Multi-type power market cooperative control method and system and storage medium

Abstract

The invention discloses a multi-type power market cooperative control method and system and a storage medium, and relates to the field of power cooperative control. Comprising the following steps: a first node receives a request from an electric energy demand side and transmits the request to a father layer node, the father layer node transmits the power supply request to a second node, and a centralized cooperative control platform determines a supply strategy according to the content of the request; the second node performs overall planning on the electric energy supply type of the supply side, generates an electric energy supply meter according to the electric energy supply mode and the electric energy supply type, and establishes an electric energy supply meter index according to the electric energy supply stability for quickly selecting the electric energy supply type; and the father layer node selects a power supply mode of a power supply side in the second node according to the supply strategy, and calls power supply of the second node. The method has the remarkable beneficial effects of improving market flexibility, optimizing resource allocation, enhancing power supply stability, promoting energy conservation and emission reduction, improving market efficiency, supporting market diversified development and the like.

Description

Technical Field

[0001] The present invention relates to the field of electric power coordinated control, and more specifically to a method, system and storage medium for coordinated control of multiple types of electric power markets. Background Art

[0002] As the installed capacity of renewable energy continues to increase, more and more backup configurations are required to deal with the uncertainty of renewable energy output, which puts great pressure on the flexibility adjustment of the system and requires optimization of backup configurations. Domestic and foreign scholars have done a lot of research on the backup optimization of power systems containing renewable energy. According to the uncertainty information of renewable energy, the modeling of renewable energy output uncertainty can generally be divided into two categories: 1) Only the upper and lower boundaries of the renewable energy prediction interval are known, and the uncertainty of renewable energy output is described by intervals, and then a robust optimization or interval optimization model is established; 2) The probability distribution information of renewable energy prediction output is known, and the uncertainty of renewable energy output is described, and then a stochastic optimization, opportunity-constrained optimization and cost / benefit trade-off optimization model are established. In the cost / benefit trade-off optimization model, the reliability cost is usually expressed in terms of expected energy not supplied (EENS) and expected wind energy spillage (EWS). By introducing the reliability cost into the objective function, it is one of the current research hotspots to balance safety and economy to find the optimal. However, the above modeling method also has many defects. For example, data processing is not flexible enough. Therefore, how to give the above model a more flexible data processing mechanism and a more flexible power access mechanism is urgently needed to be studied by technical personnel in this field. Summary of the invention

[0003] In view of this, the present invention provides a method, system and storage medium for collaborative control of multiple types of electricity markets. By studying the collaborative technology between different types of electricity markets at multiple time scales, the optimal control strategy is sought. At the same time, it has significant beneficial effects in improving market flexibility, optimizing resource allocation, enhancing power supply stability, promoting energy conservation and emission reduction, improving market efficiency and supporting the diversified development of the market.

[0004] In order to achieve the above object, the present invention adopts the following technical solution:

[0005] A multi-type power market collaborative control method, the multi-type power market includes a first node, a second node and a centralized collaborative control platform, the first node is used for the power demand side, the second node is used for the power supply side, the first node and the second node communicate with each other through the centralized collaborative control platform, the centralized collaborative control platform is internally provided with a parent node for receiving power requests from the first node and the second node, comprising the following steps:

[0006] Step 1: The first node receives a request from the power demand side and transmits it to the parent node. The parent node transmits the power supply request to the second node. The centralized collaborative control platform determines the supply strategy according to the request content.

[0007] Step 2: The second node comprehensively plans the power supply type on the supply side, generates a power supply table according to the power supply mode and the power energy supply type, and establishes a power supply table index according to the power supply stability, so as to quickly select the power supply type;

[0008] Step three: the parent node selects a power supply mode on the power supply side of the second node according to the supply strategy, and calls the power supply of the second node.

[0009] Optionally, the centralized collaborative control platform determines the supply strategy based on the request content, including determining the uncertainty of wind power, load and equipment failure. The wind power uncertainty is described by a continuous Gaussian probability distribution, and the load uncertainty is also described by a Gaussian distribution and discretized.

