Power system regulation and control equipment configuration method and device, terminal equipment and storage medium

Abstract

The invention discloses a regulation and control equipment configuration method and device of a power system, terminal equipment and a storage medium, an equipment regulation and control potential value and a demand response regulation and control potential value can be used as parameters for constructing a regulation and control equipment configuration model, and after the regulation and control equipment configuration model is solved, a regulation and control potential value is obtained. And a target configuration variable conforming to an actual regulation and control condition and an actual response characteristic of a user can be obtained. When the configuration scheme of each regulation and control device of the power system is obtained, the device regulation and control potential value and the demand response regulation and control potential value can be comprehensively considered, so that the finally obtained target configuration variable not only conforms to the actual regulation and control capability of the device, but also can enable the power system to conform to the actual response characteristic of a user, and the user experience is improved. Through the solution of the configuration variables and the configuration of the power system based on the configuration variables, the performance advantages of each regulation and control device can be brought into full play, the stable operation of the power system is ensured, and the supply and demand relationship of the power system is effectively balanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system control, and in particular to a control equipment configuration method, device, terminal equipment and storage medium for a power system. Background Art

[0002] The operating efficiency and quality of the power system directly affect people's electricity demand and quality of life. By optimizing the configuration of control equipment, we can reasonably allocate power resources, improve energy utilization efficiency, and reduce energy consumption, thereby improving the efficiency and quality of power system operation and providing better protection for people's lives.

[0003] However, the existing configuration optimization methods often use fixed rules or empirical formulas, or refer to historical load data, equipment capacity and other factors for simple calculations, so as to directly obtain the configuration optimization scheme of each control device. For example, according to the historical load data of the power system, a relatively stable load range is determined, and then a suitable equipment capacity is selected according to the load range to ensure that the equipment can operate normally at peak load. Then, the unit output or output power of each device is determined based on the suitable equipment capacity. Therefore, the existing configuration optimization method does not fully consider the actual control capability of the equipment, nor does it consider the impact of the user's load adjustment capability on the power system. Due to the failure to fully consider the equipment control potential value and the demand response control potential value, the configuration scheme is often too rigid and cannot give full play to the performance advantages of the equipment. Moreover, because the user's load adjustment capability is not fully considered, the configuration scheme obtained may not be able to effectively balance the supply and demand relationship of the power system, such as tight power supply during peak hours or waste of power resources during valley hours, thus failing to ensure the stable operation of the power system. Summary of the invention

[0004] The embodiments of the present invention provide a method, apparatus, terminal device and storage medium for configuring control equipment of an electric power system, which can comprehensively consider the equipment control potential value and the demand response control potential value, so that the target configuration variables finally obtained can not only meet the actual control capabilities of the equipment, but also make the electric power system meet the actual response characteristics of the user. It can effectively solve the problem in the prior art that the performance advantages of the equipment cannot be fully utilized and the supply and demand relationship of the electric power system cannot be effectively balanced due to the failure to fully consider the equipment control potential value and the demand response control potential value.

[0005] An embodiment of the present invention provides a method for configuring a control device of an electric power system, comprising:

[0006] According to the performance parameters and power data of the control equipment, a device control potential value is generated to characterize the control ability of the control equipment on the power system; wherein the control equipment includes: a generator set and an energy storage device; the device control potential value includes: available capacity, response rate and probability of active support;

[0007] Generate a demand response regulation potential value for characterizing the user's ability to adjust load when participating in demand response based on the user's power load data and demand response event data; wherein the demand response regulation potential value includes: capacity range, response accuracy and capacity regulation cost;

[0008] According to the cost data of the control equipment, the compensation cost data of the demand response, the equipment control potential value and the demand response control potential value, a control equipment configuration model with the goal of minimizing the operation cost of the power system is constructed; wherein the constraints corresponding to the control equipment configuration model include: output constraints of the generator set, charging and discharging constraints of the energy storage device, and capacity constraints of the energy storage device;

[0009] Under the constraints, the control device configuration model is solved to generate target configuration variables when the power system operation cost is minimized; wherein the target configuration variables include: the output power of the generator set, the charge and discharge power of the energy storage device, and the capacity of the energy storage device;

[0010] According to the target configuration variables, control equipment in the power system is configured.

[0011] Preferably, the performance parameters of the control device include: performance parameters of the generator set and performance parameters of the energy storage device;

[0012] The performance parameters of the generator set include: rated power of the set, ramp rate, output power regulation rate, load regulation rate, primary frequency regulation response time and response time of the set;

[0013] The performance parameters of the energy storage device include: charging response time, discharging response time and state of charge;

[0014] The generating, according to the performance parameters and power data of the control device, a device control potential value for characterizing the control capability of the control device on the power system comprises:

[0015] Generate the available capacity of the generator set according to the rated power of the generator set, the ramp rate and the output power of the generator set;

[0016] Generate a response rate of the generator set according to the primary frequency modulation response time of the generator set and the response time of the generator set;

[0017] Generate a probability of active support of the generator set according to the output power regulation rate and the load regulation rate of the generator set;

[0018] Generate the available capacity of the energy storage device according to the charging power, discharging power and state of charge of the energy storage device;

[0019] Generating a response rate of the energy storage device according to a charging response time and a discharging response time of the energy storage device;

[0020] According to the charge state of the energy storage device, a probability of active support of the energy storage device is generated.

