Optimal operation method and system of distribution system based on loss of sensitive equipment

Abstract

The present invention discloses an optimized operation method and system for a distribution system based on the loss of sensitive equipment, which relates to the technical field of power system optimization. The method includes: analyzing the outage conditions of sensitive equipment based on the symmetrical component method to obtain the cost loss constraints of sensitive equipment; analyzing historical statistical data, obtaining line fault data, and constructing an optimization model with the minimum operation cost of the distribution network as the goal based on the cost loss constraints of sensitive equipment, and constraining the optimization model; building a sensitive equipment grid-connected system in a visualization simulation software to verify the correctness of the optimization model, and using the optimization model to optimize the distribution system. The present invention can effectively prevent the shutdown of sensitive equipment caused by the distribution system operating with faults, and reduce the economic losses of users.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization, and in particular to a method and system for optimizing the operation of a power distribution system based on sensitive equipment loss. Background Art

[0002] The main indicators for measuring power quality are voltage, frequency and waveform. Generally speaking, power quality refers to high-quality power supply, including voltage quality, current quality, power supply quality and power quality. An ideal power system should supply power to users at a constant frequency (50Hz) and sinusoidal waveform at a specified voltage level (nominal voltage). Power quality problems can be defined as deviations in voltage, current or frequency that cause electrical equipment to fail or not work properly, including frequency deviation, voltage deviation, voltage fluctuation and flicker, three-phase imbalance, instantaneous or transient overvoltage, waveform distortion (harmonics), voltage sag, interruption, swell and power supply continuity.

[0003] Among them, voltage sag is considered a serious power quality problem due to its inevitability. When voltage-sensitive equipment is subjected to voltage sag, the related production process may be interrupted, which will not only cause economic losses to users and the power grid, but also may cause social public opinion problems. We have designed a distribution system optimization operation method and system based on sensitive equipment loss to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings in the prior art that voltage-sensitive equipment may be subjected to voltage sags and the production processes related to the voltage-sensitive equipment may be interrupted. The proposed distribution system optimization operation method and system based on sensitive equipment losses aims to prevent the shutdown of sensitive equipment caused by the faulty operation of the distribution system and reduce the economic losses of users.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for optimizing operation of a power distribution system based on loss of sensitive equipment, the method comprising the following steps:

[0007] Step 1: Analyze the shutdown conditions of sensitive equipment of power quality based on the symmetrical component method to obtain the cost loss constraints of sensitive equipment;

[0008] Step 2: Analyze historical statistical data, obtain line fault data, and build an optimization model based on sensitive equipment cost loss constraints with the goal of minimizing the distribution network operation cost, and constrain the optimization model;

[0009] Step three: Build a sensitive equipment grid-connected system in the visual simulation software, verify the correctness of the optimization model, and use the optimization model to optimize the distribution system.

[0010] Further preferably, in step 1, the shutdown conditions of sensitive equipment of power quality are analyzed based on the symmetrical component method as follows:

[0011] Since the sensitive equipment is equipped with DC undervoltage protection, and the industrial users in the park set the DC undervoltage protection setting for the sensitive equipment to ensure the yield rate, when the DC capacitor voltage of the sensitive equipment is at or lower than the DC undervoltage protection setting of the sensitive equipment, the inverter of the sensitive equipment continues to operate. The operating time depends on the voltage tolerance curve of the inverter of the sensitive equipment. Therefore, regardless of whether the DC capacitor voltage of the sensitive equipment is lower than the DC undervoltage protection setting of the sensitive equipment, the sensitive equipment is approximated as a constant power equipment. When the DC capacitor voltage of the sensitive equipment is lower than the DC undervoltage protection setting of the sensitive equipment, the sensitive equipment is forcibly cut off, causing economic losses to the industrial users in the park.

[0012] Further preferably, in step 1, the sensitive device is a variable frequency speed regulator.

[0013] Further preferably, in step 1, the expression of the sensitive equipment cost loss constraint is:

[0014] ;

[0015] In the formula, Losses caused by downtime of sensitive equipment, N t is the total running time, N s Indicates the number of sensitive devices in the power distribution system, C s,t Indicated in t Moment s The cost of producing each product with sensitive equipment, n s,t It is s Sensitive equipment in t The number of defective products caused by voltage sag at any given moment, Represents the voltage step signal of the sensitive device, is the phase voltage of the port sensitive device, yes t Voltage protection parameter values ​​of time-sensitive equipment, It is the current voltage protection value.

[0016] Further preferably, in step 2, the expression of the optimization model constructed is:

[0017] ;

[0018] In the formula, N g is the total number of distributed micro-turbines,N t is the total running time, and They are the price and power that the distribution system purchases from the upper power grid. Losses caused by downtime of sensitive equipment, C MT,g,t and P MT,t Distributed micro-turbines g The unit power generation cost and power generation capacity, C m,t For industrial parks m From the purchase price of electricity in the distribution system, P m,t For industrial parks m From the purchased power of the distribution system, is the operating cost of the energy storage equipment, F is the set of failure time periods for each failure, C FT is the loss caused by each failure in each time period, Is a collection F The fault duration corresponding to each element in For the The duration of the fault, N F is the number of failures.

