Control method and system of wind storage electric field cluster, electronic equipment and storage medium

Abstract

The invention discloses a control method and system of a wind storage electric field cluster, electronic equipment and a storage medium, and belongs to the technical field of wind power generation technologies. The control method of the wind storage electric field cluster comprises the following steps: dividing a wind storage electric field of the wind storage electric field cluster into a first wind storage electric field and a plurality of second wind storage electric fields; constructing a strategy game model comprising the first wind storage electric field and the second wind storage electric field; determining a current power dispatching demand, and performing iterative game calculation in the strategy game model according to the current power dispatching demand; determining probability distribution information when an iterative game calculation result reaches a stable state; and according to the probability distribution information, determining a target probability that the first wind storage electric field adopts a zero determinant strategy, and controlling the power of the wind storage electric field cluster under a constraint condition. According to the invention, the power of the wind storage electric field cluster can be reasonably scheduled.

Description

Technical Field

[0001] The present application relates to the technical field of wind power generation, and in particular to a control method, system, electronic equipment and storage medium for a wind power storage farm cluster. Background Art

[0002] With the rapid growth of renewable energy, especially the large-scale deployment of wind and solar energy, the power system is undergoing significant changes. However, the intermittent nature and uncertainty of renewable energy have brought new challenges to the power system. As a multi-energy hybrid system, wind farms integrate wind energy, energy storage and other energy sources, providing an important solution to these challenges. The development of wind power technology faces some common challenges, one of which is the lack of active response to collective decision-making, which makes it difficult to achieve reasonable power dispatch in the actual implementation of the dispatch plan.

[0003] Therefore, how to reasonably dispatch the power of a wind farm cluster is a technical problem that technicians in this field currently need to solve. Summary of the invention

[0004] The purpose of the present application is to provide a control method for a wind-storage power farm cluster, a control system for a wind-storage power farm cluster, an electronic device and a storage medium, which can reasonably dispatch the power of the wind-storage power farm cluster.

[0005] In order to solve the above technical problems, the present application provides a control method for a wind power plant cluster, comprising:

[0006] The wind storage power farms of the wind storage power farm cluster are divided into a first wind storage power farm and a plurality of second wind storage power farms; wherein the first wind storage power farm is a wind storage power farm directly connected to the dispatching center, and the second wind storage power farm is other wind storage power farms in the wind storage power farm cluster except the first wind storage power farm;

[0007] Constructing a strategy game model including the first wind storage power farm and the second wind storage power farm; wherein the strategy game model is a game model for describing the strategy adopted by the first wind storage power farm and the second wind storage power farm to control the wind storage power farm cluster benefits;

[0008] Determine a current power dispatch demand, and perform iterative game calculation in the strategy game model according to the current power dispatch demand;

[0009] Determine the probability distribution information when the iterative game calculation result reaches a stable state; wherein the probability distribution information is used to describe the probability that the first wind-storage power plant and the second wind-storage power plant adopt the target strategy in the current round of iterative game calculation, and the target strategy adopted by the first wind-storage power plant is the zero determinant strategy;

[0010] The target probability of the first wind-storage power plant adopting the zero-determinant strategy is determined according to the probability distribution information, and the power of the wind-storage power plant cluster is controlled under constraints; wherein the constraint condition is that the conditional probability of the first wind-storage power plant adopting the zero-determinant strategy to control the efficiency of the wind-storage power plant cluster is the target probability.

[0011] Optionally, also include:

[0012] Determine a first allocation coefficient according to the waveform similarity between the equivalent output curve and the actual output curve of each wind storage power farm; wherein the first allocation coefficient is positively correlated with the waveform similarity;

[0013] Determine a second allocation coefficient according to the excess wind energy utilization of each wind storage farm; wherein the second allocation coefficient is positively correlated with the excess wind energy utilization;

[0014] Determine a third allocation coefficient according to the voltage deviation value of each wind farm; wherein the third allocation coefficient is negatively correlated with the voltage deviation value; the voltage deviation value is determined according to the absolute value of the difference between the grid-connected node voltage and the preset voltage;

[0015] Calculate the Shapley value benefit allocation coefficient of each wind-storage power plant according to the first allocation coefficient, the second allocation coefficient and the third allocation coefficient;

[0016] The benefit allocation result of each wind-storage power plant is calculated according to the Shapley value benefit allocation coefficient.

[0017] Optionally, after calculating the benefit allocation result of each wind-storage power plant according to the Shapley value benefit allocation coefficient, the method further includes:

[0018] The grid-connected electricity price strategy of each wind-storage power plant is calculated according to the benefit allocation result.

[0019] Optionally, calculating the Shapley value benefit allocation coefficient of each wind-storage power plant according to the first allocation coefficient, the second allocation coefficient and the third allocation coefficient includes:

[0020] Calculate the weight coefficients of the first allocation coefficient, the second allocation coefficient, and the third allocation coefficient by an entropy method;

[0021] The first allocation coefficient, the second allocation coefficient and the third allocation coefficient are weightedly calculated based on the weight coefficient to obtain the Shapley value benefit allocation coefficient of each wind storage farm.

[0022] Optionally, also include:

[0023] Determining the power regulation boundary of each wind storage farm;

[0024] Constructing a wind speed model of the wind turbine generator set of the wind storage farm according to the power regulation boundary;

[0025] Determining an induction coefficient according to the wind speed model; wherein the induction coefficient is used to describe the influence of the wake effect between the wind turbines on the wind speed;

[0026] Determining a wind speed error between an actual wind speed of the wind turbine generator set and a target wind speed;

[0027] Performing empirical mode decomposition on the wind speed error to obtain an induction coefficient error;

[0028] Using a gated circulation unit to perform an inverse calculation on the inductance error to obtain a unit control strategy;

[0029] The rotor speed and pitch angle of the wind turbine set are adjusted according to the set control strategy.

[0030] Optionally, determining the power regulation boundary of each wind power storage farm includes:

[0031] Determining the upper boundary of active power regulation of each wind power storage farm according to the mechanical power limit value of the wind turbine generator set;

[0032] Determining the lower limit of active power regulation of each wind power storage farm according to the current rotation speed, current pitch angle, maximum rotation speed and maximum pitch angle of the wind turbine generator set;

[0033] The reactive power regulation upper boundary and the reactive power regulation lower boundary of each wind power storage farm are determined according to the power factor of the wind turbine generator set.

[0034] Optionally, before performing inversion calculation on the inductance error by using a gated cycle unit, the method further includes:

[0035] The modal components of the wind speed data are extracted, and the gated cyclic unit is trained using the modal components so that the gated cyclic unit learns the corresponding relationship between the control parameters and the induction coefficient; wherein the control parameters include the rotor speed and the pitch angle.

[0036] The present application also provides a control system for a wind farm cluster, the system comprising:

[0037] A role division module, used for dividing the wind storage power field of the wind storage power field cluster into one first wind storage power field and multiple second wind storage power fields; wherein the first wind storage power field is a wind storage power field directly connected to the dispatching center, and the second wind storage power field is other wind storage power fields in the wind storage power field cluster except the first wind storage power field;

[0038] A model building module, used to build a strategy game model including the first wind storage power farm and the second wind storage power farm; wherein the strategy game model is a game model used to describe the strategy adopted by the first wind storage power farm and the second wind storage power farm to control the benefits of the wind storage power farm cluster;

[0039] A game calculation module, used to determine the current power dispatch demand, and perform iterative game calculation in the strategy game model according to the current power dispatch demand;

[0040] A probability calculation module, used to determine the probability distribution information when the iterative game calculation result reaches a stable state; wherein the probability distribution information is used to describe the probability that the first wind-storage power plant and the second wind-storage power plant adopt the target strategy in the current round of iterative game calculation, and the target strategy adopted by the first wind-storage power plant is the zero determinant strategy;

[0041] A power control module is used to determine the target probability of the first wind-storage power plant adopting a zero-determinant strategy based on the probability distribution information, and to control the power of the wind-storage power plant cluster under constraints; wherein the constraint is that the conditional probability of the first wind-storage power plant adopting a zero-determinant strategy to control the benefits of the wind-storage power plant cluster is the target probability.

[0042] Furthermore, it also includes:

[0043] A benefit allocation module is used to determine a first allocation coefficient based on the waveform similarity between the equivalent output curve and the actual output curve of each wind storage farm; wherein the first allocation coefficient is positively correlated with the waveform similarity; it is also used to determine a second allocation coefficient based on the excess wind energy utilization of each wind storage farm; wherein the second allocation coefficient is positively correlated with the excess wind energy utilization; it is also used to determine a third allocation coefficient based on the voltage deviation value of each wind storage farm; wherein the third allocation coefficient is negatively correlated with the voltage deviation value; the voltage deviation value is determined based on the absolute value of the difference between the grid-connected node voltage and the preset voltage; it is also used to calculate the Shapley value benefit allocation coefficient of each wind storage farm based on the first allocation coefficient, the second allocation coefficient and the third allocation coefficient; it is also used to calculate the benefit allocation result of each wind storage farm based on the Shapley value benefit allocation coefficient.

