Micro-grid adjustment method and device based on virtual power plant, and storage medium

Abstract

The invention discloses a micro-grid adjustment method and device based on a virtual power plant and a storage medium. The method comprises the steps that the virtual power plant is determined; generating an electricity consumption baseline of each time period in the next time period for the virtual power plant; receiving a real-time adjusting signal sent by the main power grid; distributing the real-time adjusting signal to the micro-grids according to the electricity consumption base line, and sharing electric energy among the micro-grids in real time to minimize the illegal real-time adjusting signal; and when electric energy sharing is completed, real-time local optimization is carried out on each micro-grid so as to minimize the operation cost of each micro-grid. According to the embodiment, the control level is clear, the labor division is clear, and the scheduling efficiency of the micro-grid in the virtual power plant can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grids, and in particular to a microgrid regulation method, device and storage medium based on a virtual power plant. Background Art

[0002] The energy supply of traditional power grids is mainly based on fossil fuels, which has had a significant impact on the environment. The maturity of renewable energy technologies (such as photovoltaics, wind power, etc.) and the continuous reduction in costs will help alleviate the energy crisis.

[0003] Renewable energy has the characteristics of significant volatility and intermittency, which puts higher requirements on the peak regulation of the power grid. Integrating renewable energy, energy storage systems and backup generators into microgrids and controlling them using virtual power plant technology has become the mainstream method of modern power grid construction.

[0004] However, as the number of microgrids in virtual power plants continues to increase, the efficiency of energy regulation in virtual power plants and scheduling between internal microgrids is affected, which further affects the efficiency of power supply services on the demand side. Summary of the invention

[0005] In view of this, the present invention provides a microgrid regulation method, device and storage medium based on a virtual power plant, so as to improve the scheduling efficiency of the microgrid in the virtual power plant.

[0006] A first aspect of the present invention provides a microgrid regulation method based on a virtual power plant, comprising:

[0007] Determine a virtual power plant; the virtual power plant has a plurality of microgrids connected to the main power grid; the microgrids include distributed power generation devices, energy storage systems and solar photovoltaic panels;

[0008] Generating a power consumption baseline for each time period in the next time period for the virtual power plant;

[0009] Receiving a real-time regulation signal sent by the main power grid;

[0010] Distributing the real-time regulation signal to the microgrid according to the power consumption baseline, and sharing electric energy in real time among a plurality of the microgrids to minimize violations of the real-time regulation signal;

[0011] When the shared electric energy is completed, each of the microgrids is locally optimized in real time to minimize the operating cost of each of the microgrids.

[0012] A second aspect of the present invention provides a microgrid regulation device based on a virtual power plant, comprising:

[0013] A virtual power plant determination module is used to determine a virtual power plant; the virtual power plant has a plurality of microgrids connected to the main power grid; the microgrid includes a distributed power generation device, an energy storage system and a solar photovoltaic panel;

[0014] A pre-cycle adjustment module, used to generate a power consumption baseline for each time period in the next time cycle for the virtual power plant;

[0015] A real-time regulation signal receiving module, used for receiving a real-time regulation signal sent by the main power grid;

[0016] An upper control module, configured to distribute the real-time regulation signal to the microgrid according to the power consumption baseline, and share electric energy in real time among a plurality of the microgrids to minimize violations of the real-time regulation signal;

[0017] The lower control module is used to perform real-time local optimization on each of the microgrids when completing the sharing of electric energy, so as to minimize the operation cost of each of the microgrids.

[0018] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0019] at least one processor; and

[0020] a memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the microgrid regulation method based on the virtual power plant as described in the first aspect above.

[0022] The fourth aspect of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the microgrid regulation method based on the virtual power plant as described in the first aspect above.

[0023] A fifth aspect of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the microgrid regulation method based on a virtual power plant as described in the first aspect above.

[0024] In this embodiment, a virtual power plant is determined; the virtual power plant has multiple microgrids connected to the main grid; the microgrid includes a distributed power generation device, an energy storage system and a solar photovoltaic panel; the power consumption baseline of each time period in the next time period is generated for the virtual power plant; the real-time adjustment signal sent by the main grid is received; the real-time adjustment signal is allocated to the microgrid according to the power consumption baseline, and the electric energy is shared in real time between multiple microgrids to minimize the illegal real-time adjustment signal; when the shared electric energy is completed, each microgrid is locally optimized in real time to minimize the operating cost of each microgrid. This embodiment is divided into two stages: pre-cycle adjustment and real-time adjustment. The pre-cycle scheduling predicts the power consumption baseline of each time period in a future time period to provide adjustment capability, and the real-time adjustment is used to realize real-time predictive control. Real-time adjustment is to control real-time power consumption by tracking dynamic real-time adjustment signals. The upper layer allocates demand response signals from the main grid and exchanges power between microgrids based on the new energy sharing mechanism; the lower layer performs real-time power consumption control on each microgrid to minimize operating costs and improve energy utilization. The control hierarchy of this embodiment is clear, the division of labor is clear, and the scheduling efficiency of microgrids in virtual power plants can be effectively improved.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 This is a flow chart of a microgrid regulation method based on a virtual power plant provided in Example 1 of the present invention.

[0028] Figure 2 It is a structural schematic diagram of a microgrid regulation device based on a virtual power plant provided in Embodiment 2 of the present invention.

