A water, wind, light and storage integrated multi-energy complementary system regulation strategy under an extremely high temperature scenario

Abstract

The application discloses a water, wind, light and storage integrated multi-energy complementary system regulation strategy under an extreme high-temperature scene, and relates to the technical field of multi-energy complementary system optimization scheduling and resilience evaluation. The method comprises the following steps: acquiring meteorological and equipment parameters, and constructing performance models of photovoltaic power, wind power, thermal power and power transmission facilities under an extreme high-temperature condition; based on the minimum operation cost of the multi-energy complementary system, a target function is established, and constraint conditions are established according to supply-demand balance, equipment operation, peak shaving margin and load reduction; combined with a demand response strategy, the system is optimized and scheduled through an optimization tool to obtain a multi-energy collaborative operation scheme; a resilience evaluation model based on a comprehensive hierarchical-entropy weight method is used to quantitatively analyze the system resilience from the dimensions of economy, flexibility and safety, and output resilience scores and improvement suggestions. The application effectively improves the economic operation efficiency and anti-risk ability of the multi-energy complementary system under the extreme high-temperature condition, and provides scientific support for the safe and stable operation of the multi-energy complementary system.

Description

Technical Field

[0001] This invention relates to the field of power system optimization scheduling and resilience assessment technology, specifically to a control strategy for an integrated multi-energy complementary system of hydro, wind, solar and energy 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 Technology

[0002] Against the backdrop of global energy transition and escalating climate change, the frequency and intensity of extreme heat events have increased significantly. This poses enormous challenges to power system operation, primarily manifested in the following aspects: Surge in load: Air conditioning load increases significantly under extreme heat conditions, leading to record-breaking peak system loads and exacerbating pressure on supply-demand balance. Degraded equipment performance: High temperatures cause efficiency degradation in thermal power units and reduce the carrying capacity of transmission lines and transformers, increasing system operational risks. Increased volatility in renewable energy sources: High temperatures negatively impact the power generation efficiency of photovoltaic modules, further exacerbate the uncertainty of wind power, and significantly increase the difficulty of renewable energy integration and dispatch.

[0003] To address these issues, traditional power system optimization and dispatching methods, such as linear programming, genetic algorithms, and particle swarm optimization, while providing certain solutions under normal circumstances, are limited. This invention discloses a control strategy for an integrated hydro-wind-solar-storage multi-energy complementary system under extreme high-temperature scenarios, belonging to the field of multi-energy complementary system optimization, dispatching, and resilience assessment. Specific steps include: acquiring meteorological and equipment parameters; constructing performance models of photovoltaic, wind power, thermal power, and transmission facilities under extreme high temperatures; setting the objective function to minimize the operating cost of the multi-energy complementary system, and establishing conditions based on supply-demand balance, equipment operation, peak-shaving margin, and load reduction constraints; combining dynamic electricity prices and demand response strategies, using the MATLAB CPLEX optimization tool to optimize the dispatching to obtain an energy collaborative operation scheme; and then using a comprehensive hierarchical-entropy weighted resilience assessment model to quantitatively analyze the system's resilience from dimensions such as economy, flexibility, and safety, outputting scores and recommendations. This invention improves the economic operation and risk resistance of the system under extreme high temperatures, providing scientific support for its safe and stable operation. While traditional methods have achieved optimization effects, they often suffer from the following shortcomings under special scenarios such as extreme high temperatures: Lack of extreme climate modeling: Traditional methods do not fully consider the profound impact of high temperatures on equipment performance and load characteristics, making it difficult to accurately characterize the system's operating characteristics under high temperatures. Limited optimization performance: Existing algorithms are prone to getting trapped in local optima and cannot effectively solve the scheduling problem of complex multi-energy complementary systems, especially when the system's flexibility and resilience decrease under high-temperature conditions. Therefore, this paper proposes a control strategy for an integrated hydro-wind-solar-storage multi-energy complementary system under extreme high-temperature scenarios. Through precise modeling and optimized scheduling strategies, the system's resilience under extreme conditions is improved. Summary of the Invention

[0004] This invention addresses the shortcomings of traditional methods in simulating the impact of extreme high temperatures on equipment performance, the lack of flexibility in demand response strategies, and the incomplete evaluation of system resilience. The purpose of this invention is to provide a control strategy for an integrated hydro-wind-solar-storage multi-energy complementary system under extreme high-temperature scenarios. Through high-temperature impact modeling, mathematical optimization scheduling, and resilience index assessment, an optimized operation scheme is constructed for high-temperature scenarios, thereby significantly improving the system's resilience, flexibility, and risk resistance. Table 1 shows a comparison of the system's resilience score, load shedding, and peak-shaving margin; the optimized strategy significantly improves the system's resilience.

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

[0006] 1. High Temperature Impact Modeling: To address issues such as the temperature effect of photovoltaic modules, degradation of thermal power efficiency, increase in peak load, and decrease in the carrying capacity of power transmission and distribution facilities, a dynamic impact model of high temperature on multi-energy complementary systems is constructed to quantify the impact of extreme high temperatures on system performance.

