Load flexible regulation and control system and method and storage medium

Abstract

The invention provides a load flexible regulation and control system and method and a storage medium. A cloud end is used for determining a load regulation and control service based on the real-time operation state of a power grid, electric energy prediction data and the electricity price of an electricity market; the side end is used for calculating the purchase and sale electricity price by applying for 5G network resources corresponding to the business based on the electricity price of the electricity market, the load regulation and control business and the planned energy consumption data and sending the purchase and sale electricity price; the terminal is used for calculating transaction electric quantity by using a pricing algorithm based on purchase and sale electricity price calculation and updating planned energy consumption data to carry out load regulation and control; the edge end and the cloud end are connected based on a cloud-edge cooperation technology; the pricing algorithm is constructed based on a load regulation and control service by taking the maximum income of a load aggregator and the minimum total cost of a user as targets; according to the method, cloud-side cooperation is adopted, cloud computing and bearing pressure are reduced through assistance of side ends, data processing efficiency can be improved, differentiated slicing services of different businesses are achieved through the 5G technology, and a foundation is laid for making electricity selling prices for production and elimination type users and precise regulation and control of load resources.

Description

Technical Field

[0001] The present invention belongs to the technical field of load regulation, and in particular relates to a load flexible regulation system, method and storage medium. Background Art

[0002] In the context of the rapid development of new power systems, a high proportion of clean energy grid-connected and a large number of power electronic equipment connected, resulting in a large amount of data inflow, so it is particularly important to monitor and control various load resources, and it also puts forward more stringent requirements for the construction of future power load management systems. At present, most of the power load control services are based on optical fiber private network communication or 4G wireless communication. With the diversification of power load control services, 4G networks are difficult to meet the access requirements of large-capacity power loads and the rapid multi-service processing process, and at the same time cannot meet the privacy of load control business data. Considering the differentiated needs of services and the security transmission of data, traditional communication methods can no longer adapt to the diversified development of modern power services. On the other hand, the massive data contained in the terminal loads puts forward higher requirements on the resource calculation and carrying capacity of the cloud platform, and the cloud platform is urgently needed to improve the efficiency of business processing and realize rapid response on the load side. The characteristics of 5G technology such as high reliability, low-latency communication, enhanced mobile broadband and large-scale machine type communication are becoming increasingly important. How to use 5G technology to achieve rapid response of flexible loads on the demand side is still one of the problems to be solved.

[0003] Load resources can also be indirectly regulated by formulating the purchase and sale prices of energy under different businesses. The existing electricity pricing mechanism usually only targets consumer-type electricity users and only involves the electricity sales price (the user's electricity purchase price). In the context of the continuous development of the energy Internet, users are no longer just single electricity consumers. Users can also generate electricity through equipment such as distributed photovoltaics. Such users are called prosumers (prosumer users), and prosumers have the special nature of purchasing and selling electricity to the power grid. Therefore, the existing pricing mechanism cannot formulate the electricity sales price for users, prosumers lack selectivity, and it is difficult for prosumers to maximize their profits, resulting in low demand response willingness of prosumers. Therefore, how to formulate the purchase and sale prices for prosumers under different businesses is also a key issue that needs to be solved. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the present invention proposes a load flexible control system, comprising: a cloud, an edge, and a terminal communicating with each other;

[0005] The cloud is used to determine the load regulation business based on the pre-acquired real-time operation status of the power grid, power forecast data and power market electricity prices and send it to the edge;

[0006] The edge is used to input the power market price, load regulation service and planned energy consumption data into a pre-built pricing algorithm, solve the pricing algorithm by applying for 5G network resources corresponding to the load regulation service to obtain the purchase and sale price of electricity and send it to the terminal;

[0007] The terminal is used to send the planned energy consumption data to the edge, and use the pricing algorithm to solve the transaction power based on the purchase and sale electricity price and update the planned energy consumption data to perform load regulation;

[0008] The cloud end is a load regulation cloud platform, the edge end is a load aggregator, and the terminal end is a user; the edge end and the cloud end are connected based on cloud-edge collaborative technology; the pricing algorithm is based on the load regulation business and is constructed with the goal of maximizing the profit of the load aggregator and minimizing the total cost of the user.

[0009] Preferably, the edge includes: an edge server, a 5G core network UPF and a shared energy storage device;

[0010] The edge server is respectively connected to the cloud and the terminal for communication; used to obtain load regulation services from the cloud, and generate a request corresponding to the service and send it to the 5G core network UPF; and used to obtain the electricity market price from the cloud and the planned energy consumption data from the terminal, input the electricity market price and the planned energy consumption data into a pre-built pricing algorithm, solve the pricing algorithm using the 5G network slice resources corresponding to the service, obtain the purchase and sale electricity price and the charging and discharging power of the shared energy storage device, and send the purchase and sale electricity price to the terminal;

[0011] The 5G core network UPF is communicatively connected to the edge server; used to receive and apply for 5G network slice resources corresponding to the service from the 5G core network based on the request corresponding to the service;

[0012] The shared energy storage device is connected to the terminal and is used to provide power or store power for load regulation for the terminal based on the transaction power and the charge and discharge power of the shared energy storage device;

[0013] Among them, the pricing algorithm takes the electricity market price and planned energy consumption data as input, and the purchase and sale electricity price, the charging and discharging power of the shared energy storage equipment, the transaction electricity and the updated planned energy consumption data as output. It establishes a load regulation two-layer optimization model with the maximum profit of the load aggregator as the upper objective function and the minimum total cost of the user as the lower objective function, and is constructed in combination with a differentiated purchase and sale electricity pricing calculation formula set based on the load regulation business; the load regulation business includes one or more of new energy consumption, peak shaving and valley filling, and emergency demand response.

[0014] Preferably, the construction of the two-layer optimization model includes:

[0015] An upper objective function is constructed with the goal of maximizing the profit of load aggregators, the electricity market price and planned energy consumption data are used as inputs of the upper objective function, and the purchase and sale price of electricity and the charging and discharging power of the shared energy storage device are used as outputs of the upper objective function; a lower objective function is constructed with the goal of minimizing the total cost of users, the purchase and sale price of electricity is used as inputs of the lower objective function, and the amount of electric and thermal load transfer, updated planned energy consumption data and transaction power are used as outputs of the lower objective function, and the two-layer optimization model is constructed by setting load output constraints, purchase and sale power constraints, shared energy storage device power constraints, shared energy storage device capacity constraints, electric and thermal load demand response constraints and energy balance constraints;

[0016] The load includes one or more of photovoltaic, gas turbine and heat pump.

[0017] Preferably, the upper objective function is constructed based on the electricity market price, planned energy consumption data and load aggregator costs, with the goal of maximizing the load aggregator's profit;

[0018] The lower objective function is constructed based on the electricity purchase and sale price and user cost, with the goal of minimizing the total user cost;

[0019] The load output constraint is set based on the upper and lower limits of photovoltaic, gas turbine and heat pump output; the power purchase and sale constraint is set based on the upper limit of the power purchased and sold by the user to the load aggregator and the upper limit of the power purchased and sold by the load aggregator to the power market; the shared energy storage device power constraint is set based on the upper and lower limits of the storage power and the charging and discharging efficiency of the shared energy storage device; the shared energy storage device capacity constraint is set based on the upper limit of the charging and discharging power of the shared energy storage device;

[0020] The load aggregator costs include the cost of participating in the management of load regulation business, the investment cost of shared energy storage equipment, the cost of purchasing electricity from the power market and the cost of energy trading with users;

[0021] The planned energy consumption data includes the electric and thermal output of the load and the photovoltaic predicted output of the user;

[0022] The user costs include gas turbine costs, rental costs of shared energy storage equipment, unit load shedding costs of electric loads, electric and thermal demand response costs, and costs of energy trading with load aggregators.

[0023] Preferably, the upper layer objective function is expressed as follows:

[0024]

[0025] Where W is the revenue of the load aggregator, is the net income of the load aggregator from selling electricity at time t, is the cost of load aggregator participating in load regulation business management at time t, C ESS is the daily investment cost of the shared energy storage equipment for load aggregators, and T is the total duration;

[0026] Among them, the load aggregator's net income from electricity sales at time t is Calculate as follows:

[0027]

[0028] In the formula, is the cost of purchasing electricity from the load aggregator at time t for user i, is the revenue of user i from selling electricity to load aggregators at time t, P t b,E P is the power purchased by the load aggregator in the power market at time t, t s,E is the power sold by the load aggregator in the power market at time t, is the electricity purchase price in the electricity market, The electricity selling price in the electricity market;

[0029] The cost of the load aggregator participating in the load regulation business management at time t Calculate as follows:

[0030]

[0031]

[0032]

[0033]

[0034] In the formula, is the cost of load aggregators participating in emergency demand response management at time t, is the cost of load aggregator participating in peak load reduction and valley filling management at time t, is the cost of load aggregators participating in the management of new energy consumption at time t, a, b, d are all variables, λ DR1 Cost factor for emergency demand response management, is the electric load removed by user i at time t, n is the number of users, Δt is the time period, λ DR2 is the unit charging and discharging cost coefficient of the shared energy storage equipment when the load aggregator participates in peak shaving and valley filling management, λ w It is the government subsidy coefficient when load aggregators participate in peak load shaving and valley filling management. The charging power of the shared energy storage device leased to user i, The discharge power of the shared energy storage device leased to user i, λ DR3The photovoltaic consumption management cost coefficient for load aggregators participating in the new energy consumption management is Predicted photovoltaic output for user i at time t.

