An adaptive multi-energy dynamic storage method and device of a virtual power plant in different scenarios

Abstract

This invention discloses an adaptive multi-energy dynamic storage method and device for virtual power plants under different scenarios, relating to the field of collaborative scheduling and optimization technology for virtual power plants. The method constructs a three-level scheduling architecture for the virtual power plant and various equipment power generation models; based on the working states of energy storage and thermal storage devices, it constructs mathematical models for energy storage device charging and discharging and for thermal storage device charging and discharging, respectively; it calculates energy surplus and deficit data by introducing a multi-energy state comprehensive pressure index; it calculates energy storage status by introducing dynamic energy storage factors and energy surplus factors; it constructs a multi-energy compensation strategy for extreme weather conditions based on set scenarios; it constructs objective functions for power producers and power operators; and it solves these using a multi-objective optimization algorithm to obtain the optimal energy storage scheduling scheme. This invention addresses the problems of fragmented multi-energy response, lack of adaptability to normal and extreme weather conditions, and inaccurate energy storage regulation in existing virtual power plants.

Description

Technical Field

[0001] This invention relates to the field of multi-energy collaborative scheduling and optimization technology for virtual power plants, specifically to an adaptive multi-energy dynamic storage method and apparatus for virtual power plants under different scenarios. Background Technology

[0002] Virtual power plants, as a core platform integrating distributed energy resources, adjustable loads, and energy storage devices, have become a key support for improving grid resilience and promoting the consumption of renewable energy. Current applications of virtual power plants revolve around multi-timescale scheduling, resource optimization, and multi-objective optimization algorithms. They generally adopt a three-tier architecture involving power producers, power operators, and power-consuming areas. By constructing a control chain of "daytime planning - intraday dynamic adjustment - real-time deviation correction," they respond to the energy supply and demand balance requirements under both normal and extreme weather conditions, while also considering objectives such as minimizing operating costs, reducing carbon emissions, and ensuring system response robustness. Related technologies, through improved optimization algorithms and the design of adaptive energy storage strategies, provide fundamental support for the large-scale deployment of virtual power plants. Their application scenarios have expanded from single-energy dispatch to multi-energy synergy, including electricity and heat, covering diverse electricity consumption scenarios in industry, commerce, and residential sectors.

[0003] However, existing virtual power plant technologies still have significant shortcomings: In terms of multi-energy response adaptation, response regulation exhibits fragmented characteristics, lacks adaptability to normal and extreme weather scenarios, and does not fully integrate multi-energy coupling logic, making it difficult to meet the dual requirements of accurate system timing response and adaptability to extreme scenarios; In terms of resource regulation and energy storage utilization, the quantitative characterization of flexible resources suffers from parameter oversimplification, the regulation efficiency of various energy storage devices is exaggerated, and new adjustable resources are not fully incorporated. Furthermore, there is a lack of accurate consideration of multi-energy load pressure, flexible regulation mechanisms for dynamic energy storage, and multi-energy compensation logic under extreme conditions; At the optimization algorithm level, traditional multi-timescale optimization algorithms often use fixed parameter settings, have limited search capabilities, and are prone to getting trapped in local optima. Multi-objective optimization algorithms struggle to balance solution set equilibrium and convergence efficiency, directly affecting the optimization effect of virtual power plant operation and restricting its reliable application in complex scenarios.

[0004] Therefore, there is an urgent need for an adaptive multi-energy dynamic storage method for virtual power plants under different scenarios to solve the problems of fragmented multi-energy response, lack of adaptability to normal and extreme weather, and inaccurate energy storage regulation of existing virtual power plants. Summary of the Invention

[0005] To address this, the present invention provides an adaptive multi-energy dynamic storage method and apparatus for virtual power plants under different scenarios, which solves the problems of fragmented multi-energy response, lack of adaptability to normal and extreme weather, and inaccurate energy storage regulation in existing virtual power plants, and realizes multi-energy collaborative dynamic storage and efficient optimized scheduling.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an adaptive multi-energy dynamic storage method for virtual power plants under different scenarios, comprising:

[0007] The virtual power plant is divided into power producers, power operators, and power consumption areas including industrial areas, commercial areas, and residential areas. A three-level dispatch architecture for the virtual power plant is constructed. Combined with the operating characteristics of the virtual power plant, wind power generation model, photovoltaic power generation model, gas turbine power generation model, and biogas power generation model are constructed. Power generation constraints and ramp-up rate constraints are set for each device.

[0008] Based on a three-level scheduling architecture, the working states of energy storage devices and thermal storage devices are determined. Based on the working states of energy storage devices and thermal storage devices, mathematical models for charging and discharging of energy storage devices and mathematical models for charging and discharging of thermal storage devices are constructed respectively, and constraints on charging devices and thermal storage devices are set.

[0009] Based on the mathematical models of charging and discharging of energy storage devices and the mathematical models of charging and discharging of thermal storage devices, a multi-energy state comprehensive pressure index is introduced to calculate the energy surplus and deficit data of energy storage devices and thermal storage devices. Dynamic energy storage factor and energy surplus factor are introduced, and combined with energy surplus and deficit data, energy storage cost and remaining capacity, the energy storage status of energy storage devices and thermal storage devices is calculated.

[0010] In response to extreme weather, a multi-energy compensation strategy is constructed based on the given scenarios.

[0011] Based on the operational objectives of power producers and operators, objective functions for power producers and operators are constructed and integrated with carbon emission indicators and response robustness indicators to form a multi-objective system. Based on energy surplus / deficit data, energy storage status, and the setting of multi-energy compensation strategies, the multi-objective system is solved using the multi-objective optimization algorithm to obtain the optimal energy storage scheduling scheme.

[0012] As a preferred option for an adaptive multi-energy dynamic storage method for virtual power plants under different scenarios, the energy surplus and deficit data of the energy storage device and the thermal storage device include the power deficit, thermal deficit, power surplus and total energy deficit at a set time.

[0013] As a preferred scheme for an adaptive multi-energy dynamic storage method for virtual power plants under different scenarios, the configured multi-energy compensation strategy is constructed according to the specified scenario as follows:

[0014] Scenario 1: When there is a surplus of electrical energy after the electrical energy storage meets the electrical energy shortage, the surplus electrical energy is converted into heat energy to make up for the heat energy shortage. If the converted heat energy is still insufficient, heat is purchased from the power operator.

[0015] Scenario 2: When neither electrical energy storage nor thermal energy storage can fill their respective gaps, priority will be given to meeting the electrical energy gap, and all electrical energy gaps and part of the thermal energy gaps will be made up from the power operator.

[0016] As a preferred scheme for an adaptive multi-energy dynamic storage method for virtual power plants under different scenarios, the power producer aims to maximize the revenue from selling energy and minimize the operating cost, and constructs the objective function of the power producer; the power operator aims to maximize the difference between the revenue from selling energy to the electricity consumption area and the cost of purchasing energy from the power producer, and constructs the objective function of the power operator.

[0017] The objective function of the power producer is expressed as follows:

[0018] ;

[0019] In the formula, Ceb represents the economic benefit of the electricity producer; C sell For its energy sales revenue; C run Its operating cost; λ1 and λ2 are the weights of each component cost;

[0020] The objective function of the power operator is expressed as follows:

[0021] ;

[0022] In the formula, Ceo represents the comprehensive economic benefit of the power operator; C sell,e The economic benefits for power operators selling electricity and heat to electricity-consuming areas; C buy,e This refers to the cost for power operators to purchase electricity and heat from power producers.

[0023] As a preferred scheme for an adaptive multi-energy dynamic storage method for virtual power plants under different scenarios, an improved version of the Elegant Wren optimization algorithm is obtained by introducing the population activity factor "active" and white noise. Based on the improved version of the Elegant Wren optimization algorithm, the multi-objective version of the Elegant Wren optimization algorithm is constructed by adding external archiving and crowding distance sorting strategies.

