A multi-energy complementary system optimal scheduling method considering bilateral uncertainty of source and load

Abstract

A method of optimal scheduling of multi-energy complementary system considering bilateral uncertainties of supply and demand is proposed to deal with the uncertainty challenge of energy supply and load demand in multi-energy complementary system. Firstly, the supply characteristics of various energies and the dynamic variation law of load demand in multi-energy complementary system are analyzed in depth, and the random fluctuation of energy supply on the source side and the uncertainty of load on the demand side are quantified. On this basis, an optimal scheduling model is constructed with the minimum system operation cost and the maximum energy utilization efficiency as the objectives. To effectively handle the uncertainty factors, robust optimization or stochastic optimization methods are introduced to transform the bilateral uncertainties of supply and demand into processable constraint conditions or objective function items. Through the coordinated control of different energy conversion devices and energy storage devices, the reasonable allocation and optimal scheduling of energy in multi-energy complementary system are realized, and the actual example is simulated for verification.

Description

Technical Field

[0001] This invention relates to the field of multi-energy complementarity, and more particularly to an optimal scheduling method for multi-energy complementary systems that considers uncertainties on both the source and load sides. Background Technology

[0002] On the energy supply side (source side), the output of renewable energy sources such as wind and solar power is affected by natural conditions such as wind speed, sunlight, and temperature, exhibiting strong volatility, intermittency, and unpredictability, making it difficult to accurately formulate energy production plans. On the load demand side (load side), with the diversification of user energy consumption patterns (such as electric vehicle charging and fluctuations in heating and cooling loads of commercial buildings), the large-scale participation of demand response resources, and the impact of extreme weather (such as high temperatures and cold waves) on energy demand, electricity, heat, and cooling loads exhibit complex dynamic changes, further exacerbating the difficulty of balancing system supply and demand. If this uncertainty on both sides is ignored or simplified, traditional deterministic scheduling methods are prone to causing scheduling schemes to become disconnected from actual operating scenarios, potentially leading to energy waste, decreased power supply reliability, and even increased system operational risks, severely restricting the realization of the comprehensive benefits of multi-energy complementary systems.

[0003] Based on this, this paper focuses on the impact of source-load dual-side uncertainties on the scheduling of multi-energy complementary systems, aiming to construct an optimized scheduling method that balances economy, robustness, and renewable energy absorption rate. First, by integrating historical data-driven and scenario generation technologies, a source-load dual-side uncertainty model is established that can accurately describe the fluctuations in wind power and photovoltaic output and the dynamic changes of diverse loads. Second, with the goal of minimizing system operating costs, renewable energy absorption rate constraints and system operation safety constraints are introduced to construct an optimized scheduling model for multi-energy complementary systems that considers source-load dual-side uncertainties. Finally, by designing an efficient solution algorithm, the scheduling model is solved quickly, providing a scientific basis for the real-time scheduling and long-term planning of multi-energy complementary systems. This research has significant theoretical and engineering application value for improving the ability of multi-energy complementary systems to cope with uncertainties, promoting high-proportion renewable energy absorption, and assisting in the construction of new power systems. Summary of the Invention

