Voltage control method and system for power distribution network with high photovoltaic permeability

Abstract

The invention discloses a high-photovoltaic-permeability power distribution network voltage control method and system. The method comprises the following steps: constructing a high-photovoltaic-permeability power distribution network voltage control strategy; constructing a dynamic degradation cost function of the energy storage system; constructing a power distribution network node voltage approximate value calculation model; constructing a CVaR function for evaluating the voltage out-of-limit risk and the inverter capacity out-of-limit risk; constructing a centralized optimization model of high photovoltaic permeability power distribution network voltage control considering photovoltaic uncertainty, calculating a weighted sum of a node voltage upper limit exceeding risk and a power distribution network voltage control cost in a limited time domain, constructing a target function by taking minimization of the weighted sum as a target, and constructing a global optimal voltage control strategy; solving the centralized optimization model based on a distributed robust model predictive control algorithm, and outputting a voltage-reactive droop control curve; and adjusting reactive power output of the photovoltaic inverter based on the voltage-reactive droop control curve to realize voltage control. The voltage deviation can be reduced in real time, and the voltage control cost can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network voltage control, and in particular to a distribution network voltage control method and system with high photovoltaic penetration. Background Art

[0002] The installed capacity of distributed photovoltaic power generation is increasing year by year, which has caused significant changes in the operating characteristics of active distribution networks. The uncertainty and volatility of photovoltaic power generation may cause the voltage amplitude of distribution network nodes to exceed the safe range, thereby increasing the cost and difficulty of distribution network voltage control. It is necessary to seek a balance between the two conflicting goals of economy and safety.

[0003] In traditional distribution networks, voltage is often controlled by upgrading lines, installing power electronic reactive voltage regulators, adjusting on-load voltage regulators and shunt capacitors. However, due to the installation cost, action frequency and response speed of the equipment, the above methods cannot effectively deal with the voltage over-limit problem in distribution networks with high photovoltaic penetration. Photovoltaic inverters do not require additional construction costs, respond quickly, and can frequently and independently adjust active power and reactive power; electrochemical energy storage systems can respond quickly and have a decreasing construction cost, and are often connected as supporting facilities to power systems with a high proportion of renewable energy. However, the existing distribution network voltage control strategy generally has two limitations when considering energy storage scheduling: first, it fails to effectively take into account the battery degradation cost of the battery energy storage system's charge and discharge cycle; second, the mathematical representation of the energy storage operating cost mostly uses fixed cost coefficients. The above simplified model is prone to frequent charging and discharging of energy storage units, resulting in accelerated life attenuation, or causing unnecessary reductions in renewable energy absorption capacity due to deviations in operating cost estimates, ultimately restricting the economic operation efficiency of the distribution network system and the high penetration photovoltaic grid-connected absorption capacity. Therefore, how to comprehensively consider the characteristics of photovoltaic inverters and energy storage and reasonably dispatch photovoltaic inverters and energy storage systems to provide voltage control services for active distribution networks has become an issue worthy of attention. Summary of the invention

[0004] In order to overcome the defects and shortcomings of the prior art, the present invention provides a distribution network voltage control method and system with high photovoltaic penetration. The present invention is based on a high photovoltaic penetration distribution network voltage control strategy based on centralized-local coordination considering energy storage degradation, and takes into account the cycle life degradation of energy storage. In the centralized optimization stage of the control strategy, a distributed blue stick model predictive control algorithm based on Wasserstein distance is formulated to deal with the prediction error of photovoltaics. The distribution network operator is in charge, and the risk of node voltage exceeding the upper limit and the distribution network voltage control cost including the dynamic degradation cost of the energy storage system are weighed. The voltage over-limit risk can be effectively controlled, the distribution network operation cost can be reduced, and the service life of the energy storage system can be extended, so as to improve the economy and safety of the system operation. In the local control stage, the photovoltaic inverter adjusts the reactive power output according to the improved control curve in the centralized optimization stage to deal with the real-time fluctuation of photovoltaics. Based on the optimized voltage-reactive droop control curve, the photovoltaic inverter can quickly provide reactive power, realize real-time voltage control, and reduce voltage deviation. In addition, the dynamic degradation cost of the electrochemical energy storage system is incorporated into the distribution network voltage control cost to evaluate the impact of short-term scheduling on long-term battery capacity.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: The present invention provides a method for controlling voltage of a distribution network with high photovoltaic penetration, comprising the following steps: Construct voltage control strategy for distribution network with high PV penetration; Construct a dynamic degradation cost function for the energy storage system; Construct a distribution network node voltage approximate value calculation model; Construct a CVaR function for assessing voltage over-limit risk and inverter capacity over-limit risk; A centralized optimization model for voltage control of distribution networks with high photovoltaic penetration rates considering photovoltaic uncertainty is constructed. The weighted sum of the risk of node voltage exceeding the upper limit and the voltage control cost of the distribution network in a limited time domain is calculated. The objective function is constructed with the goal of minimizing the weighted sum, and the global optimal voltage control strategy is constructed. Solve the centralized optimization model based on the distributed blue stick model predictive control algorithm and output voltage-reactive power droop control curve; The reactive power output of the photovoltaic inverter is adjusted based on the voltage-reactive power droop control curve to achieve voltage control.