[0019] Further preferably, the following constraints are used to constrain the optimization model:

[0020] Power balance constraints:

[0021] ;

[0022] In the formula, φ ∈Φ, Φ is the three-phase set of distribution network abc, All power flows to the node The collection of line start and end nodes, All power outflow nodes The collection of line terminal nodes, and They are t Timeline The active and reactive power, Is with the node The connected photovoltaic active output, and They are respectively The connected upper power grid has active and reactive power output, and Outflow nodes Distribution network abc three-phase active and reactive power, and Respectively with the node Industrial Parks m Active power and reactive power obtained from the distribution system, Is a node Except industrial parks m Loads other than load, Is a node Loads other than sensitive equipment loads, Is with the node The power absorbed by the connected sensitive equipment with energy storage, Is the flow line The square term of the current, and The nodes are With Node The resistance and reactance between

[0023] For nodes j Constraints on connected devices:

[0024] ;

[0025] ;

[0026] In the formula, M Is a node The connected device, M ∈Ψ, Ψ is the collection of various loads in the distribution network, P M,j,t and They are the three-phase total active power and three-phase total reactive power of each load, and They are the active power of any phase and the reactive power of any phase of each load respectively;

[0027] Line power flow constraints after second-order cone relaxation:

[0028] ;

[0029] In the formula, For Node of t Voltage at the moment, for t Timeline The active power, for t Timeline The reactive power, for t Time Node With Node The square term of the current between

[0030] Voltage drop equation:

[0031] ;

[0032] In the formula, For Node of t Voltage at the moment, and Node With Node The resistance and reactance between them;

[0033] The upper and lower limit constraints of the node voltage of the power distribution system:

[0034] ;

[0035] In the formula, and Node The minimum and maximum voltage amplitudes, and Node exist t 1 and t The voltage at moment 2, F is the set of failure time periods for each failure;

[0036] Line transmission capacity constraints:

[0037] ;

[0038] In the formula, and The lines are The maximum active power transfer capacity and the maximum reactive power transfer capacity;

[0039] Micro turbine output upper and lower limit constraints:

[0040] ;

[0041] In the formula, P MT,t and The micro-turbine is at the time t The active and reactive outputs of P MT,min and P MT,max are the minimum and maximum values ​​of the active output of the micro-turbine, and They are the minimum and maximum values ​​of reactive power output of micro-turbine respectively;

[0042] Micro turbine climbing constraints:

[0043] ;

[0044] In the formula, R up and R down are the upward and downward climbing rates of the micro turbine, and The micro-turbine is at the time t and The meritorious contribution, and are the minimum and maximum values ​​of the active output of the micro-turbine, is a time variable;

[0045] Micro turbine constraints:

[0046] ;

[0047] In the formula, yes t The thermal output of the micro-turbine at all times, R MT is the heat-to-electricity ratio of the micro-turbine;

[0048] Sensitive equipment and energy storage constraints:

[0049] ;

[0050] In the formula, is the energy storage charging power, is the energy storage discharge power, and are the minimum and maximum energy storage charging power respectively, and are the minimum and maximum values ​​of energy storage discharge power, is the operating power of the energy storage connected to the sensitive equipment, SOC t , SOC t-1 They are t, t- 1. Battery charge status at a moment. SOC min and SOC max are the minimum and maximum values ​​of the battery state of charge, SOC 0 and SOC end are the initial and final values ​​of the battery state of charge respectively;

[0051] Cost constraints of depreciation and maintenance costs of energy storage:

[0052] ;

[0053] In the formula, It is O The operating power of energy storage is N o Energy storage, N t is the total running time, It is O The depreciation and maintenance costs of energy storage. Cost constraints for depreciation and maintenance costs of energy storage;

[0054] Photovoltaic power output constraints:

[0055] ;

[0056] In the formula, is the photovoltaic power output, is the light-to-heat conversion efficiency of photovoltaic power, is the reflection coefficient, is the solar radiation intensity;

[0057] Air conditioning constraints:

[0058] ;

[0059] In the formula, and are the minimum and maximum values ​​of the air conditioner operating power, is the cooling power of the air conditioner, is the operating power of the air conditioner, It is the efficiency of the air conditioner in converting electrical energy into cooling energy;

[0060] Ice storage system constraints:

[0061] ;

[0062] In the formula, 、 and They are t The storage capacity, minimum storage capacity and maximum storage capacity in the cold storage tank at the moment, yes t- The storage volume in the cold storage tank at 1 moment, 、 and They are tThe operating power of the ice maker at all times, the minimum and maximum values ​​of the ice maker operating power, 、 and They are t The operating power of the ice melter at all times, the minimum and maximum values ​​of the operating power of the ice melter, , They are ice making efficiency and ice melting efficiency;

[0063] Electric boiler constraints:

[0064] ;

[0065] In the formula, and are the minimum and maximum values ​​of the air conditioner operating power, is the heating power of the electric boiler, is the operating power of the electric boiler, It is the efficiency of the electric boiler in converting electrical energy into heat energy;

[0066] Energy balance constraints:

[0067] ;

[0068] In the formula, is the operating efficiency of the ice melter, is the cooling load of the industrial park, is the heat load of the industrial park, It is other fixed electrical load in the industrial park.

[0069] A system for optimizing operation of a power distribution system based on loss of sensitive equipment, the system comprising:

[0070] Sensitive equipment loss analysis module, used to analyze the shutdown conditions of sensitive equipment of power quality based on the symmetrical component method, and obtain the cost loss constraints of sensitive equipment;

[0071] The optimization model building module is used to analyze historical statistical data, obtain line fault data, and build an optimization model based on sensitive equipment cost loss constraints with the goal of minimizing the distribution network operation cost;

[0072] The verification module is used to build a sensitive equipment grid-connected system in the visual simulation software, verify the correctness of the optimization model, and use the optimization model to optimize the distribution system.

[0073] Further preferably, the sensitive equipment loss analysis module executes the expression for obtaining the sensitive equipment cost loss constraint as follows:

[0074] ;

[0075] In the formula, Losses caused by downtime of sensitive equipment, N t is the total running time, N s Indicates the number of sensitive devices in the power distribution system, C s,t Indicated in t Moment s The cost of producing each product with sensitive equipment, n s,t It is s Sensitive equipment in t The number of defective products caused by voltage sag at any given moment, Represents the voltage step signal of the sensitive device, is the phase voltage of the port sensitive device, yes t Voltage protection parameter values ​​of time-sensitive equipment, It is the current voltage protection value.