[0010] Optionally, the first node is provided with a request range for responding to the client's request in advance, and the construction of the request range is specifically as follows:

[0011] Based on the SVM algorithm, the request behavior model in the machine learning module is constructed;

[0012] The request model is determined by taking the power supply type, electrical parameter information, electric energy size and time interval parameters of different request ranges as inputs of the request behavior model.

[0013] Optionally, the second node is also connected to traditional energy to replenish new energy electricity.

[0014] Optionally, the parent node adjusts the supply strategy according to the request content of the first node and the power supply mode and power energy supply type of the second node.

[0015] Optionally, the centralized collaborative control platform is also used to provide operating backup for new energy stations through load reduction operation.

[0016] A multi-type power market collaborative control system, the multi-type power market includes a first node, a second node and a centralized collaborative control platform, the first node is used for the power demand side, the second node is used for the power supply side, the first node and the second node communicate with each other through the centralized collaborative control platform, the centralized collaborative control platform is internally provided with a parent node for receiving power requests from the first node and the second node, including the following steps:

[0017] Power information receiving module: the first node is used to receive a request from the power demand side and transmit it to the parent node, the parent node transmits the power supply request to the second node, and the centralized collaborative control platform determines the supply strategy according to the request content;

[0018] Power information analysis module: The second node is used to make overall planning for the power supply type on the supply side, generate a power supply table according to the power supply mode and power energy supply type, and establish a power supply table index according to the power supply stability, so as to quickly select the power supply type;

[0019] Power supply module: The parent node is used to select the power supply mode of the power supply side in the second node according to the supply strategy, and call the power supply of the second node.

[0020] A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any one of the steps of a multi-type electricity market collaborative control method.

[0021] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides a multi-type power market collaborative control method, system and storage medium, which have the following beneficial effects:

[0022] 1. Improve the flexibility of the power market: Through real-time information exchange between the first node (demand side) and the second node (supply side), as well as the intelligent scheduling of the centralized collaborative control platform, the power market can quickly respond to changes in supply and demand, flexibly adjust power supply strategies and methods, and meet diverse power needs.

[0023] 2. Optimize resource allocation: The second node makes overall planning for the power supply type on the supply side and establishes a power supply table and its index, which helps to achieve the optimal allocation of power resources. By quickly selecting the most suitable power supply type, various energy sources can be utilized to the maximum extent and resource waste can be reduced.

[0024] 3. Enhanced power supply stability: This method takes into account the stability of power supply when establishing the power supply meter, which means that energy types that can provide stable power supply will be given priority in the power supply selection process. This helps reduce the risk of power outages and improves the stability and reliability of the entire power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0026] Figure 1 It is a schematic diagram of the node structure of the present invention;

[0027] Figure 2 It is a node workflow diagram of the present invention. DETAILED DESCRIPTION

[0028] 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.

[0029] Example 1

[0030] The embodiment of the present invention discloses a multi-type power market collaborative control method, such as Figure 1 and Figure 2 As shown, the multi-type electricity market includes a first node, a second node and a centralized collaborative control platform, the first node is used for the power demand side, the second node is used for the power supply side, the first node and the second node communicate with each other through the centralized collaborative control platform, and the centralized collaborative control platform is internally provided with a parent node for receiving power requests from the first node and the second node, including the following steps:

[0031] Step 1: The first node receives a request from the power demand side and transmits it to the parent node. The parent node transmits the power supply request to the second node. The centralized collaborative control platform determines the supply strategy according to the request content.

[0032] Step 2: The second node comprehensively plans the power supply type on the supply side, generates a power supply table according to the power supply mode and the power energy supply type, and establishes a power supply table index according to the power supply stability, so as to quickly select the power supply type;

[0033] Step three: the parent node selects a power supply mode on the power supply side of the second node according to the supply strategy, and calls the power supply of the second node.

[0034] Furthermore, the first node is provided with a request range for responding to the client's request in advance, and the construction of the request range is specifically as follows:

[0035] Based on the SVM algorithm, the request behavior model in the machine learning module is constructed;

[0036] The request model is determined by taking the power supply type, electrical parameter information, electric energy size and time interval parameters of different request ranges as inputs of the request behavior model.

