Virtual power plant operation control method, device, equipment and medium

Abstract

The present invention discloses a virtual power plant operation control method, which relates to the technical field of virtual power plants and is used to solve the instability problem of existing virtual power plants. The method includes the following steps: establishing a virtual power plant elastic operation capability evaluation index; constructing a two-layer optimization model for the operation strategy at two time scales: day-ahead and real-time; and solving the two-layer optimization model to obtain the virtual power plant operation strategy. The present invention also discloses a virtual power plant operation control device, electronic equipment, and computer storage medium. The present invention optimizes the virtual power plant operation strategy by combining relay evaluation indicators with optimization models at two time scales.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and in particular to a method, device, equipment and medium for controlling the operation of a virtual power plant under multiple time scales taking into account elasticity. Background Art

[0002] In recent years, many flexible resources such as distributed power sources, controllable loads, and energy storage devices have appeared in distribution networks. However, these resources are highly uncertain and volatile, and their distribution is too dispersed, which increases the complexity of the system and the difficulty of regulation.

[0003] To ensure the safe and reliable operation of the power grid, existing technologies are beginning to aggregate various distributed source-load-storage resources through virtual power plants (VPPs), forming resource clusters for unified scheduling. This effectively leverages the combined power of distributed power sources and energy storage systems, thereby improving the flexibility, safety, and economic efficiency of grid regulation. However, VPP operation places high demands on both the VPP and the power grid due to the high proportion of flexible resources involved, the need to consider resource uncertainty, and various emergency situations.

[0004] In order to ensure the safe and reliable operation of the power grid, it is necessary to further evaluate and improve the ability of virtual power plants to withstand shocks, formulate operating strategies that can integrate system resources, and ensure the normal operation of the power grid. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, one of the purposes of the present invention is to provide a virtual power plant operation control method, which ensures the operating capacity of the virtual power plant by elastically evaluating the operating capacity of the power plant, and ensures that the optimization result has the best economy and the strongest elasticity by constructing a multi-time scale strategy.

[0006] One of the purposes of the present invention is achieved by the following technical solution:

[0007] A virtual power plant operation control method comprises the following steps:

[0008] Establishing indicators for evaluating the resilience of virtual power plants;

[0009] Constructing a two-level optimization model for the operation strategy at two time scales, day-ahead and real-time, and constructing the constraints and objective function of the optimization model based on the evaluation indicators;

[0010] The two-layer optimization model is solved to obtain the virtual power plant operation strategy.

[0011] Furthermore, the capability assessment indicators include branch safety margin assessment, new energy controllability assessment and emergency capability assessment. The safety margin includes active power carrying capacity and node voltage deviation. The new energy controllability includes response capability, output fluctuation and equipment failure. The emergency capability includes interconnection line failure and internal area failure.

[0012] Furthermore, the active power carrying capacity is evaluated by combining the overall active power carrying margin and active power carrying balance through a network mapping method, and the active power carrying margin satisfies: Among them, E LΣ is the total mapped elastic potential energy of the power grid, P bot is the injected active power of the grid node, θ cq is the equivalent phase difference, k LI is the equivalent branch mapping elastic coefficient; the active load balance satisfies: P L1 :P L2 :...:P Ln =k L1 :k L2 :...:k Ln , where k L1 、k L2 ,...,k Ln is the elastic coefficient of each path, P L1 、P L2 ,...,P Ln is the active load; the node voltage deviation is evaluated by voltage over-limit time, voltage over-limit amplitude, system voltage deviation level, and voltage fluctuation index.

[0013] Furthermore, the response capability is evaluated by adjusting the amplitude, response time, duration and climbing rate, wherein the adjustment amplitude includes the upper adjustment amplitude A + and downward adjustment amplitude A - , the objective functions are: max p tie (t)-p base , maxp base -p tie (t), where p base is the base line power of the tie line, p tie (t) is the transmission power of the tie line at time t; the response time objective function is: ΔP∈S ΔP , where S ΔP is a set of values of ΔP at equal intervals; m is the time traversed in the summation formula, v ΔP,m It represents whether the regulation instruction requirement is met at time m when ΔP is constant. If not, it is 1, otherwise it is 0. The objective function of the duration is: ΔP∈S ΔP , where uΔP,m Indicates whether the adjustment instruction requirement is met at mm when ΔP is constant. If it is met, it is 1, otherwise it is 0. is the response time of the obtained ΔP at a certain time; the climbing rate includes the upward and downward climbing rates R + 、R - , the objective functions are: max(∑ i∈s p i (t+1)-∑ i∈S p i (t)) t∈ψ,min(∑ i∈S p i (t+1)-∑ i∈S p i (t)) t∈ψ,

[0014] Among them, p i (t) represents the output power of device i at time t; the probability distribution function of the output fluctuation is: where ε PV is the power deviation; the equipment failure is evaluated by the average failure outage frequency, average outage duration and average annual outage time.

