Power distribution network adjustable robust decision-making method considering urban resource uncertainty set difference

Abstract

The invention belongs to the technical field of optimal dispatching of a power system, and discloses a power distribution network adjustable robust decision-making method considering urban resource uncertain set difference, which aims at the difference of uncertain resources under different time scales in the operation planning process of a virtual power plant, and carries out targeted modeling by considering the characteristics of each uncertain variable; on this basis, a virtual power plant multi-time scale resource optimization scheduling model is established, a switching scheme of discrete equipment such as a capacitor bank is obtained by considering the occurrence probability of a unit fault area day ahead, and an output adjustment plan of equipment such as a photovoltaic inverter and a battery swap station is obtained by considering the fluctuation conditions of variables such as light intensity and load day. The model comprehensively considers the power utilization cost, the equipment adjustment cost, the voltage offset degree and the decision vulnerability degree, and finally a virtual power plant comprehensive operation scheme is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization and dispatching, and in particular is a distribution network adjustable robust decision-making method taking into account the differences in uncertain sets of urban resources. Background Art

[0002] Photovoltaic power generation, as a core source of renewable energy generation, is gaining increasing attention for its clean, efficient, and low-cost characteristics. However, despite the numerous advantages of distributed photovoltaic systems, such as rapid construction, low investment, and reduced risk, their highly volatile generation characteristics pose challenges to power system planning and regulation. Furthermore, there can be a mismatch between photovoltaic active power output and user loads, with low-load periods shifting from nighttime to daytime hours when photovoltaic active power is abundant, leading to voltage over-limit issues. Current research focuses on voltage-reactive power compensation through photovoltaic inverters, with limited attention paid to the active power reduction strategy required to further expand reactive power compensation capacity, and the extent to which users accept this strategy.

[0003] On the other hand, with the rapid development of electric vehicles, battery swap stations have demonstrated their advantages over traditional charging stations with their extremely short battery swap times. By centrally managing battery charge and discharge status, battery swap stations can flexibly mitigate uncertain resource fluctuations. However, existing research has largely focused on charging strategies for traditional charging stations, with less research on scheduling strategies for battery charge and discharge within battery swap stations.

[0004] The existing technology fails to fully reflect the differences in uncertain variables in different time periods, nor does it simultaneously consider the effects of battery charging and discharging inside the battery swap station during the operation of the distribution network, and fails to achieve the coordinated unity of operational economy, safety and reliability. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides an adjustable robust optimization scheduling method for distribution networks taking into account the differences in resource uncertainty sets, thereby improving the economy and security of scheduling decisions and enhancing the robustness of the system itself.

[0006] The present invention provides a robust decision-making method for adjustable distribution networks taking into account the differences in the uncertainty set of urban resources, comprising the following steps:

[0007] Step 1: In the day-ahead planning phase, the scheduling of slow-moving devices is planned for the long-term day-ahead timescale, taking into account the fault area of ​​the photovoltaic panels. The optimization objectives are to minimize social electricity costs, voltage excursions, and decision-making vulnerability, thereby achieving optimal long-term economic security of the distribution network.

[0008] Step 2: During the intraday planning phase, a compensation plan for fast-acting devices is developed for the short-term intraday timescale. Based on the day-ahead planning, the plan takes into account the fluctuations in the solar intensity and load power of the PV units. The optimization goals are to minimize stable operating costs, minimize conventional unit adjustment costs, and minimize decision-making vulnerability, thereby achieving the best short-term distribution network economic benefits.

[0009] Step 3: Build an inverter on-site compensation model based on real-time monitoring data during the day, and carry out on-site control for voltage over-limit problems based on local monitoring data.

[0010] Furthermore, in step 1, the objective function of the optimization target in the day-ahead planning phase is constructed:

[0011]

[0012] Where f1 upp is the social electricity cost objective function, f2 upp is the voltage deviation objective function, f3 upp is the objective function of the day-ahead decision vulnerability; and are respectively the active power output and reactive power output of the conventional unit at node i at time t the day before; and C PV,P The actual active power and on-grid electricity price of the photovoltaic unit participating in the distribution network operation; and C CB,Q are the reactive power and compensation cost of capacitor bank CB participating in grid voltage regulation transaction; V i,t is the node voltage; V N To specify the rated voltage; Indicates the significance level of the PV panel SP fault area; is the power generation cost of conventional units; are the fuel coefficients; is the reactive power cost coefficient; (i, j) represents branch (i, j), that is, the branch with node i as the starting node and node j as the end node; I (i ,j ),t represents the current flowing from node i to node j; f loss (I (i,j),t ) is the branch loss; is the loss cost coefficient of network loss.

[0013] Furthermore, the operating constraints of controllable equipment today are as follows:

[0014] Conventional units are subject to two constraints: capacity and ramp rate:

[0015]

[0016]

[0017] Where, P i G,max and P i G,min are respectively the upper and lower limits of active power output of conventional generator sets; P i G,up and P i G,down They are respectively the up and down climbing rate limits;

[0018] The voltage across the branch where the on-load tap-changing transformer OLTC is located is subject to the following constraints:

[0019]

[0020] Where, v ses,t is the square of the OLTC secondary voltage at time t, v base is the square of the primary side reference voltage; is the base ratio, when t=1, K1 OLTC is the unit ratio; G t OLTC is the gear position of OLTC at time t, N OLTC,max Indicates the maximum number of times OLTC is allowed to change gears, G OLTC,max and G OLTC,min M is the maximum and minimum switching position of OLTC. OLTC is an artificial variable introduced, and its value is sufficiently large; ρ t OLTC,up and ρ t OLTC,down is the OLTC gear shift auxiliary 0-1 parameter, ρ t OLTC,up =1,ρ t OLTC,down =0 means that the OLTC gear is raised at time t, and the transformation ratio increases, ρ t OLTC,up =0,ρ t OLTC,down =1 means the gear is lowered and the ratio is reduced. Both of the above cases indicate OLTC action. t OLTC,up =0;ρ t OLTC,down =0 means the gear position remains unchanged and the ratio remains unchanged, that is, OLTC does not operate; is the difference between the squares of the strain ratios corresponding to gear positions i and i-1, is a 0-1 auxiliary variable;