[0029] Figure 3 It is a structural schematic diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] In order to simplify the description and improve readability, the professional terms and abbreviations involved in this embodiment are as follows:

[0033] VPP, virtual power plant; MG, microgrid; RES, renewable energy producer; ESS, energy storage system; RegD, dynamic regulation signal; ARegD, real-time regulation signal; DR, demand response; EMS, energy management system; MILP, mixed integer linear programming; MINLP, mixed integer nonlinear programming; DER, distributed energy producer; DSO, distribution system operator; MSE, mean square error; CSP, load reduction provider; DG, distributed generation device; ISO, independent system operator; MPC, model predictive control; RNN, recurrent neural network; ARMA, autoregressive moving average; PV, solar photovoltaic panel; SoC, surplus power.

[0034] Embodiment 1

[0035] See also Figure 1 , shows a flow chart of a microgrid regulation method based on a virtual power plant provided in the first embodiment of the present invention, the method can be executed by a microgrid regulation device based on a virtual power plant, the microgrid regulation device based on a virtual power plant can be implemented in the form of hardware and / or software, and the microgrid regulation device based on a virtual power plant can be configured in an electronic device. Figure 1 As shown, the method includes:

[0036] Step 101: Determine a virtual power plant.

[0037] In actual applications, virtual power plants use energy Internet technology to integrate thousands of microgrids' scattered controllable loads, charging piles, distributed photovoltaics, distributed energy storage, etc., into load resources and power generation capacity with a certain scale and adjustable power generation capacity. Through this invisible power plant, it cooperates with the demand response of the main power grid to solve problems such as large peak-to-valley differences in the power grid and tight local power supply.

[0038] In this embodiment, the virtual power plant has multiple microgrids connected to the main power grid; the microgrids include distributed power generation devices, energy storage systems and solar photovoltaic panels.

[0039] Step 102: Generate a power consumption baseline for each time period in the next time cycle for the virtual power plant.

[0040] In this embodiment, the time axis can be divided into multiple time periods (such as 1 day), and each time period can be divided into multiple time periods (such as 24 hours).

[0041] The scheduling of the microgrid in the virtual power plant is divided into two stages. The first stage of the scheduling of the microgrid in the virtual power plant is pre-cycle scheduling (such as day-ahead scheduling), that is, generating a power consumption baseline for each time period in the next time cycle for the virtual power plant.

[0042] In one embodiment of the present invention, step 102 may include the following steps:

[0043] Step 1021: Determine each time period in the next time cycle.

[0044] In the current time period, you can query each time period in the next time period.

[0045] Step 1022: Calculate the power consumption baseline input from the main power grid to the virtual power plant in each time period.

[0046] In this embodiment, various parameters in the virtual power plant can be used to calculate the power consumption baseline input from the main grid to the virtual power plant in various time periods, thereby calculating the power consumption input from the main grid to the virtual power plant based on the power consumption baseline.

[0047] In the specific implementation, the power consumption baseline is expressed as:

[0048]

[0049] in, is the power consumption baseline for time period t, P load (t) is the power consumption of the virtual power plant in time period t, is the power generation of distributed generation device i in time period t, is the charge and discharge amount of energy storage system k in time period t, is the solar power generation of the solar photovoltaic panel in time period t predicted by the RNN model.

[0050] For example, Among them, F is the RNN model, and the features used include time, temperature, air pressure, humidity and sunshine intensity.

[0051] Step 1023: Calculate the operating cost of the distributed power generation device in each time period.

[0052] In this embodiment, various parameters in the distributed power generation device may be used to calculate the operating cost of the distributed power generation device in each time period.

[0053] In a specific implementation, the operating cost of a distributed power generation device is in a quadratic function relationship with the output power of the distributed power generation device. At this time, the operating cost of the distributed power generation device is expressed as:

[0054]

[0055] in, is the operating cost of distributed generation device i, is the conversion function, is the output power of distributed generation device i in time period t, a i 、b i and c i are coefficients of quadratic functions.

[0056] The quadratic function is converted into a piecewise linear function, which is then encoded into a MILP using the Gurobi optimizer. The following constraints are met:

[0057]

[0058] in, is the maximum output power of distributed generation device i in time period t, is the minimum output power of distributed generation device i in time period t.

[0059] For example,

[0060] Step 1024: Calculate the operating cost of the energy storage system in each time period.

[0061] In this embodiment, various parameters in the energy storage system may be used to calculate the operating cost of the energy storage system in various time periods.

[0062] In the specific implementation, the operating cost of the energy storage system is composed of the deviation cost (the difference between the remaining power in the previous time step and the initial power) and the loss cost (the difference between the remaining power in the current time step and the remaining power in the previous time step). Then, the operating cost of the energy storage system is expressed as:

[0063]

[0064] S k (t) = S k (t-1)+ΔS k (t)

[0065]

[0066] in, is the operating cost of energy storage system k in time period t, S k (T) is the remaining power of energy storage system k in time period T, S k,ini is the initial charge of energy storage system k, β ESS is the cost coefficient associated with charging and discharging the energy storage system, S k (t) is the remaining power of energy storage system k in time period t, S k (t-1) is the remaining power of energy storage system k in time period t-1, ΔS k (t) is the charge and discharge amount of energy storage system k in time period t, EC k is the storage capacity of energy storage system k, η c is the charging efficiency of the energy storage system, η d is the discharge efficiency of the energy storage system, and σ is the time step.

[0067] For example, n c =0.95.

[0068] With S k (t) There are the following constraints:

[0069]

[0070]

[0071] in, is the maximum charge and discharge rate of energy storage system k, is the lower limit of the storage capacity of the energy storage system k, is the upper limit of the storage capacity of the energy storage system k.

[0072] Step 1025, calculate the total cost of the virtual power plant based on the power consumption baseline, the operating cost of the distributed power generation device and the operating cost of the energy storage system.