[0024] This invention also provides an adaptive multi-energy dynamic storage device for virtual power plants under different scenarios, based on the above-mentioned adaptive multi-energy dynamic storage method for virtual power plants under different scenarios, comprising:

[0025] The virtual power plant three-level architecture and equipment model construction module is used to divide the virtual power plant into power producers, power operators, and power consumption areas including industrial areas, commercial areas, and residential areas. It constructs a three-level dispatch architecture for the virtual power plant and, in combination with the operating characteristics of the virtual power plant, constructs wind power generation models, photovoltaic power generation models, gas turbine power generation models, and biogas power generation models, and sets power generation constraints and ramp-up rate constraints for each device.

[0026] The module for constructing charging / discharging / energy mathematical models for energy storage devices is used to determine the working status of energy storage devices and thermal storage devices based on a three-level scheduling architecture; based on the working status of energy storage devices and thermal storage devices, it constructs charging / discharging mathematical models for energy storage devices and charging / discharging mathematical models for thermal storage devices, and sets constraints for charging devices and thermal storage devices.

[0027] The energy storage data acquisition module for energy storage devices is used to calculate the energy surplus and deficit data of energy storage devices and thermal storage devices based on the mathematical models of charging and discharging of energy storage devices and the mathematical models of charging and discharging of thermal storage devices, and by introducing a multi-energy state comprehensive pressure index; it also introduces dynamic energy storage factors and energy surplus factors, and combines energy surplus and deficit data, energy storage costs and remaining capacity to calculate the energy storage status of energy storage devices and thermal storage devices.

[0028] The multi-energy compensation strategy construction module is used to construct and set multi-energy compensation strategies for extreme weather based on the given scenarios.

[0029] The optimal energy storage scheduling scheme acquisition module is used to construct objective functions for power producers and power operators based on their operational objectives, and integrate them with carbon emission indicators and response robustness indicators to form a multi-objective system. Based on energy surplus and shortage data, energy storage status, and the setting of multi-energy compensation strategies, the multi-objective system is solved through the multi-objective optimization algorithm to obtain the optimal energy storage scheduling scheme.

[0030] As a preferred solution for an adaptive multi-energy dynamic storage device for virtual power plants under different scenarios, the energy surplus and deficit data of the energy storage device and the thermal storage device in the energy storage data acquisition module include the power deficit, thermal deficit, power surplus and total energy deficit at a set time.

[0031] As a preferred embodiment of an adaptive multi-energy dynamic storage device for virtual power plants under different scenarios, the multi-energy compensation strategy construction module constructs the following set multi-energy compensation strategy based on the set scenario:

[0032] Scenario 1: When there is a surplus of electrical energy after the electrical energy storage meets the electrical energy shortage, the surplus electrical energy is converted into heat energy to make up for the heat energy shortage. If the converted heat energy is still insufficient, heat is purchased from the power operator.

[0033] Scenario 2: When neither electrical energy storage nor thermal energy storage can fill their respective gaps, priority will be given to meeting the electrical energy gap, and all electrical energy gaps and part of the thermal energy gaps will be made up from the power operator.

[0034] As a preferred solution for an adaptive multi-energy dynamic storage device for virtual power plants under different scenarios, in the optimal energy storage scheduling scheme acquisition module, the power producer aims to maximize energy sales revenue and minimize operating costs, and constructs the power producer objective function; the power operator aims to maximize the difference between the revenue from selling energy to the electricity consumption area and the cost of purchasing energy from the power producer, and constructs the power operator objective function.

[0035] The objective function of the power producer is expressed as follows:

[0036] ;

[0037] In the formula, Ceb represents the economic benefit of the electricity producer; C sell For its energy sales revenue; C run Its operating cost; λ1 and λ2 are the weights of each component cost;

[0038] The objective function of the power operator is expressed as follows:

[0039] ;

[0040] In the formula, Ceo represents the comprehensive economic benefit of the power operator; C sell,e The economic benefits for power operators selling electricity and heat to electricity-consuming areas; C buy,e This refers to the cost for power operators to purchase electricity and heat from power producers.

[0041] As a preferred solution for an adaptive multi-energy dynamic storage device for virtual power plants under different scenarios, the optimal energy storage scheduling scheme acquisition module obtains an improved Elegant Warbler optimization algorithm by introducing a population activity factor (active) and white noise based on the Elegant Warbler optimization algorithm. Based on the improved Elegant Warbler optimization algorithm, the multi-objective Elegant Warbler optimization algorithm is constructed by adding external archiving and crowding distance sorting strategies.

[0042] This invention has the following advantages: It constructs a three-level scheduling architecture for the virtual power plant by dividing it into power producers, power operators, and three types of power consumption areas: industrial areas, commercial areas, and residential areas. Based on this three-level scheduling architecture and considering the operating characteristics of the virtual power plant, it constructs wind power generation models, photovoltaic power generation models, gas turbine power generation models, and biogas power generation models, and sets power generation constraints and ramp-up rate constraints for each device. Based on the three-level scheduling architecture, it determines three operating states for energy storage and thermal storage devices: charging / energy, discharging / energy, and static. Based on these operating states, it constructs mathematical models for energy storage device charging / discharging and thermal storage device charging / discharging, and sets constraints for charging and thermal storage devices. Based on these mathematical models, it introduces multiple... The system uses a comprehensive energy pressure index to calculate the energy surplus / deficit data of energy storage and thermal storage devices. By introducing dynamic energy storage factors and energy surplus factors, and combining the energy surplus / deficit data, energy storage costs, and remaining capacity, the system calculates the energy storage status of the energy storage and thermal storage devices. For extreme weather conditions, a multi-energy compensation strategy is constructed based on a set scenario. Objective functions for power producers and power operators are constructed according to their respective operational goals. These objective functions are then integrated with carbon emission indicators and response robustness indicators to form a multi-objective system. Based on the energy surplus / deficit data, energy storage status, and the set multi-energy compensation strategy of the energy storage and thermal storage devices, the system is solved using a multi-objective optimization algorithm to obtain the optimal energy storage scheduling scheme. This invention constructs a three-level scheduling architecture and precise models of multiple types of power generation equipment, combined with refined mathematical modeling and constraint settings for energy and thermal storage equipment. It achieves dynamic quantification of energy storage scheduling by leveraging multi-energy state comprehensive pressure indicators, dynamic energy storage factors, and energy surplus factors. Differentiated multi-energy compensation strategies are designed for extreme weather conditions. Simultaneously, it integrates two core objective functions with carbon emissions and response robustness indicators to form a multi-objective system. Solving this system using a multi-objective optimization algorithm significantly improves the virtual power plant's adaptability to both normal and extreme weather conditions, enhances the accuracy and flexibility of multi-energy coordinated scheduling, effectively reduces operating costs, gas consumption, and pollutant emissions, ensures the stability and reliability of energy supply, and comprehensively optimizes the overall operational efficiency of the virtual power plant. Attached Figure Description

[0043] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0044] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0045] Figure 1 This is a flowchart illustrating an adaptive multi-energy dynamic storage method for virtual power plants under different scenarios, as provided in Embodiment 1 of the present invention.

[0046] Figure 2 This is a schematic diagram of the multi-objective optimization algorithm of the Magnificent Slender-tailed Warbler in the adaptive multi-energy dynamic storage method for virtual power plants under different scenarios provided in Embodiment 1 of the present invention;

[0047] Figure 3 This is a schematic diagram illustrating the iterative relationship between the optimization results of various algorithms and the revenue of the three major telecom operators in one possible embodiment of the present invention, provided in Embodiment 1 of the present invention.

[0048] Figure 4 This is a schematic diagram illustrating the normal operation of a virtual power plant system in one possible embodiment of Embodiment 1 of the present invention.