[0015] Furthermore, the tie line fault includes a tolerable power loss and a tolerable duration, and the internal area fault includes a tolerable power outage range and a tolerable duration.

[0016] Furthermore, a two-level optimization model for operation strategies at both day-ahead and real-time time scales is formulated, including: the objective function of the day-ahead operation optimization model satisfies: Among them, λ s is the importance coefficient of each resource's elasticity capability, s is the resource number, t represents the running time, Γ s is the set of all resources of the virtual power plant, Γ t is the set of all running times, f tie is the electricity purchase cost of the upper power grid, f rs is the resource operation cost, f loss is the system network loss cost. The constraints of the day-ahead operation optimization model include active power balance constraints, resource operation constraints, distribution network power flow equation constraints, and power network constraints. The objective function of the real-time operation optimization model satisfies: Among them, λ s is the importance coefficient of each resource elasticity capability, ε i is the indicator weight of the i-th indicator, s represents the resource number, t represents the running time, Γ s is the set of all resources of the virtual power plant, Γ tIt is a collection of all operating moments. The constraints of the real-time operation optimization model include the constraints of the day-ahead operation optimization model and the response capability requirements of the upper-level control center for the virtual power plant.

[0017] Furthermore, solving the two-layer optimization model to obtain a virtual power plant operation strategy includes the following steps:

[0018] Using the Yalmip modeling language and solving with the gurobi solver;

[0019] By introducing the artificial swarm algorithm of reverse learning to solve the problem, the virtual power plant operation strategy is obtained.

[0020] A second object of the present invention is to provide a virtual power plant operation control device, which completes the formulation of the best operation optimization strategy of the virtual power plant by evaluating the operation capacity and formulating the operation optimization strategy under two time scales.

[0021] The second object of the present invention is achieved by adopting the following technical solutions:

[0022] A virtual power plant operation control device, comprising:

[0023] An evaluation module, used to establish evaluation indicators for the elastic operation capability of virtual power plants;

[0024] A model building module is used to build a two-level optimization model for the operation strategy at two time scales: day-ahead and real-time, and to build the constraints and objective function of the optimization model based on the evaluation indicators;

[0025] The solution module is used to solve the two-layer optimization model to obtain the virtual power plant operation strategy.

[0026] The third object of the present invention is to provide an electronic device for performing one of the objects of the invention, which includes a processor, a storage medium and a computer program, wherein the computer program is stored in the storage medium, and when the computer program is executed by the processor, the above-mentioned virtual power plant operation control method is implemented.

[0027] A fourth object of the present invention is to provide a computer-readable storage medium for storing one of the objects of the invention, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned virtual power plant operation control method is implemented.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention evaluates the ability of virtual power plants to resist external shocks and cope with resource uncertainties by constructing a flexible operation evaluation index system. By formulating operation strategies at two time scales, day-ahead and real-time, it ensures the optimal economy on the day-ahead and the strongest real-time elasticity. In this way, while taking uncertainty into account, it ensures the optimal economy and a certain elasticity of the virtual power plant. Finally, by solving the day-ahead and real-time double-layer models, the optimal operation optimization result is obtained to ensure the safe and reliable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a virtual power plant operation control method according to embodiment 1;

[0031] Figure 2 Schematic diagram of the evaluation index system of Example 1;

[0032] Figure 3 This is a structural block diagram of a virtual power plant operation control method device according to the second embodiment;

[0033] Figure 4 It is a structural block diagram of an electronic device according to the third embodiment. DETAILED DESCRIPTION

[0034] The present invention will be described in more detail below with reference to the accompanying drawings. It should be noted that the following description of the present invention with reference to the accompanying drawings is merely illustrative and non-limiting. Various embodiments may be combined with each other to form other embodiments not shown in the following description.

[0035] Example 1

[0036] Embodiment 1 provides a virtual power plant operation control method, which aims to formulate an operation strategy by modeling the characteristics of adjustable resources such as distributed power sources, controllable loads, and energy storage equipment in the virtual power plant.

[0037] At present, scholars at home and abroad have proposed the concept of "resilience", which is mainly used to evaluate the recovery ability of individuals or systems after being disturbed. Based on this concept, a resilience index system can be established to evaluate the ability to resist external shocks and cope with resource uncertainties. There is strong uncertainty and volatility in the internal resources of virtual power plants, and the distribution of resources is too dispersed, which increases the complexity of the system and the difficulty of regulation. In order for the virtual power plant to maintain a safe and stable power generation state, it is necessary to have a certain ability to resist disturbances and maintain optimal operation, that is, resilience. Therefore, this embodiment proposes a flexible operation strategy based on the elasticity index to improve the system's ability to resist shocks and uncertainties, improve the system's ability to respond to emergencies and resource output fluctuations, and ensure the safe and reliable operation of the power system.

