A method and device for configuring source and storage capacity of a microgrid

Abstract

The present invention discloses a method and device for configuring source and storage capacity of a microgrid. By comprehensively considering uncertainty factors such as cost parameters and wind and solar power output in the process of planning source and storage capacity of a microgrid, an interval uncertainty set model is constructed to describe the uncertainty of cost parameters, and a convex hull uncertainty set model is constructed to describe the correlation of uncertain wind and solar power outputs at multiple times. With the minimum cost as the goal, an optimal configuration model of source and storage capacity of a microgrid considering uncertainty is established. The optimal configuration model of source and storage capacity of a microgrid is solved based on a dual robust algorithm, thereby realizing the optimal configuration of source and storage capacity of a microgrid considering uncertainty factors such as uncertain cost parameters and wind and solar power output, and ensuring the economic and stable operation of the microgrid system.

Description

Technical Field

[0001] The present invention relates to the field of microgrid technology, and in particular to a method and device for configuring source and storage capacity of a microgrid. Background Art

[0002] In recent years, renewable energy represented by wind turbines and photovoltaics has been widely used in microgrids, but due to the intermittent, volatile and uncertain output, it has caused great difficulties for technicians to study. Reasonable configuration of power supply and energy storage facility capacity in microgrids is a key step to effectively solve the above problems, and it is also of great significance in ensuring the economic and stable operation of the system.

[0003] At present, the configuration of source and storage capacity of microgrids has the following shortcomings: In terms of uncertainty modeling, most literatures do not consider the correlation of wind and solar output, resulting in the configuration optimization results dealing with many scenarios that will not occur, thereby increasing planning costs. In terms of solution methods, they are divided into artificial intelligence algorithms and robust optimization algorithms. The search performance and convergence of most artificial intelligence algorithms depend on the selection of control parameters, and it is difficult to guarantee that the global optimal solution can be obtained; robust optimization algorithms generally use uncertain sets to describe the uncertainty of uncertain parameters. However, robust optimization itself contains a max-min two-layer optimization model, so most solution algorithms are relatively complex in design, and there may be a situation where iteration does not converge. In terms of comprehensive measures, although energy storage and demand response loads can effectively reduce the impact of the volatility of wind and solar output on the system, this is based on certain cost parameters. Few literatures comprehensively consider the impact of the uncertainty of power cost parameters on the configuration results. If the parameter selection is inappropriate, it will lead to difficulties in solving the model, so the above measures have great limitations. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for configuring the source and storage capacity of a microgrid, which can achieve the optimal configuration of the capacity of power supply and energy storage facilities and improve the configuration economy.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for configuring source and storage capacity of a microgrid comprises the following steps:

[0007] Establish an interval uncertainty set model considering the uncertainty of cost parameters;

[0008] Establish a convex hull uncertainty set model for the correlation of uncertain wind and solar power output at multiple times;

[0009] Based on the interval uncertainty set model, a cost minimization objective function is constructed, and a microgrid source storage capacity optimization configuration model considering uncertainty is established in combination with the convex hull uncertainty set model;

[0010] The dual robust algorithm is used to solve the microgrid source storage capacity optimization configuration model to obtain the optimization configuration result.

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0012] A microgrid source storage capacity configuration device, comprising:

[0013] A model building module is used to establish an interval uncertainty set model considering the uncertainty of cost parameters, establish a convex hull uncertainty set model of uncertain wind and solar output correlation at multiple times, build a cost minimization objective function based on the interval uncertainty set model, and establish a microgrid source storage capacity optimization configuration model considering uncertainty in combination with the convex hull uncertainty set model;

[0014] The optimization solution module is used to solve the microgrid source storage capacity optimization configuration model using a dual robust algorithm to obtain an optimization configuration result.

[0015] Furthermore, the model building module includes:

[0016] A data acquisition module is used to acquire uncertain cost parameters, wherein the uncertain cost parameters include energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, wind turbine unit capacity investment cost, photovoltaic unit capacity investment cost, wind turbine unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost, and photovoltaic unit capacity operation and maintenance cost;

[0017] The data interval establishing module is used to establish an interval set of the uncertain cost parameters.

[0018] Furthermore, the model building module also includes:

[0019] The constraint model establishment module is used to establish a wind and solar capacity constraint model that does not consider the correlation time:

[0020] NC Wt ≥P Wt (t), NC PV ≥P PV (t);

[0021] Where P Wt (t), P PV (t) represent the historical output of wind turbine and photovoltaic, NC Wt, NC PV Represent the capacity of wind turbine and photovoltaic respectively.

[0022] Furthermore, the model building module is used to form a convex hull uncertainty set model from multiple linear inequalities of wind and solar power outputs:

[0023] [A Wt (t)A PV (t)]*[P Wt '(t)P PV '(t)] T ≤b(t);

[0024] In the formula, A Wt (t), A PV b(t) and b(t) represent the wind turbine output coefficient matrix, photovoltaic output coefficient matrix and constant matrix of the convex hull linear inequality, respectively. Wt '(t),P PV '(t) respectively represent the wind turbine and photovoltaic outputs considering the correlation between wind and solar outputs at time t.

[0025] Furthermore, the model building module includes:

[0026] Objective function building module is used to convert the power supply investment cost C cap , Power supply operation and maintenance cost C OM 、Energy storage replacement cost C rep，batt , generator fuel cost C fuel , Generator start-up and shutdown cost C onoff and the generator power generation cost C e As the optimization goal, establish the objective function:

[0027] min(C cap +C OM +C rep，batt +C fuel +C onoff +C e ).

[0028] Furthermore, the model building module is used to obtain the convex hull uncertain set constraint model of the historical output of wind and solar power, establish the system power constraint model, the energy storage charging and discharging state constraint model and the power constraint model, the generator minimum start and stop time constraint model and the generator output model and the microgrid demand response load power constraint model, and combine the above constraint models to obtain the microgrid source storage capacity optimization configuration model considering uncertainty.

[0029] Furthermore, the optimization solution module includes:

[0030] A model conversion module, used to describe the microgrid source storage capacity optimization configuration model as a robust optimization problem based on the interval uncertainty set model and the convex hull uncertainty set model;

[0031] The model solving module is used to transform the robust optimization problem into a deterministic problem using a dual robust algorithm, solve the deterministic problem, and obtain the cost-optimal energy storage capacity configuration result.