[0076] Further preferably, the expression for constructing the optimization model executed by the optimization model construction module is as follows:

[0077] ;

[0078] In the formula, N g is the total number of distributed micro-turbines, N t is the total running time, and They are the price and power that the distribution system purchases from the upper power grid. Losses caused by downtime of sensitive equipment, C MT,g,t and P MT,t Distributed micro-turbines g The unit power generation cost and power generation capacity, C m,t For industrial parks m From the purchase price of electricity in the distribution system, P m,t For industrial parks m From the purchased power of the distribution system, is the operating cost of the energy storage equipment, F is the set of failure time periods for each failure, C FT is the loss caused by each failure in each time period, Is a collection F The fault duration corresponding to each element in For the The duration of the fault, N F is the number of failures.

[0079] Further preferably, the verification module sets different verification scenarios to verify the correctness and effectiveness of the optimization model based on solving the DC capacitor voltage of sensitive equipment during a grid voltage sag, taking into account the losses of sensitive equipment, and considering the demand response of equipment within the industrial park and the operation of the sensitive equipment combined with the energy storage system.

[0080] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention first analyzes the shutdown conditions of sensitive equipment of power quality based on the symmetrical component method to obtain the cost loss constraints of the sensitive equipment, and then constructs an optimization model based on the cost loss constraints of the sensitive equipment with the goal of minimizing the operation cost of the distribution network. By building a sensitive equipment grid-connected system in visual simulation software, the correctness of the optimization model is verified, and the distribution system is optimized using the optimization model, so that the method proposed by the present invention can effectively prevent the shutdown of sensitive equipment caused by the faulty operation of the distribution system due to the voltage sag of voltage-sensitive equipment, thereby reducing the economic losses of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic flow chart of a method for optimizing operation of a power distribution system based on loss of sensitive equipment proposed in an embodiment of the present invention;

[0082] Figure 2 A diagram of the joint energy storage topology of the sensitive device ASD in an embodiment of the present invention;

[0083] Figure 3 A schematic diagram of the relationship between the power distribution system and the energy supply and consumption of the industrial park in an embodiment of the present invention;

[0084] Figure 4 A schematic diagram of the IEEE13 network and line layout in an embodiment of the present invention;

[0085] Figure 5 A schematic diagram of the ASD capacitor voltage of a sensitive device in scenario 1 of an embodiment of the present invention;

[0086] Figure 6 Schematic diagram of the operating power of each device in an industrial park in an embodiment of the present invention. DETAILED DESCRIPTION

[0087] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0088] Reference Figure 1 The method for optimizing the operation of a power distribution system based on the loss of sensitive equipment proposed by the present invention comprises the following steps:

[0089] Step 1: Analyze the shutdown conditions of sensitive equipment of power quality based on the symmetrical component method to obtain the cost loss constraints of the sensitive equipment, wherein the sensitive equipment in this embodiment is an adjustable speed driver (ASD).

[0090] Step 2: Analyze historical statistical data, obtain line fault data, and build an optimization model based on sensitive equipment cost loss constraints with the goal of minimizing the distribution network operation cost.

[0091] Step 3: Build a sensitive equipment grid-connected system in the visual simulation software, verify the correctness of the optimization model, and use the optimization model to optimize the distribution system. Visual simulation software can choose Simulink, which is a visual simulation tool in MATLAB launched by Mathworks. It is used for multi-domain simulation and model-based design, supporting system design, simulation, automatic code generation, and continuous testing and verification of embedded systems.

[0092] Based on the above method, this example also proposes a system for the above-mentioned distribution system optimization operation method based on sensitive equipment loss, which includes a sensitive equipment loss analysis module, an optimization model construction module and a verification module. Specifically,

[0093] Sensitive equipment loss analysis module, used to analyze the shutdown conditions of sensitive equipment of power quality based on the symmetrical component method, and obtain the cost loss constraints of sensitive equipment;

[0094] The optimization model building module is used to analyze historical statistical data, obtain line fault data, and build an optimization model based on sensitive equipment cost loss constraints with the goal of minimizing the distribution network operation cost;

[0095] The verification module is used to build a sensitive equipment grid-connected system in the visual simulation software, verify the correctness of the optimization model, and use the optimization model to optimize the distribution system.

[0096] In step 1, the sensitive equipment loss analysis module is used to analyze the shutdown conditions of sensitive equipment based on the symmetrical component method for analyzing power quality. First, the parameters are designed:

[0097] ; (1)

[0098] in, is the cut-off coefficient, which is related to the voltage unbalance coefficient Definition of voltage unbalance coefficient .

[0099] Represents the positive sequence parameter of the DC capacitor voltage, Represents the negative sequence parameter of the DC capacitor voltage, Indicates the DC undervoltage protection setting value of sensitive equipment. and They represent the positive sequence parameter and negative sequence parameter of DC undervoltage protection of sensitive equipment respectively. and They represent the positive sequence parameter value and negative sequence parameter value of the DC capacitor voltage and phase voltage respectively.

[0100] Calculate the cut-off coefficient :

[0101] ; (2)

[0102] in, and is the empirical segmentation coefficient, , ; Using the voltage unbalance coefficient Calculate the cut-off coefficient , only to reduce the The error caused by the linearization model of the constant power source when the slope of the volt-ampere characteristic curve changes sharply. If the slope of the volt-ampere characteristic curve changes less than the set threshold, the cut-off coefficient is directly given. .

[0103] DC capacitor voltage on sensitive equipment When the positive sequence power is low, U T(1) With negative sequence power supply U T(2) Capacitor C dc Charging, , , we can get formula (3).

[0104] When the sensitive device is in constant power working state, the negative resistance R CPL and constant current source The parameters remain unchanged. Therefore, formula (3) has the property of form invariance.

[0105] ; (3)

[0106] in, U T is the voltage excitation, R CPL is a negative resistance, is a constant current source, C dc is the capacitance, R asd is the resistance of the sensitive device, L asd For the inductance of sensitive equipment, yes The derivative value of is the voltage The derivative value of Represents a parameter equal to the DC current, is a time-dependent constant.

[0107] The dynamic model expression of discharge of sensitive equipment is obtained:

[0108] (4);

[0109] The approximate analysis of the capacitor voltage of the sensitive equipment and the solution of the DC voltage of the sensitive equipment during the voltage sag are as follows:

[0110] (5);

[0111] in, is the voltage of sensitive devices during charging, is the voltage The general solution of is the voltage The special solution of is the product of the resistance and the constant, is the phase a voltage, is the phase c voltage, is the b-phase voltage; Indicates the port phase voltage amplitude.

