Day-before-day management and control method for heterogeneous photovoltaic and network side resource coordinated medium and low voltage power distribution system

Abstract

The invention provides a day-ahead-intra-day management and control method for a heterogeneous photovoltaic and network side resource coordinated medium and low voltage power distribution system, and the method comprises the steps: firstly constructing a day-ahead optimization regulation and control model with the minimum single-day total operation cost of the system and the minimum node voltage deviation as targets; secondly, solving the day-ahead optimization regulation and control model by adopting a CPLEX commercial solver, determining an adjustable resource regulation and control instruction of the medium-voltage power distribution system and an OLTC and CB switching scheme of the medium-and-low-voltage power distribution system, and taking the adjustable resource regulation and control instruction and the OLTC and CB switching scheme as constraint conditions of intra-day operation optimization; on this basis, an intra-day optimization regulation and control model is established by taking the minimum deviation from the target function of the day-ahead stage as the target, and finally, a CPLEX commercial solver is adopted to solve the intra-day optimization regulation and control model, and an adjustable resource regulation and control instruction, namely a low-voltage photovoltaic, fan and energy storage regulation and control instruction, of the intra-day low-voltage power distribution system is determined. Through day-ahead and intra-day collaborative optimization, comprehensive optimization and improvement of the operation benefit and the power supply quality of the power distribution network can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power distribution networks, and in particular to a day-ahead and intra-day control method for medium and low voltage distribution systems that coordinate heterogeneous photovoltaic and grid-side resources. Background Art

[0002] With the continuous development of new distribution systems, distributed renewable energy such as photovoltaics are connected to the distribution system in different voltage levels, centralized or decentralized ways, and the scale of grid connection has increased significantly. Due to the randomness and volatility of photovoltaics themselves, a high proportion of photovoltaics connected to the grid will expose the distribution system to many risks, such as node voltage exceeding the limit and increased node voltage deviation. In this context, the impact of different operating modes of photovoltaic inverters on the voltage of the distribution system and its interaction with traditional voltage management devices such as on-load tap changers (OLTCs) and capacitor banks (CBs) have gradually become non-negligible. Therefore, how to construct a multi-resource collaborative operation control strategy that meets the adjustable characteristics of the equipment under the conditions of the collaborative operation mechanism of the inverter and the action rules of the traditional voltage management equipment in the distribution system has become one of the key issues to further improve the benefits of distributed power grid connection.

[0003] Current research on distribution system optimization is generally based on a single voltage level, but the flexible interaction of distributed renewable energy in the future is no longer limited to networks with the same voltage level. If single voltage level optimization is still used, it will be difficult to fully tap the potential of adjustable resources on the grid side of medium and low voltage distribution systems. Most studies use the reduction of photovoltaic inverter active power to suppress voltage over-limit, but the reduction of photovoltaic active power is contrary to the strategic goal of improving distributed energy consumption under the current power system energy transformation, and it is also impossible to provide voltage support for the power supply system at night. On the other hand, in reality, the active power of household photovoltaics in medium and low voltage areas cannot be reduced, and the reactive power control methods are different. It is difficult to achieve precise regulation of the distribution network without considering the actual situation of heterogeneous active and reactive output characteristics of photovoltaics. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention provides a day-ahead and intra-day control method for a medium and low voltage power distribution system in coordination with heterogeneous photovoltaic and grid-side resources.

[0005] The technical solution of the present invention is: a method for day-ahead and intra-day control of a medium and low voltage power distribution system in which heterogeneous photovoltaic and grid-side resources are coordinated, characterized in that it comprises the following steps:

[0006] S1: Construct a day-ahead optimization control model with the goal of minimizing the system's total daily operating cost and node voltage deviation;

[0007] S2: Use the CPLEX commercial solver to solve the day-ahead optimization control model, determine the adjustable resource control instructions of the medium-voltage distribution system and the OLTC and CB switching schemes of the medium- and low-voltage distribution systems, and use them as constraints for intraday operation optimization;

[0008] S3: Establish an intraday optimization control model with the goal of minimizing the deviation from the objective function of the day-ahead stage;

[0009] S4: Use CPLEX commercial solver to solve the intraday optimization control model and determine the adjustable resource control instructions of the intraday low-voltage distribution system;

[0010] S5: The day-ahead optimization and control model and the intraday optimization and control model are optimized collaboratively.

[0011] Furthermore, the objective function of the day-ahead optimization control model of S1 is shown in equations (1) and (2):

[0012] F=min(F net +F loss +F dg +F ess +F qdg +F pc ) (1)

[0013]

[0014] Where: F is the minimum daily total operating cost, F net The cost of purchasing electricity from the main grid, F loss is the line loss cost, F dg is the cost of new energy and energy storage operation and maintenance, F ess is the wind curtailment cost, F qdg is the abandoned light cost, F pc is the node voltage deviation; T is the total time period, c net is the unit's main grid electricity purchase price, c loss is the price per unit active power loss, c pv is the unit photovoltaic operation and maintenance cost, c wt is the unit wind turbine operation and maintenance cost, c ess is the unit energy storage operation and maintenance cost, c qpv is the unit abandoned light cost, c qwt is the unit wind curtailment cost; P g,t is the amount of electricity purchased by the distribution network, Δt pre is the day-ahead control cycle; is the square of the branch current between node i and node j, r ij is the branch impedance between node i and node j; P pv,t For photovoltaic, P wt,t P is the fan consumption; ch,tis the energy storage charging power, P dch,t is the energy storage discharge power; P pv’,t is the abandoned wind power, P wt’,t is the abandoned light power; U j,t is the node voltage of node j at time t.

