Multi-type resource demand response day-ahead scheduling method and system facing power grid adjustment demand

Abstract

The invention discloses a multi-type resource demand response day-ahead scheduling method and system for power grid adjustment demands, and relates to the technical field of power systems, optimal scheduling and intelligent algorithms. Comprising the following steps: acquiring data information of air conditioners, industrial loads and electric vehicles in a shopping mall; quantitatively evaluating adjustable potentials respectively; obtaining reducible quantities or schedulable quantities of various types of resources in different time periods; determining a target function and constraint conditions; and obtaining a relationship between the electricity price and the resource scheduling amount, expressing the relationship in a quadratic function form, formulating a user incentive cost model, performing optimization solution on the multi-type resource scheduling model by adopting a knapsack algorithm in combination with the user incentive cost model, and generating a resource scheduling strategy. According to the invention, efficient scheduling of resources and effective control of cost can be realized, and powerful support is provided for stable operation of a power grid and improvement of economic benefits.

Description

Technical Field

[0001] The present invention relates to the technical fields of power systems, optimal scheduling, and intelligent algorithms, and particularly to a multi-type resource demand response day-ahead scheduling method and system for grid regulation requirements. Background Art

[0002] The grid regulation requirements are increasing day by day, which puts forward higher requirements for the grid's scheduling ability and resource utilization efficiency. As the core link of the power system, the accurate scheduling of the grid's regulation requirements is of great significance for ensuring the safe, stable, economic, and efficient operation of the power system. With the diversification of power demand and the large-scale access of renewable energy, the operating environment of the grid has become more complex and changeable. When traditional scheduling methods face the participation of multi-type resources, the solution time is often too long due to the complex model, unable to meet the needs of real-time scheduling. In addition, different types of resources have different characteristics and constraint conditions when participating in grid regulation, such as mall air conditioners, industrial loads, and electric vehicles. Their adjustable potentials, response speeds, and cost-benefits vary greatly, bringing many challenges to the unified scheduling of the grid.

[0003] In order to improve the grid's regulation efficiency and economic benefits, accurate scheduling technology is particularly important. It can help grid operators reasonably plan the scheduling strategies of resources, optimize the allocation of power resources, and ensure that the grid achieves supply-demand balance and stable operation under various load demands and power supply conditions. Through precise scheduling, the grid can use various resources more efficiently, reduce regulation costs, improve the flexibility and reliability of the grid, and at the same time provide more stable and high-quality power services for power users.

[0004] However, the existing grid scheduling methods have many deficiencies in the coordinated optimization of multi-type resources. On the one hand, there is a lack of in-depth analysis and evaluation of the characteristics and response potentials of different resources, resulting in non-refined and non-personalized scheduling strategies. On the other hand, the optimization model is too complex and the solution time is long, making it difficult to meet the needs of grid real-time scheduling. In addition, the complexity and dynamics of the grid make it difficult for a single scheduling method to comprehensively reflect its operating state, and a combination of multiple technologies and methods is required for comprehensive optimization.

[0005] Therefore, providing a multi-type resource demand response day-ahead scheduling method and system for grid regulation requirements to solve the difficulties existing in the prior art is an urgent problem for those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a multi-type resource demand response day-ahead scheduling method and system for grid regulation requirements, which can achieve efficient scheduling of resources and effective control of costs, and provide strong support for the stable operation of the grid and the improvement of economic benefits.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A multi-type resource demand response day-ahead scheduling method for grid regulation requirements, comprising the following steps:

[0009] S1. Obtain the data information of shopping mall air conditioners, industrial loads and electric vehicles, quantitatively evaluate the adjustable potential respectively, and obtain the reducible amount or schedulable amount of each type of resource in different time periods;

[0010] S2. Determine the objective function and constraint conditions, transform the scheduling problem of multi-type resources into an optimization problem, and establish a multi-type resource scheduling model;

[0011] S3. Obtain the relationship between the electricity price and the resource scheduling amount, and represent the relationship between the electricity price and the resource scheduling amount in the form of a quadratic function, formulate a user incentive cost model, and allocate the user incentive cost;

[0012] S4. Use the knapsack algorithm to optimize and solve the multi-type resource scheduling model in S2 combined with the user incentive cost model to generate a resource scheduling strategy.

