Virtual power plant demand response decision optimization method and device

Abstract

The invention discloses a virtual power plant demand response decision optimization method and device, and belongs to the technical field of power system dispatching operation. The method comprises the steps of calculating an actual response amount of an uncertain adjustment resource according to a deviation between a virtual power plant demand response target value and an actual value; wherein the uncertain adjustment resources comprise response information uncertain adjustment resources and participation information uncertain adjustment resources; establishing an adjustment resource optimization model according to the actual response quantity of the uncertain adjustment resource; the total demand response deviation of the uncertain adjustment resources is calculated according to the actual response quantity; calculating subsidy cost of the virtual power plant to the uncertain adjustment resources according to the adjustment resource optimization model; and establishing a virtual power plant decision optimization model according to the total demand response deviation and the subsidy cost, and solving the decision optimization model by taking the minimum cost as an objective function to obtain an optimal demand response decision scheme. The operation cost of the virtual electric field can be reduced, and the operation income is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching operation, and particularly to a method and device for optimizing virtual power plant demand response decision-making. Background Art

[0002] The construction of a new power system mainly based on renewable energy is gradually accelerating, but the randomness and uncertainty of renewable energy output make the contradiction between power supply and demand in the power system increasingly prominent. As a carrier for aggregating various adjustable resources, the virtual power plant is expected to become an effective means to adjust the power supply-demand balance of the power system. It guides various adjustable resources to perform demand response through electricity price guidance or incentive measures to match the power output of the power supply end of the power system.

[0003] Due to the information uncertainty of the adjustable resources when the virtual power plant needs to adjust them, and the deviation between the subjective and objective response amounts and the expected value of the response amount caused by the uncertainty, a contradiction in the interest distribution between the virtual power plant and the response resources is caused. In related technologies, the impact of uncertainty on the demand response of adjustable resources is often not studied from the perspective of the combination of subjectivity and objectivity, which leads to relatively high operation and decision-making costs of the virtual power plant.

[0004] Based on this, there is an urgent need for a method and device for optimizing virtual power plant demand response decision-making to solve the above technical problems. Summary of the Invention

[0005] The present invention provides a method and device for optimizing virtual power plant demand response decision-making, which can effectively reduce the operation cost of the virtual power plant and improve the operation income. The technical solutions are as follows:

[0006] On the one hand, a method for optimizing virtual power plant demand response decision-making is provided. The method includes:

[0007] Calculating the actual response amount of the uncertain adjustable resources according to the deviation between the virtual power plant demand response target value and the actual value; wherein, the uncertain adjustable resources include adjustable resources with uncertain response information and adjustable resources with uncertain participation information;

[0008] Establishing an optimization model of adjustable resources including response information uncertainty and participation information uncertainty according to the actual response amount of the uncertain adjustable resources;

[0009] Calculating the total demand response deviation of the adjustable resources with uncertain response information and the adjustable resources with uncertain participation information according to the actual response amount;

[0010] Calculating the subsidy cost of the virtual power plant for the adjustable resources with uncertain response information and the adjustable resources with uncertain participation information according to the optimization model of adjustable resources;

[0011] Based on the total demand response deviation and the subsidy cost, a decision optimization model of a virtual power plant considering information uncertainty is established, and the decision optimization model is solved with the minimization of cost as the objective function to obtain the optimal demand response decision scheme of the virtual power plant.

[0012] On the other hand, a device for optimizing the demand response decision of a virtual power plant is provided. The device includes:

[0013] A first calculation module, configured to calculate the actual response amount of the uncertain regulation resources according to the deviation between the demand response target value and the actual value of the virtual power plant; wherein, the uncertain regulation resources include response information uncertain regulation resources and participation information uncertain regulation resources;

[0014] A first modeling module, configured to establish an optimization model of regulation resources including response information uncertainty and participation information uncertainty according to the actual response amount of the uncertain regulation resources;

[0015] A second calculation module, configured to calculate the total demand response deviation of the response information uncertain regulation resources and the participation information uncertain regulation resources according to the actual response amount;

[0016] A third calculation module, configured to calculate the subsidy cost of the virtual power plant for the response information uncertain regulation resources and the participation information uncertain regulation resources according to the regulation resource optimization model;

[0017] A second modeling module, configured to establish a decision optimization model of a virtual power plant considering information uncertainty according to the total demand response deviation and the subsidy cost, and solve the decision optimization model with the minimization of cost as the objective function to obtain the optimal demand response decision scheme of the virtual power plant.

