Distribution network optimization operation method based on cooperative scheduling of new energy station and transformer area intelligent terminal station

Abstract

The invention belongs to the technical field of power system operation and control, and particularly relates to a distribution network optimization operation method based on cooperative scheduling of a new energy field station and a transformer area intelligent terminal station. The power distribution network system comprises a distributed new energy field station, a power utilization area and a power distribution network, the power utilization area comprises roof photovoltaic and electric vehicle charging facilities and other residence loads and other resources, and the power utilization area carries out unified monitoring and management and control on the resources in the area through an area intelligent terminal station; the method comprises the following three steps of: (1) establishing an objective function of distribution network operation; (2) setting constraint conditions for solving the objective function; (3) solving an objective function; the method has the advantages that (1) the new energy consumption capability is improved: the new energy output fluctuation can be effectively stabilized and the new energy consumption capability is improved by coordinating the new energy station and the intelligent terminal station in the transformer area; (2) the operation efficiency of the power distribution network is improved: through adjustable resource scheduling, the network loss can be reduced, and the operation efficiency of the power distribution network is improved; (3) improving electric energy quality and enhancing safety and stability of the power distribution network: through real-time monitoring and adjustment of fundamental waves and harmonic power flows of the power distribution network, potential safety hazards can be effectively prevented and eliminated, the safety and stability of the power distribution network are enhanced, and electric energy management of the power distribution network is remarkably improved; and (4) promotion of distributed resource utilization: by scheduling the new energy station and the intelligent terminal station in the transformer area to participate in optimized operation of the power distribution network, the adjustment potential of the distributed resources can be fully exerted, and the economic benefit is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation and control, and particularly relates to a method for optimizing the operation of a distribution network based on the collaborative scheduling of a new energy power station and a smart terminal station in a substation area. Background Art

[0002] With the rapid development of new energy power generation technologies, the penetration rate of new energy represented by wind power and photovoltaic power in the power system has been continuously increasing. However, new energy power generation has characteristics such as intermittency, volatility, and randomness. Its large-scale access has brought huge challenges to the safe and stable operation of the distribution network. The traditional operation mode of the distribution network is difficult to effectively cope with the fluctuations in new energy output, and problems such as voltage over-limit and line overload are likely to occur, restricting the consumption of new energy.

[0003] On the other hand, with the development of power electronics technology, more and more controllable resources have emerged in the distribution network, such as distributed energy storage, flexible loads, etc. These resources have flexible adjustment capabilities and can provide support for the operation of the distribution network. However, the current utilization of these resources is often limited to local optimization, lacking collaborative scheduling with new energy power stations, and it is difficult to fully exert their adjustment potential. Therefore, there is an urgent need for a method that can effectively coordinate new energy power stations and substation area resources to achieve the optimized operation of the distribution network.

[0004] Thirdly, a load aggregator is an important participant in the power market. Its main role is to integrate dispersed and adjustable power load resources and participate in power market transactions as a virtual power plant, providing flexibility and ancillary services for the power system.

[0005] The load aggregator constructs an important node that connects the distribution network and substation area users and can effectively control the information flow and energy flow through multiple smart terminal stations in the service area, and realizes the following functions:

[0006] 1. Improve the flexibility of the power system: The traditional power system mainly relies on regulation on the power generation side to meet the load demand, while the load aggregator can aggregate demand-side resources through a large number of smart terminal stations in the substation area to achieve flexible adjustment of the load and improve the overall flexibility of the power system. When the power supply is tight, the load aggregator can reduce the aggregated load to relieve the grid pressure; when the power supply is excessive, the load aggregator can increase the aggregated load to consume the surplus power.

[0007] 2. Provide ancillary services: The load aggregator can aggregate demand-side resources through smart terminal stations in the substation area to participate in power market ancillary services (frequency modulation, reserve, black start, etc.), providing guarantee for the safe and stable operation of the power system. The load aggregator can quickly adjust the aggregated load through smart terminal stations in the substation area to participate in the frequency regulation of the power system and maintain the system frequency stability.

[0008] 3. Reduce the electricity cost for users: The load aggregator can participate in the electricity market transactions through the intelligent terminal station in the transformer area to strive for more favorable electricity prices for users. The intelligent terminal station in the transformer area can increase the electricity consumption of users when the electricity price is low and reduce the electricity consumption of users when the electricity price is high, thereby reducing the overall electricity cost of users.