[0004] The purpose of this invention is to provide an optimal scheduling method for a multi-energy complementary system that considers uncertainties on both the source and load sides, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a multi-energy complementary system optimization scheduling method considering the uncertainties on both the source and load sides, comprising the following steps to implement the proposed multi-element hybrid energy storage optimization control strategy considering partition security: S1: Construct a system source-side model that includes wind power, photovoltaics, and gas turbines; S2: Construct a system load-side uncertainty model that considers electrical, thermal, and cooling loads; S3: Scene generation and aggregation are performed using Monte Carlo and clustering algorithms; S4: Establish the objective function for the multi-energy complementary system; S5: Further establish the constraints corresponding to the multi-energy complementary system; S6: The improved particle swarm optimization algorithm is used to solve the established multi-energy complementary system model; S7: Select multiple indicators to evaluate the solution results of the multi-energy complementary system model; As a further improvement to this technical solution, the following steps are included to construct a system source-side model that includes wind power, photovoltaics, and gas turbines: Based on historical wind speed data, the Weibull distribution is used to describe the probabilistic characteristics of wind speed. The formula for wind turbine output is: (1) Where: ω t Let ω be the actual wind speed at time t. in To cut off the wind speed, ω r For the rated wind speed, ω out To cut off the wind speed, P w,r For the rated output of wind power, P w,t The wind power output is random at time t; Based on historical irradiance and temperature data, and using a Beta distribution to describe the probabilistic characteristics of irradiance, the photovoltaic output formula is as follows: (2) Among them: G t G represents the actual irradiance at time t. std For standard irradiance, T c For the temperature of the photovoltaic cell, α T P is the power temperature coefficient. pv,r For the rated output of photovoltaic power, P pv,t Let t be the random output of the photovoltaic system. The output of a gas turbine is affected by fluctuations in natural gas pressure and slight deviations in combustion efficiency. Its uncertainty is described using a normal distribution, as shown in the formula: (3) Where: P gt,r For the rated output of the gas turbine, Δ gt,t The output has a random deviation with a mean of 0, σ gt P represents the standard deviation, corresponding to a fluctuation range of ±2%. gt,t The random output of the gas turbine at time t; As a further improvement to this technical solution, the following steps are included to construct a system load-side uncertainty model that considers electrical, thermal, and cooling loads: Collect measured load data and forecast data for the same period over the past year, with a time step of 1 hour, to form a dataset. (x∈{e, h, c}, e=electrical load, h=heat load, c=cooling load); Calculate load forecast deviation and remove outliers: (4) Positive deviation indicates that the load exceeds the forecast, while negative deviation indicates that the load is lower than the forecast. Based on the statistical characteristics of load deviation, it is assumed that the deviation follows a normal distribution N(μ) x,t , σ x,t 2 ), where: μ x,t σ is the mean of the deviations. x,t The standard deviation is given by the deviation; the standard deviation σ for different time periods is estimated using the rolling window method. x,t The formula is: (5) Where: N w =30 is the size of the scrolling window, which retrieves the past 30 data points from the same period. This is the mean deviation within the window, which can be further simplified in practical applications to... (k) x The deviation coefficient is the electrical load k. e =0.08, heat load k h =0.10, cooling load k c =0.09 (calibrated based on historical data). The Kolmogorov-Smirnov test (KS test) is used to verify whether the deviation conforms to a normal distribution. The test statistic is: (6) Wherein: F n F(x) is the empirical distribution function of the bias, and F0(x) is the assumed normal distribution function. If the test result p-value > 0.05 (significance level α = 0.05), then the normal distribution hypothesis is accepted; otherwise, the distribution type is adjusted. Finally, the formula for the actual load on the load side is: (7) As a further improvement to this technical solution, the following steps are included for scene generation and aggregation using Monte Carlo and clustering algorithms: Monte Carlo simulation was used to generate N=1000 source-load scenarios (covering source-side wind power / solar power / gas turbine fluctuations and load-side load deviations), with each scenario s corresponding to a set of data {P w,t s , P pv,t s , P gt,t s , L e,t s , L h,t s , L c,t s}, Scenario probability p s =1 / N (initial equal probability); Kantorovich distance is used to measure scene similarity. Key scenes are retained through clustering algorithms (such as k-means). The number of scenes after reduction is S=30 (balancing computational accuracy and efficiency), ensuring that the probability distribution of the scenes after reduction satisfies the following: (8) Where: p s′ To reduce the probability of the post-scene, X t For source load variables, The mean of the variable; As a further improvement to this technical solution, the following steps are included to establish the objective function of the multi-energy complementary system based on economic, environmental, and reliability indicators: With "economic efficiency + environmental friendliness + reliability" as the core objectives, the optimization is transformed into a single-objective optimization using a weighted summation method (weights λ1 + λ2 + λ3 = 1), as shown in the formula: (9) Where: F1 represents minimizing operating costs for economic objectives, F2 represents minimizing carbon emission costs for environmental objectives, and F3 represents minimizing energy shortage costs for reliability objectives; As a further improvement to this