[0013] Optionally, the expression for the reducible amount of shopping mall air conditioners in S1 is as follows:

[0014]

[0015] Wherein, P i,max represents the maximum value of the reducible power range of a single shopping mall air conditioner; P i,on represents the actual operating power of the air conditioner; represents the minimum value of the historically acceptable power at temperature T;

[0016] The expression for the schedulable amount of industrial loads is as follows:

[0017]

[0018] Wherein, l i (t) is the number of adjustable loads in operation at the t-th moment of the i-th factory; represents rounding down; P i (t) is the total power of the i-th factory at the t-th moment; P i,others (t) is the load power of the i-th factory at the t-th moment excluding the adjustable load; are the upper and lower limit per-unit values of the power of the electrical equipment respectively, and P N,x is the rated power of the x-type load, are the upper and lower limits of the adjustable potential of the i-th factory at the t-th moment respectively;

[0019] The expression for the reducible amount of electric vehicles is:

[0020] Q i = B i (SOC end,i - SOC need,i ),

[0021] Among them, [0, Q i is set as the reduction interval of the electric vehicle load i, and B i represents the battery capacity of the electric vehicle i; SOC need,i represents the percentage of the required power for the future travel of the electric vehicle i; SOC end,i represents the battery SOC of the electric vehicle i at the end of the demand response period.

[0022] Optionally, in S2, with the maximum total profit of the virtual power plant as the objective function, the constraint conditions include power balance constraint, mall air conditioner reducible amount constraint, mall air conditioner reducible time period constraint, mall air conditioner maximum reducible times constraint, industrial transferable amount constraint, industrial transferable time period constraint, industrial maximum transferable times constraint, electric vehicle reducible amount constraint, and electric vehicle reducible time period constraint.

[0023] Optionally, the objective function expression is:

[0024]

[0025] Among them, x i represents the ratio of the current dispatch amount to the dispatchable amount of the i-th resource; v i represents the economic benefit of the i-th resource for grid regulation; C i represents the user incentive cost of the i-th resource, and n represents the total number of resources.

[0026] Optionally, the power balance constraint expression is:

[0027]

[0028] Among them: d t represents the power supply of the virtual power plant; x ac,t represents the power reduction amount of the mall air conditioner users; x ind,t represents the dispatch amount of the industrial users; x ev,t represents the power reduction amount of the electric vehicle users, and T represents 24 hours of a day;

[0029] The mall air conditioner reducible amount constraint, reducible time period constraint, and maximum reducible times constraint expressions are:

[0030]

[0031] Among them: Represents the upper limit of the power reduction amount of mall air - conditioner users; T ac Represents the period during which reduction is possible; N ac Represents the maximum number of reductions possible within a day;

[0032] The industrial transferable quantity constraint, transferable time - period constraint, and maximum transferable number constraint expressions are:

[0033]

[0034] Among them: Represents the upper limit of the power dispatchable quantity of industrial users; T ind Represents the industrial dispatchable time - period; N ind Represents the maximum number of dispatches possible within a day;

[0035] The electric - vehicle reduction quantity constraint and reduction time - period constraint expressions are:

[0036]

[0037] Among them: Represents the upper limit of the power reduction amount of electric - vehicle users; T ev Represents the electric - vehicle dispatchable time - period.

[0038] Optionally, in S3, obtaining the relationship between electricity price and resource dispatch quantity includes: during the demand - side response period, the higher the electricity price when the resource dispatch quantity is closer to its upper limit.

[0039] Optionally, the user cost - incentive model expression is:

[0040] P(x i )=ax i 2 +bx i +c,

[0041] x i =P i / P i,max

[0042] Among them: P(x i ) represents the electricity price of the i - th resource; x i represents the ratio of the current dispatch quantity to the dispatchable quantity of the i - th resource; P i represents the current dispatch quantity of the i - th resource; P i,max represents the maximum dispatch quantity of the i - th resource; a, b, c represent the coefficients of the quadratic electricity - price function, and a>0 ensures that the electricity price increases with the increase in the dispatch quantity.

[0043] Optionally, the optimization solution in S4 includes: regarding various adjustable resources in the power grid as "items", the capacity or adjustment ability as "weight", and the economic benefit of power grid adjustment as "value". Under the total capacity constraint that does not exceed the power grid adjustment demand, select a group of resources to maximize the total value and obtain the optimal resource scheduling plan.

[0044] Optionally, the expression for optimizing the solution using the knapsack algorithm in S4 is as follows:

[0045] dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - w[i]] + v[i]),

[0046] where i represents considering the first i resources; j represents the current knapsack capacity; w[i] represents the schedulable amount of the i-th resource; v[i] represents the economic benefit of the i-th resource for power grid adjustment; dp[i - 1][j] represents the maximum value when the i-th resource is not selected; dp[i - 1][j - w[i]] + v[i] represents the maximum value when the i-th resource is selected.