[0018] On the other hand, a computer device is provided. The computer device includes a memory and a processor. The memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the steps of the above-mentioned method for optimizing the demand response decision of a virtual power plant.

[0019] On the other hand, a computer-readable storage medium is provided. The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for optimizing the demand response decision of a virtual power plant are implemented.

[0020] On the other hand, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for optimizing the demand response decision of a virtual power plant are implemented.

[0021] The technical solution provided by the present invention can at least bring the following beneficial effects: The participation uncertainty and response uncertainty of regulation resources are characterized by the deviation between the virtual power plant demand response target value and the actual value, and an optimization model of regulation resources including response information uncertainty and participation information uncertainty is established. Subsequently, a virtual power plant decision-making model is constructed based on the total demand response deviation and subsidy cost of response information uncertain regulation resources and participation information uncertain regulation resources, and the optimal demand response decision-making scheme that minimizes the cost of the virtual power plant is calculated. This method effectively characterizes the impact of regulation resource information uncertainty on the decision-making of the virtual power plant, ensures the practicability of the virtual power plant strategy, reduces the operating cost of the virtual power grid, and improves the operating income. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] 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 use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 It is a flowchart of a virtual power plant demand response decision optimization method provided by an embodiment of the present invention;

[0024] Figure 2 It is a schematic diagram of the comparison of virtual power plant costs under different scenarios provided by an embodiment of the present invention;

[0025] Figure 3 It is a structural diagram of a virtual power plant demand response decision optimization device provided by an embodiment of the present invention;

[0026] Figure 4 It is a hardware architecture diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0028] As mentioned above, on the one hand, existing research results assume that all regulation resources will participate in the response, ignoring the participation uncertainty caused by subjective factors; on the other hand, they fail to study the impact of participation uncertainty and response uncertainty on the demand response of regulation resources from the perspective of the combination of subjectivity and objectivity.

[0029] Based on this, the concept of the present invention is to consider the participation uncertainty and response uncertainty of regulation resources, and establish a demand response model to optimize the demand response decision of the virtual power plant.

[0030] The specific implementation manners of the above concept are described below.

[0031] Please refer to Figure 1 , a method for optimizing the demand response decision of a virtual power plant provided by an embodiment of the present invention, the method includes:

[0032] Step 100, calculate the actual response amount of the uncertain regulation resources according to the deviation between the demand response target value and the actual value of the virtual power plant; wherein, the uncertain regulation resources include regulation resources with uncertain response information and regulation resources with uncertain participation information;

[0033] Step 102, establish an optimization model of regulation resources including response information uncertainty and participation information uncertainty according to the actual response amount of the uncertain regulation resources;

[0034] Step 104, calculate the total demand response deviation of the regulation resources with uncertain response information and the regulation resources with uncertain participation information according to the actual response amount;

[0035] Step 106, calculate the subsidy cost of the virtual power plant for the regulation resources with uncertain response information and the regulation resources with uncertain participation information according to the regulation resource optimization model;

[0036] Step 108, establish a decision optimization model of the virtual power plant considering information uncertainty according to the total demand response deviation and the subsidy cost, and solve the decision optimization model with the minimum cost as the objective function to obtain the optimal demand response decision scheme of the virtual power plant.

[0037] In the embodiment of the present invention, the participation uncertainty and response uncertainty of the regulation resources are characterized by the deviation between the demand response target value and the actual value of the virtual power plant, and an optimization model of regulation resources including response information uncertainty and participation information uncertainty is established. Subsequently, a decision model of the virtual power plant is constructed according to the total demand response deviation and the subsidy cost of the regulation resources with uncertain response information and the regulation resources with uncertain participation information, and the optimal demand response decision scheme that minimizes the cost of the virtual power plant is calculated. This method effectively characterizes the influence of the information uncertainty of the regulation resources on the decision of the virtual power plant, ensures the practicability of the virtual power plant strategy, reduces the operation cost of the virtual power grid, and improves the operation revenue.

[0038] The following describes Figure 1 the execution manners of each step shown.

[0039] First, for step 100, according to the deviation between the demand response target value and the actual value of the virtual power plant, calculate the actual response amount of the uncertain regulation resources.

[0040] In the embodiments of the present invention, a demand response framework is established by comprehensively considering the interaction between the virtual power plant and the regulation resources. Among them, the virtual power plant is the intersection of information and electric energy. It undertakes response tasks from the power market upwards and interacts with the regulation resources downwards to maintain the balance between supply and demand in the new power system. The specific architecture is described in detail as follows.