[0015] Furthermore, the constraints of the day-ahead optimization control model are as follows:

[0016] (1) Energy Storage System (ESS) Constraints

[0017] u dch,t +u ch,t =0 (3)

[0018] S ess,start =S ess,end (4)

[0019]

[0020] Formula (3) is the charge and discharge state constraint of the energy storage device, that is, the energy storage device cannot be in the charge and discharge state at the same time; Formula (4) is the energy state constraint of the energy storage device, the energy at the beginning and end of the energy storage device should be equal; Formula (5) is the upper and lower limit constraints of the charge state of the energy storage device; Formula (6) is the charge state constraint of the energy storage power station;

[0021] Where: u dch,t It is the charging mark of the energy storage power station. ch,t is the discharge mark of the energy storage power station, u dch,t and u ch,t is a 0-1 variable; S ess,start is the initial state of charge of the energy storage power station, S ess,end is the final state of charge of the energy storage power station; S are the upper and lower limits of the capacity of the energy storage power station; S i,t is the capacity of the energy storage power station, are the upper limits of the charging and discharging power of the energy storage power station; S ess,i,t is the power of the energy storage station at time t, P ess,ch,i,t and P ess,dch,i,t are the charging and discharging power of the energy storage station, η ch is the energy storage charging coefficient, η dch is the energy storage discharge coefficient;

[0022] (2) Heterogeneous photovoltaic characteristics constraints

[0023] Constraints of PV power station No. Ⅰ: Active power output is controllable and active power reduction is possible; reactive power output operates in constant power factor control mode;

[0024]

[0025] In the formula, is the active power output of PV power station I at time t; The maximum limit of active power that can be output by the No. Ⅰ photovoltaic power station; is the reactive power output by PV power station I at time t;

[0026] Constraints for household photovoltaics No. Ⅰ: Active output is uncontrollable and operates only in the maximum power point tracking (MPPT) mode; reactive output operates in the constant power factor control mode;

[0027]

[0028] In the formula, is the active power output of household photovoltaic system I at time t; The maximum limit of active power that can be output by household photovoltaic power generation unit I; is the reactive power output by household photovoltaic power plant No. Ⅰ at time t.

[0029] II household photovoltaic constraints: active power cannot be controlled, and it only operates in MPPT mode; the remaining capacity of the photovoltaic inverter is used to determine the reactive power output of the photovoltaic inverter;

[0030]

[0031] In the formula, is the active power output of household photovoltaic power plant II at time t; The maximum limit of active power that can be output by household photovoltaic power generation unit II; is the reactive power output of household photovoltaic power plant II at time t; S is the capacity of household photovoltaic power plant II;

[0032] III household photovoltaic constraints: active power output is controllable and active power reduction can be performed; the reactive power output of the photovoltaic inverter is determined by using the remaining capacity of the photovoltaic inverter;

[0033]

[0034] In the formula, is the active power output of household photovoltaic power plant III at time t; The maximum limit of active power that can be output by household photovoltaic power generation unit II; is the reactive power output of household photovoltaic power plant III at time t; S is the capacity of household photovoltaic power plant III;

[0035] (3) Wind Turbine (WT) Constraints

[0036]

[0037] Where: is the active power output by the i-th WT at time t; is the minimum limit of the active power output by the i-th WT, is the maximum limit of active power that can be output by the i-th WT;

[0038] 4) OLTC Constraints

[0039] OLTC plays an important role in regulating the voltage level of the distribution network. To describe the transformation ratio effect, a virtual node m is introduced. The OLTC branch satisfies the following voltage transformation equation:

[0040]

[0041] Where: is the square of the voltage amplitude of the virtual node m; is the square of the voltage amplitude at node i; P im , Q im are the active power and reactive power flowing from node i to node m respectively; r im and x im They are the sum of resistance and reactance of branch im and OLTC on the line respectively; is the line current of branch im;

[0042] The big-M method is used to linearly transform the bilinear terms in the OLTC branch constraints. The OLTC branch constraints are expressed as:

[0043]

[0044] Where: is the voltage of node m at time t, is the voltage of node j at time t, n tr k is the number of OLTC gears; ij,t,y represents the transformation ratio corresponding to OLTC gear y on line ij; e ij,t,y is a binary dummy variable for the OLTC tap position y on the branch; f ij,t,y is e after linearization by the big M method ij,t,y ; M is a large positive number;

[0045] (5) CB constraints

[0046]

[0047] Where: is the total compensation power of CB, B CB is the set of CB nodes; is the number of operational groups, which is a discrete variable value; The upper limit of the number of CB groups connected to node j; is the compensation power of each group of CB;

[0048]

[0049] Formula (16) is the total number of operations limit in multiple time periods;

[0050] In the formula, is the number of groups put into operation at time t-1, The upper limit of the number of operations;

[0051] In addition, the absolute value constraint in the above formula can be processed as follows: by adding an auxiliary variable that represents the change in CB compensation capacity between adjacent time periods: Then we can get:

[0052]

[0053] In the formula, It is an auxiliary variable that characterizes the change of CB compensation capacity between adjacent time periods;

[0054] (6) Power flow constraints

[0055] The power flow constraint of the distribution network is a non-convex constraint. In order to improve the solution rate, it is relaxed in two steps: phase angle relaxation and convex relaxation. The power flow constraint after relaxation is:

[0056]

[0057] Where i∈u(j) and k∈v(j) are the branch sets with j as the end and the beginning node respectively; P ij , Q ij are the active power and reactive power flowing from node i to node j respectively; r ij and x ij are the resistance and reactance of branch ij respectively; P jk and Q jk are the active power and reactive power flowing from node j to node k, respectively, P j and Q j are the equivalent active power and reactive power of node j respectively; is the square of the current flowing through branch ij; is the square of the voltage amplitude at node j, V i ～ is the square of the voltage amplitude at node i; P load,j , P ch,j , P dch,j , P DG,j , P G,jare the load active power, energy storage charging power, energy storage discharging power, distributed generation output and power grid purchased power of node j respectively; Q load,j , Q CB,j , Q G,j , Q pv,j They are the reactive power of the node j load, the reactive output of the CB, the reactive power generated by the generator and the reactive power generated by the heterogeneous PV.

