Capacity planning configuration method for wind-solar-storage micro-grid in cold region

Abstract

The invention discloses a capacity planning and configuration method for a wind-solar-storage micro-grid in a cold region, and relates to a capacity planning and configuration method for a micro-grid. The invention aims to solve the problem that an existing planning configuration method does not consider energy storage system life attenuation and environmental influence and is difficult to ensure safe operation constraints. According to the method, the service life attenuation and environmental influence of the energy storage system are considered, safe operation constraints are ensured, and a wind-solar storage capacity configuration scheme with optimal comprehensive benefits is obtained. In the two-level structure, the upper level is energy storage capacity configuration planning, the lower level is economic operation simulation considering security constraints, and the two-level problem is solved by adopting Camp; and G algorithm. The invention belongs to the technical field of micro-grid capacity configuration planning.

Description

Technical Field

[0001] The present invention relates to a capacity planning and configuration method for a microgrid, and belongs to the technical field of microgrid capacity configuration planning. Background Art

[0002] Amidst the global energy transition, capacity planning for wind, solar, and energy storage microgrids in cold regions presents unique challenges. For example, in Northeast my country, extreme winter temperatures not only impact photovoltaic power generation and wind turbine operations but also significantly constrain energy storage systems. In low temperatures, the storage capacity of energy storage batteries decreases significantly, charging speeds slow, and frequent charging and discharging accelerates device aging. Furthermore, cold-region microgrids must cope with the high load demands of winter heating. Energy storage systems must frequently adjust the balance between power supply and demand in these low-temperature conditions. Traditional planning methods often overlook the actual impact of temperature on energy storage performance, leading to inappropriate configurations.

[0003] Current energy storage capacity planning research is mostly based on normal temperature conditions, failing to fully consider the large temperature swings and persistent low temperatures found in cold regions. Low temperatures not only reduce the efficiency of energy storage equipment but also shorten its service life. Traditional methods, which use fixed parameters to calculate energy storage capacity, fail to accurately reflect performance variations in real-world environments. This can lead to insufficient or over-allocation of energy storage capacity in actual operation, impacting the economic viability and power supply stability of microgrids. There is an urgent need to develop wind, solar, and storage planning solutions that are more adaptable to the characteristics of cold regions.

[0004] Wang Chengshan's team at Tianjin University proposed a capacity optimization method based on energy storage charging and discharging efficiency and state of charge (SOC) constraints. By establishing a fluctuation smoothing model, they determined the minimum energy storage capacity, providing theoretical support for the smooth output of renewable energy.

[0005] In response to the problem of insufficient grid inertia caused by the high proportion of new energy access, Chen Xia's team from Huazhong University of Science and Technology established a correlation model between energy storage controller parameters and frequency dynamic characteristics, proposed a frequency response-based energy storage quantitative configuration method, and introduced confidence levels to optimize the economic efficiency of energy storage.

[0006] Zeng Ming's team at North China Electric Power University has built a cost-benefit analysis framework for the entire life cycle of energy storage, quantifying the comprehensive benefits of energy storage in reducing grid-offline assessments, solar curtailment assessments, and increasing grid-connected power, providing a systematic model for economic evaluation.

[0007] However, the existing planning and configuration methods still have the following defects:

[0008] 1. Insufficient environmental adaptability: Existing methods ignore the complex impact of extreme low temperatures in cold regions on energy storage performance. Conventional algorithms struggle to address the coupled relationship between climate factors and charge / discharge efficiency and capacity retention, resulting in insufficient planning economics and reliability.

[0009] 2. Lack of capacity degradation quantification: A dynamic model for capacity degradation has not been established. Existing research ignores the economic impact of performance degradation over the entire life cycle. Conventional algorithms cannot effectively integrate degradation characteristics, and planning deviates from actual needs.

[0010] 3. Inefficient two-level solution: Traditional methods have limited processing capacity. The computational complexity increases dramatically when running two-level optimization. Algorithms such as mixed integer programming face computing bottlenecks in large-scale scenarios, making it difficult to obtain feasible solutions.

[0011] 4. Global Optimization Limitations: Single-layer models cannot coordinate the strong coupling between capacity configuration and operation simulation. Conventional algorithms are prone to falling into local optimality, and it is difficult to coordinately optimize economic efficiency and operational stability in cold-region scenarios. Summary of the Invention

[0012] In order to solve the problem that existing planning and configuration methods do not consider the life attenuation and environmental impact of energy storage systems, making it difficult to ensure safe operation constraints, the present invention proposes a capacity planning and configuration method for cold-region wind-solar-storage microgrids.