[0021] Preferably, the demand response event data includes: a first response time corresponding to a peak shaving demand response event, a first adjustment cost corresponding to a peak shaving demand response event, a second response time corresponding to a valley filling demand response event, and a second adjustment cost corresponding to a valley filling demand response event; the first response time is used to characterize the time from when a user receives a peak shaving demand response instruction to when the user starts to adjust the load; the second response time is used to characterize the time from when a user receives a valley filling demand response instruction to when the user starts to adjust the load;

[0022] The user's electricity load data includes: a first load adjustment amount corresponding to a peak shaving demand response event and a second load adjustment amount corresponding to a valley filling demand response event;

[0023] The generating, based on the user's electricity load data and demand response event data, a demand response regulation potential value for characterizing the user's ability to adjust load when participating in demand response includes:

[0024] The first response time, the first regulation cost, the second response time, the second regulation cost, the first load adjustment amount and the second load adjustment amount are input into the regulation potential prediction model, so that the regulation potential prediction model extracts the load adjustment characteristics for characterizing the sustainable regulation capacity of the user in different types of demand response events according to the first load adjustment amount, the second load adjustment amount, the first regulation cost and the second regulation cost; extracts the response characteristics for characterizing the response ability of the user in different types of demand response events according to the first response time, the second response time, the first load adjustment amount and the second load adjustment amount; extracts the regulation cost characteristics for characterizing the cost tolerance of the user in different types of demand response events according to the first regulation cost, the second regulation cost, the first load adjustment amount and the second load adjustment amount; generates the capacity interval, response accuracy and capacity regulation cost according to the load adjustment characteristics, response characteristics and regulation cost characteristics;

[0025] The training process of the regulatory potential prediction model includes:

[0026] The power load data samples and the demand response event data samples are used as training samples; wherein the power load data samples include: load adjustment amount samples corresponding to the peak shaving demand response event and load adjustment amount samples corresponding to the valley filling demand response event; the demand response event data samples include: response time samples corresponding to the peak shaving demand response event, adjustment cost samples corresponding to the peak shaving demand response event, response time samples corresponding to the valley filling demand response event and adjustment cost samples corresponding to the valley filling demand response event;

[0027] Taking several training samples and the actual capacity range, actual response accuracy and actual capacity regulation cost corresponding to each training sample as input, and the predicted capacity range, predicted response accuracy and predicted capacity regulation cost of each training sample as output, the regulation potential prediction model to be trained is iteratively trained until the model converges to generate a preset regulation potential prediction model.

[0028] Preferably, during each iterative training, a training sample is input into the regulation potential prediction model, so that the regulation potential prediction model generates a capacity interval prediction result, a response accuracy prediction result and a capacity regulation cost prediction result corresponding to the training sample according to the load adjustment characteristics, response characteristics and regulation cost characteristics in the training sample;

[0029] The capacity interval prediction results, response accuracy prediction results and capacity regulation cost prediction results are compared with the actual capacity interval results, actual response accuracy results and actual capacity regulation cost results, and the network parameters of the regulation potential prediction model are adjusted according to the comparison results.

[0030] Preferably, the control device configuration model includes:

[0031] min F=ω1·C cost +ω2·S stability +ω3·P potential +ω4·D response ;

[0032]

[0033] C gen (t) = c i *P i,t ;

[0034]

[0035] Among them, F is the power system operation cost, C cost To control costs, S stability is the preset voltage stability index, P potentialis the equipment regulation potential value, D response is the demand response regulation potential value, ω1, ω2, ω3, ω4 are different weight coefficients respectively; C gen (t) is the power generation cost of the generator set at time t, C storage (t) is the operating cost of the energy storage equipment at time t, C demand (t) is the demand response compensation cost at time t, T is the preset time period; c i is the unit output power cost of the generator set, P i,t is the output power of the i-th generator set at time t, di is the unit power cost of the energy storage device, is the discharge power of the i-th energy storage device at time t, is the charging power of the i-th energy storage device at time t; c e is the unit capacity cost of energy storage equipment, Q i,t is the capacity of the i-th energy storage device at time t.

[0036] Preferably, the output constraint of the generator set includes:

[0037] P i,min ≤Pi,t≤P i,max ;

[0038] Among them, P i,min is the minimum output power corresponding to the i-th generator set, P i,max is the maximum output power corresponding to the i-th generator set.

[0039] Preferably, the charge and discharge constraints of the energy storage device include:

[0040]

[0041] in, is the minimum discharge power of the i-th energy storage device, is the maximum discharge power of the i-th energy storage device; is the minimum charging power of the i-th energy storage device, is the maximum charging power of the i-th energy storage device;

[0042] The capacity constraints of the energy storage device include:

[0043] Q i,min ≤Q i,t ≤Q i,max ;

[0044] Among them, Q i,min is the minimum capacity limit of the i-th energy storage device, Q i,max is the maximum capacity limit of the i-th energy storage device.