[0044] Furthermore, it also includes:

[0045] The electricity price strategy calculation module is used to calculate the grid-connected electricity price strategy of each wind-storage power farm according to the benefit allocation result after calculating the benefit allocation result of each wind-storage power farm according to the Shapley value benefit allocation coefficient.

[0046] Furthermore, the process of the benefit distribution module calculating the Shapley value benefit distribution coefficient of each wind storage power farm based on the first distribution coefficient, the second distribution coefficient and the third distribution coefficient includes: calculating the weight coefficient of the first distribution coefficient, the second distribution coefficient and the third distribution coefficient by the entropy method; performing weighted calculation on the first distribution coefficient, the second distribution coefficient and the third distribution coefficient based on the weight coefficient to obtain the Shapley value benefit distribution coefficient of each wind storage power farm.

[0047] Furthermore, it also includes:

[0048] A unit regulation module is used to determine the power regulation boundary of each wind storage farm; it is also used to construct a wind speed model of the wind turbines of the wind storage farm according to the power regulation boundary; it is also used to determine the induction coefficient according to the wind speed model; wherein the induction coefficient is used to describe the influence of the wake effect between the wind turbines on the wind speed; it is also used to determine the wind speed error between the actual wind speed of the wind turbine and the target wind speed; it is also used to perform empirical mode decomposition on the wind speed error to obtain the induction coefficient error; it is also used to perform inverse calculation on the induction coefficient error using a gated loop unit to obtain a unit control strategy; it is also used to adjust the rotor speed and pitch angle of the wind turbine according to the unit control strategy.

[0049] Furthermore, the process of the unit regulation module determining the power regulation boundary of each of the wind storage farms includes: determining the upper boundary of active power regulation of each of the wind storage farms according to the mechanical power limit value of the wind turbine unit; determining the lower boundary of active power regulation of each of the wind storage farms according to the current speed, current pitch angle, maximum speed and maximum pitch angle of the wind turbine unit; and determining the upper boundary and lower boundary of reactive power regulation of each of the wind storage farms according to the power factor of the wind turbine unit.

[0050] Furthermore, it also includes:

[0051] A training module is used to extract modal components of wind speed data before using a gated cyclic unit to perform inversion calculation on the inductance coefficient error, and use the modal components to train the gated cyclic unit so that the gated cyclic unit learns the corresponding relationship between control parameters and inductance coefficient; wherein the control parameters include rotor speed and pitch angle.

[0052] The present application also provides a storage medium on which a computer program is stored. When the computer program is executed, the steps of the control method for the wind power storage farm cluster are implemented.

[0053] The present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, the steps of the control method of the wind-storage power plant cluster are implemented.

[0054] The present application provides a control method for a wind storage farm cluster. The method divides the wind storage farm cluster into a first wind storage farm and multiple second wind storage farms, and constructs a strategy game model including the first wind storage farm and the second wind storage farm, so as to use the strategy game model to describe the strategy interaction of the first wind storage farm and the second wind storage farm in controlling the benefits of the wind storage farm cluster. According to the current power dispatching requirements, the present application performs iterative game calculations in the strategy game model to simulate various strategies that the first wind storage farm and the second wind storage farm may adopt in actual situations. When the iterative game calculation results reach a stable state, the probability distribution information at this time is determined, and the target probability of the first wind storage farm adopting the zero determinant strategy is determined, and then the power of the wind storage farm cluster is controlled based on the target probability. In the above manner, no matter what game strategy the second wind storage farm adopts, the first wind storage farm adopting the zero determinant strategy can maintain the benefits of the wind power cluster at the maximum value under the premise of meeting the power dispatching requirements. Therefore, the present application can reasonably dispatch the power of the wind storage farm cluster and reduce the interference of uncertainty factors and variability factors in the external environment. The present application also provides a control system for a wind farm cluster, a storage medium and an electronic device, which have the above-mentioned beneficial effects and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 A flow chart of a control method for a wind farm cluster provided in an embodiment of the present application;

[0057] Figure 2 A schematic diagram of a rapid capacity reduction for active power regulation provided in an embodiment of the present application;

[0058] Figure 3 A technical roadmap for optimizing the active regulation capability of a wind farm based on multi-agent game provided in an embodiment of the present application;

[0059] Figure 4 A receiving schematic diagram of an IEEE-69 node system provided in an embodiment of the present application;

[0060] Figure 5 A schematic diagram of a grid-connected strategy electricity price provided in an embodiment of the present application;

[0061] Figure 6 A schematic diagram of a wind power output coefficient provided in an embodiment of the present application;

[0062] Figure 7 A schematic diagram of energy storage output coefficient provided in an embodiment of the present application;

[0063] Figure 8 A schematic diagram of energy storage charge state provided in an embodiment of the present application;

[0064] Fig. 9 A schematic diagram of wind power cluster wind abandonment rate provided in an embodiment of the present application;

[0065] Fig.10 A schematic diagram of voltage amplitude of a node system provided in an embodiment of the present application;

[0066] Fig.11 A schematic diagram of the change of average inductance provided in an embodiment of the present application. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0068] See below Figure 1 , Figure 1 A flow chart of a control method for a wind farm cluster provided in an embodiment of the present application.

[0069] Specific steps may include:

[0070] S101: dividing the wind storage power field of the wind storage power field cluster into one first wind storage power field and multiple second wind storage power fields;

[0071] Among them, this embodiment can be applied to the control equipment of the dispatching center of the wind storage power plant cluster, the wind storage power plant cluster includes multiple wind storage power plants, the wind storage power plant includes a wind farm and an energy storage power plant, and the wind farm includes multiple wind turbines. This embodiment can determine the wind storage power plants included in the wind storage power plant cluster, and divide the identities of each wind storage power plant to obtain a first wind storage power plant and multiple second wind storage power plants.

[0072] As a feasible implementation method, this embodiment can use one wind storage farm directly connected to the dispatch center as the first wind storage farm, and other wind storage farms as the second wind storage farm. That is, the first wind storage farm is the wind storage farm directly connected to the dispatch center, and the second wind storage farm is the other wind storage farms in the wind storage farm cluster except the first wind storage farm. When there are multiple wind storage farms directly connected to the dispatch center in the wind storage farm cluster, this embodiment can use one wind storage farm directly connected to the dispatch center as the first wind storage farm.

[0073] S102: constructing a strategy game model including the first wind-storage power farm and the second wind-storage power farm;

[0074] The strategy game model is a game model used to describe the strategy used by the first wind storage farm and the second wind storage farm to control the benefits of the wind storage farm cluster. In the strategy game model, the first wind storage farm is the leader, and the second wind storage farm is the follower. The leader is responsible for setting a certain benchmark or rule, and its strategy will affect the benefits of the entire cluster. Followers can adjust their own strategies based on the leader's strategy and the behavior of other followers.

[0075] S103: determining a current power dispatching demand, and performing an iterative game calculation in the strategy game model according to the current power dispatching demand;

[0076] Among them, this embodiment can determine the current power dispatch demand based on the real-time load of the power system, the supply and demand of the power market, and the dispatch of the power grid operator. Before performing the iterative game calculation, the strategy game model can be initialized. In the iterative game calculation, the first wind storage power farm and the second wind storage power farm can update their own strategies according to the current power dispatch demand and the other party's strategy. Specifically, the first wind storage power farm first selects the optimal strategy according to the power dispatch demand and its own benefit function, and notifies the second wind storage power farm; after observing the strategy of the first wind storage power farm, the second wind storage power farm adjusts its own strategy according to its own benefit function and constraints. The above process is repeated until a stable state is reached.

[0077] S104: Determine the probability distribution information when the iterative game calculation result reaches a stable state;

[0078] Among them, the probability distribution information is used to describe the probability that the first wind storage power farm and the second wind storage power farm adopt the target strategy in the iterative game calculation of the current round. The target strategy adopted by the first wind storage power farm is the zero-determinant strategy (Zero-Determinant strategy, ZD strategy), and the target strategy adopted by the second wind storage power farm can be any strategy.

[0079] In order to determine whether the iterative game calculation results have reached a stable state, this embodiment can set corresponding judgment criteria, such as the frequency change of strategy selection in 5 consecutive rounds of iterations is less than a preset value, or the change rate of total benefit in 5 consecutive rounds is less than a preset value.

[0080] In a stable state, this embodiment can count the number of times the first wind-storage power farm and the second wind-storage power farm adopt their respective target strategies, and calculate the proportion of these times to the total number of iterations, thereby obtaining probability distribution information.

[0081] S105: determining the target probability of the first wind-storage farm adopting the zero-determinant strategy according to the probability distribution information, and controlling the power of the wind-storage farm cluster under constraint conditions;

[0082] Among them, after obtaining the probability distribution information, the target probability of the first wind storage power plant adopting the zero determinant strategy (that is, the probability of the first wind storage power plant adopting the zero determinant strategy in the iterative game) can be queried from the determined probability distribution information. In this embodiment, a constraint condition can be set based on the probability distribution information, and the constraint condition is that the conditional probability of the first wind storage power plant adopting the zero determinant strategy to control the wind storage power plant cluster benefit is the target probability. The above constraint condition can make the first wind storage power plant reach the target probability in the actual iteration.

[0083] This embodiment can control the power of each wind storage farm in the wind storage farm cluster, and in the control process ensure that the conditional probability of the first wind storage farm using the zero determinant strategy to control the benefit of the wind storage farm cluster is the target probability.