[0032] The beneficial effects of the present invention are as follows: by comprehensively considering the uncertainty factors such as cost parameters and wind and solar output in the process of microgrid source and storage capacity planning, an interval uncertainty set model is constructed to describe the uncertainty of cost parameters, and a convex hull uncertainty set model is constructed to describe the correlation of uncertain wind and solar output at multiple times, and with the minimum cost as the goal, a microgrid source and storage capacity optimization configuration model considering uncertainty is established; the microgrid source and storage capacity optimization configuration model is solved based on the dual robust algorithm, thereby realizing the optimal configuration of the microgrid source and storage capacity under the uncertainty factors such as uncertain cost parameters and wind and solar output, and ensuring the economic and stable operation of the microgrid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flow chart of a method for configuring source and storage capacity of a microgrid according to an embodiment of the present invention;

[0034] Figure 2 A schematic diagram of a microgrid source storage capacity configuration device according to an embodiment of the present invention;

[0035] Figure 3 This is a flow chart of solving the optimization model using the dual robust algorithm according to an embodiment of the present invention;

[0036] Figure 4 This is a typical daily historical data statistical chart of Example 2 of the present invention;

[0037] Figure 5 This is a graph showing the influence of uncertain cost parameters on total cost according to the second embodiment of the present invention;

[0038] Figure 6 This is a statistical diagram of wind and solar power output throughout the year at time t=7-10h according to the second embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0040] Please refer to Figure 1 The embodiment of the present invention provides a method for configuring the source and storage capacity of a microgrid, comprising the steps of:

[0041] Establish an interval uncertainty set model considering the uncertainty of cost parameters;

[0042] Establish a convex hull uncertainty set model for the correlation of uncertain wind and solar power output at multiple times;

[0043] Based on the interval uncertainty set model, a cost minimization objective function is constructed, and a microgrid source storage capacity optimization configuration model considering uncertainty is established in combination with the convex hull uncertainty set model;

[0044] The dual robust algorithm is used to solve the microgrid source storage capacity optimization configuration model to obtain the optimization configuration result.

[0045] From the above description, it can be seen that the beneficial effects of the present invention are: by comprehensively considering the uncertainty factors such as cost parameters and wind and solar output in the process of microgrid source and storage capacity planning, an interval uncertainty set model is constructed to describe the uncertainty of cost parameters, and a convex hull uncertainty set model is constructed to describe the correlation of uncertain wind and solar output at multiple times, and with the minimum cost as the goal, a microgrid source and storage capacity optimization configuration model considering uncertainty is established; the microgrid source and storage capacity optimization configuration model is solved based on the dual robust algorithm, thereby realizing the optimal configuration of the microgrid source and storage capacity considering uncertainty factors such as uncertain cost parameters and wind and solar output, and ensuring the economic and stable operation of the microgrid system.

[0046] Furthermore, the establishment of the interval uncertainty set model considering the uncertainty of the cost parameters includes:

[0047] Acquire uncertain cost parameters, wherein the uncertain cost parameters include energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, wind turbine unit capacity investment cost, photovoltaic unit capacity investment cost, wind turbine unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost, and photovoltaic unit capacity operation and maintenance cost;

[0048] A set of intervals for the uncertain cost parameters is established.

[0049] From the above description, it can be seen that the operation and maintenance costs and investment costs of wind turbines, photovoltaics, generators and energy storage are taken into account in the uncertain cost parameters, and the replacement cost of energy storage is taken as an uncertain parameter. Therefore, the optimal configuration of the source and storage capacity of the microgrid under uncertain factors such as uncertain cost parameters and wind and solar output is considered to ensure the economic and stable operation of the microgrid system.

[0050] Furthermore, the establishment of the convex hull uncertainty set model of the multi-time uncertain wind and solar power output correlation includes:

[0051] Establish a wind and solar capacity constraint model without considering the correlation time:

[0052] NC Wt ≥P Wt (t), NC PV ≥P PV (t);

[0053] Where P Wt (t), P PV (t) represent the historical output of wind turbine and photovoltaic, NC Wt , NC PV Represent the capacity of wind turbine and photovoltaic respectively.

[0054] From the above description, it can be seen that establishing a wind and solar capacity constraint model without considering the correlation time is convenient for the subsequent establishment of a convex hull uncertainty set model of uncertain wind and solar output correlation at multiple times.

[0055] Furthermore, the convex hull uncertainty set model for establishing the correlation between uncertain wind and solar power outputs at multiple times includes:

[0056] When considering the correlation, the output of wind and solar power at each moment is limited to the convex hull uncertainty set constructed by the historical output data of wind and solar power. The convex hull uncertainty set model consists of multiple linear inequalities of wind and solar power output:

[0057] [A Wt (t)A PV (t)]*[P Wt '(t)P PV '(t)] T ≤b(t);

[0058] In the formula, A Wt (t), A PV b(t) and b(t) represent the wind turbine output coefficient matrix, photovoltaic output coefficient matrix and constant matrix of the convex hull linear inequality, respectively. Wt '(t),P PV '(t) respectively represent the wind turbine and photovoltaic outputs considering the correlation between wind and solar outputs at time t.

[0059] From the above description, it can be seen that the output of renewable energy represented by wind and solar power has a strong correlation in certain time periods. If the uncertainty set considered does not take into account the correlation of wind and solar power output, the result of robust optimization will deal with many scenarios that will not occur. Therefore, considering the correlation can facilitate the subsequent optimal configuration of the source and storage capacity of the microgrid.

[0060] Furthermore, constructing a cost minimization objective function based on the interval uncertainty set model includes:

[0061] The power supply investment cost C cap , Power supply operation and maintenance cost COM 、Energy storage replacement cost C rep，batt , generator fuel cost C fuel , Generator start-up and shutdown cost C onoff and the generator power generation cost C e As the optimization goal, establish the objective function:

[0062] min(C cap +C OM +C rep，batt +C fuel +C onoff +C e ).

[0063] From the above description, it can be seen that constructing the objective function with the minimum cost is convenient for the subsequent establishment of the microgrid source and storage capacity optimization configuration model.