[0009] 4. Promote the consumption of renewable energy: The load aggregator can aggregate adjustable loads through the intelligent terminal station in the transformer area to cooperate with the volatility of local renewable energy generation and promote the consumption of renewable energy. When the output of wind power and photovoltaic power generation is large, the intelligent terminal station in the transformer area can increase the adjustable electricity load (such as fast charging of electric vehicles, charging of energy storage, etc.) to consume the excess renewable energy power.

[0011] The intelligent terminal station in the transformer area is an effective means for the load aggregator to integrate dispersed and adjustable power load resources. However, with the explosive growth of the number of active transformer areas, a large amount of state data of intelligent terminal stations in the transformer area, coordinated control strategies, and interactive coupling effects have brought great challenges to the dispatching operation analysis of the load aggregator. Therefore, the hierarchical and zonal control of aggregated resources has become an inevitable trend.

[0012] Therefore, there is an urgent need for a method to effectively coordinate the resources of the new energy power station and the intelligent terminal station in the transformer area to realize the optimal operation of the distribution network, so as to achieve multi-objective control effects such as improving the consumption rate of renewable energy, enhancing the utilization rate of distribution network equipment, reducing network line losses, improving power quality, and improving the safe and stable operation level of the power grid. Summary of the Invention

[0013] The technical problem to be solved by the present invention is: aiming at the deficiencies in the prior art, to provide a method for optimizing the operation of the distribution network based on the coordinated dispatching of the new energy power station and the intelligent terminal station in the transformer area, so as to achieve multi-objective control effects such as improving the consumption rate of renewable energy, enhancing the utilization rate of distribution network equipment, reducing network line losses, improving power quality, and improving the safe and stable operation level of the power grid.

[0014] The distribution network system involved in the present invention includes a distributed new energy power station, an electricity consumption area, and a distribution network. The electricity consumption area contains resources such as rooftop photovoltaic, electric vehicle charging facilities, and other residential loads. The intelligent terminal station in the electricity consumption area uniformly monitors and controls the resources in the area. In this system, PV, EV, and other resources in the area are connected to the distribution network through the intelligent terminal station in the transformer area.

[0015] From the perspective of optimizing the operation of the distribution network, both the new energy power station and the intelligent terminal station in the transformer area can be regarded as flexible and controllable resources. The coordinated dispatching of these resources can realize the multi-objective optimal operation of the entire distribution network.

[0016] The district intelligent terminal station monitors and collects information such as the maximum output power of PV and the initial battery power, and at the same time collects other relevant information, including but not limited to the maximum power, minimum power, vehicle charging plan, etc. calculated according to the charging and discharging contracts signed by EV owners. The district intelligent terminal station calculates the range of capabilities that can respond to the distribution network dispatching based on the status information of the district resources, uploads the results to the distribution network dispatching master station, and can respond to the instructions of the distribution network dispatching at any time.

[0017] Obtaining coordinated dispatching instructions, including: constructing a distribution network model that includes new energy power stations, district intelligent terminal stations, loads, and network topologies, and determining the mathematical models and operating constraints of each component; collecting in real time data such as the output of new energy power stations, load demands, and the status of district resources, and using prediction algorithms to predict the new energy output and load demands in a future period of time; determining the operating objectives, with the minimum operating cost of the distribution network, maximum new energy consumption, minimum network losses, etc. as the objective functions, considering network security constraints, equipment operating constraints, etc., and constructing an optimal operation model for the distribution network based on the coordinated dispatching of new energy power stations and district intelligent terminal stations; using optimization algorithms to solve the constructed optimization model to obtain optimization results such as the output plan of new energy power stations, the dispatching plan of district intelligent terminal stations, and the network reconfiguration plan; issuing the optimization results to new energy power stations and district intelligent terminal stations, and executing the corresponding output plans, dispatching plans, and corresponding operations of controllable loads (such as EVs, etc.).

[0018] To solve the above technical problems, the present invention adopts the following technical solutions:

[0019] A method for optimizing the operation of a distribution network based on the coordinated dispatching of a new energy power station and a district intelligent terminal station, characterized by comprising the following steps:

[0020] 1. Establish the objective function of the distribution network operation

[0021] f PL is the power loss of the distribution network, which can be calculated by the difference between the active power provided by all new energy power stations and district intelligent terminal stations in the distribution network and the active power consumed by all other loads, as shown in (1).