[0045] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0046] An embodiment of the present invention provides a control device configuration device for a power system, comprising: a device control potential generation module, a demand response control potential generation module, a model building module, a model solving module, and a control device configuration module;

[0047] The device regulation potential generation module is used to generate a device regulation potential value for characterizing the regulation capability of the regulation device on the power system according to the performance parameters and power data of the regulation device; wherein the regulation device includes: a generator set and an energy storage device; the device regulation potential value includes: available capacity, response rate and probability of active support;

[0048] The demand response regulation potential generation module is used to generate a demand response regulation potential value for characterizing the user's ability to adjust the load when participating in demand response based on the user's power load data and demand response event data; wherein the demand response regulation potential value includes: capacity range, response accuracy and capacity adjustment cost;

[0049] The model building module is used to build a control device configuration model with the goal of minimizing the power system operation cost according to the cost data of the control device, the compensation cost data of the demand response, the device control potential value and the demand response control potential value; wherein the constraints corresponding to the control device configuration model include: output constraints of the generator set, charging and discharging constraints of the energy storage device and capacity constraints of the energy storage device;

[0050] The model solving module is used to solve the control device configuration model under the constraint conditions to generate target configuration variables when the power system operation cost is minimized; wherein the target configuration variables include: the output power of the generator set, the charge and discharge power of the energy storage device, and the capacity of the energy storage device;

[0051] The control device configuration module is used to configure the control device in the power system according to the target configuration variable.

[0052] Based on the above method embodiments, the present invention provides corresponding terminal equipment embodiments.

[0053] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the method for configuring control equipment of an electric power system described in the above-mentioned embodiment of the invention.

[0054] Based on the above method embodiments, the present invention provides corresponding storage medium item embodiments.

[0055] Another embodiment of the present invention provides a storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a method for configuring a control device of an electric power system as described in the above-mentioned embodiment of the invention.

[0056] The following beneficial effects are achieved by implementing the present invention:

[0057] The embodiment of the present invention provides a control device configuration method, device, terminal device and storage medium for a power system, which can generate a device control potential value of the control capability according to the performance parameters and power data of the control device; and can generate a demand response control potential value according to the power load data and demand response event data of the user; so that the device control potential value and the demand response control potential value are used as parameters for constructing a control device configuration model, and after solving the control device configuration model, a target configuration variable that conforms to the actual control situation and the actual response characteristics of the user is obtained. Since the device control potential value can fully reflect the control capability of the control device on the power system, it can ensure that the model fully considers the actual control capability of the device during the solution process, and thus a target configuration variable that is more in line with the actual control situation of the power system can be obtained. The demand response control potential value represents the ability of the user to adjust the load when participating in the demand response, and the introduction of the demand response control potential value can ensure that the model fully considers the load adjustment capability of the user during the solution process, so that a target configuration variable that is more in line with the actual response characteristics of the user can be obtained, and finally the optimal configuration of each control device of the power system is achieved. Compared with the prior art, the present invention can comprehensively consider the equipment control potential value and the demand response control potential value when deriving the configuration scheme for each control device of the power system, so that the target configuration variables finally obtained can not only meet the actual control capabilities of the equipment, but also make the power system meet the actual response characteristics of the user. By solving the configuration variables of the present invention and configuring and scheduling the power system based on the configuration variables, the performance advantages of each control device can be fully utilized, the stable operation of the power system can be guaranteed, and the supply and demand relationship of the power system can be effectively balanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of a method for configuring control equipment of a power system provided in one embodiment of the present invention.

[0059] Figure 2 It is a structural schematic diagram of a control equipment configuration device for an electric power system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] like Figure 1 FIG. 1 is a flow chart of a method for configuring a control device of an electric power system provided by an embodiment of the present invention. The method for configuring a control device of an electric power system includes:

[0062] Step S1: Generate a device control potential value for characterizing the control capability of the control device on the power system according to the performance parameters and power data of the control device; wherein the control device includes: a generator set and an energy storage device; the device control potential value includes: available capacity, response rate and probability of active support;

[0063] Step S2: generating a demand response regulation potential value for characterizing the user's ability to adjust load when participating in demand response based on the user's power load data and demand response event data; wherein the demand response regulation potential value includes: capacity range, response accuracy and capacity regulation cost;

[0064] Step S3: constructing a control device configuration model with the goal of minimizing the power system operation cost according to the cost data of the control device, the compensation cost data of the demand response, the device control potential value and the demand response control potential value; wherein the constraints corresponding to the control device configuration model include: output constraints of the generator set, charging and discharging constraints of the energy storage device and capacity constraints of the energy storage device;

[0065] Step S4: Under the constraint conditions, solving the control device configuration model, generating target configuration variables when the power system operation cost is minimized; wherein the target configuration variables include: the output power of the generator set, the charge and discharge power of the energy storage device, and the capacity of the energy storage device;

[0066] Step S5: configuring control equipment in the power system according to the target configuration variables.

[0067] For step S1, in a preferred embodiment, when the present invention obtains the device regulation potential value of the regulation ability of the regulation device on the power system, the device regulation potential value of the generator set and the device regulation potential value of the energy storage device can be obtained respectively. Then the device regulation potential value is: the available capacity, response rate and probability of active support of the generator set, and the available capacity, response rate and probability of active support of the energy storage device.