[0064] Furthermore, the convex hull uncertainty set model is combined to establish a microgrid source storage capacity optimization configuration model considering uncertainty, including:

[0065] Obtain the convex hull uncertain set constraint model of wind and solar historical output, establish the system power constraint model, energy storage charging and discharging state constraint model and power constraint model, generator minimum start and stop time constraint model and generator output model and microgrid demand response load power constraint model;

[0066] Combining the above constraint model, a microgrid source and storage capacity optimization configuration model considering uncertainty is obtained.

[0067] From the above description, it can be seen that by integrating the interval uncertainty set model, the convex hull uncertainty set model, and the constraint model of energy storage and generator, an optimal configuration model of microgrid source and storage capacity considering uncertainty is established, thereby realizing the optimal configuration of microgrid source and storage capacity under uncertainty factors such as uncertain cost parameters and wind and solar output.

[0068] Furthermore, the dual robust algorithm is used to solve the microgrid source storage capacity optimization configuration model to obtain the optimization configuration result including:

[0069] Based on the interval uncertainty set model and the convex hull uncertainty set model, the microgrid source storage capacity optimization configuration model is described as a robust optimization problem;

[0070] The dual robust algorithm is used to transform the robust optimization problem into a deterministic problem, and the deterministic problem is solved to obtain the cost-optimal energy storage capacity configuration result.

[0071] From the above description, it can be seen that the dual robust algorithm is used to eliminate the uncertainty in the robust optimization problem and transform it into a deterministic problem, which is convenient for subsequent solving of the deterministic problem and obtaining the cost-optimal energy storage capacity configuration result.

[0072] Please refer to Figure 2 Another embodiment of the present invention provides a microgrid source storage capacity configuration device, comprising:

[0073] A model building module is used to establish an interval uncertainty set model considering the uncertainty of cost parameters, establish a convex hull uncertainty set model of uncertain wind and solar output correlation at multiple times, build a cost minimization objective function based on the interval uncertainty set model, and establish a microgrid source storage capacity optimization configuration model considering uncertainty in combination with the convex hull uncertainty set model;

[0074] The optimization solution module is used to solve the microgrid source storage capacity optimization configuration model using a dual robust algorithm to obtain an optimization configuration result.

[0075] From the above description, it can be seen that by comprehensively considering the uncertainty factors such as cost parameters and wind and solar output in the process of microgrid source and storage capacity planning, an interval uncertainty set model is constructed to describe the uncertainty of cost parameters, and a convex hull uncertainty set model is constructed to describe the correlation of uncertain wind and solar output at multiple times. With the goal of minimizing cost, an optimal configuration model of microgrid source and storage capacity considering uncertainty is established; the microgrid source and storage capacity optimization configuration model is solved based on the dual robust algorithm, thereby realizing the optimal configuration of microgrid source and storage capacity considering uncertainty factors such as uncertain cost parameters and wind and solar output, and ensuring the economic and stable operation of the microgrid system.

[0076] Furthermore, the model building module includes:

[0077] A data acquisition module is used to acquire uncertain cost parameters, wherein the uncertain cost parameters include energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, wind turbine unit capacity investment cost, photovoltaic unit capacity investment cost, wind turbine unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost, and photovoltaic unit capacity operation and maintenance cost;

[0078] The data interval establishing module is used to establish an interval set of the uncertain cost parameters.

[0079] From the above description, it can be seen that the operation and maintenance costs and investment costs of wind turbines, photovoltaics, generators and energy storage are taken into account in the uncertain cost parameters, and the replacement cost of energy storage is taken as an uncertain parameter. Therefore, the optimal configuration of the source and storage capacity of the microgrid under uncertain factors such as uncertain cost parameters and wind and solar output is considered to ensure the economic and stable operation of the microgrid system.

[0080] Furthermore, the model building module also includes:

[0081] The constraint model establishment module is used to establish a wind and solar capacity constraint model that does not consider the correlation time:

[0082] NC Wt ≥P Wt (t), NC PV ≥P PV (t);

[0083] Where P Wt (t), P PV (t) represent the historical output of wind turbine and photovoltaic, NC Wt , NC PV Represent the capacity of wind turbine and photovoltaic respectively.

[0084] From the above description, it can be seen that establishing a wind and solar capacity constraint model without considering the correlation time is convenient for the subsequent establishment of a convex hull uncertainty set model of uncertain wind and solar output correlation at multiple times.

[0085] Furthermore, the model building module is used to form a convex hull uncertainty set model from multiple linear inequalities of wind and solar power outputs:

[0086] [A Wt (t)A PV (t)]*[P Wt '(t)P PV '(t)] T ≤b(t);

[0087] In the formula, A Wt (t), A PV b(t) and b(t) represent the wind turbine output coefficient matrix, photovoltaic output coefficient matrix and constant matrix of the convex hull linear inequality, respectively. Wt '(t),P PV '(t) respectively represent the wind turbine and photovoltaic outputs considering the correlation between wind and solar outputs at time t.

[0088] From the above description, it can be seen that the output of renewable energy represented by wind and solar power has a strong correlation in certain time periods. If the uncertainty set considered does not take into account the correlation of wind and solar power output, the result of robust optimization will deal with many scenarios that will not occur. Therefore, considering the correlation can facilitate the subsequent optimal configuration of the source and storage capacity of the microgrid.

[0089] Furthermore, the model building module includes:

[0090] Objective function building module is used to convert the power supply investment cost C cap , Power supply operation and maintenance cost C OM 、Energy storage replacement cost C rep，batt , generator fuel cost C fuel , Generator start-up and shutdown cost C onoff and the generator power generation cost C e As the optimization goal, establish the objective function:

[0091] min(C cap +C OM +C rep，batt +C fuel +C onoff +C e ).

[0092] From the above description, it can be seen that constructing the objective function with the minimum cost is convenient for the subsequent establishment of the microgrid source and storage capacity optimization configuration model.

[0093] Furthermore, the model building module is used to obtain the convex hull uncertain set constraint model of the historical output of wind and solar power, establish the system power constraint model, the energy storage charging and discharging state constraint model and the power constraint model, the generator minimum start and stop time constraint model and the generator output model and the microgrid demand response load power constraint model, and combine the above constraint models to obtain the microgrid source storage capacity optimization configuration model considering uncertainty.