[0022]

[0023] Among them, H is the maximum harmonic order studied in this article. N is the total number of nodes in the distribution network. respectively represent the i-th node The active power generation of the new energy power station, the active power of the district intelligent terminal station, the active power of the electric vehicle charging and discharging, and the other active loads of the h-th harmonic of the phase.

[0024] f THD reflects the degree of harmonic distortion, as shown in (2):

[0025]

[0026] Among them, respectively represent the real part and the imaginary part of the h-th harmonic voltage of the i-th node phase.

[0027] f PVUR reflects the degree of voltage imbalance, which is calculated by the square of the sum of the negative-sequence voltages of each node, and is converted into the rectangular coordinate system form as shown in (3).

[0028]

[0029] Among them, VX i , VY i respectively represent the row vectors composed of the real part and the imaginary part of the fundamental-frequency three-phase voltages of the i-th node. α and β respectively represent the column vectors composed of the sine function and the cosine function.

[0030] 2. Set the constraint conditions of the objective function to be solved

[0031] The operation of the distribution network needs to meet certain power flow constraints, such as the power balance equations of the fundamental-frequency node active power and reactive power shown in (4) and (5). It should be noted that and When they are positive numbers, it means that the intelligent terminal station of the transformer substation area and the new energy power station inject power into the system. When it is a positive number, it means EV charging, that is, the system injects power into the EV battery. Similarly, When it is a negative number, it means that the EV injects power into the system. The injection of active and reactive power at the fundamental-frequency node is calculated as shown in (6)-(7).

[0032]

[0033] Among them, respectively represent the conductance and susceptance between the i-th node phase and the ψ phase of the j-th node. l represents the distribution network line. represents the active power injected at the fundamental-frequency node of the i-th node phase.

[0034] In addition to the above fundamental-frequency power balance constraints, the harmonic current should also satisfy the current balance constraint in the form of shown. Among them, represents the h-th harmonic of the current injected at the i-th node phase node, and the calculation method is shown in (8).

[0035]

[0036] The rapid fluctuations in the power generation of new energy power stations may bring unpredictable voltage violations. Therefore, the power generation of power stations is subject to certain restrictions. Under the joint constraints of the power generation of new energy power stations in the previous time period and the ramp rate limit, the power generation constraints of new energy power stations in the current time period are shown in (9)-(10). During operation, new energy power stations can operate in the first and second quadrants, that is, when injecting active power into the system, they can inject capacitive reactive power and inductive reactive power, as shown in (11)-(12).

[0037]

[0038] Among them, represents the ramp limit of the new energy power station. represents the maximum PV output. respectively represent the maximum inductive reactive power and the maximum capacitive reactive power injected by the new energy power station into the distribution network system. N PV represents the set of nodes with new energy power stations connected.

[0039] When charging, electric vehicles operate in the third and fourth quadrants, and when discharging, they operate in the first and second quadrants. Their active power satisfies the constraints shown in (13). At the same time, the reactive power satisfies the constraints shown in (14).

[0040]

[0041] Among them, respectively represent the maximum charging power and the maximum discharging power of electric vehicles, and their calculation methods are different in different charging strategies, and are usually calculated comprehensively according to factors such as the rated capacity, grid connection time, and off-grid time of electric vehicles. respectively represent the maximum inductive reactive power and the maximum capacitive reactive power injected by electric vehicles into the system. N EV represents the set of nodes with EVs connected.

[0042] The charging and discharging power of electric vehicles, as well as the harmonic current and harmonic voltage generated by them, are restricted by the capacity of the inverter, as follows:

[0043]

[0044] 3. Solving the objective function

[0045] In the present invention, by using the idea of alternating optimization, the complex multi-objective harmonic power flow model is decomposed into two low-dimensional sub-problems, one is the optimal fundamental wave power flow sub-problem, and the other is the optimal harmonic power flow sub-problem, and they are solved by an alternating iteration method.