[0007] 2. Construction of the coordination and scheduling objective function: With the goal of maximizing the net revenue of the system, the functions comprehensively consider the revenue of thermal power, hydropower, wind power, photovoltaic power and energy storage, as well as the load reduction penalty cost and demand response subsidy cost.

[0008] 3. Constraint settings: including unit output, power balance, energy constraints of energy storage system, and restrictions on the carrying capacity of transmission facilities due to high temperature.

[0009] 4. Optimization and Solution: Using MATLAB to call the CPLEX tool, the objective function is solved, and a multi-energy coordinated scheduling scheme is output to optimize the system's economy and flexibility.

[0010] 5. Resilience Assessment: Based on the Comprehensive Hierarchical-Entropy Weighted Resilience Assessment Method (CAHP-ERE), the system is quantitatively assessed from multiple dimensions such as economy, flexibility, security and topological vulnerability.

[0011] The key advantage of this invention lies in its comprehensive high-temperature impact modeling, which accurately characterizes the dynamic properties of multi-energy complementary systems under extreme high-temperature conditions. It effectively covers key influencing factors such as photovoltaic efficiency degradation, thermal power efficiency decline, load surges, and reduced transmission facility capacity, providing reliable data support for optimized scheduling. Furthermore, by optimizing the joint scheduling of thermal, hydro, wind, photovoltaic, and energy storage, this invention significantly improves system resource utilization efficiency, alleviates supply-demand imbalances under extreme high temperatures, reduces operating costs, and enhances system resilience and flexibility. More importantly, this invention proposes a method based on Comprehensive Hierarchical-Entropy Weighted Resilience Assessment (CAHP-ERE), which can quantitatively assess system resilience from multiple dimensions such as economy, flexibility, and stability, identify weak points, and provide targeted optimization guidance, helping the system achieve safer, more stable, and more efficient operation under high-temperature conditions.

[0012] This invention is achieved through the following technical solution:

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

[0014] High-temperature impact modeling: Acquire system equipment and meteorological data to construct a dynamic impact model of the multi-energy complementary system under extreme high-temperature conditions, including the temperature effect of photovoltaic modules, thermal power efficiency degradation, surge in peak load, and decline in the 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] In the formula: P t PV P represents the output power of the photovoltaic module at time t. 0,PV The rated power of photovoltaic power generation equipment under standard conditions; I t I 0 Let be the actual radiation intensity at time t, and the radiation intensity under standard conditions; The temperature power coefficient is taken as 0.005 / ℃; T t air T t PV T 0,PVLet be the air temperature at time t, the actual operating temperature of the photovoltaic module, and the operating temperature of the photovoltaic module under standard conditions (25℃); c1, c2, c3, and c4 are all correlation coefficients of the photovoltaic operating temperature; v t Let t be the ground wind speed.

[0019] Thermal power unit efficiency degradation model:

[0020]

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

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

[0023]

[0024] C l T is the temperature sensitivity coefficient of the power load. j and These represent the geographical average of the estimated temperature and the historical reference temperature, respectively.

[0025] Power transmission and distribution facility carrying capacity degradation model:

[0026]

[0027] Where I represents the allowable current carrying capacity (A); W R The radiative heat dissipation power per unit length of conductor (W / m); W F The convective heat dissipation power per unit length of conductor (W / m); W S R represents the solar heat absorption power per unit length of conductor (W / m); t 'The AC resistance (Ω / m) of the conductor at the permissible temperature.

[0028] Demand Response Strategy Design: This invention considers demand response loads primarily based on electricity price incentives, encompassing two main categories: transferable loads and reduceable loads. Transferable loads are highly adjustable, generally uninterrupted but with the ability to shift electricity usage periods across the board, such as electric vehicle charging and industrial loads. Reduceable loads refer to loads that can be temporarily disconnected or reduced in emergency situations, such as temperature-controlled loads and lighting systems.

[0029] The system's subsidy cost for transferable loads, C shift for

[0030]

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

[0032] The system's subsidy cost for load reduction is

[0033]

[0034] In the formula: c int P represents the subsidy cost per MWh of load reduction that the system can achieve in demand response. t int The power that can be reduced during period t; U i,t Let S be the load reduction state variable for node i. i,t Let N be the capacity that can be reduced for the i-th node in time period t, and N be the total number of nodes that can be reduced.

[0035] Multi-energy complementary system optimal scheduling model: Construct an optimal scheduling model for a multi-energy complementary system under extreme high temperature conditions, with the objective of maximizing the system's net benefit.

[0036] The operational objective of a multi-energy complementary system is to maximize the total system revenue, and the objective function is as follows:

[0037] max F = F income -F cost =P g +P w +P v +P h +P ESS -C DR -C shed

[0038] In the formula: P g P w P v P h and P ESS Revenue from the operation of thermal power, wind turbines, photovoltaic power, hydropower, and energy storage, respectively. C DR and C shed These are demand response subsidy costs and direct load shedding costs, respectively.