[0094] From the above description, it can be seen that by integrating the interval uncertainty set model, the convex hull uncertainty set model, and the constraint model of energy storage and generator, an optimal configuration model of microgrid source and storage capacity considering uncertainty is established, thereby realizing the optimal configuration of microgrid source and storage capacity under uncertainty factors such as uncertain cost parameters and wind and solar output.

[0095] Furthermore, the optimization solution module includes:

[0096] A model conversion module, used to describe the microgrid source storage capacity optimization configuration model as a robust optimization problem based on the interval uncertainty set model and the convex hull uncertainty set model;

[0097] The model solving module is used to transform the robust optimization problem into a deterministic problem using a dual robust algorithm, solve the deterministic problem, and obtain the cost-optimal energy storage capacity configuration result.

[0098] From the above description, it can be seen that the dual robust algorithm is used to eliminate the uncertainty in the robust optimization problem and transform it into a deterministic problem, which is convenient for subsequent solving of the deterministic problem and obtaining the cost-optimal energy storage capacity configuration result.

[0099] The above-mentioned microgrid source storage capacity configuration method and device of the present invention, based on the microgrid optimization configuration model, can consider the uncertainty of cost parameters by constructing an interval uncertainty set of power cost parameters, avoid problems such as the inability to solve the model due to unreasonable cost parameter settings, and can consider the correlation of uncertain wind and solar power outputs at multiple times to achieve the optimal configuration of power supply and energy storage facility capacity. The following is an explanation through specific implementation methods:

[0100] Embodiment 1

[0101] Please refer to Figure 1 , a method for configuring source and storage capacity of a microgrid, comprising the steps of:

[0102] S1. Establish an interval uncertainty set model considering the uncertainty of cost parameters.

[0103] S11. Obtain uncertain cost parameters, wherein the uncertain cost parameters include energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, wind turbine unit capacity investment cost, photovoltaic unit capacity investment cost, wind turbine unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost, and photovoltaic unit capacity operation and maintenance cost.

[0104] S12: Establishing an interval set of the uncertain cost parameters.

[0105] In this embodiment, the unit capacity operation and maintenance cost of energy storage, the unit capacity operation and maintenance cost of wind turbines, the unit capacity operation and maintenance cost of generators and the unit capacity operation and maintenance cost of photovoltaics are regarded as power supply operation and maintenance costs; the unit capacity investment cost of generators, the unit capacity investment cost of energy storage, the unit capacity investment cost of wind turbines and the unit capacity investment cost of photovoltaics are regarded as power supply investment costs.

[0106] Specifically, the uncertain cost parameters are as follows:

[0107]

[0108] In the formula, G represents the total number of power sources in the wind, solar, storage and diesel microgrid, i represents the type of power source; C cap represents the power supply investment cost, Ccap，unit (i) and NC(i) represent the unit capacity investment cost and total capacity of the power source, respectively, and

[0109]

[0110] In the formula, C OM represents the power supply operation and maintenance cost, C OM，unit (i) and T represent the unit capacity operation and maintenance cost and total time of the i-th power supply, respectively, and P(i,t) represents the power capacity.

[0111]

[0112] In the formula, C rep,batt represents the energy storage replacement cost, repyr, C rep，batt，unit They represent the replacement year and unit capacity replacement cost of the energy storage battery, yr represents the year, and N batt Indicates the number of energy storage batteries, P rated，batt Indicates the rated capacity of the energy storage battery, R d Represents the system discount rate.

[0113]

[0114] In the formula, C fuel represents the fuel cost, and YR represents the system operating years. fuel，unit represents the fuel price per unit volume of the system, which in the present invention refers to diesel, and

[0115] V fuel (t) = aP g，onoff (t)+bP g (t);

[0116] Where V fuel (t) represents the volume of fuel consumed at each moment, a and b are the coefficients of the fuel curve, P g，onoff (t), P g (t) refers to the power of the diesel engine when it is turned on and off and when it is in normal operation.

[0117]

[0118] In the formula, C onoff Refers to the start-up and shutdown cost of the diesel generator, C on，unit、 C off，unit They respectively represent the cost of turning the diesel generator on and off each time.

[0119]

[0120] In the formula, C e represents the cost of power generation by the generator, C g，unit represents the unit power generation cost of the diesel generator, and

[0121] S2. Establish a convex hull uncertainty set model for the correlation of uncertain wind and solar power outputs at multiple times.

[0122] S21. Establish a wind and solar capacity constraint model without considering the correlation time:

[0123] NC Wt ≥P Wt (t), NC PV ≥P PV (t);

[0124] Where P Wt (t), P PV (t) represent the historical output of wind turbine and photovoltaic, NC Wt , NC PV Represent the capacity of wind turbine and photovoltaic respectively.

[0125] S22. Establish a convex hull uncertainty set model.

[0126] Specifically, when considering the correlation, the output of wind and solar power at each moment needs to be limited to the convex hull uncertainty set constructed by the historical output data of wind and solar power. The uncertainty set consists of multiple linear inequalities of wind and solar power output, namely:

[0127] [A Wt (t)A PV (t)]*[P Wt '(t)P PV '(t)] T ≤b(t);

[0128] In the formula, A Wt (t), A PV b(t) and b(t) represent the wind turbine output coefficient matrix, photovoltaic output coefficient matrix and constant matrix of the convex hull linear inequality, respectively. Wt '(t),P PV '(t) respectively represent the wind turbine and photovoltaic outputs considering the correlation between wind and solar outputs at time t.

[0129] S3. Construct a cost minimization objective function based on the interval uncertainty set model, and establish a microgrid source and storage capacity optimization configuration model taking uncertainty into consideration in combination with the convex hull uncertainty set model.

[0130] S31. The power supply investment cost C cap , Power supply operation and maintenance cost C OM 、Energy storage replacement cost Crep，batt , generator fuel cost C fuel , Generator start-up and shutdown cost C onoff and the generator power generation cost C e As the optimization goal, establish the objective function:

[0131] min(C cap +C OM +C rep，batt +C fuel +C onoff +C e ).