[0073] In this embodiment, the total cost of the virtual power plant can be calculated by combining the electricity consumption baseline, the operating cost of the distributed power generation device and the operating cost of the energy storage system in a linear or nonlinear manner.

[0074] In the specific implementation, the total cost of the virtual power plant is expressed as:

[0075]

[0076] Among them, min means that the optimization goal is to minimize, α s (t) represents the electricity price of the main grid in time period t, γ ESS is the cost coefficient of the energy storage system.

[0077] In a time period, the energy storage system is either charging or discharging. All are positive values.

[0078] Step 1026: Calculate the regulation capacity of the virtual power plant based on the power consumption baseline.

[0079] In this embodiment, the regulation capacity of the virtual power plant can be calculated using the power consumption baseline of the virtual power plant and the maximum charge and discharge rate of the energy storage system.

[0080] In the specific implementation, the adjustment capability is expressed as:

[0081]

[0082] in, is the regulation capacity of the virtual power plant in time period t, min means the optimization goal is minimization, and A is the maximum power consumption when all controllable loads in the virtual power plant are turned on.

[0083] Step 1027: Solve the electricity consumption baseline with the optimization goal of minimizing the total cost and regulation capacity of the virtual power plant.

[0084] In this embodiment, the total cost and regulation capability of the virtual power plant are set as the objective function, and the electricity consumption baseline is solved with minimizing the total cost and regulation capability of the virtual power plant as the optimization goal.

[0085] Step 103: Receive a real-time adjustment signal sent by the main power grid.

[0086] The second stage of dispatching the microgrid in the virtual power plant is real-time regulation, that is, receiving the real-time regulation signal dynamically sent by the main power grid, and dynamically adjusting the output of the distributed power generation device and energy storage system within each microgrid to achieve real-time predictive control.

[0087] Furthermore, in the virtual power plant's electricity consumption baseline, the predicted solar power generation The value of the power consumption baseline is affected, which further affects the regulation capacity according to the expression of the regulation capacity. Therefore, the inaccuracy of the solar power generation forecast in the pre-cycle regulation will cause the MG to violate RegD in the sub-time cycle operation. The real-time regulation proposed in the second stage of this embodiment aims to minimize such violations.

[0088] In the second stage, the output of DG and ESS inside each MG is dynamically adjusted according to the received RegD signal to achieve real-time MPC energy consumption control.

[0089] The second stage is divided into two modules: the upper layer and the lower layer. The upper layer allocates RegD signals to each MG according to its electricity price and manages the energy sharing among the MGs within the VPP to minimize RegD violations. The lower layer is the real-time energy consumption control within a single MG to optimize the use of DGs and ESS therein.

[0090] In the second stage, the short-term solar forecast performance and the dimensionless RegD signal affect the real-time regulation of IMGs at each time step within the forecast horizon.

[0091] Step 104: distribute the real-time regulation signal to the microgrid according to the power consumption baseline, and share the power in real time among multiple microgrids to minimize the illegal real-time regulation signal.

[0092] The energy management system of an interconnected microgrid is implemented by the distribution system operator, which is usually divided into two layers: the interconnected microgrid control layer and the independent microgrid control layer. Among them, the microgrid can have enough distributed energy to meet the power supply demand in the island mode, and power can be exchanged between them to improve economy and reliability.

[0093] The second stage is divided into upper control and lower control.

[0094] In the upper-level control, when the real-time regulation signal is obtained, the real-time regulation signal received from the main grid is optimally distributed to each microgrid, and the energy sharing among the microgrids within the virtual power plant is managed to achieve the minimum cost of real-time regulation signal violation.

[0095] In one embodiment of the present invention, step 104 may include the following steps:

[0096] Step 1041: Calculate the electricity price for the microgrid based on the demand rate for supplying power from the main grid to the virtual power plant.

[0097] The demand rate is calculated based on the amount of electricity input from the main grid to the virtual power plant, and the electricity price of each microgrid is calculated based on the demand rate. In order to optimize the distribution of real-time regulation signals in the VPP system, the electricity price of each MG is calculated. (in $ / MW), which depends on the amount of electricity imported from the main grid to the VPP and the demand rate RegR n .

[0098] In the specific implementation, the electricity price of the microgrid is expressed as:

[0099]

[0100] Among them, RegR n is the demand rate for power supply from the main grid to the virtual power plant, is the load power consumption of microgrid n, is the real-time regulation signal allocated to the microgrid n by the virtual power plant, is the electricity price of microgrid n, γ a is the electricity price for the main grid, γ b It is the local average electricity price when the virtual power plant maximizes resource utilization.

[0101] Generally, the electricity price of the main grid is higher than that of the electricity price within the MG, so it is most economical when the demand is supplied by RES, DG and ESS themselves.

[0102] When RegR n = 0, all the electricity is provided by the main grid, and the electricity price is Highest, With RegR n decrease due to the decrease of .

[0103] When RegR n When <0, it indicates that the power provided by the main grid has exceeded the demand, and the excess power will be stored in the ESS.

[0104] Step 1042: Calculate the operating cost of the virtual power plant based on the electricity price of the microgrid.

[0105] Step 1043: With minimizing the operating cost of the virtual power plant as the optimization goal, the real-time regulation signal is distributed to the microgrid.

[0106] In this embodiment, the real-time adjustment signal of the minimum default cost (i.e., the default electricity quantity) is distributed to each microgrid to optimize resource allocation. At this time, the operating cost of the virtual power plant is calculated based on the electricity price of the microgrid.

