CN113904380B - Virtual power plant adjustable resource accurate control method considering demand response - Google Patents
Virtual power plant adjustable resource accurate control method considering demand response Download PDFInfo
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Abstract
The application discloses a virtual power plant adjustable resource accurate control method considering demand response, comprising the following steps: acquiring adjustable resource information of a virtual power plant, and evaluating the regulatory potential of the adjustable resource; classifying and layering the adjustable resources of the virtual power plant, and constructing a multi-layer control framework of the adjustable resources of the virtual power plant; establishing an adjustable resource optimization control model of a virtual power plant and a regulation constraint model of the adjustable resource optimization control model; decomposing the adjustable resource control tasks of the virtual power plant to form multi-layer subtasks, solving the optimization control problem by adopting a layered deep reinforcement learning algorithm, and automatically and optimally decomposing and issuing control task regulation and control instructions layer by layer from top to bottom. The invention can improve the accuracy of adjustable resource control and can treat nonlinearity, randomness and uncertainty in actual control.
Description
Technical Field
The invention belongs to the technical field of power resource regulation and control, and relates to an adjustable resource accurate control method of a virtual power plant taking demand response into consideration.
Background
The term "virtual power plant" originates from SHimon Awerbuch doctor in 1997, its work "virtual public facilities: description of emerging industries, technology, and competitiveness in the book of the definition of virtual public facilities: virtual public facilities are a flexible collaboration between independent and market-driven entities that can provide consumers with the efficient power services they need without having to own the corresponding assets. Just as the virtual public facilities provide electric energy service taking consumers as a guide by using the emerging technology, the virtual power plant does not change the grid connection mode of each distributed power supply, but aggregates distributed energy sources of different types such as distributed power supplies, energy storage systems, controllable loads, electric vehicles and the like by advanced technologies such as control, metering, communication and the like, and realizes coordinated and optimized operation of a plurality of distributed energy sources by a higher-level software framework, thereby being more beneficial to reasonable optimal configuration and utilization of resources.
The concept of the virtual power plant emphasizes the functions and effects presented externally, updates the operation concept and generates social and economic benefits, and the basic application scene is the electric market. The method can aggregate the stable power transmission of the distributed energy sources to the public network without modifying the power grid, provides the auxiliary service of quick response, becomes an effective method for adding the distributed energy sources into the electric power market, reduces the unbalanced risk of the independent operation of the distributed energy sources in the market, and can obtain the benefit of scale economy. Meanwhile, the visualization of the distributed energy sources and the coordination control optimization of the virtual power plants greatly reduce the impact of the conventional distributed energy source grid connection on the public network, reduce the scheduling difficulty caused by the growth of the distributed power sources, enable the distribution management to be more reasonable and orderly, and improve the running stability of the system.
The virtual power plant is an effective means for constructing the large-scale, normalized and accurate distributed resource adjustability, can effectively realize friendly interaction between the distributed resource and the power system, realizes integration and distribution of various resources, and has great application value.
Virtual power plants have been practiced many times worldwide, and development of virtual power plants also brings new challenges to power system scheduling, and unreasonable scheduling control often increases power network loss and reduces the economical efficiency of power grid operation. The existing virtual power plant control method has the following defects: the centralized control architecture is adopted, so that the algorithm is complex and the calculated amount is large; the regulation and control instruction is decomposed into specific units according to the weight, so that the optimization cannot be realized; the control algorithm cannot cope with the nonlinearities, randomness, and uncertainty of the system. Therefore, a precise control method for the adjustable resource granularity of the virtual power plant is lacking.
Disclosure of Invention
In order to solve the defects in the prior art, the application provides an adjustable resource accurate control method for a virtual power plant, which considers demand response.
In order to achieve the above object, the present invention adopts the following technical scheme:
a virtual power plant adjustable resource accurate control method considering demand response comprises the following steps:
step 1: acquiring adjustable resource information of a virtual power plant based on a cloud side architecture, and evaluating the regulatory potential of the adjustable resource;
step 2: classifying and layering the adjustable resources participating in the control tasks according to the control tasks of the response of the adjustable resources participating in the demand and the regulation and control information of the adjustable resources, and constructing a multi-layer control framework of the adjustable resources of the virtual power plant corresponding to different types of control tasks by combining the cloud side framework;
step 3: based on a multi-layer control architecture of the adjustable resources of the virtual power plant, establishing an adjustable resource optimization control model of the virtual power plant and a regulation constraint model thereof corresponding to different types of control tasks according to a demand response strategy and an evaluation result of the regulation potential of the adjustable resources;
step 4: and (3) solving the optimal control model established in the step (3) by utilizing a multi-layer deep reinforcement learning algorithm with multi-layer task cooperation to obtain a regulation and control instruction of each layer, and then controlling specific adjustable resources to participate in a control task according to an instruction issued by a cloud edge end architecture.
The invention further comprises the following preferable schemes:
preferably, the cloud side architecture in step 1 includes a cloud platform, an edge server and an intelligent terminal;
the intelligent terminal is deployed on equipment of the adjustable resources of the virtual power plant and comprises a sensor and an actuator, and is used for state sensing and control;
forming an edge proxy based on the cooperation of the cloud platform and the edge server, wherein all edge proxies are uniformly managed by the cloud platform, and the edge proxy and the cloud platform interact information through a communication network;
the edge agent distributes equipment codes, equipment numbers, user names and passwords for the intelligent terminals connected with the edge server, receives and processes data of the intelligent terminals, sends a state sensing result obtained through processing to the cloud platform, receives regulation and control instructions of the cloud platform and sends the regulation and control instructions to the intelligent terminals.