[0132] S32. Obtain the convex hull uncertain set constraint model of the historical output of wind and solar power, establish the system power constraint model, energy storage charging and discharging state constraint model and power constraint model, generator minimum start and stop time constraint model and generator output model and microgrid demand response load power constraint model, and combine the above constraint models to obtain the microgrid source storage capacity optimization configuration model considering uncertainty.

[0133] Specifically, the convex hull uncertainty set constraint of the historical wind and solar power output is established as shown in step S22.

[0134] The model of system power constraint considering the correlation time is:

[0135]

[0136] Where η inv is the photovoltaic inverter efficiency, P dis '(t),P ch (t) are the energy storage charging and discharging power, P load (t) is the load power, They represent the upward / downward power of the demand response load at time t respectively.

[0137] The energy storage charging and discharging state constraint model is:

[0138]

[0139]

[0140]

[0141] In the formula, Respectively represent the energy storage charging and discharging status. When the energy storage battery is charging is 1, otherwise is 1, which is a 0-1 variable, T represents the total time, Indicates the upper limit of the number of charge and discharge state transitions.

[0142] The charging and discharging power constraint model of the energy storage system is:

[0143] E(t)=E(t-1)(1-δΔt)+P ch (t)η ch Δt-P dis (t) / η dis Δt;

[0144] SOC min N batt P rated，batt ≤E(t)≤SOC max N batt P rated，batt ;

[0145]

[0146]

[0147] Where, E(t), P ch (t), P dis (), Δt represent the remaining capacity (kWh), charging and discharging power (kW) and time interval (h) of the energy storage battery at the tth moment, δ, η ch , η dis Respectively represent the hourly self-discharge rate and charge and discharge efficiency of the energy storage battery, N batt , P rated，batt、 SOC min、 SOC max They respectively represent the number of energy storage batteries, the rated capacity of a single energy storage battery, and the minimum and maximum values ​​of the remaining power percentage.

[0148] The minimum start-stop time constraint model of a diesel engine is:

[0149]

[0150]

[0151] Where, T min，on 、T min，off They represent the minimum on time and minimum off time of the diesel generator respectively.

[0152] The output constraint model of the diesel generator is:

[0153] P g，onoff (t)P g，min ≤P g (t)≤P g，onoff (t)P g，max ;

[0154] Where P g，max , P g，min Respectively represent the upper and lower limits of the generator output, Pg，onoff (t) represents the start / stop status of the diesel generator at each moment, with 1 for start and 0 for stop. g (t) represents the output of the diesel generator at each moment.

[0155] The microgrid demand response load power constraint model is:

[0156]

[0157] 0≤E DR (t)≤w DR max(P load (t))Δt;

[0158]

[0159]

[0160] In the formula, E DR (t) represents the power of the demand response load at time t, w DR Indicates the ratio of total load to demand response load.

[0161] S4. Use a dual robust algorithm to solve the microgrid source storage capacity optimization configuration model to obtain an optimization configuration result.

[0162] S41. Based on the interval uncertainty set model and the convex hull uncertainty set model, the microgrid source and storage capacity optimization configuration model is described as a robust optimization problem.

[0163] Considering the interval set of uncertain cost parameters and the convex hull uncertain set of historical wind and solar output, the optimal configuration model of microgrid source and storage capacity is described as a robust optimization problem:

[0164]

[0165] sG X (X)≥0

[0166]

[0167] In the formula, X represents the set of decision variables, and X = [x; y], x and y represent the set of continuous variables and the set of integer variables respectively:

[0168]

[0169]

[0170] w n , W represent uncertain parameters and uncertain sets respectively, n represents the number of uncertain parameters, and w n=[C cap_unit (i) C OM_unit (i) C rep_batt_unit , C fuel_unit , C g_unit ],i∈G;

[0171] F X (X) 、 G X (X) represents the objective function and constraints containing only continuous variables x and integer variables y, respectively;

[0172] Respectively represent continuous variables x, integer variables y and uncertain variables w n The objective function and constraints.

[0173] S42, please refer to Figure 3 , using a dual robust algorithm to transform the robust optimization problem into a deterministic problem, solving the deterministic problem to obtain the cost-optimal energy storage capacity configuration result.

[0174] S421. Add auxiliary variable function Then there is This constraint and Let’s eliminate uncertainty together.

[0175] S422. Decompose the original problem constraints into two parts containing both X and w. n Constraints The constraint G contains only X X (X) and only w n Constraints at this time That is the uncertain set W.

[0176] S423, select the dual robust algorithm according to the robust constraint characteristics and eliminate it through the following steps uncertainty.

[0177] S4231, according to whether the expression contains uncertain parameters w n , decompose the robust constraint into (c+A T X) T w n and (b T X+d).

[0178] S4232, extract separately The transposed matrix of the equality and inequality constraint coefficients is

[0179] S4233, extract separately The constant transposed matrix of the equality and inequality constraints is E eqT,E ineqT .

[0180] S4234, introduce constraint variable Z eq , Z ineq , the formula becomes a formula containing only the deterministic constraints of the decision variable X:

[0181]

[0182] S424. At this point, the robust optimization problem is transformed into a deterministic problem in the following form:

[0183]

[0184] S425. For deterministic problems, a solver may be called to solve the optimization model.

[0185] Embodiment 2

[0186] According to an embodiment of the present invention, a method and device for configuring the source and storage capacity of a microgrid, particularly taking uncertainty into consideration, is established, comprising the steps of:

[0187] S1. Establish an interval uncertainty set model considering the uncertainty of cost parameters;

[0188] S2. Establish a convex hull uncertainty set model for the correlation of uncertain wind and solar power output at multiple times;

[0189] S3, constructing a cost minimization objective function based on the interval uncertainty set model, and establishing a microgrid source storage capacity optimization configuration model considering uncertainty in combination with the convex hull uncertainty set model;

[0190] S4. Use a dual robust algorithm to solve the microgrid source storage capacity optimization configuration model to obtain an optimization configuration result.

[0191] Basic data description:

[0192] The historical data of the present invention for the whole year is derived from the actual engineering data of 32 European countries. The typical daily wind turbine, photovoltaic output and load calculation data are obtained according to the local typical wind speed and light intensity. Figure 1 The microgrid system takes 24 hours as the total planning time, the time interval is 1 hour, the discount rate of the embodiment of the present invention is 8%, and the system operation life is 20 years.