[0046] (1) Alternating iteration solution method

[0047] The alternating iteration solution will be an optimization method that decomposes the objective function of the original problem into multiple low-dimensional sub-problems for iterative solution. Let \(f(x)\) be the objective function and \(x\) be the decision variable, whose constraint domain is a bounded closed set \(C\times D\) in \(\mathbb{R}\). The variables are divided into two vectors \(\varphi\) and \(\psi\), whose constraint domains are \(C\) and \(D\) respectively. When \(\varphi\) is fixed, the objective function is a convex function of \(\psi\), and vice versa, but the objective function may not be a convex function with respect to \(x\). k in \(\mathbb{R}\). The variables are divided into two vectors \(\varphi\) and \(\psi\), whose constraint domains are \(C\) and \(D\) respectively. When is fixed, the objective function is a convex function of \(\psi\), and vice versa, but the objective function may not be a convex function with respect to \(x\).

[0048]

[0049] Using the alternating iteration method to solve the above optimization problem, alternately fix some decision variables to solve the minimum point. Taking the \(n\)th iteration as an example, fix \(\psi\) n-1 , and solve in \(C\) to obtain the minimum point Then fix and solve in \(D\) to obtain the minimum point \(\psi\) n . Repeatedly iterate and update the variables until the convergence condition is satisfied.

[0050] (2) Solving fundamental wave power flow and harmonic power flow

[0051] Using the idea of alternating optimization to establish the fundamental wave optimal power flow model and the harmonic optimal power flow model. The specific solution process is as follows:

[0052] 1) Calculate the fundamental wave and harmonic nodal admittance matrices according to the distribution network structure. Initialize the iteration number \(k = 0\), initialize the harmonic apparent power of new energy power stations, distribution transformer intelligent terminal stations, and electric vehicles, and the fundamental wave voltage, so that

[0053] 2) Fix the variables related to harmonic power flow, and use the predictor-corrector - primal-dual interior point method (PC-PDIPM) to solve the fundamental wave optimal power flow model.

[0054]

[0055] Among them, represents that the harmonic power loss is fixed in this round and regarded as a constant. The objective function is obtained by weighting (1)-(3). The harmonic power in (1) is solved according to the harmonic model of the previous round, and \(\alpha_1\) 1 , \(\alpha_2\) 2 , \(\alpha_3\) 3 are weight coefficients determined by the priority of the objective. The equality constraints include (6) and (7). The inequality constraints include (9)-(14).

[0056] 3) Determine whether the convergence condition shown in Equation (19) is satisfied. If satisfied, exit; otherwise, continue the iteration.

[0057]

[0058] 4) Fix the relevant variables of the fundamental wave power flow, and use Yalmip + Gurobi to solve the following quadratic programming model.

[0059]

[0060] When the harmonic power loss is small, the harmonic power loss in the objective function can be approximated as a constant. The harmonic power flow model is simplified to a quadratic programming model, which can be solved by various quadratic programming solvers.

[0061] 5) Update the iteration number k = k + 1, and calculate the harmonic apparent power of the new energy power station, the intelligent terminal station of the distribution transformer area, and the electric vehicle according to (21)-(22). Calculate the harmonic power loss according to (23).

[0062]

[0063] Among them, The calculation method of refers to Equation (6).

[0064] 6) Execute Step 2) again, and iterate repeatedly until the convergence condition shown in Step 3) is satisfied.

[0065] Furthermore, in Step 1, the new energy power stations involved in the objective function include wind farms, photovoltaic power stations, etc.; the intelligent terminal stations of the distribution transformer area include distributed energy storage, flexible loads, electric vehicles, etc.; the network topology includes lines, transformers, switches, etc.

[0066] Furthermore, in Step 2, the constraint conditions can be adjusted according to actual needs, such as adding conditions such as voltage deviation constraints, power supply reliability constraints, new energy consumption rate constraints, and fault loss load constraints.

[0067] Furthermore, in Step 3, the solution methods include linear programming, non-linear programming, mixed integer programming, heuristic algorithms, etc.

[0068] Beneficial Effects

[0069] A distribution network optimal operation method based on the coordinated scheduling of new energy power stations and intelligent terminal stations of the distribution transformer area provided by the present invention has the following beneficial effects:

[0070] 1. Improve the new energy consumption capacity: By coordinating the new energy power stations and the intelligent terminal stations of the distribution transformer area, the fluctuation of the new energy output can be effectively suppressed, and the new energy consumption capacity can be improved.

[0071] 2. Improve the operation efficiency of the distribution network: Through adjustable resource scheduling, network losses can be reduced and the operation efficiency of the distribution network can be improved.