[0047] A multi-type resource demand response day-ahead scheduling system for power grid adjustment requirements, which executes the multi-type resource demand response day-ahead scheduling method described in any one of the above, includes a multi-type resource adjustable potential evaluation module, a multi-type resource scheduling model establishment module, a user incentive cost model specification module, and an optimization solution module;

[0048] The multi-type resource adjustable potential evaluation module is used to obtain the data information of mall air conditioners, industrial loads, and electric vehicles, quantitatively evaluate the adjustable potential respectively, and obtain the reducible amount or schedulable amount of each type of resource at different time periods;

[0049] The multi-type resource scheduling model establishment module is connected to the multi-type resource adjustable potential evaluation module, and is used to determine the objective function and constraint conditions, transform the scheduling problem of multi-type resources into an optimization problem, and establish a multi-type resource scheduling model;

[0050] The user incentive cost model, the output end of which is connected to the second input end of the optimization solution module, is used to obtain the relationship between the electricity price and the resource scheduling amount, represent the relationship between the electricity price and the resource scheduling amount in the form of a quadratic function, formulate the user incentive cost model, and allocate the user incentive cost;

[0051] The optimization solution module, the first input end of which is connected to the output end of the multi-type resource scheduling model establishment module, uses the knapsack algorithm to optimize and solve the multi-type resource scheduling model combined with the user incentive cost model to generate a resource scheduling strategy.

[0052] As can be seen from the above technical solutions, compared with the prior art, the present invention provides a multi-type resource demand response day-ahead scheduling method and system for grid regulation requirements, having the following beneficial effects: 1) By constructing a multi-type resource adjustable potential evaluation model and an optimization scheduling model, and combining with the knapsack algorithm for optimization solving, the present invention significantly improves the resource scheduling efficiency and accuracy under grid regulation requirements; 2) This method can accurately evaluate the adjustable potential of multi-type resources such as mall air conditioners, industrial loads, and electric vehicles, and formulate an optimal scheduling plan to maximize the total profit of the virtual power plant; 3) By introducing a user incentive cost model, the present invention reasonably allocates the user incentive cost, effectively motivates users to participate in grid regulation, and improves the effect of demand response and user satisfaction; 4) The present invention can be promoted in power grid companies, virtual power plant operators, power dispatching agencies, etc., to help the above departments achieve efficient resource scheduling and effective cost control, and provide strong support for the stable operation and economic benefit improvement of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0054] Figure 1 It is a flowchart of a multi-type resource demand response day-ahead scheduling method for grid regulation requirements disclosed by the present invention;

[0055] Figure 2 It is an optimization flowchart of the knapsack algorithm disclosed by the present invention;

[0056] Figure 3 It is a block diagram of a multi-type resource demand response day-ahead scheduling system for grid regulation requirements disclosed by the present invention;

[0057] Figure 4 It is a graph of the schedulable capacity of a single mall air conditioner disclosed by the present invention;

[0058] Figure 5 It is a graph of the schedulable capacity of a single factory disclosed by the present invention;

[0059] Figure 6 It is a graph of the schedulable capacity of electric vehicles disclosed by the present invention;

[0060] Figure 7 It is a graph of the upper and lower limits of the virtual adjustable capacity of multi-type loads disclosed by the present invention;

[0061] Figure 8 It is a graph of the grid demand curve disclosed by the present invention;

[0062] Figure 9 The multi-type load response power grid demand map disclosed in the embodiments of the present invention. Specific embodiments

[0063] The following will combine the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] Refer to Figure 1 As shown, the present invention discloses a multi-type resource demand response day-ahead scheduling method for power grid regulation requirements, including the following steps:

[0065] S1. Obtain the data information of mall air conditioners, industrial loads, and electric vehicles, quantitatively evaluate the adjustable potential respectively, and obtain the reducible amount or schedulable amount of each type of resource at different time periods;

[0066] S2. Determine the objective function and constraint conditions, transform the scheduling problem of multi-type resources into an optimization problem, and establish a multi-type resource scheduling model;

[0067] S3. Obtain the relationship between electricity price and resource scheduling amount, and represent the relationship between electricity price and resource scheduling amount in the form of a quadratic function, formulate a user incentive cost model, and allocate user incentive costs;

[0068] S4. Use the knapsack algorithm to optimize and solve the multi-type resource scheduling model in S2 combined with the user incentive cost model to generate a resource scheduling strategy.

