An integrated energy system adaptive robust scheduling method considering photovoltaic and load uncertainty

Abstract

This invention discloses an adaptive robust scheduling method for integrated energy systems that considers photovoltaic (PV) and load uncertainties. This method fully considers the uncertainties of PV and load connected to the distribution network, establishes an adaptive robust scheduling model for the integrated energy system, and decomposes this model into an integrated energy system equipment state configuration model and a worst-case output model for uncertain PV loads. The Grey Wolf optimization algorithm is used to solve for and determine the worst-case PV and load output scenarios, as well as the output states and power of different equipment in the integrated energy system, achieving day-ahead-to-day economic optimization scheduling. This invention can directly construct the worst-case output range of new energy sources without assuming uncertain distributions, effectively addressing the impact of uncertain new energy output on the distribution network, enhancing system robustness and economic performance, and improving the distribution network's resilience.

Description

Technical Field

[0001] This invention relates to an adaptive robust scheduling method for integrated energy systems that takes into account photovoltaic and load uncertainties, belonging to the field of power systems, and particularly to power system operation and scheduling technology. Background Technology

[0002] The use of traditional fossil fuels emits large amounts of carbon dioxide, causing global climate change and environmental degradation. New energy sources, with their clean, environmentally friendly, and renewable characteristics, can effectively overcome the drawbacks of traditional fossil fuels. However, the large-scale integration of new energy sources into the power grid will cause power flow reversal, bidirectional voltage exceedances, and severe harmonic exceedances. Furthermore, the fluctuating and uncertain power output of new energy sources seriously affects the security and stability of the power grid.

[0003] Integrated energy systems can convert one type of energy into another through multi-energy complementarity, effectively enabling the local consumption of large-scale, high-capacity new energy sources. This greatly improves the power distribution network's capacity to accept high-penetration new energy sources. On the other hand, because integrated energy systems include fast-responding units such as combined heat and power plants and electric boilers, as well as storage devices such as distributed energy storage, distributed thermal storage, and distributed gas storage, integrated energy systems can quickly adjust equipment output to cope with the impact of uncertainties in new energy output on the power grid.

[0004] To date, renowned scholars both domestically and internationally have conducted extensive and in-depth research on the uncertain absorption of high-penetration renewable energy. Mei Fei et al. used probability density data of photovoltaic and wind power to generate random large-scale photovoltaic and wind power outputs to simulate uncertain output scenarios. They then reduced the simulated scenarios and finally optimized scheduling based on the reduced scenarios. While stochastic optimization methods are relatively mature and can effectively address the uncertainty of renewable energy output, their application inevitably assumes that uncertain output follows a certain distribution and generates a large number of scenarios. Therefore, while stochastic optimization methods are relatively simple, they often assume that the distribution cannot fully represent the true output of uncertain factors, and generating too many scenarios leads to excessively long solution times, hindering real-time scheduling and absorption. Shui Yue et al. reduced a large number of wind power output scenarios through clustering to obtain typical discrete scenarios and initial probabilities. They used the 1-norm and ∞-norm to constrain the probabilities of multiple discrete scenarios and performed a two-stage solution using a column and constraint generation algorithm to determine the worst-case scenario probability and obtain a scheduling scheme under the worst-case scenario probability. Although the scalar bar method based on multiple discrete scenarios does not require direct solution of the probability density of uncertain parameters, the initial probability distribution of typical scenarios can affect the decision-making results. Sun Xu et al. addressed the uncertainties of demand-side electricity and gas loads by using the scalar bar method based on Wasserstein distance to construct fuzzy sets of probability distributions for uncertain variables. They then transformed the proposed model into a linear programming problem for solution using duality theory and conditional risk value approximation.

[0005] Currently, most methods for addressing the uncertain integration of high-penetration renewable energy often employ stochastic optimization, generating a large amount of renewable energy output, but this requires knowledge of the probability distribution of renewable energy output. Furthermore, the solution for renewable energy integration scheduling typically uses a two-stage approach, which is complex and challenging. Therefore, constructing an adaptive robust scheduling model for a comprehensive energy system and employing an effective solution method to address the uncertain renewable energy scheduling problem can effectively solve the current issues. Summary of the Invention

[0006] The purpose of this invention is to provide an adaptive robust scheduling method for integrated energy systems that considers the uncertainties of photovoltaic (PV) and load. This method takes into account the output uncertainties of PV and load during the absorption process, and can fully address the impact of PV and load uncertainties on the distribution network during the scheduling process. It also uses the Grey Wolf optimization algorithm for effective solution, which can improve the PV absorption capacity of the distribution network, enhance the robustness of the system, and resist the impact of PV output uncertainty and high penetration on the power grid.

[0007] The technical solution to achieve the purpose of this invention is as follows:

[0008] An adaptive robust scheduling method for an integrated energy system considering photovoltaic and load uncertainties, characterized by comprising the following steps:

[0009] Step 1: Determine the location and capacity of the photovoltaic equipment connected to the distribution network, and determine the location of the integrated energy system connected to the distribution network and the parameters of each device in the integrated energy system;

[0010] Step 2: Perform day-ahead forecasts for photovoltaic and load power in the distribution network to obtain the day-ahead forecast output power of photovoltaic and load power at each node of the distribution network;

[0011] Step 3: Based on the day-ahead forecast results of photovoltaic and load power in the distribution network, construct the box-type output uncertainty set of the day-ahead forecast output of photovoltaic and load power, and determine the power range of the day-ahead output of photovoltaic and load power.

[0012] Step 4: Combining the actual photovoltaic and load output curves during the day, construct an adaptive robust scheduling optimization model for the integrated energy system, and decompose the adaptive robust scheduling optimization model for the integrated energy system into two smaller models: the integrated energy system equipment state configuration model and the worst-case output model for uncertain photovoltaic loads;

[0013] Step 5: Integrated Energy System Equipment Status Configuration Model: Configure the relevant parameters of the gray wolf optimization algorithm for the status configuration model, and initialize the position of each gray wolf, which represents the operating status of each device in the integrated energy system;

[0014] Step 6: Uncertain PV load worst-case output model: Configure the relevant parameters of the gray wolf optimization algorithm for the worst-case output model, and initialize the position of each gray wolf, which is the day-ahead output power of the PV and the load;

[0015] Step 7: Under the constraints of the integrated energy system equipment and its operating status, take the maximum value among the minimum day-ahead and intraday dispatch costs of the integrated energy system as the objective function, and solve for the day-ahead output power under the worst photovoltaic and load output conditions;

[0016] Step 8: Determine whether the termination condition has been met. If the termination condition has not been met, jump to step 7. Otherwise, take the maximum value among the minimum day-to-day scheduling costs of the integrated energy system as the fitness value of each gray wolf position in the integrated energy system equipment state model, and iteratively solve the integrated energy system equipment state configuration model with the minimum fitness value as the objective function.