technical solution, the following steps are included to achieve the economic objective function of minimizing operating costs: The economic objective F1 represents minimizing operating costs. (10) Where: T=24 represents the scheduling period, in hours; P grid,t s For scenario s at time t, the power purchased by the power grid, C grid,t Time-of-use electricity pricing; C gas Indicates the price of natural gas, η gt P represents the power generation efficiency of a gas turbine. ess,c,t s / P ess,d,ts For energy storage charging / discharging power, C ess P represents the unit cost of energy storage charging and discharging; hp,t s For heat pump output, η hp C represents the coefficient of performance (COP) of a heat pump. hp This indicates the additional costs associated with operating a heat pump; As a further improvement to this technical solution, the following steps are included to minimize carbon emission costs in order to achieve environmental protection goals: Environmental goal F2 is to minimize carbon emission costs. (11) Where: μ grid The carbon emission coefficient of grid-connected power supply, μ gas C represents the carbon emission factor of natural gas combustion. CO2 Indicates the carbon trading price; As a further improvement to this technical solution, the following steps are included to minimize the cost of power shortage in order to achieve the reliability objective: Reliability objective F3 represents minimizing the cost of power shortage. (12) Where: ΔL x,t s =max(0, L x,t s -P x,sup,t s (x∈{e, h, c}, P) x,sup,t s (C represents the total energy supply in scenario s at time t). e,loss Indicates the cost of power outage penalties, C h,loss Indicates the cost of heat deficit penalty, C c,loss This indicates the cost of penalties for lack of cooling. As a further improvement to this technical solution, the following steps are included to further establish the constraints corresponding to the multi-energy complementary system: Power balance constraints (scenario s at time t) include electrical balance, thermal balance, and cold balance; Electric balance is represented as: (13) Thermal equilibrium is expressed as: (14) Cold balance is represented as: (15) Among them, P gt,h,t s =P gt,t s·(1-η gt ) / η gt P represents waste heat from a gas turbine. ec,t s For electric cooling power, η ec P represents the coefficient of performance (COP) for electrical cooling. he,t s Power for charging thermal energy storage; P ac,t s For absorption refrigeration power (utilizing waste heat from gas turbines); Equipment operating constraints include gas turbines, energy storage systems, and grid interaction; The operating constraints of gas turbine equipment are expressed as follows: (16) Among them, P gt,min =0.3P gt,r For the minimum output requirement of the gas turbine, r gt =0.2 represents the power output ramp rate; The operating constraints of energy storage system equipment are expressed as follows: (17) Among them, SOC min =0.2 is the lower limit for SOC operation; SOC max =0.8 is the upper limit for SOC operation; η ess,c =0.9 represents the energy storage charging efficiency; η ess,d =0.9 represents the energy storage discharge efficiency; Δt = 1h; Power grid interaction is represented as: (18) Among them, P grid,max This refers to the maximum power purchase capacity allowed by the power grid. Probabilistic reliability constraints aim to ensure that the system is energy-efficient in most scenarios. The constraints are: (19) Where: I(·) is the indicator function (1 when the condition is met), ε e =0.05 (Power shortage tolerance probability), ε h =0.08 (probability of heat deficiency), ε c =0.07 (probability of cold storage shortage); As a further improvement to this technical solution, the following steps are included to solve the above-established model using an improved particle swarm optimization algorithm: The adaptive weighted particle swarm optimization (PSO) algorithm is used to solve the problem, and the steps are as follows: 1) Variable initialization: The scheduling variable is {P} grid,t s , Pgt,t s , P ess,c,t s , P ess,d,t s , P hp,t s The particle population size is M=60, the maximum number of iterations is K=150, and the initial particle positions are randomly generated. 2) Fitness calculation: For each particle, verify the constraint satisfaction and calculate the objective function value as the fitness. 3) Particle update: Adaptive inertial weight ω=0.4+0.5·exp(-(k / K)) 2 (Large weights explore in the early stages of iteration, small weights converge in the later stages), update speed and position: (20) Where c1=c2=2.0, r1r2 are uniformly random numbers in the range [0,1]; v i,k The velocity of particle i in the k-th iteration must satisfy the velocity constraint v. min ≤v i,k ≤v max ; 4) Convergence Criterion: Stop iteration and output the global optimal solution gbest if any of the following conditions are met: the number of iterations reaches the maximum number of iterations K=150; or the change in the global optimal fitness value over 10 consecutive iterations is less than 10. -6 The optimal solution satisfies all constraints, and the objective function value is lower than a preset threshold. As a further improvement to this technical solution, the following steps are included to select multiple indicators to evaluate the results of the above solution: For each output optimal scheduling scheme, we verify whether it satisfies all constraints. The core verification metrics and methods are as follows: Power balance verification: For each scenario s and time t, calculate the electrical, thermal, and cooling balance deviation rates. (twenty one) Must meet (Minor calculation errors are allowed), and there are no scenarios where "total supply < total load" (i.e., energy shortage). Equipment operation boundary verification: Check whether the output of equipment such as gas turbines, energy storage, and heat pumps is within the rated range; if there are boundary violations, the algorithm parameters need to be adjusted backtracking and the solution recalculated. Probabilistic reliability verification: Calculate the actual energy shortage probability: (twenty two) γ must be satisfied e ≤ε e =0.05、γh ≤ε h =0.08、γ c ≤ε c =0.07, ensuring that the system reliability meets the preset requirements.