[0041] First, the power market issues demand response information to the virtual power plant, including the total expected demand response, compensation price coefficient, penalty price coefficient, etc. The power market can compensate and penalize the virtual power plant according to the total expected demand response and the actual response amount of the virtual power plant. Then, the virtual power plant optimizes the subsidy price to the regulation resources according to the demand response information of the power market and settles with the power market based on the actual response amount of each regulation resource after participating in the demand response. Finally, according to the subsidy price of the virtual power plant, the regulation resources adjust the electricity load demand as a response uncertainty type or participation uncertainty type according to their actual situations. The sum of the electricity load demand adjustment amounts of different types of regulation resources is the actual response amount of the virtual power plant.

[0042] In the embodiments of the present invention, the uncertain regulation resources include regulation resources with uncertain response information and regulation resources with uncertain participation information.

[0043] The information uncertainty of the regulation resources when participating in the demand response includes participation uncertainty and response uncertainty. Participation uncertainty refers to the subjective selection information that the virtual power plant cannot accurately predict whether the regulation resources that have not participated in the demand response will participate or not in the demand response. Response uncertainty refers to the fact that the subsidy given by the virtual power plant to the regulation resources that have participated in the demand response will cause the objective response amount of the regulation resources to deviate from the expected response amount.

[0044] The deviation between the subjective and objective response amounts and the expected response amount caused by participation uncertainty and response uncertainty also results in the contradiction of interest distribution between the virtual power plant and the response resources. This deviation can be described by the information uncertainty coefficient:

[0045]

[0046] In the formula, A i (t) represents the deviation between the total expected demand response and the actual response amount; represents the total expected demand response of the i-th regulation resource at time t; a i (t) represents the actual response amount of the i-th regulation resource at time t; θ i represents the information uncertainty coefficient of user i.

[0047] According to whether the regulation resources have participated in demand response, the information uncertainty coefficient can be further decomposed into a response information uncertainty coefficient and a participation information uncertainty coefficient, as described below.

[0048] For regulation resources with uncertain response information, considering that continuous participation in demand response by regulation resources will reduce their enthusiasm and lead to uncertainty in their response capabilities. At this time, the response information uncertainty coefficient is a continuous random variable and follows a normal distribution:

[0049]

[0050] Where is the actual response volume of the i-th regulation resource with uncertain response information at time t; N A is the total number of regulation resources with uncertain response information; is the i-th response information uncertainty coefficient at time t; is the expected total demand response volume of the i-th regulation resource with uncertain response information at time t; are the expectation and standard deviation of the response information uncertainty coefficient, respectively.

[0051] For regulation resources with uncertain participation information, the participation rate of regulation resources with uncertain participation information is determined according to the incentive price of the virtual power plant, and the actual response volume of the regulation resources with uncertain participation information is calculated based on the participation rate.

[0052] Specifically, considering that the regulation resources with uncertain information have not participated in demand response and have no historical participation data for the virtual power plant, whether they participate in demand response is uncertain. Therefore, the virtual power plant uses a unified subsidy price pr B (t) to incentivize the regulation resources with uncertain participation information to participate in the response, and this subsidy price is strongly correlated with the participation rate ζ B (t). According to the price elasticity theory, a decrease in the subsidy price will lead to a decrease in the participation rate of regulation resources, and vice versa. The relationship between the two is shown in the following formula:

[0053]

[0054] In the formula: N B is the total number of regulation resources with uncertain participation information; represents the subsidy price given by the virtual power plant to the i-th regulation resource with uncertain participation information at time t; represents the minimum acceptable subsidy price of the i-th regulation resource with uncertain participation information at time t.

[0055] Furthermore, according to the central limit theorem, when the total number of regulation resources with uncertain participation information is large enough, Subject to a normal distribution, the relationship between the expected total demand response of the regulation resources participating in the information uncertainty regulation and the actual response volume is as follows:

[0056]

[0057] θ B (t)~N(μ B (t),(σ B (t)) 2 )

[0058] In the formula, N B is the total number of regulation resources participating in the information uncertainty regulation; is the actual response volume of the i-th regulation resource participating in the information uncertainty regulation at time t; θ B (t) is the information uncertainty coefficient of the regulation resources participating at time t; is the expected total demand response of the i-th regulation resource participating in the information uncertainty regulation at time t; are the expected value and standard deviation of the information uncertainty coefficient respectively.