[0017] Step 9: Determine if the termination condition has been met. If the termination condition has not been met, proceed to step 8. Otherwise, output the output status of photovoltaic systems, the worst-case load output scenario, the output status of each device in the integrated energy system, and the day-ahead to day-intraday dispatch output.

[0018] Preferably, the integrated energy system equipment in step 1 includes a combined heat and power (CHP) unit, a gas boiler (GB), an electric boiler (EB), a power to gas (P2G) system, distributed energy storage equipment, distributed gas storage equipment, and distributed thermal storage equipment.

[0019] Preferably, the integrated energy system equipment parameters in step 1 include the minimum output power, maximum output power, uphill rate, downhill rate, unit maintenance cost, unit investment cost, rated capacity, electrical conversion efficiency, thermal conversion efficiency, and lifespan of the combined heat and power unit, electric boiler, gas boiler, and electric-to-gas conversion equipment; and the initial capacity, rated capacity, minimum equipment status, maximum equipment status, unit investment cost, unit maintenance cost, charge and discharge efficiency, maximum discharge power, and lifespan of the distributed energy storage equipment, distributed gas storage equipment, and distributed thermal storage equipment.

[0020] Preferably, the box-type output uncertainty set for the day-ahead predicted output of photovoltaic and load in step 3 is constructed as follows:

[0021]

[0022] In the formula: P pv,da (t), P load,e,da (t) represents the day-ahead forecast power of photovoltaic and load at time t; P pv (t), P load (t) represents the fluctuation range of photovoltaic and load at time t; ψ1 and ψ2 are robust parameters used to adjust the magnitude of the fluctuation range of photovoltaic and load output.

[0023] Preferably, the adaptive robust scheduling optimization model of the integrated energy system in step 4 is as follows:

[0024]

[0025] It is decomposed into two models: a comprehensive energy system equipment state configuration model and a worst-case output model for uncertain photovoltaic loads;

[0026] The integrated energy system equipment status configuration model is as follows:

[0027]

[0028] Where: x is the optimization variable of the equipment state configuration model, used to represent the operating state of the integrated energy equipment at different times;

[0029] x = [U i,e (t),U i,h (t),Ui,g (t),U i,CHP (t),U i,GB (t),U i,EB (t),U i,P2G (t)] T

[0030] In the formula: U i,e (t), U i,h (t), U i,g (t), U i,CHP (t), U i,GB (t), U i,EB (t), U i,P2G (t) represents the operating status of distributed energy storage, distributed thermal storage, distributed gas storage, combined heat and power unit, electric boiler, gas boiler, and electric-to-gas conversion device at time t. If the status is 0, it means that no power is generated at that time; if the status is 1, it means that power is generated at that time.

[0031] The worst-case output model for uncertain photovoltaic loads is:

[0032]

[0033] Where: U represents the fluctuation range of uncertain output of photovoltaic and load, which is the power range of photovoltaic and load day-ahead output in step 3; N represents the number of integrated energy systems connected to the distribution network, f da,i Let f be the day-ahead dispatch cost of the i-th integrated energy system. dr,i The intraday dispatch correction cost for the i-th integrated energy system;

[0034] Among them, f da,i and f dr,i This can be specifically expressed as:

[0035]

[0036]

[0037]

[0038]

[0039] In the formula: i represents the i-th IES system; P ech,i (t), ΔP ech,i (t) represents the day-ahead charging power and intraday adjusted charging power of the distributed energy storage device at time t, respectively; P edis,i (t), ΔP edis,i (t) represents the day-ahead discharge power and intraday adjusted discharge power of the distributed energy storage device at time t, respectively; P hch,i (t), ΔP hch,i(t) represents the day-ahead thermal storage power and the intraday adjusted thermal storage power of the distributed thermal storage device at time t, respectively; P hdis,i (t), ΔP hdis,i (t) represents the daytime heat release power and the intraday adjusted heat release power of the distributed thermal storage device at time t, respectively; P gch,i (t), ΔP gch,i (t) represents the daytime gas storage power and the intraday adjusted gas storage power of the distributed gas storage device at time t, respectively; P gdis,i (t), ΔP gdis,i (t) represents the daytime gas release power and intraday adjusted gas release power of the distributed gas storage device at time t, respectively; P chp,e,i (t), ΔP chp,e,i (t) represents the daytime discharge power and intraday adjusted discharge power of the cogeneration unit at time t, respectively; P GB,h,i (t), ΔP GB,h,i (t) represents the daytime heat release power and the intraday adjusted heat release power of the gas-fired boiler at time t, respectively; P EB,e,i (t), ΔP EB,e,i (t) represents the day-ahead power consumption of the electric boiler at time t and the intraday readjusted power consumption, respectively; P P2G,e,i (t), ΔP P2G,e,i (t) The day-ahead power consumption and intraday power consumption adjustment of the electro-gas conversion device at time t; P ebuy,i (t), ΔP ebuy,i (t) represents the day-ahead power purchase and the intraday adjusted power purchase at time t, respectively; P gbuy,i (t), ΔP gbuy,i (t) represents the day-ahead gas purchase power and the intraday adjusted gas purchase power at time t, respectively; C ee Maintenance cost per unit power for distributed energy storage devices; C hh Maintenance cost per unit power for distributed thermal storage devices; C gg Maintenance cost per unit power for distributed gas storage devices; C CHP Maintenance cost per unit power of CHP units; C GB GB unit power maintenance cost; C EB Maintenance cost per unit power of the EB unit; C P2G Maintenance cost per unit power of P2G device; C i,Pbuy (t) represents the cost of purchasing electricity at time t; C ebuy Cost of electricity purchased per unit of power; C gbuy Cost of gas per unit of power; C i,in (t) represents the unit cost of equipment usage at time t; P Ns C sns represents the rated capacity, unit capacity installation cost, and service life of equipment S, where equipment S is a distributed energy storage device, a distributed thermal storage device, a distributed gas storage device, an electric-to-gas device, an electric boiler device, a gas boiler device, and a combined heat and power device, respectively; r represents the benchmark discount rate; and T is a scheduling cycle.