Preferably, the step 1 specifically comprises:
and sensing the model, parameters and state data of the adjustable resources of the virtual power plant by using the intelligent terminal, receiving the sensed data by the edge server, extracting the related information of the adjustable resources of the virtual power plant by edge calculation, and evaluating the regulation potential of the adjustable resources.
Preferably, the virtual power plant adjustable resources include gas turbine sets, photovoltaic power sets, wind power sets, energy storage devices, and demand side adjustable resources.
Preferably, the extracting information about adjustable resources of the virtual power plant, and evaluating the regulatory potential of the adjustable resources specifically includes:
acquiring an adjustable resource typical daily load curve, and predicting the maximum electricity load;
calculating an adjustable proportion of the adjustable resource, a single-user adjustment potential evaluation index and a regional adjustment potential evaluation index based on an adjustable resource typical daily load curve and a maximum electricity load;
wherein, for each type of adjustable resource, the adjustable ratio is equal to the adjustable capacity divided by the total capacity;
each individual user's regulatory potential assessment index comprising:
basic indexes are as follows: regulation capacity, regulation time, regulation precision, regulation rate and duration;
composite performance index: comprehensively regulating and controlling a performance index A, a subscription performance index B, a service time reliability index C, a service capacity reliability index D and a capacity rebound index;
and multiplying various adjustable resource potential evaluation indexes in the region for each region to obtain a region regulation potential evaluation index value.
Preferably, the composite performance index calculation formula is:
comprehensive regulation performance index a=a 1 A 2 A 3 ；
Wherein A is 1 A is the ratio of the actual regulation speed to the standard regulation speed 2 A is the ratio of the actual regulation precision to the standard regulation precision 3 The ratio of the actual preparation time to the standard preparation time;
acceptance performance index b=β 1 B 1 +β 2 B 2 +β 3 B 3 +β 4 B 4 +β 5 B 5 ；
Wherein B is 1 、B 2 、B 3 、B 4 、B 5 Respectively the ratio of the actual value to the standard value of the preparation time, the regulation speed, the regulation precision, the regulation capacity and the regulation time, beta 1 ,β 2 ,β 3 ,β 4 ,β 5 Respectively B 1 、B 2 、B 3 、B 4 、B 5 Weight value of (2);
service time reliability index c=c 1 /C 2 ；
Wherein C is 1 C for the time of the actual regulation capacity reaching 90% of the total capacity 2 Is the total regulating time;
service capacity reliability index d= (D) 1 +D 2 +D 3 )/D 4 ；
Wherein D is 1 D in response to a capacity deviation within 5% 2 D in order to respond to the capacity deviation of 5 to 10 percent 3 D in order to respond to the capacity deviation of 10 to 20 percent 4 Is the total regulating time;
capacity rebound index e=e 1 /(E 2 E 3 )；
Wherein E is 1 To regulate capacity exceeding baseline after end of regulation, E 2 For baseline load, E 3 Is the rebound time.
Preferably, the control tasks in step 2 include frequency adjustment, voltage adjustment, peak clipping and valley filling, and new energy source elimination.
Preferably, step 2 specifically comprises:
classifying and layering the adjustable resources participating in the control task according to the control task of the response of the adjustable resources participating in the demand, and the regulation potential, regulation characteristics and spatial distribution of the adjustable resources;
based on cloud side end cooperation and resource layering structure, the regulation and control instruction is issued to the edge proxy through the cloud platform and issued step by step according to the resource layering structure until the intelligent terminal at the bottommost layer receives the regulation and control instruction to complete control, and the whole process forms a multi-layer control framework of the adjustable resource of the virtual power plant;
in the multi-layer control architecture of the adjustable resources of the virtual power plant, the regulation and control instruction of each layer of corresponding resources participating in the control task can be decomposed to the next layer according to a decomposition formula until the regulation and control instruction of granularity of a specific unit or device is decomposed.
Preferably, the classifying and layering the adjustable resources participating in the control task specifically includes:
aiming at specific demand response control tasks, firstly, dividing corresponding different types of adjustable resources into a first layer, then dividing specific resources of the first layer into resource groups of a second layer, dividing the resource groups of the second layer into thinner and more specific units or devices of a third layer, and the like until granularity of the units or devices is divided, so as to obtain a layered structure of the adjustable resources of the virtual power plant.
Preferably, the step 3 specifically comprises:
the method comprises the steps of establishing a virtual power plant adjustable resource optimization control model by considering a price-based or incentive-based demand response strategy, a demand response scene, a response scale and response time, and establishing a regulation constraint model according to a regulation potential evaluation result of adjustable resources, wherein the optimization control model comprises specific regulation instructions of each layer in the multi-layer architecture.
Preferably, in step 3, the adjustable resource optimization control model of the virtual power plant establishes an objective function with the maximization of the benefit of the virtual power plant, specifically:
wherein R (t) is the benefit obtained by the adjustable resource under the price-based or incentive-based demand response strategy, C o (t) is the virtual power plant operation management cost and the operation cost of the adjustable resource, C p And (t) penalty cost for actual power output of the virtual power plant and scheduling plan deviation.
Preferably, it is assumed that there is N in a certain control task 1 The first layer of the adjustable resource cluster participates, and then the benefit R (t) is expressed as
Wherein P is i (t) is the output of the ith cluster, i.e. the regulation command of the ith cluster of the first layer, and F (t) is a benefit function;
the regulating and controlling instruction of the ith cluster of the first layer is distributed to a specific adjustable resource group of the second layer, and the decomposition formula is as follows
Wherein N is 2 For the number of adjustable resource groups corresponding to the ith cluster of the first layer in the second layer, alpha j Decomposition parameters for the j-th adjustable resource group, P ij And (t) decomposing the regulation and control instruction to the j-th adjustable resource group of the second layer, and analogizing to obtain the regulation and control instruction decomposition relation of each layer.