[0035] Preferably, the lower layer objective function is expressed as follows:

[0036]

[0037] Where C is the total cost of n users, is the gas turbine cost for user i at time t, is the cost of purchasing electricity from the load aggregator at time t for user i, The cost of leasing a shared energy storage device for electric energy storage by user i at time t, is the cost of user i participating in emergency demand response at time t, is the electric heating load cost of user i participating in electric heating demand response at time t, is the revenue of user i from selling electricity to load aggregators at time t;

[0038] The cost of purchasing electricity from the load aggregator at time t is Calculate as follows:

[0039]

[0040] In the formula, is the optimal electricity purchase price set by the load aggregator for user i at time t, is the power purchased by user i from the load aggregator at time t, and Δt is the time period;

[0041] The cost of user i leasing a shared energy storage device for energy storage at time t Calculate as follows:

[0042]

[0043] In the formula, λ ESS is the rental price of the shared energy storage equipment at time t, The charging power of the shared energy storage device leased to user i, The discharge power of the shared energy storage device leased to user i;

[0044] The cost of user i participating in emergency demand response at time t Calculate as follows:

[0045]

[0046] In the formula, λ cut is the unit load shedding cost of the electric load, is the electric load removed by user i at time t;

[0047] The electric heating load cost of user i participating in electric heating demand response at time t Calculate as follows:

[0048]

[0049] In the formula, is the cost coefficient of unit power load transfer, is the cost coefficient of heat load transfer per unit power, is the load transfer amount of user i at time t, is the heat load transfer amount of user i at time t;

[0050] The revenue of user i selling electricity to load aggregator at time t is Calculate as follows:

[0051]

[0052] In the formula, is the optimal electricity price set by the load aggregator for user i at time t, is the power sold by user i to the load aggregator at time t.

[0053] Preferably, the electric heating load demand response constraint is expressed as follows:

[0054]

[0055] In the formula, is the load transfer amount of user i at time t, is the heat load transfer amount of user i at time t, T is the total duration, is the original value of the electricity load of user i at time t, is the original value of heat load of user i at time t, ζ e is the maximum allowable transfer ratio of electric load, ζ h is the maximum allowable transfer ratio of heat load.

[0056] Preferably, the energy balance constraint is expressed as follows:

[0057]

[0058]

[0059]

[0060] In the formula, For the photovoltaic power forecast of user i at time t, is the power output of the gas turbine of user i at time t, is the thermal output of the gas turbine of user i at time t, is the power purchased by user i from the load aggregator at time t, The charging power of the shared energy storage device leased to user i, The discharge power of the shared energy storage device leased to user i, is the power sold by user i to the load aggregator at time t, is the heat output of user i’s heat pump at time t, is the electric power consumed by the heat pump of user i at time t, is the electric load removed by user i at time t, P t b,E P is the power purchased by the load aggregator in the power market at time t, t s,E is the power sold by the load aggregator in the power market at time t, is the original value of the electricity load of user i at time t, is the original value of heat load of user i at time t, is the load transfer amount of user i at time t, is the heat load transfer amount of user i at time t.

[0061] Preferably, the differentiated electricity purchase and sale pricing calculation formula includes: the electricity purchase and sale pricing calculation formula for the new energy consumption business and the electricity purchase and sale pricing calculation formula for the peak shaving and valley filling business;

[0062] The pricing calculation formula for the purchase and sale of electricity for the new energy consumption business is based on the goal of new energy consumption, with the electricity market price and planned energy consumption data as input, and the purchase and sale price as output, and is set by calculating the standard deviation of the user's net load demand distribution;

[0063] The pricing calculation formula for the purchase and sale of electricity for the peak shaving and valley filling business has the goal of peak shaving and valley filling, takes the electricity market price as input, and takes the purchase and sale price of electricity as output. It is set based on the inverse relationship between the electricity market price and the supply-demand ratio.

[0064] Preferably, the pricing calculation formula for the purchase and sale of electricity for the new energy consumption business is expressed as follows:

[0065]

[0066]

[0067]

[0068]

[0069] In the formula, Φnet is the standard deviation of the net load demand distribution on the user side, is the net electricity load demand of all users at time t, is the average net electric load demand for T time, T is the total time, is the original value of the electricity load of user i at time t, is the load transfer amount of user i at time t, For the photovoltaic power forecast of user i at time t, is the power output of the gas turbine of user i at time t, n is the number of users, θ is the price adjustment coefficient, is the optimal electricity purchase price set by the load aggregator for user i at time t, is the optimal electricity price set by the load aggregator for user i at time t, is the electricity purchase price in the electricity market, The electricity selling price in the electricity market.

[0070] Preferably, the pricing calculation formula for the purchase and sale of electricity for the peak shaving and valley filling business is expressed as follows:

[0071]

[0072]

[0073] In the formula, is the optimal electricity purchase price set by the load aggregator for user i at time t, is the optimal electricity price set by the load aggregator for user i at time t, is the electricity purchase price in the electricity market, is the electricity price in the electricity market, SDR t is the energy supply-demand ratio of the load aggregator at time t;

[0074] Among them, the energy supply-demand ratio SDR of the load aggregator at time t is t , expressed as follows:

[0075]

[0076] Where P t b,E P is the power purchased by the load aggregator in the power market at time t, t s,E is the electricity sales power of the load aggregator participating in the electricity market at time t, and α is a non-zero coefficient.

[0077] Preferably, the edge server is used for:

[0078] Based on the load regulation business, determining the differentiated power purchase and sale pricing calculation formula, and substituting the power market price and planned energy consumption data into the differentiated power purchase and sale pricing calculation formula;

[0079] Substituting the electricity market price and planned energy consumption data into the upper objective function of the two-layer optimization model;

[0080] The differentiated electricity purchase and sale pricing calculation formula is solved by utilizing the 5G network slice resources corresponding to the business, and the upper-level objective function and the lower-level objective function are combined for iterative solution to obtain the electricity purchase and sale price and the charging and discharging power of the shared energy storage device, and the electricity purchase and sale price is sent to the terminal.

[0081] Preferably, the terminal is used for:

[0082] Substituting the purchase and sale electricity price into the lower objective function of the two-layer optimization model, and iteratively solving the upper objective function and the lower objective function to obtain the transaction electricity and update the planned energy consumption data;

[0083] Sending the updated planned energy consumption data to the edge;

[0084] Based on the updated planned energy consumption data, load control is performed by adjusting the load output;

[0085] Purchase, sell or store electric energy to the edge based on the traded electric energy to perform load regulation;

[0086] The traded electricity includes one or more of the electricity purchased from the edge, the electricity sold to the edge, and the electricity stored at the edge by leasing a shared energy storage device.

[0087] Preferably, the shared energy storage device is used for:

[0088] Based on the power purchased from the edge, providing power to the terminal by discharging;

[0089] The electric energy corresponding to the electric quantity sold to the edge end is stored by charging;

[0090] Based on the amount of electricity stored at the edge, the terminal is provided with electric energy by discharging; or the electric energy corresponding to the amount of electricity stored at the edge is stored by charging.

[0091] Preferably, the 5G core network UPF is also respectively connected to the cloud and the terminal for obtaining the transaction power and planned energy consumption data from the terminal; and uploading the purchase and sale electricity price, transaction power and planned energy consumption data to the cloud through the 5G network slice corresponding to the business.

[0092] Based on the same inventive concept, the present invention also provides a load flexible control method, comprising:

[0093] Based on the real-time operation status of the power grid, power forecast data and electricity market prices acquired in advance, load regulation services are determined through the cloud;

[0094] Send planned energy consumption data through the terminal;

[0095] The electricity market price, load regulation service and planned energy consumption data are input into a pre-built pricing algorithm, and the pricing algorithm is solved to obtain the purchase and sale price of electricity by applying for 5G network resources corresponding to the load regulation service at the edge;

[0096] The purchase and sale electricity price is input into the pricing algorithm, and the transaction electricity quantity is solved through the terminal and the planned energy consumption data is updated to perform load regulation;

[0097] Wherein, the method adopts a load flexible control system to perform load flexible control;

[0098] The cloud end is a load regulation cloud platform, the edge end is a load aggregator, and the terminal end is a user; the edge end and the cloud end are connected based on cloud-edge collaborative technology; the pricing algorithm is based on the load regulation business and is constructed with the goal of maximizing the profit of the load aggregator and minimizing the total cost of the user.

[0099] Preferably, the power market electricity price, load regulation service and planned energy consumption data are input into a pre-built pricing algorithm, and the pricing algorithm is solved to obtain the purchase and sale electricity price by applying for 5G network resources corresponding to the load regulation service at the edge, including:

[0100] Based on the load regulation service, a request corresponding to the service is generated by an edge server at an edge end;

[0101] Based on the request corresponding to the service, apply for the 5G network slice resources corresponding to the service from the 5G core network through the 5G core network UPF at the edge;

[0102] Input the electricity market price and planned energy consumption data into a pre-built pricing algorithm, and solve the pricing algorithm by using the 5G network slice resources corresponding to the service through the edge server to obtain the purchase and sale electricity price;

[0103] Among them, the pricing algorithm takes the electricity market price and planned energy consumption data as input, and the purchase and sale electricity price, the charging and discharging power of the shared energy storage equipment, the transaction electricity and the updated planned energy consumption data as output. It establishes a load regulation two-layer optimization model with the maximum profit of the load aggregator as the upper objective function and the minimum total cost of the user as the lower objective function, and is constructed in combination with a differentiated purchase and sale electricity pricing calculation formula set based on the load regulation business; the load regulation business includes one or more of new energy consumption, peak shaving and valley filling, and emergency demand response.

[0104] Preferably, the construction of the two-layer optimization model includes:

[0105] An upper objective function is constructed with the goal of maximizing the profit of load aggregators, the electricity market price and planned energy consumption data are used as inputs of the upper objective function, and the purchase and sale price of electricity and the charging and discharging power of shared energy storage equipment are used as outputs of the upper objective function; a lower objective function is constructed with the goal of minimizing the total cost of users, the purchase and sale price of electricity is used as inputs of the lower objective function, and the amount of electric and thermal load transfer, updated planned energy consumption data and transaction power are used as outputs of the lower objective function, and the two-layer optimization model is constructed by setting load output constraints, purchase and sale power constraints, shared energy storage equipment power constraints, shared energy storage equipment capacity constraints, electric and thermal load demand response constraints and energy balance constraints;

[0106] The load includes one or more of photovoltaic, gas turbine and heat pump.

