A power distribution network voltage stability regulation method with distributed energy storage participating in demand response

Abstract

The application provides a power distribution network voltage stability regulation method for distributed energy participating in demand response, comprising the following steps: constructing a distributed energy prediction model according to output data and prediction error data of the distributed energy; constructing a load prediction model according to load sample data and load prediction error data; constructing a comprehensive power system operation model according to photovoltaic unit operation characteristics, energy storage system operation characteristics, load operation characteristics and power distribution network system operation characteristics; constructing a two-stage voltage regulation method based on demand response including a day-ahead scheduling stage and a real-time operation stage according to the comprehensive power system operation model, the distributed energy prediction model and the load prediction model; obtaining voltage regulation results of the comprehensive system and demand response participation results according to the two-stage demand response voltage regulation method, so as to ensure that the voltage of the power distribution network is within the allowable range and reduce the power consumption cost of users.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology, and in particular to a method for regulating the voltage stability of a distribution network by incorporating distributed energy storage in demand response. Background Technology

[0002] Currently, with the introduction of dual-carbon goals, distributed energy and electric vehicles have experienced rapid development. However, the widespread application of distributed energy and electric vehicles has also brought new challenges to the power distribution network. On the one hand, the intermittency and uncertainty of photovoltaic power generation may lead to voltage rises in the distribution system (overvoltage problem). On the other hand, the simultaneous charging of a large number of electric vehicles may lead to voltage drops in the system (undervoltage problem), especially during peak charging periods at night. These voltage problems not only affect the stability and reliability of the power system but may also shorten the lifespan of equipment. In addition, with the gradual opening of the electricity market and the intensification of competition, electricity users are becoming increasingly sensitive to electricity prices. Users of distributed energy and electric vehicles hope to reduce electricity costs by rationally arranging their energy use and charging behavior, and even gain economic benefits by selling surplus electricity to the grid. However, the intermittency of photovoltaic power generation is prone to overvoltage, and the concentrated charging of electric vehicles is prone to undervoltage, which not only affects the stable and reliable operation of the power system and shortens the lifespan of equipment, but also contradicts the needs of electricity users to rationally use energy, reduce costs, and even gain benefits in conjunction with electricity prices. Summary of the Invention

[0003] In view of this, the present invention proposes a distribution network voltage stability regulation method for distributed energy storage to participate in demand response, which can realize two-stage voltage regulation and improve the stability and economy of the distribution network.

[0004] The technical solution of this invention is implemented as follows: A method for regulating the voltage stability of a distribution network with distributed energy storage participating in demand response includes the following steps: Step S1: Establish a renewable energy output prediction model based on renewable energy sample output data and its prediction error data, and at the same time establish a load prediction model based on load sample data and its prediction error data. Based on the renewable energy output prediction model and the load prediction model, the output prediction interval and load prediction interval for each time slot before the day are predicted. Step S2: Establish a comprehensive power system operation model based on the output prediction interval and the load prediction interval. The comprehensive power system operation model includes a photovoltaic power generation unit model, a distributed energy storage unit model, a flexible load model, a user-side power exchange model, and a distribution system model. Step S3: In the day-ahead scheduling phase, based on the integrated power system operation model and day-ahead electricity price information, the day-ahead plans for distributed energy storage and flexible loads are obtained by minimizing the weighted index of user electricity cost and node voltage over-limit risk. Step S4: During the real-time operation phase, based on the day-ahead plan, the distribution operation is carried out, the voltage of each node is collected in real time and over-limit judgment is performed. When the node voltage over-limit is detected, based on the Jacobian matrix or voltage-power sensitivity matrix of the distribution network power flow equation at the current operating point, the reactive power regulation amount and / or active power regulation amount required to eliminate the over-limit is calculated. Step S5: Prioritize controlling the photovoltaic inverter to output reactive power within the apparent power and power factor constraints of the photovoltaic power generation unit. When the reactive power regulation is insufficient to eliminate the over-limit, select at least one energy storage unit from the available distributed energy storage set that meets the SOC constraint, power upper limit constraint and remaining time window constraint, and perform charging and discharging active power regulation based on the distributed energy storage unit model to restore the node voltage to the allowable range.

[0005] Preferably, the expression for the renewable energy output prediction model is:

[0006] in Forecast data on renewable energy output, Provide data for renewable energy samples. The power output prediction error for renewable energy samples has a range of values. , For renewable energy sample power output, minimum prediction error data, Given the maximum prediction error data for renewable energy output, the renewable energy output prediction model can also be expressed as:

[0007] in The coefficients for the minimum prediction error data of renewable energy sample output; The load includes on-demand load and flexible load, and the expression for the load forecasting model is as follows: (3)

[0009] in For on-demand load forecasting data, To load sample data on demand, This is the on-demand load forecasting error data, and its value range is: ,in This is the minimum forecast error data for on-demand load. This is the maximum forecast error data for on-demand load. For flexible load forecasting data, To flexibly load sample data, This is flexible load forecasting error data, and its value range is: ,in For flexible load minimum forecast error data, For the maximum forecast error data of flexible load, the expression of the load forecasting model can also be expressed as: (4)

[0011] in The coefficients for the maximum forecast error data of on-demand load. The coefficients are for the maximum prediction error data of flexible loads.

