Smart grid system for real-time adjustment of power supply and consumption

Abstract

The application provides a smart grid system for real-time power supply adjustment, which has the characteristics that the system comprises a power supplier end and a plurality of user ends, wherein the power supplier end comprises a power generation data acquisition module, a power generation data storage module, a power supply range calculation module, a real-time electricity price calculation module and a power supply control module, the real-time electricity price calculation module comprises an optimal power supply amount generation unit and a real-time electricity price generation unit, the optimal power supply amount generation unit stores an optimization problem function, the optimization problem function is calculated by using a whale optimization algorithm based on a predicted power generation range and a predicted power consumption range sent by each user end, and the optimal power supply amount and the optimal power consumption amount are obtained, and the real-time electricity price generation unit is used for calculating the optimal electricity price as the real-time electricity price according to the optimal power consumption amount and the optimal power supply amount. In summary, the method can realize power supply balance under the real-time electricity price in the smart grid system.

Description

Technical Field

[0001] This invention relates to a smart grid system, specifically a smart grid system for real-time adjustment of power supply and consumption. Background Technology

[0002] Electricity costs are primarily incurred during peak consumption periods, and peak shaving and valley filling are key measures to address this issue. However, relying solely on supply-side regulation is insufficient for peak shaving and valley filling. Demand-side management, proposed by researchers in the 1980s, can not only achieve peak shaving and valley filling to balance supply and demand, but also reduce necessary system loads, functioning similarly to traditional power plants. Simultaneously, it avoids creating new load demand and can further absorb renewable energy generation, preventing energy curtailment and demonstrating significant environmental and economic benefits.

[0003] Demand response is one of the solutions for demand-side management. Demand response refers to guiding users to proactively shift or reduce load based on given price signals by adjusting electricity prices or providing compensation, ultimately achieving the goal of saving and efficiently using electricity, while also promoting the safe, reliable, and stable operation of the power grid.

[0004] Real-time pricing in demand response is considered the most ideal method for power supply regulation, as it can fully leverage resource flexibility and facilitate optimal allocation of resources across society. However, current technologies that use real-time electricity price calculations to adjust power supply and consumption in real time, thereby achieving power supply and consumption balance, require sophisticated mathematical theories, and the predicted power supply balance effect still deviates significantly from the actual effect, making them difficult to implement in practice.

[0005] In summary, existing calculation methods for real-time power supply and consumption adjustment strategies based on real-time electricity pricing are too theoretical and difficult to implement in practice. There is an urgent need to develop real-world power supply and consumption adjustment strategies that are easy to implement and do not require complex mathematical theories. Summary of the Invention

[0006] This invention was made to solve the above-mentioned problems, and its purpose is to provide a smart grid system that adjusts power supply and consumption in real time.

[0007] This invention provides a smart grid system for real-time adjustment of power supply and consumption. It is used by power suppliers to provide users with electricity at real-time prices, guiding user electricity consumption behavior to achieve a balance between supply and demand. The system includes a power supplier terminal and multiple user terminals. Each user terminal includes a power consumption data acquisition module, a power consumption data storage module, a power consumption range calculation module, and a user data transmission module. The power consumption data acquisition module collects the user's actual electricity consumption for each time period as historical power consumption. The power consumption data storage module stores all historical power consumption data for the user. The power consumption range calculation module calculates the upper and lower limits of the user's power consumption for the next time period based on all historical power consumption data as a predicted power consumption range. The user data transmission module sends the predicted power consumption range to the power supplier terminal and receives the real-time electricity price for the next time period from the power supplier terminal. The power supplier terminal includes a power generation data acquisition module, a power generation data storage module, a power supply range calculation module, and a real-time electricity price calculation module. The system comprises a calculation module and a power supply control module. The power generation data acquisition module collects the actual power generation of the generating equipment within each time period as historical power generation data. The power generation data storage module stores all historical power generation data. The power supply range calculation module calculates the upper and lower limits of power generation for the next time period based on all historical power generation data, serving as the predicted power generation range. The real-time electricity price calculation module includes an optimal power supply generation unit and a real-time electricity price generation unit. The optimal power supply generation unit stores an optimization problem function. Based on the predicted power generation range and the predicted electricity consumption range sent by each user terminal, the whale algorithm is used to calculate the optimization problem function to obtain the optimal power supply and optimal electricity consumption. The real-time electricity price generation unit calculates the optimal electricity price based on the optimal electricity consumption and optimal power supply, serving as the real-time electricity price. The power supply control module controls the generating equipment to generate electricity based on the optimal power supply and controls the power supply equipment to deliver the generated electricity to each user terminal. The expression of the optimization problem function is as follows: In the formula Let C be the electricity consumption utility function for user i. k (L k Let ) be the power supply cost function of the power supplier. The actual electricity consumption of user i during time period k. Let L be the satisfaction parameter of user i during time period k. k Let N be the actual power supply from the power supplier during time period k, and N be the total number of users. Let i be the electricity consumption interval of user i during time period k. and These represent the minimum and maximum power supply from the power supplier during time period k, respectively, where K is the set of all time periods.