[0049] Figure 5 This is a schematic diagram illustrating the operation of a virtual power plant system under extreme conditions in one possible embodiment of Embodiment 1 of the present invention;

[0050] Figure 6 This is a schematic diagram of the architecture of an adaptive multi-energy dynamic storage device for a virtual power plant under different scenarios, as provided in Embodiment 2 of the present invention. Detailed Implementation

[0051] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0052] Example 1

[0053] See Figure 1 Embodiment 1 of the present invention provides an adaptive multi-energy dynamic storage method for virtual power plants under different scenarios, comprising the following steps:

[0054] S1. By dividing the virtual power plant into three types of power consumption areas, including power producers, power operators, industrial areas, commercial areas, and residential areas, a three-level scheduling architecture for the virtual power plant is constructed. Based on the three-level scheduling architecture and combined with the operating characteristics of the virtual power plant, wind power generation model, photovoltaic power generation model, gas turbine power generation model, and biogas power generation model are constructed respectively, and power generation constraints and ramp-up rate constraints are set for each device.

[0055] S2. Based on the three-level scheduling architecture, determine the three working states of the energy storage device and the thermal storage device: charging / energy, discharging / energy, and static. Based on the working states of the energy storage device and the thermal storage device, construct mathematical models for charging and discharging of the energy storage device and for charging and discharging of the thermal storage device, and set constraints for the charging device and the thermal storage device.

[0056] S3. Based on the charging and discharging mathematical model of the energy storage device and the charging and discharging mathematical model of the thermal storage device, the energy surplus and deficit data of the energy storage device and the thermal storage device are calculated by introducing a multi-energy state comprehensive pressure index; by introducing a dynamic energy storage factor and an energy surplus factor, combined with the energy surplus and deficit data, energy storage cost and remaining capacity, the energy storage status of the energy storage device and the thermal storage device is calculated.

[0057] S4. In response to extreme weather, construct and set multi-energy compensation strategies based on the given scenarios;

[0058] S5. Based on the operational objectives of power producers and power operators, construct objective functions for power producers and power operators respectively; integrate the objective functions of power producers and power operators with carbon emission indicators and response robustness indicators to form a multi-objective system; based on the energy surplus / deficit data of energy storage devices and thermal storage devices, the energy storage status, and the set multi-energy compensation strategy, solve the multi-objective system using the multi-objective optimization algorithm to obtain the optimal energy storage scheduling scheme.

[0059] In this embodiment, in step S1, a three-level scheduling architecture for the virtual power plant is constructed by dividing the virtual power plant into three types of power consumption areas: power producers, power operators, and industrial areas, commercial areas, and residential areas. Based on the three-level scheduling architecture and combined with the operating characteristics of the virtual power plant, wind power generation models, photovoltaic power generation models, gas turbine power generation models, and biogas power generation models are constructed respectively, and power generation constraints and ramp-up rate constraints are set for each device.

[0060] Specifically, a virtual power plant is an innovative energy management system that integrates dispersed distributed energy resources such as solar, wind, and energy storage devices through intelligent communication and optimization algorithms. This system can respond to grid demands in real time, flexibly adjust power output, and effectively balance the volatility of renewable energy, thereby improving grid stability and operational efficiency. Simultaneously, it helps promote the widespread application of clean energy and achieve sustainable energy utilization. Furthermore, to demonstrate the application of energy storage systems in the virtual power plant, this invention divides the virtual power plant into three levels: power producers, power operators, and electricity-consuming areas. The electricity-consuming areas are further divided into three types: industrial areas, commercial areas, and residential areas.

[0061] Because the equipment in the virtual power plant is similar to the equipment model in the integrated energy system, this invention draws on the equipment modeling in the integrated energy system and incorporates the characteristics of the virtual power plant to simulate the power generation models of equipment such as wind power generation, photovoltaic power generation, gas turbine, and biogas power generation, and establishes relevant power generation constraints and ramp-up constraints.

[0062] In this embodiment, in step S2, based on the three-level scheduling architecture, the three working states of the energy storage device and the thermal storage device are determined: charging / energy, discharging / energy, and static. Based on the working states of the energy storage device and the thermal storage device, mathematical models for charging and discharging of the energy storage device and for charging and discharging of the thermal storage device are constructed respectively, and constraints on the charging device and the thermal storage device are set.

[0063] Specifically, the operating states of energy storage and thermal storage devices in a virtual power plant are divided into three categories: static state, charging / energy state, and discharging / energy state. When the production capacity exceeds the load demand, the energy storage and thermal storage devices are in the charging / energy state; when the production capacity is less than the load demand, the energy storage and thermal storage devices are in the discharging / energy state; otherwise, they are in the static state.

[0064] Among them, the mathematical model for the charging and discharging state of energy storage devices is as follows:

[0065] ;

[0066] ;

[0067] In the formula, , These represent the remaining electricity of the power storage station at times t and t-1, respectively. Self-discharge rate; , These are the charging and discharging power, respectively. , These are the charging and discharging efficiencies, respectively. The time interval is 1 hour.

[0068] When a discrepancy arises between heat supply and demand due to an imbalance between system heat generation and heat load, thermal storage equipment is needed to achieve a balance. The thermal storage equipment model is as follows:

[0069] ;

[0070] In the formula, , These represent the remaining heat of the thermal storage station at times t and t-1, respectively. The self-heating rate; , These are the charging and discharging power, respectively. , For charging and releasing heat efficiency.

[0071] In this embodiment, the constraints on the energy storage device include capacity constraints. Since the energy storage device cannot be in both energy storage and energy release states simultaneously, charge / discharge constraints must also be considered. In the virtual power plant, the main energy storage devices include electrical storage devices and thermal storage devices. The constraint expression for the electrical storage device is:

[0072] ;

[0073] In the formula, Let t be the amount of energy stored in the energy storage device; and These represent the valley and peak values ​​of the energy storage capacity of the energy storage device, respectively. The value of is 0 or 1, which represents the working state of the energy storage device at time t. When the value is 0, the working state of the energy storage device is discharging, and when the value is 1, the working state of the energy storage device is charging. and These represent the valley and peak values ​​of the charging power of the energy storage device, respectively. and These are the valley and peak values ​​of the discharge power of the energy storage device, respectively. Let be the charging power of the energy storage device at time t; Let be the discharge power of the energy storage device at time t.

[0074] The constraint expression for the thermal storage device is:

[0075] ;

[0076] In the formula, Let t be the energy stored in the thermal storage device at time t; and These represent the valley and peak values ​​of the energy stored in the thermal storage equipment, respectively. The value of is 0 or 1, which represents the working state of the thermal storage device at time t. When the value is 0, the working state of the thermal storage device is energy release, and when the value is 1, the working state of the thermal storage device is energy charging. and These represent the valley and peak values ​​of the charging power of the thermal storage equipment, respectively. and These are the valley and peak values ​​of the energy release power of the thermal storage device, respectively. The energy output of the thermal storage device at time t; Let be the energy release power of the thermal storage device at time t.

[0077] In this embodiment, in step S3, based on the charging and discharging mathematical model of the energy storage device and the charging and discharging mathematical model of the thermal storage device, the energy surplus and shortage data of the energy storage device and the thermal storage device are calculated by introducing a multi-energy state comprehensive pressure index; by introducing a dynamic energy storage factor and an energy surplus factor, and combining the energy surplus and shortage data, energy storage cost and remaining capacity, the energy storage status of the energy storage device and the thermal storage device is calculated.

[0078] Specifically, in order to quantify the system load pressure at various times, this strategy introduces a multi-energy state comprehensive pressure index κ:

[0079] ;

[0080] In the formula, Let be the energy deficit at time t; Let t be the thermal energy deficit, and λ be the electro-thermal conversion coefficient. Under normal weather conditions, the surplus of electrical energy and thermal energy is sufficient to meet their respective deficits. Therefore, the conversion of electrical energy to thermal energy is not considered and the coefficient is set to 0. However, under extreme weather conditions, the conversion of electrical energy to thermal energy may be enabled, and the coefficient is set to enable. The energy surplus at time t; Let t be the total energy deficit at time t.