[0012] Preferably, the expression for the photovoltaic power generation unit model is:

[0013]

[0014]

[0015]

[0016] in The apparent power limit of the photovoltaic inverter. The active power of the photovoltaic inverter. This represents the instantaneous value of the reactive power of the photovoltaic inverter. For power factor, The reactive power of the photovoltaic inverter under the power factor limit. The adjustable reactive power of user i's p-phase photovoltaic inverter in time slot t. Let be the instantaneous reactive power value of user i's p-phase photovoltaic inverter in time slot t. Let be the reactive power of the p-phase photovoltaic inverter for user i under the power factor limit in time slot t.

[0017] Preferably, the dynamic model expression of the distributed energy storage unit model is:

[0018] in For energy storage SOC, its range is:

[0019] in and These are the minimum SOC limit and the maximum SOC limit for energy storage, respectively. for Time-slot energy storage SOC For efficiency coefficient, For time slot interval, The rated capacity of the energy storage unit, The active power of the distributed energy storage unit; definition For a feasible charge and discharge cycle of a distributed energy storage unit, the state of charge changes from... In the beginning, Previously, the battery was fully charged, when At that time, the charging and discharging power of the distributed energy storage unit is:

[0020] in For user i in time slot t, the p-phase energy storage charging and discharging power. This represents the maximum charging and discharging power of the energy storage. The SOC constraint before the deadline is:

[0021] in This is the deadline.

[0022] Preferably, the load includes on-demand load and flexible load, wherein each user's on-demand load in time slot t It is fixed, and the expression for the flexible load is:

[0023] in The total flexible load power of user i in phase p during time slot t. The rated power of flexible device A for user i's p phase in time slot t. For the total number of flexible devices, The operating status of flexible device A for user i's p phase in time slot t; definition The permissible operating range of flexible device A, that is, the range in which flexible device A operates. Then begin work, and must The work was completed previously. Described as:

[0024]

[0025] in The rated power of user i's p-phase flexible device A, For the actual startup time slot of flexible device A, The required operating time for flexible device A, For the end gap of flexible device A; The formula for calculating the total load of user i's p phase in time slot t is:

[0026] Among them flexible load ,in These are household heat load, EV charging load, and air conditioning load, respectively.

[0027] Preferably, the expression for the user-side power switching model is:

[0028] in For user i, the active power exchange of phase p in time slot t. The active power output of the p-phase photovoltaic inverter for user i.

[0029] Preferably, the expression for the power flow calculation of the power distribution system model is:

[0030]

[0031]

[0032]

[0033] in This refers to the voltage at the beginning node of the power distribution system. This is the rated bus voltage of the substation. This refers to the transformer tap position. This is the tap changer voltage adjustment coefficient. and For active power exchange and reactive power exchange of user i's p-phase, Let p be the amplitude of the phase p voltage at node i. Let p be the voltage amplitude of the adjacent node j. The total number of adjacent nodes. Let p-phase conductance be the voltage at nodes i and j. For the p-phase susceptance of nodes i and j, Let p be the phase angle difference between the p-phase voltages at nodes i and j. The p-phase current flowing through distribution lines i and j, These are the limits for the circuit.

[0034] Preferably, step S3 constructs a total optimization objective function with the goal of minimizing the weighted index of user electricity cost and node voltage exceedance risk, wherein the mathematical expression of the total optimization objective function F is:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] in Here, T represents the weighting coefficient, and T represents the total number of time slots to be scheduled. The cost of purchasing electricity for time slot t. For the revenue from selling electricity in time slot t, For the demand response benefit of time slot t, This is the violation identifier for the p-phase voltage of user i in time slot t, where c is the number of three-phase electrical phases. The total number of user nodes. For electricity purchase price, For active power exchange of user i in time slot t, phase p. For electricity sales price, The unit price is a demand response subsidy. and Separate the upper voltage limit and the lower voltage limit. For reactive power exchange of user i in time slot t, phase p Let p be the reactive power of the load for user i in time slot t. Let t be the photovoltaic reactive power of user i in phase p.

[0041] Preferably, the day-ahead schedule in step S3 is solved by combining the GA genetic algorithm and the chromosome method. The specific steps are as follows: The scheduling decision variables of distributed energy storage and flexible loads within the scheduling cycle are encoded as chromosome individuals, where chromosome genes are used to characterize the charging and discharging periods or power regulation states of distributed energy storage and the operation period adjustment strategies of flexible loads. Under the premise of satisfying the state of charge constraints, voltage constraints and equipment operation constraints of distributed energy storage, an initial chromosome population is generated randomly or semi-randomly. Based on the scheduling scheme corresponding to the chromosome, the fitness value related to electricity cost and voltage over-limit situation is calculated in combination with the overall optimization objective function F; The selection, crossover, and mutation operations are performed on the chromosome population to generate a new generation of chromosome individuals. When the preset number of generations or convergence conditions are met, the optimal chromosome individual is output as the optimization result of the day-ahead scheduling phase and is used as the day-ahead plan.

