Regulation and control strategy of underwater wind-solar-storage integrated multi-energy complementary system in extreme high-temperature scene

Abstract

The invention discloses a regulation and control strategy for a water-wind-light-storage integrated multi-energy complementary system in an extreme high-temperature scene, and relates to the technical field of optimal scheduling and toughness evaluation of the multi-energy complementary system. The method comprises the following steps: acquiring meteorological and equipment parameters, and constructing a performance model of photovoltaic, wind power, thermal power and power transmission facilities under an extreme high temperature condition; an objective function is established based on the minimum operation cost of the multi-energy complementary system, and constraint conditions are established according to supply and demand balance, equipment operation, peak regulation margin and load reduction; performing optimization scheduling on the system through an optimization tool in combination with a demand response strategy to obtain a multi-energy collaborative operation scheme; a toughness evaluation model based on a comprehensive hierarchy-entropy weight method is adopted, the toughness of the system is quantitatively analyzed from the dimensions of economy, flexibility, safety and the like, and toughness scores and improvement suggestions are output. The economic operation efficiency and the anti-risk capability of the multi-energy complementary system under the extremely high temperature condition are effectively improved, and scientific support is provided for safe and stable operation of the multi-energy complementary system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization scheduling and resilience assessment, and specifically to a control strategy for an integrated multi-energy complementary system of water, wind, solar and storage under extreme high temperature scenarios, which is used to improve the safety, stability and economy of the multi-energy complementary system under extreme high temperature conditions. Background Art

[0002] Against the backdrop of global energy transformation and climate change, the frequency and intensity of extreme heat weather have increased significantly. This poses a huge challenge to the operation of the power system, which is mainly manifested in the following aspects: Load surge: The air conditioning load has increased significantly under extreme high temperature conditions, resulting in the system load peak reaching new highs, exacerbating the pressure of balancing supply and demand. Equipment performance degradation: The high temperature environment has led to the degradation of the efficiency of thermal power units, the reduction of the carrying capacity of transmission lines and transformers, and increased the operating risk of the system. Intensified fluctuations in new energy: High temperatures have a negative impact on the power generation efficiency of photovoltaic modules, the uncertainty of wind power has been further exacerbated, and the difficulty of new energy consumption and scheduling has increased significantly.

[0003] In response to these problems, traditional power system optimization and scheduling methods, such as linear programming, genetic algorithms, and particle swarm algorithms, although they can provide certain performance in general, the present invention discloses a control strategy for an integrated multi-energy complementary system of water, wind, light, and storage under extreme high temperature scenarios, which belongs to the field of optimization, scheduling, and resilience assessment of multi-energy complementary systems. Specific steps: obtain meteorological and equipment parameters, and construct performance models of photovoltaic, wind power, thermal power, and transmission facilities under extreme high temperatures; set the objective function with the minimum operating cost of the multi-energy complementary system, and establish conditions based on supply and demand balance, equipment operation, peak regulation margin, and load reduction constraints; combine dynamic electricity prices with demand response strategies, and use MATLAB to adjust the CPLEX optimization tool to optimize the scheduling and obtain an energy collaborative operation plan; then use the comprehensive hierarchical-entropy weight method resilience assessment model to quantitatively analyze the system resilience from the dimensions of economy, flexibility, and safety, and output scores and suggestions. The present invention improves the economic operation and risk resistance of the system under extreme high temperatures, and provides scientific support for its safe and stable operation. The optimization effect is great, but in special scenarios such as extreme high temperatures, there are often the following shortcomings: Lack of modeling for extreme climates: Traditional methods do not fully consider the profound impact of high temperatures on equipment performance and load characteristics, and it is difficult to accurately characterize the operating characteristics of the system under high temperatures. Limited optimization performance: Existing algorithms are prone to fall into local optimality and cannot effectively solve the scheduling problems of complex multi-energy complementary systems, especially when the system flexibility and resilience decrease under high temperature conditions. In view of this, a control strategy for an integrated multi-energy complementary system of water, wind, solar and storage under extreme high temperature scenarios is proposed. Through precise modeling and optimized scheduling strategies, the resilience of the system under extreme conditions is improved. Summary of the invention

[0004] The present invention solves the problems of insufficient simulation of the impact of equipment performance under extreme high temperature conditions in traditional methods, poor flexibility of demand response strategies, and incomplete evaluation of system resilience. The purpose of the present invention is to provide a control strategy for an integrated multi-energy complementary system of water, wind, solar and storage under extreme high temperature scenarios. Through high temperature impact modeling, mathematical optimization scheduling and resilience index evaluation, an optimized operation plan for high temperature scenarios is constructed, thereby significantly improving the resilience, flexibility and risk resistance of the system. The comparison of the system's resilience score, load reduction and peak regulation margin is shown in Table 1. The optimization strategy significantly improves the resilience of the system.

[0005] The technical solution of the present invention comprises the following steps:

[0006] 1. Modeling of high temperature impact: In view of the temperature effect of photovoltaic modules, degradation of thermal power efficiency, increase in load peak and reduction in the carrying capacity of transmission and distribution facilities, a dynamic impact model of high temperature on the multi-energy complementary system is constructed to quantify the impact of extreme high temperature on system performance.

[0007] 2. Construction of coordinated dispatch objective function: With the goal of maximizing the net profit of the system, the benefits of thermal power, hydropower, wind power, photovoltaic power and energy storage, as well as the load reduction penalty cost and demand response subsidy cost are comprehensively considered.