[0081] The larger the value of κ, the greater the system load pressure at this time. In order to alleviate the system load pressure at this time, this invention proposes to use dynamic energy storage factor ϕ to store the surplus part of renewable energy at each time, and at the same time add energy surplus factor α to dynamically adjust the energy storage status of the energy storage system according to the remaining capacity of the energy storage system.

[0082] ;

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] In the formula, P is the dynamic energy storage factor at time t; totalThis represents the total surplus of electrical energy at all times. The cost of storing electrical energy; As a factor of energy storage efficiency; The energy surplus factor of energy storage element i; RC i (t) represents the remaining capacity of energy storage element i at time t; denoted as the energy surplus factor of the energy storage system; A is the dynamic energy storage matrix. At certain times of the day, due to insufficient renewable energy generation, the calculated dynamic energy storage factor may be negative. In this case, it is considered to set it to zero to improve the rationality of the system.

[0088] In this embodiment, the calculation methods for the thermal dynamic energy storage factor and the thermal dynamic energy storage matrix are the same as those for electrical energy:

[0089] ;

[0090] ;

[0091] ;

[0092] In the formula, H is the dynamic thermal energy storage factor at time t; surplus (t) represents the thermal surplus at time t; H total This represents the total thermal energy surplus at all times. The thermal energy storage benefit factor; Cost of storing thermal energy; B is the energy surplus factor of the thermal energy storage system; B is the dynamic thermal energy storage matrix.

[0093] After calculating the dynamic storage factor, the energy that should be stored in advance to fill the load gap caused by insufficient renewable energy capacity can be calculated:

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] In the formula, P storage The total electrical energy stored; H represents the electrical energy deficit at each moment. storage The total thermal energy stored; This refers to the deficit in thermal energy. Similarly, when the system is not deficient, this current thermal energy... or Setting it to zero improves the system's rationality.

[0099] In this embodiment, in step S4, a multi-energy compensation strategy is constructed based on a set scenario for extreme weather.

[0100] Specifically, aside from the renewable energy production capacity under normal operating conditions, extreme weather has a significant impact on the heat generation of renewable energy. Considering that the efficiency of converting electricity into heat is higher, and that heat has thermal inertia and is easier to store, in the event of a large heat shortage caused by weather, priority should be given to converting the surplus electricity that meets the electricity shortage requirement into heat. The following situations may occur in this process:

[0101] Scenario 1: When there is a surplus of electrical energy after the electrical energy storage meets the electrical energy shortage, the surplus electrical energy is converted into heat energy to make up for the heat energy shortage. If the converted heat energy is still insufficient, heat is purchased from the power operator.

[0102] Specifically, because the existing electrical energy storage was sufficient, there was still surplus electrical energy that could be converted into heat energy when the energy shortage was met:

[0103] ;

[0104] In the formula, H e-h It is the heat energy converted from excess electrical energy.

[0105] When there is a large heat energy deficit, it is necessary to determine whether the amount of excess electrical energy converted into heat energy can make up for the heat energy deficit. If it can, the remaining electrical energy will still be stored. If it cannot, then all the stored electrical energy after the deficit is made up will be converted into heat energy, and heat energy will be purchased from the power operator to meet the system's heat load deficit.

[0106] The formula that can satisfy the shortage is as follows:

[0107] ;

[0108] The formula for cases where the shortfall is not met is as follows:

[0109] ;

[0110] In the formula, H trade This refers to the heat purchased by the system from the power operator.

[0111] Scenario 2: When neither electrical energy storage nor thermal energy storage can fill their respective gaps, priority will be given to meeting the electrical energy gap, and all electrical energy gaps and part of the thermal energy gaps will be made up from the power operator.

[0112] Specifically, if neither the electrical nor thermal energy storage capacity can fill the respective gaps, priority will be given to meeting the electrical energy gap, while sacrificing some thermal energy to make up for the full electrical energy gap and part of the thermal energy gap by requesting electricity producers.

[0113] ;

[0114] In the formula, P trade This refers to the amount of electricity the system purchases from power operators.

[0115] In this embodiment, in step S5, objective functions for power producers and power operators are constructed according to their operational objectives. These objective functions are then integrated with carbon emission indicators and response robustness indicators to form a multi-objective system. Based on the energy surplus / deficit data of energy storage and thermal storage devices, the energy storage status, and the established multi-energy compensation strategy, the multi-objective system is solved using a multi-objective optimization algorithm to obtain the optimal energy storage scheduling scheme.

[0116] Specifically, the main role of electricity producers is to develop renewable resources within a region on a large scale, concentrating production capacity by utilizing clean power generation and energy conversion equipment. Their objective function is a multi-objective function comprising both energy sales revenue and operating costs. The electricity producer aims to maximize energy sales revenue and minimize operating costs, thus constructing the electricity producer's objective function.

[0117] The objective function of the electricity producer is expressed as follows:

[0118] ;

[0119] In the formula, Ceb represents the economic benefit of the electricity producer; C sell For its energy sales revenue; C run λ1 and λ2 are the operating costs of each component; λ1 and λ2 are the weights of each component cost.

[0120] ;

[0121] In the formula, c buy,e (t) represents the unit price at which the power operator purchases electricity at time t; E sell,e (t) represents the amount of electricity sold by the electricity producer to the electricity operator at time t; c buy,h (t) represents the unit price at which the power operator purchases heat energy at time t; E sell,h (t) represents the amount of heat energy sold by the power producer to the power operator at time t; c buy,g (t) represents the unit price at which the power operator purchases electricity from the external power grid at time t; E sell,g (t) represents the amount of electricity sold by the power producer to the external power grid; C p (t) represents the penalty fee incurred by the electricity producer for pollutant emissions at time t; C om(t) represents the operation and maintenance costs of various equipment in the power producer at time t, C g (t) represents the cost of natural gas purchased by the power producer at time t.

[0122] In this embodiment, from a structural perspective, the power operator acts as the connecting link between the energy supply side and the load demand side. From a system function perspective, the power operator controls and schedules energy flow within a region by setting energy purchase and sales prices. From an energy flow perspective, the power operator determines the total amount of energy to purchase from power producers based on the energy demand quotes of the electricity consumption area, thereby realizing energy supply from energy suppliers to the electricity consumption area. The power operator's objective function is constructed to maximize the difference between the revenue from selling energy to the electricity consumption area and the cost of purchasing energy from power producers.

[0123] The objective function of the power operator is expressed as follows:

[0124] ;

[0125] In the formula, Ceo represents the comprehensive economic benefit of the power operator; C sell,e The economic benefits for power operators selling electricity and heat to electricity-consuming areas; C buy,e This refers to the cost for power operators to purchase electricity and heat from power producers.

[0126] ;

[0127] ;

[0128] In the formula, e sell (t) and h sell (t) represents the unit price of electricity and heat sold by the energy management company to the electricity consumption area at time t; E buy-1 (t), E buy-2 (t), E buy-3 (t) represents the electrical energy purchased from the power operator by the first, second, and third power consumption areas at time t; H buy-1 (t), H buy-2 (t), H buy-3 (t) represents the heat energy purchased from the power operator by the first, second, and third power consumption zones at time t; e buy and h buy These represent the unit prices at which the power operator purchases electricity and heat from the power producer at time t.

[0129] In this embodiment, the objective functions of the power producers and the power operators are integrated with carbon emission indicators and response robustness indicators to form a multi-objective system. Based on the energy surplus and shortage data of the energy storage devices and thermal storage devices, the energy storage status, and the set multi-energy compensation strategy, the multi-objective system is solved using the multi-objective optimization algorithm to obtain the optimal energy storage scheduling scheme.

