A power grid congestion alleviation regulation method considering service type virtual power plant

Abstract

The present application relates to a kind of power grid congestion mitigation regulation method considering service type virtual power plant, compared with prior art, it solves the defect that renewable energy that is difficult to manage is connected to power system.The present application includes the following steps: the acquisition of power grid data;The construction of objective function;The construction of constraint condition;The solution of service type virtual power plant optimization scheduling model;The regulation of power grid congestion.The present application utilizes the characteristics of electric vehicle flexibility, optimizes and adjusts the charging and discharging schedule of electric vehicle in network congestion period, to relieve network congestion.

Description

Technical Field

[0001] This invention relates to the field of integrated power grid control technology, specifically a power grid congestion mitigation and control method that considers service-oriented virtual power plants. Background Technology

[0002] In recent years, the share of wind and solar power in the power grid has increased rapidly, driven by countries' efforts to reduce greenhouse gas emissions. However, the large-scale integration of renewable energy poses a threat to network security, and the uncertainty of its power generation further complicates security issues. Specifically, network congestion caused by the large-scale integration of renewable energy into the grid forces the network to reduce some of the incoming renewable energy to ensure the safe operation of the system.

[0003] A Virtual Power Plant (VPP) is a power supply coordination and management system that uses advanced information and communication technologies and software systems to aggregate and coordinate distributed energy sources such as distributed power generators, energy storage systems, controllable loads, and electric vehicles, and participates in the electricity market and grid operation as a special type of power plant. The core of a VPP can be summarized as "communication" and "aggregation." Key technologies of VPPs mainly include coordination and control technology, smart metering technology, and information and communication technology. Service-centric VPPs can support cooperation with distribution system operators to solve network usage problems, proposing appropriate interaction and communication schedules between aggregators, market operators, system operators, power generators, and consumers regarding electricity market and network operation. This is an important way for smart grids to achieve interactivity and intelligence on the energy supply and demand sides.

[0004] Therefore, by collaborating with system distribution operators through service-oriented virtual power plants (SOPGs), it is possible to optimize system scheduling, maximize the integration of renewable energy, and alleviate network congestion. Consequently, how to achieve grid congestion mitigation and regulation based on service-oriented virtual power plant technology has become an urgent technical challenge. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies in managing renewable energy sources connected to the power system, and to provide a grid congestion mitigation and control method that considers service-oriented virtual power plants to solve the above problems.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A grid congestion mitigation and control method considering service-oriented virtual power plants includes the following steps:

[0008] 11) Acquisition of power grid data: Acquire power grid data, including the basic parameters of each branch and the initial parameters of each node, and build a simulation model.

[0009] 12) Construction of the objective function: Under the premise of forecasting renewable energy power generation, construct the objective function for day-ahead market dispatch and intraday market dispatch;

[0010] 13) Construction of constraints: Construct constraints for day-ahead market scheduling and intraday market scheduling, including power limit constraints for CHP units, energy capacity boundary constraints for storage units, electrical power balance constraints, and thermal power balance constraints.

[0011] 14) Solving the optimal scheduling model of service-oriented virtual power plants: Based on the objective function and constraints, construct an optimal scheduling model that considers service-oriented virtual power plants, and solve the model using the scenario method;

[0012] 15) Regulation of grid congestion: In response to grid congestion, based on the data obtained from the model solution in step 14) and the data obtained in step 11), the sensitivity of each bus is calculated, and grid congestion is alleviated by adjusting the charging and discharging schedule of electric vehicles.

[0013] The acquisition of the power grid data includes the following steps:

[0014] 21) Based on the actual power network structure, build a Simulink simulation model in Matlab;

[0015] 22) Obtain data from the actual power network structure, including the maximum transmission capacity of all branches in the power network, the impedance Z and admittance Y of all branches, the node voltage V and phase angle θ of all nodes, and the initial active power P and reactive power Q of each node.

[0016] 23) Store the acquired data into each node of the simulation model.