[0107] The operating cost of a virtual power plant is expressed as:

[0108]

[0109] Among them, min means that the optimization goal is to minimize. is the conversion function, is the total power consumption of microgrid n, is the demand rate for power supply from the main grid to the microgrid n, L is the time domain, j is the time step in L, j∈{1,2,…,L}, MG n is the microgrid n, t is the time period, δ is the sampling time, is the baseline power consumption, ω dl is the weight, and R is the real-time adjustment signal.

[0110] Furthermore, the baseline And the real-time adjustment signal (ARegD)R remains unchanged within a specific time.

[0111] For example, it is assumed that there are four MGs, namely, MG n ∈{MG1,MG2,MG3,MG4}. The sum of the operating costs of all MGs is equal to the operating cost of VPP.

[0112] Step 1044: If the allocation is completed, the microgrid is adjusted with the optimization goal of minimizing the default power of the real-time dispatch signal.

[0113] When MG cannot follow When the MG is in a state of emergency, it will request power support from other MGs with surplus power to alleviate the predicted Otherwise, such violations will be recorded by the VPP and punished by the CSP.

[0114] In the specific implementation, the default power is expressed as:

[0115]

[0116] Among them, min means that the optimization goal is to minimize. is the default power of microgrid n, is the output power of distributed generation device i in microgrid n, DG i is a distributed power generation device i, is the charge and discharge capacity of energy storage system k in microgrid n, ESS k is the energy storage system k, is the solar power generation of the solar photovoltaic panels in microgrid n.

[0117] The following constraints exist:

[0118]

[0119] Among them, μ i is the maximum power fluctuation ratio that distributed generation device i can generate at each time step, is the maximum output power of distributed generation device i in microgrid n.

[0120] Step 1045: If the adjustment is completed, the power of the microgrid is dispatched with the maximum guaranteed power of the microgrid with the maximum default power of 0 as the optimization target.

[0121] In order to ensure the minimum After that, MG n Predicted illegal electricity within the scope of MPC management In the MG with the illegal power of 0 n These MGs can be shared between MGs that violate regulations provide assistance to make VPP a whole Minimize violations.

[0122] In the specific implementation, the maximum guaranteed power is expressed as:

[0123]

[0124] Among them, max means that the optimization goal is to maximize. is the maximum guaranteed power of microgrid m, is the output power of distributed generation device i in microgrid m, is the charge and discharge capacity of energy storage system k in microgrid m, is the solar power generation of microgrid m, is the real-time regulation signal allocated to the microgrid m by the virtual power plant, is the load power consumption of microgrid m.

[0125] The first three terms on the right side of the above equation represent the total power generation of the MG.

[0126] Step 1046: If the scheduling is completed, the power of the virtual power plant is scheduled under the condition that the total value of the illegal power is less than or equal to the maximum guaranteed power.

[0127] In this embodiment, the total illegal electricity Should be lower than the maximum guaranteed power Under this condition, the electricity of the virtual power plant is dispatched.

[0128] In the specific implementation, the dispatch of the power of the virtual power plant is expressed as:

[0129]

[0130] in, is the output power of microgrid m, satisfying N1 is the set of all microgrids with 0 illegal electricity, and N2 is the set of all microgrids with illegal electricity. is the default power of microgrid m, is the default power of microgrid q.

[0131] Step 105: When the shared power is completed, each microgrid is locally optimized in real time to minimize the operating cost of each microgrid.

[0132] In the lower-level control, each microgrid is locally optimized to minimize the operating cost of each microgrid.

[0133] In one embodiment of the present invention, step 105 may include the following steps:

[0134] Step 1051: In the microgrid, calculate the use cost of the energy storage system.

[0135] In this embodiment, the use cost of the energy storage system in the microgrid is calculated within a predicted time domain.

[0136] In the specific implementation, the use cost of the energy storage system is expressed as:

[0137]

[0138] S n,k (t+jδ)=S n，k (t+(j-1)δ)+ΔS n，k (t+jδ)

[0139]

[0140] in, is the cost of using energy storage system k in virtual power station n, S n,k is the remaining power of energy storage system k in virtual power station n, is the initial capacity of energy storage system k in virtual power station n, β ESS is the cost coefficient associated with charging and discharging the energy storage system, ΔS n,k is the charge and discharge capacity of the energy storage system k in the virtual power station n, is the charge and discharge capacity of energy storage system k in virtual power station n, η ESS is the charging and discharging efficiency of the energy storage system, L is the time domain, j is the time step in L, t is the time period, and δ is the sampling time.

[0141] For example, nESS =0.9.

[0142] With S n,k The following constraints exist:

[0143]

[0144] in, is the maximum charge and discharge rate of energy storage system k in virtual power station n, is the lower limit of the storage capacity of energy storage system k in virtual power station n, is the upper limit of the storage capacity of the energy storage system k in the virtual power station n;

[0145] Step 1052: Calculate the power deviation between the real-time dispatch signal and the actual energy consumption of the microgrid.

[0146] Through the operation of the upper layer control, each MG obtains the corresponding Due to the uncertainty in the forecast of solar power generation and load in the pre-cycle regulation stage, the allocation to MG n of There may be a power deviation from the actual energy consumption. At this time, the power deviation between the real-time dispatching signal and the actual energy consumption can be calculated for the microgrid.

[0147] In the specific implementation, the power deviation is expressed as:

[0148]

[0149] in, is the power deviation of microgrid n, is the output power of distributed generation device i in microgrid n, DG i is a distributed power generation device i, is the charge and discharge capacity of energy storage system k in microgrid n, ESS k is the energy storage system k, is the solar power generation of the solar photovoltaic panels in microgrid n, is the output power of microgrid n, is the load power consumption of microgrid m, The real-time regulation signal allocated by the virtual power plant to the microgrid n.

