; based on an open micro-grid business mode and a selection strategy of the user for an energy provider, establishing a response model when different types of users use cloud energy storage, and establishing a cost calculation model based on the cloud energy storage based on the response model; and based on the response model and the cost calculation model, according to a specified optimization target and constraint conditions, planning resources in the open micro-grid. According to the invention, the open micro-grid income can be effectively improved, and the user cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of energy resource planning, and in particular to a method and device for multi-objective resource planning of an open microgrid taking cloud energy storage into account. Background Art

[0002] Currently, resource planning for cloud energy storage (CES) shared energy storage is mainly based on two modes: (1) users directly share their own energy storage resources through the CES platform; (2) independent CES operators provide energy storage services to users.

[0003] The first CES shared energy storage model mostly appears in large and medium-sized industrial and commercial energy supply and demand scenarios. This is because my country's residents are relatively concentrated, the housing area is small, the self-built energy storage sites are limited, and the cost is high. Residents cannot install large-capacity energy storage to meet their own peak-shaving needs. The second CES shared energy storage model discusses open microgrids and CES operators separately, ignoring the advantages of the original power generation, energy storage, energy conversion and other equipment in the open microgrid. Separating energy production and storage will inevitably cause waste of energy storage capacity and waste of resources caused by resource storage and release, which is contrary to the meaning of CES; at the same time, as a third party, CES operators have taken away the benefits of open microgrids and increased users' energy costs.

[0004] In view of this, how to overcome the defects of the existing technology and solve the problem that the existing CES resource planning model cannot meet the needs is a problem to be solved in this technical field. Summary of the invention

[0005] In view of the above defects or improvement requirements of the prior art, the present invention solves the problem that the existing CES resource planning model cannot meet the requirements.

[0006] The embodiment of the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a multi-objective resource planning method for an open microgrid taking cloud energy storage into account, specifically: establishing an open microgrid topology structure taking cloud energy storage into account according to the connection relationship between the upper energy system, the open microgrid and the energy supply and demand system between users; wherein the upper energy system and the open microgrid respectively include one or more energy providers; based on the open microgrid topology structure, as well as the energy and information interaction between the user, the open microgrid and the cloud energy storage data center, an open microgrid business model is established when the user uses the cloud energy storage through the open microgrid, which is used to calculate the cost of the user using the cloud energy storage; based on the open microgrid business model and the user's selection strategy for the energy provider, a response model is established when different types of users use the cloud energy storage, and a cost calculation model based on the cloud energy storage is established based on the response model; based on the response model and the cost calculation model, the resources in the open microgrid are planned according to the specified optimization objectives and constraints.

[0008] Preferably, the open microgrid topology structure includes: an upper-level energy system, an open microgrid and users, specifically including: the upper-level energy system receives energy demands of the open microgrid and users, provides corresponding energy to the open microgrid and users, and receives energy provided by the open microgrid; the open microgrid exchanges energy with the upper-level energy system and users based on its own energy production equipment, energy storage equipment and energy conversion equipment; the user submits energy demands to the upper-level energy system, the open microgrid and the cloud energy storage data center to purchase and / or cloud store the required energy; wherein the user is allowed to select any energy from any energy provider at any time.

[0009] Preferably, the open microgrid business model includes: a user layer, a cloud energy storage data center and an open microgrid layer, specifically including: the user layer calculates the fees that each user needs to pay for different energy sources, as well as the loads generated by users using different types of energy; the user layer sends the user's energy demand at each moment to the cloud energy storage layer data center based on the energy cost per unit moment fed back by the upper energy system, the open microgrid and the cloud energy storage data center; the cloud energy storage data center calculates the corresponding fees based on the user's energy usage, and sends the fees to the user layer, the open microgrid layer and the upper energy system; the open microgrid layer keeps accounts based on the fees provided by the cloud energy storage data center, and calculates the benefits of each energy supplier.

[0010] Preferably, the establishment of an open microgrid business model when a user uses cloud energy storage through an open microgrid specifically includes: constructing an energy storage quantity model for cloud energy storage at a specified time according to one or more of the energy storage quantity, charge and discharge quantity, and efficiency of the cloud energy storage at a previous scheduling moment; optimizing and solving the cost per unit time of each energy provider in the cloud energy storage, and establishing the cost of energy use by the user at a specified time; and establishing an open microgrid business model based on the cost of energy use by the user and the cost per unit time of each energy provider.

[0011] Preferably, the establishment of a response model for different types of users when using cloud energy storage specifically includes: classifying users according to one or more of their price sensitivity to energy, tolerance for price fluctuations, and preference for energy providers, as well as the attractiveness of energy provided by energy suppliers to users, and obtaining a selection strategy for each classified user; and establishing a response model for each type of user to select an energy provider at a specified time according to the selection strategy of each classified user.

[0012] Preferably, the users are classified and the selection strategy of each classified user is obtained, which specifically includes: users with high price sensitivity are classified as type I users. When the sum of the rental cost and the purchase cost of cloud energy storage is less than the energy price of the open microgrid and the upper energy system, the type I users choose to use cloud energy storage as the energy supplier; users with high tolerance for price fluctuations are classified as type II users. When the cost per unit power of cloud energy storage is lower than that of the upper energy system and the open microgrid, and the price fluctuation of cloud energy storage is lower than the upper limit of price fluctuation acceptable to users, the type II users choose cloud energy storage as the energy supplier; or, when the price fluctuation of the open microgrid is higher than the price fluctuation acceptable to users. Upper limit, and the price fluctuation of the cost of cloud energy storage per unit power is less than the upper limit of price fluctuation acceptable to users, type II users choose cloud energy storage as a supplement to the open microgrid; users who have no preference for energy suppliers are regarded as type III users. When the energy provided by cloud energy storage is more attractive to users than the energy provided by the open microgrid and the upper energy system, type III users choose to use cloud energy storage as an energy supplier; users who prefer the upper energy system are regarded as type IV users. When the sum of the rental fee and the purchase fee of cloud energy storage is less than the energy commodity price of the open microgrid and the upper energy system and the upper limit of price acceptable to users, type IV users choose cloud energy storage as an energy supplier.

[0013] Preferably, the cost calculation model based on cloud energy storage is established based on the response model, specifically including: calculating the market share of each energy supplier at a specified time according to the response model, the amount of energy required by the user and the remaining energy storage of the cloud energy storage, and establishing a cost calculation model based on cloud energy storage according to the market shares of different types of users.

[0014] Preferably, the planning of resources in the open microgrid specifically includes: establishing a resource planning model taking into account cloud energy storage based on a response model and a cost calculation model; simulating and solving the resource planning model with the goal of maximizing the benefits of the open microgrid and minimizing the energy costs of users, so as to complete the multi-objective resource planning of the open microgrid.

[0015] In a second aspect, the present invention provides an open microgrid multi-objective resource planning device taking into account cloud energy storage, specifically: comprising at least one processor and a memory, at least one processor and the memory are connected via a data bus, the memory stores instructions that can be executed by at least one processor, and after the instructions are executed by the processor, they are used to complete the open microgrid multi-objective resource planning method taking into account cloud energy storage in the first aspect.

[0016] In a third aspect, the present invention further provides a non-volatile computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are executed by one or more processors to complete the method described in the first aspect.

[0017] In a fourth aspect, a chip is provided, comprising: a processor and an interface, for calling and running a computer program stored in the memory from a memory, and executing the method provided in the first aspect.

[0018] In a fifth aspect, a computer program product comprising instructions is provided, which, when executed on a computer or a processor, causes the computer or the processor to execute the method provided in the first aspect.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: an open microgrid topology structure taking cloud energy storage into account is proposed; on this basis, an open microgrid business model taking cloud energy storage into account is proposed, and a cloud energy storage strategy based on user consumption psychology characteristics is proposed, and strategies for different types of users to use cloud energy storage and a market share calculation model for open microgrids and upper-level energy systems are established; finally, for the open microgrid resource planning problem taking cloud energy storage into account, a multi-objective two-layer optimization model for open microgrids taking cloud energy storage into account is established with the goal of maximizing the open microgrid's revenue and minimizing the user's energy cost. The present invention can effectively improve the revenue of open microgrids and reduce user costs on the basis of meeting the user's energy demand and interest demands. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0021] Figure 1 A flow chart of a multi-objective resource planning method for an open microgrid taking cloud energy storage into account provided by an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of an open microgrid topology structure established in the method provided in an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of an open microgrid business model established in the method provided in an embodiment of the present invention;

[0024] Figure 4 A flow chart of another open microgrid multi-objective resource planning method taking into account cloud energy storage provided by an embodiment of the present invention;

[0025] Figure 5 A schematic diagram of a multi-objective resource planning model for a microgrid in the method provided in an embodiment of the present invention;

[0026] Figure 6 A flow chart of a multi-objective resource planning method for an open microgrid taking cloud energy storage into account provided by an embodiment of the present invention;

[0027] Figure 7 A schematic diagram of equipment capacity and power optimization results of an open microgrid in a certain scenario according to the method provided in an embodiment of the present invention.