[0042] Preferably, step S4, based on the Jacobian matrix or voltage-power sensitivity matrix of the distribution network power flow equation at the current operating point, calculates the reactive power regulation and / or active power regulation required to eliminate over-limit conditions as follows: Linearize the power flow equations:

[0043] in This is the voltage phase angle change vector. This is the voltage amplitude change vector. Let be the inverse of the Jacobian matrix. This is the vector of active power change. This is the reactive power change vector; The voltage-power sensitivity matrix S is the inverse of the Jacobian matrix, and its specific expression is:

[0044] in , , , These are the phase angle-active power sensitivity submatrix, phase angle-reactive power sensitivity submatrix, voltage amplitude-active power sensitivity submatrix, and voltage amplitude-reactive power sensitivity submatrix, respectively. Voltage amplitude change vector The expression is:

[0045] The reactive power regulation amount in time slot t and active power regulation The expression is:

[0046]

[0047] in and These are time slots t and t, respectively. and The inverse matrix, Let t be the voltage deviation vector in time slot t. , Let N be the voltage deviation vector of user i in time slot t, and N be the number of users. The p-phase voltage difference of user i in time slot t The mathematical expression is:

[0048] in The actual p-phase voltage of user i in time slot t. and These are the upper voltage threshold and the lower voltage threshold, respectively.

[0049] Compared with the prior art, the beneficial effects of the present invention are: This invention discloses a distribution network voltage stabilization regulation method involving distributed energy storage in demand response. Based on historical sample data and prediction error data of renewable energy and loads, a renewable energy output prediction model and a load prediction model are established respectively. On this basis, combined with real-time node voltage, network topology, and line parameters, a comprehensive power system operation model is established, including photovoltaic discharge units, distributed energy storage units, flexible loads, and the distribution system. Subsequently, during the day-ahead dispatch phase, energy storage regulation capacity is pre-allocated, taking into account electricity price signals, user costs, and voltage constraints to reduce the risk of voltage exceedances in advance. During the real-time operation phase, through voltage monitoring and exceedance discrimination mechanisms, based on the sensitivity relationship between node voltage and changes in active and reactive power, voltage regulation demand is quickly calculated, and distributed energy storage is prioritized for bidirectional active power regulation. When necessary, flexible loads are coordinated to participate in demand response, thereby achieving voltage recovery. At the level of solving the day-ahead plan, this invention introduces a two-stage optimization solution mechanism based on genetic algorithms. It uses chromosome encoding to express the scheduling decision variables of distributed energy storage and flexible loads, comprehensively evaluates electricity costs and voltage overruns through a fitness function, and obtains the optimal day-ahead plan by using selection, crossover and mutation operations. The optimal day-ahead plan is then combined with real-time voltage feedback control to enhance the system's adaptability to prediction uncertainties. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart of a distribution network voltage stability regulation method for distributed energy storage participating in demand response, according to the present invention. Detailed Implementation

[0052] To better understand the technical content of this invention, a specific embodiment is provided below, and the invention will be further described in conjunction with the accompanying drawings.

[0053] See Figure 1 This invention provides a method for distributed energy storage participating in demand response to stabilize distribution network voltage. It combines a community energy management system and multiple subsystems to form a hierarchical control structure. Taking the voltage exceeding the limit of distribution network nodes as the starting point, it establishes a sensitivity relationship between load power changes and node voltage. When the voltage deviates from the allowable range, it schedules the distributed energy storage system to adjust the active power at different nodes, thereby achieving voltage recovery. The method aims to minimize load regulation costs or user discomfort and performs optimization solutions under the premise of meeting voltage, grid operation, and load constraints.

[0054] Includes the following steps: Step S1: Establish a renewable energy output prediction model based on renewable energy sample output data and its prediction error data. Simultaneously, establish a load prediction model based on load sample data and its prediction error data. Based on the renewable energy output prediction model and the load prediction model, predict the output prediction interval and load prediction interval for each time slot before the day. The expression for the renewable energy output prediction model is:

[0055] in Forecast data on renewable energy output, Provide data for renewable energy samples. The power output prediction error for renewable energy samples has a range of values. , For renewable energy sample power output, minimum prediction error data, Given the maximum prediction error data for renewable energy output, the renewable energy output prediction model can also be expressed as:

[0056] in The coefficients for the minimum prediction error data of renewable energy sample output; The load includes on-demand load and flexible load, and the expression for the load forecasting model is as follows: (3)

[0058] in For on-demand load forecasting data, To load sample data on demand, This is the on-demand load forecasting error data, and its value range is: ,in This is the minimum forecast error data for on-demand load. This is the maximum forecast error data for on-demand load. For flexible load forecasting data, To flexibly load sample data, This is flexible load forecasting error data, and its value range is: ,in For flexible load minimum forecast error data, For the maximum forecast error data of flexible load, the expression of the load forecasting model can also be expressed as: (4)

[0060] in The coefficients for the maximum forecast error data of on-demand load can determine the feasible region of the electrical load forecast data. The coefficients for the maximum forecast error data of flexible loads can determine the feasible region of flexible load forecast data.