[0069] Further, the reducible amount of the mall air conditioner in S1 is specifically: the difference between the actual operating power of the user's air conditioner and the historical acceptable power consumption at the current temperature is used as a measure of the air conditioner load reduction interval. First, fit the outdoor temperature and air conditioner operating power data of a mall for multiple days; secondly, fit the air conditioner operating power and outdoor temperature curve data of a single mall for multiple days to form a scatter plot of air conditioner operating power and outdoor temperature. Each outdoor temperature corresponds to multiple power values, reflecting the historical air conditioner operating power of a mall at that outdoor temperature, forming a temperature-power graph; finally, conduct a quantitative evaluation of the adjustable potential. When the actual operating power of the user's air conditioner is greater than the lower limit of the historical acceptable power consumption at the current temperature, power reduction can be carried out. The corresponding expression is as follows:

[0070]

[0071] Wherein, P i,maxRepresents the maximum value of the air conditioner power reduction interval for a single shopping mall; P i,on Represents the actual operating power of the air conditioner; Represents the minimum value of the historical acceptable power at temperature T;

[0072] The specific adjustable amount of industrial load is as follows: using the historical load data of different electricity consumption plans of industrial users and the real-time load rolling prediction data, calculate the upper and lower adjustable capabilities of individual electrical equipment considering electricity consumption prediction and power upper and lower limits, and finally determine the adjustable potential interval of a single industrial user. Since the electricity consumption behavior of industrial loads has obvious daily periodicity, the SARIMA model is used to conduct a short-term load prediction with a span of 24 hours for industrial users in order to evaluate the DR potential of users. The expression is as follows

[0073] φ p (L)Φ P (L s )(1 - L) d (1 - L s ) D y t =θ q (L)Θ Q (L s )ε t ,

[0074] Where: y t Is the industrial power load time series; L is the lag operator; S is the change period of the seasonal sequence; d and D are the non-seasonal and seasonal differencing orders respectively; φ p 、θ q 、Φ P 、Θ Q Are the lag operator polynomials of autoregressive (AR), moving average (MA), seasonal autoregressive (SAR), and seasonal moving average (SMA) respectively; ε t Is the Gaussian noise sequence, and L s Represents the lag operator considering the change period of the seasonal sequence.

[0075] Generalize the typical adjustable load, and the adjustable potential of a single electrical equipment can be obtained, which is related to the predicted value of its electricity consumption and the upper and lower limits of power. The expression is:

[0076]

[0077] Where: x represents the type of typical adjustable load; P pre (t) is the predicted power value of the electrical equipment at time t; Are the per-unit values of the upper and lower limits of the power of the electrical equipment respectively; P N,x Is the rated power of the load of type x; They are the upper and lower adjustable potential prediction values of the electrical equipment at time t. By expanding the formula for a single adjustable load, the formula for the adjustable potential of industrial users can be obtained. The adjustable potential of a single industrial user is related to the composition of its electrical equipment and the load curve during the dispatching period. The corresponding expression is as follows:

[0078]

[0079] Among them, l i (t) is the number of adjustable loads in operation at time t in the i-th factory; denotes rounding down; P i (t) is the total power of the i-th factory at time t; P i,others (t) is the load power of the i-th factory at time t excluding adjustable loads; are the upper and lower limits of the adjustable potential of the i-th factory at time t respectively;

[0080] The specific amount of electric vehicle curtailment is to quantitatively analyze the starting power, charging power, and demand power of electric vehicles, and use the difference between the fitted power of electric vehicles at the end of demand-side response and the user's demand power as a measure of the electric vehicle power curtailment interval.

[0081] Among them, the fitted electric vehicle charging power curve includes the electric vehicle charging time, the initial power of the electric vehicle, the rated charging power of the electric vehicle, and the battery capacity of the electric vehicle.

[0082] The fitting of the expected SOC of electric vehicles uses a normal distribution to fit the expected SOC of electric vehicles. The expression is:

[0083]

[0084] Among them: SOC need represents the percentage of the power required for the future travel of the electric vehicle; μ3 and σ3 represent the expected value and the fluctuation value of the percentage of the power required for the future travel of the electric vehicle respectively. When the curtailment of the electric vehicle is less than the difference between the fitted power and the required power of the electric vehicle, curtailment of the electric vehicle will not affect the user's future travel demand. Therefore, it is classified into the adjustable potential interval of the electric vehicle. The expression is:

[0085] Q i = B i (SOC end,i - SOC need,i ),

[0086] Among them, [0, Q i is set as the curtailment interval of electric vehicle load i, B i represents the battery capacity of electric vehicle i; SOC need,iIndicates the percentage of the required power for the future travel of electric vehicle i; SOC end,i Indicates the battery SOC of electric vehicle i at the end of the demand response period.