[0072] 3. Improve power quality and enhance the safety and stability of the distribution network: Through real-time monitoring and adjustment of the fundamental wave and harmonic power flows in the distribution network, potential safety hazards can be effectively prevented and eliminated, the safety and stability of the distribution network can be enhanced, and the power management of the distribution network can be significantly improved.

[0073] 4. Promote the utilization of distributed resources: By scheduling new energy power stations and smart terminal stations in the distribution transformer area to participate in the optimal operation of the distribution network, the adjustment potential of distributed resources can be fully exploited and their economic benefits can be improved. Description of the Drawings

[0074] Figure 1 It is the circuit topology diagram of the smart terminal station unit system in a distribution network optimization operation method based on the collaborative scheduling of new energy power stations and smart terminal stations in the distribution transformer area provided by an embodiment of the present invention. The power grid supplies power to the loads in the distribution transformer area through the smart terminal station in the distribution transformer area. The smart terminal station in the distribution transformer area opens the DC bus externally and connects flexible and controllable resources in the distribution transformer area, including distributed photovoltaics, DC charging piles, and energy storage systems.

[0075] Figure 2 It is the flowchart of a distribution network optimization operation method based on the collaborative scheduling of new energy power stations and smart terminal stations in the distribution transformer area provided by an embodiment of the present invention. The process includes: 1) Establish an objective function; 2) Set constraint conditions; 3) Solve the objective function; 4) Dispatch and control. Detailed Embodiments

[0076] The following further describes the detailed embodiments of the present invention with reference to the drawings.

[0077] As Figure 1 shown, it is the circuit topology diagram of the smart terminal station unit system in a distribution network optimization operation method based on the collaborative scheduling of new energy power stations and smart terminal stations in the distribution transformer area provided by an embodiment of the present invention.

[0078] As Figure 2 shown, a distribution network optimization operation method based on the collaborative scheduling of new energy power stations and smart terminal stations in the distribution transformer area provided by an embodiment of the present invention includes the following steps:

[0079] 1. Establish an objective function for the operation of the distribution network

[0080] f PL is the power loss of the distribution network, which can be calculated by the difference between the active power provided by all new energy power stations and smart terminal stations in the distribution network and the active power consumed by all other loads, as shown in (1).

[0081]

[0082] Among them, H is the maximum harmonic order studied in this paper. N is the total number of nodes in the distribution network. respectively represent the active power generation of new energy power stations, the active power of the intelligent terminal station in the substation area, the active power of electric vehicle charging and discharging, and other active loads of the h - th harmonic of the phase of the i - th node The real and imaginary parts of the h - th harmonic voltage of the i - th node phase.

[0083] f THD reflects the degree of harmonic distortion, as shown in (2):

[0084]

[0085] Among them, respectively represent the real and imaginary parts of the h - th harmonic voltage of the i - th node phase. The real and imaginary parts of the h - th harmonic voltage of the i - th node phase.

[0086] f PVUR reflects the degree of voltage unbalance, which is calculated by the square of the sum of negative - sequence voltages of each node and is transformed into the rectangular coordinate system form as shown in (3).

[0087]

[0088] Among them, VX i , VY i respectively represent the row vectors composed of the real and imaginary parts of the fundamental - wave three - phase voltages of the i - th node. α and β respectively represent the column vectors composed of sine and cosine functions.

[0089] 2. Set the constraint conditions of the objective function to be solved

[0090] The operation of the distribution network needs to meet certain power - flow constraints, such as the balance equations of fundamental - wave node active power and reactive power shown in (4) and (5). It should be noted that and When it is a positive number, it means that the intelligent terminal station in the substation area and the new energy power station inject power into the system. When it is a positive number, it means EV charging, that is, the system injects power into the EV battery. Similarly, When it is a negative number, it means that the EV injects power into the system. The injection of active and reactive power at the fundamental - wave node is calculated as shown in (6) - (7).

[0091]

[0092]

[0093] Among them, respectively represent the conductance and susceptance between the phase of the i - th node and the ψ phase of the j - th node. l represents the distribution network line. Denote the i-th node The fundamental wave node of the phase injects active power.

[0094] In addition to the above fundamental wave power balance constraints, the harmonic current should also satisfy the current balance constraints in the form of as shown. Among them, Denote the i-th node The h-th harmonic of the current injected by the phase node is calculated as shown in (8).