[0059] Then, for step 102, according to the actual response volume of the uncertain regulation resources, an optimization model of the regulation resources including response information uncertainty and participation information uncertainty is established.

[0060] In the existing literature on demand response, usually only the response information uncertainty is considered, and the importance of participation information uncertainty in the regulation resource modeling is ignored. As more and more regulation resources participate in the demand response, the phenomenon of coexistence of response information uncertainty and participation information uncertainty will occur in large numbers. Therefore, it is necessary to comprehensively consider the above two information uncertainties in the regulation resource modeling to maximize the benefits of the regulation resources themselves.

[0061] In the embodiment of the present invention, the regulation resource optimization model is established through the following steps:

[0062] According to the willingness and characteristic parameters of the response information uncertain regulation resources for curtailment demand response or accommodation demand response, the first response cost of the response information uncertain regulation resources is calculated

[0063] According to the willingness and characteristic parameters of the participation information uncertain regulation resources for curtailment demand response or accommodation demand response, the second response cost of the participation information uncertain regulation resources is calculated

[0064] According to the actual response volume, the first response cost and the second response cost, the regulation resource optimization model that maximizes the benefits of the regulation resources is established.

[0065] Specifically, the first response cost is calculated by the following formula:

[0066]

[0067] The second response cost is calculated by the following formula:

[0068]

[0069] In the formula, represents the willingness of the regulation resource with uncertain response information to perform reduction-type demand response or absorption-type demand response; represents the characteristic parameter of the regulation resource with uncertain response information to perform reduction-type demand response or absorption-type demand response; represents the willingness of the regulation resource with uncertain participation information to perform reduction-type demand response or absorption-type demand response; represents the characteristic parameter of the regulation resource with uncertain participation information to perform reduction-type demand response or absorption-type demand response.

[0070] The regulation resource optimization model is established by the following formula:

[0071]

[0072] In the formula, is the optimization function of the regulation resource with uncertain response information; is the subsidy price given by the virtual power plant to the i-th regulation resource with uncertain response information at time t; represents the subsidy price given by the virtual power plant to the i-th regulation resource with uncertain participation information at time t; S MEA (t) characterizes the type of demand response. The S MEA (t) value for reduction-type demand response is 1, and the S MEA (t) value for absorption-type demand response is -1; is the optimization function of the regulation resource with uncertain participation information.

[0073] For step 104, calculate the total demand response deviation of the regulation resource with uncertain response information and the regulation resource with uncertain participation information according to the actual response volume.

[0074] The traditional virtual power plant cost function includes the subsidy cost for guiding the regulation resource to participate in demand response and the revenue from participating in the power market response task, as shown in the following formula:

[0075]

[0076] In the formula, F′VPP is the cost function of the traditional virtual power plant; T is the preset target time; N I is the total number of regulation resources; pr i (t) is the subsidy price given by the virtual power plant to the i-th regulation resource at time t; a i (t) is the actual response volume of the i-th regulation resource at time t; ΔL(t) is the expected total demand response volume of the virtual power plant at time t; pr e (t) represents the compensation price of the power market to the virtual power plant in the t-th period.

[0077] However, the above formula only considers the subsidy given by the virtual power plant to the regulation resources, ignoring the information uncertainty of the regulation resources, resulting in a large deviation between the expected total demand response volume and the actual response volume.

[0078] For these reasons, the embodiment of the present invention considers the contract theory of institutional economics to establish an optimization model for virtual power plant decision-making. The contract theory of institutional economics helps the virtual power plant design an optimal contract to motivate the regulation resources to complete the expected total demand response volume issued by the power market under the conditions of information uncertainty and the existence of multiple interest contradictions, so as to minimize the own cost of the virtual power plant.

[0079] Specifically, because each regulation resource is independent of each other and and θ B (t) follows a normal distribution, so the total demand response deviation M1(t) of the response information uncertain regulation resources and the participation information uncertain regulation resources at time t and the subsidy cost M2(t) of the virtual power plant to the response information uncertain regulation resources and the participation information uncertain regulation resources at time t also follow a normal distribution.

[0080] Therefore, the total demand response deviation M1(t) is calculated by the following formula:

[0081]

[0082] In the formula, μ M1 (t) is the expectation of the total demand response deviation; σ M1 (t) is the standard deviation of the total demand response deviation.

[0083] For step 106, calculate the subsidy cost of the virtual power plant to the response information uncertain regulation resources and the participation information uncertain regulation resources according to the regulation resource optimization model.