[0037] Furthermore, the second node is also connected to traditional energy to replenish new energy electricity.

[0038] Furthermore, the parent node adjusts the supply strategy according to the request content of the first node and the power supply mode and power energy supply type of the second node.

[0039] Furthermore, the centralized collaborative control platform is also used to provide operation backup for new energy stations through load reduction operation. The proposed backup optimization model aims to minimize the total cost generated by the system during the operation cycle:

[0040]

[0041] Where: NT, NG, NW are the total number of optimization periods, the total number of traditional generators and the total number of wind farms respectively; P g,t represents the output of the traditional dispatchable unit g at time t; C g (u g,t ,P g,t )and Respectively represent the operating cost of traditional units and the grid-connected cost of wind turbines, Indicates wind power; u g,t , and is a 0 / 1 variable, and when its value is 1, it means that the unit g is in the running, starting, or shutting down state during the period t; and They represent the startup and shutdown costs of the traditional unit g respectively; and Respectively represent the backup costs of the traditional unit g and the wind farm w; and They represent the up and down reserve provided by the traditional unit g and the wind farm w respectively; VOLL (value of lost load) and VOLW (value of lost wind) represent the penalty price for loss of load and the penalty price for wind abandonment respectively.

[0042] The model includes the following constraints:

[0043] 1) Power balance constraints:

[0044]

[0045] Where: NB is the total number of nodes; represents the load of node b at time t.

[0046] 2) Traditional unit standby constraints

[0047]

[0048]

[0049] Where: and are the upper and lower limits of the output power of unit g; UR g and DR g are the up and down climbing rates of unit g respectively; τ is the standby response time, which is 0.5h in the present invention; and They respectively represent the upward spinning reserve and downward spinning reserve provided by traditional generators.

[0050] 3) Line flow constraints:

[0051]

[0052] Where: H l,b represents the power injection factor of node b to the generation load transfer factor of line l; T l is the line capacity.

[0053] 4) Unit operation constraints:

[0054]

[0055] The constraint set in the above formula includes the upper and lower limits of the generator set output power, the minimum operation start and stop time constraints, the initial condition constraints, and the unit output power rate constraints, all of which are considered in the model. In order to simplify the description, they are not listed in detail one by one.

[0056] EENS refers to the expected value of power shortage caused by the possible disturbance in the power system when the system's upward reserve capacity is insufficient, while EWS refers to the expected wind curtailment caused by the system's downward reserve capacity being insufficient to cope with the possible disturbance in the power system. EENS and EWS are respectively expressed as the integral of the product of the load loss and wind curtailment with the corresponding probability density function.

[0057] The present invention considers the uncertainty of wind power, load and equipment failure at the same time. Wind power uncertainty is described by continuous Gaussian probability distribution. Load uncertainty is also described by Gaussian distribution and discretized. Equipment failure only considers single equipment failure.

[0058] EENS is expressed as follows:

[0059]

[0060] Where: N DLThe total number of probability intervals representing load forecast errors. The width of each interval is the standard deviation of the load forecast error. The corresponding probability value is p dl,t ;p g,t represents the failure probability of the traditional unit g at time t; 0 / 1 variable b g,w,dl,t Ensure that load shedding occurs only when the reserve is insufficient to cope with the uncertainty of wind power, load and equipment failure; Indicates the amount of fluctuation caused by load uncertainty; Indicates random output of wind power Less than the load reduction value The fluctuation caused by the actual wind power output is only Lower than the wind power load reduction value , the fluctuation is non-zero; ΔT represents the duration of a single optimization period, which is 1h in the present invention.

[0061] For EWS, wind curtailment will only occur if there is excess power in the system. Equipment failure will not cause excess power; excess wind power means that the actual wind power output is greater than the predicted value, and the wind power load reduction operation value is always less than the predicted value during load reduction operation, which will not cause excess power. Therefore, the only thing that can cause excess power is load disturbance.

[0062] EWS is expressed as follows:

[0063]

[0064] Where: 0 / 1 variable b w,dl,t It ensures that wind power is abandoned only when the reserve is insufficient to cope with load uncertainty.