[0068] The performance parameters of the control device may include: performance parameters of the generator set and performance parameters of the energy storage device;

[0069] The performance parameters of the generator set include: rated power of the set, ramp rate, output power regulation rate, load regulation rate, primary frequency regulation response time and response time of the set;

[0070] The performance parameters of the energy storage device include: charging response time, discharging response time and state of charge;

[0071] Therefore, when calculating the equipment regulation potential value of the generator set, it specifically includes:

[0072] Generate the available capacity of the generator set according to the rated power, ramp rate and output power of the generator set;

[0073] Generate a response rate of the generator set according to the primary frequency modulation response time of the generator set and the response time of the generator set;

[0074] According to the output power regulation rate and the load regulation rate of the generator set, the probability of active support of the generator set is generated.

[0075] When calculating the equipment regulation potential value of the energy storage equipment, it specifically includes:

[0076] Generate the available capacity of the energy storage device according to the charging power, discharging power and state of charge of the energy storage device;

[0077] Generating a response rate of the energy storage device according to a charging response time and a discharging response time of the energy storage device;

[0078] According to the charge state of the energy storage device, a probability of active support of the energy storage device is generated.

[0079] Illustratively, by separately calculating the control potential values ​​of the generator sets and the energy storage devices, the embodiments of the present invention can make a more refined evaluation of the control capability of each control device, so as to accurately understand the actual role of various devices in the power system. Therefore, when configuring the control devices, reasonable allocation can be made according to the control potential of the devices to maximize the utilization of resources.

[0080] Generators and energy storage devices have different focuses in the power system. By calculating their regulation potential values ​​separately, we can better understand their regulation capabilities in the power system, so that we can more accurately control the stability of the power system during regulation.

[0081] In a preferred embodiment, the present invention can obtain the type of generator set, rated power, ramp rate, frequency regulation capability, voltage regulation capability, primary frequency regulation response time, and AGC response time as physical control characteristic parameters; and further collect the charge and discharge power, response time, and state of charge of the energy storage device as auxiliary control characteristic parameters;

[0082] The parameter weight evaluation system can be constructed by using the hierarchical analysis method. The weight coefficient is set according to the control contribution of each parameter in the power system. The physical control characteristic parameters and auxiliary control parameters are respectively weighted with the corresponding weight coefficients to obtain the final physical control potential index.

[0083] Specifically, first, the physical regulation characteristic parameters can be divided into primary indicators, including basic regulation capability (i.e. available capacity), dynamic response characteristics (i.e. response rate) and stability support capability (i.e. the probability of active support occurring).

[0084] Among them, the basic regulation capability includes rated power and climbing rate; the dynamic response characteristics include primary frequency modulation response time and AGC response time; the stability support capability includes frequency modulation capability and voltage regulation capability. The relative importance of indicators at all levels is determined through the expert scoring method, and a judgment matrix is ​​established.

[0085] Since the rated power in the basic regulation capability reflects the capacity of the unit, it can directly determine the adjustable space, and the weighting coefficient can be set to 0.3; the ramp rate represents the speed of output power change, which affects the flexibility of regulation, and the weighting coefficient is 0.25.

[0086] In the dynamic response characteristics, the primary frequency modulation response time represents the system's ability to respond quickly after disturbance, and the weighting coefficient is 0.15; the AGC response time reflects the secondary frequency modulation effect, and the weighting coefficient is 0.1.

[0087] The frequency regulation capability in the stability support capability reflects the frequency adjustment range, and the weighting coefficient is set to 0.12; the voltage regulation capability is related to voltage control, and the weighting coefficient is set to 0.08.

[0088] Secondly, the auxiliary control characteristic parameters are divided into secondary indicators. Since the charging and discharging power determines the ability of energy storage to participate in regulation, the weighting coefficient is 0.4; the response time reflects the timeliness of regulation, and the weighting coefficient is 0.35; the state of charge reflects the availability of the energy storage equipment, and the weighting coefficient is 0.25.

[0089] Then, after determining the weight coefficient, each parameter needs to be standardized. For positive indicators (such as rated power and frequency modulation capability), the maximum value standardization method is used; for negative indicators (such as response time), the minimum value standardization method is used. The value range after standardization is unified to the [0,1] interval.

[0090] Finally, the standardized parameter value is multiplied by the corresponding weight coefficient to obtain the score of the single indicator.

[0091] Then, the comprehensive score calculation formula of the physical regulation potential index is:

[0092]

[0093] Among them, I p represents the physical regulation potential index, w i represents the weight coefficient of the i-th parameter, X′ i represents the normalized value of the i-th parameter, and n represents the total number of parameters.

[0094] In schematic form, the comprehensive score of the physical regulation potential index is the sum of the available capacity, response rate and probability of active support of the generator set, and the available capacity, response rate and probability of active support of the energy storage device. Based on the comprehensive score, the regulation capability of the regulation device on the power system can be quantified, so that the actual contribution and potential of each device in the power system can be clearly understood to achieve reasonable resource allocation.

[0095] For step S2, in a preferred embodiment, in addition to introducing the evaluation and calculation process of the equipment regulation potential value, the present invention also introduces the factor of demand response regulation potential value, thereby achieving the optimal configuration of the regulation equipment in the power system.

[0096] When obtaining the demand response regulation potential value, the present invention can predict the demand response regulation potential value according to the user's power load data and demand response event data through the trained regulation potential prediction model.