[0087] Furthermore, in S2, with the maximum total profit of the virtual power plant as the objective function, the constraint conditions include power balance constraint, mall air conditioner reducible amount constraint, mall air conditioner reducible time period constraint, mall air conditioner maximum reducible times constraint, industrial transferable amount constraint, industrial transferable time period constraint, industrial maximum transferable times constraint, electric vehicle reducible amount constraint, and electric vehicle reducible time period constraint.

[0088] Furthermore, the specific objective function is: The profit of the virtual power plant is the difference between the economic benefits generated by grid regulation and the user incentive cost, and the expression is:

[0089]

[0090] Among them, x i Represents the ratio of the current dispatch amount to the dispatchable amount of the i-th resource; v i Represents the economic benefits of the i-th resource for grid regulation; C i Represents the user incentive cost of the i-th resource, and n represents the total number of resources.

[0091] Furthermore, the power balance constraint expression is:

[0092]

[0093] Among them: d t Represents the power supply of the virtual power plant; x ac,t Represents the power reduction amount of mall air conditioner users; x ind,t Represents the dispatch amount of industrial users; x ev,t Represents the power reduction amount of electric vehicle users, and T represents 24 hours of a day;

[0094] The mall air conditioner reducible amount constraint, reducible time period constraint, and maximum reducible times constraint expressions are:

[0095]

[0096] Among them: Represents the upper limit of the power reducible amount of mall air conditioner users; T ac Represents the reducible time period; N ac Represents the maximum reducible times within a day;

[0097] The industrial transferable amount constraint, transferable time period constraint, and maximum transferable times constraint expressions are:

[0098]

[0099] Among them: represents the upper limit of the power dispatchable amount of industrial users; T ind represents the industrial dispatchable time period; N ind represents the maximum number of dispatchable times within a day;

[0100] The constraint expressions for the reducible amount of electric vehicles and the reducible time period are:

[0101]

[0102] Among them: represents the upper limit of the power reducible amount of electric vehicle users; T ev represents the dispatchable time period of electric vehicles.

[0103] Furthermore, in S3, obtaining the relationship between the electricity price and the resource dispatch amount includes: during the demand response period, the higher the electricity price when the resource dispatch amount is closer to its upper limit.

[0104] Specifically, during the demand response period, the virtual power plant needs to provide incentives to users based on the reduction amount of users. Traditional user incentive models generally start from the reducibility of all users and formulate incentive strategies uniformly. The resulting user incentive models ignore the differences among individual users when making reductions, and it is often difficult to achieve the expected effect in actual applications. Based on the principle that the higher the electricity price when the resource dispatch amount is closer to its upper limit, a user incentive cost model is formulated.

[0105] Furthermore, for a single user, at the beginning of the reduction, the reduction power has a relatively small impact on the user's comfort, and the incentive cost to be paid is also relatively small; as the reduction power increases, the impact of the reduction power on the user's comfort also increases, and the incentive cost to be paid is also relatively large; assuming that the relationship between the electricity price P and the resource dispatch amount xi can be represented by a quadratic function, where the closer the resource dispatch amount is to its upper limit, the higher the electricity price, so the user cost incentive model expression is:

[0106] P(x i ) = ax i 2 + bx i + c,

[0107] x i = P i / P i,max

[0108] Among them: P(x i ) represents the electricity price of the i-th resource; x i represents the ratio of the current dispatch amount of the i-th resource to the dispatchable amount; P irepresents the current scheduling volume of the i-th resource; P i,max represents the maximum scheduling volume of the i-th resource; a, b, c represent the coefficients of the quadratic function of the electricity price, and a>0 ensures that the electricity price increases with the increase of the scheduling volume.

[0109] Furthermore, the optimization solution in S4 includes: regarding various adjustable resources in the power grid as "items", the capacity or adjustment ability as "weight", and the economic benefit of power grid adjustment as "value", and selecting a group of resources under the total capacity constraint that does not exceed the power grid adjustment demand to maximize the total value, so as to obtain the optimal resource scheduling plan.