[0008] 3. Setting of constraints: including unit output, power balance, energy constraints of energy storage systems and restrictions on the carrying capacity of transmission facilities due to high temperatures.

[0009] 4. Optimization and solution: Use MATLAB to call the CPLEX tool to solve the objective function, output a multi-energy coordinated scheduling plan, and optimize the economy and flexibility of the system.

[0010] 5. Resilience assessment: Based on the comprehensive hierarchical-entropy weight resilience evaluation method (CAHP-ERE), the system is quantitatively evaluated from multiple dimensions such as economy, flexibility, security and topological vulnerability.

[0011] The outstanding advantage of the present invention is that its comprehensive modeling of high temperature effects can accurately characterize the dynamic characteristics of multi-energy complementary systems under extreme high temperature conditions, effectively covering key influencing factors such as photovoltaic efficiency degradation, thermal power efficiency decline, load surge and reduced carrying capacity of transmission facilities, and providing reliable data support for optimized scheduling. In addition, by optimizing the joint scheduling of thermal power, hydropower, wind power, photovoltaics and energy storage, the present invention significantly improves the utilization efficiency of system resources, alleviates the contradiction between supply and demand under extreme high temperatures, reduces operating costs and improves the resilience and flexibility of the system. More importantly, the present invention proposes a method based on comprehensive hierarchical-entropy weight resilience evaluation (CAHP-ERE), which can quantitatively evaluate system resilience from multiple dimensions such as economy, flexibility and stability, identify weak links and provide targeted optimization guidance, which helps the system to achieve safer, more stable and efficient operation under high temperature conditions.

[0012] The present invention is achieved through the following technical solutions:

[0013] In a first aspect, the present invention provides a control strategy for a water-wind-solar-storage integrated multi-energy complementary system in an extreme high temperature scenario, the method comprising the following steps:

[0014] High temperature impact modeling: Obtain system equipment and meteorological data to build a dynamic impact model of the multi-energy complementary system under extreme high temperature conditions, including the temperature effect of photovoltaic components, thermal power efficiency degradation, load peak surge, and reduced carrying capacity of transmission facilities.

[0015] Temperature effect model of photovoltaic modules:

[0016]

[0017] T t PV =c1+c2·T t air +c3·I t +c4·v t

[0018] Where: P t PV is the output power of the photovoltaic module at time t; P 0,PV is the rated power of photovoltaic power generation equipment under standard conditions; I t ,I 0 is the actual radiation intensity at time t and the radiation intensity under standard conditions; is the temperature power coefficient, which is 0.005 / ℃; T t air 、T t PV 、T 0,PVis the air temperature at time t, the actual operating temperature of the PV module, and the operating temperature of the PV module under standard conditions (25°C); c1, c2, c3, and c4 are all correlation coefficients of the PV operating temperature; v t is the ground wind speed at time t.

[0019] Efficiency degradation model of thermal power units:

[0020]

[0021] Where ρ is when T i,j Higher than T health_air The efficiency degradation rate of the thermal power unit at . health_air =10℃,ρ=0.0094.

[0022] Model of system load affected by external temperature:

[0023]

[0024] C l is the temperature sensitivity coefficient of power load, T j and Represent the geographical averages of the projected temperature and the historical reference temperature, respectively.

[0025] Transmission and distribution facilities carrying capacity degradation model:

[0026]

[0027] Where I is the allowable current carrying capacity (A); W R is the radiation heat dissipation power per unit length of wire (W / m); W F is the convection heat dissipation power per unit length of wire (W / m); W S is the solar heat absorption power per unit length of conductor (W / m); R′ t The AC resistance of the wire at the allowable temperature (Ω / m).

[0028] Demand response strategy design: The demand response loads considered in this invention are mainly based on electricity price incentives, including two categories: transferable loads and curtailable loads. Transferable loads are highly controllable and generally cannot be interrupted but can be overall shifted during the power consumption period, such as electric vehicle charging and industrial loads. Curtailable loads refer to loads that can be temporarily cut off or reduced in an emergency situation of the power grid, such as temperature control loads and lighting systems.

[0029] System subsidy cost for transferable load C shift for

[0030]

[0031] Where: c shiftis the subsidy cost per MWh of transferable load, P t shift is the power of the load that can be transferred at time t, and T is the total number of scheduling periods.

[0032] The system subsidy cost for the load that can be reduced is

[0033]

[0034]

[0035] Where: c int P is the subsidy cost of the system for each MWh of load reduction in demand response, t int is the load power that can be reduced during period t; U i,t is the load shedding state variable of node i, S i,t is the reducible capacity of the i-th node in period t, and N is the total number of reducible nodes.

[0036] Optimal scheduling model for multi-energy complementary systems: Construct an optimal scheduling model for multi-energy complementary systems under extreme high temperature conditions, with the goal of maximizing the net benefits of the system.

[0037] The operation goal of the multi-energy complementary system is to maximize the total system revenue. The objective function is as follows:

[0038] max F=F income -F cost =P g +P w +P v +P h +P ESS -C DR -C shed

[0039] Where: P g , P w , P v , P h and P ESS are the operating income of thermal power, wind turbine, photovoltaic, hydropower and energy storage respectively, C DR and C shed They are demand response subsidy cost and direct load reduction cost respectively.

[0040] Energy storage system operating benefits and costs

[0041] P ESS =I ESS -C ESS

[0042]

[0043] Where: is the on-grid electricity price, η c , η d are the energy storage charging and discharging efficiency, P t char , P t dis They are the energy storage charging and discharging power at time t respectively.