[0112] Due to the positive / negative sequence network, the full cycle rectification may not be completed in each cycle of the sensitive device rectification. The remaining time in the cycle is the discharge process of the sensitive device capacitor to the constant power load. Solving equation (4) yields equation (6).

[0113] (6);

[0114] in, is the difference voltage between the discharge process and the charging process. F u is the constant value of the second-order equation.

[0115] because R CPL is negative, so the attenuation of equation (7) is ignored e Index. Therefore, in general, the capacitor in the positive / negative sequence network includes two processes: charging and discharging. Using difference processing, that is .exist / 6 hours Extreme values ​​can be obtained. ( / 6) to approximate the DC capacitance of sensitive equipment during voltage sag. In summary, the DC capacitance voltage of sensitive equipment after voltage sag occurs on the grid side can be solved: :

[0116] (7);

[0117] According to the port phase voltage amplitude and formula (7), the DC capacitor voltage of the sensitive device is obtained: Because sensitive equipment has DC undervoltage protection, and industrial users in the park set the DC undervoltage protection value of sensitive equipment to ensure the yield rate. To set, generally Set it high, that is, the DC capacitor voltage of sensitive equipment Equal to or less than the DC undervoltage protection setting of sensitive equipment The inverter of the sensitive device will continue to operate for a period of time, and the operating time depends on its voltage tolerance curve. Therefore, regardless of the DC capacitor voltage of the sensitive device Is it less than the DC undervoltage protection setting of sensitive equipment? , the sensitive device is approximated as a constant power device. However, when the DC capacitor voltage of the sensitive device <DC undervoltage protection setting for sensitive equipment When , sensitive equipment will be forcibly removed, causing economic losses to industrial users in the park. Therefore, the expression for the cost loss constraint of sensitive equipment is:

[0118] (8);

[0119] In the formula, Losses caused by downtime of sensitive equipment, N t is the total running time, N s Indicates the number of sensitive devices in the power distribution system, Cs,t Indicated in t Moment s The cost of producing each product with sensitive equipment, n s,t It is s Sensitive equipment in t The number of defective products caused by voltage sag at any given moment, Represents the voltage step signal of the sensitive device, is the phase voltage of the port sensitive device, yes t Voltage protection parameter values ​​of time-sensitive equipment, It is the current voltage protection value.

[0120] In summary, the port phase voltage amplitude is established U DC capacitor voltage with sensitive equipment The relationship between the two is obtained, and the cost loss constraint caused by the shutdown of sensitive equipment is obtained. The sensitive equipment belongs to the three-phase load in the distribution network.

[0121] Specifically, in step 2, firstly, historical statistical data is analyzed to obtain line fault data, which specifically includes:

[0122] The forced failure rate of a single line is:

[0123] (9);

[0124] Assume that there are k lines of a certain voltage level. The following statistical method is used to calculate the failure rate of the distribution line. However, considering that the length of the distribution line is generally from several hundred meters to several thousand meters, the failure rate is calculated according to the months of the same period in history. The expression is:

[0125] (10);

[0126] in, It is the first in history Monthly failure rate, times / (km•month), It is the first in history Months of time, yes n Year The line in Number of failures in a month, L i It is The length of the line.

[0127] This embodiment performs single-phase line fault analysis and predicts the single-phase grounding fault rate in the same period in the month according to formula (10), which is expressed as:

[0128] (11);

[0129] in, η g is the probability of single-phase grounding fault accounting for the total faults, Based on the historical Monthly predicted single-phase fault rate, times / (km•month), single-phase grounding faults account for more than 80% of the total faults, so the coefficient is set η g =0.8.

[0130] In this embodiment, the industrial park is regarded as an unbalanced load, with air conditioning and ice storage system for cooling, micro-turbines and electric boilers for heating, and micro-turbines, photovoltaic power sources and distribution systems for power supply. The sensitive equipment in the distribution system contains electric energy storage devices, which are used to smooth out the losses caused by the temporary voltage drop at the park's grid connection point. The objective function is to minimize the operating cost of the distribution system with industrial parks, including the power purchase cost of the upper transmission network, the power generation cost of distributed power sources, the power purchase cost of the park, the downtime loss cost of sensitive equipment, the charging and discharging cost of energy storage equipment, and the failure loss cost.

[0131] Secondly, in step 2, the optimization model building module is used to build an optimization model based on the cost loss constraint of sensitive equipment and with the goal of minimizing the distribution network operation cost. The expression is as follows:

[0132] (12);

[0133] In the formula, N g is the total number of distributed micro-turbines, N t is the total running time, and They are the price and power that the distribution system purchases from the upper power grid. Losses caused by downtime of sensitive equipment, C MT,g,t and P MT,t Distributed micro-turbines g The unit power generation cost and power generation capacity, C m,t For industrial parks m From the purchase price of electricity in the distribution system, P m,t For industrial parks m From the purchased power of the distribution system, is the operating cost of the energy storage equipment, F is the set of failure time periods for each failure, C FT is the loss caused by each failure in each time period, For the The duration of the fault, Is a collection F The fault duration corresponding to each element (i.e. each fault) in N F is the number of failures.

[0134] The power balance constraint of the power distribution system is affected by the load of each node, the output of the micro-turbine, the power purchased by the distribution system from the upper power grid, and the load of each industrial park. This embodiment performs power flow modeling of the three-phase distribution network to analyze the impact of asymmetric operation on the operating cost of sensitive equipment. The power balance constraint expression is as follows:

[0135] (13);

[0136] In the formula, φ ∈Φ, Φ is the three-phase set of distribution network abc, All power flows to the node The collection of line start and end nodes, All power outflow nodes The collection of line terminal nodes, and They are t Timeline The active and reactive power, Is with the node j The connected photovoltaic active output, and They are respectively The connected upper power grid has active and reactive power output, and Outflow nodes Distribution network abc three-phase active and reactive power, and They are respectively Industrial Parks m Active power and reactive power obtained from the distribution system, Is a node Except industrial parks m Loads other than load, Is a node Loads other than sensitive equipment loads, Is with the node The power absorbed by the connected sensitive equipment with energy storage, Is the flow line The square term of the current, and The nodes are With Node The resistance and reactance between them.