[0021] The reactive output of the capacitor bank CB is determined by the specific gear and the capacity of each bank, and the number of its operations also needs to be limited:

[0022]

[0023] Where, is the actual switching position of the capacitor bank at node i at time t, Q CB,ave is the reactive power of each capacitor, is the actual reactive power output of the capacitor bank, and C CB,Q are the reactive power and power generation cost of the capacitor bank participating in the grid voltage regulation transaction, N CB,max The maximum number of operations specified for the capacitor bank; It is the CB virtual switching gear. Indicates that CB generates inductive reactive power. It means that capacitive reactive power is emitted. It is the reactive power auxiliary 0-1 parameter. express express M CB is an artificial variable introduced, and its value is sufficiently large; G i CB,max It is the maximum switching gear of CB; and 0-1 parameter for CB gear switching auxiliary, Indicates that the CB gear is raised at time t. Indicates that the gear is lowered. Indicates that the gear position remains unchanged, that is, the CB is not actuated;

[0024] As a device for smoothing intraday fluctuations, the battery swap station BES only needs to consider meeting the battery swap needs of electric vehicles during the day-ahead planning stage. The constraints that should be followed during this stage are as follows:

[0025]

[0026] Where, is the active power corresponding to BES, It is the minimum number of rechargeable batteries required to ensure normal operation during period t as reported by BES, P i BES,c,ave The charging power of a single battery in each period.

[0027] Furthermore, considering the fluctuation of the number of photovoltaic panel failures in the day-ahead stage, the load and light intensity are considered as the determining parameters, and the failure outage probability of a single photovoltaic panel is obtained based on historical statistical data as γ SP , the operating state at time t is ξ t SP, the failure and outage probability of a single photovoltaic panel follows a two-point distribution, and the distribution law of whether a single photovoltaic panel is operating normally or not is obtained:

[0028]

[0029] Where P{A} represents the probability of event A occurring, k i SP is a binary auxiliary variable; when ξ t SP =k i SP =1, it means that the solar panel is faulty or under maintenance and cannot work normally. t SP =k i SP =0, indicating that the solar panel is working normally;

[0030] Assume that the photovoltaic system at node i consists of N i SP The number of failed panels is n. i SP,bad No more than the expected number of faulty panels N i SP,bad The probability of is as follows:

[0031]

[0032] Where, It is the symbol for the permutation and combination operation in mathematics. is the number of faulty panels; Formula (17) indicates that the number of faulty panels follows a binomial distribution, i.e. B(a,b) represents the binomial distribution with parameters a and b; is the failure outage probability of the i-th solar photovoltaic panel;

[0033] During the day-ahead phase, PV units only participate in the distribution network operation as PV active power output. Considering that their reactive capacity is directly affected by active power, their reactive power compensation capability is not considered in day-ahead planning from the perspective of stable operation margin:

[0034]

[0035] Where, In order to consider the photovoltaic active power output under fault and capacity constraints, Indicates the ideal active power output value considering only the fault condition at this time. is the apparent power capacity of the inverter at node i, θ i PV is the inverter power factor angle constraint;

[0036] Uncertain set of active power output of photovoltaic power generation units today As follows:

[0037]

[0038] Where λ i SP is the Poisson distribution parameter, and are the upper and lower bounds of the expected number of failed panels.

[0039] Furthermore, in step 2, the objective function of the optimization target in the intraday planning stage is constructed:

[0040]

[0041] Where f1 low is the objective function for stabilizing operating costs, f2 low is the target function for regulating cost of conventional units, f3 low is the objective function of intraday decision vulnerability; and is the actual output value of the conventional units after adjustment within the day, and are the unit power regulation penalty coefficients respectively; and They represent the fluctuation of light intensity and node load respectively; The cost of peak shaving and valley filling for the power grid, and C peak They represent the active power and corresponding unit cost of BES participating in load peak shaving, and C vally They represent the active power and corresponding unit cost of participating in valley filling respectively; is the cost of PV participating in voltage regulation, is the amount of abandoned light, The reactive power compensation amount of PV participating in grid voltage regulation transactions; represents the penalty coefficient for abandoned solar power, which is calculated from the unit electricity production cost of conventional units, the on-grid electricity price of photovoltaic units and the abandoned solar power subsidy price; κ PV,com represents the discount compensation coefficient for abandoned solar power, that is, the proportion of abandoned solar power compensation price to photovoltaic grid-connected electricity price; d is the differential, is the unit electricity production cost of conventional units; C PV，P with C PV，Q It is the on-grid electricity price of the active power of photovoltaic units and the on-grid electricity price of the reactive power of photovoltaic units.

[0042] Furthermore, during the intraday planning phase, the grid power constraints are as follows:

[0043]

[0044] Where, is the reactive compensation amount of the photovoltaic inverter;

[0045] During the intraday planning phase, the controllable equipment constraints are as follows:

[0046] In addition to being constrained by output capacity and ramp rate, conventional units must also be constrained by regulation limits based on the day-ahead output plan:

[0047]

[0048] Where, and They are the adjustment limits of active power and reactive power within the day respectively;

[0049] During the intraday planning phase, battery swap stations no longer simply serve as loads but begin to take on the task of peak load shaving and valley filling. While meeting the battery swapping needs of users' electric vehicles, battery discharge is used to participate in load peak and valley regulation. This means that peak loads are reduced through battery discharge, while low-peak loads are increased by increasing the amount of rechargeable batteries. The specific constraints to be followed are as follows:

[0050]

[0051]

[0052] Where, is the active power corresponding to the BES at node i at time t, Indicates that BES is in power consumption state. Indicates that the BES is in power supply state; The active power consumed from the grid to charge the battery, The active power delivered to the grid by BES, and are the number of charging and discharging batteries, respectively, P i BES,c,ave and P i BES ,d,ave are the unit charge and discharge power of the battery respectively; Indicates the maximum number of discharged batteries that can be accepted to ensure normal operation as reported by BES. The minimum number of rechargeable batteries required as reported by BES. The maximum number of battery charging and battery replacement operations that BES can accept per unit time; It is the charge and discharge auxiliary 0-1 parameter, Indicates that it is in charging state. Indicates that it is in the discharge state at this time; and It is impossible for both to be positive at the same time, at least one of them must be 0. At this time, BES can only perform charging operations, that is, when When BES is in the state of charging or discharging, the specific The value of is determined.