[0097] Specifically, the demand response event data includes: a first response time corresponding to a peak shaving demand response event, a first adjustment cost corresponding to a peak shaving demand response event, a second response time corresponding to a valley filling demand response event, and a second adjustment cost corresponding to a valley filling demand response event; the first response time is used to represent the time from when a user receives a peak shaving demand response instruction to when the user starts to adjust the load; the second response time is used to represent the time from when a user receives a valley filling demand response instruction to when the user starts to adjust the load;

[0098] The process of generating a demand response regulation potential value for characterizing the user's ability to adjust the load when participating in demand response based on the user's power load data and demand response event data is:

[0099] The first response time, the first regulation cost, the second response time, the second regulation cost, the first load adjustment amount and the second load adjustment amount are input into the regulation potential prediction model, so that the regulation potential prediction model extracts the load adjustment characteristics for characterizing the sustainable regulation capacity of the user in different types of demand response events according to the first load adjustment amount, the second load adjustment amount, the first regulation cost and the second regulation cost; extracts the response characteristics for characterizing the response ability of the user in different types of demand response events according to the first response time, the second response time, the first load adjustment amount and the second load adjustment amount; extracts the regulation cost characteristics for characterizing the cost tolerance of the user in different types of demand response events according to the first regulation cost, the second regulation cost, the first load adjustment amount and the second load adjustment amount; generates the capacity interval, response accuracy and capacity regulation cost according to the load adjustment characteristics, response characteristics and regulation cost characteristics;

[0100] The training process of the regulatory potential prediction model includes:

[0101] The power load data samples and the demand response event data samples are used as training samples; wherein the power load data samples include: load adjustment amount samples corresponding to the peak shaving demand response event and load adjustment amount samples corresponding to the valley filling demand response event; the demand response event data samples include: response time samples corresponding to the peak shaving demand response event, adjustment cost samples corresponding to the peak shaving demand response event, response time samples corresponding to the valley filling demand response event and adjustment cost samples corresponding to the valley filling demand response event;

[0102] Taking several training samples and the actual capacity range, actual response accuracy and actual capacity regulation cost corresponding to each training sample as input, and the predicted capacity range, predicted response accuracy and predicted capacity regulation cost of each training sample as output, the regulation potential prediction model to be trained is iteratively trained until the model converges to generate a preset regulation potential prediction model.

[0103] During each iterative training, a training sample is input into the regulation potential prediction model, so that the regulation potential prediction model generates a capacity interval prediction result, a response accuracy prediction result and a capacity regulation cost prediction result corresponding to the training sample according to the load adjustment characteristics, response characteristics and regulation cost characteristics in the training sample;

[0104] The capacity interval prediction results, response accuracy prediction results and capacity regulation cost prediction results are compared with the actual capacity interval results, actual response accuracy results and actual capacity regulation cost results, and the network parameters of the regulation potential prediction model are adjusted according to the comparison results.

[0105] Specifically, historical electricity load data can be collected, including the load adjustment corresponding to peak shaving and valley filling demand response events. Historical demand response event data can be collected, including response time, adjustment cost, etc. These data can be used as training samples to train the regulation potential prediction model.

[0106] Use machine learning algorithms (such as neural networks, support vector machines, etc.) to build a regulation potential prediction model. Then input the training samples into the model and perform iterative training until the model converges. During the training process, the model will learn how to predict the capacity range, response accuracy, and capacity regulation cost based on the load adjustment characteristics, response characteristics, and regulation cost characteristics.

[0107] Therefore, when it is necessary to predict the demand response regulation potential value in the current or future period of time, the current electricity load data and demand response event data are input into the trained model. The model will then output the predicted demand response regulation potential value based on the input data, including the capacity range, response accuracy and capacity adjustment cost.

[0108] It can be understood that the embodiments of the present invention can train a regulation potential prediction model based on historical demand response event data and the user's electricity load data. After the model is deployed, the current electricity load data and demand response event data are input into the regulation potential prediction model to output the predicted value of the demand response regulation potential, that is, the user's potential for load adjustment when participating in demand response, such as the user's capacity range when participating in demand response, the user's response accuracy when participating in demand response, and the user's capacity adjustment cost when participating in demand response.

[0109] In order to improve the adaptability of the model, the embodiment of the present invention can also introduce an online learning mechanism, update the model parameters once a week, add the newly added response event data to the training set, and update the model using an incremental learning method. At the same time, a feature importance evaluation mechanism is established to analyze the contribution of each feature to the prediction result through the SHAP (SHapley Additive exPlanations) value.

[0110] In the prediction process, a sliding time window method can also be used, with the prediction window set to 24 hours and the step length set to 1 hour. For each time window, the power consumption characteristic data and the characteristics of the demand response event are combined to obtain the potential value of each demand response regulation through the trained demand response prediction model.

[0111] For step S3, based on the device regulation potential value and the demand response regulation potential value obtained in the previous steps, the two can be used as basic parameters for constructing a regulation device configuration model, so as to construct a regulation device configuration model that can generate an optimal configuration plan for the power system. The regulation device configuration model can comprehensively consider the device regulation potential value and the demand response regulation potential value, so that the final target configuration variables can not only meet the actual regulation capability of the equipment, but also make the power system meet the actual response characteristics of the user.