[0110] Furthermore, referring to Figure 2 as shown, the knapsack algorithm problem is solved using the dynamic programming method. Each state represents the maximum value that can be obtained under the given capacity limit. The expression for optimizing the solution using the knapsack algorithm is as follows:

[0111] dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - w[i]] + v[i]),

[0112] where i represents considering the first i resources; j represents the current knapsack capacity; w[i] represents the schedulable volume of the i-th resource; v[i] represents the economic benefit of the i-th resource for power grid adjustment; dp[i - 1][j] represents the maximum value when the i-th resource is not selected; dp[i - 1][j - w[i]] + v[i] represents the maximum value when the i-th resource is selected.

[0113] A multi-type resource demand response day-ahead scheduling system for power grid regulation requirements, which executes a multi-type resource demand response day-ahead scheduling method described in any one of the above, referring to Figure 3 as shown, includes a multi-type resource adjustable potential evaluation module, a multi-type resource scheduling model establishment module, a user incentive cost model specification module, and an optimization solution module;

[0114] The multi-type resource adjustable potential evaluation module is used to obtain the data information of mall air conditioners, industrial loads, and electric vehicles, quantitatively evaluate the adjustable potential respectively, and obtain the reducible volume or schedulable volume of each type of resource at different time periods;

[0115] The multi-type resource scheduling model establishment module is connected to the multi-type resource adjustable potential evaluation module, and is used to determine the objective function and constraint conditions, transform the scheduling problem of multi-type resources into an optimization problem, and establish a multi-type resource scheduling model;

[0116] The user incentive cost model has its output end connected to the second input end of the optimization and solution module. It is used to obtain the relationship between electricity price and resource dispatching volume, represent the relationship between electricity price and resource dispatching volume in the form of a quadratic function, formulate the user incentive cost model, and allocate the user incentive cost.

[0117] The optimization and solution module has its first input end connected to the output end of the multi-type resource dispatching model establishment module. It uses the knapsack algorithm to optimize and solve the multi-type resource dispatching model combined with the user incentive cost model, and generates a resource dispatching strategy.

[0118] In a specific embodiment, 16 mall air conditioners, 5 steel factories, and 30 electric vehicles in the Shandong area are selected as schedulable resources for example simulation analysis. The virtual power plant optimally dispatches these three types of resources, and under the goal of maximizing the benefits of the virtual power plant, it meets the regulation requirements of the distribution network.

[0119] The 16 mall air conditioner users are divided into 8 groups, represented by user1~8; the 5 steel factory users are divided into 5 groups, represented by user9~13; the 30 electric vehicle users are divided into 5 groups, represented by user14~18. The collected distribution network regulation demand data, mall air conditioner operation data, factory electricity consumption plan, and electric vehicle charging data in the Shandong area on December 1, 2024 are used as the original data. Using the method of this application, data simulation tests are carried out on the multi-type resource demand response. This example is divided into the following steps:

[0120] S1. Conduct an assessment of the adjustable potential of multi-type resources, quantitatively evaluate the adjustable potential of resources such as mall air conditioners, industrial loads, and electric vehicles respectively, and obtain the reducible or schedulable volume of each type of resource at different time periods. Then, the virtual power plant first reports the total adjustable capacity of each type of load. What is reported is the virtual adjustable capacity upper and lower limit curves without considering the maximum continuous duration of a single dispatch, the number of dispatches, and the interval time between two dispatches, as Figure 4 shown as the schedulable volume of a single mall air conditioner, representing the value of the power reducible volume of one mall air conditioner within a day. Figure 5 shown as the schedulable volume of a single factory, representing the value of the schedulable volume of one factory within a day. The factory can both provide power consumption services and load reduction services. Figure 6 shown as the schedulable volume of some electric vehicles, showing the schedulable potential of each electric vehicle. The adjustable volume data of multiple loads such as mall air conditioners, industry, and electric vehicles are fused and summarized to obtain the virtual adjustable capacity upper and lower limit curves as Figure 7 shown. The green line represents the upper limit of the adjustable capacity, and the red line represents the lower limit of the adjustable capacity;

[0121] S2. According to the result of S1, the distribution network formulates a real grid demand dispatch curve according to the upper and lower limits of the virtual adjustable capacity provided by the virtual power plant as Figure 8 shown; then, with the maximum total profit of the virtual power plant as the objective function and considering various constraints, a multi-type resource dispatch model is formed;