[0053] Furthermore, in step 2, during the intraday planning phase, the illumination intensity of the photovoltaic system is considered as an uncertain parameter, which obeys the Beta distribution, namely:

[0054]

[0055] Where, is the Beta distribution of the light intensity of the photovoltaic unit, is the active output of the PV system in the ideal state without damage and without considering the inverter capacity constraint, Γ(α) is the Gamma function, P i PV,max is the maximum active power output of the photovoltaic unit at node i; α t PV and β t PV is the shape parameter of the Beta distribution, which is determined by the light intensity μ at time t t PV and standard deviation σ t PV Get; r max is the maximum possible light intensity during the time period, S i PV is the photovoltaic array area, η i PV is the photoelectric conversion efficiency;

[0056] Photovoltaic units output reactive power through inverters. User satisfaction and curtailment penalty coefficients are introduced during the planning phase to constrain potential curtailment behavior:

[0057]

[0058] Where, κ PV,com is the compensation coefficient for abandoned solar power, that is, the proportion of abandoned solar power compensation price to photovoltaic grid-connected electricity price; R sat is the user's maximum economic loss acceptance, R sat The smaller the value, the less the user will accept the behavior of abandoning solar power and causing loss of economic benefits; C PV,P is the unit cost of active power output of the photovoltaic unit, is the photovoltaic active power output at node i at time t considering photovoltaic panel failure and photovoltaic inverter capacity constraints, The actual active power output of the photovoltaic unit;

[0059] In addition to delivering active power through photovoltaic panels, photovoltaic units must also participate in voltage regulation during the day, and are allowed to increase reactive power by reducing active power. In this case, the inverter is subject to the following additional constraints in addition to the corresponding day-ahead inverter constraints:

[0060]

[0061]

[0062] Where θ i PV is the ith photovoltaic power factor angle, is the reactive power transaction volume of photovoltaic inverters participating in the reactive power market, is the reactive power actually generated by the photovoltaic inverter, is the PV inverter capacity; is the photovoltaic active power output at node i at time t considering photovoltaic panel failure and photovoltaic inverter capacity constraints, The actual active power output of the photovoltaic unit; is a 0-1 auxiliary parameter, This means that the inverter is not limited by its own capacity. Indicates that the capacity is limited at this time. is a 0-1 auxiliary variable, Indicates this time The inverter generates inductive reactive power. Indicates this time The inverter generates capacitive reactive power.

[0063] Furthermore, in step 3, when the grid-connected node exceeds the limit, the photovoltaic inverter is not allowed to abandon light except for reducing active power output due to exceeding the rated power. That is, without actively sacrificing active output, the voltage is adjusted through reactive power. When the voltage exceeds the upper limit, the photovoltaic inverter absorbs inductive reactive power to prevent it from exceeding the upper limit. When the voltage exceeds the lower limit, the inverter generates inductive reactive power to avoid exceeding the lower limit. Specifically:

[0064] Assume that the voltage at node i before and after adjustment is V i,0 and V i,1 , the inverter reactive output is and According to the DistFlow equation, the following formula is obtained:

[0065]

[0066] Where V eq To balance the node voltage, Rn and X n is the upstream branch impedance of node n, is the equivalent reactance from node i to the balance node; P n PV is the photovoltaic output of n nodes, is the lower limit of photovoltaic reactive power output of n nodes, is the upper limit of the photovoltaic reactive power output of n nodes, and are the active power and reactive power consumed by the load at node n, respectively. In the formula, n is the starting node in the summation, m is a specific node in the summation, N is the total number of summation nodes, and i is node i in general;

[0067] If the reactive power compensation stage cannot solve the problem, the maximum power adjustment stage will be entered. In this stage, the maximum capacity has been reached, but the power factor angle has not yet reached the maximum. On the basis of maintaining the capacity unchanged, it is allowed to increase the reactive power by reducing a small amount of photovoltaic active power. The specific power to be adjusted is as follows:

[0068]

[0069] Where, is the equivalent resistance from node i to the equilibrium node, is the lower limit of the inverter power factor angle in the first stage;

[0070] If the over-limit problem still cannot be solved after the reactive power compensation stage and the maximum power adjustment stage, it is necessary to directly reduce the active output of the photovoltaic power generation unit, that is, enter the active power reduction stage; in this stage, the inverter power factor angle of the second stage is maintained at unchanged, by reducing the power To adjust the grid connection point voltage, the specific power that the inverter should adjust is as follows:

[0071]

[0072]

[0073] Furthermore, for the multi-objective optimization problem consisting of the constructed day-ahead objective function and the intra-day objective function, the spatial shortest multi-objective decision-making method based on solution set regularization is used to make decisions separately, as follows:

[0074] To describe the optimization results of the sub-goals, the membership function F(X) is used to normalize each sub-goal, mapping the sub-goals with different measurement units to between 0 and 1:

[0075]

[0076] Where, F i(X i ) is f i (X i )’s membership function, f i best and is the optimal value and corresponding decision variable under single-objective optimization, f i worst f i (X i ) is the worst value among the other single-objective optimization strategies Xbest, F i (X i )=0, indicating that the optimization result is completely satisfied. i (X i )=1 means that the optimization result is completely unsatisfactory;

[0077] The membership functions of the non-optimal solutions of each sub-goal are mapped into a comprehensive satisfaction space, and the Euclidean distance is used to find the Pareto solution with the highest decision maker satisfaction. The ideal optimal solution is located at the origin, and the distance between the position of each non-optimal solution mapped into the space and the origin is calculated to evaluate the quality of the decision. Among them, the non-optimal solution closest to the origin has the highest comprehensive satisfaction and can be regarded as the actual optimal solution.

