Low-voltage distribution area flexible interconnection switch optimal configuration method and system

Abstract

The invention discloses an optimal configuration method and system for a flexible interconnection switch in a low-voltage distribution area, and the method comprises the steps: determining the characteristic parameters of the flexible interconnection switch, including a rated voltage, a rated current, a switching frequency, an inductance value, a modulation ratio, response time and a communication control interface, so as to construct a characteristic vector of the flexible interconnection switch; based on the distributed energy consumption rate and the demand response instruction, taking the feature vector of the flexible interconnection switch as an optimization variable, and constructing a multi-target optimization configuration model of the flexible interconnection switch in the low-voltage distribution area; according to the method, the characteristics of the low-voltage power distribution network and the problem of high cost of the flexible interconnection switch are fully considered, so that the adaptability of flexible interconnection switch control to the low-voltage power distribution network is improved; the system optimizes energy configuration, improves the operation stability of a power system, improves the utilization efficiency of a power supply, improves the voltage level of a feeder line, and improves the capability of consuming a distributed power supply.

Description

Technical Field

[0001] The present invention belongs to the technical field of flexible interconnection switch operation control, and more specifically, relates to a method and system for optimizing the configuration of flexible interconnection switches in a low-voltage distribution station area. Background Art

[0002] Against the background of accelerating the planning and construction of a new energy system, building a new power system with a gradually increasing proportion of new energy, and promoting the large-scale optimization of clean power resources, the continuous increase in the proportion of distributed clean energy access has led to profound changes in the energy structure, operation mechanism, and balance mode within the low-voltage distribution station area, posing greater challenges to the safe, efficient, and optimized operation of the power system.

[0003] In the current energy environment, it is becoming increasingly important to improve the absorption capacity of distributed photovoltaics, ensure the reliability and quality of power supply of the power grid, achieve supply and demand balance in the substation area, and ensure the reliability of power supply for end users. The flexible interconnection switch (SOP) has the characteristics of strong adjustment ability, fast response speed, low action cost, and small impact of failure. After its access, it will greatly improve the economy, flexibility and reliability of system operation. However, the flexible interconnection switch equipment has huge differences from traditional switches in equipment characteristics and performance, and its operation optimization control faces huge challenges in analysis methods, coordination mechanisms, and optimization modeling.

[0004] Considering the complex network structure, low reliability, unbalanced load and low utilization rate of distributed energy in the current low-voltage distribution area, the use of advanced flexible interconnection technology can be used for adjustment. However, the current cost of interconnection equipment is relatively high, and the number of flexible interconnection switches is limited, so optimization control is needed.

[0005] As a recently emerging control strategy, current research on flexible interconnection switch control mainly focuses on the concept of flexible interconnection, connection mode, component composition and access function, while there is little research on the control strategy of access to low-voltage distribution network from a system perspective. The shortcomings of the existing optimization control method are that it considers a single stakeholder, does not consider the characteristics of the low-voltage distribution network and the high cost of flexible interconnection switches, and is inefficient and inaccurate, making it difficult to adapt to the needs of the low-voltage distribution network. Summary of the invention

[0006] In order to solve the deficiencies in the prior art, the present invention provides an optimal control strategy and an optimized configuration method and system for flexible interconnected switches in low-voltage distribution station areas that take into account the interests of different entities and maximize the absorption of distributed photovoltaic power generation.

[0007] The present invention adopts the following technical solution.

[0008] A first aspect of the present invention provides a method for optimizing the configuration of flexible interconnection switches in a low-voltage distribution station area, comprising the following steps:

[0009] Step 1, determining characteristic parameters of the flexible interconnection switch, including rated voltage and rated current, switching frequency, inductance value, modulation ratio, response time and communication control interface, so as to construct a characteristic vector of the flexible interconnection switch;

[0010] Step 2: Based on the distributed energy consumption rate and demand response instructions, the characteristic vector of the flexible interconnection switch is used as the optimization variable to build a multi-objective optimization configuration model of the flexible interconnection switch in the low-voltage distribution station area;

[0011] Step 3: Use the optimization algorithm to solve the multi-objective optimization configuration model and obtain the optimal configuration solution of the flexible interconnection switch.

[0012] Preferably, the multi-objective optimization configuration model includes an upper-level objective model and a lower-level objective model, wherein the upper-level objective model takes the optimal distributed energy consumption rate as the optimization objective, and the lower-level objective model takes the minimization of total annualized cost, distribution network loss and voltage imbalance as the optimization objectives.

[0013] Preferably, the distributed energy consumption rate is expressed by the following formula:

[0014]

[0015] Where:

[0016] D is the distributed energy consumption rate;

[0017] T is the total number of time periods;

[0018] s represents each scene, and N represents the set of all scenes;

[0019] P1 and W1 are the wind power and photovoltaic power absorbed at time t in the scenario set, respectively;

[0020] P2 and W2 are the wind and photovoltaic outputs at time t in the scenario set, respectively;

[0021] ω is the weight coefficient of each scene set;

[0022] k is the number of scenes included in the scene set;

[0023] K is the total number of scenes contained in all scene sets.

