Active power distribution network integrated prediction scheduling method and system considering power auxiliary service

Abstract

The invention discloses an active power distribution network integrated prediction scheduling method and system considering power auxiliary service. The method comprises the following steps: collecting weather forecast data of the active power distribution network, and constructing a net load prediction model considering distributed photovoltaic for each node in the active power distribution network; based on the net load prediction model, constructing an active and reactive intraday scheduling model of the active power distribution network considering two types of power auxiliary services of demand response active power support and distributed photovoltaic reactive power support; the net load prediction model and the active and reactive intra-day scheduling model of the active power distribution network are combined to form an integrated prediction scheduling model; solving the integrated predictive scheduling model to obtain an integrated predictive scheduling agent; and outputting an intra-day active and reactive scheduling scheme of the active power distribution network based on the integrated predictive scheduling agent. According to the method, the source load prediction stage of intraday active and reactive power scheduling can be simplified, an intraday active and reactive power scheduling scheme can be provided more quickly and efficiently, and the execution efficiency of intraday scheduling of the active power distribution network is improved.

Description

Technical Field

[0001] The present invention relates to an integrated forecasting and dispatching method and system for an active distribution network taking into account power auxiliary services, and belongs to the technical field of power system operation and control. Background Art

[0002] Developing a new modern power system with renewable energy generation is one of the key measures to achieve the current energy strategy. For the new modern power system, the active distribution network can efficiently absorb distributed renewable energy such as distributed photovoltaics on site, so the active distribution network has also become the core link in achieving energy goals. However, with the large-scale access of distributed renewable energy, its non-stationary characteristics have led to the need for more flexible power dispatching measures for active distribution networks, including promoting coordinated dispatching of sources, grids, loads and storage, and improving power auxiliary services. In this case, the active distribution network has strong non-stationary characteristics on both the power generation and power consumption sides, which greatly affects the development of power system operation and control technology.

[0003] With the development of power system operation and control technology, how to further shorten the gap between the current dispatching level and the optimal dispatching level under ideal conditions is the core issue in this field of technology. Usually, power dispatching operations follow the method of first forecasting and then optimizing dispatching, so dispatching operators cannot judge the impact of different forecast results on dispatching, and power forecasters cannot evaluate the gap between them and the optimal dispatching level. Especially in the current context of the need for more flexible power dispatching measures, this core problem is more difficult to solve. Summary of the invention

[0004] Purpose of the invention: In view of the shortcomings in the current field of power system operation and control technology in active distribution network dispatching, the present invention proposes an integrated prediction and dispatching method for active distribution networks taking into account power auxiliary services, simplifies the source-load prediction stage of intraday active and reactive power dispatching, provides intraday active and reactive power dispatching plans more quickly and efficiently, and improves the execution efficiency and economic benefits of intraday dispatching of active distribution networks, thereby increasing the new energy absorption capacity of the power system and enhancing the competitiveness of power grid enterprises.

[0005] Technical solution: In order to achieve the above-mentioned invention object, the present invention proposes an integrated prediction and dispatching method for active distribution network taking into account power auxiliary services, comprising the following steps:

[0006] Step 1: Collect the meteorological forecast data of the active distribution network and build a net load prediction model considering distributed photovoltaics for each node in the active distribution network. The formula of the model is expressed as:

[0007]

[0008] Where P i,t+1:t+Tis the net load forecast value of the ith node in the active distribution network from time t+1 to t+T, M pred,i is the net load prediction model of the ith node in the active distribution network, W i,t-D:t+T is the weather forecast data collected by the active distribution network from time tD to t+T, is the load value of the ith node in the active distribution network from time tD to t, is the distributed photovoltaic power value of the i-th node in the active distribution network from time tD to t, is the load forecast value of the i-th node in the active distribution network from time t+1 to t+T, is the predicted value of distributed photovoltaic power of the i-th node in the active distribution network from time t+1 to t+T, D is the acquisition step length, and T is the prediction step length;

[0009] Step 2: Based on the net load prediction model of each node in the active distribution network, an active distribution network active and reactive daytime dispatch model is constructed considering two types of power auxiliary services: demand response active support and distributed photovoltaic reactive support. The formula of the model is expressed as follows:

[0010]

[0011] In the formula, is the active dispatch cost of the active distribution network at time t+k, k is the step index, is the active dispatching power of the active distribution network at time t+k, is the reactive power dispatch cost of the active distribution network at time t+k, is the reactive dispatching power of the active distribution network at time t+k, is the distributed photovoltaic reactive support cost at time t+k, is the distributed photovoltaic reactive support power of the i-th node in the active distribution network at time t+k, is the active power support cost of demand response at time t+k, is the demand response active support power of the ith node in the active distribution network at time t+k, Σ is the summation function;