[0078] The beneficial effects of the present invention are as follows: 1) The present invention addresses the volatility and intermittency issues brought about by high-penetration photovoltaics by implementing a classified adjustable robust constrained uncertainty set and a multi-objective decision-making method to achieve optimized scheduling of distribution network voltage regulation and photovoltaic absorption capacity; the present invention is more targeted and flexible in handling solar panel failures, photovoltaic unit output, and load power fluctuations, thereby improving the economy and safety of scheduling decisions;

[0079] 2) This paper designs a multi-timescale distribution network optimization scheduling model and distribution characteristic modeling method. It models the distribution characteristics of solar panel failures, photovoltaic unit output, and load power fluctuations. It optimizes the economy and safety of the distribution network at the day-ahead, intraday, and real-time stages, improves photovoltaic absorption capacity, and provides reliable voltage regulation.

[0080] 3) The present invention designs a multi-objective decision-making method and an optimization scheduling method. The spatial shortest multi-objective decision-making method based on solution set regularization uses membership function and Euclidean distance to solve the Pareto solution set decision problem in the multi-objective problem, and converts the mixed integer nonlinear model into a solvable form through linearization and second-order cone relaxation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 Schematic diagram of multi-time-scale optimization scheduling model;

[0082] Figure 2is the probability distribution diagram of the number of photovoltaic panel failures;

[0083] Figure 3 It is a flow chart of real-time on-site compensation;

[0084] Figure 4 Schematic diagram of the inverter on-site compensation stage. DETAILED DESCRIPTION

[0085] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0086] The present invention proposes a robust decision-making method for adjustable distribution network taking into account the differences in the uncertainty set of urban resources. First, a multi-time-scale virtual power plant resource optimization scheduling framework is constructed based on the differences in the uncertain variables involved in different time periods. Figure 1 As shown; then, targeted and refined modeling is carried out based on the differences between different adjustable devices; finally, the actual optimal solution is obtained through the spatial shortest distance decision method based on solution set regularization.

[0087] Step 1: Construct a day-ahead slow-acting device scheduling plan: In the day-ahead stage, the objective functions are to minimize social electricity costs, minimize voltage deviation, and minimize day-ahead decision vulnerability, so as to achieve the optimal economic security of the virtual power plant on a long day-ahead time scale.

[0088] In this stage, both photovoltaic output and node load are considered as deterministic parameters. The fluctuation of the fault area of ​​the photovoltaic unit's solar panels is mainly considered, and the cost of the number of operations of discrete equipment is included in the constraint consideration. Finally, the gear switching plan of the discrete equipment and the active power output plan of the conventional unit are obtained and sent to the intraday quick-acting device compensation model.

[0089] The constructed current function is as follows:

[0090]

[0091] Where f1 upp is the social electricity cost objective function, f2 upp is the voltage deviation objective function, f3 upp is the objective function of the day-ahead decision vulnerability; and are respectively the active power output and reactive power output of the conventional unit at node i at time t the day before; and C PV,P The actual active power and on-grid electricity price of the photovoltaic unit participating in the distribution network operation; and C CB,Q are the reactive power and compensation cost of capacitor bank CB participating in grid voltage regulation transaction; V i,tis the node voltage; V N To specify the rated voltage; Indicates the significance level of the PV panel SP fault area; is the power generation cost of conventional units; a i G,P 、b i G,P 、c i G,P are the fuel coefficients; is the reactive power cost coefficient; (i, j) represents branch (i, j), that is, the branch with node i as the starting node and node j as the end node; I (i,j),t represents the current flowing from node i to node j; f loss (I (i,j),t ) is the branch loss; is the loss cost coefficient of network loss.

[0092] The current operating constraints of controllable equipment are as follows:

[0093] Conventional units are constrained by capacity and ramp rate:

[0094]

[0095] Where, is the active power output of conventional generator sets, P i G,max and P i G,min are respectively the upper and lower limits of active power output of conventional generator sets; P i G,up and P i G,down They are respectively the up and down climbing rate limits.

[0096] The voltage across the branch where the on-load tap changer (OLTC) is located is subject to the following constraints:

[0097]

[0098]

[0099] Where, v ses,t is the square of the OLTC secondary voltage at time t, v base is the square of the primary side reference voltage, is the base ratio, is the unit ratio, is the gear position of OLTC at time t, N OLTC,max Indicates the maximum number of times OLTC is allowed to change gears, G OLTC,max and GOLTC,min M is the maximum and minimum switching position of OLTC. OLTC is a very large number; ρ t OLTC,up and ρ t OLTC,down is the OLTC gear shift auxiliary 0-1 parameter, ρ t OLTC,up =1,ρ t OLTC,down =0 means that the OLTC gear is raised at time t, and the transformation ratio increases, ρ t OLTC,up =0,ρ t OLTC,down =1 means the gear is lowered and the ratio is reduced. Both of the above cases indicate OLTC action. t OLTC,up =0,ρ t OLTC,down =0 means the gear position remains unchanged and the ratio remains unchanged, that is, OLTC does not operate. is the difference between the squares of the strain ratios corresponding to gear positions i and i-1, A 0-1 auxiliary variable.

[0100] The reactive output of the capacitor bank (CB) is determined by the specific gear and the capacity of each bank, and the number of its operations also needs to be limited:

[0101]

[0102]

[0103] Where, It is the CB virtual switching gear. Indicates that CB generates inductive reactive power. It means that capacitive reactive power is emitted. It is the reactive power auxiliary 0-1 parameter. express express M CB For a very large number, It is the maximum switching gear of CB; and 0-1 parameter for CB gear switching auxiliary, Indicates that the CB gear is raised at time t. Indicates that the gear is lowered. Indicates that the gear position remains unchanged, that is, the CB is not activated.

[0104] As a device for smoothing intraday fluctuations, the Battery Exchange Station (BES) only needs to consider meeting the battery swap needs of electric vehicles during the day-ahead planning phase. The constraints to be observed during this phase are as follows:

[0105]

[0106] Where, is the active power corresponding to BES, It is the minimum number of rechargeable batteries required to ensure normal operation during period t as reported by BES, P i BES,c,ave The charging power of a single battery in each period.

[0107] During the day-ahead phase, PV units only participate in the distribution network operation as PV active power output. Considering that their reactive capacity is directly affected by active power, their reactive power compensation capability is not considered in day-ahead planning from the perspective of stable operation margin.