[0061] The renewable energy output data comes from renewable energy generating units, including photovoltaic panels and wind turbines. The load includes on-demand load and flexible load. Based on this, expressions for on-demand load and flexible load are constructed respectively, which retain the rigidity of on-demand load and highlight the dispatchable potential of flexible load. Through the constructed renewable energy output prediction model and load prediction model, the output prediction interval and load prediction interval for each time slot before the day can be predicted respectively, quantifying the uncertainty brought about by the intermittency of renewable energy and load fluctuation, and providing more reliable boundary conditions for subsequent dispatch.

[0062] Step S2: Establish a comprehensive power system operation model based on the output prediction interval and the load prediction interval. The comprehensive power system operation model includes a photovoltaic power generation unit model, a distributed energy storage unit model, a flexible load model, a user-side power exchange model, and a distribution system model. First, a photovoltaic power generation unit model is established, in which the expression controlling the reactive power output of the photovoltaic is shown in formula (5):

[0063] The power factor of photovoltaic power generation can be obtained by formula (6):

[0064] In a power distribution system, the absolute value of the power factor of photovoltaic power generation should be within the allowable range. A low absolute power factor will increase power loss; therefore, the reactive power under the power factor limit is given by formula (7).

[0065] in, Let t represent a time slot of one day. The adjustable reactive power of user i's p-phase photovoltaic inverter in time slot t is shown in formula (8):

[0066] in The apparent power limit of the photovoltaic inverter. The active power of the photovoltaic inverter. This represents the instantaneous value of the reactive power of the photovoltaic inverter. For power factor, The reactive power of the photovoltaic inverter under the power factor limit. The adjustable reactive power of user i's p-phase photovoltaic inverter in time slot t. Let be the instantaneous reactive power value of user i's p-phase photovoltaic inverter in time slot t. Let be the reactive power of the p-phase photovoltaic inverter for user i under the power factor limit in time slot t.

[0067] The photovoltaic power generation unit operates in maximum power point tracking mode, and its active power output is a known value. The photovoltaic inverter adjusts the reactive power output under apparent power and power factor constraints to provide voltage support for distribution network nodes. The constructed photovoltaic power generation unit model can provide reactive power regulation constraints for the photovoltaic inverter in the integrated power system operation model, clarify the equipment capacity constraints and power factor constraints for reactive power regulation, and provide compliance boundaries for subsequent reactive power voltage regulation.

[0068] Preferably, the dynamic model expression of the distributed energy storage unit model is:

[0069] in For energy storage SOC, its range is:

[0070] in and These are the minimum SOC limit and the maximum SOC limit for energy storage, respectively. for Time-slot energy storage SOC This is the efficiency coefficient, which is independent of charging power. For time slot interval, The rated capacity of the energy storage unit, The active power of the distributed energy storage unit; definition For a feasible charge and discharge cycle of a distributed energy storage unit, the state of charge changes from... In the beginning, Previously, the battery was fully charged, when At that time, the charging and discharging power of the distributed energy storage unit is:

[0071] in For user i in time slot t, the p-phase energy storage charging and discharging power. This represents the maximum charging and discharging power of the energy storage. Energy storage should be fully charged before the demand response deadline. The SOC constraint before the deadline is:

[0072] in This is the deadline.

[0073] Distributed energy storage units have charging and discharging states. Their charging and discharging power is constrained by the maximum charging and discharging power, and their state of charge is limited by the minimum and maximum state of charge. They are used to participate in the voltage stability regulation of the distribution network through bidirectional active power regulation during demand response. The distributed energy storage unit model can provide dynamic constraints on the state of charge (SOC) and charging and discharging power of distributed energy storage to the integrated power system operation model, ensuring the lifespan and operational safety of energy storage.

[0074] Flexible load units are specifically divided into on-demand loads and flexible loads. On-demand loads include electrical loads such as lighting and television, because their power consumption is typically not easily scheduled. In contrast, the duty cycles of flexible loads can be flexibly rearranged; for example, a customer may only care whether a washing machine completes its task within a specified timeframe. Flexible load scheduling can be used for voltage regulation. When constructing a flexible load model, it is necessary to model both on-demand and flexible loads separately. However, since on-demand loads cannot be scheduled, therefore... It can be assumed that the time slot t is fixed for each user, and the expression for the flexible load is:

[0075] in The total flexible load power of user i in phase p during time slot t. The rated power of flexible device A for user i's p phase in time slot t. For the total number of flexible devices, The operating status of flexible device A for user i's p phase in time slot t; definition The permissible operating range of flexible device A, that is, the range in which flexible device A operates. Then begin work, and must The work was completed previously. Described as:

[0076]

[0077] in The rated power of user i's p-phase flexible device A, For the actual startup time slot of flexible device A, The required operating time for flexible device A, For the end gap of flexible device A; The formula for calculating the total load of user i's p phase in time slot t is:

[0078] Among them flexible load ,in These are household heat load, EV charging load, and air conditioning load, respectively.

[0079] Flexible loads have a working time window that can be delayed. By adjusting their operating time, they can participate in demand response regulation. The constructed flexible load model can provide the integrated power system operation model with adjustable time window constraints for flexible loads. By defining the adjustable time window, the potential for load shifting can be preserved while meeting the energy needs of users.

[0080] Preferably, the expression for the user-side power switching model is:

[0081] in For user i, the active power exchange of phase p in time slot t. The active power output of the p-phase photovoltaic inverter for user i.