[0107] Preferably, the differentiated electricity purchase and sale pricing calculation formula includes: the electricity purchase and sale pricing calculation formula for the new energy consumption business and the electricity purchase and sale pricing calculation formula for the peak shaving and valley filling business;

[0108] The pricing calculation formula for the purchase and sale of electricity for the new energy consumption business is based on the goal of new energy consumption, with the electricity market price and planned energy consumption data as input, and the purchase and sale price as output, and is set by calculating the standard deviation of the user's net load demand distribution;

[0109] The pricing calculation formula for the purchase and sale of electricity for the peak shaving and valley filling business has the goal of peak shaving and valley filling, takes the electricity market price as input, and takes the purchase and sale price of electricity as output. It is set based on the inverse relationship between the electricity market price and the supply-demand ratio.

[0110] Preferably, the power market electricity price and planned energy consumption data are input into a pre-built pricing algorithm, and the pricing algorithm is solved using the 5G network slice resources corresponding to the service to obtain the purchase and sale electricity price and the charging and discharging power of the shared energy storage device, including:

[0111] Based on the load regulation business, determining the differentiated power purchase and sale pricing calculation formula, and substituting the power market price and planned energy consumption data into the differentiated power purchase and sale pricing calculation formula;

[0112] Substituting the electricity market price and planned energy consumption data into the upper objective function of the two-layer optimization model;

[0113] The differentiated electricity purchase and sale pricing calculation formula is solved by utilizing the 5G network slice resources corresponding to the business, and the upper-level objective function and the lower-level objective function are combined for iterative solution to obtain the electricity purchase and sale price and the charging and discharging power of the shared energy storage device, and the electricity purchase and sale price is sent to the terminal.

[0114] Preferably, the step of inputting the purchase and sale electricity price into the pricing algorithm, solving the transaction electricity quantity through the terminal and updating the planned energy consumption data for load regulation includes:

[0115] Substituting the purchase and sale electricity price into the lower objective function of the two-layer optimization model, and iteratively solving the upper objective function and the lower objective function to obtain the transaction electricity and update the planned energy consumption data;

[0116] Based on the updated planned energy consumption data, load control is performed by adjusting the load output;

[0117] Purchase, sell or store electric energy to the edge based on the traded electric energy to perform load regulation;

[0118] The traded electricity includes one or more of the electricity purchased from the edge, the electricity sold to the edge, and the electricity stored at the edge by leasing a shared energy storage device.

[0119] Based on the same inventive concept, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed, the load flexible control method is implemented.

[0120] Compared with the closest prior art, the present invention has the following beneficial effects:

[0121] 1. The present invention provides a load flexible regulation system and method, the system comprising: a cloud, an edge and a terminal communicating with each other; the cloud is used to determine the load regulation service based on the pre-acquired real-time operation status of the power grid, the electric energy forecast data and the electricity market price and send it to the edge; the edge is used to input the electricity market price, the load regulation service and the planned energy consumption data into a pre-built pricing algorithm, solve the pricing algorithm by applying for the 5G network resources corresponding to the load regulation service to obtain the purchase and sale electricity price and send it to the terminal; the terminal is used to send the planned energy consumption data to the edge, and solve the transaction electricity volume and update the planned energy consumption data based on the purchase and sale electricity price by using the pricing algorithm to perform load regulation; the cloud is a load regulation cloud platform, the edge is a load aggregator, and the terminal is a user; The edge and the cloud are connected based on cloud-edge collaborative technology; the pricing algorithm is based on the load regulation business, and is constructed with the goal of maximizing the revenue of load aggregators and minimizing the total cost of users; the present invention uses the load regulation cloud platform as the cloud and the load aggregator as the edge, and sends data to the edge for calculation through cloud-edge collaborative technology, thereby realizing the power autonomy of the edge. With the assistance of the edge, the computing and carrying pressure of the cloud is reduced, the data processing efficiency is improved, and the data is sent to the terminal to realize the rapid response of the terminal; the advantages of 5G technology are used to target the load regulation business, and differentiated slicing services for different businesses are realized. By formulating the purchase and sale electricity prices under differentiated businesses, the problem of traditional load regulation not being able to adapt to the precise regulation of various distributed load resources in the new power system is solved, laying the foundation for realizing the precise regulation of distributed load resources;

[0122] 2. The pricing algorithm proposed in the present invention takes the lowest total cost for users as the goal, and can set electricity sales prices for prosumer users, improve the demand response willingness of prosumer users, achieve precise regulation of adjustable load resources, and improve the economic benefits of each prosumer user participating in flexible load regulation;

[0123] 3. The shared energy storage equipment provided by the present invention can be used for leasing energy storage, and can also be used for users to purchase and sell electricity and energy storage, breaking the single nature of energy storage in the form of leasing in most current research, providing users with a variety of options, and facilitating load regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0124] Figure 1 A schematic diagram of the architecture of a load flexible control system provided by the present invention;

[0125] Figure 2 A schematic flow chart of a load flexible control method provided by the present invention. DETAILED DESCRIPTION

[0126] At present, 4G networks cannot adapt to the diversified development of power grid services and cannot provide differentiated needs for each business, while 5G technology has the characteristics of high-reliability, low-latency communication, enhanced mobile broadband and large-scale machine-type communication. Some literatures have fully demonstrated the feasibility and necessity of applying 5G technology in the power system with coordinated optimization of source, grid, load and storage; some literatures have built a system architecture for 5G-oriented adjustable loads to participate in demand response based on the physical architecture of cloud-pipe-edge-end. At the edge layer of the architecture, some demand response devices with edge computing functions can analyze the user's adjustable potential by collecting real-time electricity consumption data. As mentioned above, the characteristics of 5G technology can meet the needs of digital transformation in the power industry. However, the above literature does not substantially analyze how to apply 5G communication to the flexible load control business process.

[0127] In the context of the continuous development of energy Internet, the role of prosumers (prosumer users) has emerged. How to make full use of the energy storage system of prosumers to achieve win-win benefits in the region is one of the current hot issues. Some literatures have proposed that when all prosumers in the region configure energy storage systems separately, there may be a phenomenon of capacity waste, that is, the energy storage of prosumers in a research cycle has idle periods or excess capacity that cannot be used; some literatures have proposed a "storage capacity leasing" model, where the lessee stores the remaining electricity to the provider, and the provider charges the lessee according to the rented energy storage capacity and time; some literatures have proposed a shared energy storage service mechanism for multiple photovoltaic prosumers under the community energy Internet, to achieve a win-win situation between shared energy storage and prosumer groups. In summary, existing research usually focuses on capacity sharing or energy sharing to utilize energy storage systems. Although a single method is conducive to improving the utilization rate of energy storage systems, it does not give full play to the potential of shared energy storage technology, especially the lack of selectivity, which limits the shared benefits of prosumers and makes it difficult to maximize the benefits of prosumers.

[0128] In terms of differentiated energy purchase and sale pricing, most literature only considers the user's electricity purchase price. Some literature has formulated time-of-use electricity prices considering the energy consumption characteristics over time, but has not formulated a personalized and differentiated pricing mechanism for each user, which is less targeted, so the user's willingness to respond is low; some literature has formulated a corresponding time-of-use electricity price based on the linear correlation between the current regional consumption / reduction target and the consumption / reduction behavior of the producer and consumer, and formulated electricity price incentives / penalties based on the behavior of the producer and consumer, but has not formulated a corresponding user electricity sales price, making it difficult to maximize the interests of users; some literature has proposed a purchase and sale electricity price pricing mechanism based on the supply-demand ratio, but considering the privacy of the producer and consumer's own energy consumption data, its specific energy consumption data and distributed photovoltaic power generation data are difficult to obtain, so the above mechanism is difficult to promote and apply.

[0129] In order to solve the above problems, the present invention proposes a load flexible control system, method and storage medium. The specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings.

[0130] Embodiment 1:

[0131] The present invention provides a load flexible control system, the system architecture diagram of which is as follows: Figure 1 As shown, it includes: cloud, edge and terminal that communicate with each other;

[0132] The cloud is used to determine the load regulation business based on the pre-acquired real-time operation status of the power grid, power forecast data and power market electricity prices and send it to the edge;

[0133] The edge is used to input the power market price, load regulation service and planned energy consumption data into a pre-built pricing algorithm, solve the pricing algorithm by applying for 5G network resources corresponding to the load regulation service to obtain the purchase and sale price of electricity and send it to the terminal;

[0134] The terminal is used to send the planned energy consumption data to the edge, and use the pricing algorithm to solve the transaction power based on the purchase and sale electricity price and update the planned energy consumption data to perform load regulation;

[0135] The cloud end is a load regulation cloud platform, the edge end is a load aggregator, and the terminal end is a user; the edge end and the cloud end are connected based on cloud-edge collaborative technology; the pricing algorithm is based on the load regulation business and is constructed with the goal of maximizing the profit of the load aggregator and minimizing the total cost of the user.

[0136] Considering that traditional load control systems are difficult to achieve precise control of various distributed resources in future new power systems, 4G networks cannot adapt to the diversified development of modern power grid services and cannot provide differentiated needs for each business. However, information and communication technologies such as 5G technology, edge computing, and smart terminal interconnection have been successfully applied in various fields. Therefore, the establishment of a new load flexible control system can provide ideas for solving current problems, adapt to the development of new power systems and communication technologies, solve the shortcomings of 4G applications, realize different business needs, and improve the precise control of distributed resources. The system can be divided into three levels in terms of architecture: cloud, edge, and end. The functions of each level are as follows:

[0137] Cloud: The cloud is a load control cloud platform, which is connected to the edge for issuing control instructions and receiving feedback information from the edge. The load control cloud platform combines the real-time operation status of the power grid and the power forecast data (source-grid-load-storage forecast data) to determine the load control business (business scenario), generate day-ahead, intra-day and real-time control requirements (i.e. control instructions, including power market electricity prices and load control business), and issue them to the edge. The load control business includes new energy consumption business, peak shaving and valley filling business, and emergency demand response business.