[0008] The smart grid system for real-time power supply and consumption adjustment provided by this invention may also have the following feature: wherein the expression of the power consumption utility function is: In the formula, x represents the user's actual electricity consumption, w is the satisfaction parameter, and α is the preset parameter.

[0009] The smart grid system for real-time power supply and consumption adjustment provided by this invention may also have the following feature: wherein the expression of the power supply cost function is: In the formula a k The coefficient of the quadratic term and a k >0, b k The coefficient of the linear term and b k ≥0, c k The coefficient of the constant term and c k ≥0.

[0010] The smart grid system for real-time power supply and consumption adjustment provided by this invention may also have the following feature: The real-time electricity price generation unit stores a KKT system function, and the optimal electricity price is calculated by substituting the optimal electricity consumption and optimal power supply into the KKT system function. The expression of the KKT system function is as follows: In the formula, λ represents the optimal electricity price.

[0011] The role and effect of invention

[0012] According to the smart grid system for real-time adjustment of power supply and consumption involved in this invention, a real-time electricity pricing method is constructed based on a social welfare maximization model and transformed into an optimization problem function. The optimal electricity consumption and optimal power supply are obtained by solving this optimization problem function using the whale algorithm. Then, the optimal electricity consumption and optimal power supply are substituted into the KKT system function to calculate the real-time electricity price. Power generation equipment is controlled to produce electricity based on the optimal power supply, and the real-time electricity price ensures that the actual electricity consumption of users is close to the optimal electricity consumption, thereby achieving real-time power supply balance. Therefore, the smart grid system for real-time adjustment of power supply and consumption of this invention can achieve a good power supply balance under real-time electricity pricing within a smart grid system. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the framework of a smart grid system in an embodiment of the present invention;

[0014] Figure 2 This is a schematic diagram of the user terminal framework in an embodiment of the present invention;

[0015] Figure 3 This is a schematic diagram of the power supplier's frame in an embodiment of the present invention;

[0016] Figure 4 This is a schematic diagram of the electrical efficiency function in an embodiment of the present invention;

[0017] Figure 5 This is a schematic diagram illustrating the simulation effects of Lagrange duality and gradient projection descent in an embodiment of the present invention;

[0018] Figure 6 This is a schematic diagram illustrating the simulation effect of the real-time electricity price calculation module when the maximum number of iterations of the whale algorithm is 200 in an embodiment of the present invention;

[0019] Figure 7 This is a schematic diagram illustrating the simulation effect of the real-time electricity price calculation module when the maximum number of iterations of the whale algorithm is 20 in an embodiment of the present invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of the present invention easy to understand, the following embodiments, in conjunction with the accompanying drawings, specifically illustrate the smart grid system for real-time adjustment of power supply and consumption of the present invention.

[0021] Figure 1 This is a schematic diagram of the framework of a smart grid system in an embodiment of the present invention.

[0022] like Figure 1 As shown, the smart grid system 100 includes multiple user terminals 10 and one power supplier terminal 20, which are used by the power supplier to provide users with electricity with real-time electricity prices and guide users' electricity consumption behavior to achieve a balance between supply and demand.

[0023] Figure 2 This is a schematic diagram of the user terminal framework in an embodiment of the present invention.

[0024] like Figure 2 As shown, the user terminal 10 includes an electricity data acquisition module 101, an electricity data storage module 102, an electricity range calculation module 103, a user data transmission module 104, and a user control module 105 that controls the above modules.

[0025] The electricity data acquisition module 101 is used to collect the actual electricity consumption of users in each time period as historical electricity consumption.

[0026] The electricity data storage module 102 is used to store all the user's historical electricity consumption.

[0027] The electricity consumption range calculation module 103 is used to calculate the upper limit and lower limit of the user's electricity consumption for the next time period based on all historical electricity consumption data, as a predicted electricity consumption range.

[0028] The user data transmission module 104 is used to send the predicted electricity consumption range to the power supplier terminal 10, and then receive the real-time electricity price for the next time period sent by the power supplier terminal 10.

[0029] User control module 105 stores computer programs for controlling the various constituent modules of user terminal 10.