[0024] Preferably, the objective function of minimizing the total annualized cost is expressed as follows:

[0025] minC=C SOP +C P +C DG

[0026]

[0027] Where: C is the total annual cost of the distribution network; C SOP is the annual investment cost of the flexible interconnection switch; C P is the annual electricity purchase cost of the distribution network; C DG is the annual electricity purchase cost of the distribution network from the distributed power generator; r is the discount rate; ij is the branch between node i and node j, n SOP is the branch set where the flexible interconnection switch is located, ij∈n SOP Indicates that branch ij is the branch where the flexible interconnection switch is located; c SOP S is the unit apparent power investment cost of the flexible interconnection switch; ij,SOP is the capacity of the flexible interconnection switch on branch ij; c P is the electricity purchase cost per kWh of the distribution network; c DG is the unit kilowatt-hour electricity purchase cost of distributed power generators; P is the amount of electricity purchased by the power grid from distributed power generators; L is the percentage of line loss; K T is the total number of scenes; P l is the active power of the substation in scenario l; T l is the number of hours of scene l;

[0028] The objective function of minimizing the distribution network loss is expressed as follows:

[0029] minF1=ω1(f line +f SOP )

[0030]

[0031] Where: minF1 is the minimum loss of the distribution network; f line and f SOP are the average line loss and the average flexible interconnection switch loss respectively; ω1 is the weight coefficient of distribution network loss; T an =8760h is the number of hours in a year; ij∈n l Indicates that branch ij belongs to branch set n l ; φ=A,B,C represents three different phase values ​​of A, B, C; r ij,φ represents the resistance value of the φ phase of branch ij; z ij，φ is the impedance value of the φ phase of branch ij; and are the real and imaginary parts of the voltage of phase φ at node i in scenario l, respectively; and are the active power losses of phase φ of voltage source converter i and voltage source converter j respectively;

[0032] The objective function of minimizing voltage unbalance is expressed as follows:

[0033] minF2=ω2f U

[0034]

[0035] Where: minF2 is the minimum voltage unbalance; f U is the voltage unbalance degree; ω2 is the weight coefficient; T an =8760h is the number of hours in a year; and are the real and imaginary parts of the voltage negative sequence unbalance at node i in scenario l, respectively; i∈Ω b Indicates that scene i belongs to all scenes Ω b .

[0036] Preferably, the constraints of the multi-objective optimization control model include distributed generation capacity and power constraints, flexible interconnection switch capacity and power constraints, node power balance constraints, three-phase power flow constraints and network security constraints.

[0037] Preferably, the flexible interconnect switch capacity and power constraint are expressed as follows:

[0038]

[0039] Where: is the maximum allowable capacity of the flexible interconnection switch installed at the node ij; s SOP and ij,SOP are the rated capacity of a single flexible interconnection switch and the number of flexible interconnection switches installed in branch ij; b 1,ij ,b 2,ij ,…,b nij,ij is a binary variable describing the number of flexible interconnection switches; n ij is the binary variable dimension;

[0040] and are the active injected powers of phase φ of voltage source converter i and voltage source converter j in scenario l respectively; and are the losses of the φ phase of the voltage source converter i and the voltage source converter j in scenario l; A i,SOP and A j,SOP are the active power loss coefficients of voltage source converter i and voltage source converter j respectively; and are the reactive injection powers of phase φ of voltage source converter i and voltage source converter j in scenario l respectively; and are the lower and upper limits of the reactive injection power of phase φ of voltage source converter i and voltage source converter j respectively; ξ min and max are the coefficients of the lower and upper limits of the reactive power of the flexible interconnection switch respectively; S ij,φ,SOP is the capacity of the two voltage source converters of the flexible interconnection switch φ phase; S ij,3φ,SOP is the capacity of the three phases of the two voltage source converters of the flexible interconnection switch.

[0041] Preferably, step 3 comprises:

[0042] Step 3.1, using a distributed estimation algorithm to solve each optimization objective in the multi-objective optimization configuration model established in step 2;

[0043] Step 3.2, perform constraint checks on the solutions obtained for each optimization objective and generate a set of feasible solutions;

[0044] Step 3.3, perform multi-objective optimization in the feasible solution set to generate the final flexible interconnect switch configuration solution.

[0045] Preferably, the fireworks algorithm is selected as the distributed estimation algorithm in step 3.1, and the solution process includes:

[0046] Step A.1, randomly generate a certain number of flexible interconnect switch configurations as initial fireworks, each configuration defines the on / off state of the flexible interconnect switch and the power transmission direction;

[0047] Step A.2, calculating the proportion of renewable energy power generation actually absorbed by the grid under the current flexible interconnection switch configuration, as well as the grid operation cost and the flexible interconnection switch operation cost;

[0048] Step A.3, for each firework, calculate the explosion radius and number of sparks according to its fitness value; based on the current flexible interconnect switch configuration, generate new sparks through random mutation;

[0049] Step A.4, calculate the fitness values ​​of all sparks, retain a certain number of the best sparks as part of the next generation of fireworks, and select the remaining positions probabilistically based on the fitness values ​​of the sparks. Sparks with higher fitness have a higher probability of being selected.

[0050] Step A.5, repeat steps A.3-A.4 until the maximum number of iterations is reached. In each iteration, the currently found optimal flexible interconnect switch configuration and its corresponding fitness value are recorded.