[0084] In this embodiment, a wind storage farm cluster is divided into a first wind storage farm and a plurality of second wind storage farms, and a strategy game model including the first wind storage farm and the second wind storage farm is constructed, so as to use the strategy game model to describe the strategy interaction of the first wind storage farm and the second wind storage farm in controlling the benefits of the wind storage farm cluster. According to the current power dispatching requirements, this embodiment performs iterative game calculations in the strategy game model to simulate various strategies that the first wind storage farm and the second wind storage farm may adopt in actual situations. When the iterative game calculation results reach a stable state, the probability distribution information at this time is determined, and the target probability of the first wind storage farm adopting the zero determinant strategy is determined, and then the power of the wind storage farm cluster is controlled based on the target probability. In the above manner, no matter what game strategy the second wind storage farm adopts, the first wind storage farm adopting the zero determinant strategy can maintain the benefits of the wind power cluster at the maximum value under the premise of meeting the power dispatching requirements. Therefore, this embodiment can reasonably dispatch the power of the wind storage farm cluster and reduce the interference of uncertainty factors and variability factors in the external environment.

[0085] The above scheme is explained below through an embodiment in practical application. In this embodiment, the leader is the first wind-storage farm in the above embodiment, and the follower is the second wind-storage farm in the above embodiment. The specific scheme is as follows:

[0086] Assume that all wind farms in a wind farm cluster are equipped with energy storage power stations that meet the construction requirements, which is a wind-storage farm cluster. The power control of this wind-storage farm cluster is modeled as an iterative game with N players. Each wind-storage farm can determine its own continuous output power in the game. Assume that the wind-storage farm directly connected to the dispatch center is the controlling player in the game, that is, the leader. The other N-1 wind farms are followers. In order to achieve the best utilization of the output power of the wind farm, each wind farm is equipped with energy storage equipment to support or store wind energy when the output of the wind farm is insufficient or excessive. The strategy is expressed as ,in , express Similarly, for the other N-1 followers The strategy is expressed as ,in , express Active output and power factor angle. When a wind farm cluster jointly completes a power dispatching task, the leader of the wind farm cluster first receives the power command from the dispatching center. Assuming that the wind speed conditions are sufficient and the output power of each wind farm is too large, wind abandonment will occur. The excess wind energy needs to be stored or utilized. In order to avoid the unit switching in the wind farm and the possible frequent start and shutdown, the energy conversion loss of the energy storage power station charging and discharging is considered, and the dispatching strategy is reasonably optimized to solve the wind abandonment phenomenon, so that the wind power cluster can obtain greater public benefits.

[0087] This embodiment provides a wind farm upper strategy game model, which is specifically described as follows:

[0088] Benefits to wind turbines in wind farms and energy storage benefits Define them separately:

[0089] (1);

[0090] (2);

[0091] In the formula, is the grid-connected electricity price; and are the active dispatch response coefficients of the leader and other followers, respectively; The power demand of the dispatching center (i.e., power dispatching demand); and are the maximum active output capabilities of the leader and follower wind farms, respectively; It is the maximum active output of the wind power cluster.

[0092] (3);

[0093] (4);

[0094] (5);

[0095] In the formula, and are the energy storage benefits of the leader and the follower, respectively; Charging efficiency for energy storage; and is the utilization rate of excess wind energy in the leader and follower wind farms. The adjustment of this utilization rate is the core of the strategy of this layer model. At this time, the game strategy can be further expressed as:

[0096] (6);

[0097] Therefore, the economic benefits of wind farm clusters Can be defined as:

[0098] (7);

[0099] For the zero-rank strategy, no matter what game strategy the followers adopt, the leader who adopts the zero-rank strategy can unilaterally maintain the total economic benefits of the wind power cluster at the maximum value while meeting the system scheduling requirements. Therefore, it can be assumed that the followers all adopt the average strategy, that is, the probability of choosing any action in each round is the same. and followers benefits They can be defined as:

[0100] (8);

[0101] In the formula, and Represent the auxiliary costs of the leader and follower respectively, which can be expressed as follows:

[0102] (9);

[0103] In the above formula, is the unit cost of energy storage auxiliary services.

[0104] In each round of iterative calculation, the expected benefit of the leader in the game model is and the expected benefits to followers It can be expressed as:

[0105] (10);

[0106] In the formula, represents the probability distribution of the leader and follower adopting the corresponding strategy in round h. According to the properties of Markov stationary distribution, this distribution is consistent with the probability distribution of the corresponding strategy in round h-1. There is the following relationship matrix M:

[0107] (11);

[0108] (12);

[0109] (13);

[0110] In the formula, and are the conditional probabilities of the leader and follower in round h-1, respectively. Indicates that the cluster strategy is When the leader selects The conditional probability of Indicates that the cluster strategy is When the follower chooses When the probability distributions of the hth round and the h-1th round are equal, the iterative process reaches a stable point, that is, the steady-state distribution is obtained. , according to the definition of zero-determinant strategy, the expected benefit of the leader at this time is and the expected benefits to followers The following linear relationship can be maintained:

[0111] (14);

[0112] Where v1, v2 and v3 are constant parameters.

[0113] At this point, for a multi-agent multi-strategy model with one leader and multiple followers, the goal is to find the maximum economic benefit of the wind farm cluster. The scheduling policy problem can be transformed into the following maximization of the total expected benefit The optimization problem is:

[0114] (15);

[0115] (16);

[0116] The conditional probability when the leader adopts the zero-rank strategy to control the total expected benefit of the wind farm is Sometimes, there is , the maximum benefit of the cluster can converge to , then the optimization objective can be simplified to formula (16). This is the target probability mentioned above. is the utilization rate of excess wind energy in the leader’s wind farm in round h, is the utilization rate of excess wind energy in the wind farm of the leader in round h-1, is the benefit of the N-1th follower.

[0117] In this optimization process, when the leader adopts the zero-rank strategy, the strategies of the followers will not change. The maximum expected benefit of the wind power cluster under certain conditions is .

[0118] As a feasible implementation method, the benefits can be distributed among wind farms in the following ways:

[0119] Step A1: determining a first allocation coefficient according to the waveform similarity between the equivalent output curve and the actual output curve of each wind storage power farm;

[0120] Wherein, the first allocation coefficient is positively correlated with the waveform similarity;

[0121] Step A2: determining a second allocation coefficient according to the excess wind energy utilization of each wind storage power farm;

[0122] Wherein, the second allocation coefficient is positively correlated with the excess wind energy utilization;

[0123] Step A3: determining a third allocation coefficient according to the voltage deviation value of each wind power storage farm;

[0124] Wherein, the third allocation coefficient is negatively correlated with the voltage deviation value; the voltage deviation value is determined according to the absolute value of the difference between the grid-connected node voltage and the preset voltage;

[0125] Step A4: calculating the Shapley value benefit allocation coefficient of each wind-storage power plant according to the first allocation coefficient, the second allocation coefficient and the third allocation coefficient;

[0126] Specifically, this embodiment can calculate the weight coefficients of the first allocation coefficient, the second allocation coefficient and the third allocation coefficient by the entropy method; based on the weight coefficients, the first allocation coefficient, the second allocation coefficient and the third allocation coefficient are weightedly calculated to obtain the Shapley value benefit allocation coefficient of each wind storage farm.

[0127] Step A5: Calculate the benefit allocation result of each wind-storage power plant according to the Shapley value benefit allocation coefficient.

[0128] Furthermore, after calculating the benefit allocation result of each wind-storage power plant according to the Shapley value benefit allocation coefficient, the grid-connected electricity price strategy of each wind-storage power plant can also be calculated according to the benefit allocation result.

[0129] The above scheme is explained below through the wind farm cluster lower-level revenue game model in actual application:

[0130] In the upper-level strategy game model of wind-storage power plants, the output strategy that maximizes the expected benefits while fully utilizing excess wind energy is calculated by determining the leader and the follower, ignoring the contribution of the wind farm to the stability of the node system and to reducing wind curtailment.

[0131] In this embodiment, the cooperative game model can be used to allocate the marginal contributions of N participants. The improved Shaply value method is adopted. For the wind farm cluster, the participants of the cooperative game model are , the strategy set of the wind farm is the grid-connected electricity price strategy , The grid-connected electricity price for the leader, The grid-connected electricity price for followers. Due to the particularity of the zero-sum determinant strategy, the lower-level cooperative game model will not change the maximum economic benefit, but the distribution of Shaply values ​​can be improved in combination with the contribution of wind farms to reformulate the grid-connected electricity price strategy. , and then fed back to the upper-level strategy model, so that while ensuring the maximum benefit, the strategy result has the characteristics of taking into account the stability of the node system and reducing the contribution of wind abandonment.

[0132] The Shapley value of participant i is defined as:

[0133] (17);

[0134] In the above formula, S represents the alliance composed of all participants, and v (S) represents the total benefits of all participants in the alliance.

[0135] (18);

[0136] In the Shaply method calculation formula, member i has (|S|-1)! sorting options when participating in alliance S, |S| represents the number of members included in alliance S, and there are (n-|S|)! sorting options for the remaining (n-|S|) members. The different sorting combinations in which all member i participate divided by the random sorting combinations of n members is the weight of the benefits that member i should share with the entire alliance, recorded as [(|S|-1)!(n-|S|)!] / (n!).