[0095]

[0096] The rapid fluctuation of the power generation of the new energy power station may bring unexpected voltage over-limit. Therefore, the power generation of the power station is subject to certain restrictions. Under the joint constraints of the power generation of the new energy power station in the previous time period and the ramp rate limit, the power generation constraints of the new energy power station in the current time period are as shown in (9)-(10). The new energy power station can operate in the first and second quadrants during operation, that is, when injecting active power into the system, it can inject capacitive reactive power and inductive reactive power, as shown in (11)-(12).

[0097]

[0098] Among them, Denote the ramp limit of the new energy power station. Denote the maximum PV output. Denote the maximum inductive reactive power and the maximum capacitive reactive power injected by the new energy power station into the distribution network system respectively. N PV Denote the set of nodes with new energy power stations connected.

[0099] When charging, the electric vehicle operates in the third and fourth quadrants, and when discharging, it operates in the first and second quadrants. Its active power satisfies the constraints shown in (13). At the same time, the reactive power satisfies the constraints shown in (14).

[0100]

[0101] Among them, Denote the maximum charging power and the maximum discharging power of the electric vehicle respectively, and the calculation methods are different in different charging strategies, usually calculated comprehensively according to factors such as the rated capacity, grid connection time, and off-grid time of the electric vehicle. Denote the maximum inductive reactive power and the maximum capacitive reactive power injected by the electric vehicle into the system respectively. N EV Denote the set of nodes with EVs connected.

[0102] The power of electric vehicle charging and new energy stations, as well as the harmonic current and harmonic voltage generated by them, are restricted by the capacity of the inverter, as shown below:

[0103]

[0104] 3. Solve the objective function

[0105] In the present invention, by using the idea of alternating optimization, the complex multi-objective harmonic power flow model is decomposed into two low-dimensional sub-problems, one is the optimal fundamental power flow sub-problem, and the other is the optimal harmonic power flow sub-problem, and they are solved by means of alternating iteration.

[0106] (1) Alternating iteration solution method

[0107] Alternating iteration solution will be an optimization method that decomposes the objective function of the original problem into multiple low-dimensional sub-problems for iterative solution. Let f(x) be the objective function, x be the decision variable, and its restricted domain be a bounded closed set C×D in R k . Divide its variables into two vectors ψ, and their restricted domains are C and D respectively. When is fixed, the objective function is a convex function of ψ, and vice versa, but the objective function is not necessarily a convex function with respect to x.

[0108]

[0109] Use the alternating iteration method to solve the above optimization problem, and alternately fix some decision variables to solve the minimum point. Taking the nth iteration as an example, fix ψ n-1 , and solve in C to obtain the minimum point Then fix and solve in D to obtain the minimum point ψ n . Iterate repeatedly, update the variables until the convergence condition is satisfied.

[0110] (2) Fundamental power flow and harmonic power flow solution

[0111] Use the idea of alternating optimization to establish the fundamental optimal power flow model and the harmonic optimal power flow model. The specific solution process is as follows:

[0112] 1) Calculate the fundamental and harmonic nodal admittance matrices according to the distribution network structure. Initialize the iteration number k = 0, and initialize the harmonic apparent power, the harmonic apparent power of electric vehicles, and the fundamental voltage of new energy stations and distribution transformer area intelligent terminal stations, so that

[0113] 2) Fix the variables related to the harmonic power flow, and use the predictor-corrector - primal-dual interior point method PC-PDIPM to solve the fundamental optimal power flow model.

[0114]

[0115] Among them, It means that the harmonic power loss is fixed in this round and regarded as a constant. The objective function is obtained by weighting (1)-(3). The harmonic power in (1) is solved according to the harmonic model of the previous round, and α 1 , α 2 , α 3 are weight coefficients, which are determined by the priority of the objectives. The equality constraints include (6) and (7). The inequality constraints include (9)-(14).

[0116] 3) Judge whether the convergence condition shown in formula (19) is satisfied. If it is satisfied, exit; otherwise, continue the iteration.

[0117]

[0118] 4) Fix the relevant variables of the fundamental wave power flow, and use yalmip+gurobi to solve the following quadratic programming model.