[0051] Preferably, the multi-objective optimization solution in step 3.3 is performed based on the Pareto optimal solution set, including the following steps:

[0052] Step B.1, obtain a set of Pareto optimal solutions through the optimization algorithm, each solution represents a flexible interconnection switch configuration scheme, and form a Pareto frontier in the target space;

[0053] Step B.2, assign a weight to each objective and develop decision criteria based on the preferences of distribution network operators or policymakers;

[0054] Step B.3, for each solution on the Pareto front, use the decision criterion of step B.2 for quantitative evaluation;

[0055] Step B.4, based on the results of the quantitative evaluation, select the solution with the highest score or the one that best meets the preference as the optimal flexible interconnect switch configuration solution.

[0056] A second aspect of the present invention provides a low-voltage distribution station area flexible interconnection switch optimization configuration system, which runs the low-voltage distribution station area flexible interconnection switch optimization configuration method, including:

[0057] A feature vector module, used to determine the feature parameters of the flexible interconnection switch and construct a feature vector of the flexible interconnection switch;

[0058] Model building module, used to build a multi-objective optimization configuration model for flexible interconnection switches in low-voltage distribution substations;

[0059] Model solving module, used to solve multi-objective optimization configuration models;

[0060] The result output module is used to generate and output the optimal flexible interconnect switch configuration solution.

[0061] Compared with the prior art, the beneficial effects of the present invention include at least:

[0062] (1) The present invention sets an optimal control strategy for the flexible interconnection switch in the low-voltage distribution station area, fully considers the characteristics of the low-voltage distribution network and the high cost of the flexible interconnection switch, and improves the adaptability of the flexible interconnection switch control to the low-voltage distribution network.

[0063] (2) The present invention optimizes energy configuration, improves the operational stability of the power system and increases power utilization efficiency, thereby achieving accurate, efficient and coordinated optimal operational control.

[0064] (3) The present invention comprehensively considers the interests of multiple entities such as distribution companies, distributed photovoltaic power generators and users.

[0065] (4) Under the existing economic and technical conditions, the present invention optimizes the control of the flexible interconnection switch in combination with the actual operation of the distribution network, thereby improving the economy and reliability of the distribution system operation, and improving the feeder voltage level and the ability to absorb distributed power sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flow chart of a method for optimizing configuration of flexible interconnection switches in low-voltage distribution station areas provided in accordance with an embodiment of the present invention;

[0067] Figure 2 It is a flow chart of the optimization solution of the fireworks algorithm provided in accordance with an embodiment of the present invention;

[0068] Figure 3 It is a schematic diagram of an IEEE33 node distribution network provided according to an embodiment of the present invention;

[0069] Figure 4 It is a schematic diagram of the Pareto optimal solution and the optimal frontier under the dual-objective optimization problem provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0071] The present invention proposes an optimization configuration method and system for flexible interconnection switches in low-voltage distribution station areas that take into account the improvement of the maximum absorption of distributed photovoltaic power. The method comprehensively considers the economy, technology, environmental protection of the system operation and the complexity of the low-voltage distribution network, and integrates the interests of multiple subjects such as distribution companies, distributed photovoltaic power generators and users into the control strategy. It also considers factors such as the load characteristics of the low-voltage distribution network and the randomness of the access location of distributed energy, and realizes more accurate, efficient and coordinated optimal operation control through flexible interconnection switches.

[0072] The present invention first gives the typical wiring mode of the flexible interconnection switch of the low-voltage distribution line and determines the characteristic parameters of the flexible interconnection switch. Then, from the perspective of the distribution network, various flexible interconnection switches are centrally regulated, and a multi-objective optimization control model is established from the perspectives of "peak shaving and valley filling", load balancing, and distributed photovoltaic utilization. In view of the nonlinearity of the established multi-objectives, a distributed estimation algorithm is used to solve them. For the multi-solution of the obtained optimization scheduling strategy, a multi-objective decision analysis is performed based on the Pareto optimal solution set.

[0073] like Figure 1 As shown, embodiment 1 of the present invention provides a method for optimizing the configuration of flexible interconnection switches in low-voltage distribution stations to maximize the absorption of distributed photovoltaic power generation, including the following steps:

[0074] Step 1: determine the characteristic parameters of the flexible interconnection switch, including rated voltage and rated current, switching frequency, inductance value, modulation ratio, response time and communication control interface, so as to construct a characteristic vector suitable for optimizing and describing different flexible interconnection switches.

[0075] Among them, the rated voltage and rated current refer to the maximum voltage that the flexible interconnection switch can withstand during normal operation and the maximum current value allowed to pass under long-term continuous operation. This parameter is designed according to specific application scenarios and requirements to ensure the stability and safety of the switch.

[0076] Switching frequency refers to the switching speed or operating frequency of power electronic devices in flexible interconnect switches. High-frequency switching enables faster response and more precise control, but also leads to higher switching losses.

[0077] Inductance is one of the important parameters in flexible interconnect switches, which affects the rate of change of current and the stability of the system. In flexible interconnect switches, inductance is usually used for filtering and current limiting to improve power quality and protect equipment from current shocks.

[0078] In a flexible interconnection switch, the modulation ratio refers to the ratio of the amplitude of the output voltage (or current) to the DC side voltage (or current). The size of the modulation ratio directly affects the power transmission capacity and efficiency of the switch.

[0079] Response time refers to the time it takes for a flexible interconnection switch to start executing an action after receiving a control signal. A shorter response time can respond faster to changes and faults in the power grid, improving the stability and reliability of the power grid.