[0084] According to the previous step, the subsidy cost M2(t) is calculated by the following formula:

[0085]

[0086] where μ M2 (t) is the expectation of the subsidy cost; σ M2 (t) is the standard deviation of the subsidy cost.

[0087] For step 108, according to the total demand response deviation and the subsidy cost, a virtual power plant decision optimization model considering information uncertainty is established, and the decision optimization model is solved with the minimization of cost as the objective function to obtain the optimal demand response decision scheme of the virtual power plant.

[0088] In the embodiment of the present invention, the virtual power plant decision optimization model based on the institutional economics contract theory and considering information uncertainty is established by the following formula:

[0089]

[0090] where F VPP is the virtual power plant cost function; pr pun (t) is the penalty price of the power market for the virtual power plant in the t-th period; ΔL(t) is the expected total demand response of the virtual power plant at time t; pr e (t) represents the compensation price of the power market for the virtual power plant in the t-th period; M1(t) is the total demand response deviation of the response information uncertain regulation resources and the participating information uncertain regulation resources at time t; M2(t) is the subsidy cost of the virtual power plant for the response information uncertain regulation resources and the participating information uncertain regulation resources at time t.

[0091] Furthermore, the solution process of obtaining the optimal demand response decision scheme of the virtual power plant using this model is well-known to those skilled in the art and will not be elaborated here.

[0092] To verify the effectiveness of the model of the present invention, comparative scenarios are built from the perspectives of whether to consider participation information uncertainty and response information uncertainty for analysis and description.

[0093] As shown in Table 1, Table 1 is the description of the main characteristics of four comparative scenarios. 200 users and 10 periods in a day are selected for simulation in each scenario. Each period includes curtailment-type and accommodation-type demand responses. The expected values of the demand response volume and the initial load are shown in Table 2, and Table 2 is the expected value of the demand response volume and the initial load. The demand response volume of the regulation resources is in the interval [-2, 2] MW, the lowest acceptable subsidy price of the regulation resources is in the interval [4, 5] ¥ / MW, the willingness of the regulation resources to respond to electric energy is in the interval [3, 6], and the variance of the information uncertainty coefficient of the regulation resources is randomly generated in the interval [0.0, 0.25].

[0094] Table 1

[0095]

[0096] Table 2

[0097]

[0098] To analyze the impact of the uncertainty of the regulation resource response information on the virtual power plant strategy, the comparison of the demand response deviations between Scenario 1 and Scenario 2 in each time period is shown in Table 3. Table 3 shows the regulation resource demand response deviations of Scenario 1 and Scenario 2 considering the response uncertainty. Since the virtual power plant considers the lack of regulation resource response information during decision-making, the virtual power plant can more accurately depict the actual demand response of the regulation resources. By setting effective subsidy price coefficients, it guides the regulation resources to complete the total expected demand response in the electricity market and reduces the demand response deviation. Therefore, the demand response deviations of Scenario 2 are all smaller than those of Scenario 1.

[0099] Table 3

[0100]

[0101]

[0102] The comparison of the virtual power plant costs and the regulation resource benefits between Scenario 1 and Scenario 2 in each time period is shown in Table 4. Since the demand response deviations of Scenario 2 in each time period in Table 3 are smaller, the deviation costs of Scenario 2 in each time period are all smaller than those of Scenario 1. Although the incentive costs of Scenario 2 in each time period are higher, its lower deviation costs make the sum of the deviation costs and the incentive costs in each time period always lower than that of Scenario 1. The main reason is that the virtual power plant in Scenario 2 considers the demand response deviation caused by the uncertainty of the regulation resource response information, and thus formulates a decision that takes into account both effectiveness and economy. Similarly, the regulation resource benefits after considering the response information uncertainty in Scenario 2 are better than those of Scenario 1 in each time period. Therefore, considering the response information uncertainty in the adjustable resource modeling and the virtual power plant strategy modeling can not only reduce the virtual power plant costs, but also improve the regulation resource benefits, achieving a win-win situation for both.

[0103] Table 4

[0104]

[0105]

[0106] To analyze the impact of the uncertainty of the regulation resource participation information on the virtual power plant strategy, the comparison of the demand response deviations between Scenario 1 and Scenario 3 in each time period is shown in Table 5. Since the virtual power plant considers the lack of regulation resource participation information during decision-making, the virtual power plant can more accurately formulate subsidy prices to guide the enthusiasm of the regulation resources to participate in the demand response, so as to reduce the deviation between the total expected demand response and the actual demand response. Therefore, the demand response deviations of Scenario 3 are all smaller than those of Scenario 1.