[0030] Figure 3 This is a schematic diagram of the power supplier's frame in an embodiment of the present invention.

[0031] like Figure 3 As shown, the power supplier terminal 20 includes a power generation data acquisition module 201, a power generation data storage module 202, a power supply range calculation module 203, a real-time electricity price calculation module 204, a power supply control module 205, and a power supplier control module 206.

[0032] The power generation data acquisition module 201 is used to collect the actual power generation of the power generation module in each time period as historical power generation.

[0033] The power generation data storage module 202 is used to store all historical power generation data.

[0034] The power supply range calculation module 203 is used to calculate the upper and lower limits of power generation for the next time period based on all historical power generation data, as the predicted power generation range.

[0035] The real-time electricity price calculation module 204 includes an optimal power supply generation unit 2041 and a real-time electricity price generation unit 2042.

[0036] The optimal power supply generation unit 2041 stores an optimization problem function. Based on the predicted power generation range and the predicted power consumption range sent by each user terminal, the optimization problem function is calculated using the whale algorithm to obtain the optimal power supply as the optimal power supply and the optimal power consumption.

[0037] In this embodiment, based on the goals of maximizing user utility and minimizing power supplier costs, the user side (i.e., all users at user terminals 10) and the power supplier are considered as a social system, taking into account the interests of both sides. Therefore, the difference between user utility and power supplier costs is taken as social welfare. Under the constraint that the total electricity consumption of users is limited by the power supplier's available electricity, a nonlinear real-time pricing model based on the goal of maximizing social welfare is constructed, i.e., an optimization problem function. The expression of this optimization problem function is as follows:

[0038]

[0039] In the formula Let C be the electricity consumption utility function for user i. k (L k Let ) be the power supply cost function of the power supplier. The actual electricity consumption of user i during time period k. Let L be the satisfaction parameter of user i during time period k. k Let N be the actual power supply from the power supplier during time period k, and N be the total number of users. Let i be the electricity consumption interval of user i during time period k. and These represent the minimum and maximum power supply from the power supplier during time period k, respectively, where K is the set of all time periods.

[0040] The expression for the electrical efficiency function:

[0041]

[0042] In the formula, x represents the user's actual electricity consumption, w is the satisfaction parameter, and α is the preset parameter.

[0043] Figure 4 This is a schematic diagram of the electrical efficiency function in an embodiment of the present invention.

[0044] like Figure 4 As shown, the horizontal axis represents time, and the vertical axis represents electricity price. The three curves passing through the origin represent the electricity utility functions for w=1, w=0.5, and w=0.3 respectively from top to bottom. The dashed curve is the marginal utility function curve when w=0.3.

[0045] The expression for the power supply cost function:

[0046]

[0047] In the formula a k The coefficient of the quadratic term and a k >0, b k The coefficient of the linear term and b k ≥0, c k The coefficient of the constant term and c k ≥0.

[0048] The real-time electricity price generation unit 2042 stores the KKT system function, which is used to calculate the optimal electricity price by substituting the optimal electricity consumption and optimal power supply into the KKT system function.

[0049] In this embodiment, the optimization problem function is written as a standard convex optimization problem, and its expression is as follows:

[0050] maxf(x), stg(x)≥0.

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] Based on the Slater condition, we can further derive that this convex optimization problem has the same solution as the KKT system, that is, the KKT point exists and is unique. The expression for this KKT system is:

[0057]

[0058] Substituting the optimal electricity consumption and optimal power supply into the first term of the KKT system, i.e., the KKT system function, we can obtain the optimal Lagrange multiplier, i.e., the optimal electricity price λ. Then, by rearranging, we obtain the expression for the KKT system function:

[0059]

[0060]

[0061]

[0062] In the formula, λ represents the optimal electricity price.

[0063] The power supply control module 205 is used to control the power generation equipment to generate electricity according to the optimal power supply, and to control the power supply equipment to deliver the power generated by the power generation equipment to each user terminal 10.

[0064] The power supplier control module 206 stores a computer program for controlling the various constituent modules of the power supplier terminal 20.

[0065] In this embodiment, the optimal electricity price is also solved for the optimization problem function using Lagrange duality and gradient projection descent. The derivation process is as follows:

[0066] First, construct a Lagrange function to solve the optimization problem function. and L k Further separation yields the following expression for the Lagrange function:

[0067]

[0068] In the formula λ k For shadow prices, Indicates user-side benefits, λ k L k -C k (L k () indicates the benefits of the power supplier.