[0058] Furthermore, the objective function of the intraday optimization control model of S3 is shown in equations (19) and (20):

[0059] F=min(F net +F loss +F dg +F ess +F qdg +F pc ) (19)

[0060]

[0061] Where: Δt int is the intraday regulation cycle; T is the total period, F net 、F loss 、F dg 、F ess 、F qdg They are the main grid power purchase cost, active power loss cost, new energy and energy storage operation and maintenance cost, and wind and solar power abandonment cost; F pc is the node voltage deviation; c net 、c loss 、c pv 、c wt 、c ess 、c qpv 、c qwt They are the unit main grid power purchase price, unit active power loss price, unit photovoltaic operation and maintenance cost, unit wind turbine operation and maintenance cost, unit energy storage operation and maintenance cost, unit solar curtailment cost, and unit wind curtailment cost; P g,t , r ij are the power purchase amount of the distribution network, the square of the branch current between node i and node j, and the branch impedance between node i and node j. pv,t , P wt,t are the photovoltaic and wind turbine consumption respectively; P ch,t , P dch,t are the energy storage charging and discharging power respectively; P pv’,t , P wt’,t are wind power abandonment and solar power abandonment respectively; U j,t is the node voltage of node j at time t.

[0062] Furthermore, the constraints of the intraday optimization control model are as follows:

[0063] (1) Energy Storage System (ESS) Constraints

[0064] u dch,t +u ch,t =0 (21)

[0065] S ess,start =S ess,end (twenty two)

[0066]

[0067] Formula (21) is the charge and discharge state constraint of the energy storage device, that is, the energy storage device cannot be in the charge and discharge state at the same time; Formula (22) is the energy state constraint of the energy storage device, and the energy at the beginning and end of the energy storage device should be equal; Formula (23) is the upper and lower limit constraints of the charge state of the energy storage device; Formula (24) is the charge state constraint of the energy storage power station; where: u dch,t and u ch,t It is a 0-1 variable, indicating the charge and discharge mark of the energy storage power station; S ess,start and S ess,end They are the initial and final states of charge of the energy storage power station; They are the upper and lower limits of the capacity of the energy storage power station; are the upper limits of the charging and discharging power of the energy storage power station; S ess,i,t is the power of the energy storage station at time t, S i,t is the capacity of the energy storage power station; P ess,ch,i,t and P ess,dch,i,t are the charging and discharging power of the energy storage station, η ch is the energy storage charging coefficient, η dch is the energy storage discharge coefficient;

[0068] (2) Wind Turbine (WT) Constraints

[0069]

[0070] Where: is the active power output by the i-th WT at time t; is the minimum limit of the active power output by the i-th WT, is the maximum limit of active power that can be output by the i-th WT;

[0071] (3) Flow uniformity constraint

[0072]

[0073] Where Vg_int is the low voltage side voltage of OLTC in the intraday optimization regulation model, V g_pre To solve the OLTC low-voltage side voltage obtained by the day-ahead optimization control model; K int is the CB gear in the intraday optimization control model, K pre To solve the CB gear position obtained by the day-ahead optimization control model; is the active power of the medium-voltage photovoltaic power station in the intraday optimization control model, To solve the day-ahead optimization control model to obtain the active power of the medium-voltage photovoltaic power station; For the active power of the medium-pressure fan in the daily optimization control model, To solve the medium-pressure fan active power obtained by the day-ahead optimization control model; It is the charging and discharging power of medium voltage energy storage in the daily optimization control model. To solve the medium voltage energy storage charging and discharging power obtained by the day-ahead optimization control model;

[0074] Preferably, the adjustable resource control instructions of S4 are low-voltage photovoltaic, wind turbine, and energy storage control instructions.

[0075] The beneficial effects of the present invention are:

[0076] 1. The day-ahead and day-intraday control strategy for the medium and low voltage distribution system with coordinated heterogeneous photovoltaic and grid-side resources proposed in the present invention adjusts the OLTC low-voltage side voltage and the voltage at the new energy access point by adjusting the OLTC tap position, while ensuring that the overall voltage does not exceed the limit, so as to promote the further output of new energy.

[0077] 2. Considering the actual situation of heterogeneous active and reactive output characteristics of photovoltaic power, the reactive power of the system is flexibly adjusted to reduce the voltage deviation of system nodes, so as to achieve dual optimization and improvement of the operating efficiency and power supply quality of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0079] Figure 1 It is a diagram of medium and low voltage power distribution system;

[0080] Figure 2 This is a comparison chart of the per unit voltage values ​​of 33 nodes;

[0081] Figure 3 It is a comparison diagram of active power of branch 34;

[0082] Figure 4 It is the 24-hour node voltage diagram of scenario 1;

[0083] Figure 5 This is the 24-hour node voltage diagram for scenario 2;

[0084] Figure 6 This is the 24-hour node voltage diagram for scenario 3;

[0085] Figure 7 This is the CB gear action change diagram for scenario 2;

[0086] Figure 8 This is a comparison chart of reactive power emitted by the main grid side in three scenarios;

[0087] Fig. 9 This is the reactive power diagram generated by heterogeneous photovoltaics in scenario 3. DETAILED DESCRIPTION

[0088] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0089] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0090] Reference Figures 1 to 9 , considering that the resource control instructions on the 10kV medium-voltage feeder side are usually constant at the hourly level, while the resource control instructions on the low-voltage substation side change at the minute level, the medium-voltage resources and the OLTC and CB on the medium and low voltage sides are controlled on a long-term scale the day before, and the low-voltage resources, namely low-voltage photovoltaics, wind turbines, and energy storage, are controlled on a short-term scale within the day. In the day-ahead stage, the present invention optimizes and generates the adjustable resource control instructions of the medium-voltage distribution system and the OLTC and CB switching schemes of the distribution system for the next 24 hours with the goal of minimizing the system economic cost and node voltage deviation, and uses them as constraints for intraday operation. In the intraday stage, an intraday optimization control model is established with the goal of minimizing the deviation from the objective function of the day-ahead stage, and the low-voltage resource control instructions are solved. Through the collaborative optimization within the day before, the comprehensive optimization and improvement of the distribution network operation efficiency and power supply quality can be achieved.