Preferably, in step 3, the following regulation constraint model is established in consideration of the gas turbine set, the energy storage device, the photovoltaic generator set, the wind turbine set and the demand side adjustable load of the virtual power plant:
adjusting capacity constraint P min ≤P≤P max ；
Wherein P is min ,P max Respectively the minimum and the maximum of the capacityA large value;
adjusting the speed constraint S min ≤P(t)-P(t-1)≤S max ；
Wherein S is min ,S max Respectively a minimum value and a maximum value of the speed;
the time constraint P (t) =0 is adjusted,
wherein T is P A period of time during which regulation may be participated;
adjusting accuracy constraints
Wherein P is real To adjust the actual output of the resource,
respectively the minimum value and the maximum value of the precision;
comprehensive regulation performance constraint A min ≤A≤A max ；
A min ,A max Respectively minimum and maximum values of comprehensive performances;
acceptance and payment performance constraint B min ≤B≤B max ；
B min ,B max Respectively minimum and maximum of the acceptance performance.
Preferably, in step 4, the optimization control model established in step 3 is solved by using a multi-layer deep reinforcement learning algorithm with multi-layer task cooperation, the adjustable resource control task of the virtual power plant is decomposed to form multi-layer subtasks, each layer seeks a local optimal solution to each current layer subtask through the deep reinforcement learning algorithm, a regulation and control instruction of each layer is obtained, and the optimization solution of the whole control problem is completed jointly through the multi-layer deep reinforcement learning algorithm, and the method is as follows:
when the regulating and controlling instructions are automatically and optimally decomposed and issued layer by layer from top to bottom, decomposition parameters are introduced, the decomposing and issuing of the regulating and controlling instructions of each layer are realized by a deep reinforcement learning algorithm of the layer, a reward function is the sum of output deviation and running cost of adjustable resources of the layer, an observed quantity is that the layer receives the regulating and controlling instructions of the upper layer, actions are the decomposition parameters of the regulating and controlling instructions of the layer, and a reinforcement learning strategy is that a mapping relation between the observed quantity and the actions is established by a deep neural network.
The beneficial effect that this application reached:
1. according to the method, the regulation and control potential of the adjustable resources of the virtual power plant is evaluated, the evaluation result can be used for subsequent classification and layering of the adjustable resources and formation of regulation and control constraints, and the practicability and accuracy of the accurate control method can be improved.
2. The invention adopts the technologies of edge calculation, cloud-edge-end cooperation, network reconstruction and the like, and provides a reliable and rapid communication framework for realizing the adjustable resource accurate control method of the virtual power plant.
3. According to the method, the adjustable resources are layered, the adjustable resource control tasks of the virtual power plant are decomposed to form multiple layers of subtasks, and the method for automatically and optimally decomposing and issuing the regulation and control instructions layer by layer from top to bottom is provided, so that the accuracy of adjustable resource control can be improved, and the accurate control of the adjustable resources of the virtual power plant with fine granularity under different demand response scenes can be realized.
4. The invention solves the problem of optimal control of layering by adopting a layering depth reinforcement learning algorithm, and can treat nonlinearity, randomness and uncertainty in actual control.
Drawings
FIG. 1 is a flow chart of a method for precisely controlling adjustable resources of a virtual power plant in consideration of demand response in accordance with the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical solutions of the present invention and are not intended to limit the scope of protection of the present application.
As shown in FIG. 1, the method for precisely controlling the adjustable resources of the virtual power plant, which considers the demand response, comprises the following steps:
step 1: based on cloud side architecture, obtaining adjustable resource information of a virtual power plant, and evaluating the regulation potential of the adjustable resource, wherein the method specifically comprises the following steps:
the intelligent terminal is utilized to perceive data such as a model, parameters, states and the like of adjustable resources of the virtual power plant, the edge server receives the perceived data and extracts relevant information of the adjustable resources of the virtual power plant through edge calculation, and the adjustable potential of the adjustable resources is evaluated, and the intelligent terminal comprises the following steps:
acquiring an adjustable resource typical daily load curve, and predicting the maximum electricity load;
calculating an adjustable proportion of the adjustable resource, a single-user adjustment potential evaluation index and a regional adjustment potential evaluation index based on an adjustable resource typical daily load curve and a maximum electricity load;
wherein, for each type of adjustable resource, the adjustable ratio is equal to the adjustable capacity divided by the total capacity;
each individual user's regulatory potential assessment index comprising:
basic indexes such as regulation capacity, regulation time, regulation precision, regulation speed, duration and the like;
comprehensively regulating and controlling composite performance indexes such as a performance index A, a recognition performance index B, a service time reliability index C, a service capacity reliability index D, a capacity rebound index E and the like;
and multiplying various adjustable resource potential evaluation indexes in the region for each region to obtain a region regulation potential evaluation index value.
The composite performance index calculation formula is as follows:
comprehensive regulation performance index a=a 1 A 2 A 3 ；
Wherein A is 1 A is the ratio of the actual regulation speed to the standard regulation speed 2 A is the ratio of the actual regulation precision to the standard regulation precision 3 The ratio of the actual preparation time to the standard preparation time;
acceptance performance index b=β 1 B 1 +β 2 B 2 +β 3 B 3 +β 4 B 4 +β 5 B 5 ；
Wherein B is 1 、B 2 、B 3 、B 4 、B 5 Respectively the ratio of the actual value to the standard value of the preparation time, the regulation speed, the regulation precision, the regulation capacity and the regulation time, beta 1 ,β 2 ,β 3 ,β 4 ,β 5 Respectively B 1 、B 2 、B 3 、B 4 、B 5 Weight value of (2);
service time reliability index c=c 1 /C 2 ；
Wherein C is 1 C for the time of the actual regulation capacity reaching 90% of the total capacity 2 Is the total regulating time;
service capacity reliability index d= (D) 1 +D 2 +D 3 )/D 4 ；
Wherein D is 1 D in response to a capacity deviation within 5% 2 D in order to respond to the capacity deviation of 5 to 10 percent 3 D in order to respond to the capacity deviation of 10 to 20 percent 4 Is the total regulating time;
capacity rebound index e=e 1 /(E 2 E 3 )；
Wherein E is 1 To regulate capacity exceeding baseline after end of regulation, E 2 For baseline load, E 3 Is the rebound time.