[0130] The Superb Fairy-wren Optimization Algorithm (SFOA) is a metaheuristic optimization algorithm inspired by biological behavior. It simulates the social behavior and breeding strategies of the Fairy-wren (Maluruscyaneus) to solve multi-objective optimization problems. These birds form flocks during the breeding season, where individuals cooperate and compete to find food and breeding opportunities. Inspired by this behavior, the SFOA algorithm treats individuals within the population as candidate solutions in the optimization process and searches for the optimal solution by simulating these behaviors.

[0131] As can be seen from the optimization algorithm for the Elegant Wren, although the strategy of the optimization algorithm for the Elegant Wren is relatively complete, it still has some limitations.

[0132] First, in solving the optimization algorithm for the Magnificent Slender-tailed Warbler, the current parameters in the algorithm are either fixed or simply change with the number of iterations. An adaptive mechanism can be introduced to dynamically adjust these parameters based on the activity level and convergence status of the current population. Addressing the shortcomings of the aforementioned algorithm parameters, C can be adjusted according to the population activity level, using a larger step size when the population activity is high and a smaller step size when the activity level is low.

[0133] Specifically, the activity index of a population is calculated using the Euclidean mean distance:

[0134] ;

[0135] ;

[0136] In the formula, is the Euclidean average distance; N is the population size; dim is the dimension of each individual; x i and x j For two individuals in the population; x i,k and x j,kLet C be the value of these two individuals in the k-th dimension. This formula calculates the average Euclidean distance between all individuals in the population as an indicator of population activity. When half of the individuals in the population are active (i.e., active>0.5), C equals 0.8, indicating that the optimal solution is explored over a large area with a larger step size. Conversely, C is 0.2, indicating that the optimal solution is searched with a smaller step size.

[0137] By reasonably adjusting the trend of C, the algorithm can avoid getting stuck in local optima in the early stages of the search process, thus finding the optimal solution better. At the same time, the population activity factor parameter allows the algorithm to automatically adjust the search strategy at different stages of the iteration, which not only improves the automation of the algorithm, but also reduces the problem of unstable algorithm performance caused by manually setting parameters.

[0138] Secondly, during the exploration phase of the optimization algorithm for the Elegant Wren, the guidance of the best individual may cause the entire population to fall into a local optimum, unable to escape the local optimum, which may affect the algorithm's results. Therefore, this invention introduces a population activity factor (active) and also adds white noise perturbation to help the algorithm escape local optima and continue exploring new solution spaces.

[0139] Specifically, during the optimization process, the algorithm may linger around a local optimum, struggling to escape this local region. Introducing white noise perturbation provides a mechanism for the algorithm to randomly explore new solution spaces, potentially leading to better solutions. If, during the search for the optimal solution, the random number rand is less than the white noise perturbation probability p, then the position of the new individual can be represented as:

[0140] ;

[0141] In the formula, r is a dim-dimensional random vector; each component r k Generated from the standard normal distribution N(0,1); ε is the perturbation amplitude, set to 0.1 in the code; x i For the current individual; X' i The individuals are perturbed; the white noise perturbation probability p is 0.1. By introducing white noise perturbation, the diversity of the population is increased, helping the algorithm avoid getting trapped in local optima, thereby improving global search capability and robustness.

[0142] In this embodiment, as Figure 2 As shown, a multi-objective optimization algorithm for the Magnificent Wren-Wren (MOSFOA) is constructed by incorporating external archives and crowding distance sorting based on the single-objective optimization algorithm for the Magnificent Wren-Wren.

[0143] Specifically, first, all non-dominated solutions are identified from the initial population to form an external archive:

[0144] ;

[0145] In the formula, F is the reverse archive composed of non-dominated solutions; Y is the initial population; Let b represent the dominant solution a in the population.

[0146] Secondly, the crowding distance of each solution in the non-dominated front is calculated to determine the quality of the solution in the objective function space:

[0147] ;

[0148] In the formula, The congestion distance of solution o is represented; N represents the number of objective functions; and Let represent the maximum and minimum values ​​of the nth function at the current non-dominated frontier, respectively; M is the number of solutions at the current non-dominated frontier. and These are the values ​​of the solutions adjacent to solution o in the nth objective function.

[0149] Finally, solutions are selected based on the crowding distance calculated from the non-dominated level. Solutions with higher non-dominated levels are given the highest priority. When solutions are compared and are at the same non-dominated level, solutions with smaller crowding distances are retained, while solutions with larger crowding distances are discarded. This ensures the uniformity and diversity of the retained solution set.

[0150] Repeat the above process and continue iterating until the number of iterations reaches the maximum number of iterations or converges to meet the preset conditions, and then output the optimal solution at this point.

[0151] In this embodiment, based on the energy surplus / deficit data of energy storage devices and thermal storage devices, the energy storage status, and the set multi-energy compensation strategy, the multi-objective optimization algorithm described above is used to solve the multi-objective system that integrates the objective function of power producers, the objective function of power operators, carbon emission indicators, and response robustness indicators, so as to obtain the optimal energy storage scheduling scheme.

[0152] In one possible embodiment, a comparative verification example is provided as follows:

[0153] To evaluate the effectiveness of the algorithm and strategy, this invention applies the MOSFOA algorithm to a virtual power plant. By comparing it with MOMVO, MOPSO, and the original SFOA algorithm, its superiority in terms of convergence, distribution uniformity, and diversity is verified. This embodiment also compares the advantages of the strategy in improving energy utilization efficiency and system operation reliability under normal and extreme conditions by using power and economic data related to the virtual power plant.

[0154] I. Comparative Analysis of Multi-Objective Optimization Algorithms

[0155] To verify the feasibility of the proposed MOSFOA, the algorithm's performance was tested using the multi-objective test functions ZDT1, ZDT2, UF1, and UF2, which are commonly used in current research. Furthermore, to compare the performance of MOSFOA, two other representative multi-objective optimization algorithms (MOMVO and MOPSO) and the original SFOA were selected as comparison algorithms. Through algorithm comparison, the superiority and potential of the proposed MOSFOA can be verified. The parameter settings of each multi-objective optimization algorithm are shown in Table 1.

[0156] Table 1 Parameter settings for various multi-objective optimization algorithms

[0157]

[0158] Numerical verification of the proposed MOSFOA solution performance was conducted using standard multi-objective benchmark sets, such as the Zitzler–Deb–Thiele (ZDT) and CEC-2009 test sets. This embodiment selects the ZDT1, ZDT2, UF1, and UF2 test functions for evaluation. These test functions cover different optimization problem characteristics and can effectively evaluate the algorithm's performance in complex optimization problems. Existing metrics for evaluating the quality of the Pareto front include Inverted Generational Distance (IGD), Spacing Metric (SP), Knee-driven dissimilarity (KD), and Hypervolume (HV). IGD, SP, and HV are selected to evaluate the algorithm's convergence, uniformity of solution set distribution, and diversity of solution set. The test results of each multi-objective algorithm are shown in Tables 2-4.

[0159] Table 2 Comparison of IGD algorithms

[0160]

[0161] Table 3 Comparison of SPs for each algorithm

[0162]

[0163] Table 4 Comparison of HV values ​​for each algorithm

[0164]

[0165] As shown in Table 2, MOSFOA's IGD metric ranked first across all four test functions, lower than MOMVO, MOPSO, and SFOA by 89.89%, 44.12%, and 75.43%, respectively. This indicates that MOSFOA closely fits the original function curves. In Table 3, compared to SFOA, MOSFOA's SP metric decreased across all four test functions, by 73.93%, 82.27%, 86.97%, and 42.30%, respectively, and ranked first in ZDT1, demonstrating a uniformly distributed solution. Table 4 shows that MOSFOA's IGD metric in UF1 was only 1.66% lower than the top-ranked MOGWCA. It ranked first in the other three test functions, exceeding the second-ranked solution by 2.01%, 16.90%, and 1.16% in ZDT1, ZDT2, and UF2, respectively. This implies that MOSFOA's solution set is closer to the true Pareto front in terms of convergence and diversity, exhibiting better overall performance.

