Power distribution network voltage collaborative optimization method considering load of oxygen pumping machine

Abstract

The invention discloses a power distribution network voltage collaborative optimization method considering the load of an oxygen pumping machine, and the method comprises the steps: 1, building a reference model of the active power of the load of the oxygen pumping machine, and building a dynamic correction model of the active power of the load of the oxygen pumping machine by considering the influence of water temperature and water depth; 2, establishing a dissolved oxygen kinetic model, a relationship between the mechanical oxygenation rate of the load of the oxygenator and the standard oxygen transfer rate, and a dissolved oxygen concentration safety constraint; 3, establishing a power flow model of the power distribution network; 4, establishing an oxygen pumping machine control-water quality dynamic-power grid voltage cooperative control model and a target function; and 5, solving the cooperative control model to obtain node voltage and photovoltaic output which enable the dissolved oxygen concentration of the aquaculture pond to be maintained in a safe range. According to the method, the collaborative model containing the load of the oxygen pumping machine can be constructed, so that accurate control and optimal scheduling of the power distribution network-aquaculture pond system are realized.

Description

Technical Field

[0001] This invention relates to a method for coordinated optimization of distribution network voltage considering oxygenator load, belonging to the field of power system operation and control technology. Background Technology

[0002] In recent years, with the rapid development of renewable energy utilization, the scale and electrification level of aquaculture have increased rapidly. In aquaculture areas, dissolved oxygen is considered an important water quality parameter for crab farming. The dissolved oxygen content affects both the degree of water pollution and the metabolic activities of crabs. The increasingly frequent occurrence of oxygen deficiency in water bodies is harmful to aquatic animals, and oxygenators are key electrical equipment to ensure aquaculture production, with their load requirements being concentrated and seasonal. At the same time, in order to reduce electricity costs and achieve clean energy substitution, the penetration rate of distributed photovoltaic power generation in these areas is also increasing.

[0003] However, this "photovoltaic + aquaculture" model brings new challenges to the operation of the power distribution network. On the one hand, photovoltaic output is intermittent and fluctuates. During midday when sunlight is abundant, it may supply a large amount of power to the grid, causing line voltage to exceed limits and power backflow. At night or on cloudy or rainy days, photovoltaic output drops sharply or becomes zero, which may lead to low voltage at the end of the line, making it difficult to meet load demands. On the other hand, aerator loads, as typical constant-power motor loads, usually require centralized, high-capacity start-up and shutdown operations to ensure that the dissolved oxygen concentration in the water remains within the safe range for aquaculture. This process may cause drastic fluctuations in grid voltage, posing a serious threat to the power distribution network. Traditional power distribution network voltage regulation response speed is slow and cannot cope with the rapid voltage changes caused by distributed photovoltaic and aerator loads. In addition, simply abandoning photovoltaic power when the voltage is too high would waste clean energy and would not conform to the purpose of energy conservation and emission reduction. Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies by proposing a power distribution network voltage co-optimization method that considers the load of aerators. The aim is to construct a refined load-photovoltaic co-optimization model that integrates the dynamic operating characteristics of aerators, thereby more accurately describing and controlling the dynamic balance of dissolved oxygen in ponds. This will improve the economy and stability of the power distribution network while ensuring aquaculture safety, providing reliable and efficient technical support for power distribution networks in aquaculture areas with high photovoltaic grid integration.

[0005] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: The present invention provides a method for coordinated optimization of distribution network voltage considering oxygenator load, characterized by the following steps: Step 1: Based on the standard oxygenation efficiency and standard oxygen transfer rate of the aerator load, establish a benchmark model of the active power of the aerator load, and consider the influence of water temperature and water depth to construct a dynamic correction model of the active power of the aerator load. Step 2: Establish a dissolved oxygen kinetic model based on the baseline model; Establish the relationship between the mechanical oxygenation rate of the oxygenator load and the standard oxygen transfer rate; Establish safety constraints for dissolved oxygen concentration; Step 3: Establish a power flow model for the distribution network; Step 4: Based on the models in Steps 1-3, construct a collaborative control model for aerator control, water quality dynamics, and grid voltage. Step 5: Solve the collaborative control model to obtain the node voltage and photovoltaic output that maintain the dissolved oxygen concentration in the aquaculture pond under safe constraints, and send them to the control system of the power distribution network and the controller of the aerator to achieve closed-loop control of the dissolved oxygen concentration.