[0138] Edge: The edge is a load aggregator. It is connected to the regional power grid and the terminal for power transmission. It is also connected to the terminal for communication. The edge includes edge servers, 5G core network UPF (User Plane Function) and shared energy storage devices.

[0139] The edge server is respectively connected to the cloud and the terminal for communication, and is used to obtain load regulation services from the cloud, and generate a request corresponding to the service and send it to the 5G core network UPF.

[0140] The 5G core network UPF is connected in communication with the edge server; it is used to receive and apply for network resources for hybrid services from the 5G core network based on the request corresponding to the service. After receiving the application, the 5G core network deploys 5G network slice resources for different services according to different service requirements to achieve differentiated service slice services. The 5G core network UPF is also connected in communication with the cloud and the terminal respectively, and is used to obtain the transaction power and planned energy consumption data from the terminal; and upload the purchase and sale power price, transaction power and planned energy consumption data to the cloud through the 5G network slice corresponding to the service.

[0141] Subsequently, the edge server inputs the load control service sent from the cloud, the electricity price in the power market, and the planned energy consumption data (energy consumption plan) submitted by the terminal into the pre-built pricing algorithm, and uses the 5G network slice resources corresponding to the service to iteratively solve the pricing algorithm to obtain the purchase and sale price, purchase and sale power, and the charge and discharge power of the shared energy storage device, and sends the purchase and sale price to the terminal;

[0142] The pricing algorithm takes the electricity market price and planned energy consumption data as input, and the purchase and sale electricity price, the charging and discharging power of the shared energy storage equipment, the transaction electricity and the updated planned energy consumption data as output. It is constructed by establishing a load regulation double-layer optimization model with the maximum profit of the load aggregator as the upper objective function and the minimum total cost of the user as the lower objective function, and combining it with a differentiated purchase and sale electricity pricing calculation formula set based on the load regulation business.

[0143] Considering that when all users in a region configure energy storage devices separately, capacity waste may occur, that is, the user's energy storage has idle periods or excess capacity that cannot be used within a research cycle. In order to avoid this phenomenon, the present invention introduces shared energy storage devices. Users can adjust their own energy use strategies through the shared energy storage devices of load aggregators to achieve optimal configuration of idle energy storage resources, bring additional economic benefits to users and load aggregators, and further improve energy sustainability. As a major member participating in load regulation, the load aggregator is equipped with a shared energy storage device, which is connected to the terminal to provide or store electrical energy to the terminal based on the transaction power and the charging and discharging power of the shared energy storage device.

[0144] The present invention proposes a shared energy storage mode based on shared energy storage equipment. This mode has both energy sharing and capacity sharing in existing research, and can realize energy sharing or capacity sharing among individual loads based on shared energy storage equipment. Prosumers can choose the energy storage method that suits them from the two to maximize their own benefits, avoid waste of resources, and facilitate precise regulation of adjustable loads.

[0145] The capacity sharing is as follows: when a user has surplus electricity, the user can lease the capacity of the load aggregator's shared energy storage device and pay a certain lease fee to the load aggregator. After negotiation, the user can store the excess electricity in the load aggregator's shared energy storage device in the form of leasing. When the user's own electricity is insufficient or the current electricity price is too high, the user can discharge the electricity through the shared energy storage device. While saving their own costs, the user also avoids waste of resources.

[0146] The energy sharing is as follows: when a user has excess electricity, in addition to capacity sharing with load aggregators, the user can also share electricity with load aggregators. Specifically, the user no longer rents energy storage, but directly sells excess energy to load aggregators. Load aggregators can supply this part of electricity to other users with insufficient energy, without wasting energy due to abandonment.

[0147] Therefore, the dual modes of energy sharing and capacity sharing can make greater use of shared energy storage equipment, providing a variety of choices for each load individual (user) to participate in the demand response process, which is conducive to the precise control of adjustable loads.

[0148] Terminal: The terminal is a user (specifically, a prosumer user), and the terminal is connected to the regional power grid to obtain electric energy. In the present invention, the user is equipped with loads (equipment) such as photovoltaics, gas turbines, and heat pumps, and participates in the demand response under 5G hybrid services. The user calculates the overall adjustable potential of the load based on the adjustable potential of its own flexible load, generates the purchase and sale energy plan (i.e., planned energy consumption data) for each period and reports it to the load aggregator. The load aggregator adjusts its own price (obtains the purchase and sale electricity price) based on the planned energy consumption data uploaded by the user, and sends it to the user. The user calculates the transaction power and updates the planned energy consumption data based on the purchase and sale electricity price using the pricing algorithm, and realizes load control by adjusting the energy consumption plan (including purchase and sale of electricity, leasing excess electricity, reducing load operation power / duration, adjusting the working status of photovoltaic and other power generation equipment, etc.). Finally, the planned energy consumption data and transaction power are returned to the load aggregator at the edge. After the load aggregator completes the transaction with the user, the electric heat clearing information of the day (including purchase and sale electricity price, transaction power and updated planned energy consumption data) is fed back to the cloud.

[0149] The process of calculating the transaction power and planned energy consumption data and performing load regulation at the terminal is as follows:

[0150] Substituting the purchase and sale electricity price into the lower objective function of the two-layer optimization model, and solving the upper objective function and the lower objective function alternately and iteratively to obtain the transaction electricity and update the planned energy consumption data;

[0151] and sending the updated planned energy consumption data to the edge;

[0152] Based on the updated planned energy consumption data, load control is performed by adjusting the load output;

[0153] Purchase, sell or store electric energy to the edge based on the traded electric energy to perform load regulation;

[0154] The traded electricity includes one or more of the electricity purchased from the edge, the electricity sold to the edge, and the electricity stored at the edge by leasing a shared energy storage device.

[0155] The load control double-layer optimization model in the present invention constructs an upper-layer objective function with the goal of maximizing the profit of load aggregators, takes the electricity market price and planned energy consumption data as the input of the upper-layer objective function, and takes the purchase and sale price of electricity and the charging and discharging power of shared energy storage equipment as the output of the upper-layer objective function; constructs a lower-layer objective function with the goal of minimizing the total user cost, takes the purchase and sale price of electricity as the input of the lower-layer objective function, and takes the electric and thermal load transfer amount, updated planned energy consumption data and transaction power as the output of the lower-layer objective function, and constructs the double-layer optimization model by setting load output constraints, purchase and sale power constraints, shared energy storage equipment power constraints, shared energy storage equipment capacity constraints, electric and thermal load demand response constraints and energy balance constraints;.

[0156] The upper objective function is constructed based on the electricity market price, planned energy consumption data and load aggregator costs, with the goal of maximizing the load aggregator's profit; the load aggregator costs include the cost of participating in load regulation business management, the investment cost of shared energy storage equipment, and the cost of purchasing electricity from the electricity market; the planned energy consumption data includes the electric and thermal output of the load and the photovoltaic predicted output of the user; the upper objective function is expressed as follows:

[0157]

[0158] Where W is the revenue of the load aggregator, is the net income of the load aggregator from selling electricity at time t, is the cost of load aggregator participating in load regulation business management at time t, C ESS is the daily investment cost of the shared energy storage equipment for load aggregators, and T is the total duration;

[0159] Among them, the load aggregator's net income from electricity sales at time t is Calculate as follows:

[0160]

[0161] In the formula, is the cost of purchasing electricity from the load aggregator at time t for user i, is the revenue of user i from selling electricity to load aggregators at time t, P is the power purchased by the load aggregator in the power market at time t, t s,E is the power sold by the load aggregator in the power market at time t, is the electricity purchase price in the electricity market, The electricity selling price in the electricity market;

[0162] The cost of the load aggregator participating in the load regulation business management at time t Calculate as follows:

[0163]

[0164]

[0165]

[0166]

[0167] In the formula, is the cost of load aggregators participating in emergency demand response management at time t, is the cost of load aggregator participating in peak load reduction and valley filling management at time t, is the cost of load aggregators participating in the management of new energy consumption at time t, a, b, d are all variables, λ DR1 Cost factor for emergency demand response management, is the electric load removed by user i at time t, n is the number of users, Δt is the time period, λ DR2 is the unit charging and discharging cost coefficient of the shared energy storage equipment when the load aggregator participates in peak shaving and valley filling management, λ w It is the government subsidy coefficient when load aggregators participate in peak load shaving and valley filling management. The charging power of the shared energy storage device leased to user i, The discharge power of the shared energy storage device leased to user i, λ DR3 The photovoltaic consumption management cost coefficient for load aggregators participating in the new energy consumption management is Predict the photovoltaic output of user i at time t;

[0168] The load aggregator shares the daily investment cost C of the energy storage equipment ESS , calculated as follows:

[0169]

[0170] In the formula, λ E is the capacity factor of the shared energy storage device, λ p is the power cost coefficient of the shared energy storage device, SOC max is the maximum energy storage capacity of the shared energy storage device, P max is the maximum charge and discharge power of the shared energy storage device, T U is the service life of the shared energy storage equipment, and M is the daily maintenance cost of the shared energy storage equipment.