[0112] In a preferred embodiment, the control device configuration model includes:

[0113] min F=ω1·C cost +ω2·S stability +ω3·P potential +ω4·D response ;

[0114]

[0115] C gen (t) = c i *P i,t ;

[0116]

[0117] Among them, F is the power system operation cost, C cost To control costs, S stability is the preset voltage stability index, P potential is the equipment regulation potential value, D response is the demand response regulation potential value, ω1, ω2, ω3, ω4 are different weight coefficients respectively; C gen (t) is the power generation cost of the generator set at time t, C storage (t) is the operating cost of the energy storage equipment at time t, C demand (t) is the demand response compensation cost at time t, T is the preset time period; c i is the unit output power cost of the generator set, P i,t is the output power of the i-th generator set at time t, di is the unit power cost of the energy storage device, is the discharge power of the i-th energy storage device at time t, is the charging power of the i-th energy storage device at time t; c e is the unit capacity cost of energy storage equipment, Q i,t is the capacity of the i-th energy storage device at time t.

[0118] Furthermore, the output constraint of the generator set includes:

[0119] P i,min ≤P i,t ≤P i,max ;

[0120] Among them, P i,min is the minimum output power corresponding to the i-th generator set, P i,max is the maximum output power corresponding to the i-th generator set.

[0121] The charging and discharging constraints of the energy storage device include:

[0122]

[0123]

[0124] in, is the minimum discharge power of the i-th energy storage device, is the maximum discharge power of the i-th energy storage device; is the minimum charging power of the i-th energy storage device, is the maximum charging power of the i-th energy storage device.

[0125] The capacity constraints of the energy storage device include:

[0126] Q i,min ≤Q i,t ≤Q i,max ;

[0127] Among them, Q i,min is the minimum capacity limit of the i-th energy storage device, Q i,max is the maximum capacity limit of the i-th energy storage device.

[0128] Illustratively, the control equipment configuration model of the present invention comprehensively considers multiple factors such as power system operating cost, control cost, voltage stability index, equipment control potential value and demand response control potential value, so as to more comprehensively reflect the actual situation of the power system and generate a better configuration plan.

[0129] It can be understood that the control device configuration model of the present invention takes the device control potential value as one of the basic parameters, which can ensure that the configuration scheme finally obtained can give full play to the control potential of the device and improve the utilization rate of the device. And by considering the demand response control potential value, the user response characteristics can be predicted more accurately, so as to formulate a response strategy that better meets the actual needs of the user, so as to obtain a configuration scheme that meets the actual response characteristics of the user.

[0130] Furthermore, by considering the preset voltage stability index, it can be ensured that the final configuration scheme can meet the voltage stability requirements of the power system. And by considering the output constraints and capacity constraints of the generator set and energy storage equipment, it can avoid the occurrence of equipment overload, thereby ensuring the stable operation of the power system.

[0131] In summary, the construction of the control equipment configuration model and constraint conditions can make the final solution of the configuration variables (i.e., configuration scheme) improve the optimized operation, cost control, equipment utilization, response characteristic matching and system stability of the power system.

[0132] For step S4 and step S5, in a preferred embodiment, the objective function and constraint conditions of the control device configuration model can be input into a selected optimization algorithm (such as linear programming, nonlinear programming, dynamic programming, genetic algorithm, particle swarm algorithm, etc.) for solving. In the solution process, based on the objective function and constraint conditions, the output power of the generator set, the charging and discharging power and capacity of the energy storage device and other variables are continuously adjusted to seek the optimal solution, thereby obtaining the target configuration variables that not only meet the actual control capability of the equipment, but also make the power system meet the actual response characteristics of the user.

[0133] Therefore, by solving the configuration variables of the present invention and configuring and scheduling the power system based on the configuration variables, the performance advantages of each control device can be fully utilized, the stable operation of the power system can be guaranteed, and the supply and demand relationship of the power system can be effectively balanced.

[0134] In a preferred embodiment, the present invention can also solve the optimal control configuration scheme under different time scales based on the established control device configuration model.

[0135] After constructing the control equipment configuration model based on the cost data of the control equipment in the current time period, the compensation cost data of the demand response, the equipment control potential value and the demand response control potential value, the output plan of each unit can be accurately planned to meet the control needs within different time granularities, and the output power of the generator set, the charging and discharging power of the energy storage equipment and the capacity of the energy storage equipment under different time scales can be allocated.

[0136] In addition, the embodiments of the present invention can also clarify the response scale, call timing and duration of each type of user, so as to derive compensation prices and incentive mechanisms to encourage users to participate in demand response, so as to improve the demand response regulation potential value, and thus more accurately derive the final configuration plan.