[0013] The technical solution adopted by the present invention to solve the above problems is: the steps of the present invention include:

[0014] Step 1: Establish mathematical models of photovoltaic power generation, wind power generation and energy storage systems;

[0015] Step 2: Evaluate the complementary characteristics of wind and solar power, and cluster a set of typical wind power output and load scenarios;

[0016] Step 3: Preliminary planning configuration model considering construction and operation costs;

[0017] Step 4: Daily operation planning model considering safety constraints;

[0018] Step 5: Based on the preliminary planning configuration model considering construction and operation costs in step 3, set the initial wind-solar-storage microgrid capacity configuration plan and set the convergence conditions;

[0019] Step 6: Based on the current wind, solar, and storage microgrid capacity configuration plan, build an upper-level capacity planning model; use the optimization algorithm to solve the upper-level problem and obtain a preliminary microgrid capacity and power configuration plan; and pass the solution results to the lower-level problem;

[0020] Step 7: Based on the wind, solar, and energy storage microgrid capacity configuration plan provided by the upper-level problem, build a lower-level simulation operation model. Simulate various operating conditions of the microgrid in actual operation and evaluate the effectiveness of the microgrid configuration plan; extract the key constraints and objective function values of the lower-level problem;

[0021] Step 8: Add the key constraints of the lower-level problem to the model of the upper-level problem to form a new constraint set; update the objective function of the upper-level problem, taking into account the operating results of the lower-level problem;

[0022] Step 9: Repeat steps 6 to 8, iterating alternately between the upper and lower layers, and gradually optimize the wind-solar-storage microgrid capacity configuration plan; after each iteration, check whether the change of the objective function meets the convergence condition; if so, stop the iteration; otherwise, continue the optimization;

[0023] Step 10: When the iterative process converges, the optimal microgrid capacity configuration plan is output, including the capacity, power, and charging and discharging strategies of the wind, solar, and storage microgrid system; while taking into account the operating performance indicators of the microgrid.

[0024] Furthermore, step 1 specifically includes:

[0025] Step 101: Assume that the wind speed of the wind turbine is v, and the output power of the wind turbine is P WT The approximate relationship between and wind speed is expressed by the following piecewise function:

[0026]

[0027] In formula (1), v ci Indicates the cut-in wind speed, v co Indicates the cut-out wind speed, v r Indicates rated wind speed, P r Indicates the rated output power of the wind turbine generator set;

[0028] When the wind speed is between v ci and v r When the wind power output power is expressed as the wind speed function η(v), it is approximately a linear relationship, that is:

[0029] η(v)=P r (vv ci ) / (v r -v ci )(2),

[0030] In formula (2), the rated output power P of the wind turbine generator set is r The dynamic model of the wind turbine is obtained as follows:

[0031] P r =0.5πρR 2 C p (λ,β)v 3 N WT (3),

[0032] In formula (3), ρ is the air density, R is the blade radius, and C p is the wind energy utilization coefficient, λ is the tip speed ratio, β is the pitch angle, N WT is the number of wind turbines, ω ris the angular velocity of the wind turbine; in addition, there is:

[0033]

[0034] Step 102: The actual output power of the photovoltaic array can be obtained from the output power under standard rated conditions, light intensity, and ambient temperature:

[0035]

[0036] In formula (6), P PV is the output power of the working point, STC refers to the solar irradiance G STC is 1kW / m2, the battery surface temperature T STC The relative atmospheric optical quality is AM1.5 under the condition of 25℃, G c is the irradiance at the working point, k is the power temperature coefficient, P STC is the rated output power of the photovoltaic array under standard rated conditions, if n PV is the total number of photovoltaic cells in the photovoltaic array, p stc is the rated output power of the photovoltaic cell under rated test conditions, then P STC =n PV p stc ;T c is the battery surface temperature at the operating point, which is the ambient temperature T a And the function of wind speed:

[0037] T c =T a +αG c (7),

[0038] The coefficient α is an exponential function of the wind speed v:

[0039]

[0040] In formula (8), c1, c2, and c3 are constant coefficients;

[0041] Step 103: Use the battery pack as the energy storage element. The actual available capacity E of the energy storage battery pack is ess is a function of battery temperature:

[0042] E ess =E STC [1+δ B (T ess -T essSTC )](9),

[0043] In formula (9), T bat is the battery temperature at the operating point, E STCThe rated capacity of the battery under standard conditions is usually provided by the manufacturer. The temperature under standard conditions is T batSTC At 25℃, δ B is the capacity temperature coefficient, which is taken as 0.6%;

[0044] Based on the type of energy storage technology used in the microgrid, a cyclic charge and discharge test of the energy storage system is conducted, and the performance parameters after each cycle are recorded. Through statistical analysis, a life decay model of the energy storage system is established. The energy storage system life decay model can be expressed as:

[0045]

[0046] B(C)=a1C 3 +a2C 2 +a3C+a4(11),

[0047]

[0048] In formulas (10), (11) and (12), E loss is the capacity loss of the energy storage system, C is the discharge rate, Ah is the current throughput of the energy storage system in the corresponding time, B is the constant coefficient under different discharge rates C, a1, a2, a3 and a4 are fitting coefficients, and SOH is the health status of the energy storage system;

[0049] Step 104: Return-to-investment ratio of energy storage capacity;

[0050] Phase 1 energy storage deployment plan The optimization planning model considering the economic benefits of operation is used to obtain different energy storage configuration schemes for the first phase. The daily operation simulation of the configuration scheme with different percentages was carried out to obtain the return on investment ratio when configuring different energy storage capacities. Correction is made; the return on investment ratio of energy storage capacity is obtained by accumulating the dual variables of the energy storage energy upper limit constraint in the operation simulation, and the calculation method is as follows:

[0051]

[0052] In formula (13), is the return on investment ratio of energy storage capacity, is the dual variable corresponding to the upper limit constraint of energy storage at time h on day d, A ee It is the annuity value of the investment cost per unit capacity of energy storage.

[0053] Furthermore, step 2 specifically includes: clustering the annual wind power, photovoltaic output data and daily load data using the K-means clustering algorithm to obtain typical curves of wind power, photovoltaic output and load in the four seasons of spring, summer, autumn and winter, and the four typical scenarios are used as the scenario set for stage planning.

[0054] Furthermore, step 3 specifically includes: considering economic benefits to construct a one-stage energy storage configuration planning model. The economic objective function includes two parts: investment cost and operating cost. The investment cost mainly refers to the equivalent annual investment cost of energy storage, while the operating cost includes the distribution network interaction cost and the operation and maintenance cost of each unit.

[0055] minC=min{C int +C ope}(14),

[0056] C int =C ess +C PV +C WT (15),

[0057]

[0058] In formulas (14), (15), (16), (17), (18) and (19), C is the comprehensive cost, C int is the equal annual investment cost, C ope is the annual operating cost of the microgrid; ρ is the discount rate, r is the number of years of discount, c ess.int is the unit capacity investment cost of energy storage, K d E is the loss cost caused by unit charge and discharge after conversion, ess.max Allocate capacity for energy storage; C bat.i 、C grid.i and C om.i are the energy storage life daily loss cost, electricity purchase and sales cost, and each unit operation and maintenance cost corresponding to the i-th typical day, c grid is the grid electricity price, c om.W is the operation and maintenance cost coefficient of the W-th unit.

[0059] Furthermore, step 4 specifically includes:

[0060] Configure grid interaction:

[0061]

[0062] In formula (20), P buy (t), P sell (t) are the power purchase and sales of the microgrid in the tth period, P grid.max is the upper limit of the interaction power of the tie line between the microgrid and the distribution network, S grid (t) is the state variable of the microgrid power purchase and sale during period t, 1 represents power purchase, and 0 represents power sale;

[0063] Energy storage charging and discharging power constraints:

[0064]

[0065] P ess.max =μE ess.max (twenty two),

[0066] In formulas (21) and (22), P dis (t) and P ch (t) are the discharging and charging power of the energy storage system at time t, P bat.max is the upper limit of energy storage charging and discharging power, μ is the fixed proportional coefficient between the upper limit of energy storage power and capacity;

[0067] Energy storage state of charge constraints:

[0068]

[0069] In formula (23), Δt is the time step length, which is 1 hour, E(0) is the initial energy storage capacity, SOC max and SOC min are the upper and lower limits of the state of charge of the energy storage, respectively, and η is the energy storage charge and discharge efficiency;

[0070] Charge and discharge balance constraints:

[0071]

[0072] Power balance constraints:

[0073] P dis (t)-P ch (t)+P buy (t)-P sell (t)+u PV (t)+u WT (t) = u L (t)(25),

[0074] In formula (25), u PV (t),u WT (t) and u L (t) are the uncertain variables of photovoltaic power generation, wind turbine output and load power in the t-th period respectively;

[0075] Loop count variable Linearization:

[0076]

[0077] Depth of discharge:

[0078]

[0079] In formula (27), d d(t) represents the discharge depth of the energy storage system in the tth period and the dth segment, d d are the upper and lower limits of the discharge depth of the dth segment respectively;

[0080] Since there is uncertainty in wind and solar power output and load in the microgrid system, the box uncertainty set with equal scale of upper and lower bounds is expressed as:

[0081] U={(1-τ)u0≤u≤(1+τ)u0}(28),

[0082] In formula (28), u0 is the predicted value of wind and solar power output and load power, and τ is the scaling ratio, i.e., uncertainty, which are 0.05, 0.1, and 0.15 respectively.

[0083] The beneficial effects of the present invention are:

[0084] 1. Enhanced climate adaptability of the present invention: By integrating the dynamic impact mechanism of extreme low temperatures in cold regions on energy storage performance, a multi-dimensional climate coupling model is constructed to improve the reliability and cost-effectiveness of planning results in special environments;

[0085] 2. This invention implements dynamic modeling of full-life degradation: quantifying the correlation between battery capacity degradation and maintenance costs, establishing a long-term optimization model for "performance-economy" coupling, and avoiding the deviation of solutions caused by traditional static planning;

[0086] 3. This invention achieves efficient two-layer global optimization: It uses the C&CG algorithm to accurately decouple the two-layer optimization problem of capacity configuration and operation simulation, and simultaneously achieves the global optimal solution for economic objectives and operational stability constraints, significantly improving the solution efficiency in complex scenarios.

[0087] 4. Under a typical load scenario of 200kW, the present invention recommends the coordinated configuration of an 80kWh / 20kW energy storage system (C / P=4), a 170kW photovoltaic system, and a 12kW wind turbine. This combination, through a scientific ratio of energy storage capacity to power of 4:1, can not only meet the peak-shaving needs under extremely low temperatures, but also achieve a coordinated power supply structure with photovoltaics as the main source (accounting for 85%), energy storage for regulation, and wind turbines as a supplement. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 This is a schematic diagram of the capacity planning and configuration method for wind-solar-storage microgrids in cold regions;

[0089] Figure 2 Schematic diagram of the topology of a cold-region wind-solar-storage microgrid in a specific implementation manner;

[0090] Figure 3 It is a schematic diagram of the CC&G algorithm process in a specific implementation method;

[0091] Figure 4 is a schematic diagram of photovoltaic and wind power generation output on a typical day in cold regions in a specific implementation method;

[0092] Figure 5 This is a schematic diagram of typical daily load electricity consumption in cold regions in a specific implementation method;

[0093] Figure 6 It is a schematic diagram of the iterative convergence process of the upper and lower layers of economic goal optimization in a specific implementation method;

[0094] Figure 7 is a schematic diagram of a typical day cold region microgrid operation plan in a specific implementation method;

[0095] Figure 8 It is the capacity configuration result of the cold region microgrid in the specific implementation method. DETAILED DESCRIPTION

[0096] Specific implementation method 1: Figures 1 to 8 As shown in FIG, a capacity planning and configuration method for a wind-solar-storage microgrid in cold regions includes the following specific steps:

[0097] Step 1: Establish mathematical models of photovoltaic power generation, wind power generation and energy storage systems;

[0098] Step 101: Assume that the wind speed of the wind turbine is v, and the output power of the wind turbine is P WT The approximate relationship between and wind speed is expressed by the following piecewise function:

[0099]

[0100] In formula (1), v ci Indicates the cut-in wind speed, v co Indicates the cut-out wind speed, v r Indicates rated wind speed, P r Indicates the rated output power of the wind turbine generator set;

[0101] When the wind speed is between v ci and v r When the wind power output power is expressed as the wind speed function η(v), it is approximately a linear relationship, that is:

[0102] η(v)=P r (vv ci ) / (v r -v ci )(2),

[0103] In formula (2), the rated output power P of the wind turbine generator set is r The dynamic model of the wind turbine is obtained as follows:

[0104] P r =0.5πρR 2 C p (λ,β)v 3 N WT (3),

[0105] In formula (3), ρ is the air density, R is the blade radius, and C p is the wind energy utilization coefficient, λ is the tip speed ratio, β is the pitch angle, N WT is the number of wind turbines, ω r is the angular velocity of the wind turbine; in addition, there is:

[0106]

[0107] Step 102: The actual output power of the photovoltaic array can be obtained from the output power under standard rated conditions, light intensity, and ambient temperature:

[0108]

[0109] In formula (6), P PV is the output power of the working point, STC refers to the solar irradiance G STC is 1kW / m2, the battery surface temperature T STC The relative atmospheric optical quality is AM1.5 under the condition of 25℃, G c is the irradiance at the working point, k is the power temperature coefficient, P STC is the rated output power of the photovoltaic array under standard rated conditions, if n PV is the total number of photovoltaic cells in the photovoltaic array, p stc is the rated output power of the photovoltaic cell under rated test conditions, then P STC =n PV p stc ;T c is the battery surface temperature at the operating point, which is the ambient temperature T a And the function of wind speed:

[0110] T c =T a +αG c (7),

[0111] The coefficient α is an exponential function of the wind speed v:

[0112]

[0113] In formula (8), c1, c2, and c3 are constant coefficients;

[0114] Step 103: Use the battery pack as the energy storage element. The actual available capacity E of the energy storage battery pack is essis a function of battery temperature:

[0115] E ess =E STC [1+δ B (T ess -T essSTC )](9),

[0116] In formula (9), T bat is the battery temperature at the operating point, E STC The rated capacity of the battery under standard conditions is usually provided by the manufacturer. The temperature under standard conditions is T batSTC At 25℃, δ B is the capacity temperature coefficient, which is taken as 0.6%;

[0117] Based on the type of energy storage technology used in the microgrid, a cyclic charge and discharge test of the energy storage system is conducted, and the performance parameters after each cycle are recorded. Through statistical analysis, a life decay model of the energy storage system is established. The energy storage system life decay model can be expressed as:

[0118]

[0119] B(C)=a1C 3 +a2C 2 +a3C+a4(11),

[0120]

[0121] In formulas (10), (11) and (12), E loss is the capacity loss of the energy storage system, C is the discharge rate, Ah is the current throughput of the energy storage system in the corresponding time, B is the constant coefficient under different discharge rates C, a1, a2, a3 and a4 are fitting coefficients, and SOH is the health status of the energy storage system;

[0122] Step 104: Return-to-investment ratio of energy storage capacity;

[0123] Phase 1 energy storage deployment plan The optimization planning model considering the economic benefits of operation is used to obtain different energy storage configuration schemes for the first phase. The daily operation simulation of the configuration scheme with different percentages was carried out to obtain the return on investment ratio when configuring different energy storage capacities. Correction is made; the return on investment ratio of energy storage capacity is obtained by accumulating the dual variables of the energy storage energy upper limit constraint in the operation simulation, and the calculation method is as follows:

[0124]

[0125] In formula (13), is the return on investment ratio of energy storage capacity, is the dual variable corresponding to the upper limit constraint of energy storage at time h on day d, A ee is the annuity value of the investment cost per unit capacity of energy storage;

[0126] Step 2: Evaluate the complementary characteristics of wind and solar power, and cluster a set of typical wind power output and load scenarios;

[0127] Step 2 specifically includes: clustering the annual wind power, photovoltaic output data and daily load data using the K-means clustering algorithm to obtain typical curves of wind power, photovoltaic output and load in the four seasons of spring, summer, autumn and winter. The four typical scenarios are used as the scenario set for stage planning;

[0128] Step 3: Preliminary planning configuration model considering construction and operation costs;

[0129] A one-stage energy storage configuration planning model is constructed considering economic benefits. The economic objective function includes two parts: investment cost and operating cost. The investment cost mainly refers to the annual investment cost of energy storage, while the operating cost includes the distribution network interaction cost and the operation and maintenance cost of each unit.