[0171] The lower objective function is constructed based on the electricity purchase and sale price and user cost, with the goal of minimizing the total user cost; the user cost includes the gas turbine cost, the rental price of the shared energy storage device and the unit load shedding cost of the electric load; the lower objective function is expressed as follows:

[0172]

[0173] Where C is the total cost of n users, is the gas turbine cost for user i at time t, is the cost of purchasing electricity from the load aggregator at time t for user i, The cost of leasing a shared energy storage device for electric energy storage by user i at time t, is the cost of user i participating in emergency demand response at time t, is the electric heating load cost of user i participating in electric heating demand response at time t, is the revenue of user i from selling electricity to load aggregators at time t;

[0174] Among them, the gas turbine cost of user i at time t is Calculate as follows:

[0175]

[0176] In the formula, C gas is the natural gas unit price, is the power generation efficiency of the gas turbine, Q Lgas is the calorific value of natural gas, is the power output of the gas turbine of user i at time t;

[0177] The cost of user i purchasing electricity from the load aggregator at time t Calculate as follows:

[0178]

[0179] In the formula, is the optimal electricity purchase price set by the load aggregator for user i at time t, is the power purchased by user i from the load aggregator at time t, and Δt is the time period;

[0180] The cost of user i leasing a shared energy storage device for energy storage at time t Calculate as follows:

[0181]

[0182] In the formula, λ ESS is the rental price of the shared energy storage equipment at time t, The charging power of the shared energy storage device leased to user i, The discharge power of the shared energy storage device leased to user i;

[0183] The cost of user i participating in emergency demand response at time t Calculate as follows:

[0184]

[0185] In the formula, λ cut is the unit load shedding cost of the electric load, is the electric load removed by user i at time t;

[0186] The electric heating load cost of user i participating in electric heating demand response at time t Calculate as follows:

[0187]

[0188] In the formula, is the cost coefficient of unit power load transfer, is the cost coefficient of heat load transfer per unit power, is the load transfer amount of user i at time t, is the heat load transfer amount of user i at time t;

[0189] The revenue of user i selling electricity to load aggregator at time t is Calculate as follows:

[0190]

[0191] In the formula, is the optimal electricity price set by the load aggregator for user i at time t, is the power sold by user i to the load aggregator at time t.

[0192] The load output constraint is set based on the upper and lower limits of the photovoltaic, gas turbine and heat pump outputs; the load output constraint is expressed as follows:

[0193]

[0194]

[0195]

[0196]

[0197]

[0198] In the formula, is the power output of the gas turbine of user i at time t, and are the upper and lower limits of the gas turbine power output of user i, is the heat output of the heat pump for user i at time t, is the thermal output of the gas turbine of user i at time t, and are the upper and lower limits of the heat output of the heat pump of user i, For the photovoltaic power forecast of user i at time t, is the upper limit of photovoltaic output of user i, and They are the power generation efficiency, heat loss rate and comprehensive heating coefficient of the gas turbine. is the electric power consumed by the heat pump of user i at time t, η HP is the heating efficiency of the heat pump.

[0199] The power purchase and sale constraint is set based on the upper limit of the power that the user can purchase and sell from the load aggregator and the upper limit of the power that the load aggregator can purchase and sell from the power market; the power purchase and sale constraint is expressed as follows:

[0200]

[0201]

[0202]

[0203]

[0204] In the formula, is the power purchased by user i from the load aggregator at time t, and are the upper limits of the power that user i can purchase and sell from the load aggregator, and are the upper limits of the power that load aggregators can purchase and sell from the upper power market. is the power sold by user i to the load aggregator at time t, P t b,E and P t s,E They are respectively the power purchased and sold by the load aggregators in the superior power market at time t.

[0205] The power constraint of the shared energy storage device is set based on the upper and lower limits of the power storage capacity and the charging and discharging efficiency of the shared energy storage device;

[0206]

[0207] SOC min ≤SOC t ≤SOC max

[0208]

[0209]

[0210]

[0211] In the formula, SOC t is the storage capacity of the shared energy storage device at time t, SOC t-1 is the storage capacity of the shared energy storage device at time t-1, P t c is the total charging power of the shared energy storage device at time t, P t d is the total discharge power of the shared energy storage device at time t, δ is the self-loss coefficient of the energy storage device, η c is the charging efficiency of the shared energy storage device, η d is the discharge efficiency of the shared energy storage device, Δt is the time period, SOC min is the minimum energy storage capacity of the shared energy storage device, SOC max is the maximum energy storage capacity of the shared energy storage device, Both are binary time variables (used to ensure that charging and discharging cannot occur at the same time). The upper limit of the charging power of the shared energy storage device. It is the upper limit of the energy discharging power of the shared energy storage device.

[0212] The shared energy storage device capacity constraint is set based on the upper limit of the charge and discharge power of the shared energy storage device; the shared energy storage device capacity constraint is expressed as follows:

[0213]

[0214]

[0215] In the formula, The charging power of the shared energy storage device leased to user i, The discharge power of the shared energy storage device leased to user i, The upper limit of charging power for users to rent shared energy storage equipment, The discharge power limit of shared energy storage equipment leased to users.

[0216] The electric heating load demand response constraint is expressed as follows:

[0217]

[0218]

[0219] In the formula, is the load transfer amount of user i at time t, is the heat load transfer amount of user i at time t, T is the total duration, is the original value of the electricity load of user i at time t, is the original value of heat load of user i at time t, ζ e is the maximum allowable transfer ratio of electric load, ζ h is the maximum allowable transfer ratio of heat load.

[0220] The energy balance constraint is expressed as follows (from top to bottom, they are user power balance constraint, thermal power balance constraint, and load aggregator power balance constraint):

[0221]

[0222]

[0223]

[0224] In the formula, For the photovoltaic power forecast of user i at time t, is the power output of the gas turbine of user i at time t, is the thermal output of the gas turbine of user i at time t, is the power purchased by user i from the load aggregator at time t, The charging power of the shared energy storage device leased to user i, The discharge power of the shared energy storage device leased to user i, is the power sold by user i to the load aggregator at time t, is the heat output of user i’s heat pump at time t, is the electric power consumed by the heat pump of user i at time t, is the electric load removed by user i at time t, P t b,E P is the power purchased by the load aggregator in the power market at time t, t s,E is the power sold by the load aggregator in the power market at time t, is the original value of the electricity load of user i at time t, is the original value of heat load of user i at time t, is the load transfer amount of user i at time t, is the heat load transfer amount of user i at time t.

[0225] The differentiated electricity purchase and sale pricing calculation formula (also known as the differentiated electricity purchase and sale pricing mechanism under different business scenarios) is used to perform differentiated pricing for different businesses, that is, to determine the electricity purchase and sale price.

[0226] The differentiated electricity purchase and sale pricing calculation formula includes: the electricity purchase and sale pricing calculation formula for the new energy consumption business and the electricity purchase and sale pricing calculation formula for the peak shaving and valley filling business;

[0227] New energy consumption business: To make full use of renewable energy and realize the local consumption of renewable energy. In a high-proportion renewable energy system, the traditional method of dividing the peak and valley price cycle according to the total user demand in each period no longer conforms to the coupling characteristics of renewable energy output and electricity demand curves. It is difficult to incentivize users to transfer loads to promote the consumption of renewable energy. Therefore, the purchase and sale pricing calculation formula of the new energy consumption business takes into account the fluctuation characteristics of user power demand and renewable power output (photovoltaic) to promote the consumption of renewable energy. The purchase and sale pricing calculation formula of the new energy consumption business is based on the goal of new energy consumption, with power market electricity prices and planned energy consumption data as inputs, and purchase and sale electricity prices as outputs. It is set by calculating the standard deviation of the user's net load demand distribution; the purchase and sale pricing calculation formula of the new energy consumption business is expressed as follows:

[0228]

[0229]

[0230]

[0231]

[0232] In the formula, Φ net is the standard deviation of the net load demand distribution on the user side (and takes its minimum value), is the net electricity load demand of all users at time t, is the average net electricity load demand over 24 hours, is the original value of the electricity load of user i at time t, is the load transfer amount of user i at time t, For the photovoltaic power forecast of user i at time t, is the power output of the gas turbine of user i at time t, n is the number of users, θ is the price adjustment coefficient (a constant greater than 0), is the optimal electricity purchase price set by the load aggregator for user i at time t, is the optimal electricity price set by the load aggregator for user i at time t, is the electricity purchase price in the electricity market, is the electricity selling price in the electricity market, and N is the number of data (i.e. 24).

[0233] Peak shaving and valley filling business: Since there is no obvious leader in the energy sharing model, the reflection of supply and demand is the basis for determining the internal price, and the internal price can be jointly determined by all producers. In order to give full play to the flexible adjustment capabilities of shared energy storage equipment and realize the optimization strategy of shared energy storage equipment participating in the peak shaving and valley filling of the power grid. The purchase and sale pricing calculation formula of the peak shaving and valley filling business is based on the peak shaving and valley filling as the goal, the electricity price in the power market as the input, and the purchase and sale price of electricity as the output. The electricity price in the power market is set according to the inverse relationship between the price and the supply and demand ratio, and the purchase and sale price of electricity between the user and the load aggregator is defined as SDR respectively. t The calculation formula for the purchase and sale of electricity pricing for the peak shaving and valley filling business is expressed as follows:

[0234]

[0235]

[0236] In the formula, is the optimal electricity purchase price set by the load aggregator for user i at time t, is the optimal electricity price set by the load aggregator for user i at time t, is the electricity purchase price in the electricity market, is the electricity price in the electricity market, SDR t is the energy supply-demand ratio of the load aggregator at time t;

[0237] Among them, the energy supply-demand ratio SDR of the load aggregator at time t is t , expressed as follows:

[0238]

[0239] Where P t b,E P is the power purchased by the load aggregator in the power market at time t, t s,E is the electricity sales power of the load aggregator participating in the electricity market at time t, and α is a non-zero coefficient (used to ensure that both the numerator and the denominator are not 0).

[0240] For users, when their net electricity load is positive, the price Purchase electricity from load aggregators and pay a price when the net load is negative Selling electricity to load aggregators.

[0241] In particular, the emergency demand response service refers to the implementation of the emergency demand response service according to the load control instructions, which is to cut the load to protect the safe operation of the system when the user's own electricity and heat output cannot match the load. Assuming that the priority of electric load shedding in the area is higher, when carrying out this service, the load aggregator and the user only need to pay the additional service response cost.