[0017] The construction of the objective function includes the following steps:

[0018] 31) Based on the forecasting of renewable energy power generation, a day-ahead market dispatch objective function was constructed;

[0019] The objective function F1 for day-ahead market dispatch is composed of the CHP fuel cost of the cogeneration unit, storage operation costs, the predicted day-ahead market energy price, and the scenario probability p. s constitute:

[0020]

[0021] In the formula, s represents the s-th renewable energy prediction scenario, and t h For horizontal time, N chp N represents the number of combined heat and power (CHP) units. Sp represents the number of current-day renewable energy generation forecast scenarios. s Let H be the probability of the s-th renewable energy forecast scenario occurring. da For the planned time range in the previous day, τ da N represents the day-ahead horizontal time step. e,sto C represents the number of energy storage units. f,chp,i,s (t h Let C be the fuel cost of the i-th cogeneration unit under scenario s. op,e,sto,i,s (t h Let P be the operating cost of the i-th energy storage unit in scenario s. m,da (t h c represents the active power exchanged by the VPP to the day-ahead energy market. m,da (t h (This refers to the predicted market price per unit of energy)

[0022] 32) Based on the forecasting of renewable energy power generation, construct the intraday market dispatch objective function:

[0023] In intraday planning, the purpose of VPP is to provide day-ahead market electricity exchange and internally compensate for imbalances caused by updated renewable energy generation forecasts;

[0024] If the imbalance within its pool cannot be compensated, then VPP will exchange active power P in the intraday market. imb,id (t h );

[0025] Assume that the scheduling of VPP resources is based on the active power imbalance P of intraday transactions. imb,id (t h The objective is to minimize the intraday imbalance penalty cost C during resource scheduling optimization. pen,imb (t h Therefore, the objective function F2 for intraday market scheduling includes intraday imbalance penalty costs, CHP fuel costs, and storage operating costs, and its expression is as follows:

[0026]

[0027] In the formula, H id For the intraday scheduling time range, τ id For intraday time steps, C f,chp,i (t h Let C be the fuel cost of the i-th combined heat and power unit. op,e,sto,i (t h Let C be the operating cost of the i-th energy storage unit. pen,imb (t h This represents the cost of penalties for intraday imbalances.

[0028] The construction of the constraints includes the following steps:

[0029] 41) Set power limitation constraints for the CHP unit:

[0030] P e,chp,i,min ≤P e,chp,i (t h )≤P e,chp,i,max ,

[0031] In the formula, P e,chp,i,min For the minimum power injection of the i-th cogeneration unit, P e,chp,i (t h P is the power injection for the i-th combined heat and power unit. e,chp,i,max For the maximum power injection of the i-th cogeneration unit;

[0032] 42) Set energy capacity boundary constraints for storage cells:

[0033] E e,sto,i,min ≤E e,sto,i (t h )≤E e,sto,i,max ,

[0034] E th,sto,i,min ≤E th,sto,i (t h )≤E th,sto,i,max ,

[0035] In the formula, E e,sto,i,min E represents the minimum energy level of the i-th energy storage unit. e,sto,i (t h Let E be the energy level of the i-th energy storage unit. e,sto,i,max E represents the maximum energy level of the i-th energy storage unit. th,sto,i,min E represents the minimum energy level of the i-th thermal storage unit. th,sto,i (t h Let E be the energy level of the i-th thermal storage unit. th,sto,i,max This represents the maximum energy level of the i-th thermal storage unit;

[0036] 43) Set day-ahead power balance constraints:

[0037]

[0038] In the formula, P e,chp,i,s (t h P represents the power injection of the i-th cogeneration unit under scenario s. e,sto,i,s (t h P represents the power injection into the i-th energy storage unit under scenario s. res,s (t h P represents the electricity injection from renewable energy sources under scenario s.e,1 (t h ) represents the total electrical load in the VPP;

[0039] 44) Set day-ahead thermal power balance constraints:

[0040]

[0041] In the formula, P th,chp,i,s (t h ) represents the heat and electricity injection of the i-th cogeneration unit under scenario s, and N is the total heat and electricity injection. th,sto P represents the number of thermal storage units. th,sto,i,s (t h P represents the thermoelectric injection of the i-th energy storage unit under scenario s. th,1 (t h The total heat load in the VPP is denoted as .

[0042] 45) Set intraday power balance constraints:

[0043]

[0044] In the formula, P e,chp,i (t h P represents the power injection of the i-th combined heat and power unit during the day. e,sto,i (t h P represents the power injection into the i-th energy storage unit during the day. res (t h P is the electricity injection from renewable energy sources within the day. imb,id (t h This refers to the imbalance of active power exchanged in the intraday energy market.

[0045] 46) Set intraday heat power balance constraints:

[0046]

[0047] In the formula, P th,chp,i (t h P represents the heat injection for the i-th combined heat and power unit during the day. th,sto,i (t h ) represents the thermal injection of the i-th energy storage unit within the day.