[0006] Compared with existing technologies, the beneficial effects of this invention are as follows: This paper proposes an optimal scheduling method for multi-energy complementary systems that considers uncertainties on both the source and load sides to address the challenges of uncertainty in energy supply and load demand in multi-energy complementary systems. First, the supply characteristics of various energy sources and the dynamic changes in load demand in the multi-energy complementary system are analyzed in depth, quantifying the random fluctuations in energy supply on the source side and the uncertainty of load on the load side. Based on this, an optimal scheduling model is constructed with the objectives of minimizing system operating costs and maximizing energy utilization efficiency. To effectively handle uncertainties, robust optimization or stochastic optimization methods are introduced to transform the uncertainties on both the source and load sides into manageable constraints or objective function terms. Through coordinated control of different energy conversion and storage devices, the rational allocation and optimal scheduling of energy within the multi-energy complementary system are achieved. Simulation verification using actual examples shows that this method can significantly improve the adaptability and stability of multi-energy complementary systems under uncertainties on both the source and load sides, effectively reduce system operating costs, and improve comprehensive energy utilization efficiency, providing a practical and feasible optimal scheduling scheme for the reliable and economical operation of multi-energy complementary systems.

[0007] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description

[0008] Figure 1 The flowchart is a process for the optimal scheduling method of a multi-energy complementary system considering the uncertainties on both the source and load sides, as studied in this invention. Figure 2 This presents the case study results of the optimal scheduling method for a multi-energy complementary system considering uncertainties on both the source and load sides, as studied in this invention. Figure 3 This presents the case study results of the optimal scheduling method for a multi-energy complementary system considering uncertainties on both the source and load sides, as studied in this invention. Figure 4 This presents the case study results of the optimal scheduling method for a multi-energy complementary system considering uncertainties on both the source and load sides, as studied in this invention. Figure 5 This presents the case study results of the optimal scheduling method for a multi-energy complementary system that considers uncertainties on both the source and load sides, as studied in this invention. Detailed Implementation

[0009] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0010] This embodiment is based on Figure 1 The flowchart shown is a multi-energy complementary system optimization scheduling method that considers uncertainties on both the source and load sides. The method is analyzed.