[0012] Step 3: Combine the net load forecasting model and the active distribution network active and reactive daytime dispatching model to form an integrated forecasting and dispatching model, and unify the safety constraints. The formula of the integrated forecasting and dispatching model is expressed as follows:

[0013]

[0014] sC i,t+k (s t ,a t )≤0

[0015] In the formula, F t+k is the active and reactive power dispatch objective function of the active distribution network at time t+k, s t is the operating state of the active distribution network at time t, which is a subset of the state set including the meteorological forecast data collected by the active distribution network from time tD to t+T, the load value of the i-th node in the active distribution network from time tD to t, and the distributed photovoltaic power value, D is the collection step length, and T is the prediction step length; a t is the dispatching action of the active distribution network at time t, which is a subset of the action set including the active dispatching power, reactive dispatching power, distributed photovoltaic reactive support power, and demand response active support power of the active distribution network at time t+k; C i,t+k is the safety constraint of the ith node in the active distribution network at time t+k;

[0016] Step 4: Use the deep deterministic policy gradient algorithm driven by the multi-environment model to solve the integrated prediction and scheduling model to obtain the integrated prediction and scheduling agent, which is expressed as follows:

[0017]

[0018] In the formula, Q π is the evaluation model of the integrated forecasting and dispatching intelligent agent, π is the action strategy of the active distribution network, s is the operating state of the active distribution network, a is the dispatching action of the active distribution network, is the expected value function, γ is the discount factor, π * is the strategy model of the integrated prediction and scheduling agent, and argmax is the maximum value parameter function;

[0019] Step 5: Based on the integrated prediction and dispatching intelligent body, the active distribution network’s daily active and reactive dispatching scheme is output to achieve daily active and reactive dispatching. The formula is:

[0020]

[0021] Where A is the daily active and reactive power dispatching scheme of the active distribution network.

[0022] Wherein, the net load prediction model M pred,i It is a deep neural network model. The input data include weather forecast from tD to t+T, load value from time tD to t, and distributed photovoltaic power value from time tD to t. The output data include load prediction value from time t+1 to t+T, and distributed photovoltaic power prediction value from time t+1 to t+T.

[0023] The constraints of the active distribution network active and reactive daytime dispatch model are:

[0024]

[0025] Where V i is the voltage amplitude of the ith node in the active distribution network, V min is the minimum constraint on voltage amplitude, V max is the maximum constraint on the voltage amplitude, is the energy storage power value of the i-th node in the active distribution network at time t+k, P i bm is the energy storage power constraint of the i-th node in the active distribution network, P i dm is the demand response active support power constraint of the ith node in the active distribution network, is the distributed photovoltaic power value of the i-th node in the active distribution network at time t+k, is the distributed photovoltaic reactive power value of the i-th node in the active distribution network at time t+k, is the distributed photovoltaic complex power constraint of the i-th node in the active distribution network, arctan is the inverse tangent function, is the distributed photovoltaic phase angle constraint of the i-th node in the active distribution network.

[0026] The unified safety constraints are:

[0027]

[0028] In the formula, C i,t+k is the safety constraint of the ith node in the active distribution network at time t+k, is a conditional summation operation, which means to sum the constraints of each row on the right side; (·≥·) and (·<·) are conditional judgment operations, which means that the value is 1 when the inequality condition is met and 0 otherwise.

[0029] Preferably, a multi-environment model-driven deep deterministic policy gradient algorithm is used to solve the integrated prediction scheduling model, including the following steps:

[0030] S1, construct the environmental model of M active distribution networks, the model formula is expressed as:

[0031] s t+1 =M φ,m (s t ,a t ),1≤m≤M

[0032] Where M φ,m is the mth environmental model of the active distribution network, m is the environmental model index, M is the number of environmental models, s t+1 is the operating state of the active distribution network at time t+1, s t is the operating state of the active distribution network at time t, at is the dispatching action of the active distribution network at time t;

[0033] S2, based on the historical operation samples of the active distribution network, solve the environmental model of the active distribution network;

[0034] S3, based on the deep deterministic policy gradient algorithm, brings the environmental model of M active distribution networks into the integrated predictive scheduling model and solves it.

[0035] Among them, the solution formula of the environmental model of the active distribution network in step S2 is:

[0036]

[0037] Where N s is the number of historical operation samples of the active distribution network, Sum of squares function.

[0038] The solution formula of the integrated forecasting and scheduling model in step S3 is:

[0039]

[0040] sC i,t+k (M φ,m (s t ,a t ))≤0,1≤m≤M

[0041] In the formula, φ DDPG are the parameters of the deep deterministic policy gradient algorithm.