[0028] Figure 8 The price of different resources of the open microgrid at each moment obtained by planning in a certain scenario according to the method provided in the embodiment of the present invention;

[0029] Fig. 9 The price of different resources at each moment of the CES planned in a certain scenario by the method provided in the embodiment of the present invention;

[0030] Fig.10 The market share of different resources of the open microgrid at each moment obtained by planning in a certain scenario according to the method provided in the embodiment of the present invention;

[0031] Fig.11 The method provided in the embodiment of the present invention plans the market share of different resources of CES at each moment in a certain scenario;

[0032] Fig.12 The method provided in the embodiment of the present invention provides the market share of electric energy of different types of users at each moment in an open microgrid in a certain scenario;

[0033] Fig.13 The method provided in the embodiment of the present invention provides the market share of hydrogen energy of different types of users at each moment in an open microgrid in a certain scenario;

[0034] Fig.14 The market share of hydrogen energy of different types of users at each moment in a certain scenario CES according to the method provided by the embodiment of the present invention;

[0035] Fig.15 The market share of hydrogen energy of different types of users at each moment in a certain scenario CES according to the method provided by the embodiment of the present invention;

[0036] Fig.16 A schematic diagram of the structure of an open microgrid multi-objective resource planning device taking cloud energy storage into account provided by an embodiment of the present invention;

[0037] The reference numerals are as follows:

[0038] 11: processor; 12: memory. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] The present invention is an architecture of a specific functional system, so the specific embodiments mainly illustrate the functional logical relationship between the various structural modules, and do not limit the specific software and hardware implementation methods.

[0041] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other. The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0042] Embodiment 1:

[0043] The method provided in this embodiment introduces CES into the open microgrid model from the user's perspective, establishes an open microgrid business model that takes cloud energy storage into account, distributes the benefits of third parties to users and open microgrids, and reduces the scale and cost of CES energy storage by utilizing the advantages of existing power generation, energy storage, energy conversion and other equipment in the microgrid. CES assists in energy supply during price peak periods, reduces user demand during peak energy consumption, smoothes user load levels, further improves the benefits of open microgrids, and reduces user energy costs.

[0044] like Figure 1 As shown, the specific steps of the multi-objective resource planning method for an open microgrid taking into account cloud energy storage provided by an embodiment of the present invention are as follows:

[0045] Step 101: Based on the connection relationship between the upper energy system, the open microgrid and the energy supply and demand system between users, an open microgrid topology structure taking into account cloud energy storage is established.

[0046] For general users, energy commodities can be obtained through the public upper-level energy system; after the user is connected to the open microgrid, energy commodities can be obtained from both the upper-level energy system and the open microgrid at the same time; after the CES is introduced in the open microgrid, when the user obtains energy commodities from the open microgrid, in addition to directly obtaining energy commodities directly generated by the open microgrid, the user can also lease the CES in the open microgrid and use the CES to store and use the energy produced by the user.

[0047] like Figure 2 As shown, the open microgrid topology structure established in this embodiment includes: an upper-level energy system, an open microgrid, and a user. Specifically, the upper-level energy system receives the energy demands of the open microgrid and the user, provides the corresponding energy to the open microgrid and the user, and receives the energy provided by the open microgrid. The open microgrid exchanges energy with the upper-level energy system and the user based on its own energy production equipment, energy storage equipment, and energy conversion equipment. The user submits energy demands to the upper-level energy system, the open microgrid, and the cloud energy storage data center to purchase and / or cloud store the energy required for use.

[0048] The upper energy system and the open microgrid each include one or more energy providers, each of which is operated by one or more operators and can provide different types of energy commodities or CES services in different forms. Each operator charges different fees for providing different commodities or services at different times. In actual implementation, users are allowed to select any energy from any energy provider at any time. In this embodiment, two types of energy, electricity and hydrogen, are used as examples for illustration. In actual implementation, different types of energy or energy combinations can be planned with reference to the specific implementation methods and mathematical models in this embodiment. Figure 2 In the above, the energy providers of the upper energy system include the upper power system and the upper hydrogen system; the energy providers of the open microgrid include its own power production equipment such as wind turbines, photovoltaic panels and fuel cells, hydrogen energy generation equipment such as electrolyzers, power storage equipment such as batteries, and hydrogen storage equipment such as hydrogen storage tanks.

[0049] based on Figure 2 The open microgrid topology can obtain a free choice model for users to choose energy commodity operators such as electricity or hydrogen.

[0050]

[0051] In the formula, u CES(a) (t) is the operator of energy products such as electricity / hydrogen selected by the user at time t; CES(a) (t) = 1, indicating that the user selects the operator of energy commodity a as the open microgrid at time t; CES(a) (t) = 0, indicating that the user selects the operator of energy commodity a as the upper energy system at time t; CES(a) (t)=-1, indicating that the operator selected by the user at time t as energy commodity a is CES.

[0052] Step 102: Based on the open microgrid topology, and the energy and information interaction between the user, the open microgrid, and the cloud energy storage data center, an open microgrid business model is established when the user uses the cloud energy storage through the open microgrid, which is used to calculate the cost of the user using the cloud energy storage.

[0053] In the method provided in this embodiment, one of the planning goals is to maximize the revenue of the open microgrid. In order to facilitate the calculation of the revenue of the open microgrid, it is necessary to establish an open microgrid business model that takes cloud energy storage into account. This business model can calculate the CES energy commodity purchase fee paid by the user based on the energy commodity price at the time of energy storage, and the CES energy storage rental fee paid based on the energy storage duration and required power at the time of energy use.

[0054] The open microgrid business model includes: user layer, cloud energy storage data center and open microgrid layer. Specifically, the user layer calculates the fees that each user needs to pay for different energy sources, as well as the load generated by the user using different types of energy. The user layer sends the user's energy demand at each moment to the cloud energy storage layer data center based on the energy cost per unit moment fed back by the upper energy system, open microgrid and cloud energy storage data center; the cloud energy storage data center calculates the corresponding fees based on the user's energy usage and sends the fees to the user layer, open microgrid layer and upper energy system; the open microgrid layer keeps accounts based on the fees provided by the cloud energy storage data center and calculates the benefits of each energy supplier.

[0055] In actual implementation, each layer of the open microgrid business model can handle multiple types of energy or cost data. Figure 3 As shown, the user layer calculates the electricity load and hydrogen load obtained by the user from the open microgrid, as well as the cloud electricity storage fee and hydrogen fee generated by the user's CES rental. The cloud energy storage data center processes the user's storage request, the electricity used, the operator who purchased electricity, the electricity request and the required power, etc. when the user purchases electricity, the electricity purchase unit price and the required power when the user rents CES to store electricity, and various data when the user purchases hydrogen and rents CES to store hydrogen. The open microgrid layer accounts for data such as the cost of the power network and the hydrogen network, and manages the accounting of data such as the use of CES by users for cloud electricity storage and cloud hydrogen storage.

[0056] Figure 3In the context of open microgrids, CES consumers are residential users, and CES operators are open microgrids. Through the virtual cloud database in the CES data center, users can simulate the use of energy storage to replace the use of physical energy storage; at the same time, the user's energy experience using CES is no different from using physical energy storage, but the energy cost is lower, and the trouble of installing and maintaining physical energy storage equipment is eliminated. In the context of smart grids, open microgrids can interact with users in real time on energy and information. While providing energy and price information, they can also receive users' energy demand data. The specific manifestation is: taking electricity as an example, when leasing CES, CES consumers can log in to the CES at t Stor Send CES energy storage requests and CES electricity consumption to CES operators at all times. CES operators keep accounts, and CES consumers need to pay according to t Stor The electricity price at the moment is used to pay for the purchase of electricity, which is collected by CES; after leasing CES, the CES consumer sends a CES electricity request and required electric power to the CES operator at time t. At the same time, the CES consumer needs to pay the storage rental fee based on the storage duration and required electric power, which is collected by the CES operator.