[0166] To further verify MOSFOA's ability to solve multi-energy dynamic storage models, MOMVO, MOPSO, and the original SFOA were selected as comparison algorithms and analyzed against MOSFOA. In the model, after the power operators set energy trading prices, the electricity-consuming areas and power producers will optimize the scheduling of internal energy based on these prices. The optimization results obtained by the final iterations of the four algorithms and the revenues of the three major operators are as follows: Figure 3 As shown.

[0167] Figure 3 Figure (a) shows the optimal solutions obtained through different algorithm iterations. Analyzing the optimal solutions in the figure, the optimal solution for MOSFOA has a power area cost of ¥7236.3, and the revenue for power producers and operators is ¥15858.6 and ¥1494.2, respectively. The power area cost obtained by MOMVO is ¥14284.6, which is about 97.40% higher than that of MOSFOA. Similarly, the costs of MOPSO and SFOA are also about 139.48% and 148.53% higher, respectively. This indicates that MOSFOA has a very significant competitive advantage in optimizing power area costs. Regarding the revenue of power producers, the optimal solution for MOSFOA yields a revenue of ¥15858.6, ranking first among the four algorithms. MOSFOA is ¥234.3 higher than the second-ranked MOPSO. MALAP, in optimizing the multi-energy dynamic storage model, not only helps reduce the cost of power areas but also increases the revenue of power producers, demonstrating stronger competitiveness. Figure 3As shown in the iterative process diagrams (b), (c), (d), and (e), MOSFOA outperforms other algorithms, exhibiting better stability and convergence during the iterative process, with its objective function value gradually stabilizing. The MOSFOA algorithm gradually stabilizes after the 41st iteration. The iterative process diagrams reveal significant fluctuations in MOMVO during iteration, indicating a clear disadvantage in solving this type of model. In contrast, MOSFOA demonstrates stronger stability, thus showing higher practicality and reliability in solving this problem.

[0168] II. Comparative Analysis of Adaptive Multi-Energy Dynamic Storage Strategies

[0169] An adaptive multi-energy dynamic storage strategy under normal and extreme weather conditions was introduced into the virtual power plant. This aims to mitigate energy fluctuations, effectively reduce system load deficits caused by the virtual power plant, and achieve complementarity and mutual support between different energy sources. For example... Figure 4 As shown, this illustrates the system load and operation of a virtual power plant under normal conditions, driven by an adaptive multi-energy dynamic storage strategy.

[0170] contrast Figure 4 (a)(b)(c), Figure 4 (a) The operating status of the energy storage device fluctuates relatively smoothly, and the discharge capacity is low, indicating that the load demand in the industrial power consumption area is relatively low. The robust control strategy effectively ensures the system's operational stability. Meanwhile, the discharge power of the energy storage device in the commercial power consumption area is 500kW between 5:00 and 9:00, indicating that the system relies on the energy storage device's discharge to meet load demand during peak hours. The continuous operating status of the energy storage device also gradually decreases over time, reaching 0.2 at 12:00. Combined with... Figure 4 (e) During peak photovoltaic power generation, the energy storage device's charging power is up to 500kW, serving as a backup for subsequent peak loads. If the energy storage device discharges again between 14:00 and 17:00 to meet load demand, combined with... Figure 4 (d) and (f) show higher electricity load demand in commercial areas. During 6:00-8:00, 9:00-12:00, and 17:00-20:00, the energy release capacity of thermal storage equipment in residential areas is 500kW. Compared to the operating status of thermal storage equipment in industrial and commercial areas, the operating status of thermal storage equipment in residential areas during 6:00-8:00 is between 0.1 and 0.2. Figure 4 (f) The residential electricity consumption area has the highest heat load demand between 6:00 and 8:00, ranging from 570.87 to 620.03 kW, indicating that the residential electricity consumption area releases heat energy from the heat storage equipment to make up for the peak load gap in a timely manner.

[0171] To demonstrate the reliability of the virtual power plant system under the adaptive multi-energy dynamic storage strategy in extreme conditions, the system's energy storage status, electricity trading, and carbon emissions were observed under these conditions. Figure 5 .

[0172] from Figure 5 As shown in (a), under extreme conditions, the discharge power of the energy storage device in the industrial power consumption area is 500kW at 7:00 and 22:00, and 377.90kW and 315.63kW at 2:00 and 17:00, respectively. To ensure stable operation of the energy storage device during peak load periods, it is charged at 5:00 and 19:00, with charging powers of 467.64kW and 500kW, respectively. An adaptive multi-energy dynamic storage strategy ensures robust system control. Figure 5 (b) It was observed that the discharge power of the energy storage devices in the commercial area was 500kW during 2:00-4:00, 9:00, 11:00-12:00, 16:00-17:00, and 21:00-22:00. Figure 5 (e) The commercial electricity consumption area had the lowest carbon emissions, indicating that the adaptive multi-energy dynamic storage strategy effectively achieves significant emission reductions by using energy storage to replace fossil fuel consumption during peak hours. Observation Figure 5 (d) Electricity is sold in residential areas from 10:00 to 16:00, with a maximum of 1430.14 kW sold at 13:00. Figure 5 (e) Between 5:00 and 8:00, the demand for electricity increases, and the purchase volume of electricity in the electricity consumption area remains stable at 197.28-1576.35kW. This indicates that under the influence of the adaptive multi-energy dynamic storage strategy, the system formulates a stable purchase and sale of electricity to avoid generating high peak electricity prices.

[0173] To further observe the effectiveness of the virtual power plant under the adaptive multi-energy dynamic storage strategy under normal and extreme conditions, Table 5 compares the economics of the system under normal and extreme conditions.

[0174] Table 5. Comparison of the economic situation of virtual power plants under normal and extreme conditions.

[0175]

[0176] As can be seen from Table 5, the economic cost of the virtual power plant remains basically the same under normal and extreme conditions. For example, the economic cost of the power producer is ¥33,908.51 under normal conditions and ¥33,653.84 under extreme conditions, while the economic cost of the industrial power consumption area is ¥6,680.19 under normal conditions and ¥6,679.19 under extreme conditions. This indicates that under extreme weather conditions, the virtual power plant effectively maintains the stability of its economic cost through adaptive multi-energy dynamic adjustment. Observing pollution costs, the pollution cost for electricity producers, normally priced at ¥730.04, decreased by ¥573.26 compared to ¥156.78 under extreme conditions. Furthermore, the pollution cost for residential electricity consumption areas decreased by 24.42% under extreme conditions compared to the normal level, indicating that virtual power plants reduced their use of fossil fuels under extreme conditions. The gas cost for electricity producers decreased by 84.23% from ¥7350.55 under normal conditions to ¥1159.47 under extreme conditions, and the gas cost for residential electricity consumption areas decreased by ¥3140.43 from ¥6692.08 under normal conditions to ¥3551.65 under extreme conditions, indicating a significant reduction in the virtual power plants' dependence on gas under extreme conditions. Due to the adaptive adjustment of the energy storage system, equipment wear and tear is reduced to some extent, leading to a decrease in system operation and maintenance costs. For example, the normal operation and maintenance cost of the industrial power area is ¥1417.80, compared to ¥747.80 under extreme conditions, a decrease of ¥670.00. Similarly, the normal operation and maintenance cost of the commercial power area is ¥507.41, compared to ¥257.41 under extreme conditions, a decrease of 49.27%. In summary, the virtual power plant, through adaptive multi-energy dynamic storage adjustment, achieves effective improvement in energy utilization and stability under extreme conditions.

[0177] The application scenarios of this invention are as follows:

[0178] City-level virtual power plant multi-energy collaborative dispatch scenario: In response to the diverse electricity load differences in industrial areas, commercial areas and residential areas within a city, this invention uses a three-level architecture and precise modeling to dynamically match the output of renewable energy sources such as wind power and photovoltaics with the demand for various types of loads. With the help of energy storage dynamic regulation and extreme weather compensation strategies, it can smooth energy fluctuations, ensure the stable operation of the urban power grid, and reduce overall operating costs and carbon emissions.