[0042] The present invention also provides an active distribution network integrated prediction and dispatching system taking into account power auxiliary services, comprising:

[0043] The net load prediction model building module is used to collect the meteorological forecast data of the active distribution network and build a net load prediction model considering distributed photovoltaics for each node in the active distribution network. The formula of the model is expressed as follows:

[0044]

[0045] Where P i,t+1:t+T is the net load forecast value of the ith node in the active distribution network from time t+1 to t+T, M pred,i is the net load prediction model of the ith node in the active distribution network, W i,t-D:t+T is the weather forecast data collected by the active distribution network from time tD to t+T, is the load value of the ith node in the active distribution network from time tD to t, is the distributed photovoltaic power value of the i-th node in the active distribution network from time tD to t, is the load forecast value of the i-th node in the active distribution network from time t+1 to t+T, is the predicted value of distributed photovoltaic power of the i-th node in the active distribution network from time t+1 to t+T, D is the acquisition step length, and T is the prediction step length;

[0046] The active and reactive daytime dispatch model construction module is used to construct the active and reactive daytime dispatch model of the active distribution network considering two types of power auxiliary services, namely demand response active support and distributed photovoltaic reactive support, based on the net load prediction model of each node in the active distribution network. The formula of the model is expressed as follows:

[0047]

[0048] In the formula, is the active dispatch cost of the active distribution network at time t+k, k is the step index, is the active dispatching power of the active distribution network at time t+k, is the reactive power dispatch cost of the active distribution network at time t+k, is the reactive dispatching power of the active distribution network at time t+k, is the distributed photovoltaic reactive support cost at time t+k, is the distributed photovoltaic reactive support power of the i-th node in the active distribution network at time t+k, is the active power support cost of demand response at time t+k, is the demand response active support power of the ith node in the active distribution network at time t+k, Σ is the summation function;

[0049] The integrated prediction and dispatch model construction module is used to combine the net load prediction model and the active distribution network active and reactive daytime dispatch model to form an integrated prediction and dispatch model and unify the safety constraints. The formula of the integrated prediction and dispatch model is expressed as follows:

[0050]

[0051] sC i,t+k (s t ,a t )≤0

[0052] In the formula, F t+k is the active and reactive power dispatch objective function of the active distribution network at time t+k, s t is the operating state of the active distribution network at time t, which is a subset of the state set including the meteorological forecast data collected by the active distribution network from time tD to t+T, the load value of the i-th node in the active distribution network from time tD to t, and the distributed photovoltaic power value, D is the collection step length, and T is the prediction step length; a tis the dispatching action of the active distribution network at time t, which is a subset of the action set including the active dispatching power, reactive dispatching power, distributed photovoltaic reactive support power, and demand response active support power of the active distribution network at time t+k; C i,t+k is the safety constraint of the ith node in the active distribution network at time t+k;

[0053] The integrated prediction and scheduling model solving module is used to solve the integrated prediction and scheduling model using a deep deterministic policy gradient algorithm driven by a multi-environment model to obtain an integrated prediction and scheduling agent. The agent formula is expressed as:

[0054]

[0055] In the formula, Q π is the evaluation model of the integrated forecasting and dispatching intelligent agent, π is the action strategy of the active distribution network, s is the operating state of the active distribution network, a is the dispatching action of the active distribution network, is the expected value function, γ is the discount factor, π * is the strategy model of the integrated prediction and scheduling agent, and argmax is the maximum value parameter function;

[0056] The active and reactive power dispatching scheme determination module is used to output the active and reactive power dispatching scheme of the active distribution network within a day based on the integrated prediction and dispatching intelligent body, so as to realize the active and reactive power dispatch within a day. The formula is expressed as follows:

[0057]

[0058] Where A is the daily active and reactive power dispatching scheme of the active distribution network.

[0059] The present invention also provides a computer device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the active distribution network integrated predictive scheduling method taking into account power auxiliary services as described above are implemented.

[0060] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the active distribution network integrated prediction and scheduling method taking into account power auxiliary services as described above are implemented.

[0061] Beneficial effects:

[0062] (1) The present invention collects meteorological forecast data of the active distribution network, and constructs a net load prediction model considering distributed photovoltaics for each node in the active distribution network; based on the net load prediction model, constructs an active distribution network active and reactive daytime scheduling model considering two types of power auxiliary services, namely, demand response active support and distributed photovoltaic reactive support; combines the net load prediction model and the active distribution network active and reactive daytime scheduling model to form an integrated prediction and scheduling model; solves the integrated prediction and scheduling model to obtain an integrated prediction and scheduling intelligent body; and outputs an active distribution network active and reactive daytime scheduling plan based on the integrated prediction and scheduling intelligent body. Compared with the existing active distribution network scheduling technology, the method of the present invention can simplify the source-load prediction stage of intraday active and reactive scheduling, and provide intraday active and reactive scheduling plans more quickly and efficiently.

[0063] (2) The method of the present invention can meet the dispatching requirements of the active distribution network including power auxiliary services, and ensure the flexibility and stability of the regulation of various resources in the active distribution network.

