Comprehensive energy system optimization scheduling method and device considering environment and cost

Abstract

The invention discloses a comprehensive energy system optimization scheduling method and device considering environment and cost. Comprising the following steps: establishing a day-ahead prediction integrated energy system model consisting of multi-energy and a multi-energy coupling device, an integrated energy system energy conversion model, a multi-cost mathematical model of day-ahead scheduling and real-time scheduling of an integrated energy system, and an optimal income objective function; establishing two-stage thermal and electric power balance constraint conditions and thermal and electric load rate constraint conditions of the integrated energy system; and solving the integrated energy system through a multi-target artificial hummingbird optimization algorithm to obtain a two-stage optimization scheduling scheme. Reasonable distribution of energy is achieved through flexible energy conversion, a day-ahead optimal scheduling scheme can be output one day in the day-ahead scheduling stage, early-stage guidance is provided for operation preparation of the comprehensive energy system, the real-time scheduling stage is synchronous with actual operation of the comprehensive energy system, and the real-time scheduling efficiency is improved. And adjusting and outputting a real-time scheduling scheme on the basis of the day-ahead scheduling scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system optimization, and particularly to an integrated energy system optimal scheduling method and device considering environment and cost. Background Art

[0002] In recent years, China has vigorously developed renewable energy (RE), combined it with traditional fossil energy power generation methods, and formed an integrated energy system with excellent economy and environmental protection. The many uncertainties of renewable energy threaten the performance of the integrated energy system. How to promote the consumption of renewable energy under strong uncertainty conditions is a key issue in the integrated energy system scheduling.

[0003] Against the background of the increasing proportion of renewable energy, due to its special strong uncertainty, a large amount of waste energy will be generated, causing serious energy waste and damaging the economic benefits of the integrated energy system. To improve the RE utilization efficiency, the operation of the IES is guided by optimizing the scheduling strategy, so as to store the wasted wind energy and solar energy in energy storage batteries, or transfer them to cold and heat loads such as air conditioners and boilers. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the specification of this application, to avoid obscuring the purpose of this part, the abstract of the specification, and the title of the invention. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides an integrated energy system optimal scheduling method considering environment and cost to solve the problem of serious energy waste in the integrated energy system.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an integrated energy system optimal scheduling method considering environment and cost, including: establishing a day-ahead prediction integrated energy system model composed of multiple energies and multiple energy coupling devices according to the uncertainty of renewable energy;

[0008] Based on the day-ahead prediction integrated energy system model, for the multiple energy coupling devices, an integrated energy system energy conversion model is established by a multi-energy collaborative hybrid modeling method;

[0009] Based on the energy conversion model of the integrated energy system, considering energy operation factors and environmental factors, a multi-cost mathematical model for the day-ahead scheduling and real-time scheduling of the integrated energy system and an optimal revenue objective function are established;

[0010] Based on the energy conversion model of the integrated energy system and the optimal revenue objective function, two-stage thermal and electric power balance constraints and thermal and electric load rate constraints of the integrated energy system are established;

[0011] Based on the optimal revenue and constraints of day-ahead scheduling and real-time scheduling, the integrated energy system is solved by a multi-objective artificial hummingbird optimization algorithm to obtain a two-stage optimal scheduling scheme.

[0012] As a preferred scheme of the integrated energy system optimal scheduling method considering environment and cost according to the present invention, wherein: the day-ahead prediction integrated energy system model includes a wind power generation output power prediction model, a photovoltaic power generation output power prediction model, and a storage battery charge and discharge power prediction model;

[0013] Using past real-time wind speeds and output powers, a reaction uncertainty fuzzy set is constructed, and combined with the wind power generation equation, a wind power generation output power prediction model considering wind speed uncertainty is constructed;

[0014] Using past real-time illuminations and output powers, a reaction uncertainty fuzzy set is constructed, and combined with the photovoltaic power generation equation, a photovoltaic power generation output power prediction model considering illumination uncertainty is constructed;

[0015] According to the internal physical characteristics of the storage battery, a storage battery charge and discharge power prediction model is established.