[0242] The following is a brief description of the process of load regulation at the edge and terminal:

[0243] The terminal (user) sends its planned energy consumption data to the edge (load aggregator);

[0244] Determine the differentiated electricity purchase and sale pricing calculation formula based on the load regulation service through the edge server at the edge, and substitute the electricity market price and planned energy consumption data; at the same time, substitute the electricity market price and planned energy consumption data into the upper objective function of the two-layer optimization model; use the 5G network slice resources corresponding to the service to solve the differentiated electricity purchase and sale pricing calculation formula, and alternately iterate and solve the upper objective function and the lower objective function to obtain the electricity purchase and sale price, electricity purchase and sale power, and the charging and discharging power of the shared energy storage device; retain the electricity purchase and sale price and send it to the terminal;

[0245] The terminal (user) inputs the purchase and sale electricity price from the load aggregator into the lower objective function, and uses the CPLEX solver to alternately iterate and solve the upper objective function and the lower objective function to obtain the traded electricity, the user's total cost (the total cost of the user's load regulation) and the traded electricity, and updates the planned energy consumption data (including the user's total electric and thermal load distribution and the electric and thermal load reduction), retaining the user's total cost; the traded electricity and the updated planned energy consumption data are returned to the load aggregator;

[0246] The load aggregator continues to adjust the purchase and sale prices based on the updated planned energy consumption data and transaction power, and repeats the above process iteratively until convergence, saving the final calculation results of the upper and lower layers (the charging and discharging power of the shared energy storage device, the planned energy consumption data and the transaction power);

[0247] Based on the charging and discharging power and transaction power of shared energy storage equipment, users’ excess power can be stored in shared energy storage equipment at the edge by selling electricity to load aggregators, or by purchasing electricity from shared energy storage equipment from load aggregators, or by leasing capacity from load aggregators to store excess power in shared energy storage equipment, or by extracting power stored in shared energy storage equipment to achieve load regulation; based on planned energy consumption data, load regulation can be achieved by adjusting load output;

[0248] After completing the load regulation, the electricity purchase and sales price, transaction volume and planned energy consumption data will be sent to the cloud through the 5G core network UPF at the edge.

[0249] The present invention first establishes a load flexible control system, which uses the load control cloud platform as the cloud and the load aggregator as the edge. Through the cloud-edge collaborative technology, the data is sent to the edge for calculation, realizing the power autonomy of the edge. With the assistance of the edge, the computing and carrying pressure of the cloud is reduced, the data processing efficiency is improved, and the data is sent to the terminal to realize the rapid response of the terminal. The advantages of 5G technology can be fully utilized to take the load control business as the target, realize differentiated slicing services for different businesses, meet the differentiated needs of each business, solve the problem that traditional load control cannot adapt to the precise control of various distributed resources in the new power system, and lay the foundation for realizing the precise control of distributed resources.

[0250] The pricing algorithm proposed in the present invention aims to minimize the total cost of users, and can set electricity sales prices for prosumer users, improve the demand response willingness of prosumer users, achieve mixed business goals such as peak load shaving and valley filling and local consumption of new energy, and improve the economic benefits of each prosumer user participating in flexible load regulation;

[0251] The energy sharing and capacity sharing strategies based on shared energy storage devices proposed in the present invention break the single mode of using shared energy storage devices for energy storage in the form of leasing in most current studies, make greater use of shared energy storage devices, and provide a variety of options for users to participate in demand response, which is conducive to the precise regulation of adjustable loads.

[0252] Embodiment 2:

[0253] Based on the same inventive concept, the present invention also provides a load flexible control method, the flow chart of which is as follows: Figure 2 As shown, including:

[0254] Step 1: Based on the real-time operation status of the power grid, power forecast data and power market electricity prices acquired in advance, the load regulation business is determined through the cloud;

[0255] Step 2: Send planned energy consumption data through the terminal;

[0256] Step 3: Input the electricity market price, load regulation service and planned energy consumption data into a pre-built pricing algorithm, and solve the pricing algorithm to obtain the purchase and sale price of electricity by applying for 5G network resources corresponding to the load regulation service at the edge;

[0257] Step 4: input the purchase and sale electricity price into the pricing algorithm, solve the transaction electricity volume through the terminal and update the planned energy consumption data to perform load regulation;

[0258] The method adopts a load flexible control system described in Example 1 to perform load flexible control;

[0259] The cloud end is a load regulation cloud platform, the edge end is a load aggregator, and the terminal end is a user; the edge end and the cloud end are connected based on cloud-edge collaborative technology; the pricing algorithm is based on the load regulation business and is constructed with the goal of maximizing the profit of the load aggregator and minimizing the total cost of the user.

[0260] Preferably, the power market electricity price, load regulation service and planned energy consumption data are input into a pre-built pricing algorithm, and the pricing algorithm is solved to obtain the purchase and sale electricity price by applying for 5G network resources corresponding to the load regulation service at the edge, including:

[0261] Based on the load regulation service, a request corresponding to the service is generated by an edge server at an edge end;

[0262] Based on the request corresponding to the service, apply for the 5G network slice resources corresponding to the service from the 5G core network through the 5G core network UPF at the edge;

[0263] Input the electricity market price and planned energy consumption data into a pre-built pricing algorithm, and solve the pricing algorithm by using the 5G network slice resources corresponding to the service through the edge server to obtain the purchase and sale electricity price;

[0264] Among them, the pricing algorithm takes the electricity market price and planned energy consumption data as input, and the purchase and sale electricity price, the charging and discharging power of the shared energy storage equipment, the transaction electricity and the updated planned energy consumption data as output. It establishes a load regulation two-layer optimization model with the maximum profit of the load aggregator as the upper objective function and the minimum total cost of the user as the lower objective function, and is constructed in combination with a differentiated purchase and sale electricity pricing calculation formula set based on the load regulation business; the load regulation business includes one or more of new energy consumption, peak shaving and valley filling, and emergency demand response.

[0265] Preferably, the construction of the two-layer optimization model includes:

[0266] An upper objective function is constructed with the goal of maximizing the profit of load aggregators, the electricity market price and planned energy consumption data are used as inputs of the upper objective function, and the purchase and sale price of electricity and the charging and discharging power of shared energy storage equipment are used as outputs of the upper objective function; a lower objective function is constructed with the goal of minimizing the total cost of users, the purchase and sale price of electricity is used as inputs of the lower objective function, and the amount of electric and thermal load transfer, updated planned energy consumption data and transaction power are used as outputs of the lower objective function, and the two-layer optimization model is constructed by setting load output constraints, purchase and sale power constraints, shared energy storage equipment power constraints, shared energy storage equipment capacity constraints, electric and thermal load demand response constraints and energy balance constraints;

[0267] The load includes one or more of photovoltaic, gas turbine and heat pump.

[0268] Preferably, the differentiated electricity purchase and sale pricing calculation formula includes: the electricity purchase and sale pricing calculation formula for the new energy consumption business and the electricity purchase and sale pricing calculation formula for the peak shaving and valley filling business;

[0269] The pricing calculation formula for the purchase and sale of electricity for the new energy consumption business is based on the goal of new energy consumption, with the electricity market price and planned energy consumption data as input, and the purchase and sale price as output, and is set by calculating the standard deviation of the user's net load demand distribution;

[0270] The pricing calculation formula for the purchase and sale of electricity for the peak shaving and valley filling business has the goal of peak shaving and valley filling, takes the electricity market price as input, and takes the purchase and sale price of electricity as output. It is set based on the inverse relationship between the electricity market price and the supply-demand ratio.

[0271] Preferably, the power market electricity price and planned energy consumption data are input into a pre-built pricing algorithm, and the pricing algorithm is solved by the edge server using the 5G network slice resources corresponding to the service to obtain the purchase and sale electricity price, including:

[0272] Based on the load regulation business, determining the differentiated power purchase and sale pricing calculation formula, and substituting the power market price and planned energy consumption data into the differentiated power purchase and sale pricing calculation formula;

[0273] Substituting the electricity market price and planned energy consumption data into the upper objective function of the two-layer optimization model;

[0274] The differentiated electricity purchase and sale pricing calculation formula is solved by utilizing the 5G network slice resources corresponding to the business, and the upper-level objective function and the lower-level objective function are combined for iterative solution to obtain the electricity purchase and sale price and the charging and discharging power of the shared energy storage device.

[0275] Preferably, the step of inputting the purchase and sale electricity price into the pricing algorithm, solving the transaction electricity quantity through the terminal and updating the planned energy consumption data for load regulation includes:

[0276] Substituting the purchase and sale electricity price into the lower objective function of the two-layer optimization model, and iteratively solving the upper objective function and the lower objective function to obtain the transaction electricity and update the planned energy consumption data;

[0277] Based on the updated planned energy consumption data, load control is performed by adjusting the load output;

[0278] Purchase, sell or store electric energy to the edge based on the traded electric energy to perform load regulation;

[0279] The traded electricity includes one or more of the electricity purchased from the edge, the electricity sold to the edge, and the electricity stored at the edge by leasing a shared energy storage device.

[0280] The present invention uses the load control cloud platform as the cloud and the load aggregator as the edge. Through the cloud-edge collaborative technology, the data is sent to the edge for calculation, realizing the power autonomy of the edge. With the assistance of the edge, the computing and carrying pressure of the cloud is reduced, the data processing efficiency is improved, and the data is sent to the terminal for rapid response of the terminal. The advantages of 5G technology can be fully utilized to target the load control business, realize differentiated slicing services for different businesses, meet the differentiated needs of each business, solve the problem that traditional load control cannot adapt to the precise control of various distributed resources in the new power system, and lay a foundation for realizing the precise control of distributed resources. The pricing algorithm proposed in the present invention takes the lowest total cost for users as the goal, can formulate electricity sales prices for prosumer users, enhance the demand response willingness of prosumer users, achieve mixed business goals such as peak shaving and valley filling and local consumption of new energy, and improve the economic benefits of each prosumer user participating in flexible load regulation; the energy sharing and capacity sharing strategy based on shared energy storage equipment proposed in the present invention breaks the singleness of most current studies on using shared energy storage equipment for energy storage in the form of leasing, makes greater use of shared energy storage devices, and also provides a variety of options for each user to participate in demand response, which is conducive to precise regulation of adjustable loads.