[0064] (3) Since the method of the present invention can provide a daily active and reactive power dispatching plan more quickly and efficiently, it helps to improve the execution efficiency and economic benefits of the daily dispatching of the active distribution network, thereby increasing the new energy absorption capacity of the power system and improving the competitiveness of power grid enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 Schematic diagram of the principle of the method of the present invention.

[0066] Figure 2 This is a diagram of the distributed photovoltaic power prediction results of the method of the present invention.

[0067] Figure 3 This is a diagram showing the scheduling cost-benefit analysis results of the method of the present invention. DETAILED DESCRIPTION

[0068] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0069] The present invention proposes an integrated forecasting and dispatching method for active distribution networks taking into account power auxiliary services, such as Figure 1 As shown, the method comprises the following steps:

[0070] (1) Collect meteorological forecast data of the active distribution network and construct a net load prediction model considering distributed photovoltaics for each node in the active distribution network;

[0071] (2) Based on the net load prediction model of each node in the active distribution network, a daily active and reactive dispatch model of the active distribution network is constructed, which takes into account two types of power auxiliary services: demand response active support and distributed photovoltaic reactive support;

[0072] (3) Combine the net load forecasting model and the active distribution network active and reactive daily dispatching model to form an integrated forecasting and dispatching model, and unify the safety constraints;

[0073] (4) The integrated prediction and scheduling model is solved using a deep deterministic policy gradient algorithm driven by a multi-environment model to obtain an integrated prediction and scheduling agent;

[0074] (5) Based on the integrated prediction and dispatching intelligent body, the active distribution network’s daily active and reactive power dispatching scheme is output to achieve daily active and reactive power dispatching.

[0075] The following is a detailed description of the specific implementation process of the method of the present invention for intraday active and reactive power dispatching of active distribution networks in combination with specific implementation cases. The present invention selects a standard distribution network example of IEEE-33 nodes, connects each load node in the example to a distributed photovoltaic and energy storage system, and the collection step length D = 72 hours and the prediction step length T = 24 hours.

[0076] Based on the above case, the specific implementation steps of the method of the present invention are as follows:

[0077] Step 1: Collect the meteorological forecast data of the active distribution network and build a net load prediction model considering distributed photovoltaics for each node in the active distribution network. The model formula is expressed as:

[0078]

[0079] Where P i,t+1:t+T is the net load forecast value of the ith node in the active distribution network from time t+1 to t+T, M pred,i is the net load prediction model of the ith node in the active distribution network, W i,t-D:t+T is the weather forecast data collected by the active distribution network from time tD to t+T, is the load value of the ith node in the active distribution network from time tD to t, is the distributed photovoltaic power value of the i-th node in the active distribution network from time tD to t, is the load forecast value of the i-th node in the active distribution network from time t+1 to t+T, is the distributed photovoltaic power prediction value of the i-th node in the active distribution network from time t+1 to t+T, D is the acquisition step length, and T is the prediction step length. Net load prediction model M pred,i is a deep neural network model. The input data include weather forecast from tD to t+T, load value from time tD to t, and distributed photovoltaic power value from time tD to t. The output data include load forecast value from time t+1 to t+T, and distributed photovoltaic power forecast value from time t+1 to t+T. Figure 2Shown is a diagram of distributed photovoltaic power prediction results in an embodiment of the present invention.

[0080] Step 2: Based on the net load prediction model of each node in the active distribution network, an active distribution network active and reactive daytime dispatch model is constructed considering two types of power auxiliary services: demand response active support and distributed photovoltaic reactive support. The model formula is expressed as:

[0081]

[0082] In the formula, is the active dispatch cost of the active distribution network at time t+k, k is the step index, is the active dispatching power of the active distribution network at time t+k, is the reactive power dispatch cost of the active distribution network at time t+k, is the reactive dispatching power of the active distribution network at time t+k, is the distributed photovoltaic reactive support cost at time t+k, is the distributed photovoltaic reactive support power of the i-th node in the active distribution network at time t+k, is the active power support cost of demand response at time t+k, is the demand response active support power of the ith node in the active distribution network at time t+k, V i is the voltage amplitude of the ith node in the active distribution network, V min is the minimum constraint on voltage amplitude, V max is the maximum constraint on the voltage amplitude, is the energy storage power value of the i-th node in the active distribution network at time t+k, P i bm is the energy storage power constraint of the i-th node in the active distribution network, P i dm is the demand response active support power constraint of the ith node in the active distribution network, is the distributed photovoltaic power value of the i-th node in the active distribution network at time t+k, is the distributed photovoltaic reactive power value of the i-th node in the active distribution network at time t+k, is the distributed photovoltaic complex power constraint of the i-th node in the active distribution network, arctan is the inverse tangent function, is the distributed photovoltaic phase angle constraint of the i-th node in the active distribution network, Σ is the summation function;

[0083] Step 3: The prediction error in the net load prediction model is used to calculate the energy storage power in the active distribution network active and reactive daytime dispatch model to achieve the combination of the two models. The calculation formula is:

[0084]

[0085] In the formula, is the energy storage power value of the i-th node in the active distribution network at time t+k, P i,t+k is the net load forecast value of the ith node in the active distribution network at time t+k, is the true value of the net load of the ith node in the active distribution network at time t+k, [k] is the output index of the net load prediction model, that is, to predict the kth step, the kth output of the prediction model is taken;

[0086] Taking the active dispatching power, reactive dispatching power, distributed photovoltaic reactive support power, and demand response active support power of the active distribution network at time t+k as the action set, and the active distribution network states such as meteorological forecast data, load value, and distributed photovoltaic power value as the state set, an integrated prediction and dispatching model is constructed, and the formula is expressed as follows:

[0087]

[0088] In the formula, F t+k is the integrated forecasting and dispatching objective function of the active distribution network at time t+k; is a subset operator; the unified safety constraints are expressed as follows:

[0089]

[0090] In the formula, C i,t+k is the safety constraint of the ith node in the active distribution network at time t+k; is a conditional summation operation, which means to sum up each row of constraints on the right side; (·≥·) and (·<·) are conditional judgment operations, which means that the value is 1 when the inequality condition is met and 0 otherwise; the formula of the integrated forecasting and scheduling model is expressed as:

[0091]

[0092] sC i,t+k (s t ,a t )≤0

[0093] In the formula, F t+k is the active and reactive power dispatch objective function of the active distribution network at time t+k, s t is the operating state of the active distribution network at time t, a t is the dispatching action of the active distribution network at time t, C i,t+k is the safety constraint of the i-th node in the active distribution network at time t+k, and the constraint value must be less than or equal to 0;

[0094] Step 4: The integrated prediction and dispatching model is solved by using a deep deterministic policy gradient algorithm driven by multiple environment models. The algorithm constructs an environment model of M active distribution networks. The model formula is expressed as:

[0095] s t+1 =M φ,m (s t ,a t ),1≤m≤M

[0096] Where M φ,m is the mth environmental model of the active distribution network, m is the environmental model index, M is the number of environmental models, s t+1 is the operating state of the active distribution network at time t+1, s t is the operating state of the active distribution network at time t, a t is the dispatching action of the active distribution network at time t; based on the historical operation samples of the active distribution network, the environmental model of the active distribution network is solved, and the solution formula is:

[0097]

[0098] Where N s is the number of historical operation samples of the active distribution network, Square sum function; Based on the deep deterministic policy gradient algorithm, the environmental model of M active distribution networks is brought into the integrated prediction and scheduling model and solved. The solution formula is:

[0099]

[0100] sC i,t+k (M φ,m (s t ,a t ))≤0,1≤m≤M

[0101] In the formula, φ DDPG is the parameter of the deep deterministic policy gradient algorithm; the integrated prediction scheduling agent is obtained, and the agent formula is expressed as:

[0102]

[0103] In the formula, Q π is the evaluation model of the integrated forecasting and dispatching intelligent agent, π is the action strategy of the active distribution network, s is the operating state of the active distribution network, a is the dispatching action of the active distribution network, is the expected value function, γ is the discount factor, π * is the strategy model of the integrated prediction and scheduling agent, and argmax is the maximum value parameter function;

[0104] Step 5: Based on the integrated prediction and dispatching intelligent body, the active distribution network’s daily active and reactive dispatching scheme is output to achieve daily active and reactive dispatching. The formula is:

[0105]

[0106] Where A is the daily active and reactive power dispatching scheme of the active distribution network.

[0107] The calculation time comparison between the integrated prediction scheduling of the present invention and the traditional prediction scheduling is shown in Table 1. It can be seen that the present invention can simplify the source-load prediction stage of intra-day active and reactive scheduling, reduce the total calculation time, and thus improve the execution efficiency.

[0108] Table 1 Comparison results of prediction scheduling calculation time

[0109]

[0110] In addition, the present invention can improve the economic benefits of intraday dispatching of active distribution networks. Figure 3 The figure shows the results of the dispatching cost-benefit analysis in this embodiment. It can be seen that the method of the present invention can improve the economic benefits of the intra-day dispatching of the existing active distribution network.

[0111] In summary, the integrated prediction and dispatching method for active distribution network taking into account power auxiliary services of the present invention can simplify the source-load prediction stage of intraday active and reactive power dispatching, provide intraday active and reactive power dispatching schemes more quickly and efficiently, meet the dispatching needs of active distribution network including power auxiliary services, ensure the flexibility and stability of various resource regulation of active distribution network, and because it can provide intraday active and reactive power dispatching schemes more quickly and efficiently, the method of the present invention helps to improve the execution efficiency and economic benefits of intraday dispatching of active distribution network, thereby increasing the new energy absorption capacity of the power system and enhancing the competitiveness of power grid enterprises.