[0057] Step 103: Based on the open microgrid business model and the user's selection strategy for energy providers, a response model for different types of users using cloud energy storage is established, and a cost calculation model based on cloud energy storage is established based on the response model.

[0058] In the topology structure established in step 101, users can freely select different energy providers. In the actual energy use process, users have different preferences for different energy providers, and the energy prices and price fluctuations of different energy providers are also different. Therefore, in this embodiment, according to the different selection strategies of different users for energy suppliers, a response model for users using cloud energy storage is established to obtain the energy suppliers selected by users at different times.

[0059] Once the response model is established, the market share of each energy supplier at a specified time can be calculated based on the response model of each type of user, the amount of energy required by the user, and the remaining energy storage of the cloud energy storage. A cost calculation model based on cloud energy storage can be established based on the market share of different types of users.

[0060] Step 104: Based on the response model and the cost calculation model, and in accordance with the specified planning objectives and constraints, the resources in the open microgrid are planned.

[0061] After the response model and the cost calculation model are established, a resource planning model taking into account cloud energy storage can be established based on the response model and the cost calculation model. The resource planning model includes the optimization objectives and constraints of the upper resource system, the open microgrid itself, and the CES. In actual implementation, the optimization objectives and constraints can be determined according to the actual situation. For example, the highest open microgrid revenue and the lowest user cost are set as the optimization objectives, and the capacity and power of the resource provider are set as constraints.

[0062] Then, with the goal of maximizing the benefits of the open microgrid and minimizing the energy costs of users, the resource planning model is simulated and solved to complete the multi-objective resource planning of the open microgrid.

[0063] After steps 101 to 104 provided in this embodiment, the multi-objective resource planning of the open microgrid taking cloud energy storage into consideration can be completed, and the required resource planning scheme can be obtained.

[0064] like Figure 4 As described above, the open microgrid business model in step 102 may be established in the following manner.

[0065] Step 201: Construct an energy storage quantity model of the cloud energy storage at a specified time according to one or more of the energy storage quantity, charging and discharging quantity and efficiency of the cloud energy storage at the previous scheduling time.

[0066] CES energy storage is similar to battery energy storage. The energy storage at time t is related to the energy storage quantity, charge and discharge quantity, and efficiency at the previous scheduling time t-Δt. The energy storage quantity model at time t can be expressed by the following mathematical model.

[0067] During the charging process, the amount of energy stored in CES at time t is:

[0068] W CES (t) = (1-σ CES )W CES (t-Δt)+η CES,ch W CES,ch (Δt);

[0069] During the energy release process, the amount of energy stored by CES at time t is:

[0070]

[0071] Where W CES (t), W CES (t-Δt) is the amount of energy stored by CES at time t and t-Δt respectively; σ CES is the self-discharge rate of CES; W CES,ch (Δt), W CES,dis (Δt) are the amount of energy charged / discharged by CES in the Δt period; η CES,ch, η CES,dis are the charge / discharge efficiency of CES respectively.

[0072] Step 202: Optimize and solve the cost per unit time of each energy provider in the cloud energy storage, and establish the cost of energy use by the user at a specified time.

[0073] in accordance with Figure 3 As shown in Figure 2, after CES is introduced into the open microgrid, the usage fee per unit power of CES by CES consumers at time t is the sum of the unit power rental fee and the unit power purchase fee of CES, as shown below:

[0074] p CES (t) = p CES,Stor (t Stor )+p CES,Rent (t),t Stor =1,2,…t-1;

[0075] In the formula, p CES (t) is the usage fee per unit power of CES by CES consumers at time t; p CES,Stor (t Stor ) for CES consumers at t Stor The CES unit power purchase cost at the moment; p CES,Rent (t) is the unit power rental fee of CES by CES consumers at time t; t Stor The CES consumer sends a CES energy storage request and energy storage quantity to the CES data center at this time. CES sends a CES energy storage request and energy storage quantity at t Stor The CES is charged at all times, and the stored energy is used by the CES consumer at time t.

[0076] In the resource planning process, users Stor The cost incurred at time t Stor The amount of CES energy storage W sent by CES consumers to the CES data center at the moment CES,ch (t Stor ), an open microgrid multi-objective resource planning model taking into account cloud energy storage is optimized and solved according to the user's CES usage fee model.

[0077] Among them, at time t, the unit power rental fee of CES is the product of the unit time rental price of CES unit power and the rental time, as shown below:

[0078] p CES,rent (t) = p CES,Rent,Δt ×(tt Stor );

[0079] In the formula, p CES,Rent,ΔtAfter the introduction of CES in the open microgrid, the unit time rental price of CES unit power will be optimized and solved by the open microgrid multi-objective resource planning model taking into account cloud energy storage based on the multi-objective optimization model.

[0080] Step 203: Establish an open microgrid business model based on the cost of energy used by the user and the cost per unit time of each energy provider.

[0081] In t Stor At this moment, the source of CES unit energy purchase costs for CES consumers can be open microgrids or upper energy systems. In the case of the same energy storage container, the energy quality of electricity, hydrogen and other energy commodities from different operators is the same after being stored and released. Therefore, when CES consumers choose energy commodity operators to charge CES, they do not consider their own selection preferences, but only depend on the prices of energy commodities from different operators.

[0082] Considering only the cost, at t Stor At this moment, based on the user's free choice model for operators and the CES unit power purchase cost, the open microgrid business model is established as follows:

[0083]

[0084] p CES,Stor (t Stor )=min(p OM (t Stor ),p UES (t Stor ));

[0085] In the formula, u CES,Stor (t Stor ) for CES consumers at t Stor Energy commodity operators who are always charging CES; when u CES,Stor (t Stor )=1, indicating that at t Stor The energy commodity price of the microgrid is always open and lower than that of the upper energy system. CES consumers choose to open the microgrid to charge CES. CES,Stor (t Stor )=0, indicating that at t Stor At any given moment, the energy commodity price of the upper energy system is lower than that of the open microgrid, and CES consumers choose the upper energy system to charge CES.

[0086] After steps 201 to 203 provided in this embodiment, the establishment of the open microgrid business model can be completed.

[0087] In step 103, the user's selection strategy for energy providers is obtained through the user's consumption psychology characteristics, and a CES response model is established based on this. The response model can more accurately describe the changes in consumption psychology and consumption behavior of different users after the introduction of CES, so as to more accurately estimate the user's energy cost and the benefits of open microgrids and CES operators. Specifically: according to one or more of the user's price sensitivity to energy, price fluctuation tolerance and energy provider preference, as well as the attractiveness of the energy provided by the energy supplier to the user, the users are classified and the selection strategy of each classified user is obtained; according to the selection strategy of each classified user, a response model for each type of user to select an energy provider at a specified time is established.

[0088] The following provides some common user type selection strategies and the response model establishment method corresponding to the selection strategies. In actual implementation, the following methods can be referred to to classify users in other ways, or to establish a response model based on other selection strategies.

[0089] (I) CES response model for type I users.

[0090] Users who are highly sensitive to price are considered as Type I users. When choosing energy commodity operators, they will give priority to using low-priced energy commodities in order to reduce their own costs. Therefore, when Type I users choose energy commodity operators, they only depend on the price of energy commodities, without considering their preference for choosing energy commodity operators. In order to reduce their own costs, when the sum of the rental fee and the purchase fee of cloud energy storage is less than the energy price of the open microgrid and the upper energy system, Type I users choose to use cloud energy storage as an energy supplier.

[0091] The CES response model for Type I users can be established in the following manner.

[0092] (1) After CES is introduced into the open microgrid, the model for type I users to select energy commodity operators at time t is:

[0093]

[0094] In the formula, u CES,I (t) is the energy commodity operator selected by the Type I user at time t after the introduction of CES in the open microgrid.

[0095] When u CES,I (t) = 1, indicating that at time t, the energy commodity price of the open microgrid is lower than the energy commodity price of the upper energy system and the usage fee per unit power of CES, and the first type of user chooses the open microgrid.

[0096] When u CES,I(t) = 0, indicating that at time t, the energy commodity price of the upper energy system is lower than the energy commodity price of the open microgrid and the usage fee per unit power of CES, and the first type of user chooses the upper energy system.

[0097] When u CES,I (t) = -1, indicating that at time t, the usage fee per unit power of CES is lower than the energy commodity price of the open microgrid and the upper energy system. At this time, type I users choose to use CES.

