Micro-grid energy storage capacity configuration method and system based on power energy characteristics

Abstract

The invention discloses a micro-grid energy storage capacity configuration method and system based on power energy characteristics, and the method comprises the steps: calculating the net load of a micro-grid through building a micro-grid energy storage capacity configuration net load model; according to the stable operation demand of the system and the actual demand of the load, determining a micro-grid source-load output constraint condition based on the power energy characteristic; constructing a scene set influencing the operation of the micro-grid; performing classification according to energy characteristics and power characteristics of the micro-grid system to obtain scene subsets; using an uncertainty fusion method of power energy characteristics to convert the uncertainty of the uncertainty factors into the uncertainty of generation power and load power, and clustering in different scene subsets to obtain an uncertainty scene set of the micro-grid system; energy storage capacity configuration is optimized based on power energy characteristics, an energy storage capacity optimization model is constructed, and the energy storage capacity is calculated and optimized according to the net load in the uncertainty scene and constraint conditions of the energy storage capacity optimization model.

Description

Technical Field

[0001] The present invention relates to an optimization configuration technology for an electric power system, and in particular to a method and system for configuring energy storage capacity of a microgrid based on power energy characteristics. Background Art

[0002] In the current power system, the energy storage capacity configuration method of microgrids faces many challenges, especially for microgrid systems that include renewable energy such as small hydropower and photovoltaic power generation, because the power output of renewable energy has a high degree of uncertainty, which affects the operational stability of the entire power grid and the reliability of energy supply. Conventional energy storage configuration strategies rely on historical data and static load models, which are difficult to adapt to rapidly changing grid conditions and fluctuating energy supply and demand. In addition, existing methods often lack the ability to dynamically respond to power changes of small hydropower and photovoltaic power generation, making it impossible for microgrids to effectively adjust and optimize the response of energy storage systems when facing sudden energy fluctuations. Therefore, as the proportion of renewable energy in the global power structure gradually increases, how to effectively manage and optimize energy storage systems in microgrids to adapt to different power and energy requirements has become an urgent research topic. Summary of the invention

[0003] Purpose of the invention: The present invention aims to provide a method for configuring the energy storage capacity of a microgrid containing small hydropower and taking into account the power and energy characteristics in order to improve the energy utilization efficiency and operation stability of the microgrid; another purpose of the present invention is to provide a microgrid energy storage capacity configuration system based on the power and energy characteristics.

[0004] Technical solution: The microgrid energy storage capacity configuration method based on power energy characteristics described in the present invention comprises the following steps:

[0005] Step S1: Establish a net load model for configuring the energy storage capacity of a microgrid and calculate the net load of the microgrid;

[0006] Step S2: According to the stable operation requirements of the system and the actual requirements of the load, determine the microgrid source-load output constraint conditions based on power and energy characteristics;

[0007] Step S3: Decompose the microgrid scenario, analyze and identify the key uncertainty factors that affect the stability of the microgrid, and construct a scenario set A that affects the operation of the microgrid; preliminarily classify the scenario set A according to the energy characteristics of the microgrid system, and then classify it again according to the power characteristics of the microgrid system to obtain a subset of scenarios B that affect the operation of the microgrid with different power and energy characteristics;

[0008] Step S4: using the uncertainty fusion method of power energy characteristics, the uncertainty of the uncertainty factor is converted into the uncertainty of the power generation power and the load power, and the scenario subset B obtained in step S3 is clustered in different scenario subsets to obtain the uncertainty scenario set C of the microgrid system;

[0009] Step S5: Optimize the energy storage capacity configuration based on the power and energy characteristics, build an energy storage capacity optimization model, and calculate and optimize the energy storage capacity according to the net load under the uncertainty scenario and the constraints of the energy storage capacity optimization model.

[0010] Furthermore, in step S1, the net load power ΔP(t) at time t is

[0011] ΔP(t)=P PV (t)+P WA (t)-P L (t);

[0012]

[0013] Where P PV (t) is the photovoltaic power generation power at time t; P WA (t) is the power generation of small hydropower at time t; P L (t) is the load power at time t; N is the number of nodes; are respectively the photovoltaic power generation power, small hydropower power generation power and load power consumption of the i-th node at time t.

[0014] Furthermore, in step S2, it is determined that the microgrid source-load output constraint conditions based on power and energy characteristics include small hydropower generation uncertainty constraint, photovoltaic power generation uncertainty constraint and load power consumption uncertainty constraint.

[0015] Furthermore, the uncertainty constraints of small hydropower generation are as follows:

[0016] H i,t +Y i-1,t +w i-1,t =Y i,t +w i,t ;

[0017] In the formula, H i,t is the natural water inflow of the i-level small hydropower at time t, Y i,t and w i,t are the power generation runoff and abandoned water volume of the i-level small hydropower at time t respectively;

[0018] On the premise of ensuring the safe operation and dispatching of the hydropower station, the reservoir capacity, power generation flow limit and abandoned water flow of the hydropower station shall meet the following conditions:

[0019]

[0020] In the formula, are the maximum power generation runoff and abandoned water volume of the i-th level small hydropower at time t respectively;

[0021] Small hydropower generation capacity for:

[0022]

[0023] In the formula, are the minimum and maximum output of the ith small hydropower unit respectively; P WA,min , P WA,max are the maximum and minimum total output of small hydropower unit i within period T respectively.