[0039] Energy storage system operating benefits and costs

[0040] P ESS =I ESS -C ESS

[0041]

[0042] In the formula: For the feed-in tariff, η c ηd These represent the energy storage charging and discharging efficiencies, P0. t char P t dis These are the energy storage charging and discharging power at time t, respectively.

[0043] Operating benefits and costs of thermal power units

[0044] P g =I g -C g

[0045]

[0046] C g =C coal +C ss

[0047]

[0048] In the formula: C g For thermal power plant operating costs, I g For the revenue from thermal power grid connection, Let p be the grid-connected power of the i-th thermal power unit at time t. t,grid Let C be the on-grid electricity price for thermal power at time t. coal C ss These are the unit's coal consumption cost and start-up / shutdown cost, respectively; a i b i c i These are the secondary, primary, and constant coefficients of the i-th unit, respectively; The startup cost of the i-th unit. The downtime cost for unit i. These are the start-up and shutdown status variables of the unit.

[0049] Clean energy unit operating benefit model

[0050]

[0051]

[0052] In the formula: p w p v p h The on-grid electricity prices for wind power, solar power, and hydropower are respectively, P t w P t v P t h θ represents the output power of wind power, photovoltaic power, and hydropower at time t, respectively; w θ v θh These are the penalty coefficients for wind curtailment, solar curtailment, and hydropower curtailment, respectively. These represent the power of wind, solar, and hydropower curtailment at time t, respectively.

[0053] Demand response subsidy costs

[0054] C DR =C shift +C int

[0055]

[0056] In the formula: c shift The subsidy cost per MWh of transferable load, Let c be the power of the load that node i can transfer at time t. int The system provides subsidies for load reduction during demand response. The power of interruptible loads, where T is the total number of scheduling periods.

[0057] Direct load reduction penalty cost

[0058]

[0059] In the formula: To directly reduce load power, To directly reduce the load and penalize the base cost, Let t be the direct load reduction penalty coefficient for node i at time t, taking into account node vulnerability.

[0060] Constraints:

[0061] Unit output constraints

[0062]

[0063] In the formula: This represents the maximum theoretical output of the hydropower at time t. These are the charging and discharging state variables of energy storage, respectively. These are the upper and lower limits of the charging power, respectively. These represent the upper and lower limits of the discharge power, respectively.

[0064] Thermal power unit ramping constraints

[0065] -r i,down ≤P i,t -P i,(t-1) ≤r i,up

[0066] In the formula: r i,up and r i,downThese are the maximum upward and downward climbing rates of the thermal power unit, respectively.

[0067] Energy constraints and charge / discharge constraints of energy storage systems

[0068]

[0069]

[0070] In the formula: Let E be the state of charge (SOC) of the stored energy at time t, where δ represents the self-discharge rate of the stored energy. ESS (MWh) represents the energy storage capacity, S max S min These are the upper and lower limits of the state of charge of energy storage, respectively; These represent the states of charge of the stored energy at the beginning and end of the time interval, respectively. These represent the minimum and maximum charging power of the energy storage device, respectively. These represent the minimum and maximum discharge power of the energy storage device, respectively.

[0071] Line transmission capacity constraints

[0072]

[0073] In the formula: B i,j (S) represents the susceptance between nodes i and j, θ i,t θ j,t P represents the voltage phase angles at nodes i and j, respectively. i,j,Lmax This represents the per-unit value of the maximum allowable transmission power of the line between nodes i and j. and Let represent the capacity attenuation coefficients of the line and transformer between nodes i and j at time t due to high temperature, respectively. When the line is at its rated operating temperature, The value is 1. Similarly, when the transformer is at its normal operating temperature or there is no transformer between nodes i and j, The value is 1.

[0074] Power balance constraints

[0075]

[0076] In the formula: Let be the load transfer power that node i can transfer at time t. Interruptible load power, This is to directly reduce the load power.

[0077] Alternate constraints

[0078]

[0079] In the formula: Let λ be the maximum available grid power of unit i at time t, and λ be the reserve factor.

[0080] Demand response constraints

[0081] Within a scheduling cycle, the total load on each node of the system remains unchanged before and after the demand response.

[0082]

[0083] In the formula: Let be the maximum value of the total load that node i can transfer at time t. The maximum amount of load that can be reduced.

[0084] Optimization Solution: The rationality of the established model is verified by analyzing the power system optimization scheduling results for 96 time periods before the current date. Based on the MATLAB 2022a platform, the CPLEX solver in MATLAB is used to solve the multi-energy complementary system optimization model, outputting the optimal operation schemes for photovoltaic, wind power, thermal power, hydropower, and energy storage.

[0085] A Resilience Assessment Method for Multi-Energy Complementary Systems: In the context of the current global energy transition and climate change, multi-energy complementary systems are playing an increasingly important role in providing reliability and sustainability. To effectively assess the resilience of these systems under different scenarios, this paper proposes a Combined Analytic Hierarchy Process-Entropy Resilience Evaluation Method (CAHP-ERE). The CAHP-ERE method integrates the subjective Analytic Hierarchy Process (AHP) with the objective entropy weight method to evaluate the resilience performance of multi-energy complementary systems under various scenarios. This method combines expert judgment with objective data-driven approaches and is suitable for system resilience evaluation under comprehensive indicator systems.