[0016] As a preferred scheme of the integrated energy system optimal scheduling method considering environment and cost according to the present invention, wherein: establishing the energy conversion model of the integrated energy system includes:

[0017] By analyzing the internal physical characteristics of the energy coupling device and establishing the energy conversion model of the integrated energy system in matrix form, expressed as:

[0018]

[0019] Wherein, P t l,e 、Q t l,c 、Q t l,h and V t l,g respectively represent the electric load, cooling load, heating load and gas load per time period, P t buy,e and V t buy,grespectively represent the electricity and gas purchase quantities in each time period, P t WT,e , P t PV,e and P t BESS,e respectively represent the predicted power generation of wind turbines and photovoltaic arrays and the predicted charge and discharge power of energy storage batteries in each time period, v ec , v eb , v chp and v gb respectively represent the distribution coefficients of air conditioners, electric boilers, combined heat and power units, and gas boilers, η chp,e and η chp,h respectively represent the power generation coefficient and heat supply coefficient of combined heat and power units, η eb,h and η gb,h respectively represent the heating coefficients of electric boilers and gas boilers, COP ec and COP ac respectively represent the refrigeration coefficients of air conditioners and absorption chillers, u 1 and u 2 is a binary variable representing the season.

[0020] As a preferred scheme of the integrated energy system optimal scheduling method considering environment and cost described in the present invention, wherein: the multi-cost mathematical model and the optimal revenue objective function for day-ahead scheduling include:

[0021] Considering the operating factors of historical energy supply, energy conversion efficiency, and load, as well as the environmental factors of historical wind speed, sunlight, and temperature, the multi-cost mathematical model and the optimal revenue objective function for the day-ahead scheduling scheme are obtained, expressed as:

[0022]

[0023] Among them, C 1 obj represents the optimal revenue objective function for the day-ahead scheduling stage, C ope represents the operating cost of the integrated energy system, C 1 e represents the electricity purchase cost for the day-ahead scheduling stage, C g represents the gas purchase cost, C 1 env represents the environmental protection cost for the day-ahead scheduling stage, α 1 and α 2 represent the economic benefit and environmental benefit weight coefficients, C 1 chp represents the cost of the combined heat and power unit for the day-ahead scheduling stage, C eb , C ec , C gb and C acrespectively represent the costs of the electric boiler, air conditioner, gas boiler, and chiller, C t e represents the time-of-use electricity price of the upper power grid, Δt represents the cycle duration, c g represents the natural gas price, c CO2 represents the carbon emission cost, η CO2 represents the carbon emission coefficient of natural gas, V t CHP,g and V t GB,g respectively represent the gas consumption of the combined heat and power unit and the gas boiler in each time period.

[0024] As a preferred solution of the integrated energy system optimal scheduling method considering environment and cost described in the present invention, wherein: the multi-cost mathematical model and the optimal revenue objective function for real-time scheduling include:

[0025] Considering the operating factors of real-time energy supply, energy conversion efficiency, and load, as well as the environmental factors of real-time wind speed, sunlight, and temperature, the multi-cost mathematical model and the optimal revenue objective function for the real-time scheduling plan are obtained, expressed as:

[0026]

[0027] wherein, C 2 obj represents the optimal revenue objective function in the real-time scheduling stage, ΔC chp 、ΔC e and ΔC env respectively represent the change amounts of C chp 、C e and C env between the day-ahead scheduling stage and the real-time scheduling stage, C 2 e represents the electricity purchase cost in the real-time scheduling stage, C 2 env represents the environmental protection cost in the real-time scheduling stage, C 2 chp represents the cost of the combined heat and power unit in the real-time scheduling stage.

[0028] As a preferred solution of the integrated energy system optimal scheduling method considering environment and cost described in the present invention, wherein: the total power balance constraint condition and the load rate constraint condition of the day-ahead scheduling plan are expressed as:

[0029]

[0030] wherein, P t chp,e represents the output power of the combined heat and power unit at time t, P max chp,eIndicates the maximum output of the combined heat and power unit, P max buy,e and V max buy,e respectively represent the maximum power purchase and the maximum gas purchase, P t eb,e and P t ec,e respectively represent the loads of the electric boiler and the air conditioner in each time period, P min BESS is the minimum output power of the energy storage battery, P max BESS is the maximum output power of the energy storage battery, SOC min is the minimum capacity of the energy storage battery, SOC max is the maximum capacity of the energy storage battery.