[0040] Preferably, the relevant parameters in the gray wolf optimization algorithm in step 5 are the population size, number of iterations, coefficient vector, and position vector of the gray wolf optimization.

[0041] Preferably, the relevant parameters in the gray wolf optimization algorithm in step 6 are the population size, number of iterations, coefficient vector, and position vector of the gray wolf optimization.

[0042] Preferably, the constraints on the integrated energy system equipment and its operating status in step 7 are as follows:

[0043] (1) Electric power constraint conditions

[0044]

[0045] Where: ΔP pv,i,di (t) represents the photovoltaic power to be adjusted at time t; P load,e,i (t) represents the electrical load power at time t; P pv,i,ac (t) represents the actual photovoltaic power generated at time t; P load,e,i,ac (t) represents the actual power consumed by the load at time t;

[0046] (2) Thermal power constraint

[0047]

[0048] In the formula: P chp,h,i (t), ΔP chp,h,i (t) represents the daytime heat release power and intraday adjusted heat release power of the cogeneration unit at time t, respectively; P GB,h,i (t), ΔP GB,h,i (t) represents the daytime heat release power and the intraday adjusted heat release power of the gas-fired boiler at time t, respectively; P EB,h,i (t), ΔP EB,h,i (t) represents the daytime heat release power and the adjusted heat release power of the electric boiler at time t, respectively; P load,h,i (t) represents the thermal power of the heat load at time t;

[0049] (3) Gas power constraint

[0050]

[0051] In the formula: P P2G,gas,i (t), ΔP P2G,gas,i(t) represents the daytime gas release power and intraday adjusted gas release power of the electro-gas conversion device at time t, respectively; P Gbuy,i (t), ΔP Gbuy,i (t) represents the day-ahead gas purchase power and the intraday adjusted gas purchase power at time t, respectively; P chp,gas,i (t), ΔP chp,gas,i (t) represents the day-ahead gas power required by the cogeneration unit at time t and the day-ahead gas power required for adjustment; P GB,g,i (t), ΔP GB,g,i (t) represents the required gas power of the gas boiler unit before time t and the required gas power for intraday adjustments, respectively; P load,g,i (t) represents the gas load and gas power at time t;

[0052] (4) Output constraints of combined heat and power units

[0053]

[0054] In the formula: P chp,i,min P chp,i,max For the minimum and maximum electrical power generated by a combined heat and power (CHP) unit; U i,CHP (t) represents the output state of the cogeneration unit at time t. When it is 0, it means that the cogeneration unit is not producing power at time t; when it is 1, it means that the cogeneration unit is producing power at time t. R chpdo,i R chpup,i These represent the downhill and uphill ramp rates for combined heat and power (CHP) units, respectively; P chp,e,i (t-1), ΔP chp,e,i (t-1) represents the daytime discharge power and intraday adjusted discharge power of the cogeneration unit at time t-1, respectively.

[0055] (5) Output constraints of gas-fired boilers

[0056]

[0057] In the formula: P GB,i,min P GB,i,max The lower and upper limits of the output of the gas-fired boiler unit; R GBdo,i R GBup,i For the downhill and uphill ramp rates of the gas-fired boiler unit; U i,GB (t) represents the output state of the gas-fired boiler at time t. When it is 0, it means that the gas-fired boiler is not producing output at time t; when it is 1, it means that the gas-fired boiler is producing output at time t. GB,h,i (t-1), ΔP GB,h,i (t-1) represents the daytime heat release power and the intraday adjusted heat release power of the gas-fired boiler at time t-1, respectively.

[0058] (6) Output constraints of electric boilers

[0059]

[0060] In the formula: P EB,i,min P EB,i,max The lower and upper limits of the output of the electric boiler unit; R EBdo,i R EBup,i For the downhill and uphill ramp rates of the electric boiler unit; U i,EB (t) represents the output state of the electric boiler at time t. When it is 0, it means that the electric boiler is not producing power at time t; when it is 1, it means that the electric boiler is producing power at time t. EB,h,i (t-1), ΔP EB,h,i (t-1) represents the daytime heat release power and the intraday adjusted heat release power of the electric boiler at time t-1, respectively.

[0061] (7) Constraints of the electro-pneumatic conversion device

[0062]

[0063] In the formula: P P2Gmin P represents the minimum output of the electro-pneumatic converter; P2Gmax U represents the maximum output of the electro-pneumatic converter; i,P2G (t) represents the output state of the electro-gas converter at time t. When it is 0, it means that the electro-gas converter is not producing power at time t. When it is 1, it means that the electro-gas converter is producing power at time t.

[0064] (8) Constraints of Distributed Energy Storage Devices

[0065]

[0066] In the formula: P emin P emax S represents the minimum and maximum output power of the distributed energy storage device. emin S emax For the minimum and maximum capacity of distributed energy storage devices; U i,e (t) represents the output state of the distributed energy storage device at time t. When it is 0, it means that the distributed energy storage device is not generating power at time t; when it is 1, it means that the distributed energy storage device is generating power at time t. edis Indicates the discharge efficiency of a distributed energy storage device; η ech Indicates the charging efficiency of a distributed energy storage device;

[0067] (9) Constraints of distributed thermal storage devices

[0068]

[0069] In the formula: P hmin P hmax S represents the minimum and maximum output power of the distributed thermal storage device. hminS hmax For the minimum and maximum capacity of the distributed thermal storage device; U i,h (t) represents the output state of the distributed thermal storage device at time t. When it is 0, it means that the distributed thermal storage device is not generating power at time t; when it is 1, it means that the distributed thermal storage device is generating power at time t. hdis Indicates the heat release efficiency of a distributed thermal storage device; η hch Indicates the thermal storage efficiency of a distributed thermal storage device;

[0070] (10) Constraints of distributed gas storage devices

[0071]

[0072] In the formula: P gmin P gmax S represents the minimum and maximum output power of the distributed gas storage device. gmin S gmax For the minimum and maximum capacity of the distributed gas storage device; U i,g (t) represents the output state of the distributed gas storage device at time t. When it is 0, it means that the distributed gas storage device is not producing power at time t; when it is 1, it means that the distributed gas storage device is producing power at time t. gdis Indicates the venting efficiency of a distributed gas storage device; η gch This indicates the gas storage efficiency of a distributed gas storage device.