[0193] The model parameters include parameters of energy storage, diesel generator, photovoltaic inverter and demand response load model, and their values ​​are as follows:

[0194] The energy storage used in the embodiment of the present invention is a lead-acid battery with a rated capacity of 6kWh, a rated power of 1.2kW, a charge and discharge efficiency of 86%, a self-discharge rate of 0.01%, a maximum value of the initial power and the remaining power percentage of 100%, a minimum value of 20%, an upper limit of the number of charge and discharge state conversions of 4 times, and a design life of 10 years.

[0195] The diesel generator used in the embodiment of the present invention has a minimum start and stop time of 2 hours, a minimum and maximum output of 0 and 20 kW respectively, a start and stop cost of 2 yuan / time and 0.5 yuan / time respectively, and a coefficient a and b of the fuel curve are 0.280L and 0.251L / kW respectively. In addition, the efficiency of the photovoltaic inverter is 97%, and the proportion coefficient of the total load used for demand response load is 30%.

[0196] Since most literature does not consider the replacement cost of wind, solar and diesel, the embodiment of the present invention only considers the replacement cost of energy storage as an uncertain parameter, and converts the units of energy storage cost parameters, fuel prices and residual costs into yuan / kW, yuan / L and yuan / kW respectively. The embodiment of the present invention considers a total of 11 uncertain cost parameters, ignoring the cost parameters that are too high, as shown in Table 1. When the uncertainty of the parameters is not considered, the parameter values ​​are shown on the right side of Table 1.

[0197] Table 1 Summary of uncertain cost parameters

[0198]

[0199]

[0200] Furthermore, step S1 is specifically as follows:

[0201] Based on the robust optimization model, the capacity of the microgrid system is optimized and configured. In order to verify the influence of uncertain cost parameters on the total cost of the system, the embodiment of the present invention is based on Figure 4 Based on the typical daily data of , we consider the scenarios with only certain cost parameters, 1 uncertain cost parameter, and 11 uncertain cost parameters, and compare the total cost in each scenario. Figure 5 As shown in Figure 1. C0 represents the case where only the cost parameters are determined, and C1 to 11 represent the case where 11 uncertain cost parameters are considered. Figure 5 It can be seen that the presence or absence of uncertain cost parameters and the number of parameters have a great impact on the total cost. When the microgrid power cost parameters are all certain, the total cost is 572,500 yuan. When considering that the system has only a single uncertain cost parameter, the first 7 results are not much different from those without considering the uncertain cost parameter. This is because when the above parameters participate in the robust optimization calculation, appropriate optimization scenarios are selected, resulting in results that are basically consistent with those when the uncertainty of the parameters is not considered.

[0202] C11 is the photovoltaic unit capacity operation and maintenance cost, which has the highest total cost. This is because the interval variation range of this parameter is 0.0096 to 375 yuan / kW, and the deterministic parameter is 0.12 yuan / kW. When this parameter is involved in the robust optimization calculation, it is easy to get a worse scenario, that is, the value of photovoltaic unit capacity operation and maintenance cost is far more than 0.12 yuan / kW. When the upper limit of this parameter is changed to 230 yuan / kW, it can be calculated that the total cost at this time is 1.0039 million yuan, which is 272,000 yuan less than the previous total cost. In addition, the result of considering 11 uncertain cost parameters is the superposition of various scenarios, so the total cost is the highest.

[0203] In order to study the impact of interval sets on capacity configuration results, the embodiment of the present invention changes C10, i.e., the upper limit of the unit capacity operation and maintenance cost of the generator, to 96 yuan / kW and 1408.1145 yuan / kW respectively. The planning results are shown in Table 2. As can be seen from Table 2, when the unit capacity operation and maintenance cost of the generator is increased, in order to effectively reduce the economy, the diesel generator will reduce its output, resulting in a reduction in the capacity of the diesel generator. In order to ensure the normal operation of the system, the planned capacity of the energy storage is increased, resulting in a sharp increase in the total cost.

[0204] Table 2 Impact of C10 upper limit on planning results

[0205] C10 upper limit / (yuan / kW) Energy storage / kWh Diesel generator / kW Total cost / 10,000 yuan 96 6 19.5583 91.63 1408.1145 90 13.8844 533.87

[0206] Furthermore, step S2 is specifically as follows:

[0207] For a certain microgrid system, the output of renewable energy represented by wind and solar power has a strong correlation in certain time periods. If the uncertainty set considered does not consider the correlation of wind and solar power output, the result of robust optimization will deal with many scenarios that will not occur, such as Figure 6 When the convex hull uncertainty set is used to describe the uncertain parameters, it is assumed that the parameters affect each other, and the convex hull uncertainty set is transformed into Figure 6 The linear inequality constraint represented by the red solid line in the middle greatly improves the economy of the system because it does not need to deal with redundant scenarios.

[0208] In order to better study the correlation between wind and solar output, ignore the moment when photovoltaic output is 0, and calculate the absolute value of the correlation coefficient r between the two at t=3h~20h. Generally speaking, the closer |r| is to 1, the stronger the correlation between the two quantities. Conversely, the closer |r| is to 0, the weaker the correlation between the two quantities. When t=7h~10h, |r| is in the range of 0.4~0.69, and the two are moderately correlated, with the strongest correlation. Therefore, the embodiment of the present invention only takes t=7h~10h as an example to analyze the impact of the correlation between wind and solar output on the planning results.

[0209] Furthermore, step S3 is specifically as follows:

[0210] In order to verify the effectiveness of the solution algorithm of the embodiment of the present invention, the Enumeration Robust Counterpart (ERC), Explicit Maximization Robust Counterpart (EMRC) and Duality Robust Counterpart (DRC) algorithms are used for calculation. The upper and lower limits of the interval uncertainty set are determined by statistically analyzing the output data of wind turbines and photovoltaics at t=7-10h to determine the maximum and minimum outputs of wind turbines and photovoltaics at that moment. The wind and solar outputs are independent of each other, and the correlation of wind and solar outputs can be considered. The solution results are shown in Table 3.