[0048] Solving the optimal scheduling model of the service-oriented virtual power plant includes the following steps:

[0049] 51) Based on the objective function and relevant constraints, construct an optimal scheduling model that considers service-oriented virtual power plants. This model takes service-oriented virtual power plants as the center, integrates renewable energy, energy storage devices, cogeneration units, loads and electric vehicles, and VPPs participate in the electricity market for electricity exchange.

[0050] The role of VPP is to maintain power balance within the system, adjust the output of cogeneration units and energy storage devices according to the uncertainty of renewable energy power generation, and minimize unnecessary reduction of renewable energy while ensuring the safe and reliable operation of the system.

[0051] 52) Due to the uncertainty of renewable energy power generation, the scenario method is used to predict the power generation scenarios and obtain the renewable power generation prediction scenarios; then, the concept of probability distance is used to reduce the predicted scenarios to a specified number; finally, the reduced scenario information is sent to VPP, and the model is solved using the CPLEX solver according to the objective function and related constraints, and the voltage V and phase angle θ of each node in the solved network are obtained.

[0052] 53) Using the branch impedance Z and admittance Y obtained in the power grid data acquisition step, and the node voltage V and phase angle θ solved in step 52), calculate the magnitude of the active power flow on each branch:

[0053] P f,mn =|V m | 2 g mn -|V m ||V n |[g mn cos(θ m -θ n )+b mn sin(θ m -θ n )],

[0054] In the formula, P f,mn V represents the active power flow from node m to node n. m V represents the voltage magnitude at node m. n The voltage magnitude at node n; g mn Let b be the conductance of the mn branch; mn θ is the susceptance of the mn branch; m θ is the voltage phase angle at node m. n Let n be the voltage phase angle at node n.

[0055] The regulation of power grid congestion includes the following steps:

[0056] 61) The power distribution system operator monitors the power flow of each branch in the system in real time. When the power flow P of a certain branch... f,mn Greater than the maximum transmission capacity P of the branch fmax,mn If the network is overloaded, the network is reconfigured to resolve the issue. If the network congestion is not fully alleviated after reconfiguration, the reconfiguration solution is supplemented.

[0057] 62) The DSO complements the "reconfiguration" scheme by providing congestion mitigation services to request-service VPPs:

[0058] The DSO first calculates the sensitivity of the active power flow on the congested branch to changes in the active and reactive power injection of the VPP resources at node i, based on the data from step 11) and the data obtained after solving step 14). The process for solving the sensitivity is as follows:

[0059] 621) Based on the initial voltage V and phase angle θ of each node, the initial power P and Q of each node, and the voltage, phase angle, active power and reactive power of each node after solving, the change in active power ΔP, change in reactive power ΔQ, change in node voltage ΔV and change in phase angle Δθ of each node are obtained.

[0060] 622) Based on the data obtained in step 621), the Jacobian matrix J of the entire network is obtained by solving. θV,PQ :

[0061]

[0062] 623) Using the above data, calculate the sensitivity α of each branch to the active and reactive power in VPP. P,mn α Q,mn :

[0063]

[0064] 63) After the DSO completes the data preparation for the congestion mitigation request, it sends the relevant data to the VPP; based on the DSO's request and the data it sends, the VPP comprehensively considers the sensitivity α of each branch to active and reactive power for the time period in which congestion occurs. P,mn α Q,mn Under the premise that the ratio of active and reactive power input does not exceed the maximum carrying capacity of the line, the charging and discharging schedules of electric vehicles on relevant branches in the system are adjusted to alleviate network congestion.

[0065] Beneficial effects

[0066] This invention presents a grid congestion mitigation and control method considering service-oriented virtual power plants (VAPs). Compared with existing technologies, this method involves cooperation between VAPs and distribution system operators, and the introduction of electric vehicles (EVs) to alleviate network congestion. (Existing methods primarily alleviate network congestion by reducing renewable energy generation.) This invention leverages the flexibility of EVs to optimize their charging and discharging schedules during periods of network congestion, thereby mitigating congestion. This method avoids unnecessary reductions in renewable energy generation, reduces the security threats to the grid posed by large-scale integration of renewable energy, and improves grid flexibility due to the addition of EVs. Attached Figure Description

[0067] Figure 1 This is a sequence diagram of the method of the present invention;

[0068] Figure 2 This is a chart showing the day-ahead and intraday renewable energy power generation forecasts in an embodiment of the present invention.