[0080] Communication control interface: Flexible interconnected switches usually need to communicate and control with power grid dispatching systems or other intelligent devices. Having a complete communication and control interface is the key to achieving intelligent management and remote monitoring.

[0081] It can be understood that, in view of the diversity of flexible interconnection switches, the present invention establishes a universal characteristic vector to describe the flexible interconnection switch and gives its value characteristics, which facilitates the unified optimization control of multiple types of flexible switches in complex distribution networks.

[0082] Step 2: Based on the distributed energy consumption rate and demand response instructions, the characteristic vector of the flexible interconnection switch in step 1 is used as the optimization variable to construct a multi-objective optimization control model of the flexible interconnection switch in the low-voltage distribution station area.

[0083] Flexible interconnected switches can not only achieve load power balance through load-specific supply, improve the absorption of distributed power sources, and adjust demand response, but also achieve peak shaving and valley filling through energy storage.

[0084] In a preferred but non-limiting embodiment of the present invention, based on the flexible interconnection switch and taking into account the network operation constraints of the distribution network, a model is established with a low-voltage distribution station area double-layer multi-subject distribution network with the minimum total annualized cost, the minimum distribution network loss, the minimum voltage imbalance and the maximum wind and solar power consumption rate. Step 2 specifically includes:

[0085] Step 2.1: Construct the upper-level target model. Specifically, from the perspective of independent distributed power producers, analyze the wind and solar power consumption rate and use it as the optimization target, expressed as the following formula:

[0086]

[0087] Where:

[0088] D is the distributed energy consumption rate;

[0089] T is the total number of time periods;

[0090] s represents each scene, and N represents the set of all scenes;

[0091] P1 and W1 are the wind power and photovoltaic power absorbed at time t in the scenario set, respectively;

[0092] P2 and W2 are the wind and photovoltaic outputs at time t in the scenario set, respectively;

[0093] ω is the weight coefficient of each scene set;

[0094] k is the number of scenes included in the scene set;

[0095] K is the total number of scenes contained in all scene sets.

[0096] Step 2.2, construct the lower-level target model. Specifically, establish the target function from the perspective of the distribution network, analyze the total annualized cost, distribution network loss, and voltage imbalance, and use them as optimization targets, including:

[0097] The objective function of minimizing the total annualized cost is expressed as follows:

[0098] minC=C SOP +C P +C OM (0-3)

[0099]

[0100] Where: C is the total annual cost of the distribution network; C SOP is the annual investment cost of the flexible interconnection switch; C P is the annual electricity purchase cost of the distribution network; C DG is the annual electricity purchase cost of the distribution network from the distributed power generator; r is the discount rate; ij is the branch between node i and node j, n SOPis the branch set where the flexible interconnection switch is located, ij∈n SOP Indicates that branch ij is the branch where the flexible interconnection switch is located; c SOP S is the unit apparent power investment cost of the flexible interconnection switch; ij,SOP is the capacity of the flexible interconnection switch on branch ij; c P is the electricity purchase cost per kWh of the distribution network; c DG is the unit kilowatt-hour electricity purchase cost of distributed power generators; P is the amount of electricity purchased by the power grid from distributed power generators; L is the percentage of line loss; K T is the total number of scenes; P l is the active power of the substation in scenario l; T l is the number of hours of scene l.

[0101] The objective function of minimizing the distribution network loss is expressed as follows:

[0102] minF1=ω1(f line +f SOP ) (0-7)

[0103]

[0104] Where: minF1 is the minimum loss of the distribution network; f line and f SOP are the average line loss and the average loss of the flexible interconnection switch respectively; ω1 is the weight coefficient of the distribution network loss; T an =8760h is the number of hours in a year; ij∈n l Indicates that branch ij belongs to branch set n l ; φ=A,B,C represents three different phase values ​​of A, B, C; r ij,φ represents the resistance value of the φ phase of branch ij; z ij，φ is the impedance value of the φ phase of branch ij; and are the real and imaginary parts of the voltage of phase φ at node i in scenario l, respectively; and are the active power losses of phase φ of voltage source converter (VSC) i and voltage source converter j, respectively.

[0105] The objective function of minimizing voltage unbalance is expressed as follows:

[0106] minF2=ω2f U (0-10)

[0107]

[0108] Where: minF2 is the minimum voltage unbalance; f Uis the voltage unbalance degree; ω2 is the weight coefficient; T an =8760h is the number of hours in a year; and are the real and imaginary parts of the voltage negative sequence unbalance at node i in scenario l, respectively; i∈Ω b Indicates that scene i belongs to all scenes Ω b .

[0109] It can be understood that the present invention incorporates the interests of multiple entities such as distribution companies, distributed photovoltaic power generators and users into the control strategy by setting multiple optimization objectives.