In specific implementation, the adjustable resources of the virtual power plant comprise a gas turbine set, a photovoltaic generator set, a wind turbine set, energy storage equipment, adjustable resources on the demand side such as industrial load, commercial load and resident load, and the like.
The cloud side end framework comprises a cloud platform, an edge server and an intelligent terminal;
the intelligent terminal is deployed on equipment of the adjustable resources of the virtual power plant and comprises a sensor and an actuator, and is used for state sensing and control;
forming an edge proxy based on the cooperation of the cloud platform and the edge server, wherein all edge proxies are uniformly managed by the cloud platform, and the edge proxy and the cloud platform perform information interaction through communication networks such as public networks, optical fibers and the like;
the edge agent distributes information such as equipment codes, equipment numbers, user names, passwords and the like for the intelligent terminals connected with the edge server, receives and processes data of the intelligent terminals, sends a state sensing result obtained by processing to the cloud platform, receives a regulation and control instruction of the cloud platform and sends the regulation and control instruction to the intelligent terminals;
the cooperation of the cloud platform and the edge server comprises the supplement and cooperation of cloud and edge computing resources, and the cooperative security defense of the data communication process, and the cooperative completion of the accurate control task of the adjustable resources of the virtual power plant.
Yun Bianduan architecture corresponds to a layered structure of adjustable resources of a virtual power plant, a cloud platform is responsible for management and regulation of the whole virtual power plant, an intelligent terminal is responsible for perception and control of the specific adjustable equipment at the bottommost layer, an edge proxy is responsible for management and regulation of resources at the middle layer, and information interaction is carried out among the cloud platform, an edge server and the intelligent terminal through a communication network.
When the types, the quantity, the distribution, the management authority, the regulation modes and the like of the adjustable resources are changed, a network reconstruction technology is utilized to establish a virtual power plant communication network architecture which can adapt to the change.
The middle layer resource responsible for the edge proxy comprises a multi-layer structure, comprises a cluster layer, an adjustable resource group, specific adjustable resources and other layers, and adopts an optimization method to complete the arrangement of the edge server and the connection of the intelligent terminal and the edge proxy.
Step 2: classifying and layering the adjustable resources participating in the control task according to the control task of the response of the adjustable resources participating in the demand and the regulation and control information of the adjustable resources, and constructing a multi-layer control framework of the adjustable resources of the virtual power plant corresponding to different types of control tasks by combining the cloud side framework, wherein the multi-layer control framework specifically comprises the following steps:
according to the control tasks of the response of the participation demands of the adjustable resources and the factors such as the regulation potential, the regulation characteristics and the spatial distribution of the adjustable resources, the adjustable resources participating in the control tasks are classified and layered, specifically:
aiming at specific demand response control tasks, firstly, dividing corresponding different types of adjustable resources into a first layer, then dividing specific resources of the first layer into resource groups of a second layer, dividing the resource groups of the second layer into thinner and more specific units or devices of a third layer, and the like until granularity of the units or devices is divided, so as to obtain a layered structure of the adjustable resources of the virtual power plant.
Based on cloud side end cooperation and resource layering structure, the regulation and control instruction is issued to the edge agent through the cloud platform, and issued step by step according to the resource layering structure until the intelligent terminal at the bottommost layer receives the regulation and control instruction to finish control, and the whole process forms a multi-layer control framework of the adjustable resource of the virtual power plant;
in the multi-layer control architecture of the adjustable resources of the virtual power plant, the regulation and control instruction of each layer of corresponding resources participating in the control task can be decomposed to the next layer according to a decomposition formula until the regulation and control instruction of granularity of a specific unit or device is decomposed.
The specific embodiment of step 2 is as follows:
firstly, the adjustable resources with the same regulation and control characteristics and relatively close spatial distribution distance are integrated into polymers with larger capacity, such as polymers of electronic factories, air-conditioning polymers, lithium battery energy storage polymers and the like.
The virtual plant adjustable resources are then layered from the system level according to the adjustable capacity and speed that can be provided by the participating frequency modulation.
According to the sequence of participating in frequency modulation, the first layer is a cluster layer, and comprises: the load cluster, the energy storage cluster, the gas turbine set cluster, the wind turbine set cluster and the photovoltaic generator set cluster can be adjusted.
The second tier is an adjustable resource group, such as for industrial load groups including industrial load group 1, industrial load group 2, industrial load group 3, etc., ordered in order of participation in frequency modulation.
The third layer is a specific adjustable resource, for example, the industrial load group 1 comprises an electronics factory polymer, a metal processing factory polymer, a cement manufacturing factory polymer, and the like, and is also ordered according to the order of participation in frequency modulation.
Finally, to achieve precise control of the adjustable resources, the units or the equipment are further layered until the granularity of the equipment is achieved, such as specific adjustable equipment of an electronic factory, specific energy storage devices of an energy storage station and the like.
Based on the network reconstruction technology among the cloud platform, the edge server and the edge proxy, the collaborative-self-made coupling optimization control and the small micro-terminal-level edge optimization method are combined, and a multi-layer control framework of the virtual power plant adjustable resources with multiple types, multiple scenes and multiple time scales can be formed.