[0011] Please see the appendix Figure 1 A method for optimal scheduling of a multi-energy complementary system considering uncertainties on both the source and load sides includes the following steps to implement the proposed multi-element hybrid energy storage optimal control strategy considering partition security: S1: Construct a system source-side model that includes wind power, photovoltaics, and gas turbines; S2: Construct a system load-side uncertainty model that considers electrical, thermal, and cooling loads; S3: Scene generation and aggregation are performed using Monte Carlo and clustering algorithms; S4: Establish the objective function of the multi-energy complementary system with economic, environmental protection and reliability as indicators; S5: Further establish the constraints corresponding to the multi-energy complementary system; S6: The improved particle swarm optimization algorithm is used to solve the model established above; S7: Select multiple indicators to evaluate the results of the above solution; Furthermore, the following steps are included for constructing a source-side model of a system that includes wind power, photovoltaics, and gas turbines: Based on historical wind speed data, the Weibull distribution is used to describe the probabilistic characteristics of wind speed. The formula for wind turbine output is: (1) Where: ω t Let ω be the actual wind speed at time t. in To cut off the wind speed, ω r For the rated wind speed, ω out To cut off the wind speed, P w,r For the rated output of wind power, P w,t The wind power output is random at time t; Based on historical irradiance and temperature data, and using a Beta distribution to describe the probabilistic characteristics of irradiance, the photovoltaic output formula is as follows: (2) Among them: G t G represents the actual irradiance at time t. stdFor standard irradiance, T c For the temperature of the photovoltaic cell, α T P is the power temperature coefficient. pv,r For the rated output of photovoltaic power, P pv,t Let t be the random output of the photovoltaic system. Gas turbine output is affected by fluctuations in natural gas pressure and minor deviations in combustion efficiency. Its uncertainty is described using a normal distribution (the fluctuation amplitude is small, typically within the rated output). The formula is: (3) Where: P gt,r For the rated output of the gas turbine, Δ gt,t The output has a random deviation with a mean of 0, σ gt P represents the standard deviation, corresponding to a fluctuation range of ±2%. gt,t The random output of the gas turbine at time t; Furthermore, the following steps are included for constructing a system load-side uncertainty model that considers electrical, thermal, and cooling loads: Collect measured load data and forecast data for the same period over the past year, with a time step of 1 hour, to form a dataset. (x∈{e, h, c}, e=electrical load, h=heat load, c=cooling load); Calculate load forecast deviation and remove outliers: (4) Positive deviation indicates that the load exceeds the forecast, while negative deviation indicates that the load is lower than the forecast. Based on the statistical characteristics of load deviation, it is assumed that the deviation follows a normal distribution N(μ) x,t , σ x,t 2 ), where: μ x,t σ is the mean of the deviations. x,t The standard deviation is given by the deviation; the standard deviation σ for different time periods is estimated using the rolling window method. x,t The formula is: (5) Where: N w =30 is the size of the scrolling window, which retrieves the past 30 data points from the same period. This is the mean deviation within the window, which can be further simplified in practical applications to... (k) x The deviation coefficient is the electrical load k. e =0.08, heat load k h =0.10, cooling load k c =0.09 (calibrated based on historical data). The Kolmogorov-Smirnov test (KS test) is used to verify whether the deviation conforms to a normal distribution. The test statistic is: (6) Wherein: F n F(x) is the empirical distribution function of the bias, and F0(x) is the assumed normal distribution function. If the test result p-value > 0.05 (significance level α = 0.05), then the normal distribution hypothesis is accepted; otherwise, the distribution type is adjusted. Finally, the formula for the actual load on the load side is: (7) Furthermore, the process includes the following steps for scene generation and aggregation using Monte Carlo and clustering algorithms: Monte Carlo simulation was used to generate N=1000 source-load scenarios (covering source-side wind power / solar power / gas turbine fluctuations and load-side load deviations), with each scenario s corresponding to a set of data {P w,t s , P pv,t s , P gt,t s , L e,t s , L h,t s , L c,t s}, Scenario probability p s =1 / N (initial equal probability); Kantorovich distance is used to measure scene similarity. Key scenes are retained through clustering algorithms (such as k-means). The number of scenes after reduction is S=30 (balancing computational accuracy and efficiency), ensuring that the probability distribution of the scenes after reduction satisfies the following: (8) Where: p s′ To reduce the probability of the post-scene, X t For source load variables, The mean of the variable; Furthermore, the following steps are included to establish the objective function for a multi-energy complementary system using economic, environmental, and reliability indicators: With "economic efficiency + environmental friendliness + reliability" as the core objectives, the optimization is transformed into a single-objective optimization using a weighted summation method (weights λ1 + λ2 + λ3 = 1), as shown in the formula: (9) Where: F1 represents minimizing operating costs for economic objectives, F2 represents minimizing carbon emission costs for environmental objectives, and F3 represents minimizing energy shortage costs for reliability objectives; Furthermore, the following steps are included to achieve the economic objective function of minimizing operating costs: The economic objective F1 represents minimizing operating costs. (10) Where: T=24 represents the scheduling period, in hours; P grid,t s For scenario s at time t, the power purchased by the power grid, C grid,t Time-of-use electricity pricing; C gas Indicates the price of natural gas, η gt P represents the power generation efficiency of a gas turbine. ess,c,t s / P ess,d,t s For energy storage charging / discharging power, C ess P represents the unit cost of energy storage charging and discharging; hp,t s For heat pump output, η hp C represents the coefficient of performance (COP) of a heat pump. hp This indicates the additional costs associated with operating a heat pump; Furthermore, the following steps are included to minimize carbon emission costs in order to achieve environmental goals: Environmental goal F2 is to minimize carbon emission costs. (11) Where: μ grid The carbon emission coefficient of grid-connected power supply, μ gas C represents the carbon emission factor of natural gas combustion. CO2 Indicates the carbon trading price; Furthermore, the following steps are included to minimize the cost of power shortage in order to achieve the reliability objective: Reliability objective F3 represents minimizing the cost of power shortage. (12) Where: ΔL x,t s =max(0, L x,t s -P x,sup,t s (x∈{e, h, c}, P) x,sup,t s (C represents the total energy supply in scenario s at time t). e,loss Indicates the cost of power outage penalties, C h,loss Indicates the cost of heat deficit penalty, C c,loss This indicates the cost of penalties for lack of cooling. Furthermore, the following steps are included to further establish the constraints corresponding to the multi-energy complementary system: Power balance constraints (scenario s at time t) include electrical balance, thermal balance, and cold balance; Electric balance is represented as: (13) Thermal equilibrium is expressed as: (14) Cold balance is represented as: (15) Among them, P gt,h,t s =P gt,t s ·(1-η gt ) / η gt P represents waste heat from a gas turbine. ec,t s For electric cooling power, η ec P represents the coefficient of performance (COP) for electrical cooling. he,t s Power for charging thermal energy storage; P ac,t s For absorption refrigeration power (utilizing waste heat from gas turbines); Equipment operating constraints include gas turbines, energy storage systems, and grid interaction; The operating constraints of gas turbine equipment are expressed as follows: (16) Among them, P gt,min =0.3P gt,r For the minimum output requirement of the gas turbine, r gt =0.2 represents the power output ramp rate; The operating constraints of energy storage system equipment are expressed as follows: (17) Among them, SOC min =0.2 is the lower limit for SOC operation; SOC max =0.8 is the upper limit for SOC operation; η ess,c =0.9 represents the energy storage charging efficiency; η ess,d =0.9 represents the energy storage discharge efficiency; Δt = 1h; Power grid interaction is represented as: (18) Among them, P grid,max This refers to the maximum power purchase capacity allowed by the power grid. Probabilistic reliability constraints aim to ensure that the system is energy-efficient in most scenarios. The constraints are: (19) Where: I(·) is the indicator function (1 when the condition is met), ε e =0.05 (Power shortage tolerance probability), ε h =0.08 (probability of heat deficiency), ε c =0.07 (probability of cold storage shortage); Furthermore, the following steps are included for solving the model established above using an improved particle swarm optimization algorithm: The adaptive weighted particle swarm optimization (PSO) algorithm is used to solve the problem, and the steps are as follows: 1) Variable initialization: The scheduling variable is {P} grid,t s , P gt,t s , P ess,c,t s , P ess,d,t s , P hp,t s The particle population size is M=60, the maximum number of iterations is K=150, and the initial particle positions are randomly generated. 2) Fitness calculation: For each particle, verify the constraint satisfaction and calculate the objective function value as the fitness. 3) Particle update: Adaptive inertial weight ω=0.4+0.5·exp(-(k / K)) 2 (Large weights explore in the early stages of iteration, small weights converge in the later stages), update speed and position: (20) Where c1=c2=2.0, r1r2 are uniformly random numbers in the range [0,1]; v i,k The velocity of particle i in the k-th iteration must satisfy the velocity constraint v. min ≤v i,k ≤v max ; 4) Convergence Criterion: Stop iteration and output the global optimal solution gbest if any of the following conditions are met: the number of iterations reaches the maximum number of iterations K=150; or the change in the global optimal fitness value over 10 consecutive iterations is less than 10. -6 The optimal solution satisfies all constraints, and the objective function value is lower than a preset threshold. Furthermore, the following steps are included to select multiple metrics to evaluate the results of the above solution: For each output optimal scheduling scheme, we verify whether it satisfies all constraints. The core verification metrics and methods are as follows: Power balance verification: For each scenario s and time t, calculate the electrical, thermal, and cooling balance deviation rates. (twenty one) Must meet (Minor calculation errors are allowed), and there are no scenarios where "total supply < total load" (i.e., energy shortage). Equipment operation boundary verification: Check whether the output of equipment such as gas turbines, energy storage, and heat pumps is within the rated range; if there are boundary violations, the algorithm parameters need to be adjusted backtracking and the solution recalculated. Probabilistic reliability verification: Calculate the actual energy shortage probability: (twenty two) γ must be satisfied e ≤ε e =0.05、γ h ≤ε h =0.08、γ c ≤ε c =0.07, ensuring that the system reliability meets the preset requirements.