[0044] The operating benefits and costs of thermal power units

[0045] P g =I g -C g

[0046]

[0047] C g =C coal +C ss

[0048]

[0049] Where: C g is the thermal power operation cost, I g For the income from thermal power grid connection, is the grid-connected power of the i-th thermal power unit at time t, p t,grid C is the thermal power grid price at time t. coal , C ss are the coal consumption cost and start-up and shutdown cost of the unit respectively; a i 、b i 、c i are the secondary, primary and constant coefficients of the consumption of the i-th unit respectively; is the startup cost of the i-th unit, is the shutdown cost of the i-th unit, It is the start and stop state variable of the unit.

[0050] Clean energy unit operation profit model

[0051]

[0052]

[0053] Where: p w 、p v 、p h are the on-grid electricity prices of wind power, photovoltaic power and hydropower, respectively. t w , P t v , P t hRespectively represent the output power of wind power, photovoltaic power and hydropower at time t; θ w ,θ v ,θ h are the penalty coefficients for wind, solar and hydro abandonment, They represent the wind power, solar power and hydro power abandonment at time t respectively.

[0054] Demand response subsidy costs

[0055] C DR =C shift +C int

[0056]

[0057] Where: c shift is the subsidy cost per MWh of shiftable load, is the load power that can be transferred from node i at time t. int is the subsidy cost of the system for load reduction in demand response, The power of the interruptible load, T is the total number of dispatch periods.

[0058] Direct load reduction penalty costs

[0059]

[0060] Where: To directly cut load power, To directly cut loads, the base cost is penalized. is the direct load reduction penalty coefficient of node i at time t considering the node vulnerability.

[0061] Constraints:

[0062] Unit output constraints

[0063]

[0064] Where: is the maximum theoretical output of hydropower at time t; are the charging and discharging state variables of energy storage, are the upper and lower limits of charging power respectively, They are the upper and lower limits of the discharge power respectively.

[0065] Thermal power unit climbing constraints

[0066] -r i,down ≤P i,t -P i,(t-1) ≤r i,up

[0067] Where: r i,up and r i,down They are the maximum upward climbing rate and maximum downward climbing rate of thermal power units respectively.

[0068] Energy constraints and charging and discharging constraints of energy storage systems

[0069]

[0070] Where: is the state of charge (SOC) of the energy storage at time t, δ represents the self-discharge rate of the energy storage, E ESS (MWh) is the energy storage capacity, S max , S min They are the upper and lower limits of the state of charge of the energy storage, respectively; They are the charge states of energy storage at the start and end times respectively. Respectively represent the minimum and maximum values ​​of the charging power of the energy storage device, They respectively represent the minimum and maximum values ​​of the discharge power of the energy storage device.

[0071] Line transmission capacity constraints

[0072]

[0073] Where: B i,j (S) represents the susceptance between nodes i and j, θ i,t ,θ j,t Represents the voltage phase angle of nodes i and j, P i,j,Lmax It represents the per-unit value of the maximum transmission power allowed by the line between nodes i and j. and They represent the capacity attenuation coefficient of the line and transformer between nodes i and j due to high temperature at time t. When the line is at the rated operating temperature, is 1. Similarly, when the transformer is at normal operating temperature or there is no transformer between nodes i and j, is 1.

[0074] Power balance constraints

[0075]

[0076] Where: is the load transfer power that can be transferred to node i at time t, Interruptible load power, To directly cut load power.

[0077] Alternative constraints

[0078]

[0079] Where: is the maximum available grid-connected power of unit i at time t, and λ is the reserve coefficient.

[0080] Demand response constraints

[0081] In a scheduling cycle, the total amount of load before and after the demand response of each node in the system remains unchanged

[0082]

[0083] Where: is the maximum value of the total amount of load that can be transferred to node i at time t, The maximum value of the total amount of load that can be reduced.

[0084] Optimization solution: The rationality of the model is verified by analyzing the optimization dispatch results of the power system in 96 time periods in the past. Based on the MATLAB 2022a platform, the CPLEX solver in MATLAB is called to solve the optimization model of the multi-energy complementary system and output the optimal operation plan of photovoltaic, wind power, thermal power, hydropower and energy storage.

[0085] Multi-energy complementary system resilience evaluation method: In the context of global energy transformation and climate change, multi-energy complementary systems play an increasingly important role in providing reliability and sustainability. In order to effectively evaluate the resilience of these systems in different scenarios, this paper proposes a combined analytic hierarchy process-entropy resilience evaluation method (CAHP-ERE). The CAHP-ERE method evaluates the resilience performance of multi-energy complementary systems in different scenarios by integrating the subjective analytic hierarchy process (AHP) and the objective entropy weight method. This method combines expert judgment and objective data-driven, and is suitable for system resilience evaluation under a comprehensive indicator system.

[0086] In the second aspect, the present invention also provides a multi-energy complementary system optimization and resilience assessment system under extreme high temperature conditions. The system implements the above method and aims to solve the complex problems in the operation of energy systems under extreme high temperature conditions and improve their operation economy, stability and pressure resistance. The system includes the following functional modules:

[0087] High temperature impact modeling unit: used to collect environmental meteorological data and equipment parameters, and build performance models of photovoltaic, wind power, thermal power and other energy equipment under high temperature conditions.

[0088] Objective function and constraint condition establishment unit: used to construct the multi-energy complementary system optimization scheduling model and define the objective function and constraint conditions.

[0089] Optimization solving unit: Call CPLEX based on MATLAB to solve the model and generate optimization scheduling results.

[0090] Demand response execution unit: used to design and simulate user-side load reduction and load transfer strategies, combined with dynamic electricity prices to achieve response targets.

