Energy storage optimization configuration method for load side of power distribution network

Abstract

The invention relates to a power distribution network load side energy storage optimization configuration method, which comprises the following steps: firstly, collecting power grid load data, obtaining the load range of each node and the load range of each line through load flow calculation, then judging the regulation requirement of a new energy power system to be accessed according to the head end voltage of a feeder line, new energy active power output and new energy reactive power output, and finally determining the energy storage optimization configuration of the load side of the power distribution network. Based on an objective function and constraint adjustment, an independent energy storage economic model and a multi-objective hybrid energy storage optimal configuration model are respectively established, simulation operation and sensitivity analysis are performed on the independent energy storage economic model, and optimization solution is performed through a genetic algorithm, so that optimal energy storage capacity and charging and discharging power are obtained. And performing a particle swarm optimization algorithm on the multi-target hybrid energy storage optimal configuration model so as to obtain proper hybrid energy storage rated capacity and rated power. In general, the method has the advantages of being reasonable, efficient and good in overall economical efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid energy storage optimization, and in particular relates to an energy storage optimization configuration method on the load side of a distribution network. Background Art

[0002] With the steady improvement of the global economy and the rapid development of the power industry, the total electricity consumption of various countries has increased significantly. However, the difference between the peak and valley of power load is also expanding, which increasingly highlights the problem of imbalance between power supply and demand. Among them, energy storage, as a backup power source on the load side, can improve the reliability of power supply on the load side, and can also participate in the system power regulation. When the user demand is large and the system supply is insufficient, the output power of energy storage can relieve the system pressure and meet the load side demand in time. In the event of faults and maintenance power outages, uninterrupted power supply can be achieved through energy storage, thereby improving the reliability of power supply and improving the power quality of the system. When a large number of new energy sources are connected to the grid, energy storage can absorb the power that the system cannot absorb, improve the stability of the system power, and achieve demand response. Therefore, energy storage technology can serve as an important technical support for energy transformation and effectively solve the contradiction between power supply and demand in the power grid. To this end, it is necessary to develop a reasonable, efficient and overall economical energy storage optimization configuration method for the load side of the distribution network. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a method for optimizing energy storage configuration on the load side of a distribution network that is reasonable, efficient and has good overall economic performance.

[0004] The object of the present invention is achieved by: a method for optimizing energy storage configuration on the load side of a distribution network, comprising the following steps: Step 1: Collect grid load data, then use the grid load data to perform power flow calculation to obtain the load range of each node and the load range of each line; Step 2: According to the voltage at the head end of the feeder, the active output of the renewable energy source and the reactive output of the renewable energy source, the voltage difference change of the renewable energy power system to be connected is obtained. Then, according to the voltage difference range and the voltage difference change of the nominal voltage, the regulation demand of the renewable energy power system to be connected is determined. Step 3: Taking the initial investment cost, supporting facilities cost, operation and maintenance cost, and replacement cost as the objective function, and the initial power shortage rate and power surplus rate as the constraint adjustment, an independent energy storage economic model is established under the conditions that the energy storage capacity meets the load demand and the charging and discharging power meets the load change rate; Step 4: Perform simulation operations based on the independent energy storage economic model to obtain the objective function curve, and then perform sensitivity analysis to study the relationship between the net profit of the energy storage system and the rated capacity of the energy storage, the relationship between the optimal capacity of the energy storage system and the penalty coefficient, and the relationship between the maximum net profit of the wind-storage combined system and the penalty coefficient. Finally, use the genetic algorithm to optimize the capacity of the independent energy storage system to obtain the relationship between the cost of the energy storage system and the rated capacity / rated power. Under the premise of meeting the constraints, find the optimal energy storage capacity and charge and discharge power to minimize the total cost of the energy storage system and achieve the most economically optimal configuration of the energy storage system. Step 5: Taking the cost and the number of energy storage charge and discharge conversions as the objective function, and the power fluctuation limit and the basic constraints of the normal operation of the hybrid energy storage system as the constraint adjustment, by analyzing the characteristics of the hybrid energy storage system, a multi-objective hybrid energy storage optimization configuration model is established under the conditions of meeting the load demand balance and the charge and discharge power of various energy storage devices meeting the load change rate limit; Step 6: Perform simulation operations based on the multi-objective hybrid energy storage optimization configuration model to obtain the objective function curve. Then, optimize the hybrid energy storage system configuration through the particle swarm optimization algorithm to obtain the appropriate hybrid energy storage rated capacity and rated power, thereby reducing the cost and charge-discharge conversion times of the hybrid energy storage system. Finally, the optimal configuration of the energy storage system is obtained by simulating and verifying the obtained hybrid energy storage rated capacity and rated power.