[0179] High proportion of new energy distributed energy integration projects: In the face of the strong fluctuations in wind power and photovoltaic output, this invention accurately quantifies energy surplus and deficit through multi-energy state comprehensive pressure index, dynamically adjusts the energy storage ratio, maximizes the absorption of renewable energy, reduces wind and solar curtailment, and combines multi-objective optimization solution to balance the utilization efficiency of new energy and the stability of system operation, adapting to the impact of extreme climates such as continuous rain and insufficient sunshine on energy supply.

[0180] Peak-valley regulation scenario in power systems: During peak electricity consumption periods, the load gap is supplemented by discharging / energy through energy storage and thermal storage equipment, reducing peak electricity purchase costs; during off-peak periods, surplus electricity is used for charging / energy replenishment, realizing energy "peak shifting and valley filling", alleviating the contradiction between power grid supply and demand, and at the same time, the optimal scheduling scheme is output through optimization algorithms to improve the overall operating efficiency and resilience of the power system.

[0181] Emergency Energy Security Scenario: When encountering the risk of energy supply interruption due to extreme weather such as typhoons and cold waves, the multi-energy compensation strategy of this invention prioritizes the protection of power supply. Through adaptive conversion of power to heat and external energy purchase, it meets the core load demand, reduces the impact of extreme weather on energy supply, and enhances the emergency response capability of the virtual power plant.

[0182] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0183] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0184] Example 2

[0185] See Figure 6 Embodiment 2 of the present invention also provides an adaptive multi-energy dynamic storage device for virtual power plants under different scenarios, comprising:

[0186] The virtual power plant three-level architecture and equipment model construction module 001 is used to construct a three-level scheduling architecture for the virtual power plant by dividing the virtual power plant into three types of power consumption areas: power producers, power operators, industrial areas, commercial areas, and residential areas. Based on the three-level scheduling architecture and combined with the operating characteristics of the virtual power plant, wind power generation models, photovoltaic power generation models, gas turbine power generation models, and biogas power generation models are constructed respectively, and power generation constraints and ramp-up rate constraints are set for each device.

[0187] The energy storage / energy device charging / discharging / energy mathematical model construction module 002 is used to determine the three working states of energy storage devices and thermal storage devices—charging / energy, discharging / energy, and static—based on the three-level scheduling architecture; and to construct the energy storage device charging / discharging mathematical model and the thermal storage device charging / discharging mathematical model based on the working states of the energy storage device and the thermal storage device, respectively, and to set the charging device constraints and thermal storage device constraints.

[0188] The energy storage data acquisition module 003 for energy storage devices is used to calculate the energy surplus and deficit data of the energy storage devices and the thermal storage devices based on the charging and discharging mathematical model of the energy storage devices and the charging and discharging mathematical model of the thermal storage devices, by introducing a multi-energy state comprehensive pressure index; and to calculate the energy storage status of the energy storage devices and the thermal storage devices by introducing a dynamic energy storage factor and an energy surplus factor, combined with the energy surplus and deficit data, energy storage cost and remaining capacity.

[0189] The multi-energy compensation strategy construction module 004 is used to construct and set multi-energy compensation strategies for extreme weather based on the set scenarios.

[0190] The optimal energy storage scheduling scheme acquisition module 005 is used to construct objective functions for power producers and power operators respectively, based on the operational objectives of power producers and power operators; integrate the objective functions of power producers and power operators with carbon emission indicators and response robustness indicators to form a multi-objective system; and solve the multi-objective system using a multi-objective optimization algorithm based on the energy surplus / deficit data of energy storage devices and thermal storage devices, the energy storage status, and the set multi-energy compensation strategy to obtain the optimal energy storage scheduling scheme.

[0191] In this embodiment, the energy data acquisition module 003 for the energy storage / energy storage device includes the energy surplus / deficit data of the energy storage device and the thermal storage device at a set time, including the power deficit, thermal deficit, power surplus and total energy deficit.

[0192] In this embodiment, the multi-energy compensation strategy construction module 004 constructs the following multi-energy compensation strategy based on a set scenario:

[0193] Scenario 1: When there is a surplus of electrical energy after the electrical energy storage meets the electrical energy shortage, the surplus electrical energy is converted into heat energy to make up for the heat energy shortage. If the converted heat energy is still insufficient, heat is purchased from the power operator.

[0194] Scenario 2: When neither electrical energy storage nor thermal energy storage can fill their respective gaps, priority will be given to meeting the electrical energy gap, and all electrical energy gaps and part of the thermal energy gaps will be made up from the power operator.

[0195] In this embodiment, in the optimal energy storage scheduling scheme acquisition module 005, the power producer aims to maximize energy sales revenue and minimize operating costs, and constructs the power producer objective function; the power operator aims to maximize the difference between the revenue from selling energy to the electricity consumption area and the cost of purchasing energy from the power producer, and constructs the power operator objective function.

[0196] The objective function of the power producer is expressed as follows:

[0197] ;

[0198] In the formula, Ceb represents the economic benefit of the electricity producer; C sell For its energy sales revenue; C run Its operating cost; λ1 and λ2 are the weights of each component cost;

[0199] The objective function of the power operator is expressed as follows:

[0200] ;

[0201] In the formula, Ceo represents the comprehensive economic benefit of the power operator; C sell,e The economic benefits for power operators selling electricity and heat to electricity-consuming areas; C buy,e This refers to the cost for power operators to purchase electricity and heat from power producers.

[0202] In this embodiment, the optimal energy storage scheduling scheme acquisition module 005 obtains an improved optimization algorithm for the Magnificent Wren by introducing the population activity factor "active" and white noise based on the optimization algorithm for the Magnificent Wren. Based on the improved optimization algorithm for the Magnificent Wren, the multi-objective optimization algorithm for the Magnificent Wren is constructed by adding external archiving and crowding distance sorting strategies.

[0203] It should be noted that the information interaction and execution process between the modules of the above system are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0204] Example 3

[0205] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium storing program code for an adaptive multi-energy dynamic storage method for a virtual power plant under different scenarios. The program code includes instructions for executing the adaptive multi-energy dynamic storage method for a virtual power plant under different scenarios according to Embodiment 1 or any possible implementation thereof.

[0206] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0207] Example 4

[0208] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0209] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can call the program instructions to execute an adaptive multi-energy dynamic storage method for a virtual power plant under different scenarios, as described in Embodiment 1 or any possible implementation thereof.