[0069] Figure 3 This is a daily renewable energy power generation forecast chart in an embodiment of the present invention;

[0070] Figure 4 This is an optimal operating plan diagram for the daily power output of the VPP in this embodiment of the invention;

[0071] Figure 5 This is the optimal operating plan diagram for the daily heat output of the VPP in this embodiment of the invention;

[0072] Figure 6 This is a comparison diagram of the power flow and maximum power transmission capability on branch 4-3 in an embodiment of the present invention;

[0073] Figure 7 This is a comparison diagram of power flow and maximum power transmission capability on branches 14-13 in an embodiment of the present invention;

[0074] Figure 8 This is a flowchart illustrating the technical verification and congestion management process in an embodiment of the present invention. Detailed Implementation

[0075] To provide a better understanding of the structural features and effects achieved by the present invention, a detailed description is provided below, accompanied by preferred embodiments and accompanying drawings:

[0076] like Figure 1 As shown, the present invention provides a grid congestion mitigation and control method considering service-oriented virtual power plants, comprising the following steps:

[0077] The first step is to acquire power grid data, including the basic parameters of each branch and the initial parameters of each node, and to build a simulation model.

[0078] (1) Based on the actual power network structure, build a Simulink simulation model in Matlab;

[0079] (2) Obtain data from the actual power network structure, including the maximum transmission capacity of all branches in the power network, the impedance Z and admittance Y of all branches, the node voltage V and phase angle θ of all nodes, and the initial active power P and reactive power Q of each node.

[0080] (3) Store the acquired data into each node of the simulation model.

[0081] The second step is to construct the objective function: based on the forecast of renewable energy power generation, construct the objective function for day-ahead market dispatch and intraday market dispatch. This can comprehensively consider the power exchange in the day-ahead and intraday markets, which is conducive to making more accurate decisions. The construction of the objective function needs to take into account the uncertainty of renewable energy power generation.

[0082] (1) Based on the forecasting of renewable energy power generation, a day-ahead market dispatch objective function was constructed;

[0083] The objective function F1 for day-ahead market dispatch is composed of the CHP fuel cost of the cogeneration unit, storage operation costs, the predicted day-ahead market energy price, and the scenario probability p. s constitute:

[0084]

[0085] In the formula, s represents the s-th renewable energy prediction scenario, and t h For horizontal time. N chp N represents the number of combined heat and power (CHP) units. S p represents the number of current-day renewable energy generation forecast scenarios. s Let H be the probability of the s-th renewable energy forecast scenario occurring. da For the planned time range in the previous day, τ da N represents the day-ahead horizontal time step. e,sto C represents the number of energy storage units. f,chp,i,s (t h Let C be the fuel cost of the i-th cogeneration unit under scenario s. op,e,sto,i,s (t h Let P be the operating cost of the i-th energy storage unit in scenario s. m,da (t h c represents the active power exchanged by the VPP to the day-ahead energy market. m,da(t h ( ) represents the predicted market price per unit of energy the day before.

[0086] (2) Based on the forecasting of renewable energy power generation, construct the intraday market dispatch objective function:

[0087] In intraday planning, the purpose of VPP is to provide day-ahead market electricity exchange and internally compensate for imbalances caused by updated renewable energy generation forecasts;

[0088] If the imbalance within its pool cannot be compensated, then VPP will exchange active power P in the intraday market. imb,id (t h );

[0089] Assume that the scheduling of VPP resources is based on the active power imbalance P of intraday transactions. imb,id (t h The objective is to minimize the intraday imbalance penalty cost C during resource scheduling optimization. pen,imb (t h Therefore, the objective function F2 for intraday market scheduling includes intraday imbalance penalty costs, CHP fuel costs, and storage operating costs, and its expression is as follows:

[0090]

[0091] In the formula, H id For the intraday scheduling time range, τ id For intraday time steps, C f,chp,i (t h Let C be the fuel cost of the i-th combined heat and power unit. op,e,sto,i (t h Let C be the operating cost of the i-th energy storage unit. pen,imb (t h This represents the cost of penalties for intraday imbalances.

[0092] The third step is to construct the constraints: construct constraints for day-ahead and intraday market dispatch, including power limit constraints for CHP units, energy capacity boundary constraints for storage units, electrical power balance constraints, and thermal power balance constraints. Accurately and comprehensively setting constraints is beneficial for optimizing the construction of the dispatch model and can provide more accurate data preparation for network congestion mitigation requests. The construction of constraints requires accurate calculation of the active power exchanged by VPPs to the electricity market and the amount of renewable energy generation injected.

[0093] (1) Set power limit constraints for the CHP unit:

[0094] P e,chp,i,min ≤P e,chp,i (t h)≤P e,chp,i,max ,

[0095] In the formula, P e,chp,i,min For the minimum power injection of the i-th cogeneration unit, P e,chp,i (t h P is the power injection for the i-th combined heat and power unit. e,chp,i,max This represents the maximum power injection for the i-th combined heat and power unit.