[0091] Resilience evaluation unit: Based on the CAHP-ERE method, it quantitatively analyzes the resilience indicators of the multi-energy complementary system and outputs the resilience evaluation results.

[0092] Compared with the prior art, the system of the present invention has the following significant advantages:

[0093] The system of the present invention has significant technical advantages. It can comprehensively model the performance changes of photovoltaic, thermal power, wind power and transmission facilities under high temperature conditions, accurately simulate the impact of high temperature on multi-energy complementary systems, and provide reliable data support for scheduling optimization; by jointly optimizing the operation of multiple energy sources, it improves the economy and flexibility of the system, significantly reduces the risk of supply and demand imbalance under high temperature conditions, and ensures the safe operation of the system; adopts the CAHP-ERE method that combines subjective and objective factors to quantify the system resilience from three aspects: economy, flexibility and safety, and identify the weak links in extreme high temperature scenarios; by optimizing the electricity consumption sequence to reduce peak loads and smooth load curves, the operating pressure of the power grid is reduced, and the overall stability and economy are improved; combined with optimization solution and result visualization technology, intuitive and easy-to-interpret operation plans and resilience scores are generated to provide scientific support for power grid scheduling and strategy formulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0095] Figure 1 This is an overall flow chart of the control strategy of the integrated water, wind, solar and storage multi-energy complementary system in an extreme high temperature scenario of the present invention;

[0096] Figure 2 This is a schematic diagram of the topological structure of the multi-energy complementary system of Example 1 of the present invention;

[0097] Figure 3 This is a comparison data diagram of direct load reduction of the system before and after optimization of Example 1 of the present invention;

[0098] Figure 4 A detailed flow chart of the control strategy of the integrated water, wind, solar and storage multi-energy complementary system in an extreme high temperature scenario of the present invention;

[0099] Figure 5 This is a structural block diagram of the functional modules of a multi-energy complementary system optimization and toughness assessment system under extremely high temperature conditions of the present invention. DETAILED DESCRIPTION

[0100] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail in combination with the embodiments and drawings. The schematic implementation mode of the present invention and its description are only used to explain the present invention and are not intended to limit the present invention. The present invention proposes a control strategy for an integrated multi-energy complementary system of water, wind, light and storage under extreme high temperature scenarios, which includes three main links: high temperature impact modeling, multi-energy complementary system optimization scheduling and resilience assessment. Among them, the first step is to build an equipment performance model under high temperature conditions to provide underlying data support for subsequent optimization; the second step is the optimization part, which mainly realizes the scheduling optimization of the multi-energy complementary system by calling CPLEX through MATLAB; the third step is resilience assessment, which uses the CAHP-ERE method to quantify the resilience performance of the system in different scenarios. The present invention solves the problems of economy, flexibility and safety of system operation under extreme high temperature conditions by combining optimization and evaluation, and provides scientific support for power grid planning and operation.

[0101] Example 1

[0102] Method flow

[0103] like Figure 1 As shown, the control strategy of the integrated multi-energy complementary system of water, wind, solar and storage in an extreme high temperature scenario of the present invention includes the following steps:

[0104] S1: Modeling the impact of high temperature conditions

[0105] Acquire environmental meteorological data, equipment operating parameters and historical operating data, and build a performance model of photovoltaic, wind power, thermal power and transmission and distribution facilities in a multi-energy complementary system under extreme high temperature conditions. The system topology is as follows: Figure 2 Specifically including:

[0106] The temperature effect of photovoltaic modules is modeled, and the temperature power coefficient is used to calculate the photovoltaic output power under high temperature conditions.

[0107] The efficiency degradation modeling of thermal power units uses a piecewise linear function to describe the efficiency decline law under high temperature conditions.

[0108] The carrying capacity of transmission and distribution facilities is modeled, and the heat balance equation is used to analyze the impact of high temperature on line current carrying capacity and transformer operating capacity.

[0109] System load modeling combines the temperature sensitivity coefficient of air conditioning load and historical meteorological data to predict peak load under extreme high temperatures.

[0110] S2: Demand Response Optimization

[0111] Design demand response strategies to optimize load distribution and smooth load curves. Strategies include:

[0112] Reduce non-essential loads, such as residential air conditioning and some industrial loads.

[0113] Through dynamic electricity prices, users are guided to shift peak load to off-peak hours.

[0114] Simulate the curtailment effects of demand response and incorporate it into dispatch models.

[0115] S3: Optimal scheduling of multi-energy complementary systems

[0116] Based on the above modeling results, a multi-energy complementary system optimization scheduling model is constructed. The objective function is to minimize the system operation cost. The constraints include power output range, supply and demand balance, line capacity, peak regulation margin, etc. The specific steps include:

[0117] Obtain input data (photovoltaic and wind power forecast output, thermal power and hydropower parameters, load demand, etc.).

[0118] The CPLEX optimization tool integrated in MATLAB is called to solve the objective function and generate a multi-energy coordinated operation plan, including scheduling strategies for photovoltaic, wind power, thermal power, hydropower and energy storage.

[0119] Output optimization results, including power generation cost, peak load regulation capacity and load balance status. The scheduling optimization results show that under high temperature conditions, the direct load reduction can be effectively reduced by rationally allocating photovoltaic, wind power, thermal power and energy storage resources. The specific changes are as follows: Figure 3 shown.