[0082] Based on the photovoltaic power generation capacity, the distributed energy storage charging and discharging capacity, and the load power, the active and reactive power exchange relationship between the user and the distribution network is determined.

[0083] Preferably, the power flow calculation of the radial power distribution system model can be modeled and described as follows:

[0084]

[0085]

[0086]

[0087] in This refers to the voltage at the beginning node of the power distribution system. This is the rated bus voltage of the substation. This refers to the transformer tap position. This is the tap changer voltage adjustment coefficient. and For active power exchange and reactive power exchange of user i's p-phase, Let p be the amplitude of the phase p voltage at node i. Let p be the voltage amplitude of the adjacent node j. The total number of adjacent nodes. Let p-phase conductance be the voltage at nodes i and j. For the p-phase susceptance of nodes i and j, Let p be the phase angle difference between the p-phase voltages at nodes i and j. The p-phase current flowing through distribution lines i and j, These are the limits for the circuit.

[0088] The distribution system model is used to describe the response relationship of the distribution network node voltage to the changes in the charging and discharging power of the distributed energy storage and the load power. By constructing the distribution system model, the allowable range constraints of the distribution network nodes can be provided for the integrated power system operation model, ensuring that the voltage regulation process does not exceed the safe operation limit of the distribution network.

[0089] In day-ahead optimization, the usage period of distributed energy storage and flexible loads is determined based on the day-ahead electricity price. The energy management system receives the day-ahead electricity price from the electricity market and then sends it to each sub-energy management system. Each sub-energy management system returns a provisional dispatch plan to the energy management system. The energy management system collects all information and sends it to the distribution network operator, who estimates the photovoltaic forecast information and on-demand load data for the next day using historical data. Based on the load data and estimated photovoltaic data, power flow calculations are performed on the distribution system. Finally, the operator sends the transformer voltage to the energy management system, which then sends the specific master node voltage to the sub-energy management systems. Each sub-energy management system considers the following two objectives to optimize the dispatch of distributed energy storage and flexible loads: minimizing the electricity cost and voltage violation frequency of its subsystem. Therefore, in step S3, during the day-ahead dispatch phase, based on the integrated power system operation model and day-ahead electricity price information, a total optimization objective function F is constructed with the goal of minimizing the weighted index of user electricity cost and node voltage violation risk. Solving this function yields the day-ahead plan for distributed energy storage and flexible loads, as well as the voltage regulation reserve capacity for distributed energy storage. The mathematical expression of the total optimization objective function F is:

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] in Here, T represents the weighting coefficient, and T represents the total number of time slots to be scheduled. The cost of purchasing electricity for time slot t. For the revenue from selling electricity in time slot t, For the demand response benefit of time slot t, This is the violation identifier for the p-phase voltage of user i in time slot t, where c is the number of three-phase electrical phases. The total number of user nodes. For electricity purchase price, For active power exchange of user i in time slot t, phase p. For electricity sales price, The unit price is a demand response subsidy. and Separate the upper voltage limit and the lower voltage limit. For reactive power exchange of user i in time slot t, phase p Let p be the reactive power of the load for user i in time slot t. Let t be the photovoltaic reactive power of user i in phase p.

[0096] In the overall optimization objective function, the first part represents the total electricity cost for all users under the same sub-energy management system on the next day, and the second part is the total number of voltage violations for all user nodes. The larger the λ in the overall optimization objective function F, the more emphasis is placed on minimizing electricity costs. By using the minimization of electricity costs plus the minimization of voltage limit risk as weighted objectives, the probability of voltage limit violations is actively reduced while ensuring user economic benefits, achieving a win-win situation for economic benefits and grid stability. At the same time, the charging and discharging periods of energy storage and the operation periods of flexible loads can be planned in advance, reserving sufficient regulation capacity for real-time voltage regulation and avoiding voltage regulation failure due to resource depletion during the real-time stage. After the dispatch results are generated, the distribution network voltage is checked to correct the limit risk points in advance, transforming passive voltage regulation into proactive prevention and significantly reducing the triggering frequency of real-time voltage regulation.

[0097] The current plan is to combine the GA genetic algorithm and the chromosome method to solve the problem. The specific steps are as follows: The scheduling decision variables of distributed energy storage and flexible loads within the scheduling cycle are encoded as chromosome individuals, where chromosome genes are used to characterize the charging and discharging periods or power regulation states of distributed energy storage and the operation period adjustment strategies of flexible loads. Under the premise of satisfying the state of charge constraints, voltage constraints and equipment operation constraints of distributed energy storage, an initial chromosome population is generated randomly or semi-randomly. Based on the scheduling scheme corresponding to the chromosome, the fitness value related to electricity cost and voltage over-limit situation is calculated in combination with the overall optimization objective function F; The selection, crossover, and mutation operations are performed on the chromosome population to generate a new generation of chromosome individuals. When the preset number of generations or convergence conditions are met, the optimal chromosome individual is output as the optimization result of the day-ahead scheduling phase and is used as the day-ahead plan.