[0086] Secondly, this invention also provides an optimization and resilience assessment system for multi-energy complementary systems under extreme high-temperature conditions. This system implements the aforementioned methods and aims to solve complex problems in the operation of energy systems under extreme high-temperature conditions, improving their operational economy, stability, and stress resistance. The system includes the following functional modules:

[0087] High Temperature Impact Modeling Unit: Used to collect environmental meteorological data and equipment parameters to construct performance models of energy equipment such as photovoltaic, wind power, and thermal power under high temperature conditions.

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

[0089] Optimization and solution unit: Based on MATLAB, CPLEX is used to solve the model and generate optimized scheduling results.

[0090] Demand Response Execution Unit: Used to design and simulate load reduction and load shifting strategies on the user side, and combine dynamic electricity pricing to achieve response objectives.

[0091] Toughness assessment unit: Based on the CAHP-ERE method, quantitatively analyze the toughness index of multi-energy complementary systems and output toughness assessment results.

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

[0093] This invention's system possesses 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 temperatures on multi-energy complementary systems, and provide reliable data support for dispatch optimization. By jointly optimizing the operation of multiple energy sources, it improves the system's economy and flexibility, significantly reduces the risk of supply-demand imbalance under high-temperature conditions, and ensures the system's safe operation. Employing a combined subjective and objective CAHP-ERE method, it quantifies the system's resilience from three aspects: economy, flexibility, and safety, and identifies weak links in extreme high-temperature scenarios. By optimizing electricity consumption timing to reduce peak loads and smooth load curves, it reduces grid operation pressure and improves overall stability and economy. Combining optimization solution and result visualization technologies, it generates intuitive and easily interpretable operation plans and resilience scores, providing scientific support for grid dispatch and strategy formulation. Attached Figure Description

[0094] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0095] Figure 1 This is an overall flowchart of the control strategy for an integrated multi-energy complementary system of water, wind, solar and storage under extreme high temperature scenarios according to the present invention.

[0096] Figure 2 This is a schematic diagram of the topology of the multi-energy complementary system in Embodiment 1 of the present invention;

[0097] Figure 3 This is a comparison chart of direct load shedding data before and after optimization in Embodiment 1 of the present invention;

[0098] Figure 4 This is a detailed flowchart of the control strategy for an integrated multi-energy complementary system of water, wind, solar and storage under extreme high-temperature scenarios according to the present invention.

[0099] Figure 5 This is a functional module structure diagram of a multi-energy complementary system optimization and toughness assessment system under extreme high temperature conditions according to the present invention. Detailed Implementation

[0100] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for explanation only and are not intended to limit the invention. This invention proposes a control strategy for an integrated hydro-wind-solar-storage multi-energy complementary system under extreme high-temperature scenarios, comprising three main stages: high-temperature impact modeling, multi-energy complementary system optimization scheduling, and resilience assessment. Specifically, the first step involves constructing a performance model of equipment under high-temperature conditions to provide underlying data support for subsequent optimization; the second step is the optimization part, mainly using MATLAB to call CPLEX to achieve multi-energy complementary system scheduling optimization; the third step is resilience assessment, using the CAHP-ERE method to quantify the system's resilience performance under different scenarios. This invention, through a combination of optimization and assessment, solves the problems of economy, flexibility, and safety in system operation under extreme high-temperature conditions, and provides scientific support for power grid planning and operation.

[0101] Example 1

[0102] Method and Flow

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

[0104] S1: Modeling the Influence of High Temperature Conditions

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

[0106] The temperature effect of photovoltaic modules is modeled, and the output power of photovoltaics under high temperature conditions is calculated using the temperature power coefficient.

[0107] The efficiency degradation model of thermal power units is modeled using a piecewise linear function to describe the efficiency decline under high-temperature conditions.

[0108] Modeling of the carrying capacity of power transmission and distribution facilities and analyzing the impact of high temperature on line current carrying capacity and transformer operating capacity using heat balance equations.

[0109] System load modeling, combined with the temperature sensitivity coefficient of air conditioning load and historical meteorological data, predicts peak load under extreme high temperatures.

[0110] S2: Demand Response Optimization

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

[0112] Reduce unnecessary loads, such as residential air conditioning and some industrial loads.

[0113] Dynamic electricity pricing guides users to shift peak loads to off-peak hours.

[0114] The effects of demand response reduction were simulated and incorporated into the scheduling model.

[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 operating cost, and the constraints include power output range, supply and demand balance, line capacity, and peak-shaving margin. Specific steps include:

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

[0118] The objective function is solved by calling the CPLEX optimization tool integrated in MATLAB, generating a multi-energy coordinated operation scheme, including scheduling strategies for photovoltaic, wind power, thermal power, hydropower and energy storage.

[0119] The output optimization results include generation costs, peak-shaving capacity, and load balance status. Dispatch optimization results show that under high-temperature conditions, by rationally allocating photovoltaic, wind power, thermal power, and energy storage resources, the direct load shedding can be effectively reduced. Specific changes are as follows: Figure 3 As shown.