[0110] Step 2.3, establishing the constraint conditions of the multi-objective optimization control model. In a further preferred but non-limiting embodiment of the present invention, step 2.3 specifically includes:

[0111] Step 2.3.1, construct the DG (distributed generation) capacity and power constraints, expressed as the following formula,

[0112]

[0113] Where: S i,DG is the maximum allowed DG capacity installed at node i; s DG and i,DG are the rated capacity of a single DG and the number of DGs installed at node i; a 1,i ,a 2,i ,…,a mi,i is a binary variable describing the number of DGs; m i is the binary variable dimension; is the maximum active power of the φ phase of DGi in scenario l; and are the φ-phase active power and reactive power injection of DGi in scenario l, respectively; and are the lower and upper limits of the reactive power of the φ phase of DGi respectively; ξ min and max are the coefficients of the lower and upper limits of the DG reactive power coefficient respectively; S i,φ,DG is the capacity of the DGi converter phase φ; S i,3φ,DG is the capacity of the 3-phase DGi converter.

[0114] Step 2.3.2, construct the SOP capacity and power constraints, expressed as the following formula,

[0115]

[0116] Where: is the maximum allowable capacity of the flexible interconnection switch installed at the node ij; sSOP and ij,SOP are the rated capacity of a single SOP and the number of SOPs installed in branch ij respectively; b 1,ij ,b 2,ij ,…,b nij,ij is a binary variable describing the number of flexible interconnection switches; n ij is the binary variable dimension; and are the active injected powers of phase φ of voltage source converter i and voltage source converter j in scenario l respectively; and are the losses of the φ phase of the voltage source converter i and the voltage source converter j in scenario l; A i,SOP and A j,SOP are the active power loss coefficients of voltage source converter i and voltage source converter j respectively;

[0117] and are the reactive injection powers of phase φ of voltage source converter i and voltage source converter j in scenario l respectively; and are the lower and upper limits of the reactive injection power of phase φ of voltage source converter i and voltage source converter j respectively; ξ min and max are the coefficients of the lower and upper limits of the reactive power of the flexible interconnection switch respectively; S ij,φ,SOP is the capacity of the two voltage source converters of the flexible interconnection switch φ phase; S ij,3φ,SOP is the capacity of the three phases of the two voltage source converters of the flexible interconnection switch.

[0118] Step 2.3.3, construct the node power balance constraint, expressed as the following formula:

[0119]

[0120] Where: and are the active and reactive injected powers of phase φ of node i in scenario l, respectively;

[0121] and is the active and reactive power consumption of phase φ of load i in scenario l.

[0122] Step 2.3.4, construct the three-phase power flow constraint, expressed as the following formula:

[0123]

[0124] Where: and are the voltage and injection current of phase φ of node i in scenario l respectively; Yi,φ，j,φ′ are the elements in the three-phase node admittance matrix.

[0125] Step 2.3.5, network security constraints are expressed as follows:

[0126]

[0127] Where: U min and U max are the lower and upper limits of the node voltage, which are 0.95 pu and 1.05 pu respectively; is the current of the φ phase of branch ij at time t; is the current limit of phase φ of branch ij.

[0128] It can be understood that the multi-objective optimization control model of the present invention comprehensively considers the economy, technology, environmental protection and complexity of the operation of the low-voltage distribution station area, and can control the flexible interconnection switch more accurately and efficiently.

[0129] Step 3: Select an optimization algorithm to solve the multi-objective optimization control model of step 2 to obtain the optimal flexible interconnection switch configuration solution.

[0130] In a preferred but non-limiting embodiment of the present invention, step 3 specifically comprises:

[0131] Step 3.1, for the optimization objectives and nonlinear constraints of the multi-objective optimization control model established in step 2, a distributed estimation algorithm is used to solve each optimization objective separately.

[0132] Further preferably, a fireworks algorithm is used for solving. The fireworks algorithm (FWA) is an optimization algorithm based on swarm intelligence. In the algorithm, the explosion position of fireworks is regarded as a potential solution, and the sparks generated by the explosion of fireworks are used to search in the solution space. By adjusting the number of fireworks and the explosion range, the fireworks algorithm continuously performs iterative search to find the optimal solution.

[0133] In the fireworks algorithm, by comparing the quality of sparks, the best spark is selected as the starting point of a new round of iteration, and the poor sparks are discarded, thereby optimizing the solution. Since the fireworks algorithm fully utilizes the advantages of swarm intelligence and adaptively adjusts the breadth and depth of the search according to the complexity of the problem, it has a good global search capability and can effectively avoid falling into the local optimum. The algorithm flow chart is as follows Figure 2 shown.

[0134] The basic fireworks algorithm mainly includes four parts: fireworks explosion, fireworks mutation, mapping rules and selection strategy. The fireworks algorithm is used to solve the multi-objective optimization control model of hierarchical optimization of the optimal control of the flexible interconnection switch in the low-voltage distribution station area of ​​the present invention, and the distributed estimation idea is introduced into the fireworks algorithm to generate a new population. The elite retention strategy is used to control the generation mode of each spark. Specifically, the following steps are included:

[0135] Step A.1, Initialization: Randomly generate a certain number of SOP configurations as initial fireworks (i.e. individuals in the population). Each configuration defines the on / off state of the SOP and the possible power transmission direction.

[0136] Step A.2, design fitness function: calculate the proportion of renewable energy power generation actually absorbed by the grid under the current SOP configuration, as well as the grid operation cost and possible SOP operation cost. N fireworks explode, calculate the explosion radius and the number of sparks S produced by each firework.