[0281] Embodiment 3:

[0282] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a load flexibility control method in the above embodiment.

[0283] Through the storage medium provided in this embodiment, a load flexible regulation method is implemented. By taking the load regulation cloud platform as the cloud and the load aggregator as the edge, the data is sent to the edge for calculation through the cloud-edge collaborative technology, thereby realizing the power autonomy of the edge. With the assistance of the edge, the computing and carrying pressure of the cloud is reduced, the data processing efficiency is improved, and the data is sent to the terminal to realize the rapid response of the terminal. By taking the advantages of 5G technology and targeting the load regulation business, differentiated slicing services for different businesses are realized. By formulating the purchase and sale electricity prices under differentiated businesses, the problem that traditional load regulation cannot adapt to new electricity is solved. The insufficiency of precise regulation of various distributed load resources in the power system is solved, laying the foundation for realizing precise regulation of distributed load resources; through the proposed pricing algorithm, with the lowest total cost for users as the goal, the electricity sales price can be set for production and consumption users, the demand response willingness of production and consumption users can be improved, the precise regulation of adjustable load resources can be realized, and the economic benefits of various production and consumption users participating in flexible load regulation can be increased; through the proposed shared energy storage equipment, energy storage can be leased, and users can also purchase and sell electricity and energy storage, which breaks the singleness of energy storage in the form of leasing in most current studies, provides users with a variety of choices, and is conducive to load regulation.

[0284] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0285] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0286] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0287] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0288] 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 its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims of the invention.

Claims

1. A load flexible control system, characterized in that: include: The cloud, edge, and terminals communicate with each other; The cloud is used to determine the load regulation business based on the pre-acquired real-time operation status of the power grid, power forecast data and power market electricity prices and send it to the edge; The edge is used to input the power market price, load regulation service and planned energy consumption data into a pre-built pricing algorithm, solve the pricing algorithm by applying for 5G network resources corresponding to the load regulation service to obtain the purchase and sale price of electricity and send it to the terminal; The terminal is used to send the planned energy consumption data to the edge, and use the pricing algorithm to solve the transaction power based on the purchase and sale electricity price and update the planned energy consumption data to perform load regulation; The cloud end is a load regulation cloud platform, the edge end is a load aggregator, and the terminal end is a user; the edge end and the cloud end are connected based on cloud-edge collaborative technology; the pricing algorithm is based on the load regulation business and is constructed with the goal of maximizing the profit of the load aggregator and minimizing the total cost of the user.

2. The system according to claim 1, characterized in that The edge includes: edge server, 5G core network UPF and shared energy storage equipment; The edge server is respectively connected to the cloud and the terminal for communication; used to obtain load regulation services from the cloud, and generate a request corresponding to the service and send it to the 5G core network UPF; and used to obtain the electricity market price from the cloud and the planned energy consumption data from the terminal, input the electricity market price and the planned energy consumption data into a pre-built pricing algorithm, solve the pricing algorithm using the 5G network slice resources corresponding to the service, obtain the purchase and sale electricity price and the charging and discharging power of the shared energy storage device, and send the purchase and sale electricity price to the terminal; The 5G core network UPF is communicatively connected to the edge server; used to receive and apply for 5G network slice resources corresponding to the service from the 5G core network based on the request corresponding to the service; The shared energy storage device is connected to the terminal and is used to provide power or store power for load regulation for the terminal based on the transaction power and the charge and discharge power of the shared energy storage device; Among them, the pricing algorithm takes the electricity market price and planned energy consumption data as input, and the purchase and sale electricity price, the charging and discharging power of the shared energy storage equipment, the transaction electricity and the updated planned energy consumption data as output. It establishes a load regulation two-layer optimization model with the maximum profit of the load aggregator as the upper objective function and the minimum total cost of the user as the lower objective function, and is constructed in combination with a differentiated purchase and sale electricity pricing calculation formula set based on the load regulation business; the load regulation business includes one or more of new energy consumption, peak shaving and valley filling, and emergency demand response.

3. The system according to claim 2, characterized in that The construction of the two-layer optimization model includes: An upper objective function is constructed with the goal of maximizing the profit of load aggregators, the electricity market price and planned energy consumption data are used as inputs of the upper objective function, and the purchase and sale price of electricity and the charging and discharging power of the shared energy storage device are used as outputs of the upper objective function; a lower objective function is constructed with the goal of minimizing the total cost of users, the purchase and sale price of electricity is used as inputs of the lower objective function, and the amount of electric and thermal load transfer, updated planned energy consumption data and transaction power are used as outputs of the lower objective function, and the two-layer optimization model is constructed by setting load output constraints, purchase and sale power constraints, shared energy storage device power constraints, shared energy storage device capacity constraints, electric and thermal load demand response constraints and energy balance constraints; The load includes one or more of photovoltaic, gas turbine and heat pump.

4. The system according to claim 3, characterized in that The upper objective function is constructed based on the electricity market price, planned energy consumption data and load aggregator costs, with the goal of maximizing the load aggregator's profit; The lower objective function is constructed based on the electricity purchase and sale price and user cost, with the goal of minimizing the total user cost; The load output constraint is set based on the upper and lower limits of photovoltaic, gas turbine and heat pump output; the power purchase and sale constraint is set based on the upper limit of the power purchased and sold by the user to the load aggregator and the upper limit of the power purchased and sold by the load aggregator to the power market; the shared energy storage device power constraint is set based on the upper and lower limits of the storage power and the charging and discharging efficiency of the shared energy storage device; the shared energy storage device capacity constraint is set based on the upper limit of the charging and discharging power of the shared energy storage device; The load aggregator costs include the cost of participating in the management of load regulation business, the investment cost of shared energy storage equipment, the cost of purchasing electricity from the power market and the cost of energy trading with users; The planned energy consumption data includes the electric and thermal output of the load and the photovoltaic predicted output of the user; The user costs include gas turbine costs, rental costs of shared energy storage equipment, unit load shedding costs of electric loads, electric and thermal demand response costs, and costs of energy trading with load aggregators.

5. The system according to claim 4, characterized in that The upper layer objective function is expressed as follows: Where W is the revenue of the load aggregator, is the net revenue of the load aggregator from selling electricity at time t, is the cost of load aggregator participating in load regulation business management at time t, C ESS is the daily investment cost of the shared energy storage equipment for load aggregators, and T is the total duration; Among them, the load aggregator's net income from electricity sales at time t is Calculate as follows: In the formula, is the cost of purchasing electricity from the load aggregator at time t for user i, is the revenue of user i from selling electricity to load aggregators at time t, P t b,E P is the power purchased by the load aggregator in the power market at time t, t s,E is the power sold by the load aggregator in the power market at time t, is the electricity purchase price in the electricity market, The electricity selling price in the electricity market; The cost of the load aggregator participating in the load regulation business management at time t Calculate as follows: In the formula, is the cost of load aggregators participating in emergency demand response management at time t, is the cost of load aggregator participating in peak load reduction and valley filling management at time t, is the cost of load aggregators participating in the management of new energy consumption at time t, a, b, d are all variables, λ DR1 is the cost factor for emergency demand response management, is the electric load removed by user i at time t, n is the number of users, Δt is the time period, λ DR2 is the unit charging and discharging cost coefficient of the shared energy storage equipment when the load aggregator participates in peak shaving and valley filling management, λ w It is the government subsidy coefficient when load aggregators participate in peak load shaving and valley filling management. The charging power of the shared energy storage device leased to user i, The discharge power of the shared energy storage device leased to user i, λ DR3 The photovoltaic consumption management cost coefficient for load aggregators participating in the new energy consumption management is Predicted photovoltaic output for user i at time t.

6. The system according to claim 4, characterized in that The lower layer objective function is expressed as follows: Where C is the total cost of n users, is the gas turbine cost for user i at time t, is the cost of purchasing electricity from the load aggregator at time t for user i, The cost of leasing a shared energy storage device for electric energy storage by user i at time t, is the cost of user i participating in emergency demand response at time t, is the electric heating load cost of user i participating in electric heating demand response at time t, is the revenue of user i from selling electricity to load aggregators at time t; The cost of purchasing electricity from the load aggregator at time t is Calculate as follows: In the formula, is the optimal electricity purchase price set by the load aggregator for user i at time t, is the power purchased by user i from the load aggregator at time t, and Δt is the time period; The cost of user i leasing a shared energy storage device for energy storage at time t Calculate as follows: In the formula, λ ESS is the rental price of the shared energy storage equipment at time t, The charging power of the shared energy storage device leased to user i, The discharge power of the shared energy storage device leased to user i; The cost of user i participating in emergency demand response at time t Calculate as follows: In the formula, λ cut is the unit load shedding cost of the electric load, is the electric load removed by user i at time t; The electric heating load cost of user i participating in electric heating demand response at time t Calculate as follows: In the formula, is the cost coefficient of unit power load transfer, is the cost coefficient of heat load transfer per unit power, is the load transfer amount of user i at time t, is the heat load transfer amount of user i at time t; The revenue of user i selling electricity to load aggregator at time t is Calculate as follows: In the formula, is the optimal electricity price set by the load aggregator for user i at time t, is the power sold by user i to the load aggregator at time t.

7. The system according to claim 3, characterized in that The electric heating load demand response constraint is expressed as follows: In the formula, is the load transfer amount of user i at time t, is the heat load transfer amount of user i at time t, T is the total duration, is the original value of the electricity load of user i at time t, is the original value of heat load of user i at time t, ζ e is the maximum allowable transfer ratio of electric load, ζ h is the maximum allowable transfer ratio of heat load.