Corresponding to the layered structure of the adjustable resources of the virtual power plant, the cloud platform is responsible for the management and regulation of the whole virtual power plant, the intelligent terminal is responsible for the perception and control of the specific adjustable equipment at the bottommost layer, the edge proxy is responsible for the management and regulation of the resources of the middle layer, and the cloud platform, the edge server and the intelligent terminal are in information interaction through a communication network.
When the types, the quantity, the distribution, the management authority, the regulation modes and the like of the adjustable resources are changed, a network reconstruction technology is utilized to establish a virtual power plant communication network architecture which can adapt to the change.
The middle layer resource responsible for the edge proxy comprises a multi-layer structure, comprises a cluster layer, an adjustable resource group, specific adjustable resources and other layers, and adopts an optimization method to complete the arrangement of the edge server and the connection of the intelligent terminal and the edge proxy.
The adjustable resources of the virtual power plant can be matched to be corresponding to the specific types in the multi-type adjustable resources according to different control tasks, the regulation and control instructions are issued to the edge proxy through the cloud platform and are issued to the lower system step by step according to the layered structure until the intelligent terminal at the bottommost layer receives the regulation and control instructions to finish control, and a multi-layer control framework of the multi-type, multi-scene and multi-time-scale virtual power plant adjustable resources is formed.
Step 3: the method comprises the steps of establishing a virtual power plant adjustable resource optimization control model by considering a price-based or incentive-based demand response strategy, a demand response scene, a response scale and response time, and establishing a regulation constraint model according to a regulation potential evaluation result of adjustable resources, wherein the optimization control model comprises specific regulation instructions of each layer in the multi-layer architecture.
The optimization control model is not built in a layered manner, but a layered structure is considered, specific regulation and control instructions of each layer are considered, and the process of decomposing the regulation and control instructions of the upper layer to the next layer is realized by the reinforcement learning task corresponding to each layer. And 4, solving the optimal control model established in the step 3 to obtain the regulation and control instruction of each layer.
Specifically, the virtual power plant adjustable resource optimization control model establishes an objective function with the maximization of the virtual power plant profit:
wherein R (t) is the benefit obtained by the adjustable resource under the price-based or incentive-based demand response strategy, C o (t) is the virtual power plant operation management cost and the operation cost of the adjustable resource, C p And (t) penalty cost for actual power output of the virtual power plant and scheduling plan deviation.
Assuming N in a certain control task 1 The first layer of the adjustable resource cluster participates, and then the benefit R (t) is expressed as
Wherein P is i (t) is the output of the ith cluster, i.e. the regulation command of the ith cluster of the first layer, and F (t) is a benefit function;
the regulating and controlling instruction of the ith cluster of the first layer is distributed to a specific adjustable resource group of the second layer, and the decomposition formula is as follows
Wherein N is 2 For the number of adjustable resource groups corresponding to the ith cluster of the first layer in the second layer, alpha j Decomposition parameters for the j-th adjustable resource group, P ij And (t) decomposing the regulation and control instruction to the j-th adjustable resource group of the second layer, and analogizing to obtain the regulation and control instruction decomposition relation of each layer.
Considering the adjustable loads of a gas turbine set, energy storage equipment, a photovoltaic generator set, a wind generating set and a demand side of a virtual power plant, and establishing the following regulation and control constraint model:
adjusting capacity constraint P min ≤P≤P max ；
Wherein P is min ,P max Respectively minimum and maximum of capacity;
adjusting the speed constraint S min ≤P(t)-P(t-1)≤S max ；
Wherein S is min ,S max Respectively a minimum value and a maximum value of the speed;
the time constraint P (t) =0 is adjusted,
wherein T is P A period of time during which regulation may be participated;
adjusting accuracy constraints
Wherein P is real To adjust the actual output of the resource,
respectively the minimum value and the maximum value of the precision;
comprehensive regulation performance constraint A min ≤A≤A max ；
A min ,A max Respectively minimum and maximum values of comprehensive performances;
acceptance and payment performance constraint B min ≤B≤B max ；
B min ,B max Respectively minimum and maximum of the acceptance performance.
The system can also comprise equality constraint that the sum of lower regulation and control instructions is equal to upper issuing instructions, inequality constraint that the climbing rate of the gas turbine unit output is limited and the upper and lower capacity is regulated, charge and discharge constraint and residual capacity constraint of energy storage equipment, regulation capacity constraint of resources such as a photovoltaic generator unit, a wind turbine unit and a demand side adjustable load and the like.
Step 4: solving the optimal control model established in the step 3 by utilizing a multi-layer deep reinforcement learning algorithm with multi-layer task cooperation to obtain a regulation and control instruction of each layer, and then issuing an instruction according to a cloud edge end architecture to control specific adjustable resources to participate in a control task:
solving the optimal control model established in the step 3 by utilizing a multi-layer deep reinforcement learning algorithm with multi-layer task cooperation, decomposing an adjustable resource control task of a virtual power plant to form multi-layer subtasks, searching a local optimal solution of each current-layer subtask by using the deep reinforcement learning algorithm in each layer to obtain a regulation and control instruction of each layer, and jointly completing the optimal solution of the whole control problem by using the multi-layer deep reinforcement learning algorithm, wherein the specific steps are as follows:
when the regulating and controlling instructions are automatically and optimally decomposed and issued layer by layer from top to bottom, decomposition parameters are introduced, the decomposing and issuing of the regulating and controlling instructions of each layer are realized by a deep reinforcement learning algorithm of the layer, a reward function is the sum of output deviation and running cost of adjustable resources of the layer, an observed quantity is that the layer receives the regulating and controlling instructions of the upper layer, actions are the decomposition parameters of the regulating and controlling instructions of the layer, and a reinforcement learning strategy is that a mapping relation between the observed quantity and the actions is established by a deep neural network.