[0108]

[0109] Where, In order to consider the photovoltaic active power output under fault and capacity constraints, It represents the ideal active power output value considering only the fault condition at this time, S i PV is the apparent power capacity of the inverter at node i, θ i PV is the inverter power factor angle constraint.

[0110] The day-ahead phase mainly considers the fluctuation of the number of photovoltaic panel failures, and the load and light intensity are considered as deterministic parameters. The day-ahead active output of the photovoltaic power generation unit is an uncertain set U i SP As follows, Figure 2 As shown:

[0111]

[0112] Where λ i SP is the Poisson distribution parameter, γ SP is the failure outage probability of a single solar panel, and the photovoltaic unit is composed of It is composed of 100 solar panels. The number of panels that may be damaged, and The upper and lower limits of the possible damage amount.

[0113] Step 2: Constructing an intraday fast-acting device response plan: During the intraday phase, the objective functions are to minimize stable operating costs, minimize conventional unit adjustment costs, and minimize intraday decision-making vulnerability, aiming to achieve optimal economic benefits for the virtual power plant over short timescales. Based on the slow-acting device scheduling plan issued the day before, the plan takes into account the actual number of PV power unit failures and the operating status of other electrical equipment, primarily considering the randomness and volatility of light intensity and node loads. Ultimately, the plan develops a reactive power compensation plan for the PV inverter, a charging and discharging plan for the battery swap station, and the intraday output adjustment for the conventional units.

[0114] During the intraday phase, the main considerations include stable operation costs, conventional unit adjustment costs, and decision-making vulnerability. Stable operation costs include the costs of peak-shaving and valley-filling by power swap stations, reactive power compensation costs for inverters, and the resulting potential curtailment costs. Conventional unit adjustment costs refer to the cost of adjusting conventional units' output when it deviates from the day-ahead planned output, involving both active and reactive power adjustments.

[0115]

[0116]

[0117] Where, and is the actual output value of the conventional units after adjustment within the day, τ t PG and τ t QG are the unit power regulation penalty coefficients respectively; and They represent the fluctuation of light intensity and node load respectively; The cost of peak shaving and valley filling for the power grid, and C peak They represent the active power and corresponding unit cost of BES participating in load peak shaving, and C vally They represent the active power and corresponding unit cost of participating in valley filling respectively; is the cost of PV participating in voltage regulation, is the amount of abandoned light, is the reactive compensation amount of PV participating in grid voltage regulation transactions, represents the penalty coefficient for abandoned solar power, which can be calculated from the unit electricity production cost of conventional units, the on-grid electricity price of photovoltaic units and the abandoned solar power subsidy price, κ PV,com It represents the discount compensation coefficient for abandoned solar power, that is, the proportion of abandoned solar power compensation price to photovoltaic grid-connected electricity price.

[0118] The grid power constraints during the day are as follows:

[0119]

[0120] Where, is the reactive compensation amount of the photovoltaic inverter.

[0121] The controllable equipment constraints during the day are as follows:

[0122] In addition to being constrained by output capacity and ramp rate, conventional units in the intraday phase should also be constrained by regulation limits based on the day-ahead output plan:

[0123]

[0124] Where, and They are the adjustment limits of active power and reactive power within the day respectively.

[0125] During the intraday planning phase, battery swap stations no longer simply serve as loads but begin to take on the task of peak load shaving and valley filling. While meeting the battery swapping needs of users' electric vehicles, battery discharge is used to participate in load peak and valley regulation. This means that peak loads are reduced through battery discharge, while low-peak loads are increased by increasing the amount of rechargeable batteries. The specific constraints to be followed are as follows:

[0126]

[0127]

[0128] Where, is the active power corresponding to BES, Indicates that BES is in power consumption. Indicates that it is in power supply state; The active power consumed from the grid to charge the battery, The active power delivered to the grid by BES, and are the number of charging and discharging batteries, respectively, P i BES,c,ave and P i BES,d,ave are the unit charge and discharge power of the battery respectively; Indicates the maximum number of discharged batteries that can be accepted to ensure normal operation as reported by BES. The minimum number of rechargeable batteries required as reported by BES. The maximum number of batteries that can be charged and replaced by BES per unit time. It is the charge and discharge auxiliary 0-1 parameter, Indicates that it should be in charging state at this time, ρ i c ,t =0 means it is in the discharge state. and It is impossible for both to be positive at the same time, at least one of them must be 0. At this time, the battery swap station can only perform charging operations, that is, when At this time, the battery swap station can choose to be in either charging or discharging state, which is determined by The value of is determined.

[0129] In addition to delivering active power through photovoltaic panels, photovoltaic units must also participate in voltage regulation during the day, and are allowed to increase reactive power by reducing active power. In this case, the inverter is subject to the following additional constraints in addition to the corresponding day-ahead inverter constraints:

[0130]

[0131]

[0132] Where R sat is the user's maximum economic loss acceptance, R sat The smaller it is, the less users accept the behavior of abandoning light. is a 0-1 auxiliary parameter, This means that the inverter is not limited by its own capacity. Indicates that the capacity is limited at this time. is a 0-1 auxiliary variable, Indicates this time The inverter generates inductive reactive power. Indicates this time The inverter generates capacitive reactive power.

[0133] The main constraints during the day are light intensity and load fluctuation. The present invention believes that the fluctuation of light intensity obeys Beta distribution, and the fluctuation of load obeys normal distribution.

[0134] Step 3: Construct an on-site inverter compensation model based on real-time daily monitoring data: Based on the current optimized scheduling plan, according to the monitoring data of local node voltage and power, according to the relationship between voltage and power and the regulation characteristics of the photovoltaic inverter, make full use of its rapid response capability, and achieve the control of voltage over-limit problems by adjusting the reactive compensation amount.

[0135] The overall process of real-time multi-stage on-site compensation is as follows Figure 3 As shown, the power angle relationship of each stage of local compensation of the inverter is as follows: Figure 4 As shown in Table 1, the oab segment corresponds to the reactive power compensation stage, the bc segment corresponds to the maximum power adjustment stage, and the co segment corresponds to the active power reduction stage.