[0006] As a preferred technical solution, a voltage control strategy for distribution networks with high photovoltaic penetration is constructed, which specifically includes: Construct a centralized-local coordinated voltage control strategy for high photovoltaic penetration distribution networks. In the centralized optimization stage of the control strategy, a decision is made once based on the purchase and sale prices of electricity reported by the distribution network, photovoltaic forecast information, load forecast information, and the state of charge of the energy storage system. By controlling the electric energy exchanged with the main grid, the photovoltaic active output, the active power output of the energy storage, and the slope of the inverter voltage-reactive droop control curve, the risk of node voltage exceeding the upper limit in a limited time domain and the weighted sum of the distribution network voltage control cost are minimized to generate a global optimal strategy. In the local control stage of the control strategy, the reactive power output of the PV inverter is adjusted according to the voltage of the distribution network node based on the voltage-reactive power droop control curve.

[0007] As a preferred technical solution, a dynamic degradation cost function of the energy storage system is constructed, which specifically includes: Node n Energy storage system in time t State of charge As input, construct the degradation cost density function of the energy storage system with respect to the state of charge , expressed as: ; in, is the cost per unit capacity of the energy storage system, is the total capacity of the energy storage system, is the charge and discharge efficiency of the energy storage system, a and b are the empirical parameters of battery degradation; Based on the least squares method, the degradation cost density function is simplified into a piecewise linear function, and the dynamic degradation cost function of the energy storage system is constructed, which is expressed as: ; in, For Node n Energy storage system in time t The output power, The time interval for decision making.

[0008] As a preferred technical solution, a distribution network node voltage approximate value calculation model is constructed, which specifically includes: Based on the linear approximation method of the AC power flow equation, the moment considering the influence of photovoltaic prediction error is constructed. t Approximate voltage value of distribution network node The calculation model is expressed as: ; ; ; ; ; in, is the node admittance matrix excluding the equilibrium node, and are the corresponding resistance matrix and reactance matrix respectively, To balance the mutual admittance between the node and the PQ node, To balance the node voltage, For photovoltaic at all times The prediction error, represents the active power prediction error vector affected by the uncertainty of photovoltaic output, represents the reactive power prediction error vector affected by the uncertainty of PV output, , and are the reactive power, active power and node voltage vector of the predetermined photovoltaic output, is the node voltage vector The phase of , j represents the complex imaginary part.

[0009] As a preferred technical solution, a CVaR function is constructed to evaluate the voltage over-limit risk and the inverter capacity over-limit risk, which specifically includes: ; ; ; in, Indicates the risk of voltage exceeding the upper limit. Indicates the risk of voltage exceeding the lower limit. Indicates the risk of inverter capacity exceeding the limit. , and are all CVaR auxiliary variables, Representation Node n Photovoltaic at the moment t The reactive power injected into the distribution network, For a given confidence level, For Node n At the moment t The first m PV prediction errors, M is the number of samples contained in the PV prediction error dataset, is the probability density function, and The photovoltaic prediction errors are The node voltage amplitude and the maximum available active power of photovoltaic power, V max Represents the upper limit of the node voltage amplitude, V min Represents the lower limit of the node voltage amplitude allowed, express If the value is positive, it takes the value itself; if it is not positive, it takes 0. is the photovoltaic power generation at node n at time t The proportion of active power cut, Representation Node n The square of the rated capacity of the photovoltaic power plant.

[0010] As a preferred technical solution, the weighted sum of the node voltage exceeding the upper limit risk and the distribution network voltage control cost in the limited time domain is calculated to construct an objective function with the goal of minimizing the weighted sum, which is specifically expressed as: ; ; in, For prediction time The voltage control cost of the distribution network is is the risk factor, represents a finite time domain, is the risk of node voltage exceeding the upper limit, N is the total number of distribution network nodes, It represents the cost of power purchased by the distribution network from the public grid. Represents the reactive power output cost of photovoltaic inverters in distribution network, It represents the penalty of the droop control curve change of the photovoltaic inverter in the distribution network. represents the distribution network abandonment penalty affected by PV forecast uncertainty, Represents the sum of the dynamic degradation costs of all distribution network energy storage systems.

[0011] As a preferred technical solution, the calculation formula of each cost in the distribution network voltage control cost is expressed as: ; ; ; ; ; in, is the set of all nodes in the distribution network. Indicates the prediction time The cost of purchasing electricity from the public grid for the distribution network is is the time interval for decision making, is the electricity purchase price, For time The distribution network purchases electricity from the public grid. For prediction time The reactive power output cost of photovoltaic inverters in the distribution network, is the reactive power output cost price of the photovoltaic inverter, For Node n At prediction time The reactive power output, For time The penalty for the change of the droop control curve of the photovoltaic inverter in the distribution network, is the penalty cost coefficient for the inverter voltage-reactive power droop curve change, and It is the third and fourth node positions of the standard voltage-reactive power droop curve. and For Node n The adjustable variables of the variable slope droop curve of the photovoltaic inverter at the centralized control stage are: Indicates the prediction time The distribution network curtailment penalty affected by PV forecast uncertainty, is the light abandonment penalty coefficient, For Node n Photovoltaic in the forecast time The active power reduction ratio is For Node n Photovoltaic in the forecast time The maximum available active power taking into account the PV prediction error, For prediction time The sum of the dynamic degradation costs of all distribution network energy storage systems, For Node n At prediction time Dynamic degradation cost of energy storage system.