[0098] The amount of CES energy consumption at time t is the amount of CES remaining energy storage at time t or the energy commodity market share obtained by CES among type I users. The specific expression is as follows:

[0099] W CES,dis,I (t) = min(S CES,I (t)×P L (t)×1h,W CES,I (t));

[0100] Where W CES,dis,I (t) is the CES energy consumption of type I users at time t; S CES,I (t) is the market share of energy commodities obtained by CES in the first type of users at time t after CES is introduced in the open microgrid; W CES,I (t) is the remaining CES energy storage amount of type I user at time t.

[0101] Among them, the energy commodity market share obtained by CES in the first type of users at time t is:

[0102]

[0103] In the formula, u CES,I (t) is the energy commodity operator selected by the Type I user at time t after the introduction of CES in the open microgrid; f Sens1 is the minimum relative value of higher price sensitivity among users, f Bias is the relative value after selection bias normalization, f Sens is the relative value after normalization of price sensitivity, F SensH is the fuzzy membership function with higher price sensitivity; p(f Bias ,f Sens ) is the choice bias, and the relative value of price sensitivity is (f Bias ,f Sens ) is the probability when .

[0104] (2) After CES is introduced into the open microgrid, at time t, the market share of energy commodities obtained by the open microgrid among type I users is:

[0105]

[0106] In the formula, S OM,Ι,CES (t) is the energy commodity market share obtained by the open microgrid among type I users at time t after the introduction of CES in the open microgrid.

[0107] (3) After CES is introduced into the open microgrid, at time t, the market share of energy commodities obtained by the upper energy system in the first type of users is:

[0108]

[0109] in,

[0110] P OM,Ι (t) = S OM,Ι (t)×P L (t)+u CES,Stor (t)×W CES,dis,I (t')

[0111] In the formula, S UES,Ι,CES (t) is the market share of energy commodities obtained by the upper energy system in the first type of users at time t after the introduction of CES in the open microgrid; P OM,Ι (t) is the energy load obtained by the open microgrid from the type I user at time t; t' is the CES energy storage request and energy storage quantity sent by the CES consumer to the CES data center at time t, and used by the CES consumer at time t'.

[0112] (II) CES response model for type II users.

[0113] Users with a high tolerance for price fluctuations are considered Type II users. Since the price of CES energy commodities is lower than that of open microgrids and upper-level energy systems in most cases, Type II users tend to prefer CES when choosing operators of energy commodities. At the same time, since electricity and hydrogen are important basic resources for users' production and life, Type II users do not want energy commodity prices to fluctuate too much or change too frequently, but can tolerate a certain degree of energy commodity price fluctuations. When the cost per unit power of cloud energy storage is lower than that of the upper-level energy system and open microgrid, and the price fluctuation of cloud energy storage is lower than the upper limit of price fluctuations acceptable to users, users choose cloud energy storage as an energy supplier; or when the price fluctuation of the open microgrid is higher than the upper limit of price fluctuations acceptable to users, and the price fluctuation of the cost per unit power of cloud energy storage is lower than the upper limit of price fluctuations acceptable to users, Type II users choose cloud energy storage as a supplement to the open microgrid.

[0114] The CES response model for type II users can be established in the following manner.

[0115] (1) After CES is introduced into the open microgrid, the model for type II users to select energy commodity operators at time t is:

[0116]

[0117] in,

[0118]

[0119] In the formula, u CES,II (t) is the energy commodity operator selected by the type II user at time t after the introduction of CES in the open microgrid; is the average value of CES unit power usage fee in one day; d CES (t) is the fluctuation of CES unit power usage fee at time t.

[0120] When u CES,II (t) = 1, indicating that the energy commodity price of the open microgrid is lower than the unit power usage fee of the upper energy system and CES in most cases, and the price fluctuation of the open microgrid at time t is lower than the upper limit of price fluctuation acceptable to type II users; or, the unit power usage fee of CES is lower than the upper limit of price fluctuation acceptable to type II users in most cases, but the price fluctuation of the unit power usage fee of CES is higher than the upper limit of price fluctuation acceptable to type II users, and the price fluctuation of the open microgrid is lower than the upper limit of price fluctuation acceptable to type II users; at this time, type II users will choose the open microgrid at time t.

[0121] When u CES,II (t) = -1, indicating that the unit power usage fee of CES is lower than that of the upper-level energy system and the open microgrid in most cases, and the price fluctuation of the unit power usage fee of CES at time t is lower than the upper limit of price fluctuation acceptable to type II users; or, the price of energy commodities in the open microgrid is lower than that of the upper-level energy system and CES in most cases, but the price fluctuation of the open microgrid is higher than the upper limit of price fluctuation acceptable to type II users, and the fluctuation of the unit power usage fee of CES is lower than the upper limit of price fluctuation acceptable to type II users; in this case, they choose to use CES at time t.

[0122] When u CES,II (t) = 0, indicating that the energy commodity price of the open microgrid and the unit power usage fee of CES are higher than those of the upper energy system in most cases, or their price fluctuations are lower than the upper limit of price fluctuations acceptable to type II users, which does not meet the reasons for type II users to choose open microgrids and CES, and they choose the upper energy system at time t.

[0123] The CES energy consumption at time t is the CES remaining energy storage at time t or the energy commodity market share obtained by CES among type II users. The specific expression is as follows:

[0124] W CES,dis,II (t) = min(S CES,II (t)×P L (t)×1h,W CES,II (t))

[0125] Where W CES,dis,II (t) is the CES energy consumption of the second type of user at time t; S CES,II (t) is the energy commodity market share obtained by CES among type II users at time t after CES is introduced into the open microgrid; W CES,II (t) is the remaining CES energy storage amount of type II user at time t.

[0126] At time t, the market share of energy commodities obtained by CES among type II users is as follows:

[0127]

[0128] in,

[0129]

[0130] Where, d CESmax d CESmin It is the maximum and minimum fluctuation of CES unit power usage fee.

[0131] (2) After CES is introduced into the open microgrid, at time t, the market share of energy commodities obtained by the open microgrid among type II users is:

[0132]

[0133] In the formula, S OM,ΙΙ,CES (t) is the energy commodity market share obtained by the open microgrid among type II users at time t after the introduction of CES in the open microgrid.

[0134] (3) After CES is introduced into the open microgrid, at time t, the market share of energy commodities obtained by the upper energy system in type II users is:

[0135]

[0136] in,

[0137] P OM,ΙI (t) = S OM,ΙI (t)×P L (t)+uCES,Stor (t)×W CES,dis,II (t');

[0138] In the formula, S UES,II,CES (t) is the market share of energy commodities obtained by the upper energy system among type II users at time t after CES is introduced in the open microgrid; P OM,ΙI (t) is the energy load obtained by the open microgrid from type II users at time t.

[0139] (III) CES response model for type III users.

[0140] In the process of demand response, not all users have the willingness and ability to respond in real time, and a considerable number of users have no preference or a low degree of preference when choosing energy commodity operators. Users who have no preference for energy suppliers are regarded as Type III users, and they are relatively fixed in choosing energy commodity operators and will not change frequently. At this time, users choose operators based on the attractiveness of energy commodities. Type III users still choose operators based on the attractiveness of energy commodities when choosing energy commodity operators; when the energy provided by cloud energy storage is more attractive to users than the energy provided by open microgrids and upper-level energy systems, Type III users choose to use cloud energy storage as an energy supplier.

[0141] The CES response model for type III users can be established in the following manner.

[0142] (1) After CES is introduced into the open microgrid, the model for type III users to select energy commodity operators at time t is:

[0143]

[0144] In the formula, A CES (t) is the attractiveness of CES energy commodities at time t after CES is introduced in the open microgrid; u CES,III (t) is the energy commodity operator selected by the type III user at time t after the introduction of CES in the open microgrid.

[0145] When u CES,III (t) = 1, indicating that at time t, the attractiveness of open microgrid energy commodities is higher than that of upper-level energy systems and CES energy commodities, and type III users choose open microgrids.

[0146] When u CES,III (t) = 0, indicating that at time t, the attractiveness of the energy commodities of the upper energy system is higher than that of the open microgrid and CES energy commodities, and the third type of users choose the upper energy system.

[0147] When u CES,III(t) = -1, indicating that at time t, the attractiveness of CES energy commodities is higher than that of open microgrids and upper-level energy system energy commodities, and type III users choose the upper-level energy system.