[0006] The present invention provides a method for coordinated optimization of distribution network voltage based on oxygenator load, wherein step 1 includes the following steps: Step 1.1: Construct a benchmark model of the active power of the oxygenator load using equation (1); (1) In equation (1), express t Time of the first The active power of the oxygenator installed at each node. express t Time of the first Standard oxygen transfer rate of the oxygen generator load installed at each node; express t Time of the first The standard oxygenation efficiency of the oxygenators installed at each node. Step 1.2: Construct a dynamic correction model for the active power of the aerator load, taking into account the effects of water temperature and water depth, using equation (2): (2) In equation (2), express After constantly considering water temperature and depth, the first The active power of the oxygenator installed at each node. express t Time of the first The water temperature of the pond corresponding to the load of the aerators installed at each node. For the first The water depth of the pond corresponding to the load of the aerators installed at each node. This is a function to correct for the effects of water temperature and water depth.

[0007] Furthermore, step 2 includes the following steps: Step 2.1: Construct a dissolved oxygen kinetic model using equation (3): (3) In equation (3), express t Time of the first The dissolved oxygen concentration in the pond corresponding to the load of the aerator installed at each node; express t Time of the first The mechanical oxygenation rate of the oxygenator installed at each node. , and They represent t Time of the first The load of the aerators installed at each node corresponds to the reoxygenation rate of the pond surface, the mineralization of organic matter, and the respiration of farmed crabs. Step 2.2: Using equations (4) and (5), construct the relationship between the mechanical oxygenation rate of the oxygenator load and the standard oxygen transfer rate: (4) (5) In equations (4) and (5), express t Time of the first The dissolved oxygen saturation value in the pond corresponding to the load of the aerator installed at each node; This indicates the standard test water temperature of the pond; This indicates the saturation value of dissolved oxygen that a body of water can reach at a standard test water temperature; Indicates the oxygenation temperature coefficient; Indicates the total duration; Indicates the first The volume of the pond corresponding to the load of the aerator installed at each node; It is used to correct the first The environmental correction constant for the error between the pond and the test water body corresponding to the load of the aerator installed at each node; yes t Time of the first The working status of the oxygen generators installed at each node under load; , , , This represents four fitting coefficients; Step 2.3: Construct a safety constraint on dissolved oxygen concentration in water using equation (6): (6) In equation (6), and They represent t Time of the first The upper and lower limits of dissolved oxygen concentration in the pond corresponding to the load of each aerator.

[0008] Furthermore, step 3 includes the following steps: Step 3.1: Construct power flow constraints using equations (7) and (8): (7) (8) In equations (7) and (8), Represents the set of serial numbers for distribution network lines; and They represent t Time of the first j The net active power load and net reactive power load of each node. and They represent t Time flows through the first i The node and the first j The lines between nodes ij Active power and reactive power; and They represent t Time flows through the first j The node and the first k The lines between nodes jk Active power and reactive power; and They represent the first i The node and the first j The lines between nodes ij Resistance and reactance; and They represent t Time of the first i The node and the first j The voltage of each node; Step 3.2: Construct branch power capacity constraints using equation (9): (9) In equation (9), The 2-norm of a vector; Step 3.3: Construct node voltage and branch current constraints using equations (10) and (11): (10) (11) In equations (10) and (11), and They represent t Time of the first i The minimum and maximum values ​​of the node voltages; and They represent t Time flows through the first i The node and the first j The lines between nodes ij The minimum and maximum values ​​of the current flowing through it; Step 3.4: Construct the power constraints for distributed photovoltaic power in the distribution network using equations (12)-(15): (12) (13) (14) (15) In equations (12)-(15), and They represent t Time of the first The active and reactive power transmitted from distributed photovoltaic power at each node to the distribution network; express t Time of the first The maximum active power of distributed photovoltaic power at each node; express t Time of the first The reduction rate of active power of distributed photovoltaic power at each node; They represent t Time of the first The maximum reactive power of distributed photovoltaic power at each node; Indicates the first The capacity of distributed photovoltaic power at each node; Step 3.5: Linearize equation (15) using equations (16) and (17): (16) (17) In equation (17), The angle variable represents the polygonal linear approximation of the capacity constraints of distributed photovoltaic power. Indicates the index of any side of the polygon; NThis indicates the number of sides of the polygon.