[0024] The photovoltaic power generation uncertainty constraints are as follows:

[0025] Photovoltaic light radiation intensity and the total photovoltaic radiation area A i,t The following conditions are met:

[0026]

[0027] In the formula, are the maximum and minimum historical photovoltaic light radiation intensity, respectively; They are the maximum total area and minimum total area of ​​photovoltaic radiation, respectively;

[0028] Photovoltaic power generation The following conditions are met:

[0029]

[0030] In the formula, are the maximum and minimum photovoltaic power generation at node i at time t respectively.

[0031] The load power consumption uncertainty constraints are as follows:

[0032] Load power for

[0033]

[0034] In the formula, are the maximum and minimum load power consumption at the i-th node at time t respectively.

[0035] Furthermore, step S3 is specifically as follows:

[0036] Decompose the microgrid scenario, analyze and identify the key uncertain factors that affect the stability of the microgrid, and construct a scenario set A that affects the operation of the microgrid; preliminarily classify the scenario set A according to the energy characteristics of the microgrid system, and then classify it again according to the power characteristics of the microgrid system to obtain a subset of scenarios B that affect the operation of the microgrid with different power and energy characteristics;

[0037] When the energy characteristic of the scenario subset is net energy ΔW>0, and the power characteristic is net load ΔP(t)≥0 at any time, then there is a surplus of power generation at every moment in this scenario, and there must be power curtailment;

[0038] When the energy characteristic of the scenario subset is net energy ΔW>0, and the power characteristic is that there is a net load ΔP(t)<0 at a certain moment, then there is a surplus of power generation at a certain moment in the scenario, and there must be power abandonment;

[0039] When the energy characteristic of the scenario subset is net energy ΔW<0, and the power characteristic is that there is a net load ΔP(t)>0 at a certain moment, then there is a power generation shortage at a certain moment in the scenario, and there must be a power outage;

[0040] When the energy characteristic of the scenario subset is net energy ΔW<0, and the power characteristic is net load ΔP(t)≤0 at any time, then there is a power generation shortage or just balance at each moment in the scenario, and there must be power outages.

[0041] Further, step S4 is specifically as follows:

[0042] According to the historical statistical data of input water flow, light radiation intensity and load, the probability distribution function and cumulative probability function of each factor are fitted;

[0043] Through random sampling, the scenario and the corresponding uncertainty factors are determined, the power generation and verification power of the scenario are calculated, and the uncertainty of each uncertainty factor is converted into the uncertainty of the power generation and load power.

[0044] The scenario subset B obtained in step S3 is clustered in different scenario subsets and fused to obtain the uncertainty scenario set C containing the small hydropower microgrid system to characterize the power energy characteristics and uncertainty of the entire small hydropower microgrid system.

[0045] Furthermore, in step S5, an energy storage capacity optimization model is established with energy storage investment and maintenance costs, operation and power outage penalty costs minimized as optimization objectives. The objective function of the energy storage capacity optimization model is as follows:

[0046]

[0047] Where F1 is the optimization objective function, Investment cost of controllable power supply, The investment cost for capacity allocation, f mai For maintenance costs, is the investment cost of small hydropower, is the operating cost of the energy storage system, f CL For power outage penalty fee, f CF Penalty fees for curtailing electricity.

[0048] Furthermore, the controllable power supply investment cost Capacity configuration investment cost Maintenance cost mai , Small hydropower investment cost Energy storage system operating costs Power outage penalty fee CL and penalty fee for power abandonment CF The details are as follows:

[0049]

[0050] In the formula, c MT 、c invp 、c invw are the unit investment cost of the controllable power supply, the unit power and capacity investment cost of the energy storage, respectively; r is the discount rate; m is the operation cycle of the controllable power supply; a is the equipment operation cycle; P MT , P BS , W BS are the controllable power configuration power, energy storage configuration power and capacity respectively; c WA 、c BS are the maintenance costs of small hydropower stations and energy storage units respectively; P WA is the power of small hydropower; b is the operation cycle of small hydropower; c is the power of small hydropower; wa is the unit power capacity investment cost of small hydropower station; c deg 、c cos are the battery degradation cost and the turbine operating cost coefficient per unit time respectively; c CL is the unit power outage penalty coefficient; c CP P is the penalty coefficient for unit power abandonment; CL (t), P CP (t) is the power outage and power abandonment power of the energy storage configuration at time t.