[0137] is the Shapley value of participant i; is the total benefit of the alliance formed except for participant i; is the weighting factor; is the number of all permutations of the game participants. Therefore, the Shapley value considers the average marginal contribution under all possible sequences, ensuring fair distribution of benefits. Considering the contribution of wind farms to the stability of the node system, the Shaply allocation results are reconstructed mainly from the following aspects:

[0138] Aspect 1: Similarity between the total load waveform of the wind power cluster and the alliance.

[0139] For the node system where the wind power cluster is located, when the wind power output change trend is similar to the load waveform, that is, the less the exchange with the regional thermal power units and energy storage energy, the lower the impact on the stability of the power system itself and the corresponding cost. The equivalent output of each node wind farm is calculated using the equal power-shunning load method. , the waveform similarity between the equivalent output and actual output curves is calculated using the cosine similarity algorithm , the greater the waveform similarity, the more the wind farm meets the load demand, the greater the contribution to reducing electricity costs, and the corresponding allocation coefficient The bigger. This is the first distribution coefficient mentioned above.

[0140] (19);

[0141] (20);

[0142] In the above formula, It represents the actual output of the wind farm node, T represents the total time interval, and t is the time point corresponding to the current moment.

[0143] Aspect 2: The level of excess wind energy utilization in wind power clusters.

[0144] In the case of excess wind power, the minimum output requirement of thermal power plants is considered. In order to reduce wind curtailment, if the excess wind energy can be connected to the grid or charged to energy storage during the entire calculation period, that is, the excess wind energy is fully utilized through reasonable scheduling, the excess wind energy utilization can be calculated. For the redistribution of alliance benefits, the more the excess wind energy is fully utilized, the more benefits can be distributed, and the corresponding distribution coefficient The bigger. This is the second distribution coefficient mentioned above.

[0145] (twenty one);

[0146] (twenty two);

[0147] In the formula, is the unit coefficient corresponding to the time scale; and They are excess wind energy utilization coefficient, maximum output, dispatch response coefficient and dispatch instruction respectively.

[0148] Aspect 3: Steady-state level of node system.

[0149] In the node system, the power flow process will cause losses in the transmission line, thereby reducing the voltage level at the remote node. The wind storage system can be used as a reactive supplement device to support the node voltage, reduce the voltage support from the regional thermal power balance node, and reduce the inductive voltage drop in the transmission line. Therefore, by calculating the error between the actual voltage of the wind farm grid-connected node and 1p.u. For the redistribution of alliance benefits, the smaller the error, the more effective the use of excess wind energy to support local node voltage. The corresponding allocation coefficient The smaller. is the voltage deviation value mentioned above, is the third distribution coefficient mentioned above.

[0150] (twenty three);

[0151] (twenty four);

[0152] In the above formula, It represents the actual voltage of the grid-connected node i of the wind farm at time t, i represents the serial number of the wind farm, t represents the voltage at the time; T represents the calculation cycle time.

[0153] In summary, the improved Shapley value benefit allocation coefficient , improved allocation results And the corresponding grid-connected electricity price strategy for the previous period of time for:

[0154] (25);

[0155] (26);

[0156] (27);

[0157] In the formula, represents the Shapley value before improvement.

[0158] , , is the weight coefficient, which is determined by the entropy method. The greater the information entropy of the factor, the greater its weight ratio; For the The economic benefits of a wind farm.

[0159] As a feasible implementation method, the wind turbine generator system can be adjusted in the following ways:

[0160] Step B1: determining the power regulation boundary of each wind power storage farm;

[0161] Among them, the power regulation boundary includes an active power regulation boundary and a reactive power regulation boundary, which can be determined specifically in the following ways: determining the upper active power regulation boundary of each wind storage farm (also called the active power regulation upper boundary) according to the mechanical power limit value of the wind turbine set; determining the lower active power regulation boundary of each wind storage farm (also called the active power regulation lower boundary) according to the current speed, current pitch angle, maximum speed and maximum pitch angle of the wind turbine set; determining the upper reactive power regulation boundary and the lower reactive power regulation boundary (also called the reactive power regulation boundary) of each wind storage farm according to the power factor of the wind turbine set.

[0162] Step B2: constructing a wind speed model of the wind turbine generator set of the wind storage farm according to the power regulation boundary;

[0163] Step B3: determining an induction coefficient according to the wind speed model; wherein the induction coefficient is used to describe the influence of the wake effect between the wind turbines on the wind speed;

[0164] Step B4: determining a wind speed error between an actual wind speed of the wind turbine generator set and a target wind speed;

[0165] Step B5: performing empirical mode decomposition on the wind speed error to obtain an induction coefficient error;

[0166] Step B6: using a gated loop unit to perform inverse calculation on the inductance error to obtain a unit control strategy;

[0167] Step B7: adjusting the rotor speed and pitch angle of the wind turbine generator set according to the unit control strategy.

[0168] As a feasible implementation mode, before using a gated loop unit to perform inversion calculation on the induction coefficient error, the modal components of the wind speed data can also be extracted, and the gated loop unit can be trained using the modal components so that the gated loop unit learns the corresponding relationship between the control parameters and the induction coefficient; wherein the control parameters include the rotor speed and the pitch angle.

[0169] The above implementation is explained below by using an active adjustment model of a wind turbine in actual application.

[0170] The upper limit of active power regulation is calculated as follows:

[0171] Affected by factors such as weather, the output of wind turbines is random and volatile, resulting in the frequency regulation capability of wind farms achieved through load reduction operation being time-varying. This will bring about a series of problems, so it is particularly important to accurately evaluate the frequency regulation capability of wind farms.

[0172] When the wind farm receives the dispatching instruction from the regional power grid dispatching center When the power of each unit in the wind farm needs to be increased, the active regulation capacity of the wind farm can be obtained. , the specific calculation formula can be written as:

[0173] (28);

[0174] In the formula, is the theoretical maximum output of the i-th wind turbine, is the current actual output of the i-th unit, and It can be directly obtained through the data acquisition and monitoring system of the wind farm, where n is the number of wind turbines.

[0175] The upper limit of wind farm regulation is By mechanical power limit Determine as follows:

[0176] (29);

[0177] In the formula, is the air density of the wind farm; is the impeller radius of the i-th unit; is the wind speed of the i-th unit; 0° pitch angle , optimal tip speed ratio The corresponding wind energy utilization efficiency is as follows.

[0178] The lower limit of active power regulation is calculated as follows:

[0179] When the system frequency is higher than the rated frequency, the wind turbines need to reduce their output to participate in frequency regulation to reduce the system frequency, that is, the dispatching instructions received by the wind farm The wind farm active power regulation capacity that can be continuously reduced by wind turbines during frequency regulation is changed to the following formula: ,in is the lower bound of the active regulation capacity of the i-th wind turbine. It can be seen that the key to the evaluation and calculation of the active regulation capacity of the wind farm is of determination.

[0180] (30);

[0181] There are two ways for wind turbines to reduce their own output:

[0182] (1) Through rotor overspeed control, the rotor speed is increased, causing the output power of the wind turbine to deviate from its maximum power tracking point , converting part of the electromagnetic energy into rotor kinetic energy, thereby reducing the active output of the unit.

[0183] (2) Increase the pitch angle by controlling the pitch angle To reduce the wind energy utilization factor , which reduces the wind energy captured by the unit and further reduces the active output. Pitch angle control can achieve load reduction control at all wind speeds, but due to the mechanical part action, the response speed is slow, and frequent adjustment of the pitch angle will increase the wear of the wind turbine, reduce its lifespan, and increase operating costs.

[0184] In comparison between the two methods, the wind energy reduced by pitch angle control is directly discarded, and the utilization rate of wind energy is reduced. In comparison, rotor overspeed control is achieved by PWM control, which has a fast response speed. Part of the wind energy discarded by load reduction can be stored in the form of rotational kinetic energy, but when the rotor speed reaches 1.2pu, overspeed control can no longer be performed, and it is not suitable for control at full wind speed. The two control methods have their own advantages and disadvantages. In order to fully tap the active power reduction capability of wind turbines, it is necessary to combine the two control methods and give full play to their respective advantages, that is, overspeed control is used in the maximum power tracking area, and pitch angle control is used in the constant speed area and constant power area.

[0185] In the maximum power tracking area, the optimal rotor speed The maximum speed is not reached , it is possible to increase the speed to reduce the active output. Figure 2 , Figure 2 A schematic diagram of a rapid capacity reduction of active power regulation provided in an embodiment of the present application, wherein the intersection points in the figure include point A, point B and point C, and the horizontal axis WS is the generator speed, the ordinate P m is the active power of the unit, β is the pitch angle, β max is the maximum pitch angle, point A is the maximum power tracking point, point B is the maximum speed that can be achieved by overspeed control, and the captured mechanical power difference between A and B is the rapid reduction capacity of the unit.