[0073] Preferably, the minimum day-to-day scheduling cost of the integrated energy system in step 7 is solved using the cplex and yalmip solvers.

[0074] Compared with existing technologies, this invention has significant advantages: The adaptive robust scheduling method for integrated energy systems, considering both photovoltaic and load uncertainties, eliminates the need to assume uncertain distributions in renewable energy output. It directly constructs the adverse output range of renewable energy, effectively addressing the impact of uncertain renewable energy output on the distribution network, enhancing system robustness, and improving the distribution network's resilience. Furthermore, this invention fully leverages the advantages of multi-energy complementarity and rapid response in integrated energy systems to effectively address uncertainties in photovoltaic and load output, improving the robustness and economic performance of the distribution network. Simultaneously, by employing the Grey Wolf optimization algorithm, this invention can simply and effectively solve traditional robust optimization problems, avoiding their complexity. Attached Figure Description

[0075] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings required in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0076] Figure 1 This is a schematic diagram of the integrated energy system model structure of the present invention.

[0077] Figure 2 The flowchart of the adaptive robust scheduling process for the integrated energy system considering photovoltaic and load uncertainties in this invention is shown.

[0078] Figure 3 This is a topology diagram of the power distribution network model for the integrated energy system and photovoltaic system of this invention.

[0079] Figure 4 This is a schematic diagram of photovoltaic power output under the worst-case scenario.

[0080] Figure 5 This is a schematic diagram of the load output power under the worst-case scenario.

[0081] Figure 6 A schematic diagram for adjusting the output power of electrical power.

[0082] Figure 7 A schematic diagram showing the adjustment of output power for thermal power.

[0083] Figure 8 A schematic diagram for adjusting the output power of the gas. Detailed Implementation

[0084] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0085] like Figure 1 and Figure 2 As shown, this invention provides an adaptive robust scheduling method for an integrated energy system that considers photovoltaic and load uncertainties, comprising:

[0086] Step 1: Determine the location and capacity of the photovoltaic equipment connected to the distribution network, and determine the location of the integrated energy system connected to the distribution network, as well as the parameters of each device in the integrated energy system.

[0087] The integrated energy system equipment includes combined heat and power (CHP) units, gas boilers (GB), electric boilers (EB), power to gas (P2G) equipment, distributed energy storage equipment, distributed gas storage equipment, and distributed thermal storage equipment.

[0088] The integrated energy system equipment parameters include the minimum output power, maximum output power, uphill rate, downhill rate, unit maintenance cost, unit investment cost, rated capacity, electrical conversion efficiency, thermal conversion efficiency, and lifespan of the combined heat and power unit, electric boiler, gas boiler, and electric-to-gas conversion equipment; and the initial capacity, rated capacity, minimum equipment status, maximum equipment status, unit investment cost, unit maintenance cost, charge and discharge efficiency, maximum discharge power, and lifespan of the distributed energy storage equipment, distributed gas storage equipment, and distributed thermal storage equipment.

[0089] Step 2: Perform day-ahead forecasts for photovoltaic and load power in the distribution network to obtain the day-ahead forecast output power of photovoltaic and load power at each node of the distribution network.

[0090] Step 3: Based on the day-ahead forecast results of photovoltaic and load power distribution networks, construct the box-type output uncertainty set of the day-ahead forecast output of photovoltaic and load power, and determine the power range of the day-ahead output of photovoltaic and load power.

[0091] The box-type output uncertainty set for the day-ahead predicted output of photovoltaic and load power is constructed as follows:

[0092]

[0093] In the formula: P pv,da (t), P load,e,da (t) represents the day-ahead forecast power of photovoltaic and load at time t; P pv (t), P load (t) represents the fluctuation range of photovoltaic and load at time t; ψ1 and ψ2 are robust parameters used to adjust the magnitude of the fluctuation range of photovoltaic and load output, which can be set by the user.

[0094] Step 4: Combining the actual photovoltaic and load output curves during the day, construct an adaptive robust scheduling optimization model for the integrated energy system, and decompose the adaptive robust scheduling optimization model for the integrated energy system into two smaller models: the integrated energy system equipment state configuration model and the worst-case output model for uncertain photovoltaic load.

[0095] The adaptive robust scheduling optimization model of the integrated energy system is as follows:

[0096]

[0097] It is decomposed into two models: the integrated energy system equipment state configuration model and the worst-case output model of uncertain photovoltaic load.

[0098] The integrated energy system equipment status configuration model is as follows:

[0099]

[0100] Where: x is the optimization variable of the equipment state configuration model, used to represent the operating state of the integrated energy equipment at different times;

[0101] x = [U i,e (t),U i,h (t),U i,g (t),U i,CHP (t),U i,GB (t),U i,EB (t),U i,P2G (t)] T

[0102] In the formula: U i,e (t), U i,h (t), U i,g (t), U i,CHP (t), U i,GB (t), U i,EB (t), U i,P2G (t) represents the operating status of distributed energy storage, distributed thermal storage, distributed gas storage, combined heat and power unit, electric boiler, gas boiler, and electric-to-gas conversion device at time t. If the status is 0, it means that no power is generated at that time; if the status is 1, it means that power is generated at that time.

[0103] The worst-case output model for uncertain photovoltaic loads is:

[0104]

[0105] Where: U represents the fluctuation range of uncertain output of photovoltaic and load, which is the power range of photovoltaic and load day-ahead output in step 3; N represents the number of integrated energy systems connected to the distribution network, f da,i Let f be the day-ahead dispatch cost of the i-th integrated energy system. dr,i The daily dispatch correction cost for the i-th integrated energy system.