[0012] As a preferred technical solution, a global optimal voltage control strategy is constructed, which specifically includes: The global optimal voltage control strategy satisfies the power balance constraint: ; in, For the moment t The sum of the distribution network load power, For the moment t The sum of the powers absorbed by the energy storage system, For the moment t The power delivered from the public grid to the distribution grid, For the moment t The sum of active power output from photovoltaics; The global optimal voltage control strategy satisfies the inverter voltage-reactive power droop control curve constraint: ; ; in, is the upper limit of reactive power output of the PV inverter, For Node n In time t The node voltage approximation is The standard voltage-reactive power droop curve k The location of the nodes, , Respectively represent nodes n The adjustable variables of the variable slope droop curve of the photovoltaic inverter at the centralized control stage; The global optimal voltage control strategy satisfies the energy storage system constraints: ; ; ; ; in, node n Energy storage system in time t The state of charge, For Node n The charging and discharging efficiency of the energy storage system, is the collection of distribution network nodes connected to energy storage, Representation Node n The capacity of battery B at Representation Node n Energy storage in time t The absolute value of the output power, For Node n Energy storage system in time t The input energy, For Node n Energy storage system in time t Output energy; The direct sample average approximation method is used to deal with the node voltage exceeding the lower limit CVaR and the photovoltaic inverter capacity exceeding the limit CVaR. The global optimal voltage control strategy satisfies the following constraints: ; ; ; Where M is the number of samples contained in the photovoltaic prediction error dataset, V min Represents the lower limit of the node voltage amplitude allowed, and are the node voltage amplitude and the maximum available active power of photovoltaic, , and All are CVaR auxiliary values, express If the value is positive, it takes the value itself; if it is not positive, it takes 0. For a given confidence level, is the photovoltaic power generation at node n at time t The proportion of active power cut, Representation Node n Photovoltaic at the moment t The reactive power injected into the distribution network, Representation Node n The square of the rated capacity of the photovoltaic power plant.

[0013] As a preferred technical solution, the centralized optimization model is solved based on the distributed robust model predictive control algorithm, specifically including: Update the photovoltaic prediction value and load prediction value within a limited time domain; Construct a Wasserstein metric model based on a limited historical data set, expressed as: ; in, is the empirical probability distribution and the true probability distribution The Wasserstein distance between and They are and The random vectors in the set of values ​​are , Represents an M-dimensional real space, the matrix and vector are the coefficients and vectors of the uncertainty support set of the PV prediction error, Represents a two-dimensional random vector All value sets, For and is the joint probability distribution of the marginal probability distribution; The probability distribution fuzzy set based on Wasserstein distance is established and expressed as: ; represents the probability distribution fuzzy set based on Wasserstein distance, is the true probability distribution The fuzzy set is distributed in empirical probability Centered on is the radius; Based on the probability distribution fuzzy set, a distributed robust optimization model is established, which is expressed as: ; in, Represents probability distribution Expected value under The distributed robust optimization model is reconstructed based on the data-driven method. The reconstructed model is as follows: ; ; ; ; ; ; in, and Respectively represent matrices and No. n A vector of rows, , and is the auxiliary variable of data-driven distributed robust optimization, M represents the number of samples contained in the photovoltaic prediction error dataset, T is the transposed sign, n Indicates the corresponding grid node, The order is N The identity matrix of Indicates at time t The active power reduction ratio vector of the PV inverter is: is an integer auxiliary variable, The order is N The zero matrix of is the auxiliary variable in CVaR, For a given confidence level, , and Represent the distribution network at time t The active power vector injected by photovoltaic, the active power vector absorbed by energy storage system, and the active power vector absorbed by load, For at the moment t The reactive power vector injected into the distribution network; Solve the multi-stage distributed blue stick optimization problem, and determine the amount of electricity purchased from the main grid, the proportion of photovoltaic active power reduction, the energy absorbed by the energy storage, and the voltage-reactive power droop control curve of the photovoltaic inverter within the control time domain according to the solution results.

[0014] The present invention also provides a distribution network voltage control system with high photovoltaic penetration rate, which is used to implement the above-mentioned distribution network voltage control method with high photovoltaic penetration rate, and the system includes: a control strategy construction module, a dynamic degradation cost function construction module, a voltage calculation model construction module, a CVaR function construction module, a centralized optimization model construction module, a centralized optimization model solving module, and a voltage control module; The control strategy building module is used to build a voltage control strategy for a high photovoltaic penetration distribution network; The dynamic degradation cost function construction module is used to construct a dynamic degradation cost function of the energy storage system; The voltage calculation model building module is used to build a distribution network node voltage approximate value calculation model; The CVaR function building module is used to build a CVaR function for evaluating voltage over-limit risk and inverter capacity over-limit risk; The centralized optimization model building module is used to build a centralized optimization model for voltage control of a high photovoltaic penetration distribution network taking into account photovoltaic uncertainty, calculate the weighted sum of the node voltage exceeding the upper limit risk and the distribution network voltage control cost in a limited time domain, build an objective function with the goal of minimizing the weighted sum, and build a global optimal voltage control strategy; The centralized optimization model solving module is used to solve the centralized optimization model based on the distributed robust model predictive control algorithm and output the voltage-reactive power droop control curve; The voltage control module is used to adjust the reactive power output of the photovoltaic inverter based on the voltage-reactive power droop control curve to achieve voltage control.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: (1) The present invention adopts a technical solution of integrating the mathematical expression of the local control strategy into the centralized optimization model, which solves the technical problem that the decisions at the centralized control level and the local control level are made in isolation, and achieves the technical effect of global optimization and local rapid response.