[0122] S3. Specify the user incentive cost model. According to the relationship between the resource dispatch volume and the electricity price, a quadratic function form is used to represent the relationship between the electricity price and the resource dispatch volume, and the user incentive cost is reasonably allocated to encourage users to actively participate in grid regulation. The compensation electricity price for the virtual power plant to participate in demand response by the distribution network is 3 yuan / kWh, and the user cost incentive cost is 0.8 - 3 yuan / kWh. The user cost incentive cost model is P(x i ) = 1.6x i 2 + 0.6x i + 0.8;

[0123] S4. Use the knapsack algorithm to optimize and solve the multi-type resource dispatch model combined with the user incentive cost model. The virtual power plant will consider the maximum continuous duration of a single dispatch, the daily dispatchable times, the interval time between two dispatches, and the dispatch cost at each moment, and use internal adjustable resources to meet the grid requirements with the maximum profit. The limitation conditions for the dispatch of various types of resources are shown in Table 1.

[0124] Table 1 Limitations for the Dispatch of Various Types of Resources

[0125]

[0126] Use multi-type resources such as mall air conditioners, industries, and electric vehicles to respond to grid demands. As Figure 9 shown, demand response mainly provides power consumption services during the evening period to absorb the renewable energy of the main grid. During the remaining periods, it provides load curtailment services to maintain the grid voltage. All resources together maintain the grid voltage within the required range. It can be seen that using the knapsack algorithm can effectively allocate various resources to meet the grid regulation requirements. Table 2 shows the comparison table of performance indicators of different algorithms. Compared with the MOPSO and MILP methods, it can be seen from the table that using the knapsack algorithm for optimization increases the profit by 4.42% and 7.31%, and the solution time is increased by 10.50% and 13.01%.

[0127] Table 2 Comparison of Performance Indicators of Different Algorithms

[0128] Category MOPSO MILP Knapsack algorithm Profit (yuan) 58302.8 56737.3 60883.5 Solving time (sec) 426.9 439.2 382.1

[0129] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A day-ahead scheduling method for multi-type resource demand response oriented to grid regulation requirements, characterized in that It includes the following steps: S1. Obtain the data information of mall air conditioners, industrial loads, and electric vehicles, quantitatively evaluate the adjustable potential respectively, and obtain the reducible or schedulable amounts of various types of resources at different time periods; S2. Determine the objective function and constraint conditions, transform the scheduling problem of multi-type resources into an optimization problem, and establish a multi-type resource scheduling model; S3. Obtain the relationship between electricity price and resource scheduling volume, represent the relationship between electricity price and resource scheduling volume in the form of a quadratic function, formulate a user incentive cost model, and allocate user incentive costs; S4. Use the knapsack algorithm to optimize and solve the multi-type resource scheduling model in S2 combined with the user incentive cost model to generate a resource scheduling strategy.

2. A day-ahead scheduling method for multi-type resource demand response for grid regulation requirements according to claim 1, characterized in that The expression for the reducible amount of mall air conditioners in S1 is as follows: Among them, P i,max represents the maximum value of the cuttable range of the air-conditioning power of a single shopping mall; P i,on represents the actual operating power of the air conditioner; represents the minimum value of the historically acceptable power at temperature T; The expression for the schedulable amount of industrial loads is as follows: where l i (t) is the number of adjustable loads that are operating at the i-th factory at time t; represents rounding down; P i (t) is the total power of the i-th factory at time t; P i,others (t) is the load power of the i-th factory at time t other than the adjustable load; are the per-unit upper and lower limit values of the power of the electrical equipment, respectively, and P N,x is the rated power of the x-type load, are the upper and lower limits of the adjustable potential of the i-th factory at time t, respectively; The expression for the reducible amount of electric vehicles is: Q i = B i (SOC end,i - SOC need,i ) Among them, [0, Q i is set as the curtailment interval of electric vehicle load i, and B i represents the battery capacity of electric vehicle i; SOC need,i represents the percentage of electricity required for the future travel of electric vehicle i; SOC end,i represents the battery SOC of electric vehicle i at the end of the demand response period.

3. A day-ahead scheduling method for multi-type resource demand response for grid regulation requirements according to claim 1, characterized in that In S2, taking the maximum total profit of the virtual power plant as the objective function, the constraint conditions include power balance constraint, reducible amount constraint of mall air conditioners, reducible time period constraint of mall air conditioners, maximum reducible times constraint of mall air conditioners, transferable amount constraint of industry, transferable time period constraint of industry, maximum transferable times constraint of industry, reducible amount constraint of electric vehicles, reducible time period constraint of electric vehicles.