[0096] (2) Set energy capacity boundary constraints for storage cells:

[0097] E e,sto,i,min ≤E e,sto,i (t h )≤E e,sto,i,max ,

[0098] E th,sto,i,min ≤E th,sto,i (t h )≤E th,sto,i,max ,

[0099] In the formula, E e,sto,i,min E represents the minimum energy level of the i-th energy storage unit. e,st o, i (t h Let E be the energy level of the i-th energy storage unit. e,sto,i,max E represents the maximum energy level of the i-th energy storage unit. th,sto,i,min E represents the minimum energy level of the i-th thermal storage unit. th,st o, i (t h Let E be the energy level of the i-th thermal storage unit. th,sto,i,max is the maximum energy level of the i-th thermal storage unit.

[0100] (3) Set day-ahead power balance constraints:

[0101]

[0102] In the formula, P e,chp,i,s (t h P represents the power injection of the i-th cogeneration unit under scenario s. e,sto,i,s (t h P represents the power injection into the i-th energy storage unit under scenario s. res,s (t h P represents the electricity injection from renewable energy sources under scenario s. e,1 (t h ) represents the total electrical load in the VPP.

[0103] (4) Set day-ahead thermal power balance constraints:

[0104]

[0105] In the formula, P th,chp,i,s (t h ) represents the heat and electricity injection of the i-th cogeneration unit under scenario s, and N is the total heat and electricity injection. th,st o represents the number of heat storage units, P th,sto,i,s (t h P represents the thermoelectric injection of the i-th energy storage unit under scenario s. th,1 (t h ) represents the total heat load in the VPP.

[0106] (5) Set intraday power balance constraints:

[0107]

[0108] In the formula, P e,chp,i (t h P represents the power injection of the i-th combined heat and power unit during the day. e,sto,i (t h P represents the power injection into the i-th energy storage unit during the day. res (t h P is the electricity injection from renewable energy sources within the day. imb,id (t h This refers to the imbalance of active power exchanged with the intraday energy market.

[0109] (6) Set intraday heat power balance constraints:

[0110]

[0111] In the formula, P th,chp,i (t h P represents the heat injection for the i-th combined heat and power unit during the day. th,sto,i (t h ) represents the thermal injection of the i-th energy storage unit within the day.

[0112] The fourth step is to solve the optimal scheduling model of the service-oriented virtual power plant: Based on the objective function and constraints, an optimal scheduling model considering the service-oriented virtual power plant is constructed, and the model is solved using the scenario method.

[0113] (1) Based on the objective function and relevant constraints, an optimal scheduling model considering service-oriented virtual power plants is constructed using linear programming. This model takes service-oriented virtual power plants as the center and integrates renewable energy, energy storage devices, cogeneration units, loads and electric vehicles. At the same time, VPPs participate in the electricity market for electricity exchange.

[0114] The main function of VPP is to maintain power balance within the system, adjust the output of cogeneration units and energy storage devices according to the uncertainty of renewable energy power generation, and minimize unnecessary reduction of renewable energy while ensuring the safe and reliable operation of the system.

[0115] (2) Due to the uncertainty of renewable energy power generation, the scenario method is used to predict the power generation scenario and obtain a certain number of renewable power generation prediction scenarios. Then, the concept of probability distance is used to reduce the predicted scenarios to a specified number. Finally, the reduced scenario information is sent to VPP, and the model is solved using the CPLEX solver according to the objective function and related constraints. The voltage V and phase angle θ of each node in the solved network are obtained.

[0116] (3) Using the branch impedance Z and admittance Y obtained in the power grid data acquisition step, as well as the node voltage V and phase angle θ after solving in step (2), the magnitude of the active power flow on each branch is calculated:

[0117] P f,mn =|V m | 2 g mn -|V m ||V n [g mn cos(θ m -θ n )+b mn sin(θ m -θ n )],

[0118] In the formula, P f,mn V represents the active power flow from node m to node n. m V represents the voltage magnitude at node m. n The voltage magnitude at node n; g mn Let b be the conductance of the mn branch; mn θ is the susceptance of the mn branch; m θ is the voltage phase angle at node m. n Let n be the voltage phase angle at node n.

[0119] The fifth step is to regulate grid congestion: In response to grid congestion, based on the data obtained from the model solution in the fourth step and the data obtained in the first step, the sensitivity of each bus is calculated, and grid congestion is alleviated by adjusting the charging and discharging schedule of electric vehicles.