[0107] Table 5

[0108]

[0109] The comparison of the virtual power plant costs and the benefits of regulation resources for Scenarios 1 and 3 at each time period is shown in Table 6. For Scenario 3, the sum of the deviation cost and the incentive cost of the virtual power plant at each time period is less than that of Scenario 1, and the values of the regulation resource benefits at each time period are better than those of Scenario 1. Therefore, considering the information uncertainty of the participation of regulation resources helps to achieve a win-win situation between the virtual power plant and the regulation resources.

[0110] Table 6

[0111]

[0112]

[0113] To verify the necessity of comprehensively considering the uncertainty of response information and the uncertainty of participation information proposed in the present invention, the deviation costs, incentive costs, and the sum of the two types of costs of the virtual power plant for Scenarios 1 to 4 are as Figure 2 shown. Since Scenario 1 ignores the information uncertainty, the decision-making effect of the virtual power plant is poor, and its deviation cost and total cost are higher than those of other scenarios. Compared with Scenario 2, the incentive cost of Scenario 3 is close, but the deviation cost and the sum of the two types of costs are higher. The main reason is that the regulation resources with uncertain participation information have lower controllability than the regulation resources with uncertain response information, resulting in a higher deviation cost in Scenario 3. After comprehensively considering the uncertainty of response information and the uncertainty of participation information, although the incentive cost of Scenario 4 is high, the virtual power plant fully mobilizes the enthusiasm of the regulation resources through the subsidy price, reduces the deviation between the expected total demand response and the actual demand response volume, thereby reducing the deviation cost and the sum of the two types of costs. Therefore, both the uncertainty of response information and the uncertainty of participation information have a greater impact on the response ability of regulation resources. The present invention can effectively ensure the effectiveness of the virtual power plant demand response decision by comprehensively considering the uncertainties of the two types of information, improve the benefits of regulation resources while reducing the costs of the virtual power plant, and achieve a win-win situation for both.

[0114] Please refer to Figure 3 , an embodiment of the present invention provides a virtual power plant demand response decision optimization device, which includes:

[0115] A first calculation module 300, configured to calculate the actual response amount of the uncertain regulation resources according to the deviation between the virtual power plant demand response target value and the actual value; wherein, the uncertain regulation resources include regulation resources with uncertain response information and regulation resources with uncertain participation information;

[0116] The first modeling module 302 is used to establish an optimization model of the regulation resources that includes the uncertainty of response information and the uncertainty of participation information according to the actual response amount of the uncertain regulation resources;

[0117] The second calculation module 304 is used to calculate the total demand response deviation of the regulation resources with uncertain response information and the regulation resources with uncertain participation information according to the actual response amount;

[0118] The third calculation module 306 is used to calculate the subsidy cost of the virtual power plant for the regulation resources with uncertain response information and the regulation resources with uncertain participation information according to the regulation resource optimization model;

[0119] The second modeling module 308 is used to establish a decision optimization model of the virtual power plant considering information uncertainty according to the total demand response deviation and the subsidy cost, and solve the decision optimization model with the minimum cost as the objective function to obtain the optimal demand response decision scheme of the virtual power plant.

[0120] In the embodiment of the present invention, the actual response amount of the regulation resources with uncertain response information is calculated by the following formula:

[0121]

[0122] Where is the actual response amount of the i-th regulation resource with uncertain response information at time t; N A is the total number of regulation resources with uncertain response information; is the response information uncertainty coefficient at time t; is the expected total demand response of the i-th regulation resource with uncertain response information at time t; are the expectation and standard deviation of the response information uncertainty coefficient respectively;

[0123] Determine the participation rate of the regulation resources with uncertain participation information according to the incentive price of the virtual power plant, and calculate the actual response amount of the regulation resources with uncertain participation information according to the participation rate:

[0124]

[0125] θ B (t)~N(μ B (t),(σ B (t)) 2 )

[0126] Where ζ B (t) is the participation rate of the regulation resources with uncertain participation information; N B is the total number of regulation resources with uncertain participation information; is the actual response volume of the i-th regulation resource participating in information uncertainty at time t; θ B (t) is the information uncertainty coefficient of participation at time t; is the expected total demand response of the i-th regulation resource participating in information uncertainty at time t; are the expectation and standard deviation of the information uncertainty coefficient of participation, respectively.