[0150] Among them, the autoregressive moving average model ARMA (2,1) is used to predict short-term solar power generation. The resolution of the forecast is 5 minutes.

[0151] Step 1053: Calculate the use cost of the distributed power generation device in the microgrid.

[0152] In this embodiment, the use cost of the distributed power generation device can be obtained by using the output power of the distributed power generation device and the operation cost of the distributed power generation device.

[0153] In the specific implementation, the use cost of distributed generation equipment is expressed as:

[0154]

[0155] in, is the cost of using distributed generation device i in microgrid n, is the conversion function, is the output power of distributed generation device i in microgrid n.

[0156] Step 1054: Calculate the operating cost of the microgrid based on the use cost of the energy storage system, the power deviation and the use cost of the distributed power generation device.

[0157] In this embodiment, the use cost of the energy storage system, the power deviation and the use cost of the distributed power generation device can be integrated into the operation cost of the microgrid in a linear or nonlinear manner.

[0158] In the specific implementation, the operating cost of the microgrid is expressed as:

[0159]

[0160] Among them, min means that the optimization goal is to minimize, γ v With γ ESS are the cost coefficients of penalty for violations, γ v >>γ ESS .

[0161] Step 1055: Perform real-time local optimization on the microgrid with minimizing the operating cost of the microgrid as the optimization goal.

[0162] In this embodiment, the operation cost of the microgrid is set as the objective function, and the microgrid is locally optimized in real time with minimizing the operation cost of the microgrid as the optimization goal.

[0163] In this embodiment, a virtual power plant is determined; the virtual power plant has multiple microgrids connected to the main grid; the microgrid includes a distributed power generation device, an energy storage system and a solar photovoltaic panel; the power consumption baseline of each time period in the next time period is generated for the virtual power plant; the real-time adjustment signal sent by the main grid is received; the real-time adjustment signal is allocated to the microgrid according to the power consumption baseline, and the electric energy is shared in real time between multiple microgrids to minimize the illegal real-time adjustment signal; when the shared electric energy is completed, each microgrid is locally optimized in real time to minimize the operating cost of each microgrid. This embodiment is divided into two stages: pre-cycle adjustment and real-time adjustment. The pre-cycle scheduling predicts the power consumption baseline of each time period in a future time period to provide adjustment capability, and the real-time adjustment is used to realize real-time predictive control. Real-time adjustment is to control real-time power consumption by tracking dynamic real-time adjustment signals. The upper layer allocates demand response signals from the main grid and exchanges power between microgrids based on the new energy sharing mechanism; the lower layer performs real-time power consumption control on each microgrid to minimize operating costs and improve energy utilization. The control hierarchy of this embodiment is clear, the division of labor is clear, and the scheduling efficiency of microgrids in virtual power plants can be effectively improved.

[0164] Embodiment 2

[0165] See also Figure 2 , shows a schematic diagram of the structure of a microgrid regulation device based on a virtual power plant provided by the second embodiment of the present invention. Figure 2 As shown, the device comprises:

[0166] A virtual power plant determination module 201 is used to determine a virtual power plant; the virtual power plant has a plurality of microgrids connected to the main power grid; the microgrids include distributed power generation devices, energy storage systems and solar photovoltaic panels;

[0167] A pre-cycle adjustment module 202 is used to generate a power consumption baseline for each time period in the next time cycle for the virtual power plant;

[0168] A real-time regulation signal receiving module 203, used for receiving a real-time regulation signal sent by the main power grid;

[0169] The upper control module 204 is used to distribute the real-time adjustment signal to the microgrid according to the power consumption baseline, and share power in real time among the plurality of microgrids to minimize the violation of the real-time adjustment signal;

[0170] The lower control module 205 is used to perform real-time local optimization on each of the microgrids when the shared power is completed, so as to minimize the operation cost of each of the microgrids.

[0171] In one embodiment of the present invention, the pre-cycle adjustment module 202 includes:

[0172] A time period determination module, used to determine each time period in the next time period;

[0173] A power consumption baseline calculation module, used to calculate the power consumption baseline input from the main power grid to the virtual power plant in each time period;

[0174] A power generation operation cost calculation module, used to calculate the operation cost of the distributed power generation device in each time period;

[0175] An energy storage operation cost calculation module, used to calculate the operation cost of the energy storage system in each time period;

[0176] A power plant cost calculation module, configured to calculate the total cost of the virtual power plant based on the power consumption baseline, the operating cost of the distributed power generation device and the operating cost of the energy storage system;

[0177] A regulating capacity calculation module, used to calculate the regulating capacity of the virtual power plant according to the power consumption baseline;

[0178] The power consumption baseline solving module is used to solve the power consumption baseline with minimizing the total cost of the virtual power plant and the regulation capacity as the optimization goal.