[0016] (2) The present invention adopts a distributed robust model predictive control algorithm based on Wasserstein distance to solve the technical solution of the uncertainty problem caused by photovoltaic prediction errors, solves the technical problem that the existing hierarchical voltage control strategy does not fully exploit photovoltaic prediction data information, and achieves the technical effect of filtering abnormal data and rolling updating photovoltaic prediction data.

[0017] (3) The present invention adopts a technical solution of a calculation model for the dynamic degradation cost of energy storage taking into account the charge and discharge cycle, which solves the technical problem that the energy storage scheduling cost has not been reasonably estimated, and achieves the effect of improving the overall performance of the strategy by reasonably balancing the energy storage degradation cost and the photovoltaic reduction amount. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flow chart of a voltage control method for a distribution network with high photovoltaic penetration rate according to the present invention; Figure 2 A schematic diagram of a voltage control strategy for a high photovoltaic penetration distribution network with centralized-local coordination according to the present invention; Figure 3 It is a schematic diagram of the voltage-reactive power droop control curve of the photovoltaic inverter with variable slope of the present invention; Figure 4 This is a flow chart of the present invention for solving the optimal voltage control strategy for a distribution network with high photovoltaic penetration. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] Example 1 like Figure 1 As shown, this embodiment provides a method for controlling voltage of a distribution network with high photovoltaic penetration, comprising the following steps: S1: Determine the voltage control strategy of the distribution network with high PV penetration rate in a centralized-local coordinated manner. In the centralized optimization stage of the control strategy, the system voltage control cost including the dynamic degradation cost of the energy storage system and the voltage over-limit risk are weighed. In the local control stage, the reactive output of the PV inverter is controlled according to the local voltage-reactive droop control curve adjusted in the centralized stage to cope with the rapid fluctuation of PV. like Figure 2 As shown, the centralized-local coordinated high photovoltaic penetration distribution network voltage control strategy in step S1 is specifically as follows: In the centralized control layer, each control time domain T e The decision is made once based on the electricity purchase and sales price reported by the distribution network, photovoltaic forecast information, load forecast information and the state of charge (SOC) of the energy storage system. In the centralized optimization stage, the risk of node voltage exceeding the upper limit and the weighted sum of the distribution network voltage control cost are minimized within a limited time domain by controlling the electric energy exchanged with the main grid, photovoltaic active output, active power output of energy storage and the slope of the inverter voltage-reactive droop control curve, thus generating a global optimal strategy. At the local control layer, each photovoltaic inverter determines the reactive power output of the photovoltaic inverter based on the voltage-reactive droop control curve in the centralized optimization stage and the node voltage of the distribution network, ultimately achieving real-time minimization of the node voltage deviation; S2: Construct the dynamic degradation cost function of the energy storage system, including: Node nEnergy storage system in time t State of charge (State of Charge, SOC) as input, construct the degradation cost density function of the energy storage system with respect to the state of charge , specifically expressed as: ; in, is the cost per unit capacity of the energy storage system, is the total capacity of the energy storage system, is the charge and discharge efficiency of the energy storage system. Subscript B represents the battery. a and b are empirical parameters of battery degradation, which are generally determined based on experimental data provided by battery manufacturers.

[0021] Based on the least squares method, the degradation cost density function is simplified into a piecewise linear function, and a dynamic degradation cost function of the energy storage system suitable for complex problems is constructed: ; in, For Node n Energy storage system in time t The output power of the battery is represented by the subscript B. The time interval for decision making; S3: Construct a distribution network node voltage approximate value calculation model; The calculation model of the distribution network node voltage approximation in step S3 is as follows: Based on the linear approximation method of the AC power flow equation, a time model considering the influence of photovoltaic prediction error is constructed. t Approximate voltage value of distribution network node The calculation model is specifically expressed as: ; ; ; ; ; in, is the node admittance matrix excluding the equilibrium node, and are the corresponding resistance matrix and reactance matrix respectively, To balance the mutual admittance between the node and the PQ node, To balance the node voltage, For photovoltaic at all times The prediction error will lead to the prediction error of active power and reactive power of photovoltaic. represents the active power prediction error vector affected by the uncertainty of photovoltaic output, represents the reactive power prediction error vector affected by the uncertainty of PV output, , and are the reactive power, active power and node voltage vector of the predetermined photovoltaic output, is the node voltage vector The phase of , j represents the complex imaginary part.

[0022] S4: Based on the constructed distribution network node voltage approximation calculation model, a CVaR function is established to evaluate the voltage over-limit risk and inverter capacity over-limit risk; In this embodiment, the CVaR function used to evaluate the voltage over-limit risk and the inverter capacity over-limit risk is specifically: ; ; ; in, Indicates the risk of voltage exceeding the upper limit. Indicates the risk of voltage exceeding the lower limit. Indicates the risk of inverter capacity exceeding the limit. , and are all CVaR auxiliary variables, Representation Node n Photovoltaic at the moment t The reactive power injected into the distribution network, is a given confidence level, ranging from 0 to 1. For Node n At the moment t The first m PV prediction errors, M is the number of samples contained in the PV prediction error dataset, is the probability density function, and The photovoltaic prediction errors are The node voltage amplitude and the maximum available active power of photovoltaic power, V max Represents the upper limit of the node voltage amplitude, V min Represents the lower limit of the node voltage amplitude allowed, express If the value is positive, it takes the value itself; if it is not positive, it takes 0. is the photovoltaic power generation at node n at time t The ratio of active power to be reduced ranges from 0 to 1. For Node n The rated capacity of the photovoltaic Representation Node n The square of the rated capacity of the photovoltaic power plant.