[0186] Considering the actual situation, the current unit operation point may not be the maximum power tracking point, and the current unit pitch angle may not be 0, but as long as the unit speed does not reach , then the unit has a certain rapid down-regulation capacity. Assuming the current speed of the unit is , the pitch angle is , radius is R, wind speed is v w , the current tip speed ratio is , the maximum tip speed ratio is , C p To improve wind energy efficiency, the unit should be quickly reduced in capacity. The calculation formula is:

[0187] (31);

[0188] It can be seen that in the maximum power tracking area, the rapid reduction of the wind turbine capacity is only related to the current operating point of the wind turbine, specifically, it is only related to the speed, pitch angle and current wind speed. In this process, only the rotor speed of the unit is changed, and its pitch angle is always remains unchanged, at which point the regulation capacity of the entire wind farm It can be expressed as:

[0189] (32);

[0190] In the formula, is the rapid down-regulation capacity of unit i. Due to the influence of the wake effect, the down-regulation capacity of different units is also different under the same operating condition. The rapid down-regulation capacity of the unit originally operating in the maximum power tracking mode at different wind speeds can be calculated. When the wind speed gradually increases to the rated wind speed, the corresponding tip speed ratio The closer to the optimum tip speed ratio , so the difference in wind energy utilization coefficient Getting smaller and smaller, It decreases gradually, and when the wind speed reaches the rated wind speed, its rapid downward adjustment capacity is 0.

[0191] In order to further reduce the output power of the wind turbine, the pitch angle control can be used to increase the pitch angle of the turbine. Pitch angle The relationship between The bigger, The smaller the size, the smaller the mechanical power captured by the unit. However, in order to prevent the wind turbine from being disconnected from the grid due to too low captured mechanical power, its pitch angle There is a maximum value ,once , which may cause the unit to be disconnected from the grid or the load to be too high. The speed and pitch angle at point C reach the maximum value, which corresponds to the minimum output point of the unit, and the power difference between B and C is the slow down capacity of the unit. , the specific calculation formula is:

[0192] (33);

[0193] Slow capacity reduction is achieved by changing only the pitch angle, while the rotor speed remains constant. Less than the maximum pitch angle The unit will only have a slow capacity reduction when When considering the influence of two factors: one is the influence of mechanical load, and the other is the influence of power limitation when the unit is connected to the grid. The specific expression is:

[0194] (34);

[0195] In the formula, is the maximum pitch angle of the unit when the mechanical load is taken into account. is the maximum pitch angle of the group when active power limitation is considered, and the pitch angle The smaller of the two is selected. As with the rapid capacity reduction, the slow capacity reduction of wind farms The calculation formula is:

[0196] (35);

[0197] Indicates the slow capacity reduction of the i-th wind turbine.

[0198] Combined with the above analysis, it can be seen that the lower limit of the actual active power regulation capacity of wind turbines is for:

[0199] (36);

[0200] According to the relevant regulations on the allowable active output limit of wind turbines when connected to the grid, this article stipulates Not less than 15% of the rated active output. , Current pitch angle , Maximum allowed speed and the maximum allowable pitch angle , then formula (30) can be written as:

[0201] (37);

[0202] At this time, the lower limit of the active regulation capacity of the entire wind farm is for

[0203] (38);

[0204] Considering that the wind farm needs to carry out maintenance, the wind farm units are not all running all the time, but some are running and some are shut down. When the unit is shut down, it has no effect on the wind speed of the downstream units, and the wake of the entire wind farm will change. In order to deal with this situation, we define is the output coefficient of unit i after being affected by the wake; is the switching function of unit i: when unit i is running, ; When unit i is shut down, .

[0205] In summary, the active power regulation capacity of wind farms It can be described by the following formula:

[0206] (39);

[0207] The reactive power regulation boundary is calculated as follows:

[0208] The stator of the doubly-fed asynchronous wind turbine is directly connected to the power grid, and the power is transmitted to the power grid through the stator and rotor ends. According to relevant regulations, the power factor of the wind turbine is It needs to meet the requirement of leading 0.95 to lagging 0.95, so the reactive power limit of the wind farm can be calculated according to the following formula:

[0209] (40);

[0210] Where: It is the upper limit of reactive power output of wind farm; It is the lower limit of reactive power output of wind farm; is the power factor angle of the i-th unit.

[0211] According to relevant regulations, wind farms are required to operate under a restricted control strategy. However, in practice, due to factors such as inaccurate wake models, uncertainty in real-time wind speed and direction, and inaccurate interactive information, the wind farm's regulation capability will produce errors.

[0212] The wind speed model of wind turbines is introduced below:

[0213] A wind farm usually contains multiple wind turbines that affect each other. Different wind direction conditions and relative installation positions will produce different output power. During the operation of the wind farm, the downstream wind turbine may be in the wake area of ​​one or more upstream wind turbines, resulting in superimposed wake losses. Therefore, when calculating the power generated by the wind turbine, it is necessary to consider the insufficient actual wind speed of each wind turbine under the influence of the wake effect.

[0214] Based on the Park wake model, wind turbines Located in another wind turbine In the wake zone, Wind speed for:

[0215] (41);

[0216] In the formula, represents the air roughness factor (i.e., the slope of the wake expanding away from the wind turbine), represents the distance between wind turbines i and j in the wind direction, for wind speed; for Wake pair The resulting inductance; for Wind rotor diameter; For downstream Wind rotor swept area; for and Overlapping area of ​​wake region. Wind farm with 100 turbines For equivalent wind farm wind speed It can be expressed as:

[0217] (42);

[0218] In the formula, v0 represents the wind speed of the first wind turbine facing the wind in the current wind speed direction; Therefore, when the wind turbine adjustment capacity is different from the expected value, the induction coefficient between the units can be adjusted by calculation. The control strategy controls the output capacity of the wind farm. At this time, according to the Bernoulli equation, under the wake effect, the wind farm Power generated As shown below:

[0219] (43);

[0220] The induction coefficient inversion model based on EMD-GRU is introduced below.

[0221] Inductance is a set of intermediate control variables, which control the rotor speed and pitch angle Later, due to the inaccurate wake model, uncertainties such as real-time wind speed and direction, the wind speed of other wind turbines is affected, namely:

[0222] (44);

[0223] In the formula, The corresponding function is formula (41); is the intermediate control variable between fans i and j and The mapping model between .

[0224] represents the generator speed ω of wind turbine j j and the generator speed ω of wind turbine i i A sequential set of represents the pitch angle β of wind turbine j j Pitch angle β with i i A sequential set of , .

[0225] By controlling the intermediate variables, the error with the actual output is adjusted in time, and the actual control strategy is deduced through the inversion model. The feedback is fed back to the unit control system so that the unit's control strategy is transformed from passive control to active participation in regulating the output capacity of the wind farm.

[0226] The Gated Recurrent Unit (GRU) neural network is a simplified and improved model of the LSTM network. The main difference from LSTM is that a single gated unit controls the forgetting factor and the decision to update the state unit at the same time. It also solves the gradient vanishing problem that occurs in the traditional RNN training process using the BPTT algorithm, better captures the characteristics of the time series, realizes the modeling ability of long-term time series, and has good adaptability to data with complex fluctuations such as wind speed. Its mathematical model is shown in the following formula:

[0227] (45);

[0228] (46);

[0229] (47);

[0230] (48);

[0231] In the formula, , They are reset gate and update gate respectively; is the Logistic function; , They are the current moment output and the previous moment output of GRU respectively; Candidate status; Enter for the current moment; They are the reset gate, update gate and weight matrix for obtaining candidate states respectively.

[0232] In summary, the modal components of wind speed data are extracted by EMD (empirical mode decomposition), and the GRU (gated recurrent unit) neural network model is used to train the The mapping relationship ; Secondly, the optimization search algorithm combined with the two-dimensional Halton sequence is used to invert The active control strategy of the unit (i.e. the unit control strategy mentioned above); then through the existing The function characterizes the wind speed under each modal component, and finally superimposes the result to output the wind speed, thereby actively adjusting the output in a timely manner to better fit the wind farm power curve of the scheduling demand.

[0233] See also Figure 3 , Figure 3 A technical roadmap for optimizing the active regulation capability of a wind farm based on multi-agent game provided in the embodiment of the present application. The figure shows wind storage farms 1 to N, which include energy storage batteries and are connected to grid connection points 1 to n. This solution mainly includes three parts: the first part is the upper-level strategy game model of the wind storage farm, the second part is the lower-level benefit game model of the wind farm cluster, and the third part is the active regulation model of the wind turbine.

[0234] The implementation process of the active regulation model of wind turbines includes: calculating the wind farm regulation boundary, wind farm dispatching strategy instructions, actual wind farm response output, calculating the strategy response error, so as to input the strategy response error into the inversion model; in the inversion model, calculating the wind speed error corresponding to each unit, and inputting the unit wind speed error into the EMD calculation to obtain the function , the corresponding induction coefficient error is calculated by GRU inversion to obtain the corresponding unit control strategy change.