[0106] Among them, f da,i and f dr,i This can be specifically expressed as:

[0107]

[0108]

[0109]

[0110]

[0111] In the formula: i represents the i-th IES system; P ech,i (t), ΔP ech,i (t) represents the day-ahead charging power and intraday adjusted charging power of the distributed energy storage device at time t, respectively; P edis,i (t), ΔP edis,i (t) represents the day-ahead discharge power and intraday adjusted discharge power of the distributed energy storage device at time t; P hch,i (t), ΔP hch,i (t) represents the day-ahead thermal storage power and the intraday adjusted thermal storage power of the distributed thermal storage device at time t, respectively; P hdis,i (t), ΔP hdis,i (t) represents the daytime heat release power and the intraday adjusted heat release power of the distributed thermal storage device at time t, respectively; P gch,i (t), ΔP gch,i (t) represents the daytime gas storage power and the intraday adjusted gas storage power of the gas storage device at time t, respectively; P gdis,i (t), ΔP gdis,i (t) represents the daytime gas release power and intraday adjusted gas release power of the distributed gas storage device at time t, respectively; P chp,e,i (t), ΔP chp,e,i (t) represents the daytime discharge power and intraday adjusted discharge power of the cogeneration unit at time t, respectively; P GB,h,i (t), ΔP GB,h,i (t) represents the daytime heat release power and the intraday adjusted heat release power of the gas-fired boiler at time t, respectively; P EB,e,i (t), ΔP EB,e,i (t) represents the day-ahead power consumption of the electric boiler at time t and the intraday readjusted power consumption, respectively; P P2G,e,i (t), ΔP P2G,e,i (t) The day-ahead power consumption and intraday power consumption adjustment of the electro-gas conversion device at time t; P ebuy,i (t), ΔP ebuy,i (t) represents the day-ahead power purchase and the intraday adjusted power purchase at time t, respectively; P gbuy,i (t), ΔP gbuy,i (t) represents the day-ahead gas purchase power and the intraday adjusted gas purchase power at time t, respectively; C ee Maintenance cost per unit power for distributed energy storage devices; C hh Maintenance cost per unit power for distributed thermal storage devices; C gg Maintenance cost per unit power for distributed gas storage devices; C CHP Maintenance cost per unit power of CHP units; C GB GB unit power maintenance cost; CEB Maintenance cost per unit power of the EB unit; C P2G Maintenance cost per unit power of P2G device; C i,Pbuy (t) represents the cost of purchasing electricity at time t; C ebuy Cost of electricity purchased per unit of power; C gbuy Cost of gas per unit of power; C i,in (t) represents the unit cost of equipment usage at time t; P Ns C s ns represents the rated capacity, unit capacity installation cost, and service life of equipment S, where equipment S is a distributed energy storage device, a distributed thermal storage device, a distributed gas storage device, an electric-to-gas device, an electric boiler device, a gas boiler device, and a combined heat and power device, respectively; r represents the benchmark discount rate; and T is a scheduling cycle.

[0112] Step 5: Integrated Energy System Equipment Status Configuration Model: Configure the relevant parameters of the gray wolf optimization algorithm for the status configuration model, and initialize the position of each gray wolf, which represents the operating status of each device in the integrated energy system.

[0113] The relevant parameters in the gray wolf optimization algorithm are the population size, number of iterations, coefficient vector, and position vector.

[0114] Step 6: Uncertain PV load worst-case output model: Configure the relevant parameters of the gray wolf optimization algorithm for the worst-case output model, and initialize the position of each gray wolf, which is the day-ahead output power of the PV and the load.

[0115] The relevant parameters in the gray wolf optimization algorithm are the population size, number of iterations, coefficient vector, and position vector.

[0116] Step 7: Under the constraints of the integrated energy system equipment and its operating status, take the maximum value among the minimum day-ahead and intraday dispatch costs of the integrated energy system as the objective function, and solve for the day-ahead output power under the worst-case photovoltaic and load output conditions.

[0117] The constraints on the integrated energy system equipment and its operating status are as follows:

[0118] (1) Electric power constraint conditions

[0119]

[0120] Where: ΔP pv,i,di (t) represents the photovoltaic power to be adjusted at time t; P load,e,i (t) represents the electrical load power at time t; P pv,i,ac (t) represents the actual photovoltaic power generated at time t; P load,e,i,ac(t) represents the actual power consumed by the load at time t.

[0121] (2) Thermal power constraint

[0122]

[0123] In the formula: P chp,h,i (t), ΔP chp,h,i (t) represents the daytime heat release power and intraday adjusted heat release power of the cogeneration unit at time t, respectively; P GB,h,i (t), ΔP GB,h,i (t) represents the daytime heat release power and the intraday adjusted heat release power of the gas-fired boiler at time t, respectively; P EB,h,i (t), ΔP EB,h,i (t) represents the daytime heat release power and the adjusted heat release power of the electric boiler at time t, respectively; P load,h,i (t) represents the heat load heat power at time t.

[0124] (3) Gas power constraint

[0125]

[0126] In the formula: P P2G,gas,i (t), ΔP P2G,gas,i (t) represents the daytime gas release power and intraday adjusted gas release power of the electro-gas conversion device at time t, respectively; P Gbuy,i (t), ΔP Gbuy,i (t) represents the day-ahead gas purchase power and the intraday adjusted gas purchase power at time t, respectively; P chp,gas,i (t), ΔP chp,gas,i (t) represents the day-ahead gas power required by the cogeneration unit at time t and the day-ahead gas power required for adjustment; P GB,g,i (t), ΔP GB,g,i (t) represents the required gas power of the gas boiler unit before time t and the required gas power for intraday adjustments, respectively; P load,g,i (t) represents the gas load and gas power at time t.

[0127] (4) Output constraints of combined heat and power units

[0128]

[0129] In the formula: P chp,i,min P chp,i,max For the minimum and maximum electrical power generated by a combined heat and power (CHP) unit; U i,CHP (t) represents the output state of the cogeneration unit at time t. When it is 0, it means that the cogeneration unit is not producing power at time t; when it is 1, it means that the cogeneration unit is producing power at time t. R chpdo,i R chpup,i These represent the downhill and uphill ramp rates for combined heat and power (CHP) units, respectively; Pchp,e,i (t-1), ΔP chp,e,i (t-1) represents the daytime discharge power and intraday adjusted discharge power of the cogeneration unit at time t-1.

[0130] (5) Output constraints of gas-fired boilers

[0131]

[0132] In the formula: P GB,i,min P GB,i,max The lower and upper limits of the output of the gas-fired boiler unit; R GBdo,i R GBup,i For the downhill and uphill ramp rates of the gas-fired boiler unit; U i,GB (t) represents the output state of the gas-fired boiler at time t. When it is 0, it means that the gas-fired boiler is not producing output at time t; when it is 1, it means that the gas-fired boiler is producing output at time t. GB,h,i (t-1), ΔP GB,h,i (t-1) represents the daytime heat release power and the intraday adjusted heat release power of the gas-fired boiler at time t-1.