[0120] (1) The distribution system operator (DSO) monitors the power flow of each branch in the system in real time. When the power flow P of a certain branch... f,mnGreater than the maximum transmission capacity P of the branch fmax,mn If the network is overloaded, the network is reconfigured to resolve the issue. If the network congestion is not completely relieved after reconfiguration, the following step (2) is performed to supplement the "reconfiguration" solution.

[0121] (2) The DSO supplements the "reconfiguration" scheme by providing congestion mitigation services for request-service VPPs:

[0122] The DSO first calculates the sensitivity of the active power flow on the congested branch to changes in the active and reactive power injection of the VPP resources at node i, based on the data from the first step and the data obtained after solving the problem in the fourth step. The process for solving the sensitivity is as follows:

[0123] A1) Based on the initial voltage V and phase angle θ of each node, the initial power P and Q of each node, and the voltage, phase angle, active power and reactive power of each node after solving, the change in active power ΔP, change in reactive power ΔQ, change in node voltage Δ|V| and change in phase angle Δθ of each node are obtained.

[0124] A2) Based on the data obtained in step A1), the Jacobian matrix J of the entire network is obtained. θV,PQ :

[0125]

[0126] A3) Using the above data, calculate the sensitivity α of each branch to the active and reactive power in VPP. P,mn α Q,mn :

[0127]

[0128] (3) After the DSO completes the data preparation for the congestion mitigation request, it sends the relevant data to the VPP; based on the DSO's request and the data it sends, the VPP comprehensively considers the sensitivity α of each branch to active and reactive power for the time period in which congestion occurs. P,mn α Q,mn Under the premise that the ratio of active and reactive power input does not exceed the maximum carrying capacity of the line, the charging and discharging schedules of electric vehicles on relevant branches in the system are adjusted to alleviate network congestion.

[0129] The VPP congestion mitigation service is validated as follows:

[0130] First, the DSO performs technical verification to determine if a particular branch is overloaded, and then reconfigures the network to resolve the issue. If the network congestion is not fully alleviated after reconfiguration, an alternative solution needs to be explored.

[0131] The DSO's supplement to the "reconfiguration" scheme is to request the VPP to provide congestion mitigation services. Based on the DSO's request and the data it transmits, the VPP adjusts its resource scheduling and then sends the new scheduling schedule to the DSO for technical verification. The DSO verifies whether the VPP has alleviated network congestion by analyzing and comparing the power flow and maximum transmission capacity on each branch.

[0132] This embodiment was conducted on a representative medium-high voltage network in a certain region. The network is radial, but can also be operated as a ring network. First, scenarios summarizing wind and solar power generation forecasts were generated based on external data. Second, the generated scenarios were reduced using the concept of probabilistic distance and then merged into 16. Figure 2 The day-ahead integrated wind and solar power generation forecast scenario and renewable energy generation forecast are shown as inputs to the VPP. Figure 3 The intraday renewable energy forecast for the medium-voltage benchmark network, used as a technical verification input for distribution system operators, is displayed.

[0133] Based on the combined forecasts for wind and solar power generation, the VPP stochastically schedules its resources according to objective function F1 and relevant constraints, and formulates its power exchange plan for a 24-hour planning scope. During intraday operations, the VPP's resources are scheduled based on objective function F2 and relevant constraints, while also taking into account power exchange in the day-ahead market. Figure 4 and Figure 5 The optimal operating schedules for the daily electricity and heat output of the VPP are displayed separately, demonstrating that the service-oriented virtual power plant effectively coordinates the output of various energy sources within the system at different times. In this way, the loads within the VPP are served, while excess energy can be sold on the market. Furthermore, when renewable energy generation exceeds market demand, energy storage units come into play, storing excess energy and later transmitting it back into the grid.