[0137] Step A.3, Fireworks explosion and spark generation: For each firework (SOP configuration), calculate the explosion radius and number of sparks according to its fitness value. The higher the fitness, the smaller the explosion radius may be and the more sparks may be. Based on the current SOP configuration, sparks (new SOP configuration) are generated through random mutation (such as changing the on / off state of the SOP or adjusting the power transmission direction).

[0138] Step A.4, selection and update: Calculate the fitness values ​​of all sparks (new configurations). Through the elite selection strategy, retain a certain number of optimal sparks (including the optimal solution among all current fireworks) as part of the next generation of fireworks. The remaining positions are probabilistically selected according to the fitness value of the spark, and sparks with high fitness have a higher probability of being selected.

[0139] Step A.5, iteration and optimization: Repeat the process of fireworks explosion, spark generation, selection and update until the maximum number of iterations is reached. In each iteration, record the currently found optimal SOP configuration and its corresponding fitness value.

[0140] It is understandable that the convergence ability and solution accuracy of the multi-objective optimization algorithm can be improved through the distributed estimation population generation method and the elite retention strategy.

[0141] Step 3.2, perform constraint checks on the solutions obtained for each optimization objective and generate a set of feasible solutions.

[0142] like Figure 3As shown in the figure, taking the IEEE33 node as an example, due to the geographical constraints and the maximum number of ports of the existing flexible interconnection switch is three, the flexible interconnection switch connection mode has limited candidate solutions at each moment, and the diversity of the entire distribution network topology is mainly determined by the traditional switch. Due to the complex network structure of the distribution network containing flexible interconnection switches and the influence of linear and nonlinear constraints during operation, there are a large number of infeasible solutions. Therefore, in order to meet the constraints of radial operation, the candidate solution set generation strategy is adopted before the multi-objective function optimization solution is solved.

[0143] In a further preferred but non-limiting embodiment of the present invention, the step of generating a feasible solution set comprises:

[0144] Step 3.2.1, based on determining the number of rings in the distribution network, the switch combination vector of the rings.

[0145] Step 3.2.2, generate a topology matrix based on the ring information, and determine whether it satisfies the radial operation constraint according to the topology matrix.

[0146] Step 3.2.3, perform constraint checks based on power balance and operating conditions, and add those that meet the requirements to the candidate solution set. Repeat 3.2.2 and 3.2.3 until all candidate solutions are generated.

[0147] Step 3.3, perform multi-objective optimization in the feasible solution set to generate the final flexible interconnect switch configuration solution.

[0148] Problems in real life are complex and changeable, and there are often multiple objectives that need to be optimized. The solution process is constrained by multiple linear and nonlinear factors. Compared with single-objective optimization, which sacrifices the performance of other objectives, multi-objective optimization is more realistic.

[0149] When the multi-objective problem to be optimized is a minimization problem, it is assumed that there are r objective functions, namely f1(x), f2(x), …f r (x), there are q inequality constraints, namely G i (x)≤0,i=1,2,…q, there are s equality constraints, which are H j (x)≤0,j=1,2,…s. The general form of its mathematical model is as follows:

[0150] minf(x)=(f1(x),f2(x),·…·,f r (x))

[0151]

[0152] Where x and X are the decision variables and decision space respectively; f and F are the target vector and target space respectively.

[0153] It is understandable that there is generally no unique optimal solution for multi-objective optimization problems, and the final optimal solution is often obtained by setting evaluation functions or indicators. For the optimization of multi-objective problems, in most cases, the objectives are conflicting with each other. When optimizing one objective function, the performance of other functions may be damaged. Therefore, for multi-objective problems, the final result is often not a certain optimal value, but a set of Pareto optimal solutions.

[0154] The Pareto optimal solution set is a set of solutions in the solution space that are better than all other solutions of the objective function. The Pareto optimal frontier is the Pareto frontier surface obtained by mapping the optimal solution set to the solution space. For example, Figure 4 Shown are the Pareto optimal solution and optimal frontier for a bi-objective optimization problem.

[0155] In a further preferred embodiment of the present invention, the steps of performing multi-objective optimization solution based on the Pareto optimal solution set are as follows:

[0156] Step B.1, evaluate the Pareto optimal solution set: A set of Pareto optimal solutions is obtained through the optimization algorithm, which form the Pareto frontier in the objective space. Each solution represents a flexible interconnection switch configuration scheme that achieves the best balance between total annualized cost, distribution network loss and voltage imbalance, that is, no solution can improve all objectives without deteriorating at least one other objective.

[0157] Step B.2, Decision Criteria Development: Assign a weight to each objective based on the preferences of the distribution network operator or policy maker. The weights can reflect the importance or priority of different objectives. Directly consider the personal or organizational preferences of the decision maker, such as whether cost savings or system performance are more important. Consider the physical and technical constraints of the distribution network, such as voltage limits, current limits, equipment capacity, etc.

[0158] Step B.3, quantitative evaluation: For each solution on the Pareto front, use the decision criterion for quantitative evaluation. If target weights are used, weighted sums or weighted distances can be calculated to evaluate the overall performance of each solution.

[0159] Step B.4, select the optimal solution: Based on the results of the quantitative evaluation, select the solution with the highest score or the one that best meets the preference as the optimal flexible interconnection switch configuration solution. If there are multiple solutions with similar scores and it is difficult to choose, their robustness (i.e., sensitivity to changes in different parameters or conditions) or implementation difficulty can be considered.