[0133] (6) Output constraints of electric boilers

[0134]

[0135] In the formula: P EB,i,min P EB,i,max The lower and upper limits of the output of the electric boiler unit; R EBdo,i R EBup,i For the downhill and uphill ramp rates of the electric boiler unit; U i,EB (t) represents the output state of the electric boiler at time t. When it is 0, it means that the electric boiler is not producing power at time t; when it is 1, it means that the electric boiler is producing power at time t. EB,h,i (t-1), ΔP EB,h,i (t-1) represents the daytime heat release power and the intraday adjusted heat release power of the electric boiler at time t-1.

[0136] (7) Constraints of the electro-pneumatic conversion device

[0137]

[0138] In the formula: P P2Gmin P represents the minimum output of the electro-pneumatic converter; P2Gmax U represents the maximum output of the electro-pneumatic converter; i,P2G (t) represents the output state of the electro-gas converter at time t. When it is 0, it means that the electro-gas converter is not producing power at time t. When it is 1, it means that the electro-gas converter is producing power at time t.

[0139] (8) Constraints of Distributed Energy Storage Devices

[0140]

[0141] In the formula: P emin P emax S represents the minimum and maximum output power of the distributed energy storage device. emin S emax For the minimum and maximum capacity of distributed energy storage devices; U i,e (t) represents the output state of the distributed energy storage device at time t. When it is 0, it means that the distributed energy storage device is not generating power at time t; when it is 1, it means that the distributed energy storage device is generating power at time t. edis Indicates the discharge efficiency of a distributed energy storage device; η ech This indicates the charging efficiency of a distributed energy storage device.

[0142] (9) Constraints of distributed thermal storage devices

[0143]

[0144] In the formula: P hmin P hmax S represents the minimum and maximum output power of the distributed thermal storage device. hmin S hmax For the minimum and maximum capacity of the distributed thermal storage device; U i,h (t) represents the output state of the distributed thermal storage device at time t. When it is 0, it means that the distributed thermal storage device is not generating power at time t; when it is 1, it means that the distributed thermal storage device is generating power at time t. hdis Indicates the heat release efficiency of a distributed thermal storage device; η hch This indicates the thermal storage efficiency of a distributed thermal storage device.

[0145] (10) Constraints of distributed gas storage devices

[0146]

[0147] In the formula: P gmin P gmax S represents the minimum and maximum output power of the distributed gas storage device. gmin S gmax For the minimum and maximum capacity of the distributed gas storage device; U i,g (t) represents the output state of the distributed gas storage device at time t. When it is 0, it means that the distributed gas storage device is not producing power at time t; when it is 1, it means that the distributed gas storage device is producing power at time t. gdis Indicates the venting efficiency of a distributed gas storage device; η gch This indicates the gas storage efficiency of a distributed gas storage device.

[0148] The minimum day-to-day scheduling cost of the integrated energy system was solved using the cplex and yalmip solvers.

[0149] Step 8: Determine whether the termination condition has been met. If the termination condition has not been met, jump to step 7. Otherwise, take the maximum value among the minimum day-to-day scheduling costs of the integrated energy system as the fitness value of each gray wolf position in the integrated energy system equipment state model, and iteratively solve the integrated energy system equipment state configuration model with the minimum fitness value as the objective function.

[0150] Step 9: Determine if the termination condition has been met. If the termination condition has not been met, proceed to step 8. Otherwise, output the output status of photovoltaic systems, the worst-case load output scenario, the output status of each device in the integrated energy system, and the day-ahead to day-intraday dispatch output.

[0151] The invention will be further illustrated by an example below.

[0152] To verify the effectiveness of the above model, this paper will adopt the IEEE 33-node distribution network model, and the connection diagram with photovoltaic and integrated energy systems is shown in the figure. Figure 3 As shown in the figure. The system's base voltage is 12.66kV, the base power is 10MVA, bus 1 is the slack bus with a voltage of 1.04pu, and the total system load is 3.715MW + j2.3Mvar. Considering the relatively small area of ​​the distribution network in the urban area, it is assumed that the variation in sunlight within the distribution network is not significant; therefore, the sunlight intensity at each node is assumed to be the same. Considering the power company's assessment standards, the per-unit value of the node voltage must not exceed 1.05 and must not be lower than 0.95. The integrated energy system parameters selected in this example are shown in Tables 1 and 2.

[0153] Table 1. Selection parameters for CHP, EB, GB, and P2G models

[0154]

[0155]

[0156] Table 1. Selection parameters for CHP, EB, GB, and P2G models (continued)

[0157]

[0158] Table 2. Energy Storage Device Selection Parameters

[0159]

[0160]

[0161] In addition to the equipment parameters, the data also includes the benchmark discount rate, as well as peak-valley electricity and gas prices. The benchmark discount rate in this paper is uniformly set at 5%, and the peak-valley electricity and gas prices are shown in Tables 3 and 4.

[0162] Table 3 Peak-Valley Time-of-Use Electricity Prices

[0163]

[0164] Table 4 Peak-Valley Time-of-Use Gas Prices

[0165]

[0166]

[0167] Figure 4 and Figure 5 These represent the photovoltaic and load worst-case output scenarios under uncertain output conditions. Figure 6 During the intraday scheduling phase, electrical, thermal, and gas power require corresponding power adjustments. A negative adjustment power indicates that this power needs to be supplied; a positive adjustment power indicates that this power needs to be consumed. Since the uncertainties in thermal and gas power are not considered, the readjustment power for thermal and gas power during the intraday correction phase is 0. Figure 4 and Figure 5 It can be seen that, under the worst-case scenario, the photovoltaic output is more biased towards the upper limit, while the load output is more biased towards the lower limit.

[0168] During the intraday real-time scheduling phase, when the required corrected electrical power is negative and the corrected power is relatively large, such as at times 9 and 11-17, the dispatched power is reduced by decreasing the output power of the electric boiler and P2G unit to meet the intraday power demand. When the output power of the electric boiler is reduced, the reduced thermal power is compensated by the gas turbine, while the reduced gas power is mainly compensated by the purchased gas power. Since the unit cost of the gas boiler and purchased gas is higher than that of the electric boiler and P2G unit, the cost of the readjustment phase increases. When the corrected power is relatively small, such as during times 0-5, the power required for this part of the electrical demand is met by increasing the power of the CHP unit. When the power of the CHP unit increases, the output of the gas boiler is reduced to meet the thermal power rebalancing, while the gas power balancing can be achieved by using the gas boiler and purchased gas to cope with changes in CHP output.

[0169] When the required corrected electrical power is positive and the corrected power is relatively large, such as at times 7, 8, and 10, the excess electrical power is absorbed by increasing the electric boiler and P2G, corresponding to reducing the gas source and gas boiler to adjust the gas power and heat power balance. When the corrected power is relatively small, the output of the CHP unit is reduced to reduce the electrical power output.