[0120] S4: Resilience Assessment

[0121] Figure 4 This is a detailed flowchart of the toughness assessment process for this invention, illustrating the complete workflow from toughness parameter calculation to comprehensive scoring. The CAHP-ERE (Comprehensive Hierarchical-Entropy Weighted Toughness Assessment) method is employed to quantitatively analyze the system's economy, flexibility, and safety under extreme high-temperature conditions. Specific steps:

[0122] Indicator selection includes direct load shedding, load rate of important lines, peak-valley difference, and peak-shaving margin.

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

[0124] Evaluation score: Calculate the system's resilience score under different scenarios, analyze weaknesses, and propose improvement suggestions.

[0125] In this embodiment, the modeling of the influence of high-temperature conditions in part S1 is completed through the following steps:

[0126] High-Temperature Impact Modeling: Acquire system equipment and meteorological data to construct a dynamic impact model of the multi-energy complementary system under extreme high-temperature conditions, including the temperature effect of photovoltaic modules, thermal power efficiency degradation, surge in peak load, and decrease in the carrying capacity of transmission facilities. The system topology is as follows: Figure 2 As 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] In the formula: P t PV P represents the output power of the photovoltaic module at time t. 0,PV The rated power of photovoltaic power generation equipment under standard conditions; I t I 0 Let be the actual radiation intensity at time t, and the radiation intensity under standard conditions; The temperature power coefficient is taken as 0.005 / ℃; T t air T t PV T 0,PV Let be the air temperature at time t, the actual operating temperature of the photovoltaic module, and the operating temperature of the photovoltaic module under standard conditions (25℃); c1, c2, c3, and c4 are all correlation coefficients of the photovoltaic operating temperature; v t Let t be the ground wind speed.

[0131] Thermal power unit efficiency degradation model:

[0132]

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

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

[0135]

[0136] C l T is the temperature sensitivity coefficient of the power load. j and These represent the geographical average of the estimated temperature and the historical reference temperature, respectively.

[0137] Power transmission and distribution facility carrying capacity degradation model:

[0138]

[0139] Where I represents the allowable current carrying capacity (A); W R The radiative heat dissipation power per unit length of conductor (W / m); W F The convective heat dissipation power per unit length of conductor (W / m); W S R represents the solar heat absorption power per unit length of conductor (W / m); t 'The AC resistance (Ω / m) of the conductor at the permissible temperature.

[0140] In this embodiment, the demand response optimization in part S2 is accomplished through the following strategy:

[0141] Demand Response Strategy Design: This invention considers demand response loads primarily based on electricity price incentives, encompassing two main categories: transferable loads and reduceable loads. Transferable loads are highly adjustable, generally uninterrupted but with the ability to shift electricity usage periods across the board, such as electric vehicle charging and industrial loads. Reduceable loads refer to loads that can be temporarily disconnected or reduced in emergency situations, such as temperature-controlled loads and lighting systems.

[0142] The system's subsidy cost for transferable loads, C shift for

[0143]

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

[0145] The system's subsidy cost for load reduction is

[0146]

[0147] In the formula: c int P represents the subsidy cost per MWh of load reduction that the system can achieve in demand response. t int The power that can be reduced during period t; U i,t Let S be the load reduction state variable for node i. i,t Let N be the capacity that can be reduced for the i-th node in time period t, and N be the total number of nodes that can be reduced.

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

[0149] Multi-energy complementary system optimal scheduling model: Construct an optimal scheduling model for a multi-energy complementary system under extreme high-temperature conditions, with the objective of maximizing the system's net benefit. 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] In the formula: P g P w P v P h and P ESS Revenue from the operation of thermal power, wind turbines, photovoltaic power, hydropower, and energy storage, respectively. C DR and C shed These are demand response subsidy costs and direct load shedding costs, respectively.

[0152] Energy storage system operating benefits and costs

[0153] P ESS =I ESS -C ESS

[0154]

[0155] In the formula: For the feed-in tariff, η c η d These represent the energy storage charging and discharging efficiencies, P0. t char P t dis These are the energy storage charging and discharging power at time t, respectively.

[0156] 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] In the formula: Cg For thermal power plant operating costs, I g For the revenue from thermal power grid connection, Let p be the grid-connected power of the i-th thermal power unit at time t. t,grid Let C be the on-grid electricity price for thermal power at time t. coal C ss These are the unit's coal consumption cost and start-up / shutdown cost, respectively; a i b i c i These are the secondary, primary, and constant coefficients of the i-th unit, respectively; The startup cost of the i-th unit. The downtime cost for unit i. These are the start-up and shutdown status variables of the unit.

[0162] Clean energy unit operating benefit model

[0163]

[0164] In the formula: p w p v p h The on-grid electricity prices for wind power, solar power, and hydropower are respectively, P t w P t v P t h θ represents the output power of wind power, photovoltaic power, and hydropower at time t, respectively; w θ v θ h These are the penalty coefficients for wind curtailment, solar curtailment, and hydropower curtailment, respectively. These represent the power of wind, solar, and hydropower curtailment at time t, respectively.