[0012] This embodiment takes a regional multi-energy complementary system containing "wind power + photovoltaic + gas turbine + electric storage + thermal storage" as the research object, with a scheduling cycle of 1 day (24 hours). It focuses on simulating the impact of uncertainties on both the source and load sides on the system operation and verifying the effectiveness of the proposed optimization scheduling method.

[0013] Please see Figure 2 This chart compares renewable energy output on the source side with electricity load on the load side. The solid blue line represents wind power output, the dashed red line represents solar power output, and the dashed black line represents electricity load demand. The horizontal axis represents 24-hour time, and the vertical axis represents power. Wind power output exhibits a "low at night, high in the afternoon" characteristic (e.g., output reaches over 90kW between 12-14 PM), and fluctuates by ±20% (e.g., output at 8 AM is 48kW ± 9.6kW), reflecting uncertainty on the source side. Solar power output is concentrated between 6-18 AM (daytime irradiance period), reaching a peak of 80kW between 12-14 PM, with a fluctuation range of ±15% (e.g., output at 10 AM is 50kW ± 7.5kW), consistent with the intermittent nature of actual solar power output. Electricity load exhibits a "high in the morning and evening, low in the early morning" characteristic (e.g., load reaches 88-90kW at 8 AM and 6 PM, and drops to 50kW between 3-4 AM), plus ±10% random fluctuations, reflecting uncertainty in electricity demand on the load side. There is a "temporal and spatial mismatch" between renewable energy output and electricity load (such as the load not reaching its peak when photovoltaic output is at noon, and the renewable energy output dropping sharply when the load peaks in the evening), which needs to be mitigated by coordinating controllable power sources (gas turbines) and energy storage.

[0014] Please see Figure 3The graph shows the operating status of the gas turbine and energy storage. The solid green line represents the gas turbine output, the magenta dashed line represents the charging and discharging power of the energy storage (positive values ​​indicate charging, negative values ​​indicate discharging), and the black dotted line represents zero power. The horizontal axis represents time, and the vertical axis represents power. The gas turbine acts as a "peak-shaving power source," supplementing energy when renewable energy output is insufficient (e.g., from 6-8 PM, when wind / solar power output decreases, the gas turbine output increases to 15-20 kW), and reducing output when renewable energy is abundant (e.g., from 12-2 PM, only 5-8 kW is needed to maintain basic operation), effectively smoothing out source-load fluctuations. The energy storage operates according to the "peak shaving and valley filling" logic: charging (10-12 kW) from 12-2 PM (peak renewable energy output) and discharging (-5 to -12 kW) from 6-10 PM (peak load, low renewable energy output), avoiding energy waste and load gaps. The coordinated control of the gas turbine and energy storage is the core means to cope with uncertainties on both the source and load sides, ensuring real-time power balance on the electricity side.

[0015] Please see Figure 4 The thermal storage system's operating status and heat load demand are shown. The solid cyan line represents the thermal storage's charging and releasing power (positive values ​​indicate charging, negative values ​​indicate releasing), the orange dashed line represents the heat load demand, and the black dotted line represents the power zero point. The horizontal axis represents time, and the vertical axis represents power. The heat load exhibits a "high at night, low during the day" characteristic (e.g., the load reaches 50-55kW from 22:00 to 24:00, and drops to 40kW from 12:00 to 14:00), with a fluctuation of ±8%, which conforms to the actual demand pattern of building heating. The thermal storage system charges during the low heat load period (e.g., 8-10kW charging power from 12:00 to 14:00) and releases heat during the high heat load period (e.g., -5 to -8kW releasing power from 20:00 to 22:00), realizing the "spatiotemporal transfer" of heat energy. Thermal storage effectively matches the dynamic changes in heat load, reduces the frequency of adjustments to the thermal system, and improves the stability of thermal operation and energy utilization efficiency.

[0016] Please see Figure 5 Renewable energy absorption rate and hourly operating cost. The red vertical axis on the left represents the renewable energy absorption rate (%), and the blue vertical axis on the right represents the hourly operating cost (yuan). The dark red solid line represents the absorption rate, the dark blue solid line represents the cost, and the horizontal axis represents time. The renewable energy absorption rate is generally maintained at 85%-98%: the absorption rate is above 95% during the day (6-18:00) (e.g., 98% at 12:00), because energy storage can store excess electricity; the absorption rate drops slightly in the early morning (2-4:00) (85%-90%), because wind power output is low and load demand is small, and some electricity needs to be abandoned (but the abandonment has been minimized through robust optimization); the hourly operating cost is positively correlated with the gas turbine output: the cost is 15-20 yuan / hour from 18-22 (high output period of gas turbine), and only 5-8 yuan / hour from 12-14 (low output period of gas turbine), with a total daily operating cost of about 320 yuan, reflecting its economic efficiency; The proposed optimized scheduling method effectively controls operating costs while ensuring a high absorption rate (average 92%), achieving synergistic optimization of economic efficiency and environmental protection.

[0017] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A method for optimal scheduling of a multi-energy complementary system considering uncertainties on both the source and load sides, characterized in that, The following steps are included to implement the proposed multi-element hybrid energy storage optimization and regulation strategy that considers zonal security: S1: Construct a system source-side model that includes wind power, photovoltaics, and gas turbines; S2: Construct a system load-side uncertainty model that considers electrical, thermal, and cooling loads; S3: Scene generation and aggregation are performed using Monte Carlo and clustering algorithms; S4: Establish the objective function for the multi-energy complementary system; S5: Further establish the constraints corresponding to the multi-energy complementary system; S6: The improved particle swarm optimization algorithm is used to solve the established multi-energy complementary system model; S7: Select multiple indicators to evaluate the solution results of the multi-energy complementary system model.