[0170] In summary, the adaptive robust scheduling method for integrated energy systems considering photovoltaic and load uncertainties in this invention can directly construct the adverse output range of new energy sources without assuming uncertain distribution of new energy output. This effectively addresses the impact of uncertain new energy output on the distribution network, enhances system robustness, and improves the distribution network's resilience. This invention fully utilizes the advantages of multi-energy complementarity and rapid response in integrated energy systems to effectively address the uncertainties in photovoltaic and load output, improving the robustness and economic performance of the distribution network. Furthermore, this invention employs the Grey Wolf optimization algorithm to simply and effectively solve traditional robust optimization problems, avoiding the complexity of the problem.

[0171] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An adaptive robust scheduling method for an integrated energy system considering photovoltaic and load uncertainties, characterized in that, Includes the following steps: Step 1: Determine the location and capacity of the photovoltaic equipment connected to the distribution network, and determine the location of the integrated energy system connected to the distribution network and the parameters of each device in the integrated energy system; Step 2: Perform day-ahead forecasts for photovoltaic and load power in the distribution network to obtain the day-ahead forecast output power of photovoltaic and load power at each node of the distribution network; Step 3: Based on the day-ahead forecast results of photovoltaic and load power in the distribution network, construct the box-type output uncertainty set of the day-ahead forecast output of photovoltaic and load power, and determine the power range of the day-ahead output of photovoltaic and load power. Step 4: Combining the actual photovoltaic and load output curves during the day, construct an adaptive robust scheduling optimization model for the integrated energy system, and decompose the adaptive robust scheduling optimization model for the integrated energy system into two smaller models: the integrated energy system equipment state configuration model and the worst-case output model for uncertain photovoltaic loads; Step 5: Integrated Energy System Equipment Status Configuration Model: Configure the relevant parameters of the gray wolf optimization algorithm for the status configuration model, and initialize the position of each gray wolf, which represents the operating status of each device in the integrated energy system; Step 6: Uncertain PV load worst-case output model: Configure the relevant parameters of the gray wolf optimization algorithm for the worst-case output model, and initialize the position of each gray wolf, which is the day-ahead output power of the PV and the load; Step 7: Under the constraints of the integrated energy system equipment and its operating status, take the maximum value among the minimum day-ahead and intraday dispatch costs of the integrated energy system as the objective function, and solve for the day-ahead output power under the worst photovoltaic and load output conditions; Step 8: Determine whether the termination condition has been met. If the termination condition has not been met, jump to step 7. Otherwise, take the maximum value among the minimum day-to-day scheduling costs of the integrated energy system as the fitness value of each gray wolf position in the integrated energy system equipment state model, and iteratively solve the integrated energy system equipment state configuration model with the minimum fitness value as the objective function. Step 9: Determine if the termination condition has been met. If the termination condition has not been met, proceed to step 8. Otherwise, output the output status of photovoltaic systems, the worst-case load output scenario, the output status of each device in the integrated energy system, and the day-ahead to day-intraday dispatch output.

2. The adaptive robust scheduling method for integrated energy systems considering photovoltaic and load uncertainties according to claim 1, characterized in that, The integrated energy system equipment in step 1 includes a combined heat and power (CHP) unit, a gas boiler (GB), an electric boiler (EB), a power to gas (P2G) system, distributed energy storage equipment, distributed gas storage equipment, and distributed thermal storage equipment.

3. The adaptive robust scheduling method for integrated energy systems considering photovoltaic and load uncertainties according to claim 1, characterized in that, The integrated energy system equipment parameters in step 1 include the minimum output power, maximum output power, uphill rate, downhill rate, unit maintenance cost, unit investment cost, rated capacity, electrical conversion efficiency, thermal conversion efficiency, and lifespan of the combined heat and power unit, electric boiler, gas boiler, and electric-to-gas conversion equipment; and the initial capacity, rated capacity, minimum equipment status, maximum equipment status, unit investment cost, unit maintenance cost, charge and discharge efficiency, maximum discharge power, and lifespan of the distributed energy storage equipment, distributed gas storage equipment, and distributed thermal storage equipment.

4. The adaptive robust scheduling method for integrated energy systems considering photovoltaic and load uncertainties according to claim 1, characterized in that, The box-type output uncertainty set for the day-ahead predicted output of photovoltaic power and load in step 3 is constructed as follows: In the formula: , for Solar power and load day-ahead forecast power at any given time; , for The fluctuation range of photovoltaic power and load at any given time; , These are robust parameters used to adjust the range of fluctuations in photovoltaic and load output.

5. The adaptive robust scheduling method for integrated energy systems considering photovoltaic and load uncertainties according to claim 1, characterized in that, The adaptive robust scheduling optimization model for the integrated energy system in step 4 is as follows: It is decomposed into two models: a comprehensive energy system equipment state configuration model and a worst-case output model for uncertain photovoltaic loads; The integrated energy system equipment status configuration model is as follows: in: Optimization variables are configured for the equipment status model to represent the operating status of the integrated energy equipment at different times; In the formula: , , , , , , They are respectively The system displays the operating status of distributed energy storage devices, distributed thermal storage, distributed gas storage, combined heat and power units, electric boilers, gas boilers, and electric-to-gas conversion devices at all times. If the status is 0, it means that the device is not generating power at that time; if the status is 1, it means that the device is generating power at that time. The worst-case output model for uncertain photovoltaic loads is: in: The fluctuation range of uncertain output of photovoltaic and load is the power range of photovoltaic and load day-ahead output in step 3; The number of integrated energy systems connected to the distribution network, For the first The current dispatch cost of an integrated energy system For the first Daily dispatch and correction costs for an integrated energy system; in, and This can be specifically expressed as: In the formula: Representing the One IES system; , Distributed energy storage devices Current charging power as of yesterday, and adjusted charging power within the day; , Distributed energy storage devices Discharge power before the current time, and adjusted discharge power during the day; , Distributed thermal storage devices The thermal storage capacity before the current time and the thermal storage capacity adjusted within the day; , Distributed thermal storage devices Heat release power before the current day, and heat release power adjusted during the day; , Distributed gas storage devices Gas storage capacity up to the day and gas storage capacity adjusted within the day; , Distributed gas storage devices The daily gas venting power and the intraday adjustment of the gas venting power; , They are combined heat and power units Discharge power before the current time, and adjusted discharge power during the day; , Gas boilers Heat release power before the current day, and heat release power adjusted during the day; , Electric boilers The power consumption capacity before the specified time and the power consumption capacity readjusted within the day; , Separate electro-pneumatic conversion device Power consumption before the specified time, and power consumption adjusted within the day; , They are respectively Power purchased before the specified time, and power purchased adjusted within the day; , They are respectively Gas purchase capacity before the specified time, and gas purchase capacity adjusted within the day; Maintenance cost per unit power for distributed energy storage devices; Maintenance cost per unit power for distributed thermal storage devices; Maintenance cost per unit power of distributed gas storage devices; Maintenance cost per unit power of CHP units; Maintenance cost per unit power of GB units; Maintenance cost per unit power of the EB unit; Maintenance cost per unit power of P2G device; The cost of purchasing electricity at time t; Cost of electricity purchased per unit of power; Cost of gas per unit of power; The cost per unit of equipment at time t; , , Indicates equipment Rated capacity, unit capacity installation cost, and service life of the equipment. These are distributed energy storage devices, distributed thermal storage devices, distributed gas storage devices, electric-to-gas conversion devices, electric boiler devices, gas boiler devices, and combined heat and power devices. Indicates the benchmark discount rate; One scheduling cycle.