[0051] Furthermore, the constraints of the optimization model include power balance constraints, energy storage capacity constraints, energy storage charging and discharging constraints, and power outage and power abandonment risk index constraints;

[0052] Power balance constraints:

[0053] The power balance constraints that a microgrid containing small hydropower needs to meet are as follows:

[0054] ΔP(t)+P MT (t)-P BES,cha (t)-P CP (t)+P CL (t)+P BES,dis (t) = 0;

[0055]

[0056] In the formula, are the maximum and minimum photovoltaic power outputs respectively; are the maximum and minimum small hydropower power outputs respectively; are the maximum and minimum load power respectively;

[0057] Energy storage capacity constraints:

[0058]

[0059] In the formula, S SOC (t), S SOC (t-1) are the energy storage charge states at time t and time t-1 respectively; are the upper and lower limits of the capacity of the state of charge respectively; η e , η d are the charging efficiency and discharging efficiency of energy storage respectively; U s is the rated capacity of the energy storage system;

[0060] Energy storage charging and discharging constraints:

[0061]

[0062] In the formula, They are the upper limits of energy storage charging and discharging power respectively;

[0063] Power outage and power abandonment risk indicator constraints:

[0064]

[0065] In the formula, To avoid the risk of power outage, is the risk of power curtailment, ΔP - (t) and ΔP + (t) is the net load power deficit and surplus value at time t.

[0066] The microgrid energy storage capacity configuration system based on power and energy characteristics of the present invention comprises:

[0067] Model building module, used to build a net load model for energy storage capacity configuration of microgrid and an energy storage capacity optimization model. The energy storage capacity optimization model takes the minimum energy storage investment and maintenance cost, operation and power outage penalty cost as the optimization goal, and establishes constraints for power balance, energy storage capacity, energy storage charging and discharging, and power outage risk indicators;

[0068] The decomposition module is used to decompose the microgrid scenario, analyze and identify the key uncertain factors that affect the stability of the microgrid, and construct a scenario set A that affects the operation of the microgrid; the scenario set A is preliminarily classified according to the energy characteristics of the microgrid system, and then classified again according to the power characteristics of the microgrid system to obtain a subset of scenarios B that affect the operation of the microgrid with different power and energy characteristics;

[0069] The fusion module is used to convert the uncertainty of the uncertainty factor into the uncertainty of the power generation power and the load power through the uncertainty fusion method of the power energy characteristics, and cluster the different scenario subsets through the scenario subset B to obtain the uncertainty scenario set C of the microgrid system;

[0070] The solution module optimizes the energy storage capacity configuration based on the power and energy characteristics, and solves the energy storage capacity optimization model according to the net load and constraints under uncertain scenarios.

[0071] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. The present invention classifies scenario sets based on the energy characteristics and power characteristics of the microgrid system to obtain scenario subsets with different power and energy characteristics that affect the operation of the microgrid, and uses the uncertainty fusion method of the power and energy characteristics to obtain an uncertainty scenario set of the microgrid system containing small hydropower, so as to characterize the power and energy characteristics and uncertainties of the entire microgrid system containing small hydropower, and simplify the uncertainty scenarios; 2. The present invention optimizes the energy storage capacity configuration through the net load model of the microgrid energy storage capacity configuration, combined with uncertainty scenario analysis, to ensure that the microgrid can maintain efficient operation under different operating conditions, reduce energy waste, and dynamically adjust the use of energy storage according to actual energy supply and demand, significantly improving economic benefits; 3. The present invention uses efficient data clustering and optimization algorithms to make the configuration of the energy storage system more scientific and reasonable, which helps to maximize the utilization efficiency of the energy storage equipment, reduce energy waste, and provide support for the economic operation of the microgrid, and cooperate with the intermittent and uncertain nature of renewable energy to achieve more efficient energy management and cost control. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 is a flow chart of the present invention;

[0073] Figure 2 This is the net load curve clustering result diagram under the traditional k-means method;

[0074] Figure 3 This is a net load curve clustering result diagram under the method of the present invention. DETAILED DESCRIPTION

[0075] The present invention will be further described below in conjunction with the accompanying drawings.

[0076] The microgrid energy storage capacity configuration method based on power energy characteristics of the present invention comprises the following steps:

[0077] Step S1: Establish a net load model for configuring the microgrid energy storage capacity and calculate the net load of the microgrid.

[0078] The objective function of the net load model for microgrid energy storage capacity configuration is:

[0079] ΔP(t)=P PV (t)+P WA (t)-P L (t) (1)

[0080] Where ΔP(t) is the net load power at time t; P PV (t) is the photovoltaic power generation power at time t; P WA (t) is the power generation of small hydropower at time t; P L (t) is the load power at time t. PV (t), P WA (t) and P L (t) is the sum of the accumulated light-water-charge power of all nodes i, satisfying:

[0081]

[0082]

[0083] Where: N is the number of nodes; They are respectively the photovoltaic power generation power, small hydropower power generation power and load power consumption of the node at the moment.

[0084] Step S2: According to the stable operation requirements of the system and the actual requirements of the load, determine the microgrid source-load output constraint conditions based on power and energy characteristics.