[0120] S4: Resilience Assessment

[0121] Figure 4 This is a detailed flowchart of the toughness evaluation of the present invention, showing the complete process from toughness parameter calculation to comprehensive scoring. The CAHP-ERE (Comprehensive Hierarchical-Entropy Weight Toughness Evaluation) method is used to quantitatively analyze the economy, flexibility and safety of the system under extreme high temperature conditions. Specific steps:

[0122] Indicator selection: including direct load reduction, load rate of important lines, peak-to-valley difference, peak regulation margin, etc.

[0123] Indicator weight calculation: Combine the AHP method and the entropy weight method to calculate the comprehensive weight.

[0124] Assessment score: Calculate the resilience score of the system in different scenarios, analyze weak links and make improvement suggestions.

[0125] In this embodiment, the modeling of the influence of the high temperature condition of the S1 part is completed by the following steps:

[0126] High temperature impact modeling: Obtain system equipment and meteorological data to build a dynamic impact model of the multi-energy complementary system under extreme high temperature conditions, including the temperature effect of photovoltaic components, thermal power efficiency degradation, load peak surge, and transmission facility carrying capacity reduction. System topology structure such as Figure 2 shown.

[0127] Temperature effect model of photovoltaic modules:

[0128]

[0129] T t PV =c1+c2·T t air +c3·I t +c4·v t

[0130] Where: P t PV is the output power of the photovoltaic module at time t; P 0,PV is the rated power of photovoltaic power generation equipment under standard conditions; I t ,I 0 is the actual radiation intensity at time t and the radiation intensity under standard conditions; is the temperature power coefficient, which is 0.005 / ℃; T t air 、T t PV 、T 0,PV is the air temperature at time t, the actual operating temperature of the PV module, and the operating temperature of the PV module under standard conditions (25°C); c1, c2, c3, and c4 are all correlation coefficients of the PV operating temperature; v t is the ground wind speed at time t.

[0131] Efficiency degradation model of thermal power units:

[0132]

[0133] Where ρ is when T i,j Higher than T health_air The efficiency degradation rate of the thermal power unit at . health_air =10℃,ρ=0.0094.

[0134] Model of system load affected by external temperature:

[0135]

[0136] C l is the temperature sensitivity coefficient of power load, T j and Represent the geographical averages of the projected temperature and the historical reference temperature, respectively.

[0137] Transmission and distribution facilities carrying capacity degradation model:

[0138]

[0139] Where I is the allowable current carrying capacity (A); W R is the radiation heat dissipation power per unit length of wire (W / m); W F is the convection heat dissipation power per unit length of wire (W / m); W S is the solar heat absorption power per unit length of conductor (W / m); R′ t The AC resistance of the wire at the allowable temperature (Ω / m).

[0140] In this embodiment, the S2 demand response optimization is achieved through the following strategies:

[0141] Demand response strategy design: The demand response loads considered in this invention are mainly based on electricity price incentives, including two categories: transferable loads and curtailable loads. Transferable loads are highly controllable and generally cannot be interrupted but can be overall shifted during the power consumption period, such as electric vehicle charging and industrial loads. Curtailable loads refer to loads that can be temporarily cut off or reduced in an emergency situation of the power grid, such as temperature control loads and lighting systems.

[0142] System subsidy cost for transferable load C shift for

[0143]

[0144] Where: c shift is the subsidy cost per MWh of transferable load, P t shift is the power of the load that can be transferred at time t, and T is the total number of scheduling periods.

[0145] The system subsidy cost for the load that can be reduced is

[0146]

[0147] Where: c int P is the subsidy cost of the system for each MWh of load reduction in demand response, t int is the load power that can be reduced during period t; U i,t is the load shedding state variable of node i, S i,t is the reducible capacity of the i-th node in period t, and N is the total number of reducible nodes.

[0148] In this embodiment, the optimal scheduling of the multi-energy complementary system in part S3 is achieved through the following strategies:

[0149] Multi-energy complementary system optimization scheduling model: Construct a multi-energy complementary system optimization scheduling model under extreme high temperature conditions, the goal is to maximize the net benefit of the system, the objective function is as follows:

[0150] max F=F income -F cost =P g +P w +P v +P h +P ESS -C DR -C shed

[0151] Where: P g , P w , P v , P h and P ESS are the operating income of thermal power, wind turbine, photovoltaic, hydropower and energy storage respectively, C DR and C shed They are demand response subsidy cost and direct load reduction cost respectively.

[0152] Energy storage system operating benefits and costs

[0153] P ESS =I ESS -C ESS

[0154]

[0155] Where: p t grid is the on-grid electricity price, η c , η d are the energy storage charging and discharging efficiency, P t char , P t dis They are the energy storage charging and discharging power at time t respectively.

[0156] The operating benefits and costs of thermal power units

[0157] P g =I g -C g

[0158]

[0159] C g =C coal +C ss

[0160]

[0161] Where: C g is the thermal power operation cost, I g For the income from thermal power grid connection, is the grid-connected power of the i-th thermal power unit at time t, p t,grid C is the thermal power grid price at time t. coal , C ss are the coal consumption cost and start-up and shutdown cost of the unit respectively; a i , b i 、c i are the secondary, primary and constant coefficients of the consumption of the i-th unit respectively; is the startup cost of the i-th unit, is the shutdown cost of the i-th unit, It is the start and stop state variable of the unit.

[0162] Clean energy unit operation profit model

[0163]

[0164] Where: p w 、p v 、p h are the on-grid electricity prices of wind power, photovoltaic power and hydropower, respectively. t w , P t v , P t h Respectively represent the output power of wind power, photovoltaic power and hydropower at time t; θ w ,θ v ,θ h are the penalty coefficients for wind, solar and hydro abandonment, They represent the wind power, solar power and hydro power abandonment at time t respectively.