[0160] It can be understood that under the existing economic and technical conditions, the present invention optimizes the control of flexible interconnected switches in combination with the actual operation of the distribution network, which can not only balance the load on the feeder, improve the overall power flow distribution of the system, and reduce losses, but also provide voltage reactive support and achieve rapid recovery of non-fault areas in fault mode, thereby improving the economy and reliability of the distribution system operation, improving the feeder voltage level and the ability to absorb distributed power sources, which is of great significance to the economy, environmental protection, and safety and reliability of the operation of high-penetration renewable energy distribution stations.

[0161] Embodiment 2 of the present invention provides a low-voltage distribution station area flexible interconnection switch optimization configuration system, which runs the low-voltage distribution station area flexible interconnection switch optimization configuration method described in embodiment 1, including:

[0162] A feature vector module, used to determine the feature parameters of the flexible interconnection switch and construct a feature vector of the flexible interconnection switch;

[0163] Model building module, used to build a multi-objective optimization configuration model for flexible interconnection switches in low-voltage distribution substations;

[0164] Model solving module, used to solve multi-objective optimization configuration models;

[0165] The result output module is used to generate and output the optimal flexible interconnect switch configuration solution.

[0166] Compared with the prior art, the beneficial effects of the present invention include at least:

[0167] (1) The present invention sets an optimal control strategy for the flexible interconnection switch in the low-voltage distribution station area, fully considers the characteristics of the low-voltage distribution network and the high cost of the flexible interconnection switch, and improves the adaptability of the flexible interconnection switch control to the low-voltage distribution network.

[0168] (2) The present invention optimizes energy configuration, improves the operational stability of the power system and increases power utilization efficiency, thereby achieving accurate, efficient and coordinated optimal operational control.

[0169] (3) The present invention comprehensively considers the interests of multiple entities such as distribution companies, distributed photovoltaic power generators and users.

[0170] (4) Under the existing economic and technical conditions, the present invention optimizes the control of the flexible interconnection switch in combination with the actual operation of the distribution network, thereby improving the economy and reliability of the distribution system operation, and improving the feeder voltage level and the ability to absorb distributed power sources.

[0171] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the configuration of flexible interconnection switches in low-voltage distribution substations, characterized in that: The following steps are involved: Step 1, determining characteristic parameters of the flexible interconnection switch, including rated voltage and rated current, switching frequency, inductance value, modulation ratio, response time and communication control interface, so as to construct a characteristic vector of the flexible interconnection switch; Step 2: Based on the distributed energy consumption rate and demand response instructions, the characteristic vector of the flexible interconnection switch is used as the optimization variable to build a multi-objective optimization configuration model of the flexible interconnection switch in the low-voltage distribution station area; Step 3: Use the optimization algorithm to solve the multi-objective optimization configuration model and obtain the optimal configuration solution of the flexible interconnection switch.

2. The method for optimizing the configuration of flexible interconnection switches in low-voltage distribution station areas according to claim 1, characterized in that: The multi-objective optimization configuration model includes an upper-level objective model and a lower-level objective model. The upper-level objective model takes the optimal distributed energy consumption rate as the optimization objective, and the lower-level objective model takes the minimum total annualized cost, distribution network loss and voltage imbalance as the optimization objectives.

3. The method for optimizing the configuration of flexible interconnection switches in low-voltage distribution station areas according to claim 2, characterized in that: The distributed energy consumption rate is expressed by the following formula: Where: D is the distributed energy consumption rate; T is the total number of time periods; s represents each scene, and N represents the set of all scenes; P1 and W1 are the wind power and photovoltaic power absorbed at time t in the scenario set, respectively; P2 and W2 are the wind and photovoltaic outputs at time t in the scenario set, respectively; ω is the weight coefficient of each scene set; k is the number of scenes included in the scene set; K is the total number of scenes contained in all scene sets.

4. The method for optimizing the configuration of flexible interconnection switches in low-voltage distribution station areas according to claim 2, characterized in that: The objective function of minimizing the total annualized cost is expressed as follows: minC=C SOP +C P +C DG Where: C is the total annual cost of the distribution network; C SOP is the annual investment cost of the flexible interconnection switch; C P is the annual electricity purchase cost of the distribution network; C DG is the annual electricity purchase cost of the distribution network from the distributed power generator; r is the discount rate; ij is the branch between node i and node j, n SOP is the branch set where the flexible interconnection switch is located, ij∈n SOP Indicates that branch ij is the branch where the flexible interconnection switch is located; c SOP S is the unit apparent power investment cost of the flexible interconnection switch; ij,SOP is the capacity of the flexible interconnection switch on branch ij; c P is the electricity purchase cost per kWh of the distribution network; c DG is the unit kilowatt-hour electricity purchase cost of distributed power generators; P is the amount of electricity purchased by the power grid from distributed power generators; L is the percentage of line loss; K T is the total number of scenes; P l is the active power of the substation in scenario l; T l is the number of hours of scene l; The objective function of minimizing the distribution network loss is expressed as follows: minF1=ω1(f line +f SOP ) Where: minF1 is the minimum loss of the distribution network; f line and f SOP are the average line loss and the average flexible interconnection switch loss respectively; ω1 is the weight coefficient of distribution network loss; T an =8760h is the number of hours in a year; ij∈n l Indicates that branch ij belongs to branch set n l ; φ=A,B,C represents three different phase values ​​of A, B, C; r ij,φ represents the resistance value of the φ phase of branch ij; z ij，φ is the impedance value of the φ phase of branch ij; and are the real and imaginary parts of the voltage of phase φ at node i in scenario l, respectively; and are the active power losses of phase φ of voltage source converter i and voltage source converter j respectively; The objective function of minimizing voltage unbalance is expressed as follows: minF2=ω2f U Where: minF2 is the minimum voltage unbalance; f U is the voltage imbalance; ω2 is the weight coefficient; T an =8760h is the number of hours in a year; and are the real and imaginary parts of the voltage negative sequence unbalance at node i in scenario l, respectively; i∈Ω b Indicates that scene i belongs to all scenes Ω b .