[0179] In one embodiment of the present invention, the power consumption baseline is expressed as:

[0180]

[0181] in, is the power consumption baseline for time period t, P load (t) is the power consumption of the virtual power plant in time period t, is the power generation of distributed generation device i in time period t, is the charge and discharge amount of energy storage system k in time period t, is the solar power generation of the solar photovoltaic panel in time period t;

[0182] The operating cost of the distributed generation device is expressed as:

[0183]

[0184] in, is the operating cost of distributed generation device i, is the conversion function, is the output power of distributed generation device i in time period t, a i , b i and c i All are coefficients;

[0185] The following constraints are met:

[0186]

[0187] in, is the maximum output power of distributed generation device i in time period t, is the minimum output power of distributed generation device i in time period t;

[0188] The operating cost of the energy storage system is expressed as:

[0189]

[0190] S k (t) = S k (t-1)+ΔS k (t

[0191]

[0192] in, is the operating cost of energy storage system k in time period t, S k (T) is the remaining power of energy storage system k in time period T, S k,ini is the initial charge of energy storage system k, β ESS is the cost coefficient associated with charging and discharging the energy storage system, S k (t) is the remaining power of energy storage system k in time period t, S k (t-1) is the remaining power of energy storage system k in time period t-1, ΔS k (t) is the charge and discharge amount of energy storage system k in time period t, EC k is the storage capacity of energy storage system k, η c is the charging efficiency of the energy storage system, η d is the discharge efficiency of the energy storage system, σ is the time step;

[0193] With S k (t) There are the following constraints:

[0194]

[0195] in, is the maximum charge and discharge rate of energy storage system k, is the lower limit of the storage capacity of the energy storage system k, is the upper limit value of the storage capacity of the energy storage system k;

[0196] The regulation capability is expressed as:

[0197]

[0198] in, is the regulation capacity of the virtual power plant in time period t, min indicates that the optimization target is minimization, and A is the maximum power consumption when all controllable loads in the virtual power plant are turned on;

[0199] The total cost of the virtual power plant is expressed as:

[0200]

[0201] Among them, min means that the optimization goal is to minimize, α s (t) represents the electricity price of the main grid in time period t, γ ESS is the cost coefficient of the energy storage system.

[0202] In one embodiment of the present invention, the upper control module 204 includes:

[0203] An electricity price calculation module, used to calculate the electricity price for the microgrid according to the demand rate of power supply from the main grid to the virtual power plant;

[0204] A power plant operation cost calculation module, used to calculate the operation cost of the virtual power plant according to the electricity price of the microgrid;

[0205] A signal distribution module, configured to distribute the real-time adjustment signal to the microgrid with minimizing the operating cost of the virtual power plant as an optimization goal;

[0206] The default adjustment module adjusts the microgrid by minimizing the default power of the real-time dispatch signal as the optimization goal if the apartment completes the allocation;

[0207] An electricity sharing module is used to dispatch the electricity of the microgrid by maximizing the maximum guaranteed electricity of the microgrid with the default electricity being 0 as the optimization goal if the adjustment is completed;

[0208] The power plant scheduling module is used to schedule the power of the virtual power plant if the scheduling is completed, under the condition that the total value of the illegal power is less than or equal to the maximum guaranteed power.

[0209] In one embodiment of the present invention, the electricity price of the microgrid is expressed as:

[0210]

[0211] Among them, RegR n is the demand rate for power supply from the main grid to the virtual power plant, is the load power consumption of microgrid n, is the real-time regulation signal allocated to the microgrid n by the virtual power plant, is the electricity price of microgrid n, γ a is the electricity price of the main grid, γ b is the local average electricity price when the virtual power plant maximizes resource utilization;

[0212] The operating cost of the virtual power plant is expressed as:

[0213]

[0214] Among them, min means that the optimization goal is to minimize. is the conversion function, is the total power consumption of microgrid n, is the demand rate for power supply from the main grid to the microgrid n, L is the time domain, j is the time step in L, MG n is the microgrid n, t is the time period, δ is the sampling time, is the baseline power consumption, ω dl is the weight, and R is the real-time adjustment signal;

[0215] The default power is expressed as:

[0216]

[0217] Among them, min means that the optimization goal is to minimize. is the default power of microgrid n, is the output power of distributed generation device i in microgrid n, DG i is a distributed power generation device i, is the charge and discharge capacity of energy storage system k in microgrid n, ESS k is the energy storage system k, is the solar power generation of the solar photovoltaic panels in microgrid n;

[0218] The following constraints exist:

[0219]

[0220] Among them, μ i is the maximum power fluctuation ratio that distributed generation device i can generate at each time step, is the maximum output power of distributed generation device i in microgrid n;

[0221] The maximum guaranteed power is expressed as:

[0222]

[0223] Among them, max means that the optimization goal is to maximize. is the maximum guaranteed power of microgrid m, is the output power of distributed generation device i in microgrid m, is the charge and discharge capacity of energy storage system k in microgrid m, is the solar power generation of microgrid m, is the real-time regulation signal allocated to the microgrid m by the virtual power plant, is the load power consumption of microgrid m;

[0224] The dispatching of the power of the virtual power plant is expressed as:

[0225]

[0226] in, is the output power of microgrid m, satisfying N1 is the set of all microgrids with 0 illegal electricity, and N2 is the set of all microgrids with illegal electricity. is the default power of microgrid m, is the default power of microgrid q.

[0227] In one embodiment of the present invention, the lower layer control module 205 includes:

[0228] A storage usage cost calculation module, used to calculate the usage cost of the energy storage system in the microgrid;

[0229] An electric quantity deviation calculation module, used for calculating the electric quantity deviation between the real-time dispatching signal and the actual energy consumption of the microgrid;

[0230] A power generation cost calculation module, used to calculate the use cost of the distributed power generation device in the microgrid;

[0231] A microgrid operation cost calculation module, used to calculate the operation cost of the microgrid based on the use cost of the energy storage system, the power deviation and the use cost of the distributed power generation device;

[0232] The microgrid local optimization module is used to perform real-time local optimization on the microgrid with minimizing the operating cost of the microgrid as the optimization goal.