[0023] S5: Establish a centralized optimization model for voltage control of distribution networks with high PV penetration considering PV uncertainty, including: (1) Based on the model predictive control algorithm, to minimize the finite time domain T p The weighted sum of the risk of the internal node voltage exceeding the upper limit and the distribution network voltage control cost is taken as the target, and the determined objective function is: ; ; in, For prediction time The voltage control cost of the distribution network is is the risk factor. The higher the risk factor, the more attention the distribution network operator pays to the upper limit risk of the system voltage amplitude. represents a finite time domain, is the risk of node voltage exceeding the upper limit, N is the total number of distribution network nodes, with superscript Represents the quantity directly related to the PV prediction error dataset, the distribution network voltage control cost Cost of purchasing electricity from the distribution network to the public grid , Reactive power output cost of photovoltaic inverters in distribution networks , Distribution network photovoltaic inverter droop control curve change penalty , Distribution network curtailment penalty affected by PV forecast uncertainty and the sum of the dynamic degradation costs of all distribution network energy storage systems The specific calculation formula is as follows: ; ; ; ; ; in, is the set of all nodes in the distribution network. Indicates the prediction time The cost of purchasing electricity from the public grid is replace t This is because there is a prediction time domain ( t arrive t +T p ), is the time interval for decision making, is the electricity purchase price, For time The distribution network purchases electricity from the public grid. For prediction time The reactive power output cost of photovoltaic inverters in the distribution network, is the reactive power output cost price of the photovoltaic inverter, For Node n At prediction time Reactive power output, when the node n When there is no photovoltaic connection Always 0, For time The penalty for the change of the droop control curve of the photovoltaic inverter in the distribution network, is the penalty cost coefficient for the inverter voltage-reactive power droop curve change, and It is the third and fourth node positions of the standard voltage-reactive power droop curve. and For Node n The adjustable variables of the variable slope droop curve of the photovoltaic inverter at the centralized control stage are: Indicates the prediction time The distribution network curtailment penalty affected by PV forecast uncertainty, is the light abandonment penalty coefficient, For Node n Photovoltaic in the forecast time The active power reduction ratio is For Node n Photovoltaic in the forecast time The maximum available active power taking into account the PV prediction error, For prediction time The sum of the dynamic degradation costs of all distribution network energy storage systems, For Node n At prediction time Dynamic degradation cost of energy storage system.

[0024] (2) The formulated global optimal voltage control strategy should satisfy the following power balance constraints: ; in, For the moment t The sum of the distribution network load power, For the moment t The sum of the powers absorbed by the energy storage system, For the moment t The power delivered from the public grid to the distribution grid, For the moment t The sum of active power output from photovoltaics; (3) The formulated global optimal voltage control strategy should satisfy the following inverter voltage-reactive power droop control curve constraints: ; ; in, is the upper limit of reactive power output of the PV inverter, For Node n In time t The node voltage approximation is The standard voltage-reactive power droop curve k The location of the nodes, , Respectively represent nodes n The variable slope droop curve of the photovoltaic inverter at the centralized control stage is an adjustable variable. The proposed variable slope photovoltaic inverter voltage-reactive power droop control curve is as follows: Figure 3 shown.

[0025] (4) The formulated global optimal voltage control strategy should satisfy the following energy storage system constraints: ; ; ; ; in, node n Energy storage system in time t The state of charge, For Node n The charging and discharging efficiency of the energy storage system, is the collection of distribution network nodes connected to energy storage, Representation Node n The capacity of battery B at Representation Node n Energy storage in time t The absolute value of the output power, For Node n Energy storage system in time t The input energy, For Node n Energy storage system in time t The output energy, and All are positive values.

[0026] (5) In order to improve the computational efficiency, the direct sample average approximation method is further used to deal with the node voltage exceeding the lower limit CVaR and the PV inverter capacity exceeding the limit CVaR. Therefore, the global optimal voltage control strategy formulated should satisfy the following constraints: ; ; ; Where M is the number of samples contained in the photovoltaic prediction error dataset; S6: A data-driven distributed robust model predictive control algorithm is used to solve the centralized optimization model for voltage control of the high photovoltaic penetration distribution network considering photovoltaic uncertainty established in step S5, such as Figure 4 As shown, the specific process is: S61: Update limited time domain The photovoltaic forecast value and load forecast value within; S62: Building a Wasserstein metric model based on a limited historical data set , specifically expressed as: ; in, is the empirical probability distribution and the true probability distribution The Wasserstein distance between and They are and The random vector of , Represents an M-dimensional real space, the matrix and vector are the coefficients and vectors of the uncertainty support set of the PV prediction error, Represents a two-dimensional random vector All value sets, For and is the joint probability distribution of the marginal probability distribution; S63: Establishing fuzzy sets of probability distribution based on Wasserstein distance , specifically expressed as: ; in, represents the probability distribution fuzzy set based on Wasserstein distance, is the true probability distribution The fuzzy set is distributed in empirical probability Centered on For the radius, the distribution network operator can adjust is the radius control probability distribution fuzzy set Contains the confidence level of the true probability distribution of the random variable and the conservatism of the decision.