[0127] In the embodiment of the present invention, establishing an optimization model of regulation resources including response information uncertainty and participation information uncertainty based on the actual response volume of the uncertain regulation resources includes:

[0128] Calculating the first response cost of the response information uncertain regulation resource according to the willingness and characteristic parameters of the response information uncertain regulation resource for curtailment demand response or accommodation demand response

[0129] Calculating the second response cost of the participation information uncertain regulation resource according to the willingness and characteristic parameters of the participation information uncertain regulation resource for curtailment demand response or accommodation demand response

[0130] Establishing the regulation resource optimization model that maximizes the benefit of regulation resources according to the actual response volume, the first response cost, and the second response cost:

[0131]

[0132] In the formula, is the optimization function of the response information uncertain regulation resource; is the subsidy price given by the virtual power plant to the i-th response information uncertain regulation resource at time t; represents the subsidy price given by the virtual power plant to the i-th participation information uncertain regulation resource at time t; S MEA (t) characterizes the type of demand response, and the value of S for curtailment demand response MEA (t) is 1, and the value of S for accommodation demand response MEA (t) is -1; is the optimization function of the participation information uncertain regulation resource.

[0133] In the embodiment of the present invention, the total demand response deviation M1(t) is calculated by the following formula:

[0134]

[0135] In the formula, μ M1 (t) is the expectation of the total demand response deviation; σ M1(t) is the standard deviation of the total demand response deviation.

[0136] In an embodiment of the present invention, the subsidy cost M2(t) is calculated by the following formula:

[0137]

[0138] In the formula, μ M2 (t) is the expectation of the subsidy cost; σ M2 (t) is the standard deviation of the subsidy cost.

[0139] In an embodiment of the present invention, the virtual power plant decision optimization model is established by the following formula:

[0140]

[0141] In the formula, F VPP is the virtual power plant cost function; pr pun (t) is the penalty price of the power market for the virtual power plant in the t-th period; ΔL(t) is the expected total demand response of the virtual power plant at time t; pr e (t) represents the compensation price of the power market for the virtual power plant in the t-th period; M1(t) is the total demand response deviation of the response information uncertain regulation resources and participation information uncertain regulation resources at time t; M2(t) is the subsidy cost of the virtual power plant for the response information uncertain regulation resources and participation information uncertain regulation resources at time t.

[0142] It should be noted that: for the virtual power plant demand response decision optimization device provided in the above embodiment, only the above-mentioned division of each functional module is used for illustration. In actual application, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the virtual power plant demand response decision optimization device provided in the above embodiment and the virtual power plant demand response decision optimization method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0143] An embodiment of the present application also provides a computer device. Please refer to Figure 4 , this computer device includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the virtual power plant demand response decision optimization method provided in each of the above method embodiments.

[0144] Embodiments of the present application further provide a computer-readable storage medium, on which at least one instruction, at least one program segment, a code set or an instruction set is stored, and the at least one instruction, at least one program segment, a code set or an instruction set is loaded and executed by a processor to implement the virtual power plant demand response decision optimization method provided by the above method embodiments.

[0145] Embodiments of the present application further provide a computer program product, which includes a computer program. The processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the virtual power plant demand response decision optimization method described in any one of the above embodiments.

[0146] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present application.

[0147] Finally, it should also be noted that in this article, relational terms such as first, second, third, and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0148] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A method for optimizing virtual power plant demand response decision-making, characterized in that The method includes: Calculating the actual response amount of the uncertain regulation resources according to the deviation between the virtual power plant demand response target value and the actual value; wherein, the uncertain regulation resources include the regulation resources with uncertain response information and the regulation resources with uncertain participation information; Establishing a regulation resource optimization model including uncertainty of response information and uncertainty of participation information according to the actual response amount of the uncertain regulation resources; Calculating the total demand response deviation of the regulation resources with uncertain response information and the regulation resources with uncertain participation information according to the actual response amount; Calculating the subsidy cost of the virtual power plant for the regulation resources with uncertain response information and the regulation resources with uncertain participation information according to the regulation resource optimization model; Establishing a virtual power plant decision optimization model considering information uncertainty according to the total demand response deviation and the subsidy cost, and solving the decision optimization model with the minimum cost as the objective function to obtain the optimal demand response decision plan of the virtual power plant.