[0235] The implementation process of the upper-level strategy game model of wind-storage power plants is as follows: determine the leader and the follower, calculate the excess power of each wind-storage power plant, calculate the wind turbine benefit, energy storage benefit and cluster benefit, calculate the leader and follower benefits, calculate the corresponding relationship matrix, and calculate the strategy distribution V corresponding to the h-1 and h rounds. h-1 and V h; Determine whether the probability distributions of rounds h-1 and h are equal. If not, adjust the strategy. If so, get the steady-state distribution v s ; The leader strategy is a zero-rank strategy, so that there is an unchanging maximum benefit-(v3) max The real-time strategy is transmitted to the numerical dispatch center, and the benefit strategy can also be transmitted to the lower-level revenue game model of the wind power cluster. The lower-level revenue game model of the wind power cluster can upload the grid-connected electricity price adjustment information to update the node grid-connected electricity price.

[0236] The implementation process of the lower-level revenue game model of the wind-storage power plant is as follows: determine that the leader and the follower in the upper-level model are both cooperative game participants, set the leader to adopt a zero-rank strategy, set the follower to adopt a full-cooperative strategy, and calculate the Shaply value y i ; Calculate the distribution coefficient α corresponding to the similarity between the cluster and the total load waveform w , calculate the allocation coefficient α corresponding to the excess wind energy utilization level of the cluster e , calculate the distribution coefficient α corresponding to the steady-state level of the node system u ; Calculate the improved Shaply value , adjust the grid-connected electricity price strategy c i g .

[0237] It can be seen that this embodiment provides a wind farm cluster control strategy and a two-layer model for secondary distribution of benefits; this embodiment provides a definition of wind turbine regulation capability and the induction coefficient inversion model involved. This embodiment provides an overall regulation process for implementing wind farm control strategies to wind turbines.

[0238] Wind power generation and storage farms have become one of the important ways to solve the problems of renewable energy volatility and power system dispatch. In order to give full play to the potential of wind power generation and storage farms, various entities in wind power generation and storage farms are dispatched and coordinated through coordination and competition to achieve frequency stability, supply and demand balance, and energy efficiency improvement of the power system.

[0239] At present, a variety of methods have been proposed for wind farm power optimization. For example, some studies have used situational awareness technology and optimization instructions to improve the ability to resist environmental interference. Other studies have provided robust scheduling solutions for the uncertainty of wind power output through principal component analysis and clustering technology. Other studies focus on the day-ahead power scheduling of wind power clusters, and reduce the wind curtailment rate through specific technologies by considering the uncertainty of power forecast deviations and regulation capabilities. Other studies focus on emergency control strategies for wind farms, such as emergency active power control, to reduce the number and power of machine cuts. In addition, it also involves adjusting the operating strategy of doubly fed wind turbines, optimizing the frequency regulation participation of energy storage wind farms, and analyzing the capacity of wind, solar, and storage power generation systems from the perspective of scheduling load demand to evaluate their power output.

[0240] Although existing studies cover a wide range of topics, most of them focus on power optimization within wind farms, and less on the coordination between wind turbines and wind farms. To address this gap, some studies have adopted multi-agent consistency and game theory methods to propose strategies for power regulation of wind turbines and wind farms. At the same time, there are also studies based on cooperative and non-cooperative game theory to explore the coordinated operation strategy of microgrid systems and power grids to improve the absorption rate of photovoltaic power generation. The market participation strategy of energy storage power stations has also been studied to explore the best operation and quotation methods. In addition, how to optimize the use of energy storage systems in grid frequency regulation has also become a focus, by building a cooperative game model to reduce costs and improve efficiency. At the same time, in order to improve the absorption rate of distributed generation and reasonably distribute energy sharing benefits, some studies have also introduced optimization strategies based on improved Shapley values ​​for further research.

[0241] In the field of wind power generation, dealing with uncertainty and variability is one of the main challenges facing power dispatch. At present, some engineering applications try to use control technology to deal with these problems, but it is usually accompanied by high costs. With the development of wind farm technology, each wind farm pays more attention to pursuing its own interests. Therefore, it is increasingly important to introduce game theory into the power dispatch problem of wind farm clusters. This field focuses on the strategy of each wind farm, aiming to maximize its own benefits. Game theory, as one of the effective methods to resolve group decision-making contradictions, provides a useful idea for solving this problem. In large-scale wind farm projects, economic evaluation becomes particularly important to enhance the position of wind power generation in market competition, which is crucial for the sustainable development of wind farm clusters.

[0242] One of the key issues in wind power generation is to deal with its uncertainty and variability, especially the challenges in power dispatch. At present, there are some engineering applications that try to use control technology to solve these problems, but this is usually accompanied by high costs. Considering the intelligence of wind farms, each wind farm is more inclined to pursue its own interests. Therefore, it becomes very valuable to introduce game theory into the power dispatch problem of wind farm clusters. In this context, focusing on the strategies adopted by each wind farm to maximize its own benefits has become the core of the research. Game theory is considered to be one of the effective ways to resolve group decision-making contradictions in order to improve the competitiveness of wind power generation in the increasingly mature power market competition. The solution to this problem is crucial to the sustainable development of wind farm clusters.

[0243] In order to implement the wind farm dispatching instructions, the wind turbines control the speed and pitch angle control strategy to obtain the wind farm output strategy that meets the dispatching requirements. However, in the actual process, due to the influence of factors such as inaccurate wake model, uncertainty of real-time wind speed and direction, and inaccurate interactive information, the regulation ability of the wind farm will produce errors. Therefore, the power generation boundary of the wind farm energy management system often has a certain gap with the actual capacity. In order to reduce the gap, the actual output curve reported for dispatching will be more conservative. Therefore, using the regulation potential of the wind storage farm is important for improving the economic efficiency of the wind farm itself.

[0244] This embodiment constructs an active regulation capability assessment model for wind-storage power farms, clarifies the regulation capability boundaries of each wind-storage power farm from a cluster perspective, and enables each unit in the wind farm to actively regulate and respond to the regulation needs of the wind farm through an induction coefficient inversion model based on EMD-GRU. This model helps to fully utilize the regulation potential of wind-storage power farms and improve the efficiency and accuracy of dispatch response.

[0245] This embodiment also proposes a two-level optimization model based on multi-agent game. The upper model uses zero-row game theory to formulate a wind farm cluster output plan that maximizes economic benefits and reduces wind energy waste. The lower model considers the impact of the strategy on system stability and reducing wind abandonment, and innovatively applies the improved Shapley value method. Through a dynamic feedback mechanism, the economic benefit allocation results of the lower model and the adjusted grid-connected electricity price are fed back to the upper model, iteratively optimizes the dispatch strategy of the wind farm cluster, reasonably allocates the economic benefits between wind and storage farms, and improves the fairness and incentive of benefit distribution.

[0246] The following example uses the improved IEEE-69 node system for verification. Figure 4 , Figure 4 A receiving schematic diagram of an IEEE-69 node system provided in an embodiment of the present application, in which a thermal power plant is shown, numbers 1 to 69 in the figure represent connection points or branch points of the power network, WS1 to WS10 represent wind power storage farms, the capacity of the thermal power unit is 500MVA, node 1 is the thermal power balance node in the system, and the wind power storage farm access location is as follows Figure 4 As shown in the figure, different wind farms are equipped with 4-6MW wind turbines. According to current requirements, this article assumes that the minimum output of thermal power units is 30% of the rated capacity, that is, the minimum technical output reaches 150MVA; the power capacity of the energy storage configured in the wind storage farm is configured according to 15% of the wind power capacity, and the energy capacity is configured according to the 2h power supply capacity.

[0247] Assuming that WS1 is directly connected to the dispatch center, it is determined as the leader of the wind power cluster, and the other wind farms are followers. The improved convex inner approximation method is used to calculate in the improved IEEE69 node system. After iterative calculation, the optimal balance is obtained. Figure 5System electricity price levels shown. Figure 5 A schematic diagram of a grid-connected electricity price provided in an embodiment of the present application, wherein the horizontal axis is time T, the vertical axis is the wind farm number, and the vertical axis is the grid-connected electricity price.

[0248] The dispatching output of wind power plant is as follows: Figure 6 and Figure 7 As shown, Figure 6 A schematic diagram of wind power output coefficient provided in an embodiment of the present application is shown in FIG. Figure 7 A schematic diagram of energy storage output coefficient provided in an embodiment of the present application is shown in FIG. Figure 6 and Figure 7 The horizontal axis is time T, the vertical axis is the output coefficient, and WS1~WS10 represent each wind storage farm.

[0249] It can be seen that in the interval between the 45th and 62nd time points, the strategy in this paper considers reducing the shutdown risk of thermal power units. When the wind turbine output level cannot meet the maximum demand load, the energy storage maintains a certain output level at this time, but no wind power is stored in the energy storage power station. Figure 8 As shown, Figure 8 A schematic diagram of energy storage charge state provided in an embodiment of the present application, Figure 8 The horizontal axis is the wind farm number, the vertical axis is time T, and the vertical axis is the state of charge SOC / %.

[0250] The charge state of the energy storage power station dropped rapidly in the 45th-53rd time point interval and maintained a low charge level for a period of time. The charging and discharging strategy of the energy storage actively responded to the overall output strategy of the load and wind turbines, leaving capacity space for absorbing excess wind energy in subsequent wind energy surplus situations.