Specific examples are as follows:
decomposing an adjustable resource control task of a virtual power plant into a plurality of layers of subtasks, solving the optimal control problem by adopting a hierarchical deep reinforcement learning algorithm in consideration of nonlinearity, randomness, uncertainty and complexity of the optimal control problem, automatically decomposing and issuing a regulation and control instruction layer by layer from top to bottom, and performing state sensing, parameter sensing and control sensing by utilizing a cloud end framework to realize the control of fine-granularity adjustable resources of the virtual power plant under different demand response scenes.
Taking the example that the virtual power plant resources participate in the frequency modulation task of the power system, classifying and layering the adjustable resources participating in frequency modulation, decomposing the regulation and control instruction of each layer corresponding to the resources participating in frequency modulation to a second layer according to a decomposition formula, decomposing the regulation and control instruction of the second layer to a third layer according to the decomposition formula, and so on until the regulation and control instruction of the second layer is decomposed to the granularity of a specific unit or device, and establishing an optimal control model and constraint conditions of the adjustable resources according to the response strategy of an application scene.
For the regulation and control instruction decomposition task of each layer, the deep reinforcement learning algorithm of the corresponding layer is used for learning, so that the randomness caused by wind-electricity photovoltaic and the uncertainty caused by participation in demand response in the model can be processed. The established optimization control model is solved by using a multi-layer deep reinforcement learning method with multi-layer task cooperation, so that a regulation and control instruction of each layer can be obtained, then the instruction is issued according to a cloud-edge-end structure, and specific adjustable resources can be accurately controlled to participate in frequency modulation.
1. According to the method, the regulation and control potential of the adjustable resources of the virtual power plant is evaluated, the evaluation result can be used for subsequent classification and layering of the adjustable resources and formation of regulation and control constraints, and the practicability and accuracy of the accurate control method can be improved.
2. The invention adopts the technologies of edge calculation, cloud-edge-end cooperation, network reconstruction and the like, and provides a reliable and rapid communication framework for realizing the adjustable resource accurate control method of the virtual power plant.
3. According to the method, the adjustable resources are layered, the adjustable resource control tasks of the virtual power plant are decomposed to form the multi-layer subtasks, and the automatic optimal decomposition and issuing method of the regulating and controlling instructions from top to bottom layer by layer is provided, so that the accuracy of adjustable resource control can be improved.
4. The invention solves the problem of optimal control of layering by adopting a layering depth reinforcement learning algorithm, and can treat nonlinearity, randomness and uncertainty in actual control.
While the applicant has described and illustrated the embodiments of the present invention in detail with reference to the drawings, it should be understood by those skilled in the art that the above embodiments are only preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not to limit the scope of the present invention, but any improvements or modifications based on the spirit of the present invention should fall within the scope of the present invention.
Claims (12)
1. A virtual power plant adjustable resource accurate control method considering demand response is characterized in that:
the method comprises the following steps:
step 1: acquiring adjustable resource information of a virtual power plant based on a cloud side architecture, evaluating the regulatory potential of adjustable resources, and specifically comprising:
acquiring an adjustable resource typical daily load curve, and predicting the maximum electricity load;
calculating an adjustable proportion of the adjustable resource, a single-user adjustment potential evaluation index and a regional adjustment potential evaluation index based on an adjustable resource typical daily load curve and a maximum electricity load;
wherein, for each type of adjustable resource, the adjustable ratio is equal to the adjustable capacity divided by the total capacity;
each individual user's regulatory potential assessment index comprising:
basic indexes are as follows: regulation capacity, regulation time, regulation precision, regulation rate and duration;
composite performance index: comprehensively regulating and controlling a performance index A, a subscription performance index B, a service time reliability index C, a service capacity reliability index D and a capacity rebound index;
for each region, multiplying various adjustable resource potential evaluation indexes in the region to obtain a region regulation potential evaluation index value;
step 2: classifying and layering the adjustable resources participating in the control task according to the control task of the response of the adjustable resources participating in the demand and the regulation and control information of the adjustable resources, and constructing a multi-layer control framework of the adjustable resources of the virtual power plant corresponding to different types of control tasks by combining the cloud side framework, wherein the multi-layer control framework specifically comprises the following steps:
classifying and layering the adjustable resources participating in the control task according to the control task of the response of the adjustable resources participating in the demand, and the regulation potential, regulation characteristics and spatial distribution of the adjustable resources;
based on cloud side end cooperation and resource layering structure, the regulation and control instruction is issued to the edge proxy through the cloud platform and issued step by step according to the resource layering structure until the intelligent terminal at the bottommost layer receives the regulation and control instruction to complete control, and the whole process forms a multi-layer control framework of the adjustable resource of the virtual power plant;
in the multi-layer control architecture of the adjustable resources of the virtual power plant, the regulation and control instruction of each layer of corresponding resources participating in the control task can be decomposed to the next layer according to a decomposition formula until the regulation and control instruction of the granularity of a specific unit or device is decomposed;
step 3: based on a multi-layer control architecture of the adjustable resources of the virtual power plant, establishing an adjustable resource optimization control model of the virtual power plant and a regulation constraint model thereof corresponding to different types of control tasks according to a demand response strategy and an evaluation result of the regulation potential of the adjustable resources;
step 4: and (3) solving the optimal control model established in the step (3) by utilizing a multi-layer deep reinforcement learning algorithm with multi-layer task cooperation to obtain a regulation and control instruction of each layer, and then controlling specific adjustable resources to participate in a control task according to an instruction issued by a cloud edge end architecture.