[0137] For nodes Constraints on connected devices:

[0138] ;

[0139] (14);

[0140] In the formula, M Is a node The connected device, M ∈Ψ, Ψ is the collection of various loads in the distribution network, P M,j,t and They are the three-phase total active power and three-phase total reactive power of each load, and They are the active power of any phase and the reactive power of any phase of each load respectively;

[0141] Line power flow constraints after second-order cone relaxation:

[0142] (15)

[0143] In the formula, for t Timeline The active power, for t Timeline The reactive power, For Node of t Voltage at the moment, for t Timeline The active power, for t Timeline The reactive power, for t Time Node With Node The square term of the current between

[0144] Voltage drop equation:

[0145] (16);

[0146] In the formula, V j,t For Node of t Voltage at the moment, and Node With Node The resistance and reactance between them;

[0147] The upper and lower limit constraints of the node voltage of the distribution system:

[0148] (17);

[0149] In the formula, and Node The minimum and maximum voltage amplitudes, and Node exist t 1 and t After a single-phase grounding fault occurs in the system, the voltage level of some nodes will deteriorate. No matter how it is optimized, the node voltage (especially the voltage at the fault point) cannot reach the normal operating state. and Therefore, the lower limit on the node voltage during a fault is No constraints.

[0150] Line transmission capacity constraints:

[0151] (18);

[0152] In the formula, and The lines are The maximum active power transfer capacity and the maximum reactive power transfer capacity.

[0153] Micro turbine output upper and lower limit constraints:

[0154] (19);

[0155] In the formula, P MT,t and The micro-turbine is at the time t The active and reactive outputs of P MT,min and P MT,max are the minimum and maximum values ​​of the active output of the micro-turbine, and They are the minimum and maximum reactive output of the micro turbine respectively.

[0156] Micro turbine climbing constraints:

[0157] (20);

[0158] In the formula, Rup and R down are the upward and downward climbing rates of the micro turbine, and The micro-turbine is at the time t and The meritorious contribution, and are the minimum and maximum values ​​of the active output of the micro-turbine, is a time variable.

[0159] Micro turbine constraints:

[0160] (twenty one);

[0161] In the formula, yes t The thermal output of the micro-turbine at all times, R MT is the heat-to-electricity ratio of the micro-turbine.

[0162] Sensitive equipment and energy storage constraints:

[0163] The actual distribution system operates asymmetrically. Consider a single-phase short circuit fault. At this time, the voltage of the node adjacent to the fault in the distribution system is bound to decrease. If there are sensitive devices near the fault node, there is a risk of downtime for the sensitive devices. Add energy storage on the DC side of the sensitive devices, such as Figure 2 As shown, Figure 2 This is the topological structure diagram of the ASD combined with energy storage for sensitive equipment. When the voltage of the distribution system drops temporarily, the energy storage can provide the capacitor with corresponding power support to alleviate the voltage drop of the DC capacitor of the sensitive equipment and reduce the loss cost caused by the shutdown of the sensitive equipment, as shown in formula (22):

[0164] (twenty two);

[0165] In the formula, is the energy storage charging power, is the energy storage discharge power, and are the minimum and maximum energy storage charging power respectively, and are the minimum and maximum values ​​of energy storage discharge power, is the operating power of the energy storage connected to the sensitive equipment, SOC t , SOC t-1 They are t, t- 1. Battery charge status at a moment. SOC min and SOC maxare the minimum and maximum values ​​of the battery state of charge, SOC 0 and SOC end are the initial and final values ​​of the battery state of charge, respectively.

[0166] Consider Figure 2 The system topology of the sensitive device ASD combined with energy storage is shown. This topology connects the energy storage to the DC bus through a controller. The controller can control the bidirectional flow of energy storage and maintain the stability of the DC voltage of the sensitive device. The equation constraint is:

[0167] (twenty three);

[0168] In the formula, It is the active load of sensitive equipment. is the active power of the three phases abc of the distribution network, It is a step signal for sensitive equipment.

[0169] Although the energy storage charging and discharging process is very stable, its life is short when used in frequent discharge situations. Therefore, the cost constraints of quantifying the depreciation and maintenance costs of energy storage are:

[0170] (twenty four);

[0171] In the formula, It is O The operating power of energy storage is N o Energy storage, N t is the total running time, It is O The depreciation and maintenance costs of energy storage. The cost constraints are the depreciation and maintenance costs of energy storage.

[0172] Photovoltaic power output constraints:

[0173] (25);

[0174] In the formula, is the photovoltaic power output, is the light-to-heat conversion efficiency of photovoltaic power, is the reflection coefficient, is the solar radiation intensity.

[0175] Air conditioning constraints:

[0176] (26);

[0177] In the formula, and are the minimum and maximum values ​​of the air conditioner operating power, is the cooling power of the air conditioner, is the operating power of the air conditioner, It is the efficiency of the air conditioner in converting electrical energy into cold energy.

[0178] Ice storage system constraints:

[0179] The ice storage system contains a cold storage tank, an ice maker, and an ice melter. Its constraints include the cold storage capacity equality constraint, the cold storage capacity upper and lower limit constraints, the ice melting power upper and lower limit constraints, and the ice making power upper and lower limit constraints, and the expression is:

[0180] (27);

[0181] In the formula, 、 and They are t The storage capacity, minimum storage capacity and maximum storage capacity in the cold storage tank at the moment, yes t- The storage volume in the cold storage tank at 1 moment, 、 and They are t The operating power of the ice maker at all times, the minimum and maximum values ​​of the ice maker operating power, 、 and They are t The operating power of the ice melter at all times, the minimum and maximum values ​​of the operating power of the ice melter, , They are ice making efficiency and ice melting efficiency respectively.