[0098] Algorithm efficiency: By using intelligent algorithms such as genetic algorithms, the globally optimal scheduling scheme can be obtained quickly under complex constraints, adapting to the computational needs of large-scale power distribution networks.

[0099] The overall optimization objective function F consists of two parts: minimizing user electricity costs and minimizing the number of voltage violations. The decision value is the start time of all distributed energy storage systems. During the initialization of the GA, the start time of distributed energy storage scheduling is randomly determined based on satisfied constraints, and then an initial population is constructed using the decision value. Fitness evaluation is performed by calculating the objective function, starting from the start time and calculating the power of distributed energy storage during the working time. This determines the customer's electricity costs and the number of voltage violations. Next, a roulette wheel algorithm is used for chromosome selection. A signal point algorithm is used for crossover. During mutation, some genes are replaced by randomly generated start times. Subsequently, a new population is generated, and the GA repeats this process until a predetermined number of generations is reached. By using intelligent algorithms such as genetic algorithms, a globally optimal scheduling scheme can be quickly obtained under complex constraints, adapting to the computational needs of large-scale distribution networks.

[0100] During the day-ahead dispatch phase, based on the predicted load power, distributed photovoltaic output, and electricity price information, and taking into account user energy costs and distribution network voltage constraints, the usage periods of distributed energy storage and flexible loads are optimized to reduce the risk of voltage exceeding limits. Based on the day-ahead dispatch results, the voltage of the distribution network is checked, and the operating status of the distribution network voltage regulating device is coordinated to ensure that the node voltage meets the allowable range constraints. Step S4: During the real-time operation phase, based on the day-ahead plan, the distribution operation is carried out, and the voltage of each node is collected in real time and over-limit judgment is performed. When a node voltage over-limit is detected, based on the Jacobian matrix or voltage-power sensitivity matrix of the distribution network power flow equation at the current operating point, the reactive power regulation and / or active power regulation required to eliminate the over-limit are calculated. The specific steps are as follows: Since power flow is nonlinear, different electrical appliances operating at different user nodes i will produce different voltage regulation effects. Therefore, the power flow equation is linearized:

[0101] in This is the voltage phase angle change vector. This is the voltage amplitude change vector. Let be the inverse of the Jacobian matrix. This is the vector of active power change. This is the reactive power change vector; The voltage-power sensitivity matrix S is derived from the system Jacobian matrix obtained by the Newton-Raphson algorithm for solving nonlinear power flow, and is the inverse of the Jacobian matrix. Its specific expression is as follows:

[0102] in , , , These are the phase angle-active power sensitivity submatrix, phase angle-reactive power sensitivity submatrix, voltage amplitude-active power sensitivity submatrix, and voltage amplitude-reactive power sensitivity submatrix, respectively. Voltage amplitude change vector The expression is:

[0103] In real-time operation, reactive power and active power operate sequentially, therefore the reactive power adjustment in time slot t... and active power regulation The expression is:

[0104]

[0105] in and These are time slots t and t, respectively. and The inverse matrix, Let t be the voltage deviation vector in time slot t. , Let N be the voltage deviation vector of user i in time slot t, and N be the number of users. The p-phase voltage difference of user i in time slot t The mathematical expression is:

[0106] in The actual p-phase voltage of user i in time slot t. and These are the upper voltage threshold and the lower voltage threshold, respectively.

[0107] During the real-time operation phase, the voltage at distribution network nodes is continuously monitored while executing the day-ahead plan. When a voltage over-limit is detected, a real-time voltage regulation process is triggered. Based on the distribution network operating status, a sensitivity relationship is established between node voltage and changes in active and reactive power. This relationship is used to calculate the power regulation required to eliminate the voltage over-limit. and These are the reactive power and active power regulation amounts required to adjust the voltage to the allowable range. At the beginning of each time slot, when a voltage exceedance is observed, each sub-energy management system... and Search and operate on available reactive or active power separately. For example, under undervoltage conditions, the active power that needs to be turned off at user i on phase p is... First, the available active power of user i is searched. Then, a list of power values ​​is generated using different combinations of available distributed energy storage systems. Finally, a system is selected... The largest and closest value. Available distributed energy storage systems refer to those that are operational, can be interrupted, and still have sufficient time to complete their tasks. In other words, the aforementioned constraints on distributed energy storage systems are considered during real-time operation.

[0108] Step S5: Prioritize controlling the photovoltaic inverter to output reactive power within the apparent power and power factor constraints of the photovoltaic power generation unit. When the reactive power regulation is insufficient to eliminate the over-limit, select at least one energy storage unit from the available distributed energy storage set that meets the SOC constraint, power upper limit constraint and remaining time window constraint, and perform charging and discharging active power regulation based on the distributed energy storage unit model to restore the node voltage to the allowable range.