[0005] Furthermore, in step 5, the basic constraints for normal operation of the hybrid energy storage system are power constraints and state of charge constraints.

[0006] Furthermore, the genetic algorithm in step 4 is to substitute the rated capacity and charging and discharging power of the energy storage system as control variables and the total cost of the energy storage system as a relevant evaluation index into the economic model of a single type of energy storage system for simulation calculation.

[0007] Furthermore, the particle swarm optimization algorithm in step 6 is calculated with the goal of minimizing the system cost including the number of energy storage charge and discharge conversions, the state of charge and grid-connected smoothness of the hybrid energy storage system as evaluation indicators, and the rated capacity and rated power of the hybrid energy storage as decision variables.

[0008] Furthermore, the power grid load data in step 1 includes power load information of each node within the power grid, and load information of each node line segment.

[0009] Beneficial effects of the present invention: The present invention first collects grid load data, then uses the grid load data to perform flow calculations to obtain the load range of each node and the load range of each line, and then obtains the voltage difference change of the new energy power system to be connected according to the voltage at the head end of the feeder, the active output of new energy and the reactive output of new energy, and judges the regulation demand of the new energy power system to be connected, and then establishes an independent energy storage economic model based on the objective function and constraint regulation, and then performs simulation operation and sensitivity analysis on the independent energy storage economic model, and optimizes and solves it through a genetic algorithm, so as to obtain the optimal energy storage capacity and charge and discharge power, so as to realize the economic performance of the energy storage system. The invention optimizes the economical configuration, and at the same time, establishes a multi-objective hybrid energy storage optimization configuration model. Finally, the hybrid energy storage system configuration is optimized and solved by the particle swarm optimization algorithm to obtain the appropriate hybrid energy storage rated capacity and rated power. The invention increases the stability and reliability of the power grid operation through the optimized configuration application of energy storage technology. At the same time, it can also enable the power grid to stably respond to emergencies and reduce the losses caused by power outages. Moreover, the load-side energy storage optimization configuration can realize the dynamic regulation and stabilization of voltage, effectively solve the problem of excessively high or low voltage, and reduce the impact of voltage fluctuations on user equipment. In general, the invention has the advantages of reasonableness, high efficiency and good overall economy. DETAILED DESCRIPTION

[0010] The present invention is further described below.