[0182] Electric boiler constraints:

[0183] (28);

[0184] In the formula, and are the minimum and maximum values ​​of the air conditioner operating power, is the heating power of the electric boiler, is the operating power of the electric boiler, It is the efficiency of electric boiler in converting electrical energy into thermal energy.

[0185] Energy balance constraints:

[0186] (29);

[0187] In the formula, is the operating efficiency of the ice melter, is the cooling load of the industrial park, is the heat load of the industrial park, It is other fixed electrical load in the industrial park.

[0188] For step three, a sensitive equipment grid-connected system is built in the visual simulation software to verify the correctness of the optimization model, and the optimization model is used to optimize the power distribution system. This embodiment is verified in combination with actual operation.

[0189] This embodiment uses a verification module to verify the proposed optimization model based on solving the DC capacitor voltage of sensitive equipment during the grid voltage sag. The correctness and effectiveness of the calculation method.

[0190] Simulink was used to build a sensitive equipment grid-connected system with an ideal uncontrolled rectifier and a line voltage of 690V (without energy storage). After setting the parameters, a single-phase ground short-circuit fault occurred on the grid side at 2s, which was recorded as Example 1, and the relevant parameters were solved. The fault location and grounding resistance were changed and recorded as Example 2 to verify the correctness of the method. As can be seen from Table 1, changes in the fault location and grounding resistance will affect the relevant parameters. In Example 2, the error of the linearized model in the negative sequence network is large, and the error can be reduced by increasing the voltage calculation at point P4 to obtain more accurate parameters. In Example 2, the linearized model of the negative sequence network is recalculated, and the DC capacitor voltage of the sensitive equipment during the grid voltage sag is further calculated, and the error is reduced to 0.7%, thereby verifying the effectiveness of the method for calculating the approximate value of the DC capacitor voltage of the sensitive equipment proposed in the present invention.

[0191] Table 1 Calculation results of key parameters during grid voltage sag

[0192]

[0193] In Table 1, is the fault current value, E % represents the error, Represents the positive-sequence and negative-sequence phase voltage amplitudes at the fault point under different operating conditions.

[0194] In order to verify the correctness and effectiveness of the optimization model proposed in this embodiment, an improved IEEE 13-node power distribution system is used for analysis and verification, with a voltage level of 10 kV. Figure 4 As shown, the power distribution system includes an industrial park coupled system, two sensitive equipment combined energy storage systems, a photovoltaic system, and a micro-turbine. Its transformer has been equivalent to line UG3, a sensitive equipment combined energy storage system, and the parameters of the sensitive equipment combined energy storage system are shown in Table 2. The power distribution system in this embodiment and the energy supply and consumption relationship of the industrial park are as follows: Figure 3 shown.

[0195] Table 2 Configuration parameters of sensitive equipment with energy storage system

[0196]

[0197] In Table 2, P w Represents the power of ASD with energy storage device, L asd represents the equivalent inductance of ASD, L dc Represents the inductance on the DC side.

[0198] The preventive dispatch cycle is set to 24 hours, with an interval of 15 minutes, a total of 96 dispatch points, and the solver is Gurobi. The system fault data comes from the historical data of 10kV lines in a certain area in the past three years, and the single-phase grounding fault is randomly assigned to the line. F 1 It occurred at the 24th dispatching point and lasted for 45 minutes. The ground resistance of phase a was 0.015Ω. F 2 It occurred at the 33rd dispatching point and lasted for 15 minutes, with the ground resistance of phase a being 0.02Ω; F 3 It occurred at the 80th dispatching point and lasted for 105 minutes, with the grounding resistance of phase A being 0.025Ω.

[0199] The verification module of the system sets the following verification scenarios for the operation of the industrial park and sensitive equipment before and after taking into account the loss of sensitive equipment and the characteristics of the demand response of equipment within the industrial park, as well as the cost of purchasing electricity:

[0200] Scenario 1: The demand response of equipment within the industrial park is not considered, and sensitive equipment does not have energy storage.

[0201] Scenario 2: The demand response of equipment within the industrial park is not considered, and sensitive equipment is equipped with energy storage.

[0202] Scenario 3: Considering the demand response of equipment within the industrial park, sensitive equipment does not have energy storage.

[0203] Scenario 4: Considering the demand response of equipment within the industrial park, sensitive equipment is equipped with energy storage.

[0204] Combined with the operation of each ASD in the distribution network, at this time, the sensitive equipment does not have energy storage and the internal equipment demand response of the industrial park is not considered. It can be seen that in scenario 1, when each ASD encounters a fault, the DC voltage is lower than When the ASD1 is off, it will stop running and cause cost losses. F 1The voltage drop at the 24th dispatching point, which lasted for 45 minutes, was the largest. F 3 Therefore, the voltage drop at the 24th dispatch point, which lasted for 105 minutes, was the largest. F 2 The two dispatch points are far away and belong to different feeders. Therefore, the fault that occurs at the 33rd dispatch point and lasts for 15 minutes will not cause downtime if dispatched according to the method proposed in this paper. Figure 5 It shows that in scenario 1, where the demand response of internal equipment in the park is not set, the park purchases more electricity when the electricity price is low and less electricity when the electricity price is high. Regardless of the electricity price, micro-gas turbines are used to generate electricity and supply heat. Electric boilers and external power grids are also used for energy supply. This scenario does not take measures to shut down sensitive equipment, nor does it reduce the cost of heat and energy.

[0205] Figure 6 In scenario 2, sensitive equipment is equipped with energy storage but the industrial park demand response is not considered. Regardless of whether the electricity price is at a peak or a valley, industrial park users will use micro-turbines for power generation and heating, and will also use electric boilers and external power grids to supply heat and electricity within the industrial park. Sensitive equipment ASD2 will suffer shutdown losses when a fault occurs, while sensitive equipment ASD1 will not suffer losses due to energy storage under specific faults. Table 3 shows the shutdown losses of sensitive equipment ASD in this scenario.