[0069] Based on Lagrange duality theory and Slater conditions, the dual problem expressions (1) to (4) with the same solution as the optimization problem function are obtained as follows:

[0070]

[0071]

[0072]

[0073]

[0074] In the formula D(λ) k A portion of the problem can be decomposed into N independent subproblems of the form (3) that can be solved simultaneously, the solution of which is the user's optimal electricity consumption. Another subproblem is shown in form (4), the solution of which is the optimal power supply from the power supplier. Since the optimization problem function does not include the electricity price variable, its optimal Lagrange multiplier, i.e., the shadow price λ, is obtained by solving the dual problem expression (1). k* As the optimal electricity price, the dual problem expression (1) can be further solved iteratively by using the gradient projection method, and the specific expression (5) is as follows:

[0075]

[0076] In the formula, t∈T is the step size, and T is the power supplier update λ. k The set of iteration steps, This is a local optimal solution to expression (3). For a given The local optimal solution of expression (4) is as follows. For the iteration step λ k The value below.

[0077] Based on the above derivation, the specific process of obtaining the optimal electricity price for each t∈T using Lagrange duality and gradient projection descent is as follows:

[0078] Each user terminal 10 receives price signals from the power supplier terminal 20. k Substitute the values ​​into expression (3) to obtain the consumption amount. Then update the consumption amount 20, which was communicated to the power supplier.

[0079] The power supplier uses expression (5) to calculate the price. k The price l k Notify each user terminal 10, and then use expression (4) to update the battery level. Finally, receive the consumption data from all user terminals 10.

[0080] In this embodiment, the real-time electricity price for each time period of a day is solved by Lagrange duality and gradient projection descent method and the real-time electricity price calculation module 204 of the present invention, and then the corresponding supply and demand changes are simulated. The effects of the two real-time electricity prices are compared by comparing the supply and demand changes.

[0081] On the simulation platform, the number of users is set to N=10, and the day is divided into K=24 time periods. The parameter w for each user is randomly selected from the interval [1, 4], α=0.5, a k =0.01, c k =0, b k =0, the number of users in the whale algorithm population is 100, and the initial electricity consumption of each user, as well as the initial power supply and electricity price of the power supplier, are randomly generated.

[0082] Figure 5 This is a schematic diagram illustrating the simulation effects of Lagrange duality and gradient projection descent in an embodiment of the present invention.

[0083] like Figure 5 As shown, in (a), (b), (c), (d), and (e), the horizontal axis represents time, and the vertical axis represents power load. In (f), the horizontal axis represents time, and the vertical axis represents electricity price. (a) and (d) are schematic diagrams of maximum and minimum power supply, respectively, where maximum and minimum power supply represent the upper and lower limits of power supply provided by the power supplier during the corresponding time period. (f) is a schematic diagram of the real-time electricity price obtained by the Lagrange duality and gradient projection descent method for each time period of the day. (b) and (c) are schematic diagrams of the actual power supply and actual electricity consumption simulated based on the real-time electricity price, respectively. (e) is a summary diagram of (a), (b), (c), and (d). Therefore, the real-time electricity price calculated by the Lagrange duality and gradient projection descent method can ensure that the user's electricity consumption is basically consistent with the power supply provided by the power supplier. This means that by changing the electricity price, users can change their electricity consumption habits to reduce electricity costs, thereby shifting unnecessary electricity consumption during peak hours to off-peak hours, achieving the goal of peak shaving and valley filling.

[0084] Figure 6 This is a schematic diagram illustrating the simulation effect of the real-time electricity price calculation module when the maximum number of iterations of the whale algorithm is 200 in an embodiment of the present invention.

[0085] like Figure 6As shown, in (a), (b), (c), (d), and (e), the horizontal axis represents time, and the vertical axis represents power load. In (f), the horizontal axis represents time, and the vertical axis represents electricity price. (a) and (d) are schematic diagrams of maximum and minimum power supply, respectively. (f) is a schematic diagram of the real-time electricity price calculated by the real-time electricity price calculation module 204 for each time period of the day when the maximum number of iterations of the whale algorithm is 200. (b) and (c) are schematic diagrams of the actual power supply and actual power consumption simulated based on the real-time electricity price, respectively. (e) is a summary schematic diagram of (a), (b), (c), and (d). It can be seen that under this condition, there is a certain difference between the power consumption and power supply in each time period, but it remains within a relatively small range.

[0086] Figure 7 This is a schematic diagram illustrating the simulation effect of the real-time electricity price calculation module when the maximum number of iterations of the whale algorithm is 20 in an embodiment of the present invention.