[0091] A method for day-ahead and intraday control of a medium and low voltage power distribution system with coordinated heterogeneous photovoltaic and grid-side resources, characterized in that it comprises the following steps:

[0092] S1: Construct a day-ahead optimization control model with the goal of minimizing the system's total daily operating cost and node voltage deviation;

[0093] S2: Use the CPLEX commercial solver to solve the day-ahead optimization control model, determine the adjustable resource control instructions of the medium-voltage distribution system and the OLTC and CB switching schemes of the medium- and low-voltage distribution systems, and use them as constraints for intraday operation optimization;

[0094] S3: Establish an intraday optimization control model with the goal of minimizing the deviation from the objective function of the day-ahead stage;

[0095] S4: Use CPLEX commercial solver to solve the intraday optimization control model and determine the adjustable resource control instructions of the intraday low-voltage distribution system;

[0096] S5: The day-ahead optimization and control model and the intraday optimization and control model are optimized collaboratively.

[0097] Determine the objective function of the day-ahead optimization control model.

[0098] The goal of the day-ahead optimization model is to minimize the total daily operating cost and node voltage deviation. The total daily operating cost includes the main grid power purchase cost, active power loss cost, new energy and energy storage operation and maintenance cost, and wind and solar power abandonment cost. The objective function of the day-ahead optimization control model is shown in equations (1) and (2):

[0099] F=min(F net +F loss +F dg +F ess +F qdg +F pc ) (1)

[0100]

[0101] Where: F is the minimum daily total operating cost, F net The cost of purchasing electricity from the main grid, F loss is the line loss cost, F dg is the cost of new energy and energy storage operation and maintenance, F ess is the wind curtailment cost, F qdg is the abandoned light cost, F pc is the node voltage deviation; T is the total time period, c net is the unit's main grid electricity purchase price, c loss is the price per unit active power loss, c pv is the unit photovoltaic operation and maintenance cost, c wt is the unit wind turbine operation and maintenance cost, cess is the unit energy storage operation and maintenance cost, c qpv is the unit abandoned light cost, c qwt is the unit wind curtailment cost; P g,t is the power purchase amount of the distribution network, Δt pre is the day-ahead control cycle; is the square of the branch current between node i and node j, r ij is the branch impedance between node i and node j; P pv,t For photovoltaic, P wt,t P is the fan consumption; ch,t is the energy storage charging power, P dch,t is the energy storage discharge power; P pv’,t is the abandoned wind power, P wt’,t is the abandoned light power; U j,t is the node voltage of node j at time t.

[0102] The constraints for establishing the day-ahead optimization control model are as follows:

[0103] (1) Energy Storage System (ESS) Constraints

[0104] u dch,t +u ch,t =0 (3)

[0105] S ess,start =S ess,end (4)

[0106]

[0107] Formula (3) is the charge and discharge state constraint of the energy storage device, that is, the energy storage device cannot be in the charge and discharge state at the same time; Formula (4) is the energy state constraint of the energy storage device, the energy at the beginning and end of the energy storage device should be equal; Formula (5) is the upper and lower limit constraints of the charge state of the energy storage device; Formula (6) is the charge state constraint of the energy storage power station;

[0108] Where: u dch,t It is the charging mark of the energy storage power station. ch,t is the discharge mark of the energy storage power station, u dch,t and u ch,t is a 0-1 variable; S ess,start is the initial state of charge of the energy storage power station, S ess,end It is the final state of charge of the energy storage power station; are the upper and lower limits of the capacity of the energy storage power station; S i,t is the capacity of the energy storage power station, are the upper limits of the charging and discharging power of the energy storage power station; S ess,i,t is the power of the energy storage station at time t, Si,t is the capacity of the energy storage power station, P ess,ch,i,t and P ess,dch,i,t are the charging and discharging power of the energy storage station, η ch is the energy storage charging coefficient, η dch is the energy storage discharge coefficient;

[0109] (2) Heterogeneous photovoltaic characteristics constraints

[0110] Constraints of PV power station No. Ⅰ: Active power output is controllable and active power reduction is possible; reactive power output operates in constant power factor control mode;

[0111]

[0112] In the formula, is the active power output of PV power station I at time t; The maximum limit of active power that can be output by the No. Ⅰ photovoltaic power station; is the reactive power output by PV power station I at time t;

[0113] Constraints for household photovoltaics No. Ⅰ: Active output is uncontrollable and operates only in the maximum power point tracking (MPPT) mode; reactive output operates in the constant power factor control mode;

[0114]

[0115] In the formula, is the active power output of household photovoltaic system I at time t; The maximum limit of active power that can be output by household photovoltaic power generation unit I; is the reactive power output by household photovoltaic power plant No. Ⅰ at time t.

[0116] II household photovoltaic constraints: active power cannot be controlled, and it only operates in MPPT mode; the remaining capacity of the photovoltaic inverter is used to determine the reactive power output of the photovoltaic inverter;

[0117]

[0118] In the formula, is the active power output of household photovoltaic power plant II at time t; The maximum limit of active power that can be output by household photovoltaic power generation unit II; is the reactive power output of household photovoltaic power plant II at time t; S is the capacity of household photovoltaic power plant II;

[0119] III household photovoltaic constraints: active power output is controllable and active power reduction can be performed; the reactive power output of the photovoltaic inverter is determined by using the remaining capacity of the photovoltaic inverter;

[0120]

[0121] In the formula, is the active power output of household photovoltaic power plant III at time t; The maximum limit of active power that can be output by household photovoltaic power generation unit II; is the reactive power output of household photovoltaic power plant III at time t; S is the capacity of household photovoltaic power plant III;

[0122] (3) Wind Turbine (WT) Constraints

[0123]

[0124] Where: is the active power output by the i-th WT at time t; is the minimum limit of the active power output by the i-th WT, is the maximum limit of active power that can be output by the i-th WT;

[0125] 4) OLTC Constraints

[0126] OLTC plays an important role in regulating the voltage level of the distribution network. To describe the transformation ratio effect, a virtual node m is introduced. The OLTC branch satisfies the following voltage transformation equation:

[0127]

[0128] Where: is the square of the voltage amplitude of the virtual node m; is the square of the voltage amplitude at node i; P im , Q im are the active power and reactive power flowing from node i to node m respectively; r im and x im They are the sum of resistance and reactance of branch im and OLTC on the line respectively; is the line current of branch im;

[0129] The big-M method is used to linearly transform the bilinear terms in the OLTC branch constraints. The OLTC branch constraints are expressed as:

[0130]

[0131] Where: is the voltage of node m at time t, is the voltage of node j at time t, n tr k is the number of OLTC gears; ij,t,y represents the transformation ratio corresponding to OLTC gear y on line ij; e ij,t,yis a binary dummy variable of the OLTC tap position y on the branch; f ij,t,y is e after linearization by the big M method ij,t,y ; M is a large positive number;

[0132] (5) CB constraints

[0133]

[0134] Where: is the total compensation power of CB, B CB is the set of CB nodes; is the number of operational groups, which is a discrete variable value; The upper limit of the number of CB groups connected to node j; is the compensation power of each group of CB;

[0135]

[0136] Formula (16) is the total number of operations in multiple time periods; where, is the number of groups put into operation at time t-1, The upper limit of the number of operations;

[0137] In addition, the absolute value constraint in the above formula can be processed as follows: by adding an auxiliary variable that represents the change in CB compensation capacity between adjacent time periods: Then we can get:

[0138]

[0139] In the formula, It is an auxiliary variable that characterizes the change of CB compensation capacity between adjacent time periods;

[0140] (6) Power flow constraints

[0141] The power flow constraint of the distribution network is a non-convex constraint. In order to improve the solution rate, it is relaxed in two steps: phase angle relaxation and convex relaxation. The power flow constraint after relaxation is:

[0142]

[0143] Where i∈u(j) and k∈v(j) are the branch sets with j as the end and the beginning node respectively; P ij , Q ij are the active power and reactive power flowing from node i to node j respectively; r ij and x ij are the resistance and reactance of branch ij respectively; P jk and Q jk are the active power and reactive power flowing from node j to node k, respectively, P jand Q j are the equivalent active power and reactive power of node j respectively; is the square of the current flowing through branch ij; is the square of the voltage amplitude at node j, is the square of the voltage amplitude at node i; P load,j , P ch,j , P dch,j , P DG,j , P G,j are the load active power, energy storage charging power, energy storage discharging power, distributed generation output and power grid purchased power of node j respectively; Q load,j , Q CB,j , Q G,j , Q pv,j They are the reactive power of the node j load, the reactive output of the CB, the reactive power generated by the generator and the reactive power generated by the heterogeneous PV.

[0144] The CPLEX commercial solver is used to solve the day-ahead optimization control model to determine the adjustable resource control instructions of the medium-voltage distribution system and the OLTC and CB switching schemes of the distribution system, which are used as constraints for intraday operation optimization.

[0145] Determine the objective function of the intraday optimization and control model.

[0146] The goal is to minimize the total daily operating cost and voltage deviation. The total daily operating cost includes the main grid power purchase cost, active power loss cost, new energy and energy storage operation and maintenance cost, and wind and solar power abandonment cost.

[0147] The objective function of the intraday optimization control model is shown in formula (19)-formula (20):

[0148] F=min(F net +F loss +F dg +F ess +F qdg +F pc ) (19)

[0149]

[0150] Where: Δt int is the intraday regulation cycle; T is the total period, F net 、F loss 、F dg 、F ess 、F qdg They are the main grid power purchase cost, active power loss cost, new energy and energy storage operation and maintenance cost, and wind and solar power abandonment cost; F pc is the node voltage deviation; c net 、closs 、c pv 、c wt 、c ess 、c qpv 、c qwt They are the unit main grid power purchase price, unit active power loss price, unit photovoltaic operation and maintenance cost, unit wind turbine operation and maintenance cost, unit energy storage operation and maintenance cost, unit solar curtailment cost, and unit wind curtailment cost; P g,t , r ij are the power purchase amount of the distribution network, the square of the branch current between node i and node j, and the branch impedance between node i and node j. pv,t , P wt,t are the photovoltaic and wind turbine consumption respectively; P ch,t , P dch,t are the energy storage charging and discharging power respectively; P pv’,t , P wt’,t are wind power abandonment and solar power abandonment respectively; U j,t is the node voltage of node j at time t.

[0151] The constraints for setting up the intraday optimization control model are as follows:

[0152] (1) Energy Storage System (ESS) Constraints

[0153] u dch,t +u ch,t =0 (21)

[0154] S ess,start =S ess,end (twenty two)

[0155]

[0156] Formula (21) is the charge and discharge state constraint of the energy storage device, that is, the energy storage device cannot be in the charge and discharge state at the same time; Formula (22) is the energy state constraint of the energy storage device, and the energy at the beginning and end of the energy storage device should be equal; Formula (23) is the upper and lower limit constraints of the charge state of the energy storage device; Formula (24) is the charge state constraint of the energy storage power station; where: u dch,t and u ch,t It is a 0-1 variable, indicating the charge and discharge mark of the energy storage power station; S ess,start and S ess,end They are the initial and final states of charge of the energy storage power station; are the upper and lower limits of the capacity of the energy storage power station, S i,t is the capacity of the energy storage power station; are the upper limits of the charging and discharging power of the energy storage power station; S ess,i,t is the power of the energy storage power station at time t;

[0157] (2) Wind Turbine (WT) Constraints

[0158]

[0159] Where: is the active power output by the i-th WT at time t; is the minimum limit of the active power output by the i-th WT, is the maximum limit of active power that can be output by the i-th WT;

[0160] (3) Flow uniformity constraint

[0161]

[0162] Where V g_int is the low voltage side voltage of OLTC in the intraday optimization regulation model, V g_pre To solve the OLTC low-voltage side voltage obtained by the day-ahead optimization control model; K int is the CB gear in the intraday optimization control model, K pre To solve the CB gear position obtained by the day-ahead optimization control model; is the active power of the medium-voltage photovoltaic power station in the intraday optimization control model, To solve the day-ahead optimization control model to obtain the active power of the medium-voltage photovoltaic power station; For the active power of the medium-pressure fan in the daily optimization control model, To solve the medium-pressure fan active power obtained by the day-ahead optimization control model; It is the charging and discharging power of medium voltage energy storage in the daily optimization control model. To solve the medium voltage energy storage charging and discharging power obtained by the day-ahead optimization control model;

[0163] The CPLEX commercial solver is used to solve the intraday optimization scheduling model to determine the intraday low-voltage resources, namely low-voltage photovoltaic, wind turbine, and energy storage control instructions. Through the day-ahead intraday collaborative optimization, the comprehensive optimization and improvement of the distribution network operation efficiency and power supply quality can be achieved.