[0130] minC=min{C int +C ope}(14),

[0131] C int =C ess +C PV +C WT (15),

[0132]

[0133]

[0134] In formulas (14), (15), (16), (17), (18) and (19), C is the comprehensive cost, C int is the equal annual investment cost, C ope is the annual operating cost of the microgrid; ρ is the discount rate, r is the number of years of discount, c ess.int is the unit capacity investment cost of energy storage, K d E is the loss cost caused by unit charge and discharge after conversion, ess.max Allocate capacity for energy storage; C bat.i 、C grid.i and C om.i are the energy storage life daily loss cost, electricity purchase and sales cost, and each unit operation and maintenance cost corresponding to the i-th typical day, c grid is the grid electricity price, c om.W is the operation and maintenance cost coefficient of the W-th unit;

[0135] Step 4: Daily operation planning model considering safety constraints;

[0136] Configure grid interaction:

[0137]

[0138] In formula (20), P buy (t), P sell (t) are the power purchase and sales of the microgrid in the tth period, P grid.max is the upper limit of the interaction power of the tie line between the microgrid and the distribution network, S grid (t) is the state variable of the microgrid power purchase and sale during period t, 1 represents power purchase, and 0 represents power sale;

[0139] Energy storage charging and discharging power constraints:

[0140]

[0141] P ess.max =μE ess.max (twenty two),

[0142] In formulas (21) and (22), P dis (t) and P ch (t) are the discharging and charging power of the energy storage system at time t, P bat.max is the upper limit of energy storage charging and discharging power, μ is the fixed proportional coefficient between the upper limit of energy storage power and capacity;

[0143] Energy storage state of charge constraints:

[0144]

[0145] In formula (23), Δt is the time step length, which is 1 hour, E(0) is the initial energy storage capacity, SOC max and SOC min are the upper and lower limits of the state of charge of the energy storage, respectively, and η is the energy storage charge and discharge efficiency;

[0146] Charge and discharge balance constraints:

[0147]

[0148] Power balance constraints:

[0149] P dis (t)-P ch (t)+P buy (t)-P sell (t)+u PV (t)+u WT (t) = u L (t)(25),

[0150] In formula (25), u PV (t),u WT (t) and u L (t) are the uncertain variables of photovoltaic power generation, wind turbine output and load power in the t-th period respectively;

[0151] Loop count variable Linearization:

[0152]

[0153] Depth of discharge:

[0154]

[0155] In formula (27), d d (t) represents the discharge depth of the energy storage system in the tth period and the dth segment, d d are the upper and lower limits of the discharge depth of the dth segment respectively;

[0156] Since there is uncertainty in wind and solar power output and load in the microgrid system, the box uncertainty set with equal scale of upper and lower bounds is expressed as:

[0157] U={(1-τ)u0≤u≤(1+τ)u0}(28),

[0158] In formula (28), u0 is the predicted value of wind and solar power output and load power, τ is the scaling ratio, that is, the uncertainty, which is 0.05, 0.1, and 0.15 respectively;

[0159] Step 5: Based on the preliminary planning configuration model considering construction and operation costs in step 3, set the initial wind-solar-storage microgrid capacity configuration plan and set the convergence conditions;

[0160] Step 6: Based on the current wind, solar, and storage microgrid capacity configuration plan, build an upper-level capacity planning model; use the optimization algorithm to solve the upper-level problem and obtain a preliminary microgrid capacity and power configuration plan; and pass the solution results to the lower-level problem;

[0161] Step 7: Based on the wind, solar, and energy storage microgrid capacity configuration plan provided by the upper-level problem, build a lower-level simulation operation model. Simulate various operating conditions of the microgrid in actual operation and evaluate the effectiveness of the microgrid configuration plan; extract the key constraints and objective function values of the lower-level problem;

[0162] Step 8: Add the key constraints of the lower-level problem to the model of the upper-level problem to form a new constraint set; update the objective function of the upper-level problem, taking into account the operating results of the lower-level problem;

[0163] Step 9: Repeat steps 6 to 8, iterating alternately between the upper and lower layers, and gradually optimize the wind-solar-storage microgrid capacity configuration plan; after each iteration, check whether the change of the objective function meets the convergence condition; if so, stop the iteration; otherwise, continue the optimization;

[0164] Step 10: When the iterative process converges, the optimal microgrid capacity configuration plan is output, including the capacity, power, and charging and discharging strategies of the wind, solar, and storage microgrid system; while taking into account the operating performance indicators of the microgrid.