[0085] Microgrid source and load output constraints based on power and energy characteristics:

[0086] (1) Uncertainty constraints on small hydropower generation:

[0087] Taking the intermittent data of natural water as the uncertain factor, for run-of-river hydropower, there is no change in reservoir capacity, only the water coupling constraint and flow limit between the upper and lower small hydropower stations, that is:

[0088] Hi,t +Y i-1,t +w i-1,t =Y i,t +w i,t (5)

[0089] In the formula, H i,t is the natural water inflow of the i-level small hydropower at time t, Y i,t and w i,t is the power generation runoff and abandoned water volume of the i-level small hydropower at time t.

[0090] Under the premise of ensuring the safe operation and dispatching of the hydropower station, there are hydropower station storage capacity, power generation flow limit and abandoned water flow limit, as shown in formula (6):

[0091]

[0092] In the formula, are the maximum power generation flow and abandoned water volume of the i-level small hydropower station at time t respectively.

[0093] The power constraints of small hydropower are:

[0094]

[0095] In the formula, are the minimum and maximum output of small hydropower unit i respectively; P WA,min , P WA,max are the maximum and minimum total output of small hydropower unit i within period T respectively.

[0096] (2) Uncertainty constraints of photovoltaic power generation:

[0097] The lighting constraints are:

[0098]

[0099]

[0100] Where: are the maximum and minimum historical photovoltaic light radiation intensity, respectively; They are the maximum total area and the minimum total area of ​​photovoltaic radiation respectively.

[0101] The photovoltaic output constraint is:

[0102]

[0103] Where: are the maximum and minimum photovoltaic power generation at node i at time t respectively.

[0104] (3) Load power consumption uncertainty constraints:

[0105] The load power constraint is:

[0106]

[0107] In the formula, are the maximum and minimum load power consumption at node i at time t respectively.

[0108] Step S3: Decompose the microgrid scenario, analyze and identify the key uncertain factors that affect the stability of the microgrid, and construct a scenario set A that affects the operation of the microgrid; preliminarily classify the scenario set A according to the energy characteristics of the microgrid system, and then classify it again according to the power characteristics of the microgrid system to obtain a subset of scenarios B that have different power and energy characteristics and affect the operation of the microgrid.

[0109] The present invention decomposes uncertainty scenarios of microgrids, analyzes and identifies key uncertainty factors that affect the stability of microgrids, and constructs various scenarios that may affect the operation of microgrids based on the identified uncertainty factors. First, the energy characteristic model of the microgrid system containing small hydropower is used to perform preliminary classification according to the ΔW value. Then, the power characteristic model of the microgrid system containing small hydropower is used to perform classification again according to ΔP(t). Finally, a subset of scenarios with different power and energy characteristics can be obtained:

[0110] (1) The energy characteristic of the subset is net energy ΔW>0, and the power characteristic is net load ΔP(t)≥0 at any time, which means that there is a surplus of power generation at every moment in this scenario, and there must be power curtailment.

[0111] (2) The energy characteristic of the subset is net energy ΔW>0, and the power characteristic is that there is a net load ΔP(t)<0 at a certain moment, which means that there is a surplus of power generation at a certain moment in this scenario, and there must be power abandonment.

[0112] (3) The energy characteristic of the subset is net energy ΔW < 0, and the power characteristic is that there is a net load ΔP(t) > 0 at a certain moment, which means that there is a power generation shortage at a certain moment in the scenario and there must be a power outage.

[0113] (4) The energy characteristic of the subset is net energy ΔW < 0, and the power characteristic is net load ΔP(t) ≤ 0 at any time, which means that there is a power generation shortage or just balance at every moment in this scenario, and there must be power outages.

[0114] Step S4: Use the uncertainty fusion method of power energy characteristics to convert the uncertainty of uncertainty factors into the uncertainty of power generation and load power. The scenario subset B obtained in step S3 is clustered in different scenario subsets to obtain the uncertainty scenario set C of the microgrid system.

[0115] Based on the fusion clustering of power and energy characteristics, similar power and energy data are clustered together to simplify the complexity of the model. The uncertainty fusion clustering method based on power and energy characteristics first needs to fit the probability distribution function and cumulative probability function of each factor by inputting water flow, light radiation intensity, and historical load statistical data. Then, the uncertainty of each factor is converted into the uncertainty of power generation and load power through random sampling and uncertainty model. Then, the power characteristics and energy characteristics of the net load model are used to decompose the scene subsets with different power and energy characteristics, and clustering is performed in different scene subsets. Finally, the uncertainty scene set containing small hydropower microgrid system is fused to characterize the power energy characteristics and uncertainty of the entire small hydropower microgrid system.