[0011] Embodiment: A method for optimizing energy storage configuration on the load side of a distribution network comprises the following steps: Step 1: Collect grid load data, and then use the grid load data to perform power flow calculation to obtain the load range of each node and the load range of each line, where the grid load data includes the power load information of each node within the grid range and the load information of each node line section; Step 2: According to the voltage at the head end of the feeder, the active output of renewable energy and the reactive output of renewable energy, the voltage difference change of the renewable energy power system to be connected is obtained. Then, according to the voltage difference range and voltage difference change of the nominal voltage, the regulation demand of the renewable energy power system to be connected is determined. Step 3: Taking the initial investment cost, supporting facilities cost, operation and maintenance cost, and replacement cost as the objective function, and the initial power shortage rate and power surplus rate as the constraint adjustment, an independent energy storage economic model is established under the conditions that the energy storage capacity meets the load demand and the charging and discharging power meets the load change rate; Step 4: Perform simulation operations based on the independent energy storage economic model to obtain the objective function curve, and then perform sensitivity analysis to study the relationship between the net profit of the energy storage system and the rated capacity of the energy storage, the relationship between the optimal capacity of the energy storage system and the penalty coefficient, and the relationship between the maximum net profit of the wind storage combined system and the penalty coefficient. Finally, use a genetic algorithm to optimize the capacity of the independent energy storage system. The genetic algorithm uses the rated capacity and charge and discharge power of the energy storage system as control variables and the total cost of the energy storage system as a related evaluation index. Substitute them into the economic model of a single type of energy storage system for simulation calculation, thereby obtaining the relationship between the cost of the energy storage system and the rated capacity / rated power. Under the premise of satisfying the constraints, find the optimal energy storage capacity and charge and discharge power to minimize the total cost of the energy storage system, so as to achieve the most economically optimal configuration of the energy storage system. Step 5: Taking the cost and the number of energy storage charge and discharge conversions as the objective function, and taking the power fluctuation limit and the basic constraints for the normal operation of the hybrid energy storage system as the constraint adjustment, where the basic constraints for the normal operation of the hybrid energy storage system are the power constraint and the state of charge constraint, then by analyzing the characteristics of the hybrid energy storage system, a multi-objective hybrid energy storage optimization configuration model is established under the conditions of meeting the load demand balance and the charge and discharge power of various energy storage devices meeting the load change rate limit; Step 6: Perform simulation operations based on the multi-objective hybrid energy storage optimization configuration model to obtain the objective function curve. After that, optimize and solve the hybrid energy storage system configuration through the particle swarm optimization algorithm to obtain the appropriate hybrid energy storage rated capacity and rated power. The particle swarm optimization algorithm takes the minimum system cost including the number of energy storage charge and discharge conversions as the goal, the state of charge and grid-connected smoothness of the hybrid energy storage system as evaluation indicators, and the rated capacity and rated power of the hybrid energy storage as decision variables for calculation, thereby reducing the cost and charge and discharge conversion times of the hybrid energy storage system. Finally, the optimal configuration of the energy storage system is obtained by simulating and verifying the obtained hybrid energy storage rated capacity and rated power.

[0012] When the present invention is used, first, the power load information of each node within the power grid and the load information of each node line section are collected through the data collection device, and then the power grid load data is used to perform flow calculation to obtain the load range of each node and the load range of each line, and then, according to the voltage at the head end of the feeder, the active output of new energy and the reactive output of new energy, the voltage difference change of the new energy power system to be connected is obtained, and according to the voltage difference range and the voltage difference change of the nominal voltage, the regulation demand of the new energy power system to be connected is judged, and then, the initial investment cost, the supporting facilities cost, the operation and maintenance cost and the replacement cost are taken as the objective function, and the initial power shortage rate and the power surplus rate are taken as the target function. Constraint adjustment: Under the conditions that the energy storage capacity meets the load demand and the charging and discharging power meets the load change rate, an independent energy storage economic model is established. After that, the independent energy storage economic model is simulated to obtain the objective function curve. Then, a sensitivity analysis is performed to study the relationship between the net profit of the energy storage system and the rated capacity of the energy storage, the relationship between the optimal capacity of the energy storage system and the penalty coefficient, and the relationship between the maximum net profit of the wind-storage combined system and the penalty coefficient. Finally, a genetic algorithm is used to optimize the capacity of the independent energy storage system to obtain the relationship between the cost of the energy storage system and the rated capacity / rated power. Under the premise of meeting the constraints, the optimal energy storage is found. capacity and charge-discharge power, so that the total cost of the energy storage system is minimized, so as to achieve the economic optimization configuration of the energy storage system; after completing the above operations, taking the cost and the number of energy storage charge-discharge conversions as the objective function, taking the power fluctuation limit and the basic constraints of the normal operation of the hybrid energy storage system as the constraint adjustment, by analyzing the characteristics of the hybrid energy storage system, under the conditions of meeting the load demand balance and the charge-discharge power of various energy storage devices meeting the load change rate limit, a multi-objective hybrid energy storage optimization configuration model is established; and then, the multi-objective hybrid energy storage optimization configuration model is simulated to obtain the objective function curve, and then, the hybrid energy storage system configuration is optimized through the particle swarm optimization algorithm. The solution is obtained to obtain the appropriate hybrid energy storage rated capacity and rated power, thereby reducing the cost of the hybrid energy storage system and the number of charge-discharge conversions. Finally, the optimal configuration of the energy storage system is obtained by simulating and verifying the obtained hybrid energy storage rated capacity and rated power. The present invention increases the stability and reliability of power grid operation through the optimized configuration application of energy storage technology. At the same time, it can also enable the power grid to stably respond to emergencies and reduce the losses caused by power outages. Moreover, the optimized configuration of load-side energy storage can realize dynamic regulation and stabilization of voltage, effectively solve the problem of excessively high or low voltage, and reduce the impact of voltage fluctuations on user equipment. In general, the present invention has the advantages of reasonableness, high efficiency and good overall economy.