[0165] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with the present profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical content disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement of the above embodiments made according to the technical essence of the present invention, within the spirit and principles of the present invention, without departing from the content of the technical solution of the present invention, shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A capacity planning and configuration method for a wind-solar-storage microgrid in cold regions, characterized by: The specific steps include: Step 1: Establish mathematical models of photovoltaic power generation, wind power generation and energy storage systems; Step 2: Evaluate the complementary characteristics of wind and solar power, and cluster a set of typical wind power output and load scenarios; Step 3: Preliminary planning configuration model considering construction and operation costs; Step 4: Daily operation planning model considering safety constraints; Step 5: Based on the preliminary planning configuration model considering construction and operation costs in step 3, set the initial wind-solar-storage microgrid capacity configuration plan and set the convergence conditions; Step 6: Based on the current wind, solar, and storage microgrid capacity configuration plan, build an upper-level capacity planning model; use the optimization algorithm to solve the upper-level problem and obtain a preliminary microgrid capacity and power configuration plan; and pass the solution results to the lower-level problem; Step 7: Based on the wind, solar, and energy storage microgrid capacity configuration plan provided by the upper-level problem, build a lower-level simulation operation model. Simulate various operating conditions of the microgrid in actual operation and evaluate the effectiveness of the microgrid configuration plan; extract the key constraints and objective function values of the lower-level problem; Step 8: Add the key constraints of the lower-level problem to the model of the upper-level problem to form a new constraint set; update the objective function of the upper-level problem, taking into account the operating results of the lower-level problem; Step 9: Repeat steps 6 to 8, iterating alternately between the upper and lower layers, and gradually optimize the wind-solar-storage microgrid capacity configuration plan; after each iteration, check whether the change of the objective function meets the convergence condition; if so, stop the iteration; otherwise, continue the optimization; Step 10: When the iterative process converges, the optimal microgrid capacity configuration plan is output, including the capacity, power, and charging and discharging strategies of the wind, solar, and storage microgrid system; while taking into account the operating performance indicators of the microgrid.

2. A capacity planning and configuration method for a cold-region wind-solar-storage microgrid according to claim 1, characterized in that: Step 1 specifically includes: Step 101: Assume that the wind speed of the wind turbine is v, and the output power of the wind turbine is P WT The approximate relationship between and wind speed is expressed by the following piecewise function: In formula (1), v ci Indicates the cut-in wind speed, v co Indicates the cut-out wind speed, v r Indicates rated wind speed, P r Indicates the rated output power of the wind turbine generator set; When the wind speed is between v ci and v r When the wind power output power is expressed as the wind speed function η(v), it is approximately a linear relationship, that is: η(v)=P r (vv ci ) / (v r -v ci ) (2), In formula (2), the rated output power P of the wind turbine generator set is r The dynamic model of the wind turbine is obtained as follows: P r =0.5πR 2 C p (l,b)v 3 N WT (3), In formula (3), ρ is the air density, R is the blade radius, and C p is the wind energy utilization coefficient, λ is the tip speed ratio, β is the pitch angle, N WT is the number of wind turbines, ω r is the angular velocity of the wind turbine; in addition, there is: Step 102: The actual output power of the photovoltaic array can be obtained from the output power under standard rated conditions, light intensity, and ambient temperature: In formula (6), P PV is the output power of the working point, STC refers to the solar irradiance G STC is 1kW / m2, the battery surface temperature T STC The relative atmospheric optical quality is AM1.5 under the condition of 25℃, G c is the irradiance at the working point, k is the power temperature coefficient, P STC is the rated output power of the photovoltaic array under standard rated conditions, if n PV is the total number of photovoltaic cells in the photovoltaic array, p stc is the rated output power of the photovoltaic cell under rated test conditions, then P STC =n PV p stc ;T c is the battery surface temperature at the operating point, which is the ambient temperature T a And the function of wind speed: T c =T a +αG c (7), The coefficient α is an exponential function of the wind speed v: In formula (8), c1, c2, and c3 are constant coefficients; Step 103: Use the battery pack as the energy storage element. The actual available capacity E of the energy storage battery pack is ess is a function of battery temperature: E ess =E STC [1+δ B (T ess -T essSTC )] (9), In formula (9), T bat is the battery temperature at the operating point, E STC The rated capacity of the battery under standard conditions is usually provided by the manufacturer. The temperature under standard conditions is T batSTC At 25℃, δ B is the capacity temperature coefficient, which is taken as 0.6%; Based on the type of energy storage technology used in the microgrid, a cyclic charge and discharge test of the energy storage system is conducted, and the performance parameters after each cycle are recorded. Through statistical analysis, a life decay model of the energy storage system is established. The energy storage system life decay model can be expressed as: B(C)=a1C 3 +a2C 2 +a3C+a4(11), In formulas (10), (11) and (12), E loss is the capacity loss of the energy storage system, C is the discharge rate, Ah is the current throughput of the energy storage system in the corresponding time, B is the constant coefficient under different discharge rates C, a1, a2, a3 and a4 are fitting coefficients, and SOH is the health status of the energy storage system; Step 104: Return-to-investment ratio of energy storage capacity; Phase 1 energy storage deployment plan The optimization planning model considering the economic benefits of operation is used to obtain different energy storage configuration schemes for the first phase. The daily operation simulation of the configuration scheme with different percentages was carried out to obtain the return on investment ratio when configuring different energy storage capacities. Correction is made; the return on investment ratio of energy storage capacity is obtained by accumulating the dual variables of the energy storage energy upper limit constraint in the operation simulation, and the calculation method is as follows: In formula (13), is the return on investment ratio of energy storage capacity, is the dual variable corresponding to the upper limit constraint of energy storage at time h on day d, A ee It is the annuity value of the investment cost per unit capacity of energy storage.