[0148] Among the multiple energy commodity operator models that satisfy logical consistency, the multiplicative competition interaction model is relatively reasonable. The attractiveness of an operator's energy commodity A(t) at time t is:

[0149]

[0150] In the formula, ε(t) and ε0 are the influence coefficient and fixed influence coefficient of energy commodity's attractiveness to users respectively; p(t) and μ(t) are the price and price error term of energy commodity at time t respectively. Therefore,

[0151]

[0152] In the formula, ε CES (t), ε 0,CES are the influence coefficient and fixed influence coefficient of CES energy products on user attractiveness; μ CES (t) is the error term of CES unit power usage fee at time t.

[0153] The CES energy consumption at time t is the CES remaining energy storage at time t or the energy commodity market share obtained by CES among type III users. The specific expression is as follows:

[0154] W CES,dis,III (t) = min(S CES,III (t)×P L (t)×1h,W CES,III (t));

[0155] Where W CES,dis,III (t) is the CES energy consumption of the third type of user at time t; S CES,III (t) is the market share of energy commodities obtained by CES in type III users at time t after CES is introduced in the open microgrid; W CES,III (t) is the remaining CES energy storage amount of the type III user at time t.

[0156] Among them, the energy commodity market share obtained by CES in type III users at time t is as follows:

[0157]

[0158] (2) After CES is introduced into the open microgrid, at time t, the market share of energy commodities obtained by the open microgrid among type III users is:

[0159]

[0160] In the formula, S OM,ΙII,CES (t) is the energy commodity market share obtained by the open microgrid among type III users at time t after the introduction of CES in the open microgrid.

[0161] (3) After CES is introduced into the open microgrid, at time t, the market share of energy commodities obtained by the upper energy system in the third type of users is:

[0162]

[0163] in,

[0164] P OM,IIΙ (t) = S OM,IIΙ (t)×P L (t)+u CES,Stor (t)×W CES,dis,III (t')

[0165] In the formula, S UES,IIΙ,CES (t) is the market share of energy commodities obtained by the upper energy system in the third type of users at time t after the introduction of CES in the open microgrid; P OM,IIΙ (t) is the energy load obtained by the open microgrid from type III users at time t.

[0166] (IV) CES response model for type IV users.

[0167] Users who prefer upper-level energy systems are considered Type IV users. Due to the randomness, volatility, and uncertainty of renewable energy, voltage fluctuations and flicker, frequency deviation, harmonics, and insufficient supply may occur, resulting in Type IV users having low trust in the quality of energy commodities from CES and open microgrids. When choosing energy commodity operators, Type IV users tend to prefer upper-level energy systems. However, Type IV users will also weigh the pros and cons between the low price and quality of energy commodities. To gain the energy commodity market share of this type of users, CES and open microgrids need to offer lower prices. When the sum of the rental cost and the purchase cost of cloud energy storage is less than the energy commodity price of the open microgrid and the upper limit of the price acceptable to the upper energy system and users, Type IV users choose cloud energy storage as the energy supplier.

[0168] (1) After CES is introduced into the open microgrid, the model for type IV users to select energy commodity operators at time t is:

[0169]

[0170] In the formula, u CES,IV(t) is the energy commodity operator selected by the Type IV user at time t after the introduction of CES in the open microgrid.

[0171] When u CES,IV (t) = 1, indicating that at time t, the energy commodity price of the open microgrid is lower than the unit power usage fee of CES and the price upper limit acceptable to type IV users, which satisfies the reasons for type IV users to choose the open microgrid, and they choose the open microgrid at time t.

[0172] When u CES,IV (t) = 0, indicating that at time t, the energy commodity price of the open microgrid and the unit power usage fee of CES are higher than the upper limit of the price acceptable to the Type IV user, which does not meet the reasons for the Type IV user to choose the open microgrid and CES, and he chooses the upper energy system at time t.

[0173] When u CES,IV (t)=-1, indicating that at time t, the unit power usage fee of CES is lower than the energy commodity price of the open microgrid and the upper limit of the price acceptable to the Type IV user, which satisfies the reason for the Type IV user to choose CES, and he chooses to use CES at time t.

[0174] The CES energy consumption at time t is the CES remaining energy storage at time t or the energy commodity market share obtained by CES among type IV users. The specific expression is as follows:

[0175] W CES,dis,IV (t) = min(S CES,IV (t)×P L (t)×1h,W CES,IV (t));

[0176] Where W CES,dis,IV (t) is the CES energy consumption of the type IV user at time t; S CES,IV (t) is the energy commodity market share obtained by CES among type IV users at time t after CES is introduced into the open microgrid; W CES,IV (t) is the remaining CES energy storage amount of the Type IV user at time t.

[0177] Among them, the energy commodity market share obtained by CES among type IV users at time t is as follows:

[0178]

[0179] in,

[0180]

[0181] In the formula, κ CES,ΙV(t) is the proportion of Type IV users that can be attracted by the price of CES at time t after CES is introduced in the open microgrid.

[0182] (2) After CES is introduced into the open microgrid, at time t, the market share of energy commodities obtained by the open microgrid among type IV users is:

[0183]

[0184] In the formula, S OM,ΙV,CES (t) is the energy commodity market share obtained by the open microgrid among type IV users at time t after the introduction of CES in the open microgrid.

[0185] (3) After CES is introduced into the open microgrid, at time t, the market share of energy commodities obtained by the upper energy system from the type IV users is:

[0186]

[0187] in,

[0188] P OM,ΙV (t) = S OM,ΙV (t)×P L (t)+u CES,Stor (t)×κ ΙV (t')×W CES,dis,IV (t')

[0189] In the formula, S UES,ΙV,CES (t) is the market share of energy commodities obtained by the upper energy system in the type IV user at time t after the introduction of CES in the open microgrid; P OM,ΙV (t) is the energy load obtained by the open microgrid from the type IV users at time t.

[0190] Based on the CES response models of types I to IV, after CES is introduced into the open microgrid, the energy commodity market share obtained by CES at time t is:

[0191]

[0192] In the formula, S CES (t) is the energy commodity market share obtained by CES in the user market at time t after CES is introduced in the open microgrid.

[0193] The multi-objective resource planning method for an open microgrid taking cloud energy storage into consideration provided in this embodiment has the following beneficial effects compared with the prior art:

[0194] First, based on the energy supply and energy storage network structure after the introduction of CES, an open microgrid topology structure taking into account cloud energy storage is established to describe the energy supply and demand relationship between the open microgrid after the introduction of CES and the upper-level energy system and users.

[0195] Second, an open microgrid business model taking cloud energy storage into account is proposed. In this business model, users need to pay the CES energy commodity purchase fee based on the energy commodity price at the time of energy storage, and pay the CES energy storage rental fee based on the energy storage duration and demand power at the time of energy use.

[0196] Third, a CES response model based on user consumption psychology characteristics is proposed, which can more accurately describe the changes in consumption psychology and consumption behavior of different users after the introduction of CES, thereby more accurately estimating the user's energy consumption cost and the benefits of open microgrid and CES operators.

[0197] Based on the above model, the present invention introduces CES energy storage services in the open microgrid, and uniformly plans the resources of multiple energy suppliers in the open microgrid and the upper energy system. The open microgrid and CES operators are merged, and the advantages of the original power generation, energy storage, energy conversion and other equipment in the open microgrid are used to reduce the scale and cost of CES energy storage, and improve the revenue of the open microgrid and CES operators; CES is used to assist in energy supply during peak price periods, reduce user demand during peak energy consumption, smooth user load levels, thereby reducing user energy costs during peak prices, and further reducing user energy costs.

[0198] Embodiment 2:

[0199] The open microgrid multi-objective resource planning method taking into account cloud energy storage provided in Example 1 can be implemented through the specific implementation methods in this embodiment in some specific implementation methods.

[0200] In step 104, the revenue of the open microgrid can be increased as much as possible and the energy cost of users can be reduced by establishing an open microgrid multi-objective resource planning model taking cloud energy storage into account.

[0201] In this embodiment, a multi-objective resource planning model for an open microgrid taking into account cloud energy storage is provided, as well as a specific process of using the model for resource planning. The planning scenario of the model includes an upper-level energy system and an open microgrid after the introduction of CES. The energy provider can provide electricity and hydrogen commodities, as well as cloud energy storage services for electricity and hydrogen. It can be understood that the specific implementation method provided in this embodiment is only used to illustrate the specific implementation process of the method in Example 1 in certain specific scenarios, and is not intended to limit the scope of protection.