[0109] In the real-time operation phase, voltage monitoring and over-limit detection are performed, and voltage regulation demand is calculated. Based on the calculation results, it is determined whether photovoltaic inverters and distributed energy storage should respond first. After day-ahead optimization, the start-up time of distributed energy storage is determined by the sub-energy management system to minimize electricity costs and the number of voltage over-limit occurrences. However, the day-ahead dispatch plan includes estimation errors for photovoltaics. Due to these errors, voltage over-limits may still occur. Therefore, real-time operation of distributed energy storage is necessary to ensure that the voltage remains within the allowable range. In real-time operation, each sub-energy management system follows the day-ahead dispatch to observe the voltage profile of its covered system. When overvoltage occurs, the adjustable reactive power of the photovoltaic inverters is first used to reduce the voltage deviation. If all adjustable reactive power has been used and the overvoltage still cannot be regulated, the combined unactivated distributed energy storage will be deployed to reduce the voltage, and flexible loads will be coordinated to participate in demand response if necessary to achieve voltage recovery. In contrast, when undervoltage occurs, the distributed energy storage combination that can be delayed will be shut down to improve the voltage.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for voltage stabilization regulation of a distribution network in which distributed energy storage participates in demand response, characterized in that, Includes the following steps: Step S1: Establish a renewable energy output prediction model based on renewable energy sample output data and its prediction error data, and at the same time establish a load prediction model based on load sample data and its prediction error data. Based on the renewable energy output prediction model and the load prediction model, the output prediction interval and load prediction interval for each time slot before the day are predicted. Step S2: Establish a comprehensive power system operation model based on the output prediction interval and the load prediction interval. The comprehensive power system operation model includes a photovoltaic power generation unit model, a distributed energy storage unit model, a flexible load model, a user-side power exchange model, and a distribution system model. Step S3: In the day-ahead scheduling phase, based on the integrated power system operation model and day-ahead electricity price information, the day-ahead plans for distributed energy storage and flexible loads are obtained by minimizing the weighted index of user electricity cost and node voltage over-limit risk. Step S4: During the real-time operation phase, based on the day-ahead plan, the distribution operation is carried out, the voltage of each node is collected in real time and over-limit judgment is performed. When the node voltage over-limit is detected, based on the Jacobian matrix or voltage-power sensitivity matrix of the distribution network power flow equation at the current operating point, the reactive power regulation amount and / or active power regulation amount required to eliminate the over-limit is calculated. Step S5: Prioritize controlling the photovoltaic inverter to output reactive power within the apparent power and power factor constraints of the photovoltaic power generation unit. When the reactive power regulation is insufficient to eliminate the over-limit, select at least one energy storage unit from the available distributed energy storage set that meets the SOC constraint, power upper limit constraint and remaining time window constraint, and perform charging and discharging active power regulation based on the distributed energy storage unit model to restore the node voltage to the allowable range.

2. The method for voltage stabilization regulation of a distribution network with distributed energy storage participating in demand response as described in claim 1, characterized in that, The expression for the renewable energy output prediction model is: in Forecast data on renewable energy output, Provide data for renewable energy samples. The power output prediction error for renewable energy samples has a range of values. , For renewable energy sample power output, minimum prediction error data, Given the maximum prediction error data for renewable energy output, the renewable energy output prediction model can also be expressed as: in The coefficients for the minimum prediction error data of renewable energy sample output; The load includes on-demand load and flexible load, and the expression for the load forecasting model is as follows: (3) in For on-demand load forecasting data, To load sample data on demand, This is the on-demand load forecasting error data, and its value range is: ,in This is the minimum forecast error data for on-demand load. This is the maximum forecast error data for on-demand load. For flexible load forecasting data, To flexibly load sample data, This is flexible load forecasting error data, and its value range is: ,in For flexible load minimum forecast error data, For the maximum forecast error data of flexible load, the expression of the load forecasting model can also be expressed as: (4) in The coefficients for the maximum forecast error data of on-demand load. The coefficients are for the maximum prediction error data of flexible loads.

3. The method for voltage stabilization regulation of a distribution network with distributed energy storage participating in demand response as described in claim 1, characterized in that, The expression for the photovoltaic power generation unit model is: in The apparent power limit of the photovoltaic inverter. The active power of the photovoltaic inverter. This represents the instantaneous value of the reactive power of the photovoltaic inverter. For power factor, The reactive power of the photovoltaic inverter under the power factor limit. The adjustable reactive power of user i's p-phase photovoltaic inverter in time slot t. Let be the instantaneous reactive power value of user i's p-phase photovoltaic inverter in time slot t. Let be the reactive power of the p-phase photovoltaic inverter for user i under the power factor limit in time slot t.

4. The method for voltage stabilization regulation of a distribution network with distributed energy storage participating in demand response as described in claim 1, characterized in that, The dynamic model expression of the distributed energy storage unit model is: in For energy storage SOC, its range is: in and These are the minimum SOC limit and the maximum SOC limit for energy storage, respectively. for Time-slot energy storage SOC For efficiency coefficient, For time slot interval, The rated capacity of the energy storage unit, The active power of the distributed energy storage unit; definition For a feasible charge and discharge cycle of a distributed energy storage unit, the state of charge changes from... In the beginning, Previously, the battery was fully charged, when At that time, the charging and discharging power of the distributed energy storage unit is: in For user i in time slot t, the p-phase energy storage charging and discharging power. This represents the maximum charging and discharging power of the energy storage. The SOC constraint before the deadline is: in This is the deadline.