[0009] Furthermore, step 4 includes the following steps: Step 4.1: Construct the objective function of the collaborative control model with the optimization objectives of minimizing total distribution network loss, total voltage deviation, and total photovoltaic reduction using equations (18)-(21). : (18) (19) (20) (twenty one) In equations (18)-(21), Indicates the total network loss of the distribution network. Indicates the total voltage deviation of the distribution network. This represents the total photovoltaic reduction in the distribution network. , , These represent the weighting coefficients for total power loss, total voltage deviation, and total photovoltaic active power reduction in the distribution network, respectively. This represents the set of node numbers in the distribution network. This represents the set of serial numbers of distribution network nodes equipped with distributed photovoltaic systems. express t Time of the first i Reference values ​​for the voltage at each node; The present invention provides an electronic device, including a memory and a processor, characterized in that the memory is used to store a program that supports the processor in executing the power distribution network voltage collaborative optimization method, and the processor is configured to execute the program stored in the memory.

[0010] The present invention discloses a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, performs the steps of the power distribution network voltage collaborative optimization method.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention accurately quantifies the impact of special loads and innovatively models the aerator load as a rigid constraint strongly correlated with the dissolved oxygen concentration in the pond. It also introduces this load as a key factor that must be met into the optimization model. This makes the optimization results more in line with the actual operation requirements of the power distribution network in the aquaculture area. It effectively optimizes and controls the key loads from a mechanistic perspective, thus ensuring the safety of aquaculture.

[0012] 2. This invention aims to minimize the weighted average of power loss, voltage deviation, and curtailment of solar power in the power distribution network, achieving a balance between economy, safety, and environmental protection. Under the premise of ensuring dissolved oxygen levels in the pond remain within a safe range, aerators operate reliably, and node voltages do not exceed limits, this method effectively reduces the total power loss of the power distribution network, smooths voltage fluctuations, and maximizes the utilization of photovoltaic power generation, achieving comprehensive operational benefits and improving the accuracy and reliability of voltage dispatch optimization. Attached Figure Description