8. The system according to claim 3, characterized in that The energy balance constraint is expressed as follows: In the formula, For the photovoltaic power forecast of user i at time t, is the power output of the gas turbine of user i at time t, is the thermal output of the gas turbine of user i at time t, is the power purchased by user i from the load aggregator at time t, The charging power of the shared energy storage device leased to user i, The discharge power of the shared energy storage device leased to user i, is the power sold by user i to the load aggregator at time t, is the heat output of user i’s heat pump at time t, is the electric power consumed by the heat pump of user i at time t, is the electric load removed by user i at time t, P t b,E P is the power purchased by the load aggregator in the power market at time t, t s,E is the power sold by the load aggregator in the power market at time t, is the original value of the electricity load of user i at time t, is the original value of heat load of user i at time t, is the load transfer amount of user i at time t, is the heat load transfer amount of user i at time t.

9. The system according to claim 2, characterized in that The differentiated electricity purchase and sale pricing calculation formula includes: the electricity purchase and sale pricing calculation formula for the new energy consumption business and the electricity purchase and sale pricing calculation formula for the peak shaving and valley filling business; The pricing calculation formula for the purchase and sale of electricity for the new energy consumption business is based on the goal of new energy consumption, with the electricity market price and planned energy consumption data as input, and the purchase and sale price as output, and is set by calculating the standard deviation of the user's net load demand distribution; The pricing calculation formula for the purchase and sale of electricity for the peak shaving and valley filling business has the goal of peak shaving and valley filling, takes the electricity market price as input, and takes the purchase and sale price of electricity as output. It is set based on the inverse relationship between the electricity market price and the supply-demand ratio.

10. The system according to claim 9, characterized in that The calculation formula for the purchase and sale of electricity pricing for the new energy consumption business is as follows: In the formula, Φ net is the standard deviation of the net load demand distribution on the user side, is the net electricity load demand of all users at time t, is the average net electric load demand for T time, T is the total time, is the original value of the electricity load of user i at time t, is the load transfer amount of user i at time t, For the photovoltaic power forecast of user i at time t, is the power output of the gas turbine of user i at time t, n is the number of users, θ is the price adjustment coefficient, is the optimal electricity purchase price set by the load aggregator for user i at time t, is the optimal electricity price set by the load aggregator for user i at time t, is the electricity purchase price in the electricity market, The electricity selling price in the electricity market.

11. The system according to claim 9, characterized in that The calculation formula for the purchase and sale of electricity pricing for the peak shaving and valley filling business is as follows: In the formula, is the optimal electricity purchase price set by the load aggregator for user i at time t, is the optimal electricity price set by the load aggregator for user i at time t, is the electricity purchase price in the electricity market, is the electricity price in the electricity market, SDR t is the energy supply-demand ratio of the load aggregator at time t; Among them, the energy supply-demand ratio SDR of the load aggregator at time t is t , expressed as follows: Where P t b,E P is the power purchased by the load aggregator in the power market at time t, t s,E is the electricity sales power of the load aggregator participating in the electricity market at time t, and α is a non-zero coefficient.

12. The system according to claim 9, characterized in that The edge server is used for: Based on the load regulation business, determining the differentiated power purchase and sale pricing calculation formula, and substituting the power market price and planned energy consumption data into the differentiated power purchase and sale pricing calculation formula; Substituting the electricity market price and planned energy consumption data into the upper objective function of the two-layer optimization model; The differentiated electricity purchase and sale pricing calculation formula is solved by utilizing the 5G network slice resources corresponding to the business, and the upper-level objective function and the lower-level objective function are combined for iterative solution to obtain the electricity purchase and sale price and the charging and discharging power of the shared energy storage device, and the electricity purchase and sale price is sent to the terminal.

13. The system of claim 2, wherein: The terminal is used for: Substituting the purchase and sale electricity price into the lower objective function of the two-layer optimization model, and iteratively solving the upper objective function and the lower objective function to obtain the transaction electricity and update the planned energy consumption data; Sending the updated planned energy consumption data to the edge; Based on the updated planned energy consumption data, load control is performed by adjusting the load output; Purchase, sell or store electric energy to the edge based on the traded electric energy to perform load regulation; The traded electricity includes one or more of the electricity purchased from the edge, the electricity sold to the edge, and the electricity stored at the edge by leasing a shared energy storage device.

14. The system of claim 13, wherein: The shared energy storage device is used for: Based on the power purchased from the edge, providing power to the terminal by discharging; The electric energy corresponding to the electric quantity sold to the edge end is stored by charging; Based on the amount of electricity stored at the edge, providing electric energy to the terminal by discharging; Or the electric energy corresponding to the electric quantity stored at the edge is stored by charging.

15. The system of claim 2, wherein: The 5G core network UPF is also respectively connected to the cloud and the terminal for obtaining the transaction power and planned energy consumption data from the terminal; and uploading the purchase and sale electricity price, transaction power and planned energy consumption data to the cloud through the 5G network slice corresponding to the business.

16. A load flexible control method, characterized in that: include: Based on the real-time operation status of the power grid, power forecast data and electricity market prices acquired in advance, load regulation services are determined through the cloud; Send planned energy consumption data through the terminal; The electricity market price, load regulation service and planned energy consumption data are input into a pre-built pricing algorithm, and the pricing algorithm is solved to obtain the purchase and sale price of electricity by applying for 5G network resources corresponding to the load regulation service at the edge; The purchase and sale electricity price is input into the pricing algorithm, and the transaction electricity quantity is solved through the terminal and the planned energy consumption data is updated to perform load regulation; Wherein, the method adopts a load flexible control system as described in any one of claims 1 to 15 to perform load flexible control; The cloud end is a load regulation cloud platform, the edge end is a load aggregator, and the terminal end is a user; the edge end and the cloud end are connected based on cloud-edge collaborative technology; the pricing algorithm is based on the load regulation business and is constructed with the goal of maximizing the profit of the load aggregator and minimizing the total cost of the user.

17. The method according to claim 16, characterized in that The power market electricity price, load regulation service and planned energy consumption data are input into a pre-built pricing algorithm, and the pricing algorithm is solved to obtain the purchase and sale electricity price by applying for 5G network resources corresponding to the load regulation service at the edge, including: Based on the load regulation service, a request corresponding to the service is generated by an edge server at an edge end; Based on the request corresponding to the service, apply for the 5G network slice resources corresponding to the service from the 5G core network through the 5G core network UPF at the edge; Input the electricity market price and planned energy consumption data into a pre-built pricing algorithm, and solve the pricing algorithm by using the 5G network slice resources corresponding to the service through the edge server to obtain the purchase and sale electricity price; Among them, the pricing algorithm takes the electricity market price and planned energy consumption data as input, and the purchase and sale electricity price, the charging and discharging power of the shared energy storage equipment, the transaction electricity and the updated planned energy consumption data as output. It establishes a load regulation two-layer optimization model with the maximum profit of the load aggregator as the upper objective function and the minimum total cost of the user as the lower objective function, and is constructed in combination with a differentiated purchase and sale electricity pricing calculation formula set based on the load regulation business; the load regulation business includes one or more of new energy consumption, peak shaving and valley filling, and emergency demand response.

18. The method according to claim 17, characterized in that The construction of the two-layer optimization model includes: An upper objective function is constructed with the goal of maximizing the profit of load aggregators, the electricity market price and planned energy consumption data are used as inputs of the upper objective function, and the purchase and sale price of electricity and the charging and discharging power of shared energy storage equipment are used as outputs of the upper objective function; a lower objective function is constructed with the goal of minimizing the total cost of users, the purchase and sale price of electricity is used as inputs of the lower objective function, and the amount of electric and thermal load transfer, updated planned energy consumption data and transaction power are used as outputs of the lower objective function, and the two-layer optimization model is constructed by setting load output constraints, purchase and sale power constraints, shared energy storage equipment power constraints, shared energy storage equipment capacity constraints, electric and thermal load demand response constraints and energy balance constraints; The load includes one or more of photovoltaic, gas turbine and heat pump.

19. The method according to claim 17, characterized in that The differentiated electricity purchase and sale pricing calculation formula includes: the electricity purchase and sale pricing calculation formula for the new energy consumption business and the electricity purchase and sale pricing calculation formula for the peak shaving and valley filling business; The pricing calculation formula for the purchase and sale of electricity for the new energy consumption business is based on the goal of new energy consumption, with the electricity market price and planned energy consumption data as input, and the purchase and sale price as output, and is set by calculating the standard deviation of the user's net load demand distribution; The pricing calculation formula for the purchase and sale of electricity for the peak shaving and valley filling business has the goal of peak shaving and valley filling, takes the electricity market price as input, and takes the purchase and sale price of electricity as output. It is set based on the inverse relationship between the electricity market price and the supply-demand ratio.

20. The method of claim 19, wherein: The power market price and planned energy consumption data are input into a pre-built pricing algorithm, and the pricing algorithm is solved by the edge server using the 5G network slice resources corresponding to the service to obtain the purchase and sale price of electricity, including: Based on the load regulation business, determining the differentiated power purchase and sale pricing calculation formula, and substituting the power market price and planned energy consumption data into the differentiated power purchase and sale pricing calculation formula; Substituting the electricity market price and planned energy consumption data into the upper objective function of the two-layer optimization model; The differentiated electricity purchase and sale pricing calculation formula is solved by utilizing the 5G network slice resources corresponding to the business, and the upper-level objective function and the lower-level objective function are combined for iterative solution to obtain the electricity purchase and sale price and the charging and discharging power of the shared energy storage device.

21. The method of claim 17, wherein: The step of inputting the purchase and sale electricity price into the pricing algorithm, solving the transaction electricity quantity through the terminal and updating the planned energy consumption data for load regulation includes: Substituting the purchase and sale electricity price into the lower objective function of the two-layer optimization model, and iteratively solving the upper objective function and the lower objective function to obtain the transaction electricity and update the planned energy consumption data; Based on the updated planned energy consumption data, load control is performed by adjusting the load output; Purchase, sell or store electric energy to the edge based on the traded electric energy to perform load regulation; The traded electricity includes one or more of the electricity purchased from the edge, the electricity sold to the edge, and the electricity stored at the edge by leasing a shared energy storage device.

22. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, a load flexibility control method as claimed in any one of claims 16 to 21 is implemented.

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