5. A method for voltage stabilization regulation of a distribution network with distributed energy storage participating in demand response, as described in claim 1, characterized in that, The load includes on-demand load and flexible load, wherein each user's on-demand load in time slot t It is fixed, and the expression for the flexible load is: in The total flexible load power of user i in phase p during time slot t. The rated power of flexible device A for user i's p phase in time slot t. For the total number of flexible devices, The operating status of flexible device A for user i's p phase in time slot t; definition The permissible operating range of flexible device A, that is, the range in which flexible device A operates. Then begin work, and must The work was completed previously. Described as: in The rated power of user i's p-phase flexible device A, For the actual startup time slot of flexible device A, The required operating time for flexible device A, For the end gap of flexible device A; The formula for calculating the total load of user i's p phase in time slot t is: Among them flexible load ,in These are household heat load, EV charging load, and air conditioning load, respectively.

6. A method for voltage stabilization regulation of a distribution network with distributed energy storage participating in demand response, as described in claim 5, is characterized in that, The expression for the user-side power exchange model is: in For user i, the active power exchange of phase p in time slot t. The active power output of the p-phase photovoltaic inverter for user i.

7. A method for voltage stabilization regulation of a distribution network with distributed energy storage participating in demand response, as described in claim 1, is characterized in that, The expression for power flow calculation in the power distribution system model is as follows: in This refers to the voltage at the beginning node of the power distribution system. This is the rated bus voltage of the substation. This refers to the transformer tap position. This is the tap changer voltage adjustment coefficient. and For active power exchange and reactive power exchange of user i's p-phase, Let p be the amplitude of the phase p voltage at node i. Let p be the voltage amplitude of the adjacent node j. The total number of adjacent nodes. Let p-phase conductance be the voltage at nodes i and j. For the p-phase susceptance of nodes i and j, Let p be the phase angle difference between the p-phase voltages at nodes i and j. The p-phase current flowing through distribution lines i and j, These are the limits for the circuit.

8. A method for voltage stabilization regulation of a distribution network with distributed energy storage participating in demand response, as described in claim 1, is characterized in that, Step S3 constructs a total optimization objective function with the goal of minimizing the weighted index of user electricity cost and node voltage exceedance risk. The mathematical expression of the total optimization objective function F is as follows: in Here, T represents the weighting coefficient, and T represents the total number of time slots to be scheduled. The cost of purchasing electricity for time slot t. For the revenue from selling electricity in time slot t, For the demand response benefit of time slot t, This is the violation identifier for the p-phase voltage of user i in time slot t, where c is the number of three-phase electrical phases. The total number of user nodes. For electricity purchase price, For active power exchange of user i in time slot t, phase p. For electricity sales price, The unit price is a demand response subsidy. and Separate the upper voltage limit and the lower voltage limit. For reactive power exchange of user i in time slot t, phase p Let p be the reactive power of the load for user i in time slot t. Let t be the photovoltaic reactive power of user i in phase p.

9. A method for voltage stabilization regulation of a distribution network with distributed energy storage participating in demand response, as described in claim 8, is characterized in that, The day-ahead schedule in step S3 is solved by combining the GA genetic algorithm and the chromosome method. The specific steps are as follows: The scheduling decision variables of distributed energy storage and flexible loads within the scheduling cycle are encoded as chromosome individuals, where chromosome genes are used to characterize the charging and discharging periods or power regulation states of distributed energy storage and the operation period adjustment strategies of flexible loads. Under the premise of satisfying the state of charge constraints, voltage constraints and equipment operation constraints of distributed energy storage, an initial chromosome population is generated randomly or semi-randomly. Based on the scheduling scheme corresponding to the chromosome, the fitness value related to electricity cost and voltage over-limit situation is calculated in combination with the overall optimization objective function F; The selection, crossover, and mutation operations are performed on the chromosome population to generate a new generation of chromosome individuals. When the preset number of generations or convergence conditions are met, the optimal chromosome individual is output as the optimization result of the day-ahead scheduling phase and is used as the day-ahead plan.

10. A method for voltage stabilization regulation of a distribution network with distributed energy storage participating in demand response, as described in claim 1, characterized in that, The specific steps of step S4, which calculates the reactive power regulation and / or active power regulation required to eliminate over-limits based on the Jacobian matrix or voltage-power sensitivity matrix of the power flow equation at the current operating point, are as follows: Linearize the power flow equations: in This is the voltage phase angle change vector. This is the voltage amplitude change vector. Let be the inverse of the Jacobian matrix. This is the vector of active power change. This is the reactive power change vector; The voltage-power sensitivity matrix S is the inverse of the Jacobian matrix, and its specific expression is: in , , , These are the phase angle-active power sensitivity submatrix, phase angle-reactive power sensitivity submatrix, voltage amplitude-active power sensitivity submatrix, and voltage amplitude-reactive power sensitivity submatrix, respectively. Voltage amplitude change vector The expression is: The reactive power regulation amount in time slot t and active power regulation The expression is: in and These are time slots t and t, respectively. and The inverse matrix, Let t be the voltage deviation vector in time slot t. , Let N be the voltage deviation vector of user i in time slot t, and N be the number of users. The p-phase voltage difference of user i in time slot t The mathematical expression is: in The actual p-phase voltage of user i in time slot t. and These are the upper voltage threshold and the lower voltage threshold, respectively.

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