[0137] like Figure 2 As shown, based on the above-mentioned embodiments of the control equipment configuration method of various power systems, the present invention provides corresponding device item embodiments;

[0138] An embodiment of the present invention provides a control device configuration device for a power system, comprising: a device control potential generation module, a demand response control potential generation module, a model building module, a model solving module, and a control device configuration module;

[0139] The device regulation potential generation module is used to generate a device regulation potential value for characterizing the regulation capability of the regulation device on the power system according to the performance parameters and power data of the regulation device; wherein the regulation device includes: a generator set and an energy storage device; the device regulation potential value includes: available capacity, response rate and probability of active support;

[0140] The demand response regulation potential generation module is used to generate a demand response regulation potential value for characterizing the user's ability to adjust the load when participating in demand response based on the user's power load data and demand response event data; wherein the demand response regulation potential value includes: capacity range, response accuracy and capacity adjustment cost;

[0141] The model building module is used to build a control device configuration model with the goal of minimizing the power system operation cost according to the cost data of the control device, the compensation cost data of the demand response, the device control potential value and the demand response control potential value; wherein the constraints corresponding to the control device configuration model include: output constraints of the generator set, charging and discharging constraints of the energy storage device and capacity constraints of the energy storage device;

[0142] The model solving module is used to solve the control device configuration model under the constraint conditions to generate target configuration variables when the power system operation cost is minimized; wherein the target configuration variables include: the output power of the generator set, the charge and discharge power of the energy storage device, and the capacity of the energy storage device;

[0143] The control device configuration module is used to configure the control device in the power system according to the target configuration variable.

[0144] It should be noted that the device embodiments described above are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without paying creative labor.

[0145] Those skilled in the art can clearly understand that, for the sake of convenience and simplicity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0146] Based on the above-mentioned embodiments of the control equipment configuration method of various power systems, the present invention provides corresponding terminal equipment item embodiments.

[0147] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a method for configuring a control device of an electric power system as described in any method embodiment of the present invention.

[0148] The terminal device may be a computing terminal device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0149] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0150] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0151] Based on the above-mentioned embodiments of the control equipment configuration method of various power systems, the present invention provides corresponding storage medium item embodiments.

[0152] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a method for configuring a control device of an electric power system as described in any method embodiment of the present invention.

[0153] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each method embodiment described above can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0154] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for configuring control equipment of a power system, characterized in that: include: According to the performance parameters and power data of the control equipment, a device control potential value is generated to characterize the control ability of the control equipment on the power system; wherein the control equipment includes: a generator set and an energy storage device; the device control potential value includes: available capacity, response rate and probability of active support; Generate a demand response regulation potential value for characterizing the user's ability to adjust load when participating in demand response based on the user's power load data and demand response event data; wherein the demand response regulation potential value includes: capacity range, response accuracy and capacity regulation cost; According to the cost data of the control equipment, the compensation cost data of the demand response, the equipment control potential value and the demand response control potential value, a control equipment configuration model with the goal of minimizing the operation cost of the power system is constructed; wherein the constraints corresponding to the control equipment configuration model include: output constraints of the generator set, charging and discharging constraints of the energy storage device, and capacity constraints of the energy storage device; Under the constraints, the control device configuration model is solved to generate target configuration variables when the power system operation cost is minimized; wherein the target configuration variables include: the output power of the generator set, the charge and discharge power of the energy storage device, and the capacity of the energy storage device; According to the target configuration variables, control equipment in the power system is configured.

2. A control device configuration method for a power system according to claim 1, characterized in that: The performance parameters of the control equipment include: performance parameters of the generator set and performance parameters of the energy storage device; The performance parameters of the generator set include: rated power of the set, ramp rate, output power regulation rate, load regulation rate, primary frequency regulation response time and response time of the set; The performance parameters of the energy storage device include: charging response time, discharging response time and state of charge; The generating, according to the performance parameters and power data of the control device, a device control potential value for characterizing the control capability of the control device on the power system comprises: Generate the available capacity of the generator set according to the rated power of the generator set, the ramp rate and the output power of the generator set; Generate a response rate of the generator set according to the primary frequency modulation response time of the generator set and the response time of the generator set; Generate a probability of active support of the generator set according to the output power regulation rate and the load regulation rate of the generator set; Generate the available capacity of the energy storage device according to the charging power, discharging power and state of charge of the energy storage device; Generating a response rate of the energy storage device according to a charging response time and a discharging response time of the energy storage device; According to the charge state of the energy storage device, a probability of active support of the energy storage device is generated.