[0112] Based on the same technical concept as the method embodiment, the present invention also provides an active distribution network integrated prediction and dispatching system taking into account power auxiliary services, including:

[0113] The net load prediction model building module is used to collect the meteorological forecast data of the active distribution network and build a net load prediction model considering distributed photovoltaics for each node in the active distribution network. The formula of the model is expressed as follows:

[0114]

[0115] Where P i,t+1:t+T is the net load forecast value of the ith node in the active distribution network from time t+1 to t+T, M pred,i is the net load prediction model of the ith node in the active distribution network, W i,t-D:t+Tis the weather forecast data collected by the active distribution network from time tD to t+T, is the load value of the ith node in the active distribution network from time tD to t, is the distributed photovoltaic power value of the i-th node in the active distribution network from time tD to t, is the load forecast value of the i-th node in the active distribution network from time t+1 to t+T, is the predicted value of distributed photovoltaic power of the i-th node in the active distribution network from time t+1 to t+T, D is the acquisition step length, and T is the prediction step length;

[0116] The active and reactive daytime dispatch model construction module is used to construct the active and reactive daytime dispatch model of the active distribution network considering two types of power auxiliary services, namely demand response active support and distributed photovoltaic reactive support, based on the net load prediction model of each node in the active distribution network. The formula of the model is expressed as follows:

[0117]

[0118] In the formula, is the active dispatch cost of the active distribution network at time t+k, k is the step index, is the active dispatching power of the active distribution network at time t+k, is the reactive power dispatch cost of the active distribution network at time t+k, is the reactive dispatching power of the active distribution network at time t+k, is the distributed photovoltaic reactive support cost at time t+k, is the distributed photovoltaic reactive support power of the i-th node in the active distribution network at time t+k, is the active power support cost of demand response at time t+k, is the demand response active support power of the ith node in the active distribution network at time t+k, Σ is the summation function;

[0119] The integrated prediction and dispatch model construction module is used to combine the net load prediction model and the active distribution network active and reactive daytime dispatch model to form an integrated prediction and dispatch model and unify the safety constraints. The formula of the integrated prediction and dispatch model is expressed as follows:

[0120]

[0121] sC i,t+k (s t ,a t )≤0

[0122] In the formula, F t+k is the active and reactive power dispatch objective function of the active distribution network at time t+k, st is the operating state of the active distribution network at time t, which is a subset of the state set including the meteorological forecast data collected by the active distribution network from time tD to t+T, the load value of the i-th node in the active distribution network from time tD to t, and the distributed photovoltaic power value, D is the collection step length, and T is the prediction step length; a t is the dispatching action of the active distribution network at time t, which is a subset of the action set including the active dispatching power, reactive dispatching power, distributed photovoltaic reactive support power, and demand response active support power of the active distribution network at time t+k; C i,t+k is the safety constraint of the ith node in the active distribution network at time t+k;

[0123] The integrated prediction and scheduling model solving module is used to solve the integrated prediction and scheduling model using a deep deterministic policy gradient algorithm driven by a multi-environment model to obtain an integrated prediction and scheduling agent. The agent formula is expressed as:

[0124]

[0125] In the formula, Q π is the evaluation model of the integrated forecasting and dispatching intelligent agent, π is the action strategy of the active distribution network, s is the operating state of the active distribution network, a is the dispatching action of the active distribution network, is the expected value function, γ is the discount factor, π * is the strategy model of the integrated prediction and scheduling agent, and argmax is the maximum value parameter function;

[0126] The active and reactive power dispatching scheme determination module is used to output the active and reactive power dispatching scheme of the active distribution network within a day based on the integrated prediction and dispatching intelligent body, so as to realize the active and reactive power dispatch within a day. The formula is expressed as follows:

[0127]

[0128] Where A is the daily active and reactive power dispatching scheme of the active distribution network.

[0129] It should be understood that the active distribution network integrated forecasting and dispatching system taking into account power auxiliary services in the embodiment of the present invention can implement all the technical solutions in the above-mentioned method embodiment, and the functions of its various functional modules can be specifically implemented according to the methods in the above-mentioned method embodiments. The specific implementation process can refer to the relevant description in the above-mentioned embodiments, which will not be repeated here.

[0130] The present invention also provides a computer device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the active distribution network integrated predictive scheduling method taking into account power auxiliary services as described above are implemented.

[0131] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the active distribution network integrated prediction and scheduling method taking into account power auxiliary services as described above are implemented.

[0132] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices (systems), computer equipment or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0133] The present invention is described with reference to a flowchart of a method according to an embodiment of the present invention. It should be understood that each process in the flowchart and a combination of processes in the flowchart can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A device that specifies functions in a process or multiple processes.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A function specified in a process or multiple processes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 The steps of a specified function in a process or multiple processes.