[0164] In order to enable those skilled in the art to better understand the present invention, the example analysis includes the following components:

[0165] 1. Case description and simulation results analysis

[0166] In order to verify the effectiveness and superiority of the method involved in the present invention, this embodiment takes the medium voltage 10kV (IEEE33 node) connected to the low voltage 0.38kV (improved IEEE15 node, improved IEEE8 node) as an example to simulate the medium and low voltage distribution system. Figure 1As shown. OLTC1 is connected to the connection point between the medium voltage distribution system and the main grid, and CB is connected to nodes 5 and 16 respectively; node 22 is the PV access point, nodes 17 and 32 are WT access points, and nodes 16 and 32 are ESS access points. Node 15 is connected to the improved IEEE15 node through OLTC2, and node 21 is connected to the improved IEEE8 node through OLTC3. The line impedance of the low voltage distribution system is (0.650+j0.412)Ω / km. The example parameters are shown in Table 1:

[0167] Table 1 Example parameter settings

[0168]

[0169] 2. Case Comparative Analysis

[0170] Three scenarios are set for comparative analysis, with the day-ahead control cycle Δt pre Take 1h, intraday regulation cycle Δt int Take 15min; take M as 1000; take S as 0.1Mva.

[0171] Scenario 1: No consideration is given to heterogeneous PV, nor to the switching scheme of grid-side resources OLTC and CB, and only intraday regulation is performed;

[0172] Scenario 2: Based on Scenario 1, the switching plan of network-side resources OLTC and CB is considered, and the day-ahead OLTC and CB switching plan is used as a constraint for intraday operation;

[0173] Scenario 3: Based on Scenario 2, heterogeneous photovoltaics are considered, that is, the day-ahead and intraday optimization of the distribution system that coordinates heterogeneous photovoltaics with grid-side resources.

[0174] The optimization models of the three scenarios are solved, and the scheduling results are shown in Table 2.

[0175] Table 2

[0176]

[0177] As can be seen from Table 2, scenario 1 does not consider heterogeneous photovoltaics, nor does it consider the switching scheme of grid-side resources OLTC and CB. It only performs intraday regulation, with the highest total operating cost and node voltage deviation, and the lowest new energy consumption rate. Scenario 2 considers the switching scheme of grid-side resources OLTC and CB, and uses the day-ahead OLTC and CB switching schemes as constraints for intraday operation, which reduces the total operating cost of the medium and low voltage distribution system by 9.29%, reduces the cost of wind and solar abandonment by 64.91%, and reduces the node voltage deviation by 43.27%. The total operating cost of scenario 3 is further reduced compared with scenario 2, and the new energy consumption rate is increased from 95.95% to 96.1%, and the node voltage deviation is reduced by 38.98%. Therefore, the day-ahead-intraday control strategy for medium and low voltage distribution systems in which heterogeneous photovoltaics and grid-side resources are coordinated proposed in the present invention can achieve voltage regulation through OLTC and improve the new energy consumption rate. At the same time, the reactive power of the system is flexibly adjusted, line losses are reduced, and the system node voltage deviation is reduced.

[0178] Figure 2 The comparison is the 33-node voltage per unit value of scenario 1 and scenario 2. Figure 3 The comparison is the active power of branch 34 in two scenarios. The new energy consumption rate in scenario 2 is higher than that in scenario 1. The reason is that when the power of branch 34 is less than zero, the load demand of the distribution system is less than the power provided by the new energy, and the surplus new energy power is sent from the low-voltage distribution system to the medium-voltage distribution system for consumption. In scenario 1, OLTC regulation is not considered, the voltage regulation range of node 33 is limited, and the system limits the output of new energy to ensure that the voltage at the new energy access point does not exceed the upper limit. In scenario 2, the system adjusts the gear of OLTC2 to reduce the voltage of node 33. Under the premise that the voltage at the new energy access point does not exceed the limit, the new energy power sent from the low-voltage distribution system to the medium-voltage distribution system for consumption is increased as much as possible, thereby improving the new energy consumption rate.

[0179] Figure 4 , Figure 5 , Figure 6 The 24-hour node voltages of three scenarios are given respectively. Figure 7 , Figure 8 The following are the CB gear action change diagram of scenario 2 and the reactive power comparison diagram of the main grid side in three scenarios. Figure 7 It can be seen that the two groups of CBs in scenario 2 acted a total of 8 times. Compared with scenario 1, the CBs in scenario 2 provided part of the reactive power required for the operation of the distribution system, reducing the total amount of reactive power generated by the main grid side and reducing the flow of reactive power in the grid. At the same time, the line loss caused by the transmission of reactive power was reduced, and the node voltage deviation was reduced. Fig. 9 The figure shows the reactive power generated by heterogeneous photovoltaics. It can be seen from the figure that heterogeneous photovoltaics can also absorb and generate reactive power at night, providing some reactive power support for the distribution system. Figure 8It can be seen that compared with scenario 2, the total amount of reactive power generated by the main grid side in scenario 3 is further reduced, and the node voltage deviation is further optimized.

[0180] The above is only for explaining the implementation mode of the present invention and is not intended to limit the present invention. For those skilled in the art, any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention without creative work should be included in the protection scope of the present invention.