[0027] S64: A distributed robust optimization model is established based on probability distribution fuzzy sets. The specific model is: ; in, Represents probability distribution Expected value under The distributed robust optimization model is based on the fuzzy set established in step S63. The probability distribution that maximizes the voltage over-limit risk is used to make an optimized decision to consider the impact of photovoltaic prediction error uncertainty on the voltage over-limit CVaR.

[0028] S65: Reconstruct the distributed robust optimization model based on the data-driven method. The reconstructed model is as follows: ; ; ; ; ; ; and Respectively represent matrices and No. n A vector of rows, , and is the auxiliary variable of data-driven distributed robust optimization, M represents the number of samples contained in the photovoltaic prediction error dataset, T is the transposed sign, n Indicates the corresponding grid node, The order is N The identity matrix of Indicates at time t The active power reduction ratio vector of the PV inverter is: is an integer auxiliary variable, The order is N The zero matrix of is the auxiliary variable in CVaR, For a given confidence level, , and Represent the distribution network at time t The active power vector injected by photovoltaic, the active power vector absorbed by energy storage system, and the active power vector absorbed by load, For at the moment t The reactive power vector injected into the distribution network is and Together they define the uncertainty set of the prediction error .

[0029] S66: Solve the multi-stage distributed robust optimization problem and determine the control time domain based on the solution results T e The amount of electricity purchased from the main grid, the reduction ratio of photovoltaic active power, the energy absorbed by energy storage and the voltage-reactive power droop control curve of the photovoltaic inverter are all controlled at the minute level. After the control instruction is issued, the centralized controller pushes the solution time forward by one control time domain and returns to step S61 to continue rolling solution. At the same time, the photovoltaic inverter controls its own reactive output based on the voltage-reactive power droop control curve and the voltage amplitude that can be monitored locally to cope with the second-level fluctuation of photovoltaic power.

[0030] Example 2 This embodiment provides a distribution network voltage control system with high photovoltaic penetration, which is used to implement the distribution network voltage control method with high photovoltaic penetration of the above-mentioned embodiment 1, and the system includes: a control strategy construction module, a dynamic degradation cost function construction module, a voltage calculation model construction module, a CVaR function construction module, a centralized optimization model construction module, a centralized optimization model solving module, and a voltage control module; In this embodiment, the control strategy building module is used to build a voltage control strategy for a high photovoltaic penetration distribution network; In this embodiment, the dynamic degradation cost function construction module is used to construct a dynamic degradation cost function of the energy storage system; In this embodiment, the voltage calculation model construction module is used to construct a distribution network node voltage approximate value calculation model; In this embodiment, the CVaR function construction module is used to construct a CVaR function for evaluating voltage over-limit risk and inverter capacity over-limit risk; In this embodiment, the centralized optimization model construction module is used to construct a centralized optimization model for voltage control of a high photovoltaic penetration distribution network taking into account photovoltaic uncertainty, calculate the weighted sum of the node voltage exceeding the upper limit risk and the distribution network voltage control cost in a limited time domain, construct an objective function with the goal of minimizing the weighted sum, and construct a global optimal voltage control strategy; In this embodiment, the centralized optimization model solving module is used to solve the centralized optimization model based on the distributed robust model predictive control algorithm and output the voltage-reactive power droop control curve; In this embodiment, the voltage control module is used to adjust the reactive power output of the photovoltaic inverter based on the voltage-reactive power droop control curve to achieve voltage control.

[0031] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A voltage control method for a distribution network with high photovoltaic penetration, characterized in that: The steps include: Construct voltage control strategy for distribution network with high PV penetration; Construct a dynamic degradation cost function for the energy storage system; Construct a distribution network node voltage approximate value calculation model; Construct a CVaR function for assessing voltage over-limit risk and inverter capacity over-limit risk; A centralized optimization model for voltage control of distribution networks with high photovoltaic penetration rates considering photovoltaic uncertainty is constructed. The weighted sum of the risk of node voltage exceeding the upper limit and the voltage control cost of the distribution network in a limited time domain is calculated. The objective function is constructed with the goal of minimizing the weighted sum, and the global optimal voltage control strategy is constructed. Solve the centralized optimization model based on the distributed blue stick model predictive control algorithm and output voltage-reactive power droop control curve; The reactive power output of the photovoltaic inverter is adjusted based on the voltage-reactive power droop control curve to achieve voltage control.

2. The method for controlling voltage of a distribution network with high photovoltaic penetration according to claim 1, characterized in that: Construct a voltage control strategy for distribution networks with high photovoltaic penetration, including: Construct a centralized-local coordinated voltage control strategy for high photovoltaic penetration distribution networks. In the centralized optimization stage of the control strategy, a decision is made once based on the purchase and sale prices of electricity reported by the distribution network, photovoltaic forecast information, load forecast information, and the state of charge of the energy storage system. By controlling the electric energy exchanged with the main grid, the photovoltaic active output, the active power output of the energy storage, and the slope of the inverter voltage-reactive droop control curve, the risk of node voltage exceeding the upper limit in a limited time domain and the weighted sum of the distribution network voltage control cost are minimized to generate a global optimal strategy. In the local control stage of the control strategy, the reactive power output of the PV inverter is adjusted according to the voltage of the distribution network node based on the voltage-reactive power droop control curve.