2. The method according to claim 1, characterized in that, The actual response amount of the regulation resources with uncertain response information is calculated by the following formula: Among them, is the actual response amount of the i-th response information uncertainty regulation resource at time t; N A is the total number of response information uncertainty regulation resources; is the response information uncertainty coefficient at time t; is the expected total demand response of the i-th response information uncertainty regulation resource at time t; are the expected value and standard deviation of the response information uncertainty coefficient respectively; Determining the participation rate of the regulation resources with uncertain participation information according to the incentive price of the virtual power plant, and calculating the actual response amount of the regulation resources with uncertain participation information according to the participation rate: θ B (t) ~ N(μ B (t), (σ B (t)) 2 ) Among them, ζ B (t) is the participation rate of the regulation resources with uncertain participation information; N B is the total quantity of the regulation resources with uncertain participation information; is the actual response quantity of the i-th regulation resource with uncertain participation information at time t; θ B (t) is the coefficient of uncertainty of participation information at time t; is the expected total demand response of the i-th regulation resource with uncertain participation information at time t; are the expectation and standard deviation of the coefficient of uncertainty of participation information, respectively.

3. The method according to claim 2, wherein The establishing a regulation resource optimization model including uncertainty of response information and uncertainty of participation information according to the actual response amount of the uncertain regulation resources includes: Determine the willingness and characteristic parameters of the reduction-type demand response or the accommodation-type demand response according to the regulation resources with uncertain response information, and calculate the first response cost of the regulation resources with uncertain response information Based on the willingness and characteristic parameters of the regulation resources with uncertain participation information for curtailment demand response or accommodation demand response, calculate the second response cost of the regulation resources with uncertain participation information Establishing the regulation resource optimization model that maximizes the benefit of the regulation resources according to the actual response amount, the first response cost and the second response cost: In the formula, is the optimization function of the regulation resource with uncertain response information; is the subsidy price given by the virtual power plant to the i-th regulation resource with uncertain response information at time t; represents the subsidy price given by the virtual power plant to the i-th regulation resource participating in the uncertain information at time t; S MEA (t) characterizes the type of demand response. The value of S MEA (t) for the load reduction type of demand response is 1, and the value of S MEA (t) for the consumption type of demand response is -1; is the optimization function of the regulation resource participating in the uncertain information.

4. The method according to claim 2, characterized in that, The total demand response deviation M1(t) is calculated by the following formula: where, μ M1 (t) is the expectation of the total demand response deviation; σ M1 (t) is the standard deviation of the total demand response deviation.

5. The method according to claim 3, wherein The subsidy cost M2(t) is calculated by the following formula: where μ M2 (t) is the expectation of the subsidy cost; σ M2 (t) is the standard deviation of the subsidy cost.

6. The method according to claim 1, characterized in that The virtual power plant decision optimization model is established by the following formula: where F VPP is the cost function of the virtual power plant; pr pun (t) is the penalty price of the power market for the virtual power plant in the t-th period; ΔL(t) is the expected total demand response of the virtual power plant at time t; pr e (t) represents the compensation price of the power market for the virtual power plant in the t-th period; M1(t) is the total demand response deviation of the response information uncertainty regulation resources and the participation information uncertainty regulation resources at time t; M2(t) is the subsidy cost of the virtual power plant for the response information uncertainty regulation resources and the participation information uncertainty regulation resources at time t.

7. A virtual power plant demand response decision optimization device, characterized in that The device includes: A first calculation module, configured to calculate the actual response amount of the uncertain regulation resources according to the deviation between the virtual power plant demand response target value and the actual value; wherein, the uncertain regulation resources include the regulation resources with uncertain response information and the regulation resources with uncertain participation information; A first modeling module, configured to establish a regulation resource optimization model including uncertainty of response information and uncertainty of participation information according to the actual response amount of the uncertain regulation resources; A second calculation module, configured to calculate the total demand response deviation of the regulation resources with uncertain response information and the regulation resources with uncertain participation information according to the actual response amount; A third calculation module, configured to calculate the subsidy cost of the virtual power plant for the regulation resources with uncertain response information and the regulation resources with uncertain participation information according to the regulation resource optimization model; A second modeling module, configured to establish a virtual power plant decision optimization model considering information uncertainty according to the total demand response deviation and the subsidy cost, and solve the decision optimization model with the minimum cost as the objective function to obtain the optimal demand response decision plan of the virtual power plant.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the steps of the method according to any one of claims 1-6 above.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1-6 are implemented.

10. A computer program product, characterized in that, It includes a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1-6 are implemented.

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