[0233] In one embodiment of the present invention, the use cost of the energy storage system is expressed as:

[0234]

[0235] Sn,k (t+jδ)=S n,k (t+(j-1)δ)+ΔS n,k (t+jδ

[0236]

[0237] in, is the cost of using energy storage system k in virtual power station n, S n,k is the remaining power of energy storage system k in virtual power station n, is the initial capacity of energy storage system k in virtual power station n, β ESS is the cost coefficient associated with charging and discharging the energy storage system, ΔS n,k is the charge and discharge capacity of the energy storage system k in the virtual power station n, is the charge and discharge capacity of energy storage system k in virtual power station n, η ESS is the charging and discharging efficiency of the energy storage system, L is the time domain, j is the time step in L, t is the time period, and δ is the sampling time;

[0238] With S n,k The following constraints exist:

[0239]

[0240] in, is the maximum charge and discharge rate of energy storage system k in virtual power station n, is the lower limit of the storage capacity of energy storage system k in virtual power station n, is the upper limit of the storage capacity of the energy storage system k in the virtual power station n;

[0241] The electrical quantity deviation is expressed as:

[0242]

[0243] in, is the power deviation of microgrid n, is the output power of distributed generation device i in microgrid n, DG i is a distributed power generation device i, is the charge and discharge capacity of energy storage system k in microgrid n, ESS k is the energy storage system k, is the solar power generation of the solar photovoltaic panels in microgrid n, is the output power of microgrid n, is the load power consumption of microgrid m, A real-time regulation signal allocated to the microgrid n by the virtual power plant;

[0244] The cost of using distributed generation equipment is expressed as:

[0245]

[0246] in, is the cost of using distributed generation device i in microgrid n, is the conversion function, is the output power of distributed generation device i in microgrid n;

[0247] The operating cost of the microgrid is expressed as:

[0248]

[0249] Among them, min means that the optimization goal is to minimize, γ v With γ ESS Both are cost coefficients for penalties for violations.

[0250] The microgrid regulation device based on a virtual power plant provided in an embodiment of the present invention can execute the microgrid regulation method based on a virtual power plant provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the microgrid regulation method based on a virtual power plant.

[0251] Embodiment 3

[0252] See also Figure 3 , shows a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0253] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0254] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0255] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a microgrid regulation method based on a virtual power plant.

[0256] In some embodiments, the microgrid regulation method based on a virtual power plant may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the microgrid regulation method based on a virtual power plant described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the microgrid regulation method based on a virtual power plant in any other appropriate manner (e.g., by means of firmware).

[0257] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0258] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0259] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0260] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0261] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0262] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0263] Embodiment 4

[0264] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the microgrid regulation method based on a virtual power plant as provided in any embodiment of the present invention.

[0265] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0266] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0267] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A microgrid regulation method based on a virtual power plant, characterized in that: include: Identify virtual power plants; The virtual power plant has multiple microgrids connected to the main power grid; the microgrids include distributed power generation devices, energy storage systems and solar photovoltaic panels; Generating a power consumption baseline for each time period in the next time period for the virtual power plant; Receiving a real-time regulation signal sent by the main power grid; Allocating the real-time regulation signal to the microgrid according to the power consumption baseline, and sharing electric energy in real time among a plurality of the microgrids to minimize violations of the real-time regulation signal; When the shared electric energy is completed, each of the microgrids is locally optimized in real time to minimize the operating cost of each of the microgrids.

2. The method according to claim 1, characterized in that The generating of a power consumption baseline for each time period in the next time period for the virtual power plant includes: Determine the time periods in the next time period; Calculating a baseline of electricity consumption input from the main power grid to the virtual power plant in various time periods; Calculating the operating cost of the distributed generation device in each time period; Calculating the operating cost of the energy storage system in various time periods; Calculating the total cost of the virtual power plant based on the power consumption baseline, the operating cost of the distributed power generation device and the operating cost of the energy storage system; Calculating the regulation capacity of the virtual power plant based on the power consumption baseline; The electricity consumption baseline is solved with the optimization goal of minimizing the total cost of the virtual power plant and the regulation capacity.

3. The method according to claim 2, characterized in that The power consumption baseline is expressed as: in, is the power consumption baseline for time period t, P load (t) is the power consumption of the virtual power plant in time period t, is the power generation of distributed generation device i in time period t, is the charge and discharge amount of energy storage system k in time period t, is the solar power generation of the solar photovoltaic panel in time period t; The operating cost of the distributed generation device is expressed as: in, is the operating cost of distributed generation device i, is the conversion function, is the output power of distributed generation device i in time period t, a i , b i and c i All are coefficients; The following constraints are met: in, is the maximum output power of distributed generation device i in time period t, is the minimum output power of distributed generation device i in time period t; The operating cost of the energy storage system is expressed as: S k (t)=S k (t-1)+ΔS k (t) in, is the operating cost of energy storage system k in time period t, S k (T) is the remaining power of energy storage system k in time period T, S k,ini is the initial charge of energy storage system k, β ESS is the cost coefficient associated with charging and discharging the energy storage system, S k (t) is the remaining power of energy storage system k in time period t, S k (t-1) is the remaining power of energy storage system k in time period t-1, ΔS k (t) is the charge and discharge amount of energy storage system k in time period t, EC k is the storage capacity of energy storage system k, η c is the charging efficiency of the energy storage system, η d is the discharge efficiency of the energy storage system, σ is the time step; With S k (t) There are the following constraints: in, is the maximum charge and discharge rate of energy storage system k, is the lower limit of the storage capacity of the energy storage system k, is the upper limit value of the storage capacity of the energy storage system k; The regulation capability is expressed as: in, is the regulation capacity of the virtual power plant in time period t, min indicates that the optimization target is minimization, and A is the maximum power consumption when all controllable loads in the virtual power plant are turned on; The total cost of the virtual power plant is expressed as: Among them, min means that the optimization goal is to minimize, α s (t) represents the electricity price of the main grid in time period t, γ ESS is the cost coefficient of the energy storage system.