[0065] This embodiment also discloses a multi-type power market collaborative control system, the multi-type power market includes a first node, a second node and a centralized collaborative control platform, the first node is used for the power demand side, the second node is used for the power supply side, the first node and the second node communicate with each other through the centralized collaborative control platform, and the centralized collaborative control platform is internally provided with a parent node for receiving power requests from the first node and the second node, including the following steps:

[0066] Power information receiving module: the first node is used to receive a request from the power demand side and transmit it to the parent node, the parent node transmits the power supply request to the second node, and the centralized collaborative control platform determines the supply strategy according to the request content;

[0067] Power information analysis module: The second node is used to make overall planning for the power supply type on the supply side, generate a power supply table according to the power supply mode and power energy supply type, and establish a power supply table index according to the power supply stability, so as to quickly select the power supply type;

[0068] Power supply module: The parent node is used to select the power supply mode of the power supply side in the second node according to the supply strategy, and call the power supply of the second node.

[0069] Finally, this embodiment discloses a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, any one of the steps of the multi-type electricity market collaborative control method is implemented.

[0070] Example 2

[0071] The IEEE-RTS24-node system is used as an example for analysis. The system includes 24 nodes, 26 generators, and 38 transmission lines. Among them, 17 nodes are connected to loads, and the peak load is 2850MW. The detailed parameters of the example can be found in the literature. The price of the traditional unit for up and down reserve is 10% of the highest marginal cost of each unit after the linearization of the cost function. Wind turbines with an installed capacity of 200MW are connected to nodes 5 and 22, and the wind power forecast values ​​for each period and the segmented probability values ​​after discretization are from the literature. The cost of wind power grid connection is 20$ / MWh, and the cost of wind power reserve is 1$ / MW. The standard deviation of wind power is set to 15% of the predicted value, and the standard deviation of load is set to 3% of the predicted value. The load loss value VOLL and the wind abandonment value VOLW are 5000$ / MWh and 500$ / MWh respectively.

[0072] The model proposed in this invention is implemented based on platform programming, and the commercial solver CPLEX is called for solution, with a convergence accuracy of 0.1%. The computer configuration is Win10 system, Intel Core i5-4460 series, main frequency 3.2GHz, and memory 8G.

[0073] Taking wind power as an example of renewable energy, four wind power backup strategies are compared.

[0074] Strategy 1: Traditional control strategy, which assumes that wind power cannot provide backup;

[0075] Strategy 2: The most radical strategy, assuming that wind power reserve is completely reliable;

[0076] Strategy 3: The most conservative strategy, which assumes that wind power can only provide backup when the random output value of wind power is greater than the predicted value of wind power, so the reliability of wind power backup is 50%;

[0077] Strategy 4: Refined strategy, that is, the method of considering wind power reserve in the present invention.

[0078] Table 1 Comparison of results under different wind power reserve strategies

[0079]

[0080]

[0081] As can be seen from Table 1, compared with Strategy 1 where wind power cannot provide backup, the total costs of Strategies 2, 3, and 4, which consider wind power to provide backup, have all decreased, and the system economy has improved. Specifically, the total cost of Strategy 2 decreased by 5.1%, the total cost of Strategy 3 decreased by 3.2%, and the total cost of Strategy 4 decreased by 4.5%. The total cost reduction ratio of Strategy 4 is between Strategy 2 and 3, which is neither too radical like Strategy 2 nor too conservative like Strategy 3. In addition, the operating costs of traditional units corresponding to Strategies 2, 3, and 4 have all increased. The main reason is that the load reduction operation of wind power has led to an increase in the output of traditional units, which in turn has led to an increase in the operating costs of traditional units. Similarly, due to the load reduction operation of wind power, wind power can provide backup, so the backup costs of traditional units corresponding to Strategies 2, 3, and 4 are all decreasing, indicating that wind power providing backup can alleviate the pressure on traditional backup resources, and the load loss cost and wind abandonment cost are also decreasing, especially the wind abandonment cost is 0, indicating that wind power providing backup can reduce wind power uncertainty. From the calculation time comparison of the four strategies, it can be seen that after considering the backup provided by wind power, the calculation time of strategies 2 and 3 is extended, but the calculation time of strategy 4 is shortened. The main reason is that compared with strategies 1, 2, and 3, which need to discretize the entire uncertainty interval of wind power, strategy 4 only needs to discretize the interval on the left side of the wind power forecast value, which reduces the complexity of the model.