[0136] Table 1 Relationship between inverter parameters at different stages

[0137]

[0138]

[0139] When a grid-connected node exceeds the limit, the photovoltaic inverter first enters the reactive power compensation stage. In this stage, the photovoltaic inverter is not allowed to abandon light except for reducing active output due to exceeding the rated power. That is, without actively sacrificing active output, the voltage is adjusted through reactive power. When the voltage exceeds the upper limit, the photovoltaic inverter absorbs inductive reactive power to prevent it from exceeding the upper limit; when the voltage exceeds the lower limit, the inverter generates inductive reactive power to avoid it from exceeding the lower limit. Assume that the voltage at node i before and after adjustment is V i,0 and V i,1 , the inverter reactive output is and According to the DistFlow equation, the following formula can be obtained:

[0140]

[0141] Where V eq To balance the node voltage, R n and X n is the upstream branch impedance of node n, is the equivalent reactance from node i to the balance node.

[0142] If the problem cannot be solved in the previous stage, the maximum power adjustment stage will be entered. In this stage, the maximum capacity has been reached, but the power factor angle has not yet reached the maximum. On the basis of maintaining the capacity unchanged, it is allowed to increase the reactive power by reducing a small amount of photovoltaic active power. The specific power adjustment is as follows:

[0143]

[0144] Where, is the equivalent resistance from node i to the equilibrium node, is the lower limit of the inverter power factor angle.

[0145] If the over-limit problem still cannot be solved after the above two stages, the active power output of the photovoltaic power generation unit must be directly reduced, that is, the active power reduction stage is entered. In this stage, the inverter power factor angle is maintained at unchanged, by reducing the power To adjust the grid connection point voltage, the specific power that the inverter should adjust is as follows:

[0146]

[0147] Since active power reduction is relatively large in this stage, which seriously damages the operating economy, it should be used as the last resort in the adjustment strategy.

[0148] In the present invention, for the multi-objective optimization problem (i.e., the day-ahead objective function and the intraday objective function), a spatial shortest multi-objective decision-making method based on solution set regularization is adopted to make decisions, as follows:

[0149] To describe the optimization results of the sub-goals, the present invention uses the membership function F(X) to normalize each sub-goal and map the sub-goals with different measurement units to a value between 0 and 1:

[0150]

[0151] Where, F i (X i ) is f i (X i )’s membership function, f i best and is the optimal value and corresponding decision variable under single-objective optimization, f i worst f i (X i ) In the other single-objective optimization strategies X best The worst value in F i (X i )=0, indicating that the optimization result is completely satisfied. i (X i )=1 means that the optimization result is completely unsatisfactory.

[0152] The membership functions of the non-optimal solutions for each sub-goal are mapped into a comprehensive satisfaction space. Using Euclidean distance, the Pareto solution with the highest decision-maker satisfaction is found. The ideal optimal solution is located at the origin. The distance between the locations of each non-optimal solution mapped into this space and the origin is calculated to evaluate the quality of the decision. The non-optimal solution closest to the origin has the highest overall satisfaction and can be considered the actual optimal solution.

[0153] The above description is only a preferred embodiment of the present invention and is not intended to further limit the present invention. All equivalent changes made using the contents of the present invention description and drawings are within the scope of protection of the present invention.

Claims

1. A robust decision-making method for adjustable distribution network taking into account the difference of uncertain urban resource sets, characterized in that: The following steps are involved: Step 1: In the day-ahead planning stage, the scheduling of slow-moving devices is planned on a long-term scale, the fault area of ​​the photovoltaic panels is considered, and the social electricity cost, voltage deviation and decision vulnerability are minimized as the optimization goals to achieve the optimal economic security of the long-term distribution network; Step 2: In the intraday planning stage, a compensation plan for fast-acting devices is carried out for the short-term time scale within the day. Based on the day-ahead planning, combined with the fluctuation of the light intensity and load power of the photovoltaic unit, the optimization goals are to minimize the stable operation cost, minimize the adjustment cost of conventional units, and minimize the decision-making vulnerability, so as to achieve the best economic benefits of the short-term distribution network; Step 3: Build an inverter on-site compensation model based on real-time monitoring data during the day, and carry out on-site control for voltage over-limit problems based on local monitoring data.

2. According to claim 1, a distribution network adjustable robust decision method taking into account the difference of urban resource uncertainty sets is characterized in that: In step 1, the objective function of the optimization goal in the day-ahead planning phase is constructed: In the formula, is the social electricity cost objective function, is the voltage deviation objective function, is the objective function of the day-ahead decision vulnerability; and are respectively the active output and reactive output of the conventional unit at node i at time t the day before; and C PV,P The actual active power and grid-connected electricity price of the photovoltaic units participating in the operation of the distribution network; and C CB,Q are the reactive power and compensation cost of capacitor bank CB participating in grid voltage regulation transaction; V i,t is the node voltage; V N To specify the rated voltage; Indicates the significance level of the PV panel SP failure area; is the power generation cost of conventional units; are the fuel coefficients; is the reactive power output cost coefficient; (i,j) represents the branch (i,j), that is, the branch with node i as the starting node and node j as the end node; I (i,j),t represents the current flowing from node i to node j; f loss (I (i,j),t ) is the branch loss; is the loss cost coefficient of network loss.