[0202] like Figure 5As shown in the figure, the upper optimization model optimizes the time-of-use price scheme for the sale of electricity and hydrogen by the open microgrid and the unit time rental fee of CES unit power based on the CES response model of the user's consumption psychology characteristics, so as to adjust the energy load of the open microgrid from the user, and at the same time, transmits the user's energy load curve to the lower optimization model 1, and transmits the time-of-use price scheme of the open microgrid and the unit time rental fee of CES unit power to the lower optimization model 2. The lower optimization model 1 optimizes the power, capacity, and output of each device in the open microgrid and the interaction with the upper energy system based on the user's energy load curve, and feeds back the cost of the open microgrid and the interaction with the upper energy system to the upper optimization model. At the same time, the lower optimization model 2 optimizes the energy storage request time and energy storage quantity sent by various types of users to the CES data center based on the open microgrid time-of-use price scheme and the unit time rental fee of CES unit power transmitted by the upper optimization model, and feeds back the user's CES usage fee to the upper optimization model. Then, the upper optimization model modifies the revenue of the open microgrid based on the open microgrid cost fed back by the lower optimization model 1 and the interaction with the upper energy system, and modifies the user's energy cost based on the user's CES usage fee fed back by the lower optimization model 2. Finally, according to the cyclic iteration between the upper and lower optimization models, the open microgrid resource planning scheme considering cloud energy storage is solved.

[0203] (A) Upper-level optimization model.

[0204] The upper optimization model performs global optimization with the goal of maximizing the benefits of the open microgrid and minimizing the energy cost of users. Based on the CES response model of user consumption psychology characteristics, according to the open microgrid cost feedback from the lower optimization model 1, the interaction with the upper energy system and the user CES usage fee feedback from the lower optimization model 2, the time-of-use price scheme of the open microgrid for selling electricity and hydrogen, and the unit-time rental fee of CES per unit power are optimized to reasonably adjust the energy load from users, and the load task is sent to the open microgrid, and the time-of-use price scheme of the open microgrid and the unit-time rental fee of CES per unit power are passed on to users. The upper optimization model of the open microgrid can be expressed as follows:

[0205] (1) Optimization objectives.

[0206] The interest demands of the open microgrid model are to maximize the benefits of the open microgrid and minimize the energy costs of users. Its specific interest demands are:

[0207] Objective 1: Maximize the benefits of open microgrids:

[0208] After the introduction of CES in the open microgrid, the revenue sources of the open microgrid include not only the revenue from the sale of energy commodities such as electricity and hydrogen, but also the revenue obtained from leasing CES to users.

[0209] R OM =max(R OM,E +R OM,H +R CES,E +R CES,H -C OM );

[0210] In the formula, R CES,E , R CES,H They are the profits that open microgrids obtain by leasing CES to users to store electricity and hydrogen.

[0211] The income that the open microgrid obtains from users through leasing CES is the sum of CES leasing fee and energy purchase fee, R CES,E and R CES,H The expression is:

[0212] R CES,E =R CES,Stor,E +R CES,Rent,E ;

[0213] R CES,H =R CES,Stor,H +R CES,Rent,H ;

[0214] In the formula, R CES,Stor,E , R CES,Rent,E They are the CES rental fee and electricity purchase fee obtained by the open microgrid from leasing CES to users; R CES,Stor,H , R CES,Rent,H They are respectively the CES rental fee and electricity purchase fee obtained by the open microgrid by leasing CES to users to store hydrogen.

[0215] in,

[0216]

[0217]

[0218] Where W CES,ch,E (t Stor )、W CES,ch,H (t Stor ) are respectively Stor The amount of CES electricity and hydrogen storage sent by users to the CES data center at any given moment; W CES,dis,E (t), W CES,dis,H (t) are the CES electricity and hydrogen consumption of the user at time t.

[0219] Goal 2: Minimize energy costs for users:

[0220] C Uers =min(C Uers,E +CUers,H +C CES );

[0221] In the formula, C CES The CES usage fee that the user needs to bear will be described in detail in the lower-level optimization model 2.

[0222] The upper optimization model needs to send the load task to the open microgrid. The load of energy commodities such as electricity and hydrogen in the open microgrid at time t is:

[0223] P OM,E (t) = P OM,Ι,E (t)+P OM,ΙI,E (t)+P OM,IIΙ,E (t)+P OM,VΙ,E (t);

[0224] V OM,H (t) = V OM,Ι,H (t)+V OM,ΙI,H (t)+V OM,III,H (t)+V OM,VΙ,H (t);

[0225] Where P OM,Ι,E (t), P OM,ΙI,E (t), P OM,IIΙ,E (t), P OM,VΙ,E (t) are the power loads obtained by the open microgrid from users of type I, II, III, and IV at time t; V OM,Ι,H (t), V OM,ΙI,H (t), V OM,III,H (t), V OM,VΙ,H (t) are the hydrogen loads obtained by the open microgrid from type I, II, III and IV users at time t.

[0226] (2)Constraints.

[0227] After CES is introduced into the open microgrid, the prices of energy commodities such as electricity and hydrogen given by the open microgrid at time t and the usage fee of CES per unit power must still be higher than the grid connection price given by the upper energy system. Otherwise, the open microgrid will be driven by profit and will sell all the energy at time t to the upper energy system to obtain the maximum benefit.

[0228] p OM,E (t), p UES,E (t), p CES,E (t)≥p OM-UES,E (t);

[0229] p OM,H (t), p UES,H (t), p CES,H (t)≥pOM-UES,H (t).

[0230] (ii) Lower layer optimization model 1.

[0231] The lower optimization model 1 aims to minimize the cost of the open microgrid. Based on the user energy load curve given by the upper optimization model and the renewable energy generation capacity in the open microgrid, it rationally plans the rated power and capacity of each device in the open microgrid, formulates the output plan of each device, and optimizes the interaction between the open microgrid and the upper energy system. It also feeds back the cost of the open microgrid and the interaction with the upper energy system to the upper optimization model. It can be specifically expressed as:

[0232] (1) Objective function.

[0233] After CES is introduced into the open microgrid, the lower optimization model 1 aims to minimize the cost of the open microgrid, and its specific expression is as follows:

[0234] C OM =min(C Equ +C Ope ).

[0235] (2)Constraints.

[0236] Figure 2 In the multi-energy system shown, the open microgrid provides electricity load for users through the upper power system, CES, renewable energy generation (mainly wind turbines and photovoltaic cells), batteries, and fuel cell generation:

[0237] P WT (t)+P PV (t)+P OM-UES (t)-P Bat (t)-P EB (t)+P FC (t) = S OM,E (t)×P L (t)+S UES,E (t)×P L (t)+S CES,E (t)×P L (t) = P L (t).

[0238] Provide hydrogen load to users through upper hydrogen system, CES, electrolyzer, hydrogen storage tank and other equipment:

[0239] V EB (t)+V OM-UES (t)-V H (t)-V FC (t) = S OM,H (t)×V L(t)+S UES,H (t)×V L (t)+S CES,H (t)×V L (t) = V L (t).

[0240] (iii) Lower layer optimization model 2.

[0241] The lower optimization model 2 aims to minimize the CES usage fee that users need to bear. Based on the open microgrid time-of-use price plan and the unit time rental fee of CES unit power transmitted by the upper optimization model, it optimizes the energy storage request time and energy storage quantity sent by various types of users to the CES data center, and feeds back the user's CES usage fee to the upper optimization model. The specific optimization model can be expressed as follows:

[0242] (1) Objective function.

[0243] The CES usage fee that users need to bear is the sum of the user's usage fee for CES electricity storage and the user's usage fee for CES hydrogen storage, as shown in the formula:

[0244] C CES =C CES,E +C CES,H ;

[0245] In the formula, C CES,E The user fee for using CES power storage; CES,H The user shall pay the fee for using CES hydrogen storage.

[0246] in,

[0247]

[0248] In the formula, p CES,E (t), p CES,H (t) are the usage fees per unit power of energy commodities such as electricity and hydrogen stored by users using CES at time t; S CES,E (t), S CES,H (t) are the electricity and hydrogen market shares obtained by CES from the user market at time t after CES is introduced into the open microgrid.

[0249] (2)Constraints.