[0165] Demand response subsidy costs

[0166] C DR =C shift +C int

[0167]

[0168] In the formula: c shift The subsidy cost per MWh of transferable load, Let c be the power of the load that node i can transfer at time t. int The system provides subsidies for load reduction during demand response. The power of interruptible loads, where T is the total number of scheduling periods.

[0169] Direct load reduction penalty cost

[0170]

[0171] In the formula: To directly reduce load power, To directly reduce the load and penalize the base cost, Let t be the direct load reduction penalty coefficient for node i at time t, taking into account node vulnerability.

[0172] Constraints:

[0173] Unit output constraints

[0174]

[0175] In the formula: This represents the maximum theoretical output of the hydropower at time t. These are the charging and discharging state variables of energy storage, respectively. These are the upper and lower limits of the charging power, respectively. These represent the upper and lower limits of the discharge power, respectively.

[0176] Thermal power unit ramping constraints

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

[0178] In the formula: r i,up and r i,down These are the maximum upward and downward climbing rates of the thermal power unit, respectively.

[0179] Energy constraints and charge / discharge constraints of energy storage systems

[0180]

[0181] In the formula: Let E be the state of charge (SOC) of the stored energy at time t, where δ represents the self-discharge rate of the stored energy. ESS (MWh) represents the energy storage capacity, S max S min These are the upper and lower limits of the state of charge of energy storage, respectively; These represent the states of charge of the stored energy at the beginning and end of the time interval, respectively. These represent the minimum and maximum charging power of the energy storage device, respectively. These represent the minimum and maximum discharge power of the energy storage device, respectively.

[0182] Line transmission capacity constraints

[0183]

[0184] In the formula: B i,j (S) represents the susceptance between nodes i and j, θ i,t θ j,t P represents the voltage phase angles at nodes i and j, respectively. i,j,Lmax This represents the per-unit value of the maximum allowable transmission power of the line between nodes i and j. and Let represent the capacity attenuation coefficients of the line and transformer between nodes i and j at time t due to high temperature, respectively. When the line is at its rated operating temperature, The value is 1. Similarly, when the transformer is at its normal operating temperature or there is no transformer between nodes i and j, The value is 1.

[0185] Power balance constraints

[0186]

[0187] In the formula: Let be the load transfer power that node i can transfer at time t. Interruptible load power, This is to directly reduce the load power.

[0188] Alternate constraints

[0189]

[0190] In the formula: Let λ be the maximum available grid power of unit i at time t, and λ be the reserve factor.

[0191] Demand response constraints

[0192] Within a scheduling cycle, the total load on each node of the system remains unchanged before and after the demand response.

[0193]

[0194] In the formula: Let be the maximum value of the total load that node i can transfer at time t. The maximum amount of load that can be reduced.

[0195] Optimization Solution: The rationality of the established model is verified by analyzing the power system optimization scheduling results for 96 time periods before the current date. Based on the MATLAB 2022a platform, the CPLEX solver in MATLAB is used to solve the multi-energy complementary system optimization model, outputting the optimal operation schemes for photovoltaic, wind power, thermal power, hydropower, and energy storage.

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

[0197] The first step is to calculate resilience parameters. This involves calculating system operating parameters under different scenarios, including five indicators: average load factor of important lines, load shedding, rated capacity of thermal power plants, peak-to-valley difference, and peak-shaving margin.

[0198] The second step is data normalization. The raw data is normalized, distinguishing between positive and negative indicators. Positive indicators are normalized based on their proportion of the maximum value; negative indicators are normalized inversely based on the maximum value.

[0199] The third step is entropy weight calculation. Using the normalized data matrix, the objective entropy weight is calculated. The data matrix is ​​n×m in size, where n is the number of scenarios and m is the number of indicators. The specific process is as follows: Figure 4 As shown, it details each step of data processing, entropy weight calculation, and comprehensive scoring.

[0200] Calculate the entropy value for each indicator:

[0201]

[0202] Among them, P ij It is the normalized value of the j-th index. ∈ is a very small value used to avoid errors in logarithmic calculations.

[0203] Calculate objective weights:

[0204]

[0205] Step 4: Calculation of AHP subjective weights. Construct the AHP judgment matrix and specify the weight ratio w of the indicators based on expert experience. i Construct matrix A, where:

[0206]

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

[0208] Perform a consistency check:

[0209]

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

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

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

[0213] Where α is taken as 0.5.

[0214] Step 6: Comprehensive Score Calculation. Based on the comprehensive weights and normalized data, calculate the resilience score for each scenario:

[0215]

[0216] Example 2

[0217] Resilience assessment system

[0218] like Figure 5 As shown, this embodiment provides a system for optimizing and assessing the resilience of a multi-energy complementary system under extreme high-temperature conditions. This system implements the above-mentioned method and includes the following functional modules:

[0219] High Temperature Impact Modeling Module: Collects environmental meteorological data, equipment operating parameters, and historical operating data to construct performance models of energy equipment such as photovoltaic, wind power, thermal power, and energy storage under high temperature conditions. Combined with effects such as high temperature load forecasting, thermal power efficiency degradation, and line capacity reduction, it provides accurate basic data support for optimized scheduling.