4. The method according to claim 1, characterized in that: The method of allocating the real-time regulation signal to the microgrid according to the power consumption baseline and sharing electric energy in real time among a plurality of the microgrids to minimize violations of the real-time regulation signal includes: Calculating an electricity price for the microgrid based on a demand rate for supplying power from the main grid to the virtual power plant; Calculating the operating cost of the virtual power plant based on the electricity price of the microgrid; With minimizing the operating cost of the virtual power plant as an optimization goal, allocating the real-time adjustment signal to the microgrid; If the allocation is completed, the microgrid is adjusted with minimizing the default power of the real-time dispatch signal as the optimization goal; If the adjustment is completed, the power of the microgrid is dispatched with the maximum guaranteed power of the microgrid being maximized to 0 as the optimization goal; If the scheduling is completed, the electricity of the virtual power plant will be scheduled under the condition that the total value of the illegal electricity is less than or equal to the maximum guaranteed electricity.

5. The method according to claim 4, characterized in that The electricity price of the microgrid is expressed as: Among them, RegR n is the demand rate for power supply from the main grid to the virtual power plant, is the load power consumption of microgrid n, is the real-time regulation signal allocated to the microgrid n by the virtual power plant, is the electricity price of microgrid n, γ a is the electricity price of the main grid, γ b is the local average electricity price when the virtual power plant maximizes resource utilization; The operating cost of the virtual power plant is expressed as: Among them, min means that the optimization goal is to minimize. is the conversion function, is the total power consumption of microgrid n, is the demand rate for power supply from the main grid to the microgrid n, L is the time domain, j is the time step in L, MG n is the microgrid n, t is the time period, δ is the sampling time, is the baseline power consumption, ω dl is the weight, and R is the real-time adjustment signal; The default power is expressed as: Among them, min means that the optimization goal is to minimize. is the default power of microgrid n, is the output power of distributed generation device i in microgrid n, DG i is a distributed power generation device i, is the charge and discharge capacity of energy storage system k in microgrid n, ESS k is the energy storage system k, is the solar power generation of the solar photovoltaic panels in microgrid n; The following constraints exist: Among them, μ i is the maximum power fluctuation ratio that distributed generation device i can generate at each time step, is the maximum output power of distributed generation device i in microgrid n; The maximum guaranteed power is expressed as: Among them, max means that the optimization goal is to maximize. is the maximum guaranteed power of microgrid m, is the output power of distributed generation device i in microgrid m, is the charge and discharge capacity of energy storage system k in microgrid m, is the solar power generation of microgrid m, is the real-time regulation signal allocated to the microgrid m by the virtual power plant, is the load power consumption of microgrid m; The dispatching of the power of the virtual power plant is expressed as: in, is the output power of microgrid m, satisfying N1 is the set of all microgrids with 0 illegal electricity, and N2 is the set of all microgrids with illegal electricity. is the default power of microgrid m, is the default power of microgrid q.

6. The method according to any one of claims 1 to 5, characterized in that The real-time local optimization of each of the microgrids to minimize the operating cost of each of the microgrids includes: In the microgrid, calculating the use cost of the energy storage system; Calculating the power deviation between the real-time dispatch signal and the actual energy consumption of the microgrid; In the microgrid, calculating the use cost of the distributed power generation device; Calculating the operating cost of the microgrid according to the use cost of the energy storage system, the power deviation and the use cost of the distributed power generation device; Taking minimizing the operating cost of the microgrid as the optimization goal, the microgrid is locally optimized in real time.

7. The method according to claim 6, characterized in that The use cost of the energy storage system is expressed as: S n,k (t+jδ)=S n，k (t+(j-1)δ)+ΔS n，k (t+jδ) in, is the cost of using energy storage system k in virtual power station n, S n,k is the remaining power of energy storage system k in virtual power station n, is the initial capacity of energy storage system k in virtual power station n, β ESS is the cost coefficient associated with charging and discharging the energy storage system, ΔS n,k is the charge and discharge capacity of the energy storage system k in the virtual power station n, is the charge and discharge capacity of energy storage system k in virtual power station n, η ESS is the charging and discharging efficiency of the energy storage system, L is the time domain, j is the time step in L, t is the time period, and δ is the sampling time; With S n,k The following constraints exist: in, is the maximum charge and discharge rate of energy storage system k in virtual power station n, is the lower limit of the storage capacity of energy storage system k in virtual power station n, is the upper limit of the storage capacity of the energy storage system k in the virtual power station n; The electrical quantity deviation is expressed as: in, is the power deviation of microgrid n, is the output power of distributed generation device i in microgrid n, DG i is a distributed power generation device i, is the charge and discharge capacity of energy storage system k in microgrid n, ESS k is the energy storage system k, is the solar power generation of the solar photovoltaic panels in microgrid n, is the output power of microgrid n, is the load power consumption of microgrid m, A real-time regulation signal allocated to the microgrid n by the virtual power plant; The cost of using distributed generation equipment is expressed as: in, is the cost of using distributed generation device i in microgrid n, is the conversion function, is the output power of distributed generation device i in microgrid n; The operating cost of the microgrid is expressed as: Among them, min means that the optimization goal is to minimize, γ v With γ ESS Both are cost coefficients for penalties for violations.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the microgrid regulation method based on a virtual power plant as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the microgrid regulation method based on a virtual power plant as described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the microgrid regulation method based on a virtual power plant as described in any one of claims 1 to 7 is implemented.

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