3. The method for controlling voltage of a distribution network with high photovoltaic penetration according to claim 1, characterized in that: Construct the dynamic degradation cost function of the energy storage system, including: Node n Energy storage system in time t State of charge As input, construct the degradation cost density function of the energy storage system with respect to the state of charge , expressed as: ； in, is the cost per unit capacity of the energy storage system, is the total capacity of the energy storage system, is the charge and discharge efficiency of the energy storage system, a and b are the empirical parameters of battery degradation; Based on the least squares method, the degradation cost density function is simplified into a piecewise linear function, and the dynamic degradation cost function of the energy storage system is constructed, which is expressed as: ； in, For Node n Energy storage system in time t The output power, The time interval for decision making.

4. The method for controlling voltage of a distribution network with high photovoltaic penetration according to claim 1, characterized in that: Construct a distribution network node voltage approximate value calculation model, including: Based on the linear approximation method of the AC power flow equation, the moment considering the influence of photovoltaic prediction error is constructed. t Approximate voltage value of distribution network node The calculation model is expressed as: ； ； ； ； ； in, is the node admittance matrix excluding the equilibrium node, and are the corresponding resistance matrix and reactance matrix respectively, To balance the mutual admittance between the node and the PQ node, To balance the node voltage, For photovoltaic at all times The prediction error, represents the active power prediction error vector affected by the uncertainty of photovoltaic output, represents the reactive power prediction error vector affected by the uncertainty of PV output, , and are the reactive power, active power and node voltage vector of the predetermined photovoltaic output, is the node voltage vector The phase of , j represents the complex imaginary part.

5. The method for controlling voltage of a distribution network with high photovoltaic penetration according to claim 1, characterized in that: Construct a CVaR function for assessing voltage over-limit risk and inverter capacity over-limit risk, including: ； ； ； in, Indicates the risk of voltage exceeding the upper limit. Indicates the risk of voltage exceeding the lower limit. Indicates the risk of inverter capacity exceeding the limit. , and are all CVaR auxiliary variables, Representation Node n Photovoltaic at the moment t The reactive power injected into the distribution network, For a given confidence level, For Node n At the moment t The first m PV prediction errors, M is the number of samples contained in the PV prediction error dataset, is the probability density function, and The photovoltaic prediction errors are The node voltage amplitude and the maximum available active power of photovoltaic power, V max Represents the upper limit of the node voltage amplitude, V min Represents the lower limit of the node voltage amplitude allowed, express If the value is positive, it takes the value itself; if it is not positive, it takes 0. is the photovoltaic power generation at node n at time t The proportion of active power cut, Representation Node n The square of the rated capacity of the photovoltaic power plant.

6. The method for controlling voltage of a distribution network with high photovoltaic penetration according to claim 1, characterized in that: The weighted sum of the node voltage exceeding the upper limit risk and the distribution network voltage control cost in the finite time domain is calculated, and the objective function is constructed with the goal of minimizing the weighted sum, which is specifically expressed as: ； ； in, For prediction time The voltage control cost of the distribution network is is the risk factor, represents a finite time domain, is the risk of node voltage exceeding the upper limit, N is the total number of distribution network nodes, It represents the cost of power purchased by the distribution network from the public grid. Represents the reactive power output cost of photovoltaic inverters in distribution network, It represents the penalty of the droop control curve change of the photovoltaic inverter in the distribution network. represents the distribution network curtailment penalty affected by PV forecast uncertainty, Represents the sum of the dynamic degradation costs of all distribution network energy storage systems.

7. The method for controlling voltage of a distribution network with high photovoltaic penetration according to claim 6, characterized in that: The calculation formula of each cost in the distribution network voltage control cost is expressed as: ； ； ； ； ； in, is the set of all nodes in the distribution network. Indicates the prediction time The cost of purchasing electricity from the public grid for the distribution network is is the time interval for decision making, is the electricity purchase price, For time The distribution network purchases electricity from the public grid. For prediction time The reactive power output cost of photovoltaic inverters in the distribution network, is the reactive power output cost price of the photovoltaic inverter, For Node n At prediction time The reactive power output, For time The penalty for the change of the droop control curve of the photovoltaic inverter in the distribution network, is the penalty cost coefficient for the inverter voltage-reactive power droop curve change, and It is the third and fourth node positions of the standard voltage-reactive power droop curve. and For Node n The adjustable variables of the variable slope droop curve of the photovoltaic inverter at the centralized control stage are: Indicates the prediction time The distribution network curtailment penalty affected by PV forecast uncertainty, is the light abandonment penalty coefficient, For Node n Photovoltaic in the forecast time The active power reduction ratio is For Node n Photovoltaic in the forecast time The maximum available active power taking into account the PV prediction error, For prediction time The sum of the dynamic degradation costs of all distribution network energy storage systems, For Node n At prediction time Dynamic degradation cost of energy storage system.