[0165] Demand response subsidy costs

[0166] C DR =C shift +C int

[0167]

[0168] Where: c shift is the subsidy cost per MWh of shiftable load, is the load power that can be transferred from node i at time t. int is the subsidy cost of the system for load reduction in demand response, The power of the interruptible load, T is the total number of dispatch periods.

[0169] Direct load reduction penalty costs

[0170]

[0171] Where: To directly cut load power, To directly cut loads, the base cost is penalized. is the direct load reduction penalty coefficient for node i at time t considering node vulnerability.

[0172] Constraints:

[0173] Unit output constraints

[0174]

[0175] Where: is the maximum theoretical output of hydropower at time t; are the charging and discharging state variables of energy storage, are the upper and lower limits of charging power respectively, They are the upper and lower limits of the discharge power respectively.

[0176] Thermal power unit climbing constraints

[0177] -r i,down ≤P i,t -P i,(t-1) ≤r i,up

[0178] Where: r i,up and r i,down They are the maximum upward climbing rate and maximum downward climbing rate of thermal power units respectively.

[0179] Energy constraints and charging and discharging constraints of energy storage systems

[0180]

[0181] Where: is the state of charge (SOC) of the energy storage at time t, δ represents the self-discharge rate of the energy storage, E ESS (MWh) is the energy storage capacity, S max , S min They are the upper and lower limits of the state of charge of the energy storage, respectively; They are the charge states of energy storage at the start and end times respectively. Respectively represent the minimum and maximum values ​​of the charging power of the energy storage device, They respectively represent the minimum and maximum values ​​of the discharge power of the energy storage device.

[0182] Line transmission capacity constraints

[0183]

[0184] Where: B i,j (S) represents the susceptance between nodes i and j, θ i,t ,θ j,t Represents the voltage phase angle of nodes i and j, P i,j,Lmax It represents the per-unit value of the maximum transmission power allowed by the line between nodes i and j. and They represent the capacity attenuation coefficient of the line and transformer between nodes i and j due to high temperature at time t. When the line is at the rated operating temperature, is 1. Similarly, when the transformer is at normal operating temperature or there is no transformer between nodes i and j, is 1.

[0185] Power balance constraints

[0186]

[0187] Where: is the load transfer power that can be transferred to node i at time t, Interruptible load power, To directly cut load power.

[0188] Alternative constraints

[0189]

[0190] Where: is the maximum available grid-connected power of unit i at time t, and λ is the reserve coefficient.

[0191] Demand response constraints

[0192] In a scheduling cycle, the total amount of load before and after the demand response of each node in the system remains unchanged

[0193]

[0194] Where: is the maximum value of the total amount of load that can be transferred to node i at time t, The maximum value of the total amount of load that can be reduced.

[0195] Optimization solution: The rationality of the model is verified by analyzing the optimization dispatch results of the power system in 96 time periods in the past. Based on the MATLAB 2022a platform, the CPLEX solver in MATLAB is called to solve the optimization model of the multi-energy complementary system and output the optimal operation plan of photovoltaic, wind power, thermal power, hydropower and energy storage.

[0196] In this embodiment, the toughness assessment of the S4 part is completed through the following process:

[0197] The first step is to calculate the resilience parameters. The system operating parameters under different scenarios are calculated, including the average load rate of important lines, load reduction, rated capacity of thermal power, peak-to-valley difference and peak regulation margin.

[0198] The second step is data normalization. The original data is normalized to distinguish between positive and negative indicators. Positive indicators are normalized by the proportion of the maximum value; negative indicators are reverse normalized based on the maximum value.

[0199] The third step is entropy weight calculation. Use the normalized data matrix to calculate the objective entropy weight. The data matrix size is n×m, where n is the number of scenarios and m is the number of indicators. The specific process is as follows Figure 4 As shown, the steps of data processing, entropy weight calculation and comprehensive scoring are shown in detail.

[0200] Calculate the entropy value for each indicator:

[0201]

[0202] Among them, P ij is the normalized value of the jth index, ∈ is a minimum value used to avoid logarithmic calculation errors.

[0203] Calculate objective weights:

[0204]

[0205] The fourth step is to calculate the AHP subjective weight. Construct the AHP judgment matrix and specify the indicator weight ratio w according to expert experience. i , construct the matrix A, where:

[0206]

[0207] Calculate the eigenvalues ​​and eigenvectors, and extract the normalized eigenvector corresponding to the maximum eigenvalue as the subjective weight.

[0208] To perform a consistency check:

[0209]

[0210] If CR ≥ 0.1, the judgment matrix needs to be adjusted.

[0211] Step 5: Calculate the comprehensive weight. Combine the subjective (AHP) and objective (entropy weight) weights to calculate the comprehensive weight:

[0212] W combined =αW objective +(1-α)Wsubjective

[0213] Here α is set to 0.5.

[0214] Step 6: Calculate the comprehensive score. Based on the comprehensive weight and normalized data, calculate the resilience score of each scenario:

[0215]

[0216] Example 2

[0217] Resilience Assessment System

[0218] like Figure 5 As shown, this embodiment provides a multi-energy complementary system optimization and toughness assessment system under extremely high temperature conditions, which implements the above method and includes the following functional modules:

[0219] High temperature impact modeling module: collects environmental meteorological data, equipment operating parameters and historical operating data, builds performance models of energy equipment such as photovoltaic, wind power, thermal power, and energy storage under high temperature conditions, and combines high temperature load prediction, thermal power efficiency degradation, line capacity reduction and other effects to provide accurate basic data support for optimized scheduling.