[0013] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0014] In this embodiment, a distribution network voltage collaborative optimization method based on aerator load is a voltage optimization method that considers the operating characteristics of aerator load and the regulation capability of distributed photovoltaic power. Under the premise of ensuring reliable operation of the aquaculture load, it can effectively solve the problem of insufficient adaptability of voltage optimization scheduling in the "photovoltaic + aquaculture" model, thereby achieving stable operation of the distribution network voltage and efficient consumption of photovoltaic energy. Specifically, as... Figure 1 As shown, the method includes the following steps: Step 1: Based on the standard oxygenation efficiency (SAE) and standard oxygen transfer rate (SOTR) of the aerator load, establish a baseline model of the active power of the aerator load, and consider the influence of water temperature and water depth to construct a dynamic correction model of the active power of the aerator load. Step 1.1: In a diffusion aeration system, oxygen is generally supplied to the water body using an aerator load. Therefore, we can use equation (1) to construct a benchmark model of the active power of the aerator load: (1) In equation (1), express t Time of the first The active power of the oxygenator installed at each node. express t Time of the first Standard oxygen transfer rate of the oxygen generator load installed at each node; express t Time of the first The standard oxygenation efficiency of the oxygenators installed at each node. Step 1.2: Construct a dynamic correction model for the active power of the aerator load, taking into account the effects of water temperature and water depth, using equation (2): (2) In equation (2), express After constantly considering water temperature and depth, the first The active power of the oxygenator installed at each node. express t Time of the first The water temperature of the pond corresponding to the load of the aerators installed at each node. For the first The water depth of the pond corresponding to the load of the aerators installed at each node. A function to correct for the effects of water temperature and water depth; Step 2: Establish a dissolved oxygen kinetic model based on the baseline model; Establish the relationship between the mechanical oxygenation rate of the oxygenator load and the standard oxygen transfer rate; Establish safety constraints for dissolved oxygen concentration; Step 2.1: Construct a dissolved oxygen kinetic model using equation (3): (3) In equation (3), express t Time of the first The dissolved oxygen concentration in the pond corresponding to the load of the aerator installed at each node; express t Time of the first The mechanical oxygenation rate of the oxygenator installed at each node. , and They represent t Time of the first The load of the aerators installed at each node corresponds to the reoxygenation rate of the pond surface, the mineralization of organic matter, and the respiration of farmed crabs. Step 2.2: The oxygen transfer rate is affected by the dissolved oxygen content, dissolved oxygen saturation of the water body, and water temperature. Using equations (4) and (5), the relationship between the mechanical oxygenation rate of the aerator load and the standard oxygen transfer rate is constructed: (4) (5) In equations (4) and (5), express t Time of the first The dissolved oxygen saturation value in the pond corresponding to the load of the aerator installed at each node; The standard test water temperature of the pond can be selected as 20℃ in this embodiment; This represents the saturation value of dissolved oxygen that the water can reach under standard test water temperature; in this embodiment, 9.09 can be selected. Indicates the oxygenation temperature coefficient; This indicates the total duration, which can be selected as 24 hours in this embodiment; Indicates the first The volume of the pond corresponding to the load of the aerator installed at each node; It is used to correct the first The environmental correction constant for the error between the pond and the test water body corresponding to the load of the aerator installed at each node; yes t Time of the first The operating state of the oxygenator load installed at each node is a binary variable. When s =1 indicates that the oxygenator is on. s =0 indicates that the oxygenator is off. (Optimization) s The value of can be used to control the start and stop of the aerator; , , , These represent four fitting coefficients, which in this embodiment can be selected as 14.625, 0.41022, and 7.99×10⁻⁶ respectively. -3 7.7774×10 5 ; Step 2.3: Construct a safety constraint on dissolved oxygen concentration in water using equation (6): (6) In equation (6), and They represent t Time of the first The upper and lower limits of dissolved oxygen concentration in the pond corresponding to the load of each aerator are dynamically set according to the species being farmed, the growth stage, and the diurnal cycle. This constraint ensures that the dissolved oxygen concentration in the pond is always maintained within the preset safe threshold range. Step 3: Establish a power flow model for the distribution network; Step 3.1: Construct power flow constraints using equations (7) and (8): (7) (8) In equations (7) and (8), Represents the set of serial numbers for distribution network lines; and They represent t Time of the first j The net active power load and net reactive power load of each node. and They represent t Time flows through the first i The node and the first j The lines between nodes ij Active power and reactive power; and They represent t Time flows through the first j The node and the first k The lines between nodes jk Active power and reactive power; and They represent the first i The node and the first j The lines between nodes ij Resistance and reactance; and They represent t Time of the first i The node and the first j The voltage of each node; Step 3.2: Construct branch power capacity constraints using equation (9): (9) In equation (9), The 2-norm of a vector; Step 3.3: Construct node voltage and branch current constraints using equations (10) and (11): (10) (11) In equations (10) and (11), and They represent t Time of the first i The minimum and maximum values ​​of the node voltages; and They represent t Time flows through the first i The node and the first j The lines between nodes ij The minimum and maximum values ​​of the current flowing through it; Step 3.4: Construct the power constraints for distributed photovoltaic power in the distribution network using equations (12)-(15): (12) (13) (14) (15) In equations (12)-(15), and They represent t Time of the first The active and reactive power transmitted from distributed photovoltaic power at each node to the distribution network; express t Time of the first The maximum active power of distributed photovoltaic power at each node; express t Time of the first The reduction rate of active power of distributed photovoltaic power at each node; They represent t Time of the first The maximum reactive power of distributed photovoltaic power at each node; Indicates the first The capacity of distributed photovoltaic power at each node; Step 3.5: Linearize equation (15) using equations (16) and (17): (16) (17) In equation (17), The angle variable represents the polygonal linear approximation of the capacity constraints of distributed photovoltaic power. Indicates the index of any side of the polygon; N This indicates the number of sides of the polygon. N The larger the value, the more accurate the approximation, but the higher the computational complexity. Step 4: Based on the models in Steps 1-3, construct a collaborative control model for aerator control, water quality dynamics, and grid voltage. Step 4.1: Construct the objective function of the collaborative control model with the optimization objectives of minimizing total distribution network loss, total voltage deviation, and total photovoltaic reduction using equations (18)-(21). : (18) (19) (20) (twenty one) In equations (18)-(21), Indicates the total network loss of the distribution network. Indicates the total voltage deviation of the distribution network. This represents the total photovoltaic reduction in the distribution network. , , These represent the weighting coefficients for the total power loss, total voltage deviation, and total photovoltaic active power reduction of the distribution network, respectively. The weighting coefficients can be selected using the analytic hierarchy process (AHP). This represents the set of node numbers in the distribution network. This represents the set of serial numbers of distribution network nodes equipped with distributed photovoltaic systems. express t Time of the first i Reference values ​​for the voltage at each node; Step 5: Solve the collaborative control model to obtain the node voltage and photovoltaic output that maintain the dissolved oxygen concentration in the aquaculture pond under safe constraints, and send them to the control system of the power distribution network and the controller of the aerator to achieve closed-loop control of the dissolved oxygen concentration.