[0038] To ensure safe and reliable grid operation, virtual power plants (VPPs) must integrate and evaluate various internal resources to ensure coordinated operation. Externally, they must participate in market transactions as a single entity. Modeling the characteristics of VPP resources, including distributed power sources, controllable loads, and energy storage, is a crucial component of the VPP's decision-making system for formulating bidding strategies and optimizing source, storage, and load scheduling.

[0039] Based on the above principles, a virtual power plant operation strategy considering elasticity at multiple time scales is proposed. Please refer to Figure 1 As shown, a virtual power plant operation control method is characterized by comprising the following steps:

[0040] S1. Establishing evaluation indicators for the flexible operation capability of virtual power plants;

[0041] Please refer to Figure 2 As shown in the figure, the elastic operation capability of virtual power plants is evaluated, the safe operation capability of the power network, the operating status and controllability of internal resources, and the ability to respond to emergencies are considered, and an evaluation index system for the elastic operation capability of virtual power plants is established.

[0042] Specifically, the above-mentioned operating capability assessment indicators include branch safety margin assessment, new energy controllability assessment and emergency capability assessment. The safety margin includes active power carrying capacity and node voltage deviation. The new energy controllability includes response capability, output fluctuation and equipment failure. The emergency capability includes interconnection line failure and internal area failure.

[0043] Similar to the power grid, the load-bearing condition of the elastic mechanics network (hereinafter referred to as the elastic net) also depends on the grid structure, branch strength, force magnitude and distribution. Therefore, some studies have mapped the power, angular state quantities and wiring topology of the power grid into a longitudinally stressed elastic net model, which not only maintains the wiring topology connection state of the power grid, but also characterizes the physical characteristics between power and angle. It is a state topology mapping of active power and phase angle. Therefore, the force and deformation analysis in the mapped elastic net is equivalent to the power and angular state analysis in the power grid. And since the force directions of the branches in the mapped elastic net are the same, they satisfy the scalar superposition property like the elastic potential energy, so the total potential energy is related to the total load and overall load-bearing strength of the elastic net. If the total load remains unchanged, the total potential energy can characterize the overall load-bearing strength F of the elastic net. l , which can represent the active carrying capacity P of the corresponding power grid L . And some studies have verified the feasibility of mapping elastic potential energy and using it as an indicator. The state mapping relationship is

[0044]

[0045] Where P L is the active power carrying capacity of the power grid, F lis the overall bearing strength of the elastic net; θ ij is the power phase angle of the grid branch, x l is the deformation of the branch in the mapping elastic net; k L The ratio of the grid's active load capacity to the branch power phase angle, K l is the elastic coefficient of the mapping elastic net.

[0046] If the values of the above state quantities are equal, the mapped elastic potential energy E of the power line is L and the elastic potential energy E of the spring l equal:

[0047] E L =E l .

[0048] In summary, since the mapped elastic network branches are all subjected to longitudinal forces, as long as the total potential energy and total load are equal, one elastic branch can be used as equivalent. Let the mapped elastic coefficient of the equivalent branch be k LI , the equivalent phase difference between the two ends is θ cq The active power carrying capacity is evaluated by combining the overall active power carrying margin and active power carrying balance through the network mapping method. The active power carrying margin satisfies: Among them, E LΣ is the total mapped elastic potential energy of the power grid, P bot is the injected active power of the grid node, θ cq is the equivalent phase difference, k LI is the elastic coefficient of the equivalent branch mapping; therefore, under the same active load, for different power grids or the same power grid in different operating modes, if the mapped total potential energy is smaller, the overall rigidity of the power grid is greater, the active load margin is greater, and the power angle security is better.

[0049] For active load balancing, the elastic network's force balance means that branches with larger elastic coefficients should bear greater forces. Similarly, the grid's active load balance means that lines with larger elastic coefficients should bear greater active power.

[0050] Since the power grid is mapped into a longitudinally stressed elastic network, the associated nodes of the upper and lower boundaries (i.e., the top and bottom layers) and the paths between them can be merged and made equivalent. There are n branches, and the elastic coefficient of each branch is k L1 ,k L2 ,...,k Ln , the total active load is P LΣ =∑P Li , where P Li If the active power of the i-th branch is , the active power load balance is: P L1 :P L2 :...:P Ln =k L1:k L2 :...:k Ln , where k L1 、k L2 ,...,k Ln is the elastic coefficient of each path, P L1 、P L2 ,...,P Ln is the active load, if P LΣ If is a constant, the total mapped elastic potential energy is minimized when the active load is balanced. This indicates that the mapped elastic potential energy can characterize the balance of active loads in the power grid branches. When the total active load remains unchanged, the smaller the mapped elastic potential energy, the better the balance of active loads in the power grid branches.