[0220] Demand response module: Design and simulate user-side load optimization strategies, including cutting unnecessary loads and load transfer strategies. Combined with dynamic electricity prices, guide users to optimize load distribution, achieve demand response goals, and significantly reduce system operating costs and peak pressure by reducing peak loads and smoothing load curves.

[0221] Optimization scheduling module: Based on the constructed multi-energy complementary system model under high temperature conditions, the CPLEX optimization tool integrated in MATLAB is called to generate a scheduling strategy including photovoltaic, wind power, thermal power, hydropower and energy storage, with the goal of minimizing costs and meeting constraints such as system supply and demand balance and line capacity limitations.

[0222] Model solving module: According to the piecewise linearization technology, the interactive scheduling model is established and solved to verify whether the constraints are met; if not, it returns to adjust. The goal is to maximize the system benefits and minimize the load reduction through multi-energy complementarity, and improve the overall economy and resilience of the system.

[0223] Resilience assessment module: Based on the CAHP-ERE method (Comprehensive Hierarchy-Entropy Weight Resilience Evaluation), it quantitatively analyzes the economy, flexibility and safety of the system under extreme high temperature conditions, outputs resilience scores and improvement suggestions for each scenario, and scientifically evaluates the weak links of the system by combining subjective and objective weighting methods, and proposes optimization measures.

[0224] Application Cases

[0225] The present invention verifies its effectiveness through the application case of IEEE-30 node system, and the analysis results include the following:

[0226] Under high temperature conditions, the load peak reached 6045.8MW, the total thermal power output increased by about 1%, and the average line load factor slightly increased to 0.5133. Through optimized scheduling, the total load reduction was reduced from 4235.9MWh to 2235.9MWh, and the system profit increased by about 7%.

[0227] Changes in system economics and stability before and after the introduction of demand response.

[0228] After the introduction of the demand response strategy, the interruptible load of 800MWh and the transferable load of 1200MWh effectively reduced the system pressure, the peak-to-valley difference was reduced from 9.8990 to 9.7383, and the total peak-shaving margin was increased from 0.0120 to 0.0096.

[0229] Based on the CAHP-ERE evaluation method, the resilience score of scenario 1 (without demand response) is 0.2, and that of scenario 2 (introducing demand response) is increased to 0.4329, indicating that the optimization strategy significantly improves the adaptability and flexibility of the system under extreme high temperature conditions. The specific data are shown in Table 1.

[0230] Table 1

[0231]

[0232] Among them, the execution process of each unit can be executed according to the process steps of the control strategy of the integrated multi-energy complementary system of water, wind, light and storage in an extreme high temperature scenario in Example 1, and will not be repeated one by one in this embodiment.

[0233] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0234] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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 1 A device that provides the functions specified in a block or multiple blocks.

[0235] 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 including 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.

[0236] 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 specific implementation methods described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A control strategy for an integrated multi-energy complementary system of water, wind, solar and storage under extreme high temperature scenarios, characterized by The method includes: Obtain basic data of the multi-energy complementary system, including the operating parameters of photovoltaic modules, wind turbines, thermal power units, hydropower stations, energy storage systems, and transmission and distribution facilities; construct an extreme high temperature impact model, and establish a dynamic performance model of the multi-energy complementary system based on the impact of high temperature on the efficiency of photovoltaic modules, thermal power unit efficiency, load peak, transformer and transmission line carrying capacity; Construct a system coordination dispatch objective function with the goal of maximizing the total system revenue, including the operating revenue of thermal power, hydropower, wind power, photovoltaic power and energy storage, and taking into account the demand response subsidy cost and direct load reduction penalty cost; propose a multi-constraint optimization model, the constraints include unit output constraints, power balance constraints, energy constraints of energy storage systems, thermal power ramp constraints and high temperature carrying capacity restrictions of transmission and distribution facilities; use MATLAB to call CPLEX to solve, solve the multi-energy coordinated dispatch plan through mathematical optimization technology, and optimize system operation; The system resilience is evaluated based on the comprehensive hierarchical-entropy weighted resilience evaluation method (CAHP-ERE), combining multi-dimensional indicators such as the average load rate of important lines, load reduction, rated capacity of thermal power, peak-to-valley difference and peak regulation margin, to quantitatively evaluate the operating resilience of the system under extreme high temperatures.

2. According to the control strategy of the water-wind-solar-storage integrated multi-energy complementary system in extreme high temperature scenarios in claim 1, it is characterized in that: The high temperature impact model includes: Temperature effect model of photovoltaic modules: T t PV =c1+c2·T t air +c3·I t +c4·v t Where: P t PV is the output power of the photovoltaic module at time t; P 0,PV is the rated power of photovoltaic power generation equipment under standard conditions; I t ,I 0 is the actual radiation intensity at time t and the radiation intensity under standard conditions; is the temperature power coefficient, which is 0.005 / ℃; T t air , T t PV , T 0,PV is the air temperature at time t, the actual operating temperature of the PV module, and the operating temperature of the PV module under standard conditions (25°C); c1, c2, c3, and c4 are all correlation coefficients of the PV operating temperature; v t is the ground wind speed at time t. Efficiency degradation model of thermal power units: Where ρ is when T i,j Higher than T health_air The efficiency degradation rate of the thermal power unit at . health_air =10℃,ρ=0.0094. Model of system load affected by external temperature: C l is the temperature sensitivity coefficient of power load, T j and Represent the geographical averages of the projected temperature and the historical reference temperature, respectively. Transmission and distribution facilities carrying capacity degradation model: Where I is the allowable current carrying capacity (A); W R is the radiation heat dissipation power per unit length of wire (W / m); W F is the convection heat dissipation power per unit length of wire (W / m); W S is the solar heat absorption power per unit length of conductor (W / m); R t ' is the AC resistance of the wire at the allowable temperature (Ω / m).