Claims

1. A method for controlling medium and low voltage power distribution systems coordinated by heterogeneous photovoltaic and grid-side resources within one day ahead, characterized in that: The following steps are involved: S1: Construct a day-ahead optimization control model with the goal of minimizing the system's total daily operating cost and node voltage deviation; S2: Use the CPLEX commercial solver to solve the day-ahead optimization control model, determine the adjustable resource control instructions of the medium-voltage distribution system and the OLTC and CB switching schemes of the medium- and low-voltage distribution systems, and use them as constraints for intraday operation optimization; S3: Establish an intraday optimization control model with the goal of minimizing the deviation from the objective function of the day-ahead stage; S4: Use CPLEX commercial solver to solve the intraday optimization control model and determine the adjustable resource control instructions of the intraday low-voltage distribution system; S5: The day-ahead optimization and control model and the intraday optimization and control model are optimized collaboratively.

2. The method for controlling the medium and low voltage power distribution system coordinated by heterogeneous photovoltaic and grid-side resources within one day before the day according to claim 1 is characterized in that: The objective function of the day-ahead optimization control model of S1 is shown in equations (1) and (2): F=min(F net +F loss +F dg +F ess +F qdg +F pc ) (1) Where: F is the minimum daily total operating cost, F net The cost of purchasing electricity from the main grid, F loss is the line loss cost, F dg is the cost of new energy and energy storage operation and maintenance, F ess is the wind curtailment cost, F qdg is the abandoned light cost, F pc is the node voltage deviation; T is the total time period, c net is the unit's main grid electricity purchase price, c loss is the price per unit active power loss, c pv is the unit photovoltaic operation and maintenance cost, c wt is the unit wind turbine operation and maintenance cost, c ess is the unit energy storage operation and maintenance cost, c qpv is the unit abandoned light cost, c qwt is the unit wind curtailment cost; P g,t is the power purchase amount of the distribution network, Δt pre is the day-ahead control cycle; is the square of the branch current between node i and node j, r ij is the branch impedance between node i and node j; P pv,t For photovoltaic, P wt,t P is the fan consumption; ch,t is the energy storage charging power, P dch,t is the energy storage discharge power; P pv’, is the abandoned wind power, P wt’,t is the abandoned light power; U j,t is the node voltage of node j at time t.

3. The method for controlling medium and low voltage power distribution systems coordinated by heterogeneous photovoltaic and grid-side resources according to claim 2, characterized in that: The constraints of the day-ahead optimization control model are as follows: (1) Energy Storage System (ESS) Constraints in dch,t +in ch,t =0 (3) S ess,start =S ess,end (4) Formula (3) is the charge and discharge state constraint of the energy storage device, that is, the energy storage device cannot be in the charge and discharge state at the same time; Formula (4) is the energy state constraint of the energy storage device, the energy at the beginning and end of the energy storage device should be equal; Formula (5) is the upper and lower limit constraints of the charge state of the energy storage device; Formula (6) is the charge state constraint of the energy storage power station; Where: u dch,t It is the charging mark of the energy storage power station, u ch,t is the discharge mark of the energy storage power station, u dch,t and u ch,t is a 0-1 variable; S ess,start is the initial state of charge of the energy storage power station, S ess,end It is the final state of charge of the energy storage power station; and are the upper and lower limits of the capacity of the energy storage power station, S i,t is the capacity of the energy storage power station, and They are the upper limits of the charging and discharging power of the energy storage power station respectively; S ess,i,t is the power of the energy storage station at time t, P ess,ch,i,t and P ess,dch,i,t are the charging and discharging power of the energy storage station, η ch is the energy storage charging coefficient, η dch is the energy storage discharge coefficient; (2) Heterogeneous photovoltaic characteristics constraints Constraints of PV power station No. Ⅰ: Active power output is controllable and active power reduction is possible; reactive power output operates in constant power factor control mode; In the formula, is the active power output of PV power station I at time t, is the maximum limit of active power that can be output by the No. 1 photovoltaic power station, is the reactive power output by PV power station I at time t; Constraints for household photovoltaics: Active output is uncontrollable and operates only in the maximum power point tracking (MPPT) mode; reactive output operates in the constant power factor control mode; In the formula, is the active power output of household photovoltaic system I at time t, The maximum limit of active power that can be output by household photovoltaic power station I is: is the reactive power output of household photovoltaic power generation No. Ⅰ at time t; Household PV Constraint II: Active power cannot be controlled, and it only operates in MPPT mode, using the remaining capacity of the PV inverter to determine the reactive power output of the PV inverter; In the formula, is the active power output of household photovoltaic power plant II at time t, The maximum limit of active power that can be output by household photovoltaic power generation. is the reactive power output of household photovoltaic power plant II at time t, and S is the capacity of household photovoltaic power plant II; Type III household photovoltaic constraints: active power output is controllable, active power reduction can be performed, and the remaining capacity of the photovoltaic inverter is used to determine the reactive power output of the photovoltaic inverter; In the formula, is the active power output of household photovoltaic power plant III at time t, The maximum limit of active power that can be output by household photovoltaic power generation. is the reactive power output of household photovoltaic power plant III at time t, and S is the capacity of household photovoltaic power plant III; (3) Wind Turbine (WT) Constraints Where: is the active power output by the i-th WT at time t, is the minimum limit of the active power output by the i-th WT, is the maximum limit of active power that can be output by the i-th WT; (4) OLTC Constraints OLTC plays an important role in regulating the voltage level of the distribution network. To describe the transformation ratio effect, a virtual node m is introduced. The OLTC branch satisfies the following voltage transformation equation: Where: is the square of the voltage amplitude of the virtual node m, is the square of the voltage amplitude at node i, P im , Q im are the active power and reactive power flowing from node i to node m, r im and x im are the sum of resistance and reactance of branch im and OLTC on the line, respectively. is the line current of branch im; The big-M method is used to linearly transform the bilinear terms in the OLTC branch constraints. The OLTC branch constraints are expressed as: Where: is the voltage of node m at time t, is the voltage of node j at time t, n tr is the number of OLTC gears, k ij,t,y represents the transformation ratio corresponding to the OLTC gear y on line ij, e ij,t,y is a binary dummy variable for the OLTC tap position y on the branch, f ij,t,y is e after linearization by the big M method ij,t,y , M is a large positive number; (5) CB constraints Where: is the total compensation power of CB, B CB is the set of CB nodes, is the number of operational groups, which is a discrete variable value. is the upper limit of the number of CB groups connected to node j, is the compensation power of each group of CB; Formula (16) is the total number of operations in multiple time periods, where: is the number of groups put into operation at time t-1, The upper limit of the number of operations; In addition, the absolute value constraint in the above formula can be processed as follows: by adding an auxiliary variable that represents the change in CB compensation capacity between adjacent time periods: Then we can get: In the formula, It is an auxiliary variable that characterizes the change of CB compensation capacity between adjacent time periods; (6) Power flow constraints The power flow constraint of the distribution network is a non-convex constraint. In order to improve the solution rate, it is relaxed in two steps: phase angle relaxation and convex relaxation. The power flow constraint after relaxation is: Where i∈u(j) and k∈v(j) are the branch sets with j as the end and the beginning node respectively; P ij , Q ij are the active power and reactive power flowing from node i to node j respectively; r ij and x ij are the resistance and reactance of branch ij respectively; P jk and Q jk are the active power and reactive power flowing from node j to node k, respectively, P j and Q j are the equivalent active power and reactive power of node j respectively; is the square of the current flowing through branch ij; is the square of the voltage amplitude at node j, is the square of the voltage amplitude at node i; P load,j , P ch,j , P dch,j , P DG,j , P G,j are the load active power, energy storage charging power, energy storage discharging power, distributed generation output and power grid purchased power of node j respectively; Q load,j , Q CB,j , Q G,j , Q pv,j They are the reactive power of the node j load, the reactive output of the CB, the reactive power generated by the generator and the reactive power generated by the heterogeneous PV.