8. The method for controlling voltage of a distribution network with high photovoltaic penetration according to claim 1, characterized in that: Construct a global optimal voltage control strategy, including: The global optimal voltage control strategy satisfies the power balance constraint: ； in, For the moment t The sum of the distribution network load power, For the moment t The sum of the powers absorbed by the energy storage system, For the moment t The power delivered from the public grid to the distribution grid, For the moment t The sum of active power output from photovoltaics; The global optimal voltage control strategy satisfies the inverter voltage-reactive power droop control curve constraint: ； ； in, is the upper limit of reactive power output of the PV inverter, For Node n In time t The node voltage approximation is The standard voltage-reactive power droop curve k The location of the nodes, , Respectively represent nodes n The adjustable variables of the variable slope droop curve of the photovoltaic inverter at the centralized control stage; The global optimal voltage control strategy satisfies the energy storage system constraints: ； ； ； ； in, node n Energy storage system in time t The state of charge, For Node n The charging and discharging efficiency of the energy storage system, is the collection of distribution network nodes connected to energy storage, Representation Node n The capacity of battery B at Representation Node n Energy storage in time t The absolute value of the output power, For Node n Energy storage system in time t The input energy, For Node n Energy storage system in time t Output energy; The direct sample average approximation method is used to deal with the node voltage exceeding the lower limit CVaR and the photovoltaic inverter capacity exceeding the limit CVaR. The global optimal voltage control strategy satisfies the following constraints: ； ； ； Where M is the number of samples contained in the photovoltaic prediction error dataset, V min Represents the lower limit of the node voltage amplitude allowed, and are the node voltage amplitude and the maximum available active power of photovoltaic, , and All are CVaR auxiliary values, express If the value is positive, it takes the value itself; if it is not positive, it takes 0. For a given confidence level, is the photovoltaic power generation at node n at time t The proportion of active power cut, Representation Node n Photovoltaic at the moment t The reactive power injected into the distribution network, Representation Node n The square of the rated capacity of the photovoltaic power plant.

9. The method for controlling voltage of a distribution network with high photovoltaic penetration according to claim 1, characterized in that: The centralized optimization model is solved based on the distributed robust model predictive control algorithm, including: Update the photovoltaic prediction value and load prediction value within a limited time domain; Construct a Wasserstein metric model based on a limited historical data set, expressed as: ； in, is the empirical probability distribution and the true probability distribution The Wasserstein distance between and They are and The random vectors in the set of values ​​are , Represents an M-dimensional real space, the matrix and vector are the coefficients and vectors of the uncertainty support set of the PV prediction error, Represents a two-dimensional random vector All value sets, For and is the joint probability distribution of the marginal probability distribution; The probability distribution fuzzy set based on Wasserstein distance is established and expressed as: ； represents the probability distribution fuzzy set based on Wasserstein distance, is the true probability distribution The fuzzy set is distributed in empirical probability Centered on is the radius; Based on the probability distribution fuzzy set, a distributed robust optimization model is established, which is expressed as: ； in, Represents probability distribution Expected value under The distributed robust optimization model is reconstructed based on the data-driven method. The reconstructed model is as follows: ； ； ； ； ； ； in, and Respectively represent matrices and No. n A vector of rows, , and is the auxiliary variable of data-driven distributed robust optimization, M represents the number of samples contained in the photovoltaic prediction error dataset, T is the transposed sign, n Indicates the corresponding grid node, The order is N The identity matrix of Indicates at time t The active power reduction ratio vector of the PV inverter is: is an integer auxiliary variable, The order is N The zero matrix of is the auxiliary variable in CVaR, For a given confidence level, , and Represent the distribution network at time t The active power vector injected by photovoltaic, the active power vector absorbed by energy storage system, and the active power vector absorbed by load, For at the moment t The reactive power vector injected into the distribution network; Solve the multi-stage distributed blue stick optimization problem, and determine the amount of electricity purchased from the main grid, the proportion of photovoltaic active power reduction, the energy absorbed by the energy storage, and the voltage-reactive power droop control curve of the photovoltaic inverter within the control time domain according to the solution results.

10. A distribution network voltage control system with high photovoltaic penetration, characterized in that: A distribution network voltage control method for achieving high photovoltaic penetration as described in any one of claims 1 to 9, the system comprising: a control strategy building module, a dynamic degradation cost function building module, a voltage calculation model building module, a CVaR function building module, a centralized optimization model building module, a centralized optimization model solving module, and a voltage control module; The control strategy building module is used to build a voltage control strategy for a high photovoltaic penetration distribution network; The dynamic degradation cost function construction module is used to construct a dynamic degradation cost function of the energy storage system; The voltage calculation model building module is used to build a distribution network node voltage approximate value calculation model; The CVaR function building module is used to build a CVaR function for evaluating voltage over-limit risk and inverter capacity over-limit risk; The centralized optimization model building module is used to build a centralized optimization model for voltage control of a high photovoltaic penetration distribution network taking into account photovoltaic uncertainty, calculate the weighted sum of the node voltage exceeding the upper limit risk and the distribution network voltage control cost in a limited time domain, build an objective function with the goal of minimizing the weighted sum, and build a global optimal voltage control strategy; The centralized optimization model solving module is used to solve the centralized optimization model based on the distributed robust model predictive control algorithm and output the voltage-reactive power droop control curve; The voltage control module is used to adjust the reactive power output of the photovoltaic inverter based on the voltage-reactive power droop control curve to achieve voltage control.

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