3. The capacity planning and configuration method for a wind-solar-storage microgrid in cold regions according to claim 1, characterized in that: Step 2 specifically includes: clustering the annual wind power, photovoltaic output data and daily load data using the K-means clustering algorithm to obtain typical curves of wind power, photovoltaic output and load in the four seasons of spring, summer, autumn and winter. The four typical scenarios are used as the scenario set for stage planning.

4. The capacity planning and configuration method for a wind-solar-storage microgrid in cold regions according to claim 1, characterized in that: Step 3 specifically involves: Constructing a one-stage energy storage configuration planning model considering economic benefits. The economic objective function includes two components: investment cost and operating cost. The investment cost primarily refers to the equivalent annual investment cost of energy storage, while the operating cost includes the distribution network interaction cost and the operation and maintenance costs of each unit. minC=min{C int +C ope }(14), C int =C ess +C PV +C WT (15), In formulas (14), (15), (16), (17), (18) and (19), C is the comprehensive cost, C int is the equal annual investment cost, C ope is the annual operating cost of the microgrid; ρ is the discount rate, r is the number of years of discount, c ess.int is the unit capacity investment cost of energy storage, K d E is the loss cost caused by unit charge and discharge after conversion, ess.max Allocate capacity for energy storage; C bat.i 、C grid.i and C om.i are the energy storage life daily loss cost, electricity purchase and sales cost, and each unit operation and maintenance cost corresponding to the i-th typical day, c grid is the grid electricity price, c om.W is the operation and maintenance cost coefficient of the W-th unit.

5. The capacity planning and configuration method for a cold-region wind-solar-storage microgrid according to claim 1 is characterized in that: Step 4 specifically includes: Configure grid interaction: In formula (20), P buy (t), P sell (t) are the power purchase and sales of the microgrid in the tth period, P grid.max is the upper limit of the interaction power of the tie line between the microgrid and the distribution network, S grid (t) is the state variable of the microgrid power purchase and sale during period t, 1 represents power purchase, and 0 represents power sale; Energy storage charging and discharging power constraints: P ess.max =μE ess.max (22), In formulas (21) and (22), P dis (t) and P ch (t) are the discharging and charging power of the energy storage system at time t, P bat.max is the upper limit of energy storage charging and discharging power, μ is the fixed proportional coefficient between the upper limit of energy storage power and capacity; Energy storage state of charge constraints: In formula (23), Δt is the time step length, which is 1 hour, E(0) is the initial energy storage capacity, SOC max and SOC min are the upper and lower limits of the state of charge of the energy storage, respectively, and η is the energy storage charge and discharge efficiency; Charge and discharge balance constraints: Power balance constraints: P dis (t)-P ch (t)+P buy (t)-P sell (t)+u PV (t)+u WT (t)=u L (t)(25), In formula (25), u PV (t),u WT (t) and u L (t) are the uncertain variables of photovoltaic power generation, wind turbine output and load power in the t-th period respectively; Loop count variable Linearization: Depth of discharge: In formula (27), d d (t) represents the discharge depth of the energy storage system in the tth period and the dth segment, are the upper and lower limits of the discharge depth of the dth segment respectively; Since there is uncertainty in wind and solar power output and load in the microgrid system, the box uncertainty set with equal scale of upper and lower bounds is expressed as: U={(1-τ)u0≤u≤(1+τ)u0}(28), In formula (28), u0 is the predicted value of wind and solar power output and load power, and τ is the scaling ratio, i.e., uncertainty, which are 0.05, 0.1, and 0.15 respectively.

Images