[0031] As a preferred solution of the integrated energy system optimal scheduling method considering environment and cost according to the present invention, wherein: the constraint conditions of the real-time scheduling solution are expressed as:

[0032]

[0033] Among them, ΔP t buy,e 、ΔP t chp,e 、ΔP t eb,e and ΔP t ec,e are respectively the change amounts of P t buy,e 、P t chp,e 、P t eb,e and P t ec,e ; P t WT,a 、P t PV,a and P t BESS,a are respectively the actual outputs of the wind turbine, the photovoltaic array and the energy storage battery.

[0034] In a second aspect, the present invention provides an integrated energy system optimal scheduling device considering environment and cost, including:

[0035] A first construction module, configured to establish a day-ahead prediction integrated energy system model composed of multiple energies and multiple energy coupling devices according to the uncertainty of renewable energy;

[0036] A second construction module, configured to establish an integrated energy system energy conversion model for the multi-energy coupling device based on the predicted integrated energy system model of the current day through a multi-energy collaborative hybrid modeling method;

[0037] A third construction module, configured to establish a multi-cost mathematical model and an optimal revenue objective function for the day-ahead scheduling and real-time scheduling of the integrated energy system based on the integrated energy system energy conversion model, taking into account energy operation factors and environmental factors;

[0038] A fourth construction module, configured to establish two-stage thermal and electric power balance constraints and thermal and electric load rate constraints for the integrated energy system based on the integrated energy system energy conversion model and the optimal revenue objective function;

[0039] An output module, configured to solve the integrated energy system through a multi-objective artificial hummingbird optimization algorithm based on the optimal revenue and constraints of the day-ahead scheduling and real-time scheduling, and obtain a two-stage optimal scheduling scheme.

[0040] In a third aspect, the present invention provides an electronic device, including:

[0041] A memory and a processor;

[0042] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the integrated energy system optimization scheduling method considering the environment and cost are implemented.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the integrated energy system optimization scheduling method considering the environment and cost are implemented.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows: First, aiming at the problem of wind power uncertainty, the present invention constructs a fuzzy set reflecting uncertainty by using historical data, and establishes output power prediction models for wind power generation, photovoltaic power generation and energy storage batteries based on the Wasserstein fuzzy set. Subsequently, a mathematical model for energy conversion of the integrated energy system is established, and reasonable distribution of energy is achieved through flexible energy conversion. Second, a two-stage scheduling strategy is proposed, which includes a day-ahead scheduling stage and a real-time scheduling stage. The day-ahead scheduling stage can output the optimal day-ahead scheduling plan one day in advance, providing early guidance for the operation preparation of the integrated energy system. The real-time scheduling stage is synchronized with the actual operation of the integrated energy system, and is adjusted based on the day-ahead scheduling plan to output the real-time scheduling plan. Finally, by introducing an external archive set to save a fixed number of non-dominated solutions, a multi-objective artificial hummingbird algorithm is proposed as the solution algorithm for the scheduling model by using the crowding distance method based on dynamic elimination and the non-dominated sorting solution update mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0046] Figure 1 Schematic diagram of the overall process of the integrated energy system optimization scheduling method considering environment and cost according to an embodiment of the present invention;

[0047] Figure 2 Schematic diagram of the integrated energy system model structure of the integrated energy system optimization scheduling method considering environment and cost according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention shall fall within the protection scope of the present invention.

[0049] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0050] Secondly, the so-called "one embodiment" or "embodiment" herein refers to specific features, structures or characteristics that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that are mutually exclusive with other embodiments.

[0051] The present invention will be described in detail in conjunction with schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally in a non-general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width and depth should be included.