[0134] When congestion occurs in the power grid, the VPP can use optimization of the objective function F2 with relevant constraints to alleviate congestion. The process by which the VPP works with the distribution system operator to mitigate congestion in the network is as follows: Figure 8 As shown. First, the power distribution system operator receives the scheduling of resources connected to the network. Then it... Figure 8 Part A performs power flow calculations. At 09:00, congestion on branches 3-2 and 4-3 is identified. The power flow on branch 4-3 is as follows: Figure 6 As shown, the solid line of the hollow square represents the power flow without any constraints, i.e. Figure 8 The power flow calculation results for section A show that no action was taken. To alleviate congestion, the distribution system operator first attempted to reconfigure the network. To illustrate the reconfiguration attempt by the distribution system operator, the power flow on branch 14-13 is as follows: Figure 7 As shown. The solid line of the hollow square again represents an unconstrained solution where no action is taken. If the distribution system operator reconfigures the network by closing switch S1, it will exceed the maximum power transmission capacity of branch 14-13. This occurs because the wind farm at branch 14-13 is also operating at high load. The result of the reconfiguration is... Figure 7 The solid line represents a hollow square. Then, the distribution system operator prepares to request congestion mitigation services from the VPP, such as... Figure 8 As shown in sections B and C. Then VPP adjusts its schedule and moves it from... Figure 8 The C part sends the data back to the A part, and then the power distribution system operator performs another technical verification, namely power flow calculation, to check whether the congestion has been alleviated. Figure 6 and Figure 7 The black solid line in the diagram represents the power flow on the two branches after the VPP congestion mitigation service is implemented, showing that congestion on both lines has been alleviated.

[0135] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A grid congestion mitigation and control method considering service-oriented virtual power plants, characterized in that, Includes the following steps: 11) Acquisition of power grid data: Acquire power grid data, including the basic parameters of each branch and the initial parameters of each node, and build a simulation model. 12) Construction of the objective function: Based on the forecasting of renewable energy power generation, construct the objective functions for day-ahead market dispatch and intraday market dispatch; The construction of the objective function includes the following steps: 121) Based on the forecasting of renewable energy power generation, a day-ahead market dispatch objective function was constructed; The objective function F1 for day-ahead market dispatch is composed of the CHP fuel cost of the cogeneration unit, storage operation costs, the predicted day-ahead market energy price, and scenario probabilities. constitute: ， In the formula, s represents the s-th renewable energy prediction scenario. For horizontal time, For the number of combined heat and power units, The number of current-day renewable energy generation forecast scenarios. Let be the probability of the s-th renewable energy forecast scenario occurring. The planned time range is as follows. The current horizontal time step is the day-to-day time step. The number of energy storage units, Let be the fuel cost of the i-th cogeneration unit under scenario s. Let be the operating cost of the i-th energy storage unit in scenario s. The active power exchanged by the VPP to the day-ahead energy market. Forecast market price per unit of energy; 122) Based on the forecasting of renewable energy power generation, construct the intraday market dispatch objective function: In intraday planning, the purpose of VPP is to provide day-ahead market electricity exchange and internally compensate for imbalances caused by updated renewable energy generation forecasts; If the imbalance within its pool cannot be compensated, the VPP will exchange active power in the intraday market. ; Assume that the scheduling of VPP resources is based on the active power imbalance of intraday transactions. With minimization as its objective, it considers intraday imbalance penalty costs during resource scheduling optimization. Therefore, the objective function F2 for intraday market scheduling includes intraday imbalance penalty costs, CHP fuel costs, and storage operating costs, and its expression is as follows: ， In the formula, The time range for intraday scheduling. For intraday time steps, For the fuel cost of the i-th cogeneration unit, Let i be the operating cost of the i-th energy storage unit. The cost of penalties for intraday imbalances; 13) Construction of constraints: Construct constraints for day-ahead market scheduling and intraday market scheduling, including power limit constraints for CHP units, energy capacity boundary constraints for storage units, electrical power balance constraints, and thermal power balance constraints. 14) Solving the optimal scheduling model of service-oriented virtual power plants: Based on the objective function and constraints, construct an optimal scheduling model that considers service-oriented virtual power plants, and solve the model using the scenario method; Solving the optimal scheduling model of the service-oriented virtual power plant includes the following steps: 141) Based on the objective function and relevant constraints, construct an optimal scheduling model that considers service-oriented virtual power plants. This model takes service-oriented virtual power plants as the center, integrates renewable energy, energy storage devices, cogeneration units, loads and electric vehicles, and VPPs participate in the electricity market for electricity exchange. The role of VPP is to maintain power balance within the system, adjust the output of cogeneration units and energy storage devices according to the uncertainty of renewable energy power generation, and minimize unnecessary reduction of renewable energy while ensuring the safe and reliable operation of the system. 142) Due to the uncertainty of renewable energy power generation, the scenario method is used to predict the power generation scenarios and obtain the renewable power generation prediction scenarios; then, the concept of probability distance is used to reduce the predicted scenarios to a specified number; finally, the reduced scenario information is sent to VPP, and the model is solved using the CPLEX solver according to the objective function and related constraints, and the voltage V and phase angle θ of each node in the solved network are obtained. 143) Using the branch impedance Z and admittance Y obtained in the power grid data acquisition step, and the node voltage V and phase angle θ solved in step 142), calculate the magnitude of the active power flow on each branch: ， In the formula, Let m be the active power flow from node m to node n; Let be the voltage magnitude at node m; Let n be the voltage magnitude at node n; Let mn be the conductance of the branch; The susceptance of the mn branch; Let be the voltage phase angle at node m; Let n be the voltage phase angle at node n; 15) Regulation of grid congestion: In response to grid congestion, based on the data obtained from the model solution in step 14) and the data obtained in step 11), the sensitivity of each bus is calculated, and grid congestion is alleviated by adjusting the charging and discharging schedule of electric vehicles.