2. The optimal scheduling method for a multi-energy complementary system considering uncertainties on both the source and load sides as described in claim 1, characterized in that, The following steps are included for constructing a source-side model of a system that includes wind power, solar power, and gas turbines: Based on historical wind speed data, the Weibull distribution is used to describe the probabilistic characteristics of wind speed. The formula for wind turbine output is: (1) Where: ω t Let ω be the actual wind speed at time t. in To cut off the wind speed, ω r For the rated wind speed, ω out To cut off the wind speed, P w,r For the rated output of wind power, P w,t The wind power output is random at time t; Based on historical irradiance and temperature data, and using a Beta distribution to describe the probabilistic characteristics of irradiance, the photovoltaic output formula is as follows: (2) Among them: G t G represents the actual irradiance at time t. std For standard irradiance, T c For the temperature of the photovoltaic cell, α T P is the power temperature coefficient. pv,r For the rated output of photovoltaic power, P pv,t Let t be the random output of the photovoltaic system. Gas turbine output is affected by natural gas pressure fluctuations and minor deviations in combustion efficiency. Its uncertainty is described by a normal distribution, with small fluctuation amplitudes, which are within the rated output. The formula is: (3) Where: P gt,r For the rated output of the gas turbine, Δ gt,t The output has a random deviation with a mean of 0, σ gt P represents the standard deviation, corresponding to a fluctuation range of ±2%. gt,t Let t be the random output of the gas turbine.

3. The optimal scheduling method for a multi-energy complementary system considering uncertainties on both the source and load sides as described in claim 1, characterized in that, The following steps are included for constructing a system load-side uncertainty model that considers electrical, thermal, and cooling loads: Collect measured load data and forecast data from the past year, with a time step of 1 hour, to form a dataset. x∈{e, h, c}, where e = electrical load, h = heat load, and c = cooling load; Calculate load forecast deviation and remove outliers: (4) Positive deviation indicates that the load exceeds the forecast, while negative deviation indicates that the load is lower than the forecast. Based on the statistical characteristics of load deviation, it is assumed that the deviation follows a normal distribution N(μ) x,t , σ x,t 2 ), where: μ x,t σ is the mean of the deviations. x,t The standard deviation is given by the deviation; the standard deviation σ for different time periods is estimated using the rolling window method. x,t The formula is: (5) Where: N w =30 is the size of the scrolling window, which retrieves the past 30 data points from the same period. The mean deviation within the window is further simplified in practical applications to... Where, k x The deviation coefficient is the electrical load k. e =0.08, heat load k h =0.10, cooling load k c =0.09, calibrated based on historical data; The Kolmogorov-Smirnov test (KS test) is used to verify whether the deviation conforms to a normal distribution. The test statistic is: (6) Wherein: F n F(x) is the empirical distribution function of the bias, and F0(x) is the assumed normal distribution function. If the test result p-value > 0.05, the normal distribution hypothesis is accepted; otherwise, the distribution type is adjusted. Finally, the formula for the actual load on the load side is: (7)。 4. The optimal scheduling method for a multi-energy complementary system considering uncertainties on both the source and load sides as described in claim 1, characterized in that, The following steps are included for scene generation and aggregation using Monte Carlo and clustering algorithms: Monte Carlo simulation was used to generate N=1000 source-load scenarios, with each scenario s corresponding to a set of data {P}. w,t s , P pv,t s ,P gt,t s , L e,t s , L h,t s , L c,t s }, Scenario probability p s =1 / N; Kantorovich distance is used to measure scene similarity. Key scenes are retained through clustering algorithm. The number of scenes after reduction is S=30, ensuring that the probability distribution of the scenes after reduction satisfies the following: (8) Where: p s′ To reduce the probability of the post-scene, X t For source load variables, The mean of the variable.

5. The optimal scheduling method for a multi-energy complementary system considering uncertainties on both the source and load sides as described in claim 1, characterized in that, The objective function of a multi-energy complementary system is established by the following steps: The problem is transformed into a single-objective optimization using a weighted summation method, with weights λ1 + λ2 + λ3 = 1, and the formula is: (9) Wherein: F1 represents minimizing operating costs for economic objectives, F2 represents minimizing carbon emission costs for environmental objectives, and F3 represents minimizing energy shortage costs for reliability objectives.

6. The optimal scheduling method for a multi-energy complementary system considering uncertainties on both the source and load sides as described in claim 5, characterized in that, The following steps are included to achieve the economic objective function of minimizing operating costs: The economic objective F1 represents minimizing operating costs. (10) Where: T=24 represents the scheduling period, in hours; P grid,t s For scenario s at time t, the power purchased by the power grid, C grid,t Time-of-use electricity pricing; C gas Indicates the price of natural gas, η gt P represents the power generation efficiency of a gas turbine. ess,c,t s / P ess,d,t s For energy storage charging / discharging power, C ess P represents the unit cost of energy storage charging and discharging; hp,t s For heat pump output, η hp C represents the coefficient of performance (COP) of a heat pump. hp This indicates the additional costs associated with operating a heat pump.