[0052] At the same time, in the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper, lower, inner and outer" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0053] Unless otherwise clearly defined and limited in the present invention, the terms "mounted, connected, connected" should be understood in a broad sense. For example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] Referring to Figure 1 - Figure 2 , for an embodiment of the present invention, an integrated energy system optimal scheduling method considering environment and cost is provided, including:

[0055] S100, according to the uncertainty of renewable energy, establish a day-ahead prediction integrated energy system model composed of multiple energies and multiple energy coupling devices;

[0056] S200, based on the day-ahead prediction integrated energy system model, for the multiple energy coupling devices, establish an integrated energy system energy conversion model through a multi-energy collaborative hybrid modeling method;

[0057] S300, based on the integrated energy system energy conversion model, considering energy operation factors and environmental factors, establish a multi-cost mathematical model for the day-ahead scheduling and real-time scheduling of the integrated energy system and an optimal revenue objective function;

[0058] S400. Based on the energy conversion model of the integrated energy system and the optimal revenue objective function, establish the two-stage thermal and electric power balance constraints and the thermal and electric load rate constraints of the integrated energy system;

[0059] S500. Based on the optimal revenue and constraints of day-ahead scheduling and real-time scheduling, solve the integrated energy system through the multi-objective artificial hummingbird optimization algorithm to obtain the two-stage optimal scheduling scheme.

[0060] It should be noted that in step S100, based on this embodiment of the integrated energy system, multi-energy means that the integrated energy system is divided into several parts according to different forms such as wind power, photovoltaic power, combined heat and power units, air conditioners, energy storage batteries, etc., ensuring that each subsystem has specific functions and characteristics, which can not only achieve the independent integrity of each part of the system model, but also ensure that the overall system can maintain a stable state during operation, without being interfered by external factors and the independent operation of internal subsystems, so as to accurately establish the optimization model. For the specific connection method, see the appendix Figure 2 。

[0061] In a preferred embodiment, the day-ahead prediction integrated energy system model includes a wind power generation output power prediction model, a photovoltaic power generation output power prediction model, and an energy storage battery charge and discharge power prediction model;

[0062] Among them, based on the Wasserstein fuzzy set representing the system uncertainty, using the past real-time wind speed and output power, construct a reactive uncertainty fuzzy set, and combine with the wind power generation equation to build a wind power generation output power prediction model considering wind speed uncertainty, expressed as:

[0063]

[0064]

[0065] Among them, P t WT,e is the output power of the wind turbine at time t, and P ref,WT is the rated power of the wind turbine; V in,wind is the incoming wind speed, V out,wind is the outgoing wind speed, V ref,wind is the rated wind speed, V wind is the actual wind speed; K wt1 、K wt2 are wind speed parameters; v is the wind speed probability distribution; W(v, V wind ) is the wind speed fuzzy set; δ * wind is the historical wind speed data; ξ wind is the support set of the wind speed uncertain variable δ wind ; Q wind is δwind and δ * wind The joint distribution of; ||·|| is the 1-norm; D wind is the wind speed fuzzy set; Ψ(ξ wind ) is the entire wind speed probability distribution on the support set ξ wind ; ε wind is the wind speed Wasserstein ball radius;

[0066] Using past real-time illumination and output power, construct a reaction uncertainty fuzzy set, and combine with the photovoltaic power generation equation to build a photovoltaic power generation output power prediction model considering illumination uncertainty, expressed as:

[0067]

[0068] where P t PV,e is the output power of the photovoltaic array at time t under the illumination intensity I t PV and the surface temperature T t PV ; I ref,PV , T ref,PV and P max,PV are the illumination intensity, photovoltaic array temperature, and maximum output power under standard test respectively, K PV is the temperature coefficient, i PV and t PV are the illumination intensity and surface temperature probability distributions respectively, W(i PV , I t PV ) and W(t PV , T t PV ) are the illumination intensity fuzzy set and surface temperature fuzzy set respectively, δ * i and δ * t are the historical illumination intensity data and historical surface temperature data respectively, ξ i and ξ t are the support sets of the illumination intensity uncertainty variable δi and the surface temperature uncertainty variable δt respectively, Q i,PV is the joint distribution of δ i