[0051] In order to measure the node voltage deviation, the node voltage deviation is evaluated by voltage over-limit time, voltage over-limit amplitude, system voltage deviation level, and voltage fluctuation index.

[0052] The voltage limit time meets the following conditions: t ol is the duration of the voltage exceeding the limit of the distribution network as a whole; L ... t is 1 if the value is set, otherwise it is 0.

[0053] The voltage over-limit amplitude meets the following requirements:

[0054] ΔU VL,u =max U VL,u -U max

[0055] ΔU VL,d =U min -minU VL,d ,

[0056] Among them, ΔU VL,u is the maximum over-limit amplitude; ΔU VL,d is the maximum lower limit amplitude; U VL,u is the node voltage when the voltage exceeds the upper limit; U VL,d is the node voltage when the voltage exceeds the lower limit.

[0057] The system voltage deviation level meets the following requirements:

[0058] Where D reg =System voltage deviation, reflecting the degree to which the system voltage deviates from the rated voltage. Values closer to 1 indicate a higher system voltage level, closer to the rated voltage level. t represents the time of day, T represents the total number of time points, i represents the node number, and N represents the total number of nodes.

[0059] Voltage fluctuation indicators meet the following requirements: Wherein, d represents the daily voltage fluctuation of the system; is the mean voltage at node i.

[0060] The responsiveness of renewable energy controllability is primarily reflected in the level, speed, and duration of its output / absorption power. In this embodiment, four indicators are established to evaluate the overall responsiveness of a virtual power plant, including adjustment amplitude, response time, duration, and ramp rate.

[0061] The adjustment range refers to the maximum deviation value that can be achieved relative to the day-ahead operating point. The adjustment range includes the upper adjustment range A + and downward adjustment amplitude A - , the objective functions are: maxp tie (t)-p base , max p base -p tie (t), where p base is the base line power of the tie line, p tie (t) is the transmission power of the tie line at time t;

[0062] The response time refers to the shortest time from receiving the adjustment instruction to fully meeting the adjustment requirements. Its objective function is: ΔP∈S ΔP , where S ΔP is a set of values of ΔP at equal intervals; m is the time traversed in the summation formula, v ΔP,m It represents whether the regulation instruction requirement is met at time m when ΔP is constant. If not, it is 1, otherwise it is 0. When the result of the objective function is greater than 24, it is considered that the response cannot be achieved. At the same time, this objective function has a constraint condition: X(t) = X base (t), where X(t) is the decision variable matrix, including the output of each device, the water storage capacity of the hydropower station, the stored energy of the energy storage system, etc.; X base (t) is the matrix of the values of the decision variables on the baseline at time t. The above formula means that the state of each device at time t is the same as the state on the baseline, that is, the virtual power plant is operating on the baseline at time t. In addition, if v ΔP,m =0, then there are two constraints:∑ i∈S P i (t)+ΔP=∑ i∈S P i (m), v ΔP,m+1 =0, the former formula means that the regulation instruction requirement is met at time t; the latter formula means that once the regulation instruction requirement is met, the subsequent time is not included in the response time.

[0063] Duration T D It refers to the longest time that the state can be maintained after the regulation requirements are fully met. The objective function of the duration is: ΔP∈S ΔP , where uΔP,m Indicates whether the adjustment instruction requirement is met at mm when ΔP is constant. If it is met, it is 1, otherwise it is 0. is the response time when ΔP is constant. At the same time, this objective function has the constraint condition: X(t)=X base (t), and if v ΔP,m =1, then there is a constraint: v ΔP,m+1 =0.

[0064] The ramp rate refers to the change in the operating point per unit time Δt during the regulation process. + 、R - The objective functions are: max(∑ i∈S p i (t+1)-∑ i∈S p i (t)) t∈ψ,min(∑ i∈S p i (t+1)-∑ i∈s p i (t)) t∈ψ, where p i (t) represents the output power of device i at time t. This objective function has the constraint condition: X(t) = X base (t).

[0065] The virtual power plant includes resources with strong volatility, such as wind power, photovoltaic power, and temperature control load. Taking photovoltaic power generation as an example, an output fluctuation model is established. The output fluctuation models of other resources can also be obtained using similar methods. Specifically, the power deviation of the photovoltaic power generation group at time t is set to ε PV (t), then the actual output power of the entire photovoltaic generator set in the virtual power plant is: in, represents the expected output of photovoltaic generator set i in the virtual power plant at time t; ε PV Assumed to be normally distributed Then its probability density function is: The probability distribution function can be obtained from the probability density function: where ε PV is the power deviation.