[0119]

[0120] When the power loss of the harmonic is small, the harmonic power loss in the objective function can be approximated as a constant. The harmonic power flow model is simplified to a quadratic programming model, which can be solved by various quadratic programming solvers.

[0121] 5) Update the iteration number k = k + 1, and calculate the harmonic apparent power of the new energy power station, the intelligent terminal station of the distribution area, and the electric vehicle according to (21)-(22) Calculate the harmonic power loss according to (23)

[0122]

[0123]

[0124] Among them, The calculation method of is referred to formula (6).

[0125] 6) Execute step 2) again, and iterate repeatedly until the convergence condition shown in step 3) is satisfied.

[0126] Furthermore, in step 1, the new energy power stations involved in the objective function include wind farms, photovoltaic power stations, etc.; the intelligent terminal stations of the distribution area include distributed energy storage, flexible loads, electric vehicles, etc.; the network topology includes lines, transformers, switches, etc.

[0127] Further, in step 2, the constraint conditions can be adjusted according to actual requirements, such as adding conditions like voltage deviation constraint, power supply reliability constraint, new energy consumption rate constraint, fault loss load constraint, etc.

[0128] Further, in step 3, the solution methods include linear programming, non - linear programming, mixed - integer programming, heuristic algorithms, etc.

Claims

1. A distribution network optimization operation method based on the coordinated dispatch of new energy stations and intelligent terminal stations in substations, characterized in that: The following steps are involved: (1) Establishing the objective function of distribution network operation f PL The power loss of the distribution network can be calculated by the difference between the active power provided by all new energy stations and intelligent terminal stations in the distribution network and the active power consumed by all other loads, as shown in (1). Among them, H is the maximum harmonic order studied in this paper, and N is the total number of nodes in the distribution network. Respectively represent the i-th node The phase h harmonics include the active power of new energy stations, the active power of intelligent terminal stations in the substations, the active power of electric vehicle charging and discharging, and other active loads. f THD Reflects the degree of harmonic distortion, as shown in (2): in, Respectively represent the i-th node The real and imaginary parts of the hth harmonic voltage of the phase. f PVUR The degree of voltage imbalance is calculated by the square of the sum of the negative sequence voltages at each node and converted into a rectangular coordinate system as shown in (3). Among them, VX i , VY i They represent the row vectors composed of the real and imaginary parts of the three-phase fundamental voltage of the ith node. α and β represent the column vectors composed of the sine function and cosine function, respectively. (2) Set constraints for solving the objective function The operation of the distribution network needs to meet certain power flow constraints, such as the balance equations of active power and reactive power at the fundamental node shown in (4) and (5). It is worth noting that and When it is a positive number, it means that the intelligent terminal station and new energy station in the substation inject power into the system. A positive number indicates that the EV is charging, that is, the system injects power into the EV battery. When it is a negative number, it means that the EV injects power into the system. The active and reactive power injected into the fundamental node is calculated as shown in (6)-(7). in, Respectively represent the i-th node The conductance and susceptance between the phase and the jth node ψ phase. l represents the distribution network line. Represents the i-th node The fundamental node of the phase injects active power. In addition to the fundamental power balance constraints above, the harmonic currents should also satisfy the following form: The current balance constraint is shown in Figure 2. Represents the i-th node The hth harmonic of the current injected into the phase node is calculated as shown in (8). The rapid fluctuation of the power generation of the new energy station may cause unpredictable voltage over-limit, so the power generation of the station is subject to certain restrictions. Under the joint constraints of the power generation of the new energy station in the previous period and the ramp rate limit, the power generation constraints of the new energy station in the current period are shown in (9)-(10). The new energy station can operate in the first and second quadrants during operation, that is, when injecting active power into the system, it can inject capacitive reactive power and inductive reactive power, as shown in (11)-(12). in, Indicates the climbing limit of new energy stations. Indicates the maximum photovoltaic output. They represent the maximum inductive reactive power and the maximum capacitive reactive power injected by the new energy station into the distribution network system. PV Indicates the set of nodes connected to new energy stations. When the electric vehicle is charging, it works in the third and fourth quadrants, and when it is discharging, it works in the first and second quadrants. Its active power satisfies the constraints shown in (13). At the same time, its reactive power satisfies the constraints shown in (14). in, They respectively represent the