3. The adjustable robust decision method for distribution network taking into account the difference of uncertain sets of urban resources according to claim 2 is characterized in that: The operating constraints of controllable equipment are as follows: Conventional units are subject to two constraints: capacity and ramp rate: Where P i G,max and P i G,min They are respectively the upper and lower limits of active output of conventional generator sets; P i G,up and P i G,down They are respectively the up and down climbing rate limits; The voltage at both ends of the branch where the on-load tap-changing transformer OLTC is located is subject to the following constraints: In the formula, v ses,t is the square of the OLTC secondary voltage at time t, v base is the square of the primary side reference voltage; is the base ratio, when t=1, is the unit ratio; is the gear position of OLTC at time t, N OLTC,max Indicates the maximum number of times OLTC is allowed to change gears, G OLTC,max and G OLTC,min M is the maximum and minimum switching position of OLTC. OLTC is an artificial variable introduced, and its value is sufficiently large; and OLTC gear switching auxiliary 0-1 parameters, It means that at time t, the OLTC gear is raised and the transformation ratio increases. It indicates that the gear is lowered and the ratio is reduced. Both of the above situations indicate that OLTC is in action. It means the gear position remains unchanged and the ratio remains unchanged, that is, OLTC does not operate; is the difference between the square of the strain ratios corresponding to gear position i and gear position i-1, It is a 0-1 auxiliary variable; The reactive output of capacitor bank CB is determined by the specific gear and the capacity of each bank, and the number of its actions also needs to be limited: In the formula, is the actual switching position of the capacitor bank at node i at time t, Q CB,ave is the reactive power of each capacitor, is the actual reactive power output of the capacitor bank, and C CB,Q are the reactive power and power generation cost of the capacitor bank participating in the grid voltage regulation transaction, N CB,max The maximum number of operations specified for the capacitor bank; It is the CB virtual switching gear. Indicates that CB generates inductive reactive power. It means that capacitive reactive power is emitted. It is the reactive power auxiliary 0-1 parameter. express express M CB is an artificial variable introduced, and its value is sufficiently large; It is the maximum switching gear of CB; and 0-1 parameters for CB gear switching assistance. It means that the CB gear is raised at time t. Indicates that the gear is lowered. It means the gear position remains unchanged, i.e. the CB is not actuated; In the day-ahead planning stage, the battery swap station BES only needs to consider meeting the battery swap needs of electric vehicles. The constraints to be observed in this stage are as follows: In the formula, is the active power corresponding to BES, It is the minimum number of rechargeable batteries required to ensure normal operation during period t reported by BES, P i BES,c,ave The charging power of a single battery in each period.

4. The adjustable robust decision method for distribution network taking into account the difference of uncertain sets of urban resources according to claim 3 is characterized in that: In the day-ahead phase, the number of photovoltaic panel failures fluctuates, and the load and light intensity are considered as deterministic parameters. According to historical statistical data, the failure and outage probability of a single photovoltaic panel is γ SP , the operating state at time t is The failure and shutdown probability of a single photovoltaic panel follows a two-point distribution, and the distribution law of whether a single photovoltaic panel operates normally or not is obtained: In the formula, P{A} represents the probability of event A occurring. is a binary auxiliary variable; when When the battery panel fails or is under maintenance, it means that the battery panel is faulty or cannot work normally. When , it means the solar panel is working normally; Assume that the photovoltaic unit at node i is The number of photovoltaic panels that fail is No more than the expected number of failed panels The probability is as follows: In the formula, It is the symbol for the permutation and combination operation in mathematics. is the number of solar panel failures; Formula (17) indicates that the number of solar panels with failures follows a binomial distribution, that is, B(a,b) represents a binomial distribution with parameters a and b; is the failure outage probability of the i-th solar photovoltaic panel; In the day-ahead stage, the photovoltaic units only participate in the operation of the distribution network as photovoltaic active power output. Considering that their reactive capacity is directly affected by active power, from the perspective of stable operation margin, the day-ahead planning does not consider their reactive compensation capacity: In the formula, In order to consider the photovoltaic active power output under fault and capacity constraints, It indicates the ideal active output value considering only the fault condition at this time. is the apparent power capacity of the inverter at node i, is the inverter power factor angle constraint; Uncertain set of active output of photovoltaic power generation units today As follows: In the formula, is the Poisson distribution parameter, and are the upper and lower limits of the expected number of failed panels.

5. The adjustable robust decision method for distribution network taking into account the difference of uncertain sets of urban resources according to claim 4 is characterized in that: In step 2, construct the objective function of the optimization target in the intraday planning stage: In the formula, To stabilize the operating cost objective function, is the objective function of the conventional unit regulation cost, is the objective function of intraday decision vulnerability; and is the actual output value of the conventional units after adjustment within the day, and are the unit power regulation penalty coefficients respectively; and They represent the fluctuation of light intensity and node load respectively; The cost of peak load reduction for the power grid, and C peak They represent the active power and corresponding unit cost of BES participating in load peak reduction, and C vally They represent the active power and corresponding unit cost involved in valley filling respectively; is the cost of PV participating in voltage regulation, is the amount of abandoned light, The reactive power compensation amount of PV participating in grid voltage regulation transactions; represents the penalty coefficient for abandoned solar power, which is calculated from the unit electricity production cost of conventional units, the on-grid electricity price of photovoltaic units and the abandoned solar power subsidy price; κ PV,com represents the discount compensation coefficient for abandoned photovoltaic power, that is, the proportion of abandoned photovoltaic power compensation price to photovoltaic power grid price; d is the differential, is the unit electricity production cost of conventional units; C PV，P With C PV，Q It is the on-grid electricity price of the on-grid active power of photovoltaic units and the on-grid electricity price of the on-grid reactive power of photovoltaic units.

6. The adjustable robust decision method for distribution network taking into account the difference of uncertain sets of urban resources according to claim 5 is characterized in that: During the intraday planning stage, the grid power constraints are as follows: In the formula, is the reactive power compensation of the photovoltaic inverter; During the intraday planning stage, the controllable equipment constraints are as follows: Conventional units are subject to constraints of output capacity and ramp rate, and should also be subject to the regulation limit based on the day-ahead output plan: In the formula, and They are respectively the adjustment limits of active power and reactive power within the day; In the intraday planning stage, the battery swap station is no longer a simple load, but begins to take on the task of peak shaving and valley filling. On the premise of meeting the user's electric vehicle battery swap needs, it participates in load peak and valley regulation through battery discharge, that is, reducing peak loads through battery discharge and increasing low-peak loads by increasing the amount of charged batteries. The specific constraints to be followed are as follows: In the formula, is the active power corresponding to the BES at node i at time t, Indicates that BES is in power consumption state. Indicates that the BES is in power supply state; The active power consumed from the grid for battery charging, is the active power delivered to the grid by BES, and are the number of charging and discharging batteries, respectively, i BES,c,ave and P i BES ,d,ave are the battery unit charging and discharging power respectively; It indicates the maximum number of discharged batteries that can be accepted by BES to ensure normal operation. The minimum number of rechargeable batteries required as reported by BES. The maximum number of battery charging and battery replacement operations that BES can accept per unit time; It is the charge and discharge auxiliary 0-1 parameter. It means it is in charging state. Indicates that it is in the discharge state at this time; and It is impossible for both to be positive at the same time, at least one of them must be 0. At this time, BES can only perform charging operations, that is, when When BES is in the state of charging or discharging, the specific state is The value of is determined.