[0211] Table 3 Comparison of the impact of wind and solar output correlation on planning results

[0212]

[0213]

[0214] As can be seen from Table 3, the energy storage capacity has not changed, because energy storage has a good effect in absorbing the output of new energy. When considering the correlation, the total cost is 924,500 yuan. Since there is no need to deal with additional useless scenarios, the economy is better. This result is mainly achieved by reducing the configuration capacity of the generator. When considering the correlation of wind and solar output, the worst-case scenario results of diesel generator output are better at most times, which also verifies that considering the correlation of wind and solar output can optimize the configuration capacity and improve the economy of the system.

[0215] Table 4 Comparison of results of three robust optimization algorithms

[0216] algorithm Energy storage capacity / kWh Total cost / 10,000 yuan Solution time / s EMRC 48 93.53 11.07 ERC 120 92.45 3080.37 DRC 48 92.45 22.28

[0217] As shown in Table 4, the solution algorithms are compared from the three indicators of energy storage configuration capacity, total cost and solution time, and the results are compared. The MRC algorithm does not need to consider the correlation between wind and solar output, which makes the model more conservative and has the shortest solution time of 10.104250s, but the cost is also the highest and the economy is the worst. The ERC algorithm has a relatively low model conservatism and a relatively small total cost when calculating, but it has the largest energy storage capacity, resulting in a slow efficiency in calculating relatively complex robust models. The DRC algorithm avoids the defects of the previous two algorithms in terms of energy storage configuration capacity, total cost and solution time, and the calculation results are more reasonable. It is of great reference and reference significance for solving robust optimization models containing a large number of scenarios.

[0218] Embodiment 3

[0219] Please refer to Figure 2 , a microgrid source storage capacity configuration device, comprising:

[0220] A model building module is used to establish an interval uncertainty set model considering the uncertainty of cost parameters, establish a convex hull uncertainty set model of uncertain wind and solar output correlation at multiple times, build a cost minimization objective function based on the interval uncertainty set model, and establish a microgrid source storage capacity optimization configuration model considering uncertainty in combination with the convex hull uncertainty set model;

[0221] The optimization solution module is used to solve the microgrid source storage capacity optimization configuration model using a dual robust algorithm to obtain an optimization configuration result.

[0222] In some embodiments, the model building module includes:

[0223] A data acquisition module is used to acquire uncertain cost parameters, wherein the uncertain cost parameters include energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, wind turbine unit capacity investment cost, photovoltaic unit capacity investment cost, wind turbine unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost, and photovoltaic unit capacity operation and maintenance cost;

[0224] The data interval establishing module is used to establish an interval set of the uncertain cost parameters.

[0225] In some embodiments, the model building module further includes:

[0226] The constraint model establishment module is used to establish a wind and solar capacity constraint model that does not consider the correlation time:

[0227] NC Wt ≥P Wt (t), NC PV ≥P PV (t);

[0228] Where P Wt (t), P PV (t) represent the historical output of wind turbine and photovoltaic, NC Wt , NC PV Represent the capacity of wind turbine and photovoltaic respectively.

[0229] In some embodiments, the model building module is used to form a convex hull uncertainty set model from a plurality of linear inequalities of wind and solar outputs:

[0230] [A Wt(t)A PV (t)]*[P Wt '(t)P PV '(t)] T ≤b(t);

[0231] In the formula, A Wt (t), A PV b(t) and b(t) represent the wind turbine output coefficient matrix, photovoltaic output coefficient matrix and constant matrix of the convex hull linear inequality, respectively. Wt '(t),P PV '(t) respectively represent the wind turbine and photovoltaic outputs considering the correlation between wind and solar outputs at time t.

[0232] In some embodiments, the model building module includes:

[0233] Objective function building module is used to convert the power supply investment cost C cap , Power supply operation and maintenance cost C OM 、Energy storage replacement cost C rep，batt , generator fuel cost C fuel , Generator start-up and shutdown cost C onoff and the generator power generation cost C e As the optimization goal, establish the objective function:

[0234] min(C cap +C OM +C rep，batt +C fuel +C onoff +C e ).

[0235] In some embodiments, the model building module is used to obtain a convex hull uncertain set constraint model of the historical output of wind and solar power, establish a system power constraint model, an energy storage charging and discharging state constraint model and a power constraint model, a generator minimum start and stop time constraint model and a generator output model and a microgrid demand response load power constraint model, and combine the above constraint models to obtain a microgrid source and storage capacity optimization configuration model that takes into account uncertainty.

[0236] In some embodiments, the optimization solution module includes:

[0237] A model conversion module, used to describe the microgrid source storage capacity optimization configuration model as a robust optimization problem based on the interval uncertainty set model and the convex hull uncertainty set model;

[0238] The model solving module is used to transform the robust optimization problem into a deterministic problem using a dual robust algorithm, solve the deterministic problem, and obtain the cost-optimal energy storage capacity configuration result.

[0239] In summary, the present invention provides a method and device for configuring the source and storage capacity of a microgrid. By comprehensively considering the uncertainty factors such as cost parameters and wind and solar output in the process of planning the source and storage capacity of a microgrid, an interval uncertainty set model is constructed to describe the uncertainty of cost parameters, wherein the operation and maintenance costs and investment costs of wind turbines, photovoltaics, generators and energy storage are considered in the uncertain cost parameters, and the replacement cost of energy storage is used as an uncertain parameter. Therefore, the optimal configuration of the source and storage capacity of the microgrid under the uncertainty factors such as uncertain cost parameters and wind and solar output is considered. A convex hull uncertainty set model is constructed to describe the correlation of uncertain wind and solar output at multiple times with the goal of minimizing the cost. Among them, the output of new energy represented by wind and solar has a strong correlation in certain time periods. If the uncertainty set considered does not consider the correlation of wind and solar output, the result of robust optimization will deal with many scenarios that will not occur. Therefore, considering the correlation can facilitate the subsequent optimal configuration of the source and storage capacity of the microgrid. Based on the interval uncertainty set model, the objective function is established and combined with the convex hull uncertainty set model to establish the microgrid source and storage capacity optimization configuration model considering uncertainty; based on the dual robust algorithm, the microgrid source and storage capacity optimization configuration model is solved, wherein the uncertainty in the robust optimization problem is eliminated by the dual robust algorithm, and it is transformed into a deterministic problem for solution, thereby obtaining the cost-optimal energy storage capacity configuration result to ensure the economic and stable operation of the microgrid system.