[0210] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0211] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0212] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing systems. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Optionally, they can be implemented using program code executable by a computing system, thereby storing them in a storage system for execution by the computing system. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0213] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. An adaptive multi-energy dynamic storage method for virtual power plants under different scenarios, characterized in that, include: The virtual power plant is divided into power producers, power operators, and power consumption areas including industrial areas, commercial areas, and residential areas. A three-level dispatch architecture for the virtual power plant is constructed. Combined with the operating characteristics of the virtual power plant, wind power generation model, photovoltaic power generation model, gas turbine power generation model, and biogas power generation model are constructed. Power generation constraints and ramp-up rate constraints are set for each device. Based on a three-level scheduling architecture, the operating states of energy storage devices and thermal storage devices are determined. Based on these operating states, mathematical models for charging and discharging of energy storage devices and for charging and discharging of thermal storage devices are constructed, and constraints are set for both. The mathematical model for the charging and discharging states of the energy storage devices is as follows: ； ； In the formula, , These represent the remaining electricity of the power storage station at times t and t-1, respectively. Self-discharge rate; , These are the charging and discharging power, respectively. , These are the charging and discharging efficiencies, respectively. For time intervals; The thermal storage device model is as follows: ； In the formula, , These represent the remaining heat of the thermal storage station at times t and t-1, respectively. The self-heating rate; , These are the charging and discharging power, respectively. , For charging and releasing heat efficiency; Based on the mathematical models of charging and discharging of energy storage devices and the mathematical models of charging and discharging of thermal storage devices, a multi-energy state comprehensive pressure index is introduced to calculate the energy surplus and deficit data of energy storage devices and thermal storage devices. Dynamic energy storage factor and energy surplus factor are introduced, and combined with energy surplus and deficit data, energy storage cost and remaining capacity, the energy storage status of energy storage devices and thermal storage devices is calculated. To address extreme weather, a multi-energy compensation strategy is constructed based on a given scenario; the strategy derives a multi-energy state comprehensive pressure index κ: ； In the formula, Let t be the energy deficit at time t; Let λ be the heat energy deficit at time t, and λ be the electro-thermal conversion coefficient. The energy surplus at time t; This represents the total energy deficit at time t. Based on the operational objectives of power producers and operators, objective functions for power producers and operators are constructed and integrated with carbon emission indicators and response robustness indicators to form a multi-objective system. Based on energy surplus and shortage data, energy storage status, and the setting of multi-energy compensation strategies, the multi-objective system is solved using the multi-objective optimization algorithm to obtain the optimal energy storage scheduling scheme. Based on the optimization algorithm for the Magnificent Swan-warbler, an improved optimization algorithm for the Magnificent Swan-warbler was obtained by introducing the population activity factor "active" and white noise. Based on the improved optimization algorithm for the Magnificent Swan-warbler, the multi-objective optimization algorithm for the Magnificent Swan-warbler is constructed by incorporating external archives and crowding distance sorting strategies.

2. The adaptive multi-energy dynamic storage method for virtual power plants under different scenarios according to claim 1, characterized in that, The energy surplus and deficit data of the energy storage equipment and the thermal storage equipment include the power deficit, thermal deficit, power surplus and total energy deficit at a set time.

3. The adaptive multi-energy dynamic storage method for virtual power plants under different scenarios according to claim 2, characterized in that, Based on the given scenario, the constructed multi-energy compensation strategy is as follows: Scenario 1: When there is a surplus of electrical energy after the electrical energy storage meets the electrical energy shortage, the surplus electrical energy is converted into heat energy to make up for the heat energy shortage. If the converted heat energy is still insufficient, heat is purchased from the power operator. Scenario 2: When neither electrical energy storage nor thermal energy storage can fill their respective gaps, priority will be given to meeting the electrical energy gap, and all electrical energy gaps and part of the thermal energy gaps will be made up from the power operator.

4. The adaptive multi-energy dynamic storage method for virtual power plants under different scenarios according to claim 3, characterized in that, The power producer aims to maximize revenue from energy sales and minimize operating costs, and constructs its objective function accordingly. The power operator aims to maximize the difference between revenue from selling energy to the electricity consumption area and the cost of purchasing energy from the power producer, and constructs its objective function accordingly. The objective function of the power producer is expressed as follows: ； In the formula, Ceb represents the economic benefit of the electricity producer; C sell For its energy sales revenue; C run Its operating costs; λ1 and λ2 are the weights of each component cost; The objective function of the power operator is expressed as follows: ； In the formula, Ceo represents the comprehensive economic benefit of the power operator; C sell,e The economic benefits for power operators selling electricity and heat to electricity-consuming areas; C buy,e This refers to the cost for power operators to purchase electricity and heat from power producers.

5. An adaptive multi-energy dynamic storage device for virtual power plants under different scenarios, employing the adaptive multi-energy dynamic storage method for virtual power plants under different scenarios as described in any one of claims 1-4, characterized in that, include: The virtual power plant three-level architecture and equipment model construction module is used to divide the virtual power plant into power producers, power operators, and power consumption areas including industrial areas, commercial areas, and residential areas. It constructs a three-level dispatch architecture for the virtual power plant and, in combination with the operating characteristics of the virtual power plant, constructs wind power generation models, photovoltaic power generation models, gas turbine power generation models, and biogas power generation models, and sets power generation constraints and ramp-up rate constraints for each device. The module for constructing charging / discharging / energy mathematical models for energy storage devices is used to determine the working status of energy storage devices and thermal storage devices based on a three-level scheduling architecture; based on the working status of energy storage devices and thermal storage devices, it constructs charging / discharging mathematical models for energy storage devices and charging / discharging mathematical models for thermal storage devices, and sets constraints for charging devices and thermal storage devices. The energy storage data acquisition module for energy storage devices is used to calculate the energy surplus and deficit data of energy storage devices and thermal storage devices based on the mathematical models of charging and discharging of energy storage devices and the mathematical models of charging and discharging of thermal storage devices, and by introducing a multi-energy state comprehensive pressure index; it also introduces dynamic energy storage factors and energy surplus factors, and combines energy surplus and deficit data, energy storage costs and remaining capacity to calculate the energy storage status of energy storage devices and thermal storage devices. The multi-energy compensation strategy construction module is used to construct and set multi-energy compensation strategies for extreme weather based on the given scenarios. The optimal energy storage scheduling scheme acquisition module is used to construct objective functions for power producers and power operators based on their operational objectives, and integrate them with carbon emission indicators and response robustness indicators to form a multi-objective system. Based on energy surplus and shortage data, energy storage status, and the setting of multi-energy compensation strategies, the multi-objective system is solved through the multi-objective optimization algorithm to obtain the optimal energy storage scheduling scheme.

6. The adaptive multi-energy dynamic storage device for virtual power plants under different scenarios according to claim 5, characterized in that, In the energy storage data acquisition module of the energy storage / energy storage device, the energy surplus and deficit data of the energy storage device and the thermal storage device include the power deficit, thermal deficit, power surplus and total energy deficit at a set time.

7. The adaptive multi-energy dynamic storage device for virtual power plants under different scenarios according to claim 6, characterized in that, In the multi-energy compensation strategy construction module, the set multi-energy compensation strategy constructed according to the set scenario is as follows: Scenario 1: When there is a surplus of electrical energy after the electrical energy storage meets the electrical energy shortage, the surplus electrical energy is converted into heat energy to make up for the heat energy shortage. If the converted heat energy is still insufficient, heat is purchased from the power operator. Scenario 2: When neither electrical energy storage nor thermal energy storage can fill their respective gaps, priority will be given to meeting the electrical energy gap, and all electrical energy gaps and part of the thermal energy gaps will be made up from the power operator.

8. The adaptive multi-energy dynamic storage device for virtual power plants under different scenarios according to claim 7, characterized in that, In the optimal energy storage scheduling scheme acquisition module, the power producer aims to maximize energy sales revenue and minimize operating costs, and constructs the power producer objective function; the power operator aims to maximize the difference between the revenue from selling energy to the electricity consumption area and the cost of purchasing energy from the power producer, and constructs the power operator objective function. The objective function of the power producer is expressed as follows: ； In the formula, Ceb represents the economic benefit of the electricity producer; C sell For its energy sales revenue; C run Its operating costs; λ1 and λ2 are the weights of each component cost; The objective function of the power operator is expressed as follows: ； In the formula, Ceo represents the comprehensive economic benefit of the power operator; C sell,e The economic benefits for power operators selling electricity and heat to electricity-consuming areas; C buy,e This refers to the cost for power operators to purchase electricity and heat from power producers.

9. The adaptive multi-energy dynamic storage device for virtual power plants under different scenarios according to claim 8, characterized in that, In the optimal energy storage scheduling scheme acquisition module, based on the Magnificent Wren optimization algorithm, an improved Magnificent Wren optimization algorithm is obtained by introducing the population activity factor active and white noise. Based on the improved optimization algorithm for the Magnificent Swan-warbler, the multi-objective optimization algorithm for the Magnificent Swan-warbler is constructed by incorporating external archives and crowding distance sorting strategies.

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