[0116] The uncertainty model takes into account the output characteristics of small hydropower, photovoltaic power generation and load power consumption in the microgrid. These characteristics are uncertain and will affect the overall operation of the microgrid. The uncertainty model is characterized by:

[0117] 1. Net load model: Considering the differences between small hydropower, photovoltaic power generation and load power consumption, the net load power of the microgrid at each moment is defined. As well as the uncertainty constraints of small hydropower, photovoltaic power generation and load

[0118] 2. Scenario decomposition and clustering: Based on the power and energy characteristics, the uncertainty of uncertain factors (such as natural water supply, light radiation intensity and load demand) is converted into different net load scenarios. These scenarios are further decomposed into subsets and classified according to different energy characteristics and power characteristics.

[0119] The conversion process of this step can be understood as:

[0120] First, based on historical statistical data, probability distribution functions and cumulative probability functions are used to describe the uncertainty of each factor. Then, different scenarios are generated from these probability distributions by random sampling. Each scenario corresponds to a possible future state and covers different combinations of various factors. These randomly generated scenarios reflect different combinations of various factors. By substituting them into the power calculation model of the system, the power generation power and load power in each scenario can be obtained. These power values ​​reflect the power fluctuations that the microgrid may experience under different uncertainty conditions.

[0121] Step S5: Optimize the energy storage capacity configuration based on the power and energy characteristics, build an energy storage capacity optimization model, and calculate and optimize the energy storage capacity according to the net load under the uncertainty scenario and the constraints of the energy storage capacity optimization model.

[0122] The optimization model is established with the energy storage investment and maintenance costs, operation and power outage penalty costs minimized as the optimization objectives. The objective function is as follows:

[0123]

[0124] Where F1 is the optimization objective function: Investment cost of controllable power supply, The investment cost for capacity allocation, f mai For maintenance costs, is the investment cost of small hydropower, is the operating cost of the energy storage system, f CL For power outage penalty fee, f CF The penalty fee for curtailment is as follows:

[0125]

[0126] In the formula, c MT 、c invp 、c invw are the unit investment cost of the controllable power supply, the unit power and capacity investment cost of the energy storage, respectively; r is the discount rate; m is the operation cycle of the controllable power supply; a is the equipment operation cycle; P MT , P BS , W BS are the controllable power configuration power, energy storage configuration power and capacity respectively; c WA 、c BS are the maintenance costs of small hydropower stations and energy storage units respectively; P WA is the power of small hydropower; b is the operation cycle of small hydropower; c is the power of small hydropower; wa is the unit power capacity investment cost of small hydropower station; c deg 、c cos are the battery degradation cost and the turbine operating cost coefficient per unit time respectively; c CL is the unit power outage penalty coefficient; c CP P is the penalty coefficient for unit power abandonment; CL (t), P CP (t) is the power outage and power abandonment power of the energy storage configuration at time t.

[0127] Constraints of the energy storage capacity optimization model:

[0128] (1) Power balance constraints:

[0129] The power balance constraints that a microgrid containing small hydropower needs to meet are as follows:

[0130] ΔP(t)+P MT (t)-P BES,cha (t)-P CP (t)+P CL (t)+P BES,dis (t)=0 (15)

[0131]

[0132] In the formula, are the maximum and minimum photovoltaic power outputs respectively; are the maximum and minimum small hydropower power outputs respectively; are the maximum and minimum load power respectively.

[0133] (2) Energy storage capacity constraints:

[0134]

[0135] In the formula, S SOC (t), S SOC (t-1) are the energy storage charge states at time t and time t-1 respectively; are the upper and lower limits of the capacity of the state of charge respectively; η e , η d are the charging efficiency and discharging efficiency of energy storage respectively; U s is the rated capacity of the energy storage system.

[0136] (3) Energy storage charging and discharging constraints:

[0137]

[0138] In the formula, They are the upper limits of energy storage charging and discharging power respectively.

[0139] (4) Power outage and power abandonment risk indicator constraints:

[0140]

[0141] In the formula, To avoid the risk of power outage, is the risk of power curtailment, ΔP - (t) and ΔP + (t) is the net load power deficit and surplus value at time t. For the off-grid microgrid containing small hydropower studied in this invention, the capacity configuration is relatively small, only 1-2MW, and the power outage and power abandonment risk index reflects the adaptability to high-risk scenarios. The power outage risk is not higher than 5%, and the power abandonment risk is not higher than 25%, which is an acceptable risk value.

[0142] The present invention uses an actual small hydropower microgrid system in a certain place as an example for simulation calculation. The experimental platform is the MATLAB simulation platform, and the solution is performed through the cplex and gurobi solvers. The water-photovoltaic-storage microgrid system contains 1000kW photovoltaic power generation, small hydropower output, and a maximum load peak of 750kW. The remaining simulation parameter settings are shown in Table 1.