7. A multi-energy complementary system optimization scheduling method considering uncertainties on both the source and load sides as described in claim 5, characterized in that, This includes the following steps to minimize carbon emission costs in order to achieve environmental goals: Environmental goal F2 is to minimize carbon emission costs. (11) Where: μ grid The carbon emission coefficient of grid-connected power supply, μ gas C represents the carbon emission factor of natural gas combustion. CO2 This indicates the price of carbon trading.

8. A method for optimal scheduling of a multi-energy complementary system considering uncertainties on both the source and load sides, as described in claim 5, is characterized in that... The following steps are included to minimize energy shortage costs in order to achieve reliability objectives: Reliability objective F3 represents minimizing the cost of power shortage. (12) Where: ΔL x,t s =max(0, L x,t s -P x,sup,t s (x∈{e, h, c}) P x,sup,t s For a moment t Scene s Total energy supply, C e,loss Indicates the cost of power outage penalties, C h,loss Indicates the cost of heat deficit penalty, C c,loss This indicates the cost of penalties for lack of cold weather.

9. A method for optimal scheduling of a multi-energy complementary system considering uncertainties on both the source and load sides, as described in claim 1, characterized in that... The following steps are included to further establish the constraints corresponding to the multi-energy complementary system: Power balance constraints, scenario s, time t, including electrical balance, thermal balance, and cold balance; Electric balance is represented as: (13) Thermal equilibrium is expressed as: (14) Cold balance is represented as: (15) Among them, P gt,h,t s =P gt,t s ·(1-η gt ) / η gt P represents waste heat from a gas turbine; ec,t s For electric cooling power, η ec P represents the coefficient of performance (COP) for electrical cooling. he,t s Power for charging thermal energy storage; P ac,t s This refers to the absorption cooling capacity; Equipment operating constraints include gas turbines, energy storage systems, and grid interaction; The operating constraints of gas turbine equipment are expressed as follows: (16) Among them, P gt,min =0.3P gt,r For the minimum output requirement of the gas turbine, r gt =0.2 represents the power output ramp rate; The operating constraints of energy storage system equipment are expressed as follows: (17) Among them, SOC min =0.2 is the lower limit for SOC operation; SOC max =0.8 is the upper limit for SOC operation; η ess,c =0.9 represents the energy storage charging efficiency; η ess,d =0.9 represents the energy storage discharge efficiency; Δt = 1h; Power grid interaction is represented as: (18) Among them, P grid,max This refers to the maximum power purchase capacity allowed by the power grid. Probabilistic reliability constraints aim to ensure that the system is energy-efficient in most scenarios. The constraints are: (19) Where: I(·) is an indicator function, which is 1 when the condition is met, and ε e =0.05, power outage tolerance probability, ε h =0.08, heat deficit probability, ε c =0.07, probability of cold storage failure.

10. A method for optimal scheduling of a multi-energy complementary system considering uncertainties on both the source and load sides, as described in claim 1, characterized in that... The following steps are included for solving the established multi-energy complementary system model using an improved particle swarm optimization algorithm: The adaptive weighted particle swarm optimization (PSO) algorithm is used to solve the problem, and the steps are as follows: 1) Variable initialization: The scheduling variable is {P} grid,t s , P gt,t s , P ess,c,t s , P ess,d,t s , P hp,t s The particle population size is M=60, the maximum number of iterations is K=150, and the initial particle positions are randomly generated. 2) Fitness calculation: For each particle, verify the constraint satisfaction and calculate the objective function value as the fitness. 3) Particle update: Adaptive inertial weight ω=0.4+0.5·exp(-(k / K)) 2 Update speed and location: (20) Where c1=c2=2.0, r1r2 are uniformly random numbers in the range [0,1]; v i,k The velocity of particle i in the k-th iteration must satisfy the velocity constraint v. min ≤v i,k ≤v max ; 4) Convergence Criterion: Stop iteration and output the global optimal solution gbest if any of the following conditions are met: the number of iterations reaches the maximum number of iterations K=150; or the change in the global optimal fitness value over 10 consecutive iterations is less than 10. -6 The optimal solution satisfies all constraints and the objective function value is lower than a preset threshold.

11. A method for optimal scheduling of a multi-energy complementary system considering uncertainties on both the source and load sides, as described in claim 1, characterized in that... The following steps are included to select multiple metrics to evaluate the results of the above solution: For each output optimal scheduling scheme, we verify whether it satisfies all constraints. The core verification metrics and methods are as follows: Power balance verification: For each scenario s and time t, calculate the electrical, thermal, and cooling balance deviation rates. (21) Must meet Furthermore, there were no scenarios where "total supply < total load" indicating an energy shortage. Equipment operation boundary verification: Check whether the output of gas turbine, energy storage, and heat pump equipment is within the rated range; if there are boundary violations, the algorithm parameters need to be adjusted and the solution recalculated. Probabilistic reliability verification: Calculate the actual energy shortage probability: (22) γ must be satisfied e ≤ε e =0.05、γ h ≤ε h =0.08、γ c ≤ε c =0.07, ensuring that the system reliability meets the preset requirements.

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