[0015] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the power distribution network voltage collaborative optimization method, and the processor is configured to execute the program stored in the memory.

[0016] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the power distribution network voltage collaborative optimization method.

Claims

1. A method for coordinated optimization of distribution network voltage considering oxygenator load, characterized in that, Includes the following steps: Step 1: Based on the standard oxygenation efficiency and standard oxygen transfer rate of the aerator load, establish a benchmark model of the active power of the aerator load, and consider the influence of water temperature and water depth to construct a dynamic correction model of the active power of the aerator load. Step 2: Establish a dissolved oxygen kinetic model based on the baseline model; Establish the relationship between the mechanical oxygenation rate of the oxygenator load and the standard oxygen transfer rate; Establish safety constraints for dissolved oxygen concentration; Step 3: Establish a power flow model for the distribution network; Step 4: Based on the models in Steps 1-3, construct a collaborative control model for aerator control, water quality dynamics, and grid voltage. Step 5: Solve the collaborative control model to obtain the node voltage and photovoltaic output that maintain the dissolved oxygen concentration in the aquaculture pond under safe constraints, and send them to the control system of the power distribution network and the controller of the aerator to achieve closed-loop control of the dissolved oxygen concentration.

2. The method for coordinated optimization of distribution network voltage based on oxygenator load according to claim 1, characterized in that, Step 1 includes the following steps: Step 1.1: Construct a benchmark model of the active power of the oxygenator load using equation (1); (1) In equation (1), express t Time of the first The active power of the oxygenator installed at each node. express t Time of the first Standard oxygen transfer rate of the oxygen generator load installed at each node; express t Time of the first The standard oxygenation efficiency of the oxygenators installed at each node. Step 1.2: Construct a dynamic correction model for the active power of the aerator load, taking into account the effects of water temperature and water depth, using equation (2): (2) In equation (2), express After constantly considering water temperature and depth, the first The active power of the oxygenator installed at each node. express t Time of the first The water temperature of the pond corresponding to the load of the aerators installed at each node. For the first The water depth of the pond corresponding to the load of the aerators installed at each node. This is a function to correct for the effects of water temperature and water depth.