[0250] The amount of CES charging and discharging energy and the amount of remaining energy storage of residential users should be restricted within a certain range:

[0251]

[0252] Where W CES,max , W CES,minare the maximum and minimum values ​​of the CES remaining energy storage of residential users; W CES,Pmax , W CES,Pmin They are respectively the maximum and minimum values ​​of CES charging and discharging energy for residential users.

[0253] After the above model is established, simulation can be performed to solve the multi-objective two-layer optimization model of the open microgrid from the user's perspective. In actual implementation, multi-objective genetic algorithms or multi-objective evolutionary algorithms such as NSGA-II algorithm, particle swarm algorithm, ant colony algorithm, and gray wolf algorithm are used for simulation and solution.

[0254] like Figure 6 As shown, the NSGA-II algorithm can be used for programming simulation optimization in the following way to obtain the time-of-use price mechanism for the sale of electricity and hydrogen by the open microgrid and the unit time rental fee per unit power, so as to achieve the optimization goal of maximizing the open microgrid's revenue and minimizing user costs.

[0255] Step 301: Use simulated annealing hybrid genetic algorithm to solve the upper optimization model to obtain the time-of-use electricity price, time-of-use hydrogen price of the open microgrid, the power, capacity, output of each device, and the interaction with the upper energy system.

[0256] Step 302: Based on the user energy load curve transmitted by the upper optimization model, the lower optimization model 1 is solved using a simulated annealing hybrid genetic algorithm to obtain the open microgrid cost and the interaction between the open microgrid and the upper energy system, and the results are fed back to the upper optimization model.

[0257] Step 303: Based on the time-sharing price of the open microgrid and the unit time rental fee of the CES unit power, the lower optimization model 2 is solved by using a simulated annealing hybrid genetic algorithm to obtain the user's CES usage fee, and the fee is fed back to the upper optimization model.

[0258] After steps 301 to 303 provided in this embodiment, the planning of multi-objective resources can be completed.

[0259] In step 301, the upper optimization model may be solved by using a simulated annealing hybrid genetic algorithm in the following manner.

[0260] Step 401: Input parameters. The input parameters include but are not limited to: time-of-use electricity price and hydrogen price data of the upper energy system; grid-connected prices of energy commodities such as electricity and hydrogen given by the upper energy system; user electricity load and hydrogen load; unit power and capacity price of each device and model-related parameters; algorithm parameters such as population number, population size, evolutionary generations, crossover probability, mutation probability, and selection probability.

[0261] Step 402: The user's consumption psychology characteristics are obtained by Monte Carlo simulation. The probability density distribution of selection bias and price sensitivity is obtained; an initial parent population with a certain number of individuals is randomly generated, where the variables to be optimized include: the time-of-use electricity price and time-of-use hydrogen price of the open microgrid.

[0262] Step 403: The parent population undergoes crossover, mutation, and selection operations to obtain a child population.

[0263] Step 404: Based on the time-of-use electricity price and time-of-use hydrogen price schemes of the open microgrid in the parent population and the child population, the CES response model based on the user consumption psychology characteristics is used to respectively calculate the market share of energy commodities such as electricity and hydrogen obtained by the open microgrid in the user market and the market share of energy commodities such as electricity and hydrogen obtained by the upper energy system in the user market.

[0264] Step 405: The user's energy load curve is passed to the lower optimization model 1, and the lower optimization model 1 feeds back to the upper optimization model. The cost of the open microgrid and the interaction between the open microgrid and the upper energy system; the time-of-use price of the open microgrid and the unit time rental fee of the CES unit power are passed to the lower optimization model 2, and the lower optimization model 2 gives the upper optimization model: the user's CES usage fee.

[0265] Step 406: Calculate the revenue of the open microgrid and the energy cost of the user based on the upper optimization model, the open microgrid cost fed back by the lower optimization model, the interaction between the open microgrid and the upper energy system, and the CES usage fee of the user.

[0266] Step 407: Based on the elite retention strategy, the parent population and the child population are merged to obtain a new population, and the individuals in the new population are quickly non-dominated sorted; at the same time, the crowding degree of each non-dominated layer individual is calculated, and the optimal individual is selected according to the crowding degree and non-dominated relationship to form a new parent population.

[0267] Step 408: Repeat the above steps 403 to 407 until the program termination condition is met, and obtain the Pareto non-inferior solution set of the open microgrid multi-objective resource planning model from the user's perspective.

[0268] Step 409: Sort the obtained Pareto non-inferior solutions according to the fuzzy membership function to select the optimal solution for the open microgrid; and visualize the variables corresponding to the optimal solution, and present the time-of-use electricity price, time-of-use hydrogen price, power, capacity, output of each device, and interaction with the upper energy system of the open microgrid in the form of graphs and tables.

[0269] After steps 401 to 409 provided in this embodiment, the upper-layer optimization model can be solved.

[0270] In step 302, the lower optimization model 1 may be solved using a simulated annealing hybrid genetic algorithm in the following manner.

[0271] Step 501: randomly generate an initial parent population with a certain number of individuals, wherein the variables to be optimized include: the rated power, capacity, output of each device in the open microgrid, and the interaction between the open microgrid and the upper energy system.

[0272] Step 502: The parent population undergoes crossover, mutation, and selection operations to obtain a child population.

[0273] Step 503: Calculate the cost of the open microgrid based on the rated power, capacity, output of each device in the open microgrid in the parent population and the child population, the interaction between the open microgrid and the upper energy system, and the user energy load curve transmitted by the upper optimization model.

[0274] Step 504: Based on the elite retention strategy, the parent population and the child population are merged to obtain a new population, and the fitness of the individuals in the population is calculated. At the same time, the best individuals are selected according to the fitness to form a new parent population.

[0275] Step 505: Repeat the above steps 502 to 504 until the program termination condition is met.

[0276] Step 506: Feedback the open microgrid cost and the interaction between the open microgrid and the upper energy system to the upper optimization model.

[0277] After steps 501 to 506 provided in this embodiment, the solution of the lower layer optimization model 1 can be completed.

[0278] In step 303, the lower layer optimization model 2 may be solved using a simulated annealing hybrid genetic algorithm in the following manner.

[0279] Step 601: Randomly generate an initial parent population with a certain number of individuals, where the variables to be optimized include: t Stor time and at t Stor The amount of CES energy storage W sent by CES consumers to the CES data center at the moment CES,ch (t Stor )wait.

[0280] Step 602: The parent population undergoes crossover, mutation, and selection operations to obtain a child population.

[0281] Step 603: Based on the time-sharing price scheme of the open microgrid in the parent population and the child population, the unit time rental fee of the CES unit power, etc., the CES response model based on the user's consumption psychology characteristics is used to calculate the CES usage fee that the user needs to bear.

[0282] Step 604: Based on the elite retention strategy, the parent population and the child population are merged to obtain a new population, and the fitness of the individuals in the population is calculated. At the same time, the best individuals are selected according to the fitness to form a new parent population.

[0283] Step 605: Repeat the above steps 602 to 604 until the program ends.

[0284] Step 606: Feedback the CES usage fee that the user needs to bear to the upper optimization model to correct the user's energy cost.

[0285] After steps 601 to 606 provided in this embodiment, the solution of the lower layer optimization model 2 can be completed.

[0286] In a specific scenario, the simulation results show that the total revenue of the CES operator's open microgrid is 5005.37 yuan, and the user's energy cost is 19951.29 yuan; the equipment capacity and power optimization results of the open microgrid are as follows: Figure 7 shown.

[0287] The planned open microgrid, CES time-of-use price, commodity share, and energy commodity market share obtained among various types of users are as follows: Figure 8-Figure 15 shown.

[0288] In summary, the CES response model based on the user consumption psychology characteristics proposed in Example 1 and Example 2 can accurately describe the changes in the consumption psychology and consumption behavior of different users after the introduction of CES, thereby accurately estimating the user's energy cost and the benefits of the open microgrid and CES operator. By introducing CES energy storage services in the open microgrid, by merging the open microgrid and the CES operator, and taking advantage of the original power generation, energy storage, energy conversion and other equipment in the open microgrid to reduce the scale and cost of CES energy storage, the benefits of the open microgrid including the CES operator can be increased. At the same time, using CES to assist in energy supply during peak price periods can reduce user demand during peak energy consumption, smooth user load levels, thereby reducing users' energy costs at peak prices, and further reducing users' energy costs.