[0143] Table 1 Simulation parameter settings

[0144] parameter Numeric parameter Numeric <![CDATA[c MT ]]> 373.62 yuan / kWh m 10 years <![CDATA[c invp ]]> 199.28 yuan / kW <![CDATA[c invw ]]> 403.62 yuan / kWh r 0.05 a 10 years b 20 years <![CDATA[c BS ]]> 22.46 yuan / kW <![CDATA[c WA ]]> 6 yuan / kWh <![CDATA[c wa ]]> 70.3 yuan / kW <![CDATA[c deg ]]> 0.81 yuan / kWh <![CDATA[c cos ]]> 0.3 yuan / kW <![CDATA[c CL ]]> 2.17 yuan / kW <![CDATA[c CP ]]> 1.45 yuan / kW

[0145] The present invention sets up a calculation example for two scenario clustering methods, simulates and analyzes the clustering results, energy storage configuration schemes and factors affecting energy storage configuration, and compares the traditional k-means clustering method with the scenario decomposition clustering method of the present invention. Take the initial scenario as 1131 net load curves and the final clustering scenario number as 300 as an example (k m =300 / 1131), Figure 2 is the net load curve clustering result under the traditional k-means method and Figure 3 It is the net load curve clustering result under the method of the present invention.

[0146] The decomposition clustering method proposed in this invention is significantly different from the traditional k-means clustering method. Figure 2 and Figure 3 The red, blue, green and yellow curves of the four clustered scenario curves correspond to scenario subsets 1, 2, 3 and 4. The final clustered curve clearly divides the net load power distribution of the four sub-scenarios, and makes a clear distinction between power abandonment, power outage and redundancy scenarios. This is because the power energy characteristics are taken into account when clustering the scenarios, and the calculation of energy storage energy and power capacity configuration is highly targeted. It can effectively reduce extremely harsh scenarios and make effective configurations for power abandonment and power outage scenarios.

[0147] Table 2 shows the results of comparative analysis between the indicators of power abandonment, power outage and deleted scenarios in the scenarios clustered by the two methods. Compared with the traditional k-means clustering method, the number of power abandonment scenarios clustered by the method of the present invention is reduced by 9.43%, the number of power outage scenarios is reduced by 4.59%, and the number of deleted scenarios that do not require energy storage capacity configuration is increased by 17.6%, which improves the degree of adaptation of the scenarios to the objective function optimization and reduces the power abandonment and load abandonment rate. This is because the method of the present invention makes a more detailed scene division in scene clustering, and the number of extreme and severe load scenarios in the final clustered scenes is relatively small, while in the traditional k-means clustering method, the probability of extreme and severe load scenarios is highly volatile. This reflects the effectiveness of the scene decomposition clustering method of the present invention.

[0148] Table 2 Comparative analysis of configuration scenario indicators

[0149] index k-means Method of the present invention contrast Total number of scenes 300 300 —— Number of power abandonment scenarios 103 96 9.43％ Number of power outage scenarios 109 104 4.59％ Number of deleted scenes 85 100 -17.6％

[0150] The present invention sets two scenario number control groups for scenario clustering methods. The following scenario numbers correspond to the energy storage capacity configuration results for monthly, quarterly, semi-annual and annual durations, and conducts simulation experimental analysis on this basis:

[0151] (1) Traditional k-means method: The k-means clustering method was used, and the number of scenes was 30, 90, 150, and 300 for comparative analysis.

[0152] (2) The method of the present invention: The decomposition clustering method of the present invention is used to perform comparative analysis on the number of scenes: 30, 90, 150, and 300.

[0153] Table 3 Capacity configuration and calculation time of different number of scenarios under two methods

[0154]

[0155] Table 3 is a comparison of the capacity configuration results and configuration calculation time of different scene numbers of the traditional k-means method and the method of the present invention. It can be seen from Table 3 that under the k-means method, the configuration of energy storage and controllable power supply capacity values ​​decreases with the increase of the number of scenes, and eventually tends to be stable. This is because when there are few clustering scenes, the randomness of the reduction for extreme and harsh scenes is strong. In order to smooth the fluctuation of the power grid, it is necessary to configure a larger capacity of energy storage and controllable power supply. As the number of scenes increases, the configuration capacity will be more accurate, but the cost and calculation time will increase; under the method of the present invention, the configuration capacity is relatively stable, because the method of the present invention has good adaptability to extreme and harsh scenes. At the same time, as the number of clustering scenes gradually increases, the calculation time will be extended, but the method of the present invention can still ensure relative speed, and the calculation time only increases by 7.6% when the number of scenes increases from 90 to 150, indicating that the optimal number of scene clusters is 90. For the output of a quarter of a microgrid containing small hydropower, the method of the present invention has better adaptability.

[0156] For the off-grid type microgrid containing small hydropower studied in this invention, the capacity configuration is relatively small, only 1-2MW, and the power outage and power abandonment risk index reflects the adaptability to high-risk scenarios. The power outage risk is not higher than 5%, and the power abandonment risk is not higher than 25%, which is an acceptable risk value. When the method of the present invention reduces the 1331 initial scenarios to 90 scenarios, the power outage risk is 1.34%, and the power abandonment risk is 18.88%, which meets the off-grid maintenance requirements. After the maintenance of the microgrid is completed and connected to the grid, the risk of power outage and power abandonment will be further reduced.