[0220] Demand Response Module: Designs and simulates user-side load optimization strategies, including reducing unnecessary loads and load shifting strategies. Combined with dynamic electricity pricing, it guides users to optimize load distribution, achieving demand response objectives. By reducing peak loads and smoothing the load curve, it significantly reduces system operating costs and peak pressure.

[0221] Optimized 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 scheduling strategies that include photovoltaic, wind power, thermal power, hydropower and energy storage. The goal is to minimize costs and meet constraints such as system supply and demand balance and line capacity limitations.

[0222] Model Solving Module: Using piecewise linearization techniques, an interactive scheduling model is established and solved to verify whether the constraints are met; if not, adjustments are returned. The goal is to maximize system benefits through multi-energy complementarity and minimize load shedding, thereby improving the overall economy and resilience of the system.

[0223] Resilience Assessment Module: Based on the CAHP-ERE method (Comprehensive Hierarchical-Entropy Weighted Resilience Assessment), this module quantifies and analyzes the system's economy, flexibility, and safety under extreme high-temperature conditions, outputs resilience scores and improvement suggestions for each scenario, and scientifically assesses the system's weaknesses by combining subjective and objective weighting methods, proposing optimization strategies.

[0224] Application Cases

[0225] This invention validates its effectiveness through an application case study of the IEEE-30 node system. The analysis results include the following:

[0226] Under high-temperature conditions, the peak load reached 6045.8MW, the total output of thermal power increased by about 1%, the average load factor of the line rose slightly to 0.5133, and 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 economy and stability before and after the introduction of demand response.

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

[0229] Based on the CAHP-ERE evaluation method, the resilience score of scenario 1 (no demand response) is 0.2, and that of scenario 2 (introducing demand response) is improved to 0.4329, indicating that the optimization strategy significantly improves the adaptability and flexibility of the system under high temperature extreme conditions. Specific data are shown in Table 1.

[0230] Table 1

[0231]

[0232] The execution process of each unit can be carried out according to the control strategy process of an integrated multi-energy complementary system of water, wind, solar and storage under extreme high temperature scenario in Example 1. It will not be described in detail in this example.

[0233] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0234] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0235] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0236] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. The specific embodiments described above further illustrate the purpose, technical solution and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A control strategy for an integrated multi-energy complementary system combining water, wind, solar, and energy storage under extreme high-temperature scenarios, characterized in that... include: Acquire basic data for the multi-energy complementary system, including operating parameters of photovoltaic modules, wind turbines, thermal power units, hydropower stations, energy storage systems, and power 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 photovoltaic module efficiency, thermal power unit efficiency, peak load, transformer and transmission line carrying capacity; A system coordination and scheduling objective function is constructed, aiming to maximize the total system revenue, including the operating revenue of thermal power, hydropower, wind power, photovoltaic power, and energy storage, and taking into account demand response subsidy costs and direct load shedding penalty costs. A multi-constraint optimization model is proposed, with constraints including unit output constraints, power balance constraints, energy storage system energy constraints, thermal power ramping constraints, and high-temperature load-bearing capacity limitations of transmission and distribution facilities. The model is solved using MATLAB with CPLEX, employing mathematical optimization techniques to find a multi-energy coordinated scheduling scheme and optimize system operation. The system resilience is assessed using a comprehensive hierarchical-entropy weighted resilience assessment method, which combines multiple dimensions such as average load rate of important lines, load shedding, rated capacity of thermal power plants, peak-valley difference, and peak regulation margin to quantitatively assess the system's operational resilience under extreme high temperatures. High Temperature Effect Model: Temperature effect model of photovoltaic modules: In the formula: P t PV P represents the output power of the photovoltaic module at time t. 0,PV The rated power of photovoltaic power generation equipment under standard conditions; I t I 0 Let be the actual radiation intensity at time t, and the radiation intensity under standard conditions; The temperature power coefficient is taken as 0.005 / ℃; T t air T t PV T 0,PV Let be the air temperature at time t, the actual operating temperature of the photovoltaic module, and the operating temperature of the photovoltaic module under standard conditions (25℃); c1, c2, c3, and c4 are all correlation coefficients of the photovoltaic operating temperature; v t Let t be the ground wind speed; Thermal power unit efficiency degradation model: Where ρ is the value when T i,j Higher than T health_air The efficiency degradation rate of thermal power units at that time, of which T health_air =10℃, ρ=0.0094; Model of system load affected by external temperature: C l T is the temperature sensitivity coefficient of the power load. j and These represent the geographical average of the estimated temperature and the historical reference temperature, respectively. Power transmission and distribution facility carrying capacity degradation model: Where I is the allowable current carrying capacity (A); W R The radiative heat dissipation power per unit length of conductor (W / m); W F The convective heat dissipation power per unit length of conductor (W / m); W S R' represents the solar heat absorption power per unit length of conductor (W / m); t The AC resistance (Ω / m) of the conductor at the allowable temperature; The resilience assessment index system includes: direct load shedding, average load rate of important lines, rated capacity utilization rate of thermal power plants, peak-valley difference, and peak-shaving margin; The resilience score is calculated using a comprehensive hierarchical-entropy weighting method, which combines the comprehensive weights of multi-dimensional indicators with normalized results to quantify the operational resilience of the system under extreme high temperatures. Peak-shaving strategies based on optimization models include: Prioritize the dispatch of photovoltaic, wind power, hydropower, and energy storage resources to reduce reliance on thermal power dispatch; implement peak shaving and valley filling strategies during peak load periods. Based on the principle of multi-period load reduction, vulnerable nodes are prioritized for protection to reduce the risk of system operation under extreme high temperatures. In the formula, N represents the set of vulnerable points, and P t shed This indicates direct load shedding to reduce power.