[0289] Embodiment 3:

[0290] Based on the multi-objective resource planning method for an open microgrid taking into account cloud energy storage provided in the above-mentioned embodiments 1 and 2, the present invention further provides a multi-objective resource planning device for an open microgrid taking into account cloud energy storage that can be used to implement the above-mentioned method, such as Fig.16 , which is a schematic diagram of the device architecture of an embodiment of the present invention. The open microgrid multi-objective resource planning device taking into account cloud energy storage in this embodiment includes one or more processors 11 and a memory 12. Fig.16 A processor 11 is taken as an example.

[0291] The processor 11 and the memory 12 may be connected via a bus or other means. Fig.16 The example of connecting through bus is taken in the following.

[0292] The memory 12 is a non-volatile computer-readable storage medium for the multi-objective resource planning method for an open microgrid taking into account cloud energy storage, and can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the multi-objective resource planning method for an open microgrid taking into account cloud energy storage in Embodiments 1 to 2. The processor 11 executes various functional applications and data processing of the multi-objective resource planning device for an open microgrid taking into account cloud energy storage by running the non-volatile software programs, instructions and modules stored in the memory 12, that is, the multi-objective resource planning method for an open microgrid taking into account cloud energy storage in Embodiments 1 to 2 is implemented.

[0293] The memory 12 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 12 may optionally include a memory remotely arranged relative to the processor 11, and these remote memories may be connected to the processor 11 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0294] The program instructions / modules are stored in the memory 12. When executed by one or more processors 11, the multi-objective resource planning method for an open microgrid taking into account cloud energy storage in the above-mentioned embodiments 1 to 2 is executed. For example, the above-described Figure 1 , Figure 4 and Figure 6 The steps shown.

[0295] An embodiment of the present invention further provides a non-volatile computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are executed by one or more processors, such as Fig.16A processor 11 in the embodiment 1 can enable the one or more processors to execute the open microgrid multi-objective resource planning method considering cloud energy storage in embodiment 1 and embodiment 2, for example, to execute the above-described Figure 1 , Figure 4 and Figure 6 The steps shown can also be implemented Figure 3 , Figure 5 The various modules and units described.

[0296] It is worth noting that the information interaction, execution process, etc. between the modules and units within the above-mentioned devices and systems are based on the same concept as the processing method embodiment of the present invention. The specific contents can be found in the description of the method embodiment of the present invention and will not be repeated here.

[0297] A person skilled in the art may understand that all or part of the steps in the various methods of the embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0298] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-objective resource planning method for an open microgrid taking cloud energy storage into account, characterized in that: include: According to the connection relationship between the upper energy system, the open microgrid and the energy supply and demand system between users, an open microgrid topology structure taking into account cloud energy storage is established; wherein the upper energy system and the open microgrid respectively include one or more energy providers; Based on the open microgrid topology, as well as the energy and information interaction between users, open microgrids and cloud energy storage data centers, an open microgrid business model is established when users use cloud energy storage through open microgrids, which is used to calculate the cost of users using cloud energy storage; Based on the open microgrid business model and the user's selection strategy for energy providers, a response model for different types of users using cloud energy storage is established, and a cost calculation model based on cloud energy storage is established based on the response model; Based on the response model and cost calculation model, resources in the open microgrid are planned according to the specified optimization objectives and constraints.

2. The multi-objective resource planning method for an open microgrid taking cloud energy storage into account according to claim 1 is characterized in that: The open microgrid topology structure includes: an upper energy system, an open microgrid and users, specifically including: The upper energy system receives energy demands from open microgrids and users, provides corresponding energy to open microgrids and users, and receives energy provided by open microgrids; The open microgrid exchanges energy with the upper energy system and users based on its own energy production equipment, energy storage equipment and energy conversion equipment; Users submit energy and cloud energy storage data center demands to upper-level energy systems and open microgrids to purchase and / or cloud store the energy they need; users are allowed to select any energy from any energy provider at any moment.

3. The open microgrid multi-objective resource planning method taking into account cloud energy storage according to claim 1 is characterized in that: The open microgrid business model includes: user layer, cloud energy storage data center and open microgrid layer, specifically including: The user layer calculates the fees that each user needs to pay for different energy sources, as well as the load generated by the user's use of different types of energy. The user layer sends the user's energy demand at each moment to the cloud energy storage layer data center based on the energy cost per unit moment fed back by the upper energy system, open microgrid, and cloud energy storage data center. The cloud energy storage data center calculates the corresponding fees based on the user's energy usage and sends the fees to the user layer, open microgrid layer and upper energy system; The open microgrid layer keeps accounts based on the fees provided by the cloud energy storage data center and calculates the benefits of each energy supplier.

4. The multi-objective resource planning method for an open microgrid taking cloud energy storage into account according to claim 1, characterized in that: The establishment of an open microgrid business model when users use cloud energy storage through an open microgrid specifically includes: According to one or more of the energy storage quantity, charging and discharging quantity and efficiency of the cloud energy storage at the previous scheduling moment, a storage quantity model of the cloud energy storage at a specified moment is constructed; Optimize and solve the cost per unit time of each energy provider in cloud energy storage, and establish the cost of energy used by users at a specified time; An open microgrid business model is established based on the cost of energy used by users and the cost per unit time of each energy provider.

5. The open microgrid multi-objective resource planning method taking into account cloud energy storage according to claim 1 is characterized in that: The establishment of a response model for different types of users using cloud energy storage specifically includes: Classify users according to one or more of their price sensitivity to energy, tolerance to price fluctuations, and preference for energy providers, as well as the attractiveness of energy provided by energy providers to users, and obtain the selection strategy of each classified user; According to the selection strategy of each classified user, a response model for each type of user to select an energy provider at a specified time is established.

6. The multi-objective resource planning method for an open microgrid taking cloud energy storage into account according to claim 5, characterized in that: The user classification and the acquisition of the selection strategy for each classified user specifically include: Users who are highly price sensitive are considered as Type I users. When the sum of the cloud energy storage rental fee and the energy purchase fee is less than the energy price of the open microgrid and the upper energy system, Type I users choose to use cloud energy storage as an energy supplier. Users with a high tolerance for price fluctuations are considered Type II users. When the cost per unit power of cloud energy storage is lower than that of the upper-layer energy system and the open microgrid, and the price fluctuation of cloud energy storage is lower than the upper limit of price fluctuations acceptable to users, Type II users choose cloud energy storage as an energy supplier; or, when the price fluctuation of the open microgrid is higher than the upper limit of price fluctuations acceptable to users, and the price fluctuation of the cost per unit power of cloud energy storage is lower than the upper limit of price fluctuations acceptable to users, Type II users choose cloud energy storage as a supplement to the open microgrid. Users who have no preference for energy suppliers are considered Type III users. When the energy provided by cloud energy storage is more attractive to users than the energy provided by open microgrids and upper-level energy systems, Type III users choose to use cloud energy storage as their energy supplier. Users who prefer the upper-level energy system are classified as Type IV users. When the sum of the cloud energy storage rental fee and the energy purchase fee is less than the energy commodity price of the open microgrid and the upper-level energy system and the user's acceptable price cap, Type IV users choose cloud energy storage as their energy supplier.

7. The multi-objective resource planning method for an open microgrid taking cloud energy storage into account according to claim 1, characterized in that: The cost calculation model based on cloud energy storage is established based on the response model, specifically including: Based on the response model, the amount of energy required by users and the remaining energy storage of cloud energy storage, the market share of each energy supplier at a specified time is calculated, and a cost calculation model based on cloud energy storage is established according to the market share of different types of users.

8. The open microgrid multi-objective resource planning method taking into account cloud energy storage according to claim 1, characterized in that: The planning of resources in the open microgrid specifically includes: Based on the response model and cost calculation model, a resource planning model taking cloud energy storage into account is established; With the goal of maximizing the benefits of the open microgrid and minimizing the energy costs of users, the resource planning model is simulated and solved to complete the multi-objective resource planning of the open microgrid.

9. An open microgrid multi-objective resource planning device taking into account cloud energy storage, characterized in that: The method comprises at least one processor and a memory, wherein the at least one processor and the memory are connected via a data bus, and the memory stores instructions that can be executed by the at least one processor, and after being executed by the processor, the instructions are used to complete the open microgrid multi-objective resource planning method taking into account cloud energy storage as described in any one of claims 1-8.

10. A non-volatile computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, which are executed by one or more processors to complete the open microgrid multi-objective resource planning method taking into account cloud energy storage as described in any one of claims 1-8.

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