[0240] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0241] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0242] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0243] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0244] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0245] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for configuring source and storage capacity of a microgrid, characterized in that: Includes steps: Establish an interval uncertainty set model considering the uncertainty of cost parameters; Establish a convex hull uncertainty set model for the correlation of uncertain wind and solar power output at multiple times; Based on the interval uncertainty set model, a cost minimization objective function is constructed, and a microgrid source storage capacity optimization configuration model considering uncertainty is established in combination with the convex hull uncertainty set model; A dual robust algorithm is used to solve the microgrid source storage capacity optimization configuration model to obtain an optimization configuration result; The establishment of the interval uncertainty set model considering the uncertainty of cost parameters includes: Acquire uncertain cost parameters, wherein the uncertain cost parameters include energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, wind turbine unit capacity investment cost, photovoltaic unit capacity investment cost, wind turbine unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost, and photovoltaic unit capacity operation and maintenance cost; Establishing an interval set of the uncertain cost parameters; The establishment of the convex hull uncertainty set model of the multi-time uncertain wind and solar power output correlation includes: Establish a wind and solar capacity constraint model without considering the correlation time: ； In the formula, Respectively represent the historical output of wind turbines and photovoltaics, Represent the capacity of wind turbine and photovoltaic respectively; The convex hull uncertainty set model for establishing the correlation between uncertain wind and solar power outputs at multiple times includes: The convex hull uncertainty set model is composed of multiple linear inequalities of wind and solar power output: ； In the formula, They represent the wind turbine output coefficient matrix, photovoltaic output coefficient matrix and constant matrix of the convex hull linear inequality respectively, They represent the wind turbine and photovoltaic outputs considering the correlation between wind and solar outputs at time t respectively; The model of system power constraint considering the correlation time is: ； In the formula, is the photovoltaic inverter efficiency, , are the energy storage charging and discharging power, is the load power, They represent the upward / downward power of the demand response load at time t, P g ( t ) is the power of the diesel engine during normal operation.

2. A method for configuring source and storage capacity of a microgrid according to claim 1, characterized in that: Constructing the cost minimization objective function based on the interval uncertainty set model includes: Power supply investment cost , Power supply operation and maintenance costs , Energy storage replacement cost , Generator fuel costs , Generator start-up and shutdown costs and the cost of generating electricity from the generator As the optimization goal, establish the objective function: 。 3. A method for configuring source and storage capacity of a microgrid according to claim 1, characterized in that: Combining the convex hull uncertainty set model, a microgrid source storage capacity optimization configuration model considering uncertainty is established, including: Obtain the convex hull uncertain set constraint model of wind and solar historical output, establish the system power constraint model, energy storage charging and discharging state constraint model and power constraint model, generator minimum start and stop time constraint model and generator output model and microgrid demand response load power constraint model; Combining the above constraint model, a microgrid source and storage capacity optimization configuration model considering uncertainty is obtained.

4. A method for configuring source and storage capacity of a microgrid according to claim 1, characterized in that: The dual robust algorithm is used to solve the microgrid source storage capacity optimization configuration model to obtain the optimization configuration result including: Based on the interval uncertainty set model and the convex hull uncertainty set model, the microgrid source storage capacity optimization configuration model is described as a robust optimization problem; The dual robust algorithm is used to transform the robust optimization problem into a deterministic problem, and the deterministic problem is solved to obtain the cost-optimal energy storage capacity configuration result.

5. A microgrid source storage capacity configuration device, characterized in that: include: A model building module is used to establish an interval uncertainty set model considering the uncertainty of cost parameters, establish a convex hull uncertainty set model of uncertain wind and solar output correlation at multiple times, build a cost minimization objective function based on the interval uncertainty set model, and establish a microgrid source storage capacity optimization configuration model considering uncertainty in combination with the convex hull uncertainty set model; An optimization solution module is used to solve the microgrid source storage capacity optimization configuration model using a dual robust algorithm to obtain an optimization configuration result; The establishment of the interval uncertainty set model considering the uncertainty of cost parameters includes: Acquire uncertain cost parameters, wherein the uncertain cost parameters include energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, wind turbine unit capacity investment cost, photovoltaic unit capacity investment cost, wind turbine unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost, and photovoltaic unit capacity operation and maintenance cost; Establishing an interval set of the uncertain cost parameters; The establishment of the convex hull uncertainty set model of the multi-time uncertain wind and solar power output correlation includes: Establish a wind and solar capacity constraint model without considering the correlation time: ； In the formula, Respectively represent the historical output of wind turbines and photovoltaics, Represent the capacity of wind turbine and photovoltaic respectively; The convex hull uncertainty set model for establishing the correlation between uncertain wind and solar power outputs at multiple times includes: The convex hull uncertainty set model is composed of multiple linear inequalities of wind and solar power output: ； In the formula, They represent the wind turbine output coefficient matrix, photovoltaic output coefficient matrix and constant matrix of the convex hull linear inequality respectively, They represent the wind turbine and photovoltaic outputs considering the correlation between wind and solar outputs at time t respectively; The model of system power constraint considering the correlation time is: ； In the formula, is the photovoltaic inverter efficiency, , are the energy storage charging and discharging power, is the load power, They represent the upward / downward power of the demand response load at time t, P g ( t ) is the power of the diesel engine during normal operation.

6. According to a microgrid source storage capacity configuration device according to claim 5, the model building module comprises: Objective function building module, used to convert power supply investment cost , Power supply operation and maintenance costs , Energy storage replacement cost , Generator fuel costs , Generator start-up and shutdown costs and the cost of generating electricity from the generator As the optimization goal, establish the objective function: 。 7. According to a microgrid source and storage capacity configuration device described in claim 5, the model building module is used to obtain the convex hull uncertain set constraint model of the historical output of wind and solar power, establish a system power constraint model, an energy storage charging and discharging state constraint model and a power constraint model, a generator minimum start and stop time constraint model and a generator output model and a microgrid demand response load power constraint model, and combine the above constraint models to obtain a microgrid source and storage capacity optimization configuration model that takes uncertainty into account.

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