2. The method for precisely controlling adjustable resources of a virtual power plant taking into account demand response according to claim 1, wherein the method comprises the following steps:
the cloud side architecture in the step 1 comprises a cloud platform, an edge server and an intelligent terminal;
the intelligent terminal is deployed on equipment of the adjustable resources of the virtual power plant and comprises a sensor and an actuator, and is used for state sensing and control;
forming an edge proxy based on the cooperation of the cloud platform and the edge server, wherein all edge proxies are uniformly managed by the cloud platform, and the edge proxy and the cloud platform interact information through a communication network;
the edge agent distributes equipment codes, equipment numbers, user names and passwords for the intelligent terminals connected with the edge server, receives and processes data of the intelligent terminals, sends a state sensing result obtained through processing to the cloud platform, receives regulation and control instructions of the cloud platform and sends the regulation and control instructions to the intelligent terminals.
3. The method for precisely controlling adjustable resources of a virtual power plant taking into account demand response according to claim 1, wherein the method comprises the following steps:
the step 1 specifically comprises the following steps:
and sensing the model, parameters and state data of the adjustable resources of the virtual power plant by using the intelligent terminal, receiving the sensed data by the edge server, acquiring the related information of the adjustable resources of the virtual power plant by edge calculation, and evaluating the regulation potential of the adjustable resources.
4. A method for precisely controlling adjustable resources of a virtual power plant taking into account demand response according to any one of claims 1 to 3, wherein:
the virtual power plant adjustable resources comprise a gas turbine set, a photovoltaic generator set, a wind turbine set, energy storage equipment and demand side adjustable resources.
5. The method for precisely controlling adjustable resources of a virtual power plant taking into account demand response according to claim 1, wherein the method comprises the following steps:
the composite performance index calculation formula is as follows:
comprehensive regulation performance index a=a 1 A 2 A 3 ；
Wherein A is 1 A is the ratio of the actual regulation speed to the standard regulation speed 2 A is the ratio of the actual regulation precision to the standard regulation precision 3 The ratio of the actual preparation time to the standard preparation time;
acceptance performance index b=β 1 B 1 +β 2 B 2 +β 3 B 3 +β 4 B 4 +β 5 B 5 ；
Wherein B is 1 、B 2 、B 3 、B 4 、B 5 Respectively the ratio of the actual value to the standard value of the preparation time, the regulation speed, the regulation precision, the regulation capacity and the regulation time, beta 1 ,β 2 ,β 3 ,β 4 ,β 5 Respectively B 1 、B 2 、B 3 、B 4 、B 5 Weight value of (2);
service time reliability index c=c 1 /C 2 ；
Wherein C is 1 C for the time of the actual regulation capacity reaching 90% of the total capacity 2 Is the total regulating time;
service capacity reliability index d= (D) 1 +D 2 +D 3 )/D 4 ；
Wherein D is 1 D in response to a capacity deviation within 5% 2 D in order to respond to the capacity deviation of 5 to 10 percent 3 D in order to respond to the capacity deviation of 10 to 20 percent 4 Is the total regulating time;
capacity rebound index e=e 1 (E 2 E 3 )；
Wherein E is 1 To regulate capacity exceeding baseline after end of regulation, E 2 For baseline load, E 3 Is the rebound time.
6. The method for precisely controlling adjustable resources of a virtual power plant taking into account demand response according to claim 1, wherein the method comprises the following steps:
and 2, the control tasks comprise frequency adjustment, voltage adjustment, peak clipping and valley filling and new energy source elimination.
7. The method for precisely controlling adjustable resources of a virtual power plant taking into account demand response according to claim 1, wherein the method comprises the following steps:
the classification and layering of the adjustable resources participating in the control task are specifically as follows:
for a demand response control task, firstly, dividing corresponding different types of adjustable resources into a first layer, then dividing the resources of the first layer into resource groups of a second layer respectively, dividing the resource groups of the second layer into resource groups of a third layer respectively, and so on until granularity of units or equipment is divided, so as to obtain a layered structure of the adjustable resources of the virtual power plant.
8. The method for precisely controlling adjustable resources of a virtual power plant taking into account demand response according to claim 1, wherein the method comprises the following steps:
the step 3 is specifically as follows:
the method comprises the steps of establishing a virtual power plant adjustable resource optimization control model by considering a price-based or incentive-based demand response strategy, a demand response scene, a response scale and response time, and establishing a regulation constraint model according to a regulation potential evaluation result of adjustable resources, wherein the optimization control model comprises specific regulation instructions of each layer in the multi-layer architecture.
9. The method for precisely controlling adjustable resources of a virtual power plant taking into account demand response according to claim 1 or 8, wherein the method comprises the following steps:
in step 3, the adjustable resource optimization control model of the virtual power plant establishes an objective function with the maximization of the benefit of the virtual power plant, specifically:
wherein R (t) is the benefit obtained by the adjustable resource under the price-based or incentive-based demand response strategy, C o (t) is the virtual power plant operation management cost and the operation cost of the adjustable resource, C p And (t) penalty cost for actual power output of the virtual power plant and scheduling plan deviation.
10. The method for precisely controlling the adjustable resources of the virtual power plant taking into account the demand response according to claim 9, wherein the method comprises the following steps of:
assuming N in a certain control task 1 The first layer of the adjustable resource cluster participates, and then the benefit R (t) is expressed as
Wherein P is i (t) is the output of the ith cluster, i.e. the regulation command of the ith cluster of the first layer, and F (t) is a benefit function;
the regulating and controlling instruction of the ith cluster of the first layer is distributed to a specific adjustable resource group of the second layer, and the decomposition formula is as follows
Wherein N is 2 For the number of adjustable resource groups corresponding to the ith cluster of the first layer in the second layer, alpha j Decomposition parameters for the j-th adjustable resource group, P ij And (t) decomposing the regulation and control instruction to the j-th adjustable resource group of the second layer, and analogizing to obtain the regulation and control instruction decomposition relation of each layer.