[0066] Equipment failures are assessed using the average outage frequency (AFP), average outage duration (AOT), and average annual outage time (AAU). The average outage frequency (AFP) is the expected number of outages for the equipment in a year, the average outage duration (AOT) is the average duration of each outage, and the average annual outage time (AAU) is the expected total outage duration for the equipment in a year, satisfying the following equation: AAU = AFR·AOT.

[0067] Emergency response capabilities: Emergency events include tie line failures and internal zone failures. Tie line failures refer to disconnection of the tie line between the virtual power plant and the grid or power constraints. Internal zone failures refer to power outages within a virtual power plant, resulting in the loss of some resources or load.

[0068] The interconnection line fault includes the tolerable power loss and the tolerable duration. Tolerable power loss: that is, the difference between the operating baseline power and the power limit value within one day. When the power limit value is 0, it means that the interconnection line is disconnected. Tolerable duration: under the determined power loss constraint, the maximum duration for which important loads can be kept in normal operation. The internal area fault includes the tolerable power outage range and the tolerable duration. Tolerable power outage range: when a certain area is out of power, important loads can continue to operate normally, then this area is the tolerable power outage range. Tolerable duration: under the determined power outage range constraint, the maximum duration for which important loads can be kept in normal operation.

[0069] S2. Construct a two-level optimization model for the operation strategy at two time scales: day-ahead and real-time, and construct the constraints and objective function of the optimization model based on the evaluation indicators;

[0070] Resource uncertainty needs to be considered when formulating a flexible operation strategy. This embodiment adopts the method of selecting multiple typical scenarios to analyze uncertainty. Flexible resources such as wind and solar power obey probability distribution, and their possible operating states are called "scenarios". The probability distribution function of flexible resource power prediction is determined based on historical data, and a large number of random scenarios are generated using Latin hypercube sampling to ensure that all sampling points are sampled. Then, the backward scenario reduction technology is used to reduce some similar and extreme scenarios to obtain typical scenarios and probabilities. The parameters of the typical scenario are the output of various types of resources with uncertainty. When calculating the two-layer optimization model, the parameters and probabilities of the typical scenario are substituted into the optimization model and solved.

[0071] Specifically, considering the economic day-ahead operation model, its objective function satisfies: Among them, λ s is the importance coefficient of each resource's elasticity capability, s is the resource number, t represents the running time, Γ s is the set of all resources of the virtual power plant, Γ t is the set of all running times, f tie is the electricity purchase cost of the upper power grid, f rs is the resource operation cost, f loss is the system network loss cost. The cost of purchasing electricity from the upper power grid is f tie This means that after the distribution network has generated power from its internal resources, the remaining power required must be purchased from the upper-level power grid through the tie line, and the excess power can be transmitted to the upper-level power grid to generate revenue. The calculation satisfies: Among them, C d The electricity purchase cost per unit power, P tie is the power input from the tie line to the virtual power plant, ΔtΔt is the unit time. The operating cost of various resources f rs In this embodiment, the operating cost function of each resource is equivalent to a quadratic function: Where i is the resource number, Γ r is the set of all resource numbers, P i is the output power of resource i, a i 、b i 、c i is the equivalent coefficient of the cost function of resource i, and the network loss cost f loss It refers to the network loss caused by the access of resources to the distribution network in each period, and the expression is: Among them C t,loss is the cost coefficient of unit electricity loss of the network at time t, j represents the branch number, R j is the branch resistance, P t,j , Q t,j 、U t,j are the active power, reactive power and voltage at the beginning of the branch respectively.

[0072] The constraints of the day-ahead operation optimization model include active power balance constraints, resource operation constraints, distribution network power flow equation constraints, and power network constraints.

[0073] The active power balance constraint is: Where i is the device serial number, and when used as a subscript, it means that this parameter belongs to the i-th device; S res is the collection of all resource devices; p i The power of the i-th device; p tie is the transmission power of the tie line; P D is the total load power (excluding temperature control load); t represents the scheduling time; ψ is the set of all operating times in a day.