3. The control strategy of the integrated water, wind, solar and storage multi-energy complementary system in extreme high temperature scenarios according to claim 1 is characterized in that: The objective function includes: Income from thermal power, hydropower, wind power, photovoltaic power and energy storage grid access: Where: P g , P w , P v , P h and P ESS are the operating income of thermal power, wind turbine, photovoltaic, hydropower and energy storage respectively, C DR and C shed They are demand response subsidy cost and direct load reduction cost respectively. Demand response subsidy cost model, including the transferable load subsidy cost and the curtailable load subsidy cost: Where: β shift is the subsidy cost per MWh of transferable load, P t shift is the power of the transferable load. DR_shed is the subsidy cost per MWh of load reduction, P t DR_shed The power of the interruptible load, T is the total number of dispatch periods. The direct load reduction penalty cost is calculated by coupling the penalty coefficient with the load reduction power: Where: To directly cut load power, To directly cut loads, the base cost is penalized. is the direct load reduction penalty coefficient for node i at time t considering node vulnerability. The mathematical optimization model is implemented by the following steps: Establish the system's objective function and multi-constraint optimization model: Constraint 1: Power balance constraint Where: is the load transfer power that can be transferred to node i at time t, Interruptible load power, To directly cut load power. Constraint 2: Energy Constraint of Energy Storage System Where: is the state of charge (SOC) of the energy storage at time t, δ represents the self-discharge rate of the energy storage, E ESS (MWh) is the energy storage capacity. Constraint 3: Thermal power ramp limit -r i,down ≤P i,t -P i,(t-1) ≤r i,up Where: r i,up and r i,down They are the maximum upward climbing rate and maximum downward climbing rate of thermal power units respectively. Constraint 4: Transmission Line Capacity Limitations Where: B i,j (S) represents the susceptance between nodes i and j, θ i,t ,θ j,t Represents the voltage phase angle of nodes i and j, P i,j,Lmax It represents the per-unit value of the maximum transmission power allowed by the line between nodes i and j. and They represent the capacity attenuation coefficient of the line and transformer between nodes i and j due to high temperature at time t. When the line is at the rated operating temperature, is 1. Similarly, when the transformer is at normal operating temperature or there is no transformer between nodes i and j, is 1. The CPLEX optimization tool is called on the MATLAB platform to solve mixed integer linear programming problems; the optimal coordinated operation plan including photovoltaic, wind power, thermal power, hydropower and energy storage equipment is output to optimize the system operation efficiency.

4. The control strategy of the water-wind-solar-storage integrated multi-energy complementary system in extreme high temperature scenarios according to claim 1 is characterized in that: The resilience assessment index system includes: direct load reduction, average load rate of important lines, rated capacity utilization rate of thermal power, peak-to-valley difference and peak regulation margin. The resilience score is calculated through the comprehensive hierarchy-entropy weight method (CAHP-ERE), which combines the comprehensive weights and normalized results of multi-dimensional indicators to quantify the operational resilience of the system under extreme high temperatures. Peak load regulation strategies based on optimization models include: Prioritize the dispatch of photovoltaic, wind power, hydropower and energy storage resources to reduce dependence on thermal power dispatch; use demand response strategies to reduce peak loads during peak load periods; According to the principle of multi-period load reduction, vulnerable nodes are protected first to reduce the risk of system operation under extreme high temperature. The principle of multi-period load reduction gives priority to protecting vulnerable nodes and reducing the risk of system operation: Where N represents the set of vulnerable points, P t shed It means directly reducing the load and cutting the power.

5. The control strategy of the water-wind-solar-storage integrated multi-energy complementary system under extreme high temperature scenarios according to claim 1 is characterized in that: The resilience assessment comprises the following steps: Resilience parameter calculation, calculation of the operating parameters of the multi-energy complementary system under different scenarios, including direct load reduction, average load rate of important lines, rated capacity utilization of thermal power, peak-to-valley difference and peak regulation margin. Normalization of indicator data: normalization is performed according to the indicator attributes. Positive indicators are normalized according to the proportion of the maximum value, and negative indicators are normalized according to the following formula: in, represents the normalized index value, X represents the original index value, and X max is the maximum value of the indicator. Entropy weight calculation, using the normalized data matrix to calculate the objective entropy weight, the entropy value formula is: In the formula, p ij It represents the proportion of each element in the normalized data matrix to the total of the column; k is a constant, usually Where m is the number of indicators. According to the entropy weight formula, the objective weight is calculated: Where n is the total number of indicators, H j is the entropy value of the jth indicator. AHP subjective weight calculation: construct the AHP judgment matrix based on expert experience, and use eigenvalue decomposition to extract the eigenvector corresponding to the maximum eigenvalue as the subjective weight. Perform consistency check: Among them, λ max is the maximum eigenvalue, RI is the random consistency index, and if CR<0.1, the matrix consistency is passed. Comprehensive weight calculation, taking into account both subjective weight and objective weight, the comprehensive weight formula is: Among them, α is the fusion coefficient of subjective weight and objective weight. The comprehensive resilience score is calculated based on the comprehensive weight and normalized index for each scenario: Among them, S i Score the resilience of the i-th scenario.

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