Claims

1. An integrated forecasting and dispatching method for active distribution network taking into account power auxiliary services, characterized in that: The following steps are involved: Step 1: Collect the meteorological forecast data of the active distribution network and build a net load prediction model considering distributed photovoltaics for each node in the active distribution network. The formula of the model is expressed as: Where P i,t+1:t+T is the net load forecast value of the ith node in the active distribution network from time t+1 to t+T, M pred,i is the net load prediction model of the ith node in the active distribution network, W i,t-D:t+T is the weather forecast data collected by the active distribution network from time tD to t+T, is the load value of the ith node in the active distribution network from time tD to t, is the distributed photovoltaic power value of the i-th node in the active distribution network from time tD to t, is the load forecast value of the i-th node in the active distribution network from time t+1 to t+T, is the predicted value of distributed photovoltaic power of the i-th node in the active distribution network from time t+1 to t+T, D is the acquisition step length, and T is the prediction step length; Step 2: Based on the net load prediction model of each node in the active distribution network, an active distribution network active and reactive daytime dispatch model is constructed considering two types of power auxiliary services: demand response active support and distributed photovoltaic reactive support. The formula of the model is expressed as follows: In the formula, is the active dispatch cost of the active distribution network at time t+k, k is the step index, is the active dispatching power of the active distribution network at time t+k, is the reactive power dispatch cost of the active distribution network at time t+k, is the reactive dispatching power of the active distribution network at time t+k, is the distributed photovoltaic reactive support cost at time t+k, is the distributed photovoltaic reactive support power of the i-th node in the active distribution network at time t+k, is the active power support cost of demand response at time t+k, is the demand response active support power of the ith node in the active distribution network at time t+k, Σ is the summation function; Step 3: Combine the net load forecasting model and the active distribution network active and reactive daytime dispatching model to form an integrated forecasting and dispatching model, and unify the safety constraints. The formula of the integrated forecasting and dispatching model is expressed as follows: In the formula, F t+k is the active and reactive power dispatch objective function of the active distribution network at time t+k, s t is the operating state of the active distribution network at time t, which is a subset of the state set including the meteorological forecast data collected by the active distribution network from time tD to t+T, the load value of the i-th node in the active distribution network from time tD to t, and the distributed photovoltaic power value, D is the collection step length, and T is the prediction step length; a t is the dispatching action of the active distribution network at time t, which is a subset of the action set including the active dispatching power, reactive dispatching power, distributed photovoltaic reactive support power, and demand response active support power of the active distribution network at time t+k; C i,t+k is the safety constraint of the ith node in the active distribution network at time t+k; Step 4: Use the deep deterministic policy gradient algorithm driven by the multi-environment model to solve the integrated prediction and scheduling model to obtain the integrated prediction and scheduling agent, which is expressed as follows: In the formula, Q π is the evaluation model of the integrated forecasting and dispatching intelligent agent, π is the action strategy of the active distribution network, s is the operating state of the active distribution network, a is the dispatching action of the active distribution network, is the expected value function, γ is the discount factor, π * is the strategy model of the integrated prediction and scheduling agent, and argmax is the maximum value parameter function; Step 5: Based on the integrated prediction and dispatching intelligent body, the active distribution network’s daily active and reactive dispatching scheme is output to achieve daily active and reactive dispatching. The formula is: Where A is the daily active and reactive power dispatching scheme of the active distribution network.

2. The method according to claim 1, characterized in that Net load forecasting model M pred,i It is a deep neural network model. The input data include weather forecast from tD to t+T, load value from time tD to t, and distributed photovoltaic power value from time tD to t. The output data include load prediction value from time t+1 to t+T, and distributed photovoltaic power prediction value from time t+1 to t+T.

3. The method according to claim 1, characterized in that The constraints of the active distribution network active and reactive daytime dispatch model are: Where V i is the voltage amplitude of the ith node in the active distribution network, V min is the minimum constraint on voltage amplitude, V max is the maximum constraint on the voltage amplitude, is the energy storage power value of the i-th node in the active distribution network at time t+k, is the energy storage power constraint of the i-th node in the active distribution network, is the demand response active support power constraint of the ith node in the active distribution network, is the distributed photovoltaic power value of the i-th node in the active distribution network at time t+k, is the distributed photovoltaic reactive power value of the i-th node in the active distribution network at time t+k, is the distributed photovoltaic complex power constraint of the i-th node in the active distribution network, arctan is the inverse tangent function, is the distributed photovoltaic phase angle constraint of the i-th node in the active distribution network.

4. The method according to claim 3, characterized in that The unified safety constraints are: In the formula, C i,t+k is the safety constraint of the ith node in the active distribution network at time t+k, It means to sum up the constraints of each row on the right side; (·≥·) and (·<·) are conditional judgment operations, which means that the value is 1 when the inequality condition is met, and the value is 0 otherwise.