[0082] In general, when a large number of renewable energy power generation is connected, in order to cope with the uncertainty of its output, the system needs to configure additional spare capacity and reserve sufficient peak-shaving capacity to absorb renewable energy power generation. Some thermal power units even need to be started and stopped during the day, which increases the operating cost and flexibility regulation pressure of the system to a certain extent. If a suitable dispatching method is adopted, such as considering the participation of renewable energy in providing backup, the economical balance of system operation can be used in the dispatching process to reduce the spare capacity and peak-shaving pressure required for system operation, and improve the economy and reliability of system operation.

[0083] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0084] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined in the present invention may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown in the present invention, but will conform to the widest scope consistent with the principles and novel features disclosed in the present invention.

Claims

1. A multi-type electricity market collaborative control method, characterized in that: The multi-type electricity market includes a first node, a second node and a centralized collaborative control platform, wherein the first node is used for the power demand side, the second node is used for the power supply side, the first node and the second node communicate with each other through the centralized collaborative control platform, and the centralized collaborative control platform is internally provided with a parent node for receiving power requests from the first node and the second node, including the following steps: Step 1: The first node receives a request from the power demand side and transmits it to the parent node. The parent node transmits the power supply request to the second node. The centralized collaborative control platform determines the supply strategy according to the request content. Step 2: The second node comprehensively plans the power supply type on the supply side, generates a power supply table according to the power supply mode and the power energy supply type, and establishes a power supply table index according to the power supply stability, so as to quickly select the power supply type; Step three: the parent node selects a power supply mode on the power supply side of the second node according to the supply strategy, and calls the power supply of the second node.

2. A multi-type electricity market collaborative control method according to claim 1, characterized in that: The centralized collaborative control platform determines the supply strategy according to the request content, including determining the uncertainty of wind power, load and equipment failure. The wind power uncertainty is described by a continuous Gaussian probability distribution, and the load uncertainty is also described by a Gaussian distribution and discretized.

3. A multi-type electricity market collaborative control method according to claim 1, characterized in that: The first node is provided with a request range for responding to the client's request in advance. The construction of the request range is specifically as follows: Based on the SVM algorithm, the request behavior model in the machine learning module is constructed; The request model is determined by taking the power supply type, electrical parameter information, electric energy size and time interval parameters of different request ranges as inputs of the request behavior model.

4. A multi-type electricity market collaborative control method according to claim 1, characterized in that: The second node is also connected to traditional energy to replenish new energy electricity.

5. A multi-type electricity market collaborative control method according to claim 1, characterized in that: The parent node adjusts the supply strategy according to the request content of the first node and the power supply mode and power energy supply type of the second node.

6. A multi-type electricity market collaborative control method according to claim 1, characterized in that: The centralized collaborative control platform is also used to provide operating backup for new energy stations through load reduction operation.

7. A multi-type power market collaborative control system, characterized in that: The multi-type electricity market includes a first node, a second node and a centralized collaborative control platform, wherein the first node is used for the power demand side, the second node is used for the power supply side, the first node and the second node communicate with each other through the centralized collaborative control platform, and the centralized collaborative control platform is internally provided with a parent node for receiving power requests from the first node and the second node, including the following steps: Power information receiving module: the first node is used to receive a request from the power demand side and transmit it to the parent node, the parent node transmits the power supply request to the second node, and the centralized collaborative control platform determines the supply strategy according to the request content; Power information analysis module: The second node is used to make overall planning for the power supply type on the supply side, generate a power supply table according to the power supply mode and power energy supply type, and establish a power supply table index according to the power supply stability, so as to quickly select the power supply type; Power supply module: The parent node is used to select the power supply mode of the power supply side in the second node according to the supply strategy, and call the power supply of the second node.

8. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by the processor, the steps of a multi-type electricity market collaborative control method as described in any one of claims 1-6 are implemented.

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