[0087] In (a), (b), (c), (d), and (e), the x-axis represents time, and the y-axis represents power load. In (f), the x-axis represents time, and the y-axis represents electricity price. (a) and (d) are schematic diagrams of maximum and minimum power supply, respectively. (f) is a schematic diagram of the real-time electricity price calculated by the real-time electricity price calculation module 204 for each time period of the day when the maximum number of iterations of the whale algorithm is 20. (b) and (c) are schematic diagrams of the actual power supply and actual electricity consumption simulated based on the real-time electricity price, respectively. (e) is a summary schematic diagram of (a), (b), (c), and (d). Therefore, compared to the simulation results with a maximum number of iterations of 200, the gap between electricity consumption and power supply in each time period increases when the maximum number of iterations is 20.

[0088] In summary, the real-time electricity price calculated by the Lagrange duality and gradient projection descent method has a good effect on peak shaving and valley filling. However, by setting the maximum number of iterations of the whale algorithm, the real-time electricity price calculation module 204 of this invention can also achieve a similar peak shaving and valley filling effect. Moreover, the whale algorithm used in this invention has the advantages of being easy to operate and not requiring a deep mathematical foundation compared with the Lagrange duality and gradient projection descent method.

[0089] The role and effect of the embodiments

[0090] Based on the smart grid system for real-time power supply and consumption adjustment involved in this embodiment, a real-time electricity pricing method is constructed based on a social welfare maximization model and transformed into an optimization problem function. The optimal power consumption and optimal power supply are obtained by solving this optimization problem function using the whale algorithm. Then, the optimal power consumption and optimal power supply are substituted into the KKT system function to calculate the real-time electricity price. Power generation equipment is controlled to produce electricity based on the optimal power supply, and the real-time electricity price ensures that the actual power consumption of users is close to the optimal power consumption, thereby achieving real-time power supply balance. In summary, this method can effectively achieve power supply balance under real-time electricity pricing in a smart grid system.

[0091] The above embodiments are preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention.

Claims

1. A smart grid system for real-time adjustment of power supply and consumption, used by power suppliers to provide users with electricity at real-time prices, guiding users' electricity consumption behavior to achieve a balance between power supply and consumption, characterized in that, include: Powering the supplier and multiple users, The user terminal includes an electricity data acquisition module, an electricity data storage module, an electricity range calculation module, and a user data transmission module. The electricity consumption data acquisition module is used to collect the user's actual electricity consumption in each time period as historical electricity consumption. The electricity data storage module is used to store all of the user's historical electricity consumption. The electricity consumption range calculation module is used to calculate the upper and lower limits of the user's electricity consumption for the next time period based on all the historical electricity consumption data, as a predicted electricity consumption range. The user data transmission module is used to send the predicted electricity consumption range to the power supplier, and then receive the real-time electricity price for the next time period from the power supplier. The power supplier's terminal includes a power generation data acquisition module, a power generation data storage module, a power supply range calculation module, a real-time electricity price calculation module, and a power supply control module. The power generation data acquisition module is used to collect the actual power generation of the power generation equipment in each time period as historical power generation. The power generation data storage module is used to store all the historical power generation data. The power supply range calculation module is used to calculate the upper and lower limits of power generation for the next time period based on all the historical power generation data, as the predicted power generation range. The real-time electricity price calculation module includes an optimal power supply generation unit and a real-time electricity price generation unit. The optimal power supply generation unit stores an optimization problem function. Based on the predicted power generation range and the predicted power consumption range sent by each user terminal, the whale algorithm is used to calculate the optimization problem function to obtain the optimal power supply and optimal power consumption. The expression for the power supply cost function is: ， In the formula The coefficient of the quadratic term and , The coefficient of the linear term and , The coefficient of the constant term and , The real-time electricity price generation unit stores KKT system functions. The optimal electricity price is calculated by substituting the optimal electricity consumption and the optimal electricity supply into the KKT system functions. The expression for the KKT system function: ， , ， In the formula, λ represents the optimal electricity price. The power supply control module is used to control the power generation equipment to generate electricity according to the optimal power supply, and to control the power supply equipment to deliver the electricity generated by the power generation equipment to each of the user terminals. The expression for the optimization problem function is: ， ， In the formula For users The power efficiency function, The power supply cost function of the power supplier. For users In time period Actual electricity consumption For users In time period Satisfaction parameters For power suppliers during time periods The actual power supply, Total number of users For users In time period The electricity consumption area and For each power supplier in the time period The minimum and maximum power supply, This is a collection of all time periods.

2. The smart grid system for real-time adjustment of power supply and consumption according to claim 1, characterized in that: in, The expression for the power efficiency function is: ， In the formula The actual electricity consumption of the user For satisfaction parameters, These are preset parameters.

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