[0074] The operating constraints of various resources are:

[0075] P i,min (t)≤P i (t)≤P i,max (t),t∈ψ,i∈S res

[0076]

[0077] V h,min,i ≤V h,i (t)≤V h,max,i ,t∈ψ,i∈S h

[0078] V h,i (t+1)=V h,i (t)+[q i (t)-Q i (t)]·Δt,t∈ψ,i∈S HS

[0079] E b,min,i ≤E b,i (t)≤E b,max,i ,t∈ψ,i∈S ES

[0080] E b (t+1)=E b (t)-P b (t)Δt·η b ,t∈ψ,i∈S ES ;

[0081] Among them, S h 、S g 、S b 、S c are the serial number sets of hydropower station, gas turbine, energy storage system, and temperature control load cluster respectively; P i,max and P i,min are the upper and lower limits of the output of the i-th device; Q(t) represents the water diversion flow rate of the hydropower station; Q max , Q min V is the maximum and minimum water diversion flow for power generation. h 、V h,min 、V h,max are the reservoir's water storage capacity, minimum and maximum storage capacity, respectively; q is the average runoff of the river, Δt is the unit time; E b Store electrical energy for the energy storage system; P b is the charge and discharge power, with discharge power being positive; η b is the charge and discharge efficiency; E b,max and E b,min The upper and lower limits of the electrical energy stored in the energy storage system.

[0082] The distribution network power flow equation constraints are satisfied:

[0083] Where P and Q are the active power and reactive power output by the node; U is the node voltage; j and k are the node numbers; Γ bot is the set of adjacent nodes of node j; G jk 、B jk ,θ jk are the conductance, susceptance, and impedance angle of the branch between nodes j and kk.

[0084] Power network constraints satisfy: p tie,min ≤ptie (t)≤p tie,max ,t∈ψ,

[0085] U j,min ≤U j (t)≤U j,max ; where p tie,max and p tie,min U is the upper and lower limits of the transmission power of the tie line; j,max and U j,min are the upper and lower limits of the node voltage of the j-th node.

[0086] The objective function of the real-time operation model considering elasticity satisfies: Among them, λ s is the importance coefficient of each resource elasticity capability, ε i is the indicator weight of the i-th indicator, s represents the resource number, t represents the running time, Γ s is the set of all resources of the virtual power plant, Γ t It is a collection of all operating moments. The constraints of the real-time operation optimization model include the constraints of the day-ahead operation optimization model and the response capability requirements of the upper-level control center for the virtual power plant.

[0087] The requirements of the upper control center on the response capability of the virtual power plant are taken as constraints, that is,

[0088]

[0089]

[0090]

[0091]

[0092] in is the required minimum downward / upward adjustment range; is the maximum required response time; is the minimum duration required; is the required minimum downward / upward climbing rate; ΔP is the required change in the interconnection line power at time t.

[0093] S3. Solve the two-layer optimization model to obtain the virtual power plant operation strategy.

[0094] S3 specifically includes:

[0095] Using the Yalmip modeling language and solving with the gurobi solver;

[0096] By introducing the artificial swarm algorithm of reverse learning to solve the problem, the virtual power plant operation strategy is obtained.

[0097] The upper layer of the two-layer model is the optimal day-ahead economic operation model, while the lower layer is the most flexible real-time operation model. This ensures that the virtual power plant has a certain degree of flexibility while maintaining optimal economic performance.

[0098] The solution process for the established day-ahead and real-time dual-layer operation optimization model is complex. Therefore, this embodiment considers using the Yalmip modeling language in conjunction with the Gurobi solver for the first stage and an intelligent algorithm for the second stage. The artificial bee colony algorithm, a swarm intelligence algorithm, has been applied to problems such as hydropower scheduling and power grid reconstruction. The bee colony consists of bees that collect, observe, and scout. This algorithm is highly robust, but suffers from slow search speed and a tendency to get stuck in local optima. Therefore, the algorithm is improved by introducing a reverse learning population initialization method and an adaptive coefficient. This reverse learning population initialization method can generate an opposite solution that may be closer to the optimal nectar source during the initial iteration of the search space, effectively reducing the number of iterations. The adaptive coefficient dynamically adjusts the bee colony's nectar collection during the search process. The improved artificial bee colony algorithm is used to solve the day-ahead and real-time dual-layer operation model.

[0099] The above-mentioned Yalmip is a high-level modeling language that can call various optimization solvers such as Gurobi in MATLAB. Gurobi can be called to directly solve first-order nonlinear programming problems.

[0100] Compared with the traditional artificial bee colony algorithm, the improved artificial bee colony algorithm in this embodiment introduces a reverse learning population initialization method to find an initialization value closer to the optimal nectar source; it also introduces an adaptive coefficient to dynamically adjust the nectar collection situation of the bee colony during the search process.

[0101] The Gurobi solver can only solve relatively simple optimization models. For example, the upper-level day-ahead optimization model in this article is a first-order nonlinear programming problem. While traditional and improved artificial bee colony algorithms cannot achieve the same exact solutions as the Gurobi solver, they can solve complex optimization models. For example, the lower-level real-time optimization model is a high-order nonlinear programming problem. Furthermore, the improved artificial bee colony algorithm converges more easily and solves faster than the traditional artificial bee colony algorithm.