3. A method for configuring control equipment of a power system according to claim 2, characterized in that: The demand response event data includes: a first response time corresponding to a peak shaving demand response event, a first adjustment cost corresponding to a peak shaving demand response event, a second response time corresponding to a valley filling demand response event, and a second adjustment cost corresponding to a valley filling demand response event; the first response time is used to represent the time from when a user receives a peak shaving demand response instruction to when the user starts to adjust the load; the second response time is used to represent the time from when a user receives a valley filling demand response instruction to when the user starts to adjust the load; The user's electricity load data includes: a first load adjustment amount corresponding to a peak shaving demand response event and a second load adjustment amount corresponding to a valley filling demand response event; The generating, based on the user's electricity load data and demand response event data, a demand response regulation potential value for characterizing the user's ability to adjust load when participating in demand response includes: The first response time, the first regulation cost, the second response time, the second regulation cost, the first load adjustment amount and the second load adjustment amount are input into the regulation potential prediction model, so that the regulation potential prediction model extracts the load adjustment characteristics for characterizing the sustainable regulation capacity of the user in different types of demand response events according to the first load adjustment amount, the second load adjustment amount, the first regulation cost and the second regulation cost; extracts the response characteristics for characterizing the response ability of the user in different types of demand response events according to the first response time, the second response time, the first load adjustment amount and the second load adjustment amount; extracts the regulation cost characteristics for characterizing the cost tolerance of the user in different types of demand response events according to the first regulation cost, the second regulation cost, the first load adjustment amount and the second load adjustment amount; generates the capacity interval, response accuracy and capacity regulation cost according to the load adjustment characteristics, response characteristics and regulation cost characteristics; The training process of the regulatory potential prediction model includes: The power load data samples and the demand response event data samples are used as training samples; wherein the power load data samples include: load adjustment amount samples corresponding to the peak shaving demand response event and load adjustment amount samples corresponding to the valley filling demand response event; the demand response event data samples include: response time samples corresponding to the peak shaving demand response event, adjustment cost samples corresponding to the peak shaving demand response event, response time samples corresponding to the valley filling demand response event and adjustment cost samples corresponding to the valley filling demand response event; Taking several training samples and the actual capacity range, actual response accuracy and actual capacity regulation cost corresponding to each training sample as input, and the predicted capacity range, predicted response accuracy and predicted capacity regulation cost of each training sample as output, the regulation potential prediction model to be trained is iteratively trained until the model converges to generate a preset regulation potential prediction model.

4. A method for configuring control equipment of a power system according to claim 3, characterized in that: During each iterative training, a training sample is input into the regulation potential prediction model, so that the regulation potential prediction model generates a capacity interval prediction result, a response accuracy prediction result and a capacity regulation cost prediction result corresponding to the training sample according to the load adjustment characteristics, response characteristics and regulation cost characteristics in the training sample; The capacity interval prediction results, response accuracy prediction results and capacity regulation cost prediction results are compared with the actual capacity interval results, actual response accuracy results and actual capacity regulation cost results, and the network parameters of the regulation potential prediction model are adjusted according to the comparison results.

5. A method for configuring control equipment of a power system according to claim 4, characterized in that: The control device configuration model includes: min F=ω1·C cost +ω2·S stability +ω3·P potential +ω4·D response ; C gen (t)=ci*P i,t ; Among them, F is the operating cost of the power system, C cost To control costs, S stability is the preset voltage stability index, P potential is the equipment regulation potential value, D response is the demand response regulation potential value, ω1, ω2, ω3, ω4 are different weight coefficients respectively; C gen (t) is the power generation cost of the generator set at time t, C storage (t) is the operating cost of the energy storage equipment at time t, C demand (t) is the demand response compensation cost at time t, T is the preset time period; c i is the unit output power cost of the generator set, P i,t is the output power of the i-th generator set at time t, di is the unit power cost of the energy storage device, is the discharge power of the i-th energy storage device at time t, is the charging power of the i-th energy storage device at time t; c e is the unit capacity cost of energy storage equipment, Q i,t is the capacity of the i-th energy storage device at time t.

6. A method for configuring control equipment of a power system according to claim 5, characterized in that: The output constraint of the generator set includes: P i,min ≤Pi,t≤P i,max ; Among them, P i,min is the minimum output power corresponding to the i-th generator set, P i,max is the maximum output power corresponding to the i-th generator set.

7. A method for configuring control equipment of a power system according to claim 6, characterized in that: The charging and discharging constraints of the energy storage device include: in, is the minimum discharge power of the i-th energy storage device, is the maximum discharge power of the i-th energy storage device; is the minimum charging power of the i-th energy storage device, is the maximum charging power of the i-th energy storage device; The capacity constraints of the energy storage device include: Q i,min ≤Q i,t ≤Q i,max ; Among them, Q i,min is the minimum capacity limit of the i-th energy storage device, Q i,max is the maximum capacity limit of the i-th energy storage device.

8. A control equipment configuration device for a power system, characterized in that: include: Equipment regulation potential generation module, demand response regulation potential generation module, model building module, model solving module and regulation equipment configuration module; The device regulation potential generation module is used to generate a device regulation potential value for characterizing the regulation capability of the regulation device on the power system according to the performance parameters and power data of the regulation device; wherein the regulation device includes: a generator set and an energy storage device; the device regulation potential value includes: available capacity, response rate and probability of active support; The demand response regulation potential generation module is used to generate a demand response regulation potential value for characterizing the user's ability to adjust the load when participating in demand response based on the user's power load data and demand response event data; wherein the demand response regulation potential value includes: capacity range, response accuracy and capacity adjustment cost; The model building module is used to build a control device configuration model with the goal of minimizing the power system operation cost according to the cost data of the control device, the compensation cost data of the demand response, the device control potential value and the demand response control potential value; wherein the constraints corresponding to the control device configuration model include: output constraints of the generator set, charging and discharging constraints of the energy storage device and capacity constraints of the energy storage device; The model solving module is used to solve the control device configuration model under the constraint conditions to generate target configuration variables when the power system operation cost is minimized; wherein the target configuration variables include: the output power of the generator set, the charge and discharge power of the energy storage device, and the capacity of the energy storage device; The control device configuration module is used to configure the control device in the power system according to the target configuration variable.

9. A terminal device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a method for configuring control equipment of an electric power system as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the control device configuration method for a power system as described in any one of claims 1 to 7.

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