[0251] Fig. 9 A schematic diagram of a wind power cluster wind abandonment rate provided in an embodiment of the present application, wherein the horizontal axis is time T and the vertical axis is the total wind abandonment rate (%). The figure shows the difference between the traditional control strategy and the control strategy of this invention. Fig. 9 Based on the data of the wind power plant, the active regulation strategy of wind power storage proposed in this paper successfully reduced the wind curtailment rate by 8% at most and 3% on average, thus significantly improving the passive wind curtailment under the traditional strategy. This shows that the active regulation strategy has achieved remarkable results in improving the utilization rate of wind power resources.

[0252] Fig.10 A schematic diagram of a node system voltage amplitude provided in an embodiment of the present application, Fig.10 The horizontal axis is time T, the vertical axis is the node number, and the vertical axis is the voltage amplitude U / pu. Fig.11 A schematic diagram of the change of average inductance provided in an embodiment of the present application is shown in FIG. Fig.11The horizontal axis is time T, the vertical axis is the wind farm number, and the vertical axis is the voltage amplitude U / pu.

[0253] according to Fig.10 and Fig.11 From the data, it can be observed that the wind farm adjusts the output level by actively adjusting the inductance coefficient. Compared with the traditional strategy, after active adjustment, the maximum voltage amplitude of some nodes of the system increased by 2.09% relative to the original maximum amplitude. However, the average voltage deviation level of the system decreased by 6.17%. Therefore, the active adjustment strategy proposed in this paper not only does not have a negative impact on the voltage level of the node system, but also has a certain positive gain in actual situations.

[0254] Please refer to Table 1, which is the calculation result table of the improved Shaply value. According to the data in Table 1, after adopting the active adjustment strategy, the average profit of each wind-storage farm in the node increased by 9.06%. In particular, in the wind-storage farms WS7, WS9 and WS10, due to their high contributions in waveform similarity and wind energy utilization level, their profit increased by more than 11%. Although WS1 failed to fully utilize the wind energy level due to coordinated control, reducing the additional profit, it also achieved a 3.84% profit increase due to its contribution in waveform similarity.

[0255] Table 1 Improved Shaply value calculation results

[0256]

[0257] It can be seen that the active regulation strategy not only helps to improve the average income of each wind-storage farm, but also achieves significant economic benefits in different scenarios. This further verifies the effectiveness of the active regulation strategy and shows that it has potential for broad application in optimizing wind-storage farm operations.

[0258] A control system for a wind farm cluster provided in an embodiment of the present application includes:

[0259] A role division module, used for dividing the wind storage power field of the wind storage power field cluster into one first wind storage power field and multiple second wind storage power fields; wherein the first wind storage power field is a wind storage power field directly connected to the dispatching center, and the second wind storage power field is other wind storage power fields in the wind storage power field cluster except the first wind storage power field;

[0260] A model building module, used to build a strategy game model including the first wind storage power farm and the second wind storage power farm; wherein the strategy game model is a game model used to describe the strategy adopted by the first wind storage power farm and the second wind storage power farm to control the benefits of the wind storage power farm cluster;

[0261] A game calculation module, used to determine the current power dispatch demand, and perform iterative game calculation in the strategy game model according to the current power dispatch demand;

[0262] A probability calculation module, used to determine the probability distribution information when the iterative game calculation result reaches a stable state; wherein the probability distribution information is used to describe the probability that the first wind-storage power plant and the second wind-storage power plant adopt the target strategy in the current round of iterative game calculation, and the target strategy adopted by the first wind-storage power plant is the zero determinant strategy;

[0263] A power control module is used to determine the target probability of the first wind-storage power plant adopting a zero-determinant strategy based on the probability distribution information, and to control the power of the wind-storage power plant cluster under constraints; wherein the constraint is that the conditional probability of the first wind-storage power plant adopting a zero-determinant strategy to control the benefits of the wind-storage power plant cluster is the target probability.

[0264] In this embodiment, a wind storage farm cluster is divided into a first wind storage farm and a plurality of second wind storage farms, and a strategy game model including the first wind storage farm and the second wind storage farm is constructed, so as to use the strategy game model to describe the strategy interaction of the first wind storage farm and the second wind storage farm in controlling the benefits of the wind storage farm cluster. According to the current power dispatching requirements, this embodiment performs iterative game calculations in the strategy game model to simulate various strategies that the first wind storage farm and the second wind storage farm may adopt in actual situations. When the iterative game calculation results reach a stable state, the probability distribution information at this time is determined, and the target probability of the first wind storage farm adopting the zero determinant strategy is determined, and then the power of the wind storage farm cluster is controlled based on the target probability. In the above manner, no matter what game strategy the second wind storage farm adopts, the first wind storage farm adopting the zero determinant strategy can maintain the benefits of the wind power cluster at the maximum value under the premise of meeting the power dispatching requirements. Therefore, this embodiment can reasonably dispatch the power of the wind storage farm cluster and reduce the interference of uncertainty factors and variability factors in the external environment.

[0265] Furthermore, it also includes:

[0266] A benefit allocation module is used to determine a first allocation coefficient based on the waveform similarity between the equivalent output curve and the actual output curve of each wind storage farm; wherein the first allocation coefficient is positively correlated with the waveform similarity; it is also used to determine a second allocation coefficient based on the excess wind energy utilization of each wind storage farm; wherein the second allocation coefficient is positively correlated with the excess wind energy utilization; it is also used to determine a third allocation coefficient based on the voltage deviation value of each wind storage farm; wherein the third allocation coefficient is negatively correlated with the voltage deviation value; the voltage deviation value is determined based on the absolute value of the difference between the grid-connected node voltage and the preset voltage; it is also used to calculate the Shapley value benefit allocation coefficient of each wind storage farm based on the first allocation coefficient, the second allocation coefficient and the third allocation coefficient; it is also used to calculate the benefit allocation result of each wind storage farm based on the Shapley value benefit allocation coefficient.

[0267] Furthermore, it also includes:

[0268] The electricity price strategy calculation module is used to calculate the grid-connected electricity price strategy of each wind-storage power farm according to the benefit allocation result after calculating the benefit allocation result of each wind-storage power farm according to the Shapley value benefit allocation coefficient.

[0269] Furthermore, the process of the benefit distribution module calculating the Shapley value benefit distribution coefficient of each wind storage power farm based on the first distribution coefficient, the second distribution coefficient and the third distribution coefficient includes: calculating the weight coefficient of the first distribution coefficient, the second distribution coefficient and the third distribution coefficient by the entropy method; performing weighted calculation on the first distribution coefficient, the second distribution coefficient and the third distribution coefficient based on the weight coefficient to obtain the Shapley value benefit distribution coefficient of each wind storage power farm.

[0270] Furthermore, it also includes:

[0271] A unit regulation module is used to determine the power regulation boundary of each wind storage farm; it is also used to construct a wind speed model of the wind turbines of the wind storage farm according to the power regulation boundary; it is also used to determine the induction coefficient according to the wind speed model; wherein the induction coefficient is used to describe the influence of the wake effect between the wind turbines on the wind speed; it is also used to determine the wind speed error between the actual wind speed of the wind turbine and the target wind speed; it is also used to perform empirical mode decomposition on the wind speed error to obtain the induction coefficient error; it is also used to perform inverse calculation on the induction coefficient error using a gated loop unit to obtain a unit control strategy; it is also used to adjust the rotor speed and pitch angle of the wind turbine according to the unit control strategy.

[0272] Furthermore, the process of the unit regulation module determining the power regulation boundary of each of the wind storage farms includes: determining the upper boundary of the active power regulation of each of the wind storage farms according to the mechanical power limit value of the wind turbine unit; determining the lower boundary of the active power regulation of each of the wind storage farms according to the current speed, current pitch angle, maximum speed and maximum pitch angle of the wind turbine unit; determining the upper boundary and lower boundary of the reactive power regulation of each of the wind storage farms according to the power factor of the wind turbine unit.

[0273] Furthermore, it also includes:

[0274] A training module is used to extract modal components of wind speed data before using a gated cyclic unit to perform inversion calculation on the inductance coefficient error, and use the modal components to train the gated cyclic unit so that the gated cyclic unit learns the corresponding relationship between control parameters and inductance coefficient; wherein the control parameters include rotor speed and pitch angle.

[0275] Since the embodiments of the system part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the system part, which will not be repeated here.

[0276] The present application also provides a storage medium on which a computer program is stored, and when the computer program is executed, the steps provided in the above embodiment can be implemented. The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0277] The present application also provides an electronic device, which may include a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps provided in the above embodiment may be implemented. Of course, the electronic device may also include various network interfaces, power supplies and other components.

[0278] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the system 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 description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of this application.

[0279] It should also be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

Claims

1. A control method for a wind farm cluster, characterized in that: include: The wind storage power farms of the wind storage power farm cluster are divided into a first wind storage power farm and a plurality of second wind storage power farms; wherein the first wind storage power farm is a wind storage power farm directly connected to the dispatching center, and the second wind storage power farm is other wind storage power farms in the wind storage power farm cluster except the first wind storage power farm; Constructing a strategy game model including the first wind storage power farm and the second wind storage power farm; wherein the strategy game model is a game model for describing the strategy adopted by the first wind storage power farm and the second wind storage power farm to control the wind storage power farm cluster benefits; Determine a current power dispatch demand, and perform iterative game calculation in the strategy game model according to the current power dispatch demand; Determine the probability distribution information when the iterative game calculation result reaches a stable state; wherein the probability distribution information is used to describe the probability that the first wind-storage power plant and the second wind-storage power plant adopt the target strategy in the current round of iterative game calculation, and the target strategy adopted by the first wind-storage power plant is the zero determinant strategy; The target probability of the first wind-storage power plant adopting the zero-determinant strategy is determined according to the probability distribution information, and the power of the wind-storage power plant cluster is controlled under constraints; wherein the constraint condition is that the conditional probability of the first wind-storage power plant adopting the zero-determinant strategy to control the efficiency of the wind-storage power plant cluster is the target probability.