5. The method for optimizing the configuration of flexible interconnection switches in low-voltage distribution station areas according to claim 1, characterized in that: The constraints of the multi-objective optimization control model include distributed generation capacity and power constraints, flexible interconnection switch capacity and power constraints, node power balance constraints, three-phase power flow constraints and network security constraints.

6. The method for optimizing the configuration of flexible interconnection switches in low-voltage distribution station areas according to claim 5, characterized in that: The flexible interconnect switch capacity and power constraints are expressed as follows: Where: is the maximum allowable capacity of the flexible interconnection switch installed at the node ij; s SOP and ij,SOP are the rated capacity of a single SOP and the number of SOPs installed in branch ij respectively; is a binary variable describing the number of flexible interconnection switches; n ij is the binary variable dimension; and are the active injected powers of phase φ of voltage source converter i and voltage source converter j in scenario l respectively; and are the losses of the φ phase of the voltage source converter i and the voltage source converter j in scenario l; A i,SOP and A j,SOP are the active power loss coefficients of voltage source converter i and voltage source converter j respectively; and are the reactive injection powers of phase φ of voltage source converter i and voltage source converter j in scenario l respectively; and are the lower and upper limits of the reactive injection power of phase φ of voltage source converter i and voltage source converter j respectively; ξ min and max are the coefficients of the lower and upper limits of the reactive power of the flexible interconnection switch, respectively; S ij,φ,SOP is the capacity of the two voltage source converters φ phases of the flexible interconnection switch; S ij,3φ,SOP is the capacity of the three phases of the two voltage source converters of the flexible interconnection switch.

7. The method for optimizing the configuration of flexible interconnection switches in low-voltage distribution station areas according to claim 1, characterized in that: Step 3 includes: Step 3.1, using a distributed estimation algorithm to solve each optimization objective in the multi-objective optimization configuration model established in step 2; Step 3.2, perform constraint checks on the solutions obtained for each optimization objective and generate a set of feasible solutions; Step 3.3, perform multi-objective optimization in the feasible solution set to generate the final flexible interconnect switch configuration solution.

8. The method for optimizing the configuration of flexible interconnection switches in low-voltage distribution station areas according to claim 7, characterized in that: Select the Fireworks algorithm as the distributed estimation algorithm in step 3.

1. The solution process includes: Step A.1, randomly generate a certain number of flexible interconnect switch configurations as initial fireworks, each configuration defines the on / off state of the flexible interconnect switch and the power transmission direction; Step A.2, calculating the proportion of renewable energy power generation actually absorbed by the grid under the current flexible interconnection switch configuration, as well as the grid operation cost and the flexible interconnection switch operation cost; Step A.3, for each firework, calculate the explosion radius and number of sparks according to its fitness value; based on the current flexible interconnect switch configuration, generate new sparks through random mutation; Step A.4, calculate the fitness values ​​of all sparks, retain a certain number of the best sparks as part of the next generation of fireworks, and select the remaining positions probabilistically based on the fitness values ​​of the sparks. Sparks with higher fitness have a higher probability of being selected. Step A.5, repeat steps A.3-A.4 until the maximum number of iterations is reached. In each iteration, the currently found optimal flexible interconnect switch configuration and its corresponding fitness value are recorded.

9. The method for optimizing the configuration of flexible interconnection switches in low-voltage distribution station areas according to claim 7, characterized in that: The multi-objective optimization solution in step 3.3 is performed based on the Pareto optimal solution set, including the following steps: Step B.1, obtain a set of Pareto optimal solutions through the optimization algorithm, each solution represents a flexible interconnection switch configuration scheme, and form a Pareto frontier in the target space; Step B.2, assign a weight to each objective and develop decision criteria based on the preferences of distribution network operators or policymakers; Step B.3, for each solution on the Pareto front, use the decision criterion of step B.2 for quantitative evaluation; Step B.4, based on the results of the quantitative evaluation, select the solution with the highest score or the one that best meets the preference as the optimal flexible interconnect switch configuration solution.

10. A low-voltage distribution substation flexible interconnection switch optimization configuration system, which runs the low-voltage distribution substation flexible interconnection switch optimization configuration method according to any one of claims 1 to 9, characterized in that: include: A feature vector module, used to determine the feature parameters of the flexible interconnection switch and construct a feature vector of the flexible interconnection switch; Model building module, used to build a multi-objective optimization configuration model for flexible interconnection switches in low-voltage distribution substations; Model solving module, used to solve multi-objective optimization configuration models; The result output module is used to generate and output the optimal flexible interconnect switch configuration solution.

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