2. The grid congestion mitigation and control method considering service-oriented virtual power plants according to claim 1, characterized in that, The acquisition of the power grid data includes the following steps: 21) Based on the actual power network structure, build a Simulink simulation model in Matlab; 22) Obtain data from the actual power network structure, including the maximum transmission capacity of all branches in the power network, the impedance Z and admittance Y of all branches, the node voltage V and phase angle θ of all nodes, and the initial active power P and reactive power Q of each node. 23) Store the acquired data into each node of the simulation model.

3. The grid congestion mitigation and control method considering service-oriented virtual power plants according to claim 1, characterized in that, The construction of the constraints includes the following steps: 31) Set power limitation constraints for the CHP unit: ， In the formula, For the minimum power injection of the i-th cogeneration unit, For the power injection of the i-th cogeneration unit, For the maximum power injection of the i-th cogeneration unit; 32) Set energy capacity boundary constraints for storage cells: ， ， In the formula, Let be the minimum energy level of the i-th energy storage unit. Let i be the energy level of the i-th energy storage unit. The maximum energy level of the i-th energy storage unit. Let be the minimum energy level of the i-th thermal storage unit. Let i be the energy level of the i-th thermal storage unit. This represents the maximum energy level of the i-th thermal storage unit; 33) Set day-ahead power balance constraints: ， In the formula, For the power injection of the i-th cogeneration unit under scenario s, For the power injection of the i-th energy storage unit under scenario s, For the power injection of renewable energy under scenario s, This represents the total electrical load in the VPP; 34) Set day-ahead thermal power balance constraints: ， In the formula, For the heat and power injection of the i-th cogeneration unit under scenario s, The number of heat storage units. For the thermoelectric injection of the i-th energy storage unit under scenario s, This represents the total heat load in the VPP; 35) Set intraday power balance constraints: ， In the formula, For the power injection of the i-th cogeneration unit during the day, For the power injection of the i-th energy storage unit during the day, To inject electricity from renewable energy sources during the day, The imbalance of active power exchanged to the intraday energy market; 36) Set intraday heat power balance constraints: ， In the formula, For the heat injection of the i-th cogeneration unit of the day, For the thermal injection of the i-th energy storage unit within the day.

4. The grid congestion mitigation and control method considering service-oriented virtual power plants according to claim 1, characterized in that, The regulation of power grid congestion includes the following steps: 41) The power distribution system operator monitors the power flow of each branch in the system in real time. When the power flow of a certain branch... Greater than the maximum transmission capacity of the tributary If the network is overloaded, the network is reconfigured to resolve the issue. If the network congestion is not fully alleviated after reconfiguration, the reconfiguration solution is supplemented. 42) The DSO complements the "reconfiguration" scheme by providing congestion mitigation services to request-service VPPs: The DSO first calculates the sensitivity of the active power flow on the congested branch to changes in the active and reactive power injection of the VPP resources at node i, based on the data from step 11) and the data obtained after solving step 14). The process for solving the sensitivity is as follows: 421) Based on the initial voltage V and phase angle θ of each node, the initial power P and Q of each node, and the voltage, phase angle, active power and reactive power of each node after solving, calculate the change in active power of each node. Changes in reactive power Node voltage change and phase angle change ; 422) Based on the data obtained in step 421), the Jacobian matrix of the entire network is obtained. : ； 423) Using the above data, calculate the sensitivity of each branch to the active and reactive power in VPP. : ； 43) After the DSO completes the data preparation for the congestion mitigation request, it sends the relevant data to the VPP; based on the DSO's request and the data it sends, the VPP comprehensively considers the sensitivity of each branch to active and reactive power for the time period in which congestion occurred. Under the premise that the ratio of active and reactive power input does not exceed the maximum carrying capacity of the line, the charging and discharging schedules of electric vehicles on relevant branches in the system are adjusted to alleviate network congestion.

Images