3. The method for coordinated optimization of distribution network voltage based on oxygenator load according to claim 2, characterized in that, Step 2 includes the following steps: Step 2.1: Construct a dissolved oxygen kinetic model using equation (3): (3) In equation (3), express t Time of the first The dissolved oxygen concentration in the pond corresponding to the load of the aerator installed at each node; express t Time of the first The mechanical oxygenation rate of the oxygenator installed at each node. , and They represent t Time of the first The load of the aerators installed at each node corresponds to the reoxygenation rate of the pond surface, the mineralization of organic matter, and the respiration of farmed crabs. Step 2.2: Using equations (4) and (5), construct the relationship between the mechanical oxygenation rate of the oxygenator load and the standard oxygen transfer rate: (4) (5) In equations (4) and (5), express t Time of the first The dissolved oxygen saturation value in the pond corresponding to the load of the aerator installed at each node; This indicates the standard test water temperature of the pond; This indicates the saturation value of dissolved oxygen that a body of water can achieve at a standard test water temperature; Indicates the oxygenation temperature coefficient; Indicates the total duration; Indicates the first The volume of the pond corresponding to the load of the aerator installed at each node; It is used to correct the first The environmental correction constant for the error between the pond and the test water body corresponding to the load of the aerator installed at each node; yes t Time of the first The working status of the oxygen generators installed at each node under load; , , , This represents four fitting coefficients; Step 2.3: Construct a safety constraint on dissolved oxygen concentration in water using equation (6): (6) In equation (6), and They represent t Time of the first The upper and lower limits of dissolved oxygen concentration in the pond corresponding to the load of each aerator.

4. The method for coordinated optimization of distribution network voltage based on oxygenator load according to claim 3, characterized in that, Step 3 includes the following steps: Step 3.1: Construct power flow constraints using equations (7) and (8): (7) (8) In equations (7) and (8), Represents the set of serial numbers for distribution network lines; and They represent t Time of the first j The net active power load and net reactive power load of each node. and They represent t Time flows through the first i The node and the first j The lines between nodes ij Active power and reactive power; and They represent t Time flows through the first j The node and the first k The lines between nodes jk Active power and reactive power; and They represent the first i The node and the first j The lines between nodes ij Resistance and reactance; and They represent t Time of the first i The node and the first j The voltage of each node; Step 3.2: Construct branch power capacity constraints using equation (9): (9) In equation (9), The 2-norm of a vector; Step 3.3: Construct node voltage and branch current constraints using equations (10) and (11): (10) (11) In equations (10) and (11), and They represent t Time of the first i The minimum and maximum values ​​of the node voltages; and They represent t Time flows through the first i The node and the first j The lines between nodes ij The minimum and maximum values ​​of the current flowing through it; Step 3.4: Construct the power constraints for distributed photovoltaic power in the distribution network using equations (12)-(15): (12) (13) (14) (15) In equations (12)-(15), and They represent t Time of the first The active and reactive power transmitted from distributed photovoltaic power at each node to the distribution network; express t Time of the first The maximum active power of distributed photovoltaic power at each node; express t Time of the first The reduction rate of active power of distributed photovoltaic power at each node; They represent t Time of the first The maximum reactive power of distributed photovoltaic power at each node; Indicates the first The capacity of distributed photovoltaic power at each node; Step 3.5: Linearize equation (15) using equations (16) and (17): (16) (17) In equation (17), The angle variable represents the polygonal linear approximation of the capacity constraints of distributed photovoltaic power. Indicates the index of any side of the polygon; N This indicates the number of sides of the polygon.

5. The method for coordinated optimization of power distribution network voltage based on the load of aerators in aquaculture ponds according to claim 4, characterized in that, Step 4 includes the following steps: Step 4.1: Construct the objective function of the collaborative control model with the optimization objectives of minimizing total distribution network loss, total voltage deviation, and total photovoltaic reduction using equations (18)-(21). : (18) (19) (20) (21) In equations (18)-(21), Indicates the total network loss of the distribution network. Indicates the total voltage deviation of the distribution network. This represents the total photovoltaic reduction in the distribution network. , , These represent the weighting coefficients for total power loss, total voltage deviation, and total photovoltaic active power reduction in the distribution network, respectively. This represents the set of node numbers in the distribution network. This represents the set of serial numbers of distribution network nodes equipped with distributed photovoltaic systems. express t Time of the first i Reference values ​​for node voltages.

6. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store programs that support the processor in executing any of the power distribution network voltage collaborative optimization methods of claims 1-5, and the processor is configured to execute the programs stored in the memory.

7. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is run by the processor, it executes the steps of any of the power distribution network voltage collaborative optimization methods described in claims 1-5.

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