5. The method according to claim 1, characterized in that: The integrated prediction and scheduling model is solved using a deep deterministic policy gradient algorithm driven by a multi-environment model. The following steps are involved: S1, construct the environmental model of M active distribution networks, the model formula is expressed as: s t+1 =M φ,m (s t ,a t ),1≤m≤M Where M φ,m is the mth environmental model of the active distribution network, m is the environmental model index, M is the number of environmental models, s t+1 is the operating state of the active distribution network at time t+1, s t is the operating state of the active distribution network at time t, a t is the dispatching action of the active distribution network at time t; S2, based on the historical operation samples of the active distribution network, solve the environmental model of the active distribution network; S3, based on the deep deterministic policy gradient algorithm, brings the environmental model of M active distribution networks into the integrated predictive scheduling model and solves it.

6. The method according to claim 5, characterized in that The solution formula of the environmental model of the active distribution network in step S2 is: Where N s is the number of historical operation samples of the active distribution network, Sum of squares function.

7. The method according to claim 5, characterized in that The solution formula of the integrated forecasting and scheduling model in step S3 is: In the formula, φ DDPG are the parameters of the deep deterministic policy gradient algorithm.

8. An integrated forecasting and dispatching system for active distribution networks taking into account power auxiliary services, characterized in that: include: The net load prediction model building module is used to collect the meteorological forecast data of the active distribution network and build a net load prediction model considering distributed photovoltaics for each node in the active distribution network. The formula of the model is expressed as follows: Where P i,t+1:t+T is the net load forecast value of the ith node in the active distribution network from time t+1 to t+T, M pred,i is the net load prediction model of the ith node in the active distribution network, W i,t-D:t+T is the weather forecast data collected by the active distribution network from time tD to t+T, is the load value of the ith node in the active distribution network from time tD to t, is the distributed photovoltaic power value of the i-th node in the active distribution network from time tD to t, is the load forecast value of the i-th node in the active distribution network from time t+1 to t+T, is the predicted value of distributed photovoltaic power of the i-th node in the active distribution network from time t+1 to t+T, D is the acquisition step length, and T is the prediction step length; The active and reactive daytime dispatch model construction module is used to construct the active and reactive daytime dispatch model of the active distribution network considering two types of power auxiliary services, namely demand response active support and distributed photovoltaic reactive support, based on the net load prediction model of each node in the active distribution network. The formula of the model is expressed as follows: In the formula, is the active dispatch cost of the active distribution network at time t+k, k is the step index, is the active dispatching power of the active distribution network at time t+k, is the reactive power dispatch cost of the active distribution network at time t+k, is the reactive dispatching power of the active distribution network at time t+k, is the distributed photovoltaic reactive support cost at time t+k, is the distributed photovoltaic reactive support power of the i-th node in the active distribution network at time t+k, is the active power support cost of demand response at time t+k, is the demand response active support power of the ith node in the active distribution network at time t+k, Σ is the summation function; The integrated prediction and dispatch model construction module is used to combine the net load prediction model and the active distribution network active and reactive daytime dispatch model to form an integrated prediction and dispatch model and unify the safety constraints. The formula of the integrated prediction and dispatch model is expressed as follows: In the formula, F t+k is the active and reactive power dispatch objective function of the active distribution network at time t+k, s t is the operating state of the active distribution network at time t, which is a subset of the state set including the meteorological forecast data collected by the active distribution network from time tD to t+T, the load value of the i-th node in the active distribution network from time tD to t, and the distributed photovoltaic power value, D is the collection step length, and T is the prediction step length; a t is the dispatching action of the active distribution network at time t, which is a subset of the action set including the active dispatching power, reactive dispatching power, distributed photovoltaic reactive support power, and demand response active support power of the active distribution network at time t+k; C i,t+k is the safety constraint of the ith node in the active distribution network at time t+k; The integrated prediction and scheduling model solving module is used to solve the integrated prediction and scheduling model using a deep deterministic policy gradient algorithm driven by a multi-environment model to obtain an integrated prediction and scheduling agent. The agent formula is expressed as: In the formula, Q π is the evaluation model of the integrated forecasting and dispatching intelligent agent, π is the action strategy of the active distribution network, s is the operating state of the active distribution network, a is the dispatching action of the active distribution network, is the expected value function, γ is the discount factor, π * is the strategy model of the integrated prediction and scheduling agent, and argmax is the maximum value parameter function; The active and reactive power dispatching scheme determination module is used to output the active and reactive power dispatching scheme of the active distribution network within a day based on the integrated prediction and dispatching intelligent body, so as to realize the active and reactive power dispatch within a day. The formula is expressed as follows: Where A is the daily active and reactive power dispatching scheme of the active distribution network.

9. A computer device, characterized in that: include: one or more processors; Memory; And one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the active distribution network integrated predictive scheduling method taking into account power auxiliary services as described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the active distribution network integrated prediction and dispatching method taking into account power auxiliary services are implemented as described in any one of claims 1 to 7.

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