4. The method for day-ahead and intra-day control of medium and low voltage power distribution systems with coordinated heterogeneous photovoltaic and grid-side resources according to claim 1 is characterized in that: The objective function of the intraday optimization control model of S3 is shown in formula (19)-formula (20): F=min(F net +F loss +F dg +F ess +F qdg +F pc ) (19) Where: Δt int is the intraday regulation cycle; T is the total period, F net 、F loss 、F dg 、F ess 、F qdg They are the main grid power purchase cost, active power loss cost, new energy and energy storage operation and maintenance cost, and wind and solar power abandonment cost; F pc is the node voltage deviation; c net 、c loss 、c pv 、c wt 、c ess 、c qpv 、c qwt They are the unit main grid power purchase price, unit active power loss price, unit photovoltaic operation and maintenance cost, unit wind turbine operation and maintenance cost, unit energy storage operation and maintenance cost, unit solar curtailment cost, and unit wind curtailment cost; P g,t , r ij are the power purchase amount of the distribution network, the square of the branch current between node i and node j, the branch impedance between node i and node j, and P pv,t , P wt,t are the photovoltaic and wind turbine consumption respectively; P ch,t , P dch,t are the energy storage charging and discharging power respectively; P pv’,t , P wt’,t are wind power abandonment and solar power abandonment respectively; U j,t is the node voltage of node j at time t.

5. The method for day-ahead and intra-day control of medium and low voltage power distribution systems with coordinated heterogeneous photovoltaic and grid-side resources according to claim 4 is characterized in that: The constraints of the intraday optimization control model are as follows: (1) Energy Storage System (ESS) Constraints in dch,t +in ch,t =0 (21) S ess,start =S ess,end (22) Formula (21) is the charge and discharge state constraint of the energy storage device, that is, the energy storage device cannot be in the charge and discharge state at the same time; Formula (22) is the energy state constraint of the energy storage device, and the energy at the beginning and end of the energy storage device should be equal; Formula (23) is the upper and lower limit constraints of the charge state of the energy storage device; Formula (24) is the charge state constraint of the energy storage power station; where: u dch,t and u ch,t It is a 0-1 variable, indicating the charge and discharge mark of the energy storage power station; S ess,start and S ess,end They are the initial and final states of charge of the energy storage power station; and are the upper and lower limits of the capacity of the energy storage power station, S i,t is the capacity of the energy storage power station; and They are the upper limits of the charging and discharging power of the energy storage power station respectively; S ess,i,t is the power of the energy storage station at time t, P ess,ch,i,t and P ess,dch,i,t are the charging and discharging power of the energy storage station, η ch is the energy storage charging coefficient, η dch is the energy storage discharge coefficient; (2) Wind Turbine (WT) Constraints Where: is the active power output by the i-th WT at time t; is the minimum limit of the active power output by the i-th WT, is the maximum limit of active power that can be output by the i-th WT; (3) Flow uniformity constraint Where V g_int is the low voltage side voltage of OLTC in the intraday optimization regulation model, V g_pre To solve the OLTC low-voltage side voltage obtained by the day-ahead optimization control model; K int is the CB gear in the intraday optimization control model, K pre To solve the CB gear position obtained by the day-ahead optimization control model; is the active power of the medium-voltage photovoltaic power station in the intraday optimization control model, To solve the day-ahead optimization control model to obtain the active power of the medium-voltage photovoltaic power station; For the active power of the medium-pressure fan in the daily optimization control model, To solve the medium-pressure fan active power obtained by the day-ahead optimization control model; It is the charging and discharging power of medium voltage energy storage in the daily optimization control model. To solve the medium voltage energy storage charging and discharging power obtained by the day-ahead optimization control model.

6. The method for day-ahead and intra-day control of medium and low voltage power distribution systems with coordinated heterogeneous photovoltaic and grid-side resources according to claim 1, characterized in that: The adjustable resource control instructions of S4 are low-voltage photovoltaic, wind turbine, and energy storage control instructions.

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