[0102] The above-mentioned solution process belongs to conventional technical means and will not be described in detail in this embodiment.

[0103] Example 2

[0104] Example 2 discloses a device corresponding to the virtual power plant operation control method of the above embodiment, which is a virtual device structure of the above embodiment. Please refer to Figure 3 Shown, including:

[0105] Evaluation module 210, for establishing evaluation indicators for the elastic operation capability of the virtual power plant;

[0106] The model construction module 220 is used to construct a two-level optimization model for the operation strategy at two time scales: day-ahead and real-time, and to construct the constraint conditions and objective function of the optimization model based on the evaluation indicators;

[0107] The solution module 230 is used to solve the two-layer optimization model to obtain the virtual power plant operation strategy.

[0108] Preferably, the operating capability evaluation indicators include branch safety margin evaluation, new energy controllability evaluation and emergency capability evaluation. The safety margin includes active power carrying capacity and node voltage deviation. The new energy controllability includes response capability, output fluctuation and equipment failure. The emergency capability includes interconnection line failure and internal area failure.

[0109] Preferably, the tie line fault includes a tolerable power loss and a tolerable duration, and the internal area fault includes a tolerable power outage range and a tolerable duration.

[0110] Preferably, solving the two-layer optimization model to obtain a virtual power plant operation strategy comprises the following steps:

[0111] Using the Yalmip modeling language and solving with the gurobi solver;

[0112] By introducing the artificial swarm algorithm of reverse learning to solve the problem, the virtual power plant operation strategy is obtained.

[0113] Example 3

[0114] Figure 4 This is a structural diagram of an electronic device provided in the third embodiment of the present invention, such as Figure 4 As shown, the electronic device includes a processor 310, a memory 320, an input device 330 and an output device 340; the number of processors 310 in the computer device can be one or more. Figure 4 In the figure, a processor 310 is used as an example; the processor 310, memory 320, input device 330 and output device 340 in the electronic device can be connected via a bus or other means. Figure 4 The bus connection is taken as an example.

[0115] Memory 320, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the virtual power plant operation control method in the embodiments of the present invention. Processor 310 executes the software programs, instructions, and modules stored in memory 320 to execute various functional applications and data processing of the electronic device, thereby implementing the virtual power plant operation control method of the first embodiment.

[0116] The memory 320 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data created based on the use of the terminal, etc. Furthermore, the memory 320 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 320 may further include a memory remotely located relative to the processor 310, and these remote memories may be connected to the electronic device via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0117] The input device 330 may be used to receive input user identity information, grid data, etc. The output device 340 may include a display device such as a display screen.

[0118] Example 4

[0119] A fourth embodiment of the present invention further provides a storage medium containing computer-executable instructions, which can be used by a computer to execute a virtual power plant operation control method, the method comprising:

[0120] Establishing indicators for evaluating the resilience of virtual power plants;

[0121] Construct a two-level optimization model for operation strategies at both day-ahead and real-time time scales;

[0122] The two-layer optimization model is solved to obtain the virtual power plant operation strategy.

[0123] Of course, the storage medium containing computer-executable instructions provided in an embodiment of the present invention is not limited to the method operations described above, and can also execute related operations in the virtual power plant operation control method provided in any embodiment of the present invention.

[0124] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling an electronic device (which can be a mobile phone, personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0125] It is worth noting that in the above-mentioned embodiment of the virtual power plant operation control method device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0126] Those skilled in the art can make various other corresponding changes and deformations based on the technical solutions and concepts described above, and all of these changes and deformations should fall within the scope of protection of the claims of the present invention.

Claims

1. A virtual power plant operation control method, characterized in that: The following steps are involved: Establishing evaluation indicators for the flexible operation capability of virtual power plants; the evaluation indicators include branch safety margin assessment, new energy controllability assessment, and emergency response capability assessment. The safety margin includes active power carrying capacity and node voltage deviation; the new energy controllability includes response capability, output fluctuation, and equipment failure; and the emergency response includes tie line failure and internal area failure. The response capability is evaluated by adjusting the amplitude, response time, duration and climbing rate. The adjustment amplitude includes the upward adjustment amplitude. and downward adjustment , the objective functions are: , ,in, is the tie line baseline power, is the transmission power of the tie line at time t; the response time objective function is: ,in, for A collection of values taken at equal intervals; is the time traversed in the summation formula, represent A certain time Whether the time moment meets the regulation instruction requirement, if not, it is 1, otherwise it is 0; the objective function of the duration is: ,in, represent A certain time Whether the adjustment instruction requirements are met at the moment, if yes, it is 1, otherwise it is 0, For what has been sought A certain response time; the ramp rate includes both upward and downward ramp rates 、 , the objective functions are: , ,in, Indicates time equipment The output power; the probability distribution function of the output fluctuation is: ,in is the power deviation; the equipment failure is evaluated by the average failure outage frequency, average outage duration and average annual outage time; Constructing a two-level optimization model for the operation strategy at two time scales, day-ahead and real-time, and constructing the constraints and objective function of the optimization model based on the evaluation indicators; The two-layer optimization model is solved to obtain the virtual power plant operation strategy.