7. The adjustable robust decision method for distribution network taking into account the difference of uncertain sets of urban resources according to claim 6 is characterized in that: In step 2, during the intraday planning stage, the light intensity of the photovoltaic unit is considered to be an uncertain parameter, which obeys the Beta distribution, that is: In the formula, is the Beta distribution of the light intensity of the photovoltaic unit, is the active output of the PV unit under ideal conditions without damage and without considering the inverter capacity constraint, Γ(α) is the Gamma function, P i PV,max is the maximum active output of the photovoltaic unit at node i; and is the shape parameter of the Beta distribution, which is determined by the light intensity at time t. and standard deviation Get; r max is the maximum possible light intensity during the time period, is the photovoltaic array area, is the photoelectric conversion efficiency; Photovoltaic units output reactive power through inverters. User satisfaction and abandonment penalty coefficients are introduced in the planning stage to constrain potential abandonment behavior: In the formula, κ PV,com is the compensation coefficient for abandoned photovoltaic power, that is, the proportion of abandoned photovoltaic power compensation price to photovoltaic power grid price; R sat is the user's maximum economic loss acceptance, R sat The smaller the value, the less the user will accept the behavior of abandoning light and causing loss of economic benefits; C PV,P is the unit cost of active output of photovoltaic units, is the photovoltaic active power output at node i at time t considering the photovoltaic panel failure and photovoltaic inverter capacity constraints, The actual active output of the photovoltaic unit; In addition to transmitting active power through photovoltaic panels, photovoltaic units should also participate in voltage regulation and are allowed to increase reactive power by reducing active power. In this case, the inverter is subject to the following constraints in addition to the corresponding constraints of the day-ahead inverter: In the formula, is the ith photovoltaic power factor angle, is the reactive power transaction volume of PV inverters participating in the reactive power market, is the reactive power actually generated by the photovoltaic inverter, is the PV inverter capacity; is the photovoltaic active power output at node i at time t considering the photovoltaic panel failure and photovoltaic inverter capacity constraints, The actual active output of the photovoltaic unit; is a 0-1 auxiliary parameter, This means that the inverter is not limited by its own capacity. Indicates that the capacity is limited at this time. is a 0-1 auxiliary variable, Indicates that at this time The inverter generates inductive reactive power. Indicates that at this time The inverter generates capacitive reactive power.

8. The adjustable robust decision method for distribution network taking into account the difference of uncertain sets of urban resources according to claim 7 is characterized in that: In step 3, when the grid-connected node exceeds the limit, the photovoltaic inverter is not allowed to abandon light except for reducing the active output due to exceeding the rated power, that is, the voltage is adjusted through reactive power without actively sacrificing the active output; when the voltage exceeds the upper limit, the photovoltaic inverter absorbs inductive reactive power to prevent exceeding the upper limit; when the voltage exceeds the lower limit, the inverter emits inductive reactive power to avoid exceeding the lower limit; specifically: Assume that the voltage at node i before and after adjustment is V i,0 and V i,1 , the inverter reactive output is and The following equation is derived from the DistFlow equation: Where V eq To balance the node voltage, R n and X n is the upstream branch impedance of node n, is the equivalent reactance from node i to the balance node; P n PV is the photovoltaic output of n nodes, is the lower limit of the photovoltaic reactive power output of n nodes, is the upper limit of the PV reactive power output of n nodes, and are respectively the active power and reactive power consumed by the load of node n, where n is the starting node in the summation, m is a specific node in the summation, N is the total number of summation nodes, and i is the node i in general; If the reactive power compensation stage cannot solve the problem, the maximum power adjustment stage will be entered; in this stage, the maximum capacity has been reached, but the power factor angle has not yet reached the maximum. On the basis of keeping the capacity unchanged, it is allowed to increase the reactive power by reducing a small amount of photovoltaic active power. The specific power to be adjusted is as follows: In the formula, R i eq is the equivalent resistance from node i to the equilibrium node, is the lower limit of the inverter power factor angle in the first stage; If the over-limit problem still cannot be solved after the above reactive power compensation stage and maximum power adjustment stage, it is necessary to directly reduce the active output of the photovoltaic power source unit, that is, enter the active power reduction stage; in this stage, the inverter power factor angle of the second stage is maintained at unchanged, by reducing the power S i PV To adjust the grid connection point voltage, the specific power that the inverter should adjust is as follows:

9. The adjustable robust decision method for distribution network taking into account the difference of uncertain sets of urban resources according to claim 7, characterized in that: For the multi-objective optimization problem consisting of the constructed day-ahead objective function and intra-day objective function, the spatial shortest multi-objective decision-making method based on solution set regularization is used to make decisions respectively, as follows: In order to describe the optimization results of the sub-goals, the membership function F(X) is used to normalize each sub-goal and map the sub-goals with different original measurement units to between 0 and 1: In the formula, F i (X i ) is f i (X i )’s membership function, f i best and is the optimal value and corresponding decision variable under single objective optimization, f i worst f i (X i ) In the remaining single-objective optimization strategies X best The worst value in F i (X i )=0, indicating that the optimization result is completely satisfactory. i (X i )=1 means that the optimization result is completely unsatisfactory; The membership function of each sub-goal non-optimal solution is mapped to a comprehensive satisfaction space, and the Euclidean distance is used to find the Pareto solution with the highest decision maker satisfaction. The ideal optimal solution is located at the origin, and the distance between the position of each non-optimal solution mapped to the space and the origin is calculated to evaluate the quality of the decision. Among them, the non-optimal solution closest to the origin has the highest comprehensive satisfaction, so it can be regarded as the actual optimal solution.

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