[0206] Scenario 3 sets up demand response for equipment within the park. When electricity prices are high, the park uses micro-turbines to generate electricity and heat and reduces external energy supply, reducing ASD downtime losses of sensitive equipment and heat and energy costs.

[0207] In scenario 2, the loss of ASD shutdown of sensitive equipment in the distribution network is consistent with that in scenario 3, and the economic loss of shutdown is shown in Table 3. In scenario 4, due to the internal equipment demand response and energy storage configuration, micro-gas turbines and energy storage are used to generate electricity and heat during peak electricity prices, reducing external supply, ASD shutdown losses of sensitive equipment and the cost of heat and energy in the park. Table 3 shows the cost of purchasing electricity.

[0208] Industrial parks purchase more electricity when prices are low, and less electricity when prices are high. Since scenario 4 sets up the internal equipment demand response of the industrial park and configures energy storage, when the electricity price is high, the industrial park will use micro-gas turbines and energy storage for power generation and heating, and will reduce the supply of heat and electricity to the industrial park by electric boilers and external power grids; since scenario 4 takes into account the internal equipment demand response of the industrial park and configures energy storage, scenario 4 reduces the ASD downtime loss of sensitive equipment and will further reduce the heat and energy costs of the industrial park. Table 3 shows the electricity purchase cost of this scenario.

[0209] Table 3 ASD downtime loss and park electricity purchase cost

[0210]

[0211] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the operation of a power distribution system based on the loss of sensitive equipment, characterized in that: The following steps are involved: Step 1: Analyze the shutdown conditions of sensitive equipment of power quality based on the symmetrical component method and obtain the cost loss constraint of sensitive equipment, which is expressed as: ； In the formula, Losses caused by downtime of sensitive equipment, is the total running time, Indicates the number of sensitive devices in the power distribution system, represents the cost of producing each product by the s-th sensitive device at time t, is the number of defective products produced by the sth sensitive device at time t due to voltage sag, Represents the voltage step signal of the sensitive device, is the phase voltage of the port sensitive device, is the voltage protection parameter value of the sensitive equipment at time t, is the current voltage protection value; Step 2: Analyze historical statistical data, obtain line fault data, and build an optimization model based on sensitive equipment cost loss constraints with the goal of minimizing the distribution network operation cost. The optimization model is subject to power balance constraints, line flow constraints after second-order cone relaxation, upper and lower limits of distribution system node voltage constraints, line transmission capacity constraints, upper and lower limits of micro-turbine output constraints, micro-turbine climbing constraints, micro-turbine constraints, sensitive equipment and energy storage constraints, energy storage depreciation and maintenance cost constraints, photovoltaic power output constraints, air conditioning constraints, ice storage system constraints, electric boiler constraints and energy balance constraints. The expression of the constructed optimization model is: ； In the formula, N g is the total number of distributed micro-turbines, N t is the total running time, and They are the price and power that the distribution system purchases from the upper power grid. Losses caused by downtime of sensitive equipment, C MT,g,t and P MT,t Distributed micro-turbines g The unit power generation cost and power generation capacity, C m,t For industrial parks m From the purchase price of electricity in the distribution system, P m,t For industrial parks m From the purchased power of the distribution system, is the operating cost of the energy storage equipment, F is the set of failure time periods for each failure, C FT is the loss caused by each failure in each time period, Is a collection F The fault duration corresponding to each element in For the The duration of the fault, N F is the number of failures; Step three: Build a sensitive equipment grid-connected system in the visual simulation software, verify the correctness of the optimization model, and use the optimization model to optimize the distribution system.

2. The method for optimizing the operation of a power distribution system based on sensitive equipment loss according to claim 1, characterized in that: Step 1: The shutdown conditions of sensitive equipment for power quality are analyzed based on the symmetrical component method as follows: Since the sensitive equipment is equipped with DC undervoltage protection, and the industrial users in the park set the DC undervoltage protection setting for the sensitive equipment to ensure the yield rate, when the DC capacitor voltage of the sensitive equipment is at or lower than the DC undervoltage protection setting of the sensitive equipment, the inverter of the sensitive equipment continues to operate. The operating time depends on the voltage tolerance curve of the inverter of the sensitive equipment. Therefore, regardless of whether the DC capacitor voltage of the sensitive equipment is lower than the DC undervoltage protection setting of the sensitive equipment, the sensitive equipment is approximated as a constant power equipment. When the DC capacitor voltage of the sensitive equipment is lower than the DC undervoltage protection setting of the sensitive equipment, the sensitive equipment is forcibly cut off, causing economic losses to the industrial users in the park.

3. The method for optimizing the operation of a power distribution system based on sensitive equipment loss according to claim 1, characterized in that: In step 1, the sensitive device is a variable frequency speed regulator.