maximum charging power and maximum discharging power of an electric vehicle. The calculation methods are different in different charging strategies and are usually calculated based on factors such as the rated capacity of the electric vehicle, grid-connection time, and off-grid time. Respectively represent the maximum inductive reactive power and the maximum capacitive reactive power injected into the system by the electric vehicle. N EV Indicates the set of nodes to which EVs are connected. The power of electric vehicle charging and new energy stations, as well as the harmonic currents and harmonic voltages they generate, are constrained by the inverter capacity, as shown below: (3) Solving the objective function The present invention utilizes the idea of ​​alternating optimization to decompose the complex multi-objective harmonic power flow model into two low-dimensional sub-problems, one is the optimal fundamental power flow sub-problem, and the other is the optimal harmonic power flow sub-problem, and solves them through alternating iteration. 1) Alternating iterative solution method Alternating iterative solution is an optimization method that decomposes the original problem objective function into multiple low-dimensional sub-problems for iterative solution. f(x) is the objective function, x is the decision variable, and its restriction domain is R k A bounded closed set C×D in . Divide its variables into two vectors ψ, whose restriction domains are C and D respectively. When is fixed, the objective function is a convex function of ψ, and vice versa, but the objective function is not necessarily convex with respect to x. The above optimization problem is solved by using the alternating iteration method, and some decision variables are alternately fixed to solve the minimum point. Taking the nth iteration as an example, fix ψ n-1 , solve in C Get the minimum point Then fix Solve in D Get the minimum point ψ n . Repeatedly iterate and update the variables until the convergence condition is met. 2) Solving fundamental and harmonic power flows The fundamental wave optimal power flow model and the harmonic optimal power flow model are established by using the idea of ​​alternating optimization. The specific solution process is as follows: a. Calculate the fundamental wave and harmonic node admittance matrix according to the distribution network structure. Initialize the number of iterations k = 0, initialize the harmonic apparent power of new energy stations, intelligent terminal stations in the substation area, the harmonic apparent power of electric vehicles and the fundamental wave voltage, so that b. Fix the harmonic power flow related variables and use the prediction-correction-primitive-dual interior point method PC-PDIPM to solve the fundamental wave optimal power flow model. in, Indicates that the harmonic power loss is fixed in this round and is regarded as a constant. The objective function is obtained by weighting (1)-(3). In (1), the harmonic power According to the previous round of harmonic model solution, α1, α2, and α3 are weight coefficients, which are determined by the priority of the target. The equality constraints include (6) and (7). The inequality constraints include (9)-(14). c. Determine whether the convergence condition shown in formula (19) is satisfied. If so, exit; otherwise, continue iterating. d. Fix the related variables of the fundamental wave flow and use yalmip+gurobi to solve the following quadratic programming model. When the power loss of harmonics is small, the harmonic power loss in the objective function can be approximated as a constant. The harmonic power flow model is simplified to a quadratic programming model, which can be solved using various quadratic programming solvers. e. Update the number of iterations k = k + 1, and calculate the apparent power of new energy stations, intelligent terminal stations in the substation area, and electric vehicle harmonics according to (21)-(22) Calculate the harmonic power loss according to (23) in, The calculation method of refers to formula (6). f. Execute step 2) again and iterate repeatedly until the convergence condition shown in step 3) is met. Furthermore, in step 1, the new energy sites involved in the objective function include wind farms, photovoltaic power stations, etc.; the substation intelligent terminal stations include distributed energy storage, flexible loads, electric vehicles, etc.; the network topology includes lines, transformers, switches, etc. Furthermore, in step 2, the constraint conditions can be adjusted according to actual needs, such as adding voltage deviation constraints, power supply reliability constraints, new energy consumption rate constraints, fault loss load constraints and other conditions. Furthermore, in step 3, the solution method includes linear programming, nonlinear programming, mixed integer programming, heuristic algorithm, etc.

2. According to claim 1, the new energy stations involved in the objective function include wind farms, photovoltaic power stations, etc.; the substation intelligent terminal station includes distributed energy storage, flexible loads, electric vehicles, etc.; the network topology includes lines, transformers, switches, etc.

3. According to claim 1, the constraints can be adjusted according to actual needs, such as adding voltage deviation constraints, power supply reliability constraints, new energy consumption rate constraints, fault loss load constraints and other conditions.

4. The solution method according to claim 1 includes linear programming, nonlinear programming, mixed integer programming, heuristic algorithm, etc.

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