11. The method for precisely controlling the adjustable resources of the virtual power plant taking into account the demand response according to claim 9, wherein the method comprises the following steps of:
in step 3, the following regulation and control constraint model is established by considering the adjustable loads of the gas turbine set, the energy storage equipment, the photovoltaic generator set, the wind turbine set and the demand side of the virtual power plant:
adjusting capacity constraint P min ≤P≤P max ；
Wherein P is min ,P max Respectively minimum and maximum of capacity;
adjusting the speed constraint S min ≤P(t)-P(t-1)≤S max ；
Wherein S is min ,S max Respectively a minimum value and a maximum value of the speed;
the time constraint P (t) =0 is adjusted,
wherein T is P A period of time during which regulation may be participated;
adjusting accuracy constraints
Wherein P is real To adjust the actual output of the resource,
respectively the minimum value and the maximum value of the precision;
comprehensive regulation performance constraint A min ≤A≤A max ；
A min ,A max Respectively minimum and maximum values of comprehensive performances;
acceptance and payment performance constraint B min ≤B≤B max ；
B min ,B max Respectively minimum and maximum of the acceptance performance.
12. The method for precisely controlling adjustable resources of a virtual power plant taking into account demand response according to claim 1, wherein the method comprises the following steps:
in step 4, solving the optimal control model established in step 3 by using a multi-layer deep reinforcement learning algorithm with multi-layer task cooperation, decomposing the adjustable resource control task of the virtual power plant to form multi-layer subtasks, searching a local optimal solution of each current-layer subtask by using the deep reinforcement learning algorithm in each layer to obtain a regulation and control instruction of each layer, and jointly completing the optimal solution of the whole control problem by using the multi-layer deep reinforcement learning algorithm, wherein the specific steps are as follows:
when the regulating and controlling instructions are automatically and optimally decomposed and issued layer by layer from top to bottom, decomposition parameters are introduced, the decomposing and issuing of the regulating and controlling instructions of each layer are realized by a deep reinforcement learning algorithm of the layer, a reward function is the sum of output deviation and running cost of adjustable resources of the layer, an observed quantity is that the layer receives the regulating and controlling instructions of the upper layer, actions are the decomposition parameters of the regulating and controlling instructions of the layer, and a reinforcement learning strategy is that a mapping relation between the observed quantity and the actions is established by a deep neural network.
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| CN118095806B (en) * | 2024-04-28 | 2024-07-09 | 国网山东省电力公司营销服务中心(计量中心) | Virtual power plant resource scheduling method, system, medium, equipment and program product |
| CN118539433B (en) * | 2024-07-24 | 2024-10-11 | 南京邮电大学 | Virtual power plant frequency control and transmission communication joint design method under Yun Bianduan cooperation |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103903066A (en) * | 2014-04-04 | 2014-07-02 | 东南大学 | Virtual power plant stratified random optimized dispatching method |
| CN110188950A (en) * | 2019-05-30 | 2019-08-30 | 三峡大学 | Virtual plant supply side and Demand-side Optimized Operation modeling method based on multi-agent technology |
| CN111245026A (en) * | 2020-03-09 | 2020-06-05 | 国网冀北电力有限公司 | Virtual power plant regulation and control method, system and equipment |
| CN111355233A (en) * | 2020-03-18 | 2020-06-30 | 国网浙江嘉善县供电有限公司 | Multi-virtual power plant coordination optimization control method |
| CN112928749A (en) * | 2021-01-18 | 2021-06-08 | 西安交通大学 | Virtual power plant day-ahead scheduling method integrating multi-energy demand side resources |
| CN113013929A (en) * | 2021-04-20 | 2021-06-22 | 天津大学 | Load curve adjustment-oriented active power distribution network simulation optimization operation method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9300141B2 (en) * | 2010-11-18 | 2016-03-29 | John J. Marhoefer | Virtual power plant system and method incorporating renewal energy, storage and scalable value-based optimization |
-
2021
- 2021-10-08 CN CN202111171340.7A patent/CN113904380B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103903066A (en) * | 2014-04-04 | 2014-07-02 | 东南大学 | Virtual power plant stratified random optimized dispatching method |
| CN110188950A (en) * | 2019-05-30 | 2019-08-30 | 三峡大学 | Virtual plant supply side and Demand-side Optimized Operation modeling method based on multi-agent technology |
| CN111245026A (en) * | 2020-03-09 | 2020-06-05 | 国网冀北电力有限公司 | Virtual power plant regulation and control method, system and equipment |
| CN111355233A (en) * | 2020-03-18 | 2020-06-30 | 国网浙江嘉善县供电有限公司 | Multi-virtual power plant coordination optimization control method |
| CN112928749A (en) * | 2021-01-18 | 2021-06-08 | 西安交通大学 | Virtual power plant day-ahead scheduling method integrating multi-energy demand side resources |
| CN113013929A (en) * | 2021-04-20 | 2021-06-22 | 天津大学 | Load curve adjustment-oriented active power distribution network simulation optimization operation method |
Non-Patent Citations (2)
| Title |
|---|
| 适应新型电力供需的多元化友好互动体系研究;李作锋，黄奇峰，杨世海;江苏电机工程;第35卷(第5期);全文 * |
| 采用双层优化调度的虚拟电厂经济性分析;张高;王旭;蒋传文;张裕;王正宇;;电网技术(第8期);全文 * |
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