[0013] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing energy storage configuration on the load side of a distribution network, characterized in that: The following steps are involved: Step 1: Collect grid load data, then use the grid load data to perform power flow calculation to obtain the load range of each node and the load range of each line; Step 2: According to the voltage at the head end of the feeder, the active output of the renewable energy source and the reactive output of the renewable energy source, the voltage difference change of the renewable energy power system to be connected is obtained. Then, according to the voltage difference range and the voltage difference change of the nominal voltage, the regulation demand of the renewable energy power system to be connected is determined. Step 3: Taking the initial investment cost, supporting facilities cost, operation and maintenance cost, and replacement cost as the objective function, and the initial power shortage rate and power surplus rate as the constraint adjustment, an independent energy storage economic model is established under the conditions that the energy storage capacity meets the load demand and the charging and discharging power meets the load change rate; Step 4: Perform simulation operations based on the independent energy storage economic model to obtain the objective function curve, and then perform sensitivity analysis to study the relationship between the net profit of the energy storage system and the rated capacity of the energy storage, the relationship between the optimal capacity of the energy storage system and the penalty coefficient, and the relationship between the maximum net profit of the wind-storage combined system and the penalty coefficient. Finally, use the genetic algorithm to optimize the capacity of the independent energy storage system to obtain the relationship between the cost of the energy storage system and the rated capacity / rated power. Under the premise of meeting the constraints, find the optimal energy storage capacity and charge and discharge power to minimize the total cost of the energy storage system and achieve the most economically optimal configuration of the energy storage system. Step 5: Taking the cost and the number of energy storage charge and discharge conversions as the objective function, and the power fluctuation limit and the basic constraints of the normal operation of the hybrid energy storage system as the constraint adjustment, by analyzing the characteristics of the hybrid energy storage system, a multi-objective hybrid energy storage optimization configuration model is established under the conditions of meeting the load demand balance and the charge and discharge power of various energy storage devices meeting the load change rate limit; Step 6: Perform simulation operations based on the multi-objective hybrid energy storage optimization configuration model to obtain the objective function curve. Then, optimize the hybrid energy storage system configuration through the particle swarm optimization algorithm to obtain the appropriate hybrid energy storage rated capacity and rated power, thereby reducing the cost and charge-discharge conversion times of the hybrid energy storage system. Finally, the optimal configuration of the energy storage system is obtained by simulating and verifying the obtained hybrid energy storage rated capacity and rated power.

2. The method for optimizing energy storage configuration on the load side of a distribution network according to claim 1, characterized in that: In step 5, the basic constraints for normal operation of the hybrid energy storage system are power constraints and state of charge constraints.

3. The method for optimizing energy storage configuration on the load side of a distribution network according to claim 1, characterized in that: The genetic algorithm in step 4 is to substitute the rated capacity and charging and discharging power of the energy storage system as control variables and the total cost of the energy storage system as the relevant evaluation index into the economic model of the single-type energy storage system for simulation calculation.

4. The method for optimizing energy storage configuration on the load side of a distribution network according to claim 1, characterized in that: The particle swarm optimization algorithm in step 6 is calculated with the goal of minimizing the system cost including the number of energy storage charge and discharge conversions, the state of charge and grid-connected smoothness of the hybrid energy storage system as evaluation indicators, and the rated capacity and rated power of the hybrid energy storage as decision variables.

5. The method for optimizing energy storage configuration on the load side of a distribution network according to claim 1, characterized in that: The power grid load data in step 1 includes the power load information of each node within the power grid, and the load information of each node line segment.