2. The virtual power plant operation control method according to claim 1, characterized in that: The active power carrying capacity is evaluated by combining the overall active power carrying margin and active power carrying balance through a network mapping method. The active power carrying margin satisfies: ,in, is the total mapped elastic potential energy of the power grid, The injected active power of the grid node is is the equivalent phase difference, is the equivalent branch mapping elastic coefficient; the active load balance satisfies: ,in, is the elastic coefficient of each path, is the active load; the node voltage deviation is evaluated by voltage over-limit time, voltage over-limit amplitude, system voltage deviation level, and voltage fluctuation index.

3. The virtual power plant operation control method according to claim 1, characterized in that: The tie line fault includes the tolerable power loss and the tolerable duration, and the internal area fault includes the tolerable power outage range and the tolerable duration.

4. The virtual power plant operation control method according to claim 1, characterized in that: Formulate a two-level optimization model for operation strategies at both day-ahead and real-time time scales, including: The objective function of the day-ahead operation optimization model satisfies: ,in, is the importance coefficient of each resource elasticity capability, is the resource serial number, Represents the running time, is the collection of all resources of the virtual power plant. is the set of all running times, The cost of purchasing electricity from the upper power grid, is the resource operating cost, is the system network loss cost. The constraints of the day-ahead operation optimization model include active power balance constraints, resource operation constraints, distribution network power flow equation constraints, and power network constraints. The objective function of the real-time operation optimization model satisfies: ,in, is the importance coefficient of each resource elasticity capability, For the The indicator weight of each indicator, Represents the resource number, Represents the running time, is the collection of all resources of the virtual power plant. It is a collection of all operating moments. The constraints of the real-time operation optimization model include the constraints of the day-ahead operation optimization model and the response capability requirements of the upper-level control center for the virtual power plant.

5. The virtual power plant operation control method according to claim 1, characterized in that: Solving the two-layer optimization model to obtain a virtual power plant operation strategy includes the following steps: In the optimization with the goal of economic optimization, the Yalmip modeling language is used in Matlab to call the gurobi solver to solve this first-order nonlinear programming problem. In the lower-level real-time optimization with the goal of elastic optimization, the artificial bee colony algorithm is improved by introducing the reverse learning population initialization method and adaptive coefficient. The improved artificial bee colony algorithm is used to solve this high-order nonlinear programming problem, and the obtained intraday operation strategy is returned as a parameter to the upper-level day-ahead optimization model.

6. A virtual power plant operation control device, characterized in that: It includes: An evaluation module, used to establish evaluation indicators for the elastic operation capability of virtual power plants; The operational capability assessment indicators include branch safety margin assessment, new energy controllability assessment, and emergency capability assessment. The safety margin includes active power carrying capacity and node voltage deviation. The new energy controllability includes response capability, output fluctuation, and equipment failure. The emergency capability includes tie line failure and internal area failure. The response capability is evaluated by adjusting the amplitude, response time, duration and climbing rate. The adjustment amplitude includes the upward adjustment amplitude. and downward adjustment , the objective functions are: , ,in, is the tie line baseline power, is the transmission power of the tie line at time t; the response time objective function is: ,in, for A collection of values taken at equal intervals; is the time traversed in the summation formula, represent A certain time Whether the time moment meets the regulation instruction requirement, if not, it is 1, otherwise it is 0; the objective function of the duration is: ,in, represent A certain time Whether the adjustment instruction requirements are met at the moment, if yes, it is 1, otherwise it is 0, For what has been sought A certain response time; the ramp rate includes both upward and downward ramp rates 、 , the objective functions are: , ,in, Indicates time equipment The output power; the probability distribution function of the output fluctuation is: ,in is the power deviation; the equipment failure is evaluated by the average failure outage frequency, average outage duration and average annual outage time; A model building module is used to build a two-level optimization model for the operation strategy at two time scales: day-ahead and real-time, and to build the constraints and objective function of the optimization model based on the evaluation indicators; The solution module is used to solve the two-layer optimization model to obtain the virtual power plant operation strategy.

7. An electronic device comprising a processor, a storage medium, and a computer program, wherein the computer program is stored in the storage medium, When the computer program is executed by a processor, the virtual power plant operation control method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the virtual power plant operation control method according to any one of claims 1 to 5 is implemented.

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