2. The control strategy for an integrated multi-energy complementary system of water, wind, solar, and storage under extreme high-temperature scenarios as described in claim 1, characterized in that, The objective function includes: Revenue from grid connection of thermal power, hydropower, wind power, photovoltaic power, and energy storage: In the formula: P g P w P v P h and P ESS Revenue from the operation of thermal power, wind turbines, photovoltaic power, hydropower, and energy storage, respectively. C DR and C shed These are demand response subsidy costs and direct load shedding costs, respectively. Demand response subsidy cost model, including transferable load subsidy cost and reduceable load subsidy cost: Where: β shift P is the subsidy cost per MWh of transferable load. t shift For the power of the transferable load, β DR_shed To reduce the subsidy cost per MWh of load, P t DR_shed The power of interruptible loads, where T is the total number of scheduling periods; The direct load shedding penalty cost is calculated by coupling the penalty coefficient with the load shedding power: In the formula: To directly reduce load power, To directly reduce load and penalize basic costs, Let the direct load shedding penalty coefficient be given to node i at time t, taking into account node vulnerability; The mathematical optimization model is implemented through the following steps: Establish the objective function and multi-constraint optimization model of the system: Constraint 1: Power Balance Constraint In the formula: Let the load transfer power be the power that node i can transfer at time t. Interruptible load power, To directly reduce load power; Constraint 2: Energy Constraints of Energy Storage Systems In the formula: Let E be the state of charge (SOC) of the stored energy at time t, where δ represents the self-discharge rate of the stored energy. ESS For energy storage capacity; Constraint 3: Ramp-up Limitation for Thermal Power Plants -r i,down ≤P i,t -P i,(t-1) ≤r i,up In the formula: r i,up and r i,down These are the maximum upward and downward ramp rates of the thermal power unit, respectively. Constraint 4: Transmission line capacity limitation In the formula: B i,j (S) represents the susceptance between nodes i and j, θ i,t θ j,t P represents the voltage phase angles at nodes i and j, respectively. i,j,Lmax This represents the per-unit value of the maximum allowable transmission power of the line between nodes i and j. and Let represent the capacity attenuation coefficients of the line and transformer between nodes i and j at time t due to high temperature, respectively. When the line is at its rated operating temperature, The value is 1; similarly, when the transformer is at its normal operating temperature or there is no transformer between nodes i and j, =1; Using the MATLAB platform and the CPLEX optimization tool, a mixed-integer linear programming problem is solved; the output includes the optimal coordinated operation scheme of photovoltaic, wind power, thermal power, hydropower and energy storage equipment, thereby optimizing the system's operating efficiency.

3. The control strategy for an integrated multi-energy complementary system of water, wind, solar, and storage under extreme high-temperature scenarios as described in claim 1, characterized in that, The resilience assessment includes the following steps: Resilience parameter calculation: Calculate the operating parameters of the multi-energy complementary system under different scenarios, including direct load shedding, average load rate of important lines, utilization rate of thermal power rated capacity, peak-valley difference, and peak-shaving margin. Indicator data normalization is performed based on indicator attributes. Positive indicators are normalized according to their proportion of the maximum value, while negative indicators are normalized using the following formula: in, This represents the normalized index value, where X is the original index value. max This represents the maximum value of the indicator. Entropy weight calculation uses a normalized data matrix to calculate the objective entropy weight. The entropy value formula is as follows: In the formula, p ij This represents the proportion of each element in the normalized data matrix to the total sum of that column; k is a constant, usually taken as... Where m is the number of indicators; Calculate the objective weights using the entropy weight formula: Where m is the number of indicators, H j It is the entropy value of the j-th index; AHP Subjective Weight Calculation: An AHP judgment matrix is ​​constructed based on expert experience. The eigenvector corresponding to the largest eigenvalue is extracted using eigenvalue decomposition and used as the subjective weight for consistency verification. Where, λ max CR is the largest eigenvalue, and RI is the random consistency index. If CR < 0.1, the matrix is ​​considered to be consistent. The comprehensive weighting calculation takes into account both subjective and objective weights. The comprehensive weighting formula is as follows: Where α is the fusion coefficient of subjective weight and objective weight; The overall resilience score is calculated for each scenario based on a comprehensive weighting and a normalized index. Among them, S i The resilience score for the i-th scenario.

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