6. The adaptive robust scheduling method for integrated energy systems considering photovoltaic and load uncertainties according to claim 1, characterized in that, The relevant parameters in the gray wolf optimization algorithm in step 5 are the population size, number of iterations, coefficient vector, and position vector of the gray wolf optimization.

7. The adaptive robust scheduling method for integrated energy systems considering photovoltaic and load uncertainties according to claim 1, characterized in that, The relevant parameters in the gray wolf optimization algorithm in step 6 are the population size, number of iterations, coefficient vector, and position vector of the gray wolf optimization.

8. The adaptive robust scheduling method for integrated energy systems considering photovoltaic and load uncertainties according to claim 1, characterized in that, The constraints on the integrated energy system equipment and its operating status in step 7 are as follows: (1) Electric power constraint conditions In the formula: Let be the photovoltaic power to be adjusted at time t; Let t be the electrical load power at time t; Let t be the actual photovoltaic power generated at time t; The actual power consumed by the load at time t; (2) Thermal power constraint In the formula: , They are combined heat and power units Heat release power before the current day, and heat release power adjusted during the day; , Gas boilers Heat release power before the current day, and heat release power adjusted during the day; , Electric boilers Heat release power before the current day, and heat release power adjusted during the day; Let t be the heat load heat power at time t; (3) Gas power constraint In the formula: , These are respectively electro-pneumatic conversion devices The daily gas venting power and the intraday adjustment of the gas venting power; , They are respectively Gas purchase capacity before the specified time, and gas purchase capacity adjusted within the day; , These are combined heat and power units Gas power required before the specified time, and gas power required for intraday adjustments; , Gas boiler unit Gas power required before the specified time, and gas power required for intraday adjustments; Let t be the gas load and gas power. (4) Output constraints of cogeneration units In the formula: , The minimum and maximum electrical power generated by a combined heat and power unit; For combined heat and power units The output status at any given moment; when it is 0, it indicates... When the combined heat and power unit is not operating, a value of 1 indicates that the combined heat and power unit is not generating power. The combined heat and power unit is always operating at full capacity. , These are the downhill and uphill ramp rates for combined heat and power units, respectively. , They are combined heat and power units Discharge power before the current time, and adjusted discharge power during the day; (5) Output constraints of gas-fired boilers In the formula: , The lower and upper limits of the output of the gas-fired boiler unit; , For gas-fired boiler units, the downhill and uphill ramp rates are used. Gas-fired boiler The output status at any given moment; when it is 0, it indicates... The gas boiler is not producing power at any time; when the value is 1, it means... The gas-fired boiler is constantly producing power. , Gas boilers Heat release power before the current day, and heat release power adjusted during the day; (6) Output constraints of electric boilers In the formula: , The lower and upper limits of the output of the electric boiler unit; , For electric boiler units, the downhill and uphill ramp rates are used. For electric boilers The output status at any given moment; when it is 0, it indicates... The electric boiler is not producing power at all times; when the value is 1, it means... The electric boiler is always producing power. , Electric boilers Heat release power before the current day, and heat release power adjusted during the day; (7) Constraints of the electro-pneumatic conversion device In the formula: This represents the minimum output of the electro-pneumatic converter. This represents the maximum output of the electro-pneumatic converter; For electro-gas conversion device The output status at any given moment; when it is 0, it indicates... The electro-pneumatic device is not producing power at any given time; when the value is 1, it indicates... The output of the electro-pneumatic device is constantly monitored. (8) Constraints of distributed energy storage devices In the formula: , The minimum and maximum output power of the distributed energy storage device; , These represent the minimum and maximum capacities of distributed energy storage devices. Distributed energy storage devices The output status at any given moment; when it is 0, it indicates... The distributed energy storage device is not generating power at any given time; when the value is 1, it indicates that... Distributed energy storage devices are constantly generating power; Indicates the discharge efficiency of a distributed energy storage device; Indicates the charging efficiency of a distributed energy storage device; (9) Constraints of distributed thermal storage devices In the formula: , The minimum and maximum output power of the distributed thermal storage device; , For the minimum and maximum capacity of distributed thermal storage devices; Distributed thermal storage device The output status at any given moment; when it is 0, it indicates... The distributed thermal storage device is not generating power at any time; when the value is 1, it indicates that... The distributed thermal storage device outputs power at all times; This indicates the heat release efficiency of the distributed thermal storage device; Indicates the thermal storage efficiency of a distributed thermal storage device; (10) Constraints of distributed gas storage devices In the formula: , The minimum and maximum output power of the distributed gas storage device; , These represent the minimum and maximum capacities of the distributed gas storage device. Distributed gas storage device The output status at any given moment; when it is 0, it indicates... The distributed gas storage device is not generating power at any time; when the value is 1, it indicates that... The distributed gas storage device outputs power at all times; Indicates the venting efficiency of a distributed gas storage device; This indicates the gas storage efficiency of a distributed gas storage device.

9. The adaptive robust scheduling method for integrated energy systems considering photovoltaic and load uncertainties according to claim 1, characterized in that, The minimum day-to-day scheduling cost of the integrated energy system in step 7 is solved using the cplex and yalmip solvers.

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