4. A day-ahead scheduling method for multi-type resource demand response for grid regulation requirements according to claim 3, characterized in that The expression of the objective function is: Among them, x i represents the ratio of the current scheduling volume to the schedulable volume of the i-th resource; v i represents the economic benefit of the i-th resource for power grid regulation; C i represents the user incentive cost of the i-th resource, and n represents the total number of resources.

5. A day-ahead scheduling method for multi-type resource demand response for grid regulation requirements according to claim 3, characterized in that The expression of the power balance constraint is: Where: d t represents the power supply of the virtual power plant; x ac,t represents the power reduction of the mall air-conditioning users; x ind,t represents the dispatching amount of industrial users; x ev,t represents the power reduction of electric vehicle users, and T represents 24 hours of a day; The expressions of the reducible amount constraint, reducible time period constraint, and maximum reducible times constraint of mall air conditioners are: Wherein: represents the upper limit of the power reduction amount of the mall air-conditioning users; T ac represents the reducible time period; N ac represents the maximum reducible times within a day; The expressions of the transferable amount constraint, transferable time period constraint, and maximum transferable times constraint of industry are: Wherein: represents the upper limit of the power dispatchable amount for industrial users; T ind represents the industrial dispatchable time period; N ind represents the maximum dispatchable times within a day; The expressions of the reducible amount constraint and reducible time period constraint of electric vehicles are: Wherein: represents the upper limit of the power curtailment amount of electric vehicle users; T ev represents the schedulable time period of electric vehicles.

6. A day-ahead scheduling method for multi-type resource demand response for grid regulation requirements according to claim 1, characterized in that In S3, obtaining the relationship between electricity price and resource scheduling volume includes: during the demand-side response period, the higher the electricity price when the resource scheduling volume is closer to its upper limit.

7. A day-ahead scheduling method for multi-type resource demand response for grid regulation requirements according to claim 6, characterized in that The expression of the user cost incentive model is: where: P(x i ) represents the electricity price of the i-th resource; x i represents the ratio of the current dispatch volume to the dispatchable volume of the i-th resource; P i represents the current dispatch volume of the i-th resource; P i,max represents the maximum dispatch volume of the i-th resource; a, b, c represent the coefficients of the quadratic function of the electricity price, and a > 0 ensures that the electricity price increases with the increase of the dispatch volume.

8. A day-ahead scheduling method for multi-type resource demand response for grid regulation requirements according to claim 1, characterized in that The optimization solution in S4 includes: regarding various adjustable resources in the power grid as "items", the capacity or adjustment ability as "weight", and the economic benefit of power grid regulation as "value", and selecting a group of resources under the total capacity constraint not exceeding the power grid regulation demand to maximize the total value, so as to obtain the optimal resource scheduling plan.

9. A multi-type resource demand response day-ahead scheduling method for grid regulation requirements according to claim 1, characterized in that: The expression for optimizing and solving using the knapsack algorithm in S4 is as follows: dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - w[i]] + v[i]), where i represents considering the first i resources; j represents the current knapsack capacity; w[i] represents the schedulable amount of the i-th resource; v[i] represents the economic benefit of the i-th resource for grid regulation; dp[i - 1][j] represents the maximum value when the i-th resource is not selected; dp[i - 1][j - w[i]] + v[i] represents the maximum value when the i-th resource is selected.

10. A multi-type resource demand response day-ahead scheduling system for grid regulation requirements, which executes a multi-type resource demand response day-ahead scheduling method according to any one of claims 1-9, characterized in that, It includes a multi-type resource adjustable potential evaluation module, a multi-type resource scheduling model establishment module, a user incentive cost model specification module, and an optimization and solution module; The multi-type resource adjustable potential evaluation module is used to obtain data information of mall air conditioners, industrial loads, and electric vehicles, quantitatively evaluate the adjustable potential respectively, and obtain the reducible amount or schedulable amount of each type of resource at different time periods; The multi-type resource scheduling model establishment module, connected to the multi-type resource adjustable potential evaluation module, is used to determine the objective function and constraint conditions, transform the scheduling problem of multi-type resources into an optimization problem, and establish a multi-type resource scheduling model; The user incentive cost model, with its output end connected to the second input end of the optimization and solution module, is used to obtain the relationship between electricity price and resource scheduling amount, represent the relationship between electricity price and resource scheduling amount in the form of a quadratic function, formulate the user incentive cost model, and allocate the user incentive cost; The optimization and solution module, with its first input end connected to the output end of the multi-type resource scheduling model establishment module, uses the knapsack algorithm to optimize and solve the multi-type resource scheduling model combined with the user incentive cost model to generate a resource scheduling strategy.

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