4. The method for optimizing the operation of a power distribution system based on sensitive equipment loss according to claim 1, characterized in that: The following constraints are used to constrain the optimization model: Power balance constraints: ； In the formula, φ ∈Φ, Φ is the three-phase set of distribution network abc, All power flows to the node The collection of line start and end nodes, All power outflow nodes The collection of line terminal nodes, and They are t Timeline The active and reactive power, Is with the node j The connected photovoltaic active output, and They are respectively The connected upper power grid has active and reactive power output, and Outflow nodes Distribution network abc three-phase active and reactive power, and They are respectively Industrial Parks m Active power and reactive power obtained from the distribution system, Is a node Except industrial parks m Loads other than load, Is a node Loads other than sensitive equipment loads, Is with the node The power absorbed by the connected sensitive equipment with energy storage, Is the flow line The square term of the current, and The nodes are With Node The resistance and reactance between For nodes Constraints on connected devices: ； ； In the formula, M Is a node The connected device, M ∈Ψ, Ψ is the collection of various loads in the distribution network, P M,j,t and They are the three-phase total active power and three-phase total reactive power of each load, and They are the active power of any phase and the reactive power of any phase of each load respectively; Line power flow constraints after second-order cone relaxation: ； In the formula, For Node of t Voltage at the moment, for t Timeline The active power, for t Timeline The reactive power, for t Time Node With Node The square term of the current between Voltage drop equation: ； In the formula, V j,t For Node of t Voltage at the moment, and Node With Node The resistance and reactance between them; The upper and lower limit constraints of the node voltage of the distribution system: ； In the formula, and Node The minimum and maximum voltage amplitudes, and Node exist t 1 and t The voltage at moment 2, F is the set of failure time periods for each failure; Line transmission capacity constraints: ； In the formula, and The lines are The maximum active power transfer capacity and the maximum reactive power transfer capacity; Micro turbine output upper and lower limit constraints: ； In the formula, P MT,t and The micro-turbine is at the time t The active and reactive outputs of P MT,min and P MT,max are the minimum and maximum values ​​of the active output of the micro-turbine, and They are the minimum and maximum values ​​of reactive power output of micro-turbine respectively; Micro turbine climbing constraints: ； In the formula, R up and R down are the upward and downward climbing rates of the micro turbine, and The micro-turbine is at the time t and The meritorious contribution, and are the minimum and maximum values ​​of the active output of the micro-turbine, is a time variable; Micro turbine constraints: ； In the formula, yes t The thermal output of the micro-turbine at all times, R MT is the heat-to-electricity ratio of the micro-turbine; Sensitive equipment and energy storage constraints: ； In the formula, is the energy storage charging power, is the energy storage discharge power, and are the minimum and maximum energy storage charging power respectively, and are the minimum and maximum values ​​of energy storage discharge power, is the operating power of the energy storage connected to the sensitive equipment, SOC t , SOC t-1 They are t, t- 1. Battery charge status at a moment. SOC min and SOC max are the minimum and maximum values ​​of the battery state of charge, SOC 0 and SOC end are the initial and final values ​​of the battery state of charge respectively; Cost constraints of depreciation and maintenance costs of energy storage: ； In the formula, It is O The operating power of energy storage is N o Energy storage, N t is the total running time, It is O The depreciation and maintenance costs of energy storage. Cost constraints for depreciation and maintenance costs of energy storage; Photovoltaic power output constraints: ； In the formula, is the photovoltaic power output, is the light-to-heat conversion efficiency of photovoltaic power, is the reflection coefficient, is the solar radiation intensity; Air conditioning constraints: ； In the formula, and are the minimum and maximum values ​​of the air conditioner operating power, is the cooling power of the air conditioner, is the operating power of the air conditioner, It is the efficiency of the air conditioner in converting electrical energy into cooling energy; Ice storage system constraints: ； In the formula, 、 and They are t The storage capacity, minimum storage capacity and maximum storage capacity in the cold storage tank at the moment, yes t- The storage volume in the cold storage tank at 1 moment, 、 and They are t The operating power of the ice maker at all times, the minimum and maximum values ​​of the ice maker operating power, 、 and They are t The operating power of the ice melter at all times, the minimum and maximum values ​​of the operating power of the ice melter, , They are ice making efficiency and ice melting efficiency; Electric boiler constraints: ； In the formula, and are the minimum and maximum values ​​of the air conditioner operating power, is the heating power of the electric boiler, is the operating power of the electric boiler, It is the efficiency of the electric boiler in converting electrical energy into heat energy; Energy balance constraints: ； In the formula, is the operating efficiency of the ice melter, is the cooling load of the industrial park, is the heat load of the industrial park, It is other fixed electrical load in the industrial park.

5. A system for optimizing the operation of a power distribution system based on sensitive equipment loss according to any one of claims 1 to 4, characterized in that the system include: Sensitive equipment loss analysis module, used to analyze the shutdown conditions of sensitive equipment of power quality based on the symmetrical component method, and obtain the cost loss constraints of sensitive equipment; The optimization model building module is used to analyze historical statistical data, obtain line fault data, and build an optimization model based on sensitive equipment cost loss constraints with the goal of minimizing the distribution network operation cost; The verification module is used to build a sensitive equipment grid-connected system in the visual simulation software, verify the correctness of the optimization model, and use the optimization model to optimize the distribution system.

6. The system of the power distribution system optimization operation method based on sensitive equipment loss according to claim 5 is characterized in that: The sensitive equipment loss analysis module executes the expression for obtaining the sensitive equipment cost loss constraint as follows: ； In the formula, Losses caused by downtime of sensitive equipment, N t is the total running time, N s Indicates the number of sensitive devices in the power distribution system, C s,t Indicated in t Moment s The cost of producing each product with sensitive equipment, n s,t It is s Sensitive equipment in t The number of defective products caused by voltage sag at any given moment, Represents the voltage step signal of the sensitive device, is the phase voltage of the port sensitive device, yes t Voltage protection parameter values ​​of time-sensitive equipment, It is the current voltage protection value.

7. The system of the power distribution system optimization operation method based on sensitive equipment loss according to claim 5 is characterized in that: The expression for constructing the optimization model by the optimization model construction module is as follows: ； In the formula, N g is the total number of distributed micro-turbines, N t is the total running time, and They are the price and power that the distribution system purchases from the upper power grid. Losses caused by downtime of sensitive equipment, C MT,g,t and P MT,t Distributed micro-turbines g The unit power generation cost and power generation capacity, C m,t For industrial parks m From the purchase price of electricity in the distribution system, P m,t For industrial parks m From the purchased power of the distribution system, is the operating cost of the energy storage equipment, F is the set of failure time periods for each failure, C FT is the loss caused by each failure in each time period, Is a collection F The fault duration corresponding to each element in For the The duration of the fault, N F is the number of failures.

8. The system of the power distribution system optimization operation method based on sensitive equipment loss according to claim 5 is characterized in that: The verification module is based on solving the DC capacitor voltage of sensitive equipment during the temporary drop of grid voltage, taking into account the loss of sensitive equipment, and considering the demand response of equipment inside the industrial park and the operation of the sensitive equipment combined with the energy storage system, setting different verification scenarios to verify the correctness and effectiveness of the optimization model.

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