Claims

1. A method for configuring energy storage capacity of a microgrid based on power and energy characteristics, characterized in that: The following steps are involved: Step S1: Establish a net load model for configuring the energy storage capacity of a microgrid and calculate the net load of the microgrid; Step S2: According to the stable operation requirements of the system and the actual requirements of the load, determine the microgrid source-load output constraint conditions based on power and energy characteristics; Step S3: Decompose the microgrid scenario, analyze and identify the key uncertainty factors that affect the stability of the microgrid, and construct a scenario set A that affects the operation of the microgrid; preliminarily classify the scenario set A according to the energy characteristics of the microgrid system, and then classify it again according to the power characteristics of the microgrid system to obtain a subset of scenarios B that affect the operation of the microgrid with different power and energy characteristics; Step S4: using the uncertainty fusion method of power energy characteristics, the uncertainty of the uncertainty factor is converted into the uncertainty of the power generation power and the load power, and the scenario subset B obtained in step S3 is clustered in different scenario subsets to obtain the uncertainty scenario set C of the microgrid system; Step S5: Optimize the energy storage capacity configuration based on the power and energy characteristics, build an energy storage capacity optimization model, and calculate and optimize the energy storage capacity according to the net load under the uncertainty scenario and the constraints of the energy storage capacity optimization model.

2. The microgrid energy storage capacity configuration method based on power and energy characteristics according to claim 1 is characterized in that: In step S1, the net load power ΔP(t) at time t is ΔP(t)=P PV (t)+P WA (t)-P L (t); Where P PV (t) is the photovoltaic power generation power at time t; P WA (t) is the power generation of small hydropower at time t; P L (t) is the load power at time t; N is the number of nodes; are respectively the photovoltaic power generation power, small hydropower power generation power and load power consumption of the i-th node at time t.

3. The microgrid energy storage capacity configuration method and system based on power and energy characteristics according to claim 1 is characterized in that: In step S2, the microgrid source-load output constraint conditions based on power and energy characteristics are determined to include small hydropower generation uncertainty constraints, photovoltaic power generation uncertainty constraints and load power consumption uncertainty constraints.

4. The microgrid energy storage capacity configuration method based on power and energy characteristics according to claim 3 is characterized in that: The uncertainty constraints of small hydropower generation are as follows: H i,t +Y i-1,t +w i-1,t =Y i,t +w i,t ; In the formula, H i,t is the natural water inflow of the i-level small hydropower at time t, Y i,t and w i,t are the power generation runoff and abandoned water volume of the i-level small hydropower at time t respectively; On the premise of ensuring the safe operation and dispatching of the hydropower station, the reservoir capacity, power generation flow limit and abandoned water flow of the hydropower station shall meet the following conditions: In the formula, are the maximum power generation runoff and abandoned water volume of the i-th level small hydropower at time t respectively; Small hydropower generation capacity for: In the formula, are the minimum and maximum output of the ith small hydropower unit respectively; P WA,min , P WA,max are the maximum and minimum total output of small hydropower unit i within period T respectively. The photovoltaic power generation uncertainty constraints are as follows: Photovoltaic light radiation intensity and the total photovoltaic radiation area A i,t The following conditions are met: In the formula, are the maximum and minimum historical photovoltaic light radiation intensity, respectively; They are the maximum total area and minimum total area of ​​photovoltaic radiation, respectively; Photovoltaic power generation The following conditions are met: In the formula, are the maximum and minimum photovoltaic power generation at node i at time t respectively. The load power consumption uncertainty constraints are as follows: Load power for In the formula, are the maximum and minimum load power consumption at the i-th node at time t respectively.

5. The microgrid energy storage capacity configuration method based on power and energy characteristics according to claim 4 is characterized in that: Step S3 is as follows: Decompose the microgrid scenario, analyze and identify the key uncertain factors that affect the stability of the microgrid, and construct a scenario set A that affects the operation of the microgrid; preliminarily classify the scenario set A according to the energy characteristics of the microgrid system, and then classify it again according to the power characteristics of the microgrid system to obtain a subset of scenarios B that affect the operation of the microgrid with different power and energy characteristics; When the energy characteristic of the scenario subset is net energy ΔW>0, and the power characteristic is net load ΔP(t)≥0 at any time, then there is a surplus of power generation at every moment in this scenario, and there must be power curtailment; When the energy characteristic of the scenario subset is net energy ΔW>0, and the power characteristic is that there is a net load ΔP(t)<0 at a certain moment, then there is a surplus of power generation at a certain moment in the scenario, and there must be power abandonment; When the energy characteristic of the scenario subset is net energy ΔW<0, and the power characteristic is that there is a net load ΔP(t)>0 at a certain moment, then there is a power generation shortage at a certain moment in the scenario, and there must be a power outage; When the energy characteristic of the scenario subset is net energy ΔW<0, and the power characteristic is net load ΔP(t)≤0 at any time, then there is a power generation shortage or just balance at each moment in the scenario, and there must be power outages.