2. The control method of the wind power plant cluster according to claim 1 is characterized in that: Also includes: Determine a first allocation coefficient according to the waveform similarity between the equivalent output curve and the actual output curve of each wind storage power farm; wherein the first allocation coefficient is positively correlated with the waveform similarity; Determine a second allocation coefficient according to the excess wind energy utilization of each wind storage farm; wherein the second allocation coefficient is positively correlated with the excess wind energy utilization; Determine a third allocation coefficient according to the voltage deviation value of each wind farm; wherein the third allocation coefficient is negatively correlated with the voltage deviation value; the voltage deviation value is determined according to the absolute value of the difference between the grid-connected node voltage and the preset voltage; Calculate the Shapley value benefit allocation coefficient of each wind-storage power plant according to the first allocation coefficient, the second allocation coefficient and the third allocation coefficient; The benefit allocation result of each wind-storage power plant is calculated according to the Shapley value benefit allocation coefficient.

3. The control method of the wind farm cluster according to claim 2 is characterized in that: After calculating the benefit allocation result of each wind-storage power plant according to the Shapley value benefit allocation coefficient, the method further includes: The grid-connected electricity price strategy of each wind-storage power plant is calculated according to the benefit allocation result.

4. The control method of the wind farm cluster according to claim 2 is characterized in that: Calculating the Shapley value benefit allocation coefficient of each wind-storage power plant according to the first allocation coefficient, the second allocation coefficient and the third allocation coefficient includes: Calculate the weight coefficients of the first allocation coefficient, the second allocation coefficient, and the third allocation coefficient by an entropy method; The first allocation coefficient, the second allocation coefficient and the third allocation coefficient are weightedly calculated based on the weight coefficient to obtain the Shapley value benefit allocation coefficient of each wind storage farm.

5. The control method of the wind power plant cluster according to claim 1 is characterized in that: Also includes: Determining the power regulation boundary of each wind storage farm; Constructing a wind speed model of the wind turbine generator set of the wind storage farm according to the power regulation boundary; Determining an induction coefficient according to the wind speed model; wherein the induction coefficient is used to describe the influence of the wake effect between the wind turbines on the wind speed; Determining a wind speed error between an actual wind speed of the wind turbine generator set and a target wind speed; Performing empirical mode decomposition on the wind speed error to obtain an induction coefficient error; Using a gated circulation unit to perform an inverse calculation on the inductance error to obtain a unit control strategy; The rotor speed and pitch angle of the wind turbine set are adjusted according to the set control strategy.

6. The control method of the wind farm cluster according to claim 5 is characterized in that: Determining the power regulation boundary of each wind power storage farm includes: Determining the upper boundary of active power regulation of each wind power storage farm according to the mechanical power limit value of the wind turbine generator set; Determining the lower limit of active power regulation of each wind power storage farm according to the current rotation speed, current pitch angle, maximum rotation speed and maximum pitch angle of the wind turbine generator set; The reactive power regulation upper boundary and the reactive power regulation lower boundary of each wind power storage farm are determined according to the power factor of the wind turbine generator set.

7. The control method of the wind farm cluster according to claim 5 is characterized in that: Before using the gated cycle unit to perform inversion calculation on the inductance error, the method further includes: The modal components of the wind speed data are extracted, and the gated cyclic unit is trained using the modal components so that the gated cyclic unit learns the corresponding relationship between the control parameters and the induction coefficient; wherein the control parameters include the rotor speed and the pitch angle.

8. A control system for a wind farm cluster, characterized in that: include: A role division module, used for dividing the wind storage power field of the wind storage power field cluster into one first wind storage power field and multiple second wind storage power fields; wherein the first wind storage power field is a wind storage power field directly connected to the dispatching center, and the second wind storage power field is other wind storage power fields in the wind storage power field cluster except the first wind storage power field; A model building module, used to build a strategy game model including the first wind storage power farm and the second wind storage power farm; wherein the strategy game model is a game model used to describe the strategy adopted by the first wind storage power farm and the second wind storage power farm to control the benefits of the wind storage power farm cluster; A game calculation module, used to determine the current power dispatch demand, and perform iterative game calculation in the strategy game model according to the current power dispatch demand; A probability calculation module, used to determine the probability distribution information when the iterative game calculation result reaches a stable state; wherein the probability distribution information is used to describe the probability that the first wind-storage power plant and the second wind-storage power plant adopt the target strategy in the current round of iterative game calculation, and the target strategy adopted by the first wind-storage power plant is the zero determinant strategy; A power control module is used to determine the target probability of the first wind-storage power plant adopting a zero-determinant strategy based on the probability distribution information, and to control the power of the wind-storage power plant cluster under constraints; wherein the constraint is that the conditional probability of the first wind-storage power plant adopting a zero-determinant strategy to control the benefits of the wind-storage power plant cluster is the target probability.

9. The control system of the wind farm cluster according to claim 8, characterized in that: Also includes: A benefit allocation module is used to determine a first allocation coefficient based on the waveform similarity between the equivalent output curve and the actual output curve of each wind storage farm; wherein the first allocation coefficient is positively correlated with the waveform similarity; it is also used to determine a second allocation coefficient based on the excess wind energy utilization of each wind storage farm; wherein the second allocation coefficient is positively correlated with the excess wind energy utilization; it is also used to determine a third allocation coefficient based on the voltage deviation value of each wind storage farm; wherein the third allocation coefficient is negatively correlated with the voltage deviation value; the voltage deviation value is determined based on the absolute value of the difference between the grid-connected node voltage and the preset voltage; it is also used to calculate the Shapley value benefit allocation coefficient of each wind storage farm based on the first allocation coefficient, the second allocation coefficient and the third allocation coefficient; it is also used to calculate the benefit allocation result of each wind storage farm based on the Shapley value benefit allocation coefficient.

10. The control system of the wind farm cluster according to claim 9, characterized in that: Also includes: The electricity price strategy calculation module is used to calculate the grid-connected electricity price strategy of each wind-storage power farm according to the benefit allocation result after calculating the benefit allocation result of each wind-storage power farm according to the Shapley value benefit allocation coefficient.

11. The control system of the wind farm cluster according to claim 9, characterized in that: The process of the benefit allocation module calculating the Shapley value benefit allocation coefficient of each wind-storage power plant based on the first allocation coefficient, the second allocation coefficient and the third allocation coefficient includes: calculating the weight coefficient of the first allocation coefficient, the second allocation coefficient and the third allocation coefficient by the entropy method; performing weighted calculation on the first allocation coefficient, the second allocation coefficient and the third allocation coefficient based on the weight coefficient to obtain the Shapley value benefit allocation coefficient of each wind-storage power plant.

12. The control system of the wind farm cluster according to claim 8, characterized in that: Also includes: A unit regulation module is used to determine the power regulation boundary of each wind storage farm; it is also used to construct a wind speed model of the wind turbines of the wind storage farm according to the power regulation boundary; it is also used to determine the induction coefficient according to the wind speed model; wherein the induction coefficient is used to describe the influence of the wake effect between the wind turbines on the wind speed; it is also used to determine the wind speed error between the actual wind speed of the wind turbine and the target wind speed; it is also used to perform empirical mode decomposition on the wind speed error to obtain the induction coefficient error; it is also used to perform inverse calculation on the induction coefficient error using a gated loop unit to obtain a unit control strategy; it is also used to adjust the rotor speed and pitch angle of the wind turbine according to the unit control strategy.

13. The control system of the wind farm cluster according to claim 12, characterized in that: The process of the unit regulation module determining the power regulation boundary of each wind storage farm includes: determining the upper boundary of active power regulation of each wind storage farm according to the mechanical power limit value of the wind turbine unit; determining the lower boundary of active power regulation of each wind storage farm according to the current speed, current pitch angle, maximum speed and maximum pitch angle of the wind turbine unit; determining the upper boundary and lower boundary of reactive power regulation of each wind storage farm according to the power factor of the wind turbine unit.

14. The control system of the wind farm cluster according to claim 12, characterized in that: Also includes: A training module is used to extract modal components of wind speed data before using a gated cyclic unit to perform inversion calculation on the inductance coefficient error, and use the modal components to train the gated cyclic unit so that the gated cyclic unit learns the corresponding relationship between control parameters and inductance coefficient; wherein the control parameters include rotor speed and pitch angle.

15. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, the steps of the control method of the wind-storage power plant cluster according to any one of claims 1 to 7 are implemented.

16. A storage medium, characterized in that: The storage medium stores computer executable instructions, and when the computer executable instructions are loaded and executed by the processor, the steps of the control method of the wind power plant cluster according to any one of claims 1 to 7 are implemented.

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