6. The microgrid energy storage capacity configuration method based on power and energy characteristics according to claim 5 is characterized in that: Step S4 is specifically as follows: According to the historical statistical data of input water flow, light radiation intensity and load, the probability distribution function and cumulative probability function of each factor are fitted; Through random sampling, the scenario and the corresponding uncertainty factors are determined, the power generation and verification power of the scenario are calculated, and the uncertainty of each uncertainty factor is converted into the uncertainty of power generation and load power; The scenario subset B obtained in step S3 is clustered in different scenario subsets and fused to obtain an uncertainty scenario set C containing a small hydropower microgrid system.

7. The microgrid energy storage capacity configuration method based on power and energy characteristics according to claim 6 is characterized in that: In step S5, an energy storage capacity optimization model is established with the energy storage investment and maintenance costs, operation and power outage penalty costs minimized as the optimization objectives. The objective function of the energy storage capacity optimization model is as follows: Where F1 is the optimization objective function, Investment cost of controllable power supply, The investment cost for capacity allocation, f mai For maintenance costs, is the investment cost of small hydropower, is the operating cost of the energy storage system, f CL For power outage penalty fee, f CF Penalty fees for curtailing electricity.

8. The microgrid energy storage capacity configuration method based on power and energy characteristics according to claim 7 is characterized in that: Controllable power supply investment cost Capacity configuration investment cost Maintenance cost mai , Small hydropower investment cost Energy storage system operating costs Power outage penalty fee CL and power abandonment penalty fee CF The details are as follows: In the formula, c MT 、c invp 、c invw are the unit investment cost of the controllable power supply, the unit power and capacity investment cost of the energy storage, respectively; r is the discount rate; m is the operation cycle of the controllable power supply; a is the equipment operation cycle; P MT , P BS , W BS are the controllable power configuration power, energy storage configuration power and capacity respectively; c WA 、c BS are the maintenance costs of small hydropower stations and energy storage units respectively; P WA is the power of small hydropower; b is the operation cycle of small hydropower; c is the power of small hydropower; wa is the unit power capacity investment cost of small hydropower station; c deg 、c cos are the battery degradation cost and the turbine operating cost coefficient per unit time respectively; c CL is the unit power outage penalty coefficient; c CP P is the penalty coefficient for unit power abandonment; CL (t), P CP (t) is the power outage and power abandonment power of the energy storage configuration at time t.

9. The microgrid energy storage capacity configuration method based on power and energy characteristics according to claim 8 is characterized in that: The constraints of the optimization model include power balance constraints, energy storage capacity constraints, energy storage charging and discharging constraints, and power outage and power abandonment risk index constraints; Power balance constraints: The power balance constraints that a microgrid containing small hydropower needs to meet are as follows: ΔP(t)+P MT (t)-P BES,cha (t)-P CP (t)+P CL (t)+P BES,dis (t)=0; In the formula, are the maximum and minimum photovoltaic power outputs respectively; are the maximum and minimum small hydropower power outputs respectively; are the maximum and minimum load power respectively; Energy storage capacity constraints: In the formula, S SOC (t), S SOC (t-1) are the energy storage charge states at time t and time t-1 respectively; They are the upper and lower limits of the capacity of the state of charge respectively; η e , η d are the charging efficiency and discharging efficiency of energy storage respectively; U s is the rated capacity of the energy storage system; Energy storage charging and discharging constraints: In the formula, They are the upper limits of energy storage charging and discharging power respectively; Power outage and power abandonment risk indicator constraints: In the formula, To avoid the risk of power outage, is the risk of power curtailment, ΔP - (t) and ΔP + (t) is the net load power deficit and surplus value at time t.

10. A microgrid energy storage capacity configuration system based on power and energy characteristics, characterized in that: include: Model building module, used to build a net load model for energy storage capacity configuration of microgrid and an energy storage capacity optimization model. The energy storage capacity optimization model takes the minimum energy storage investment and maintenance cost, operation and power outage penalty cost as the optimization goal, and establishes constraints for power balance, energy storage capacity, energy storage charging and discharging, and power outage risk indicators; The decomposition module is used to decompose the microgrid scenario, analyze and identify the key uncertain factors that affect the stability of the microgrid, and construct a scenario set A that affects the operation of the microgrid; The scenario set A is preliminarily classified according to the energy characteristics of the microgrid system, and then classified again according to the power characteristics of the microgrid system to obtain a subset of scenarios B that have different power and energy characteristics and affect the operation of the microgrid; The fusion module is used to convert the uncertainty of the uncertainty factors into the uncertainty of the power generation power and the load power through the uncertainty fusion method of the power energy characteristics, and cluster the different scenario subsets through the scenario subset B to obtain the uncertainty scenario set C containing the small hydropower microgrid system; The solution module optimizes the energy storage capacity configuration based on the power and energy characteristics, and solves the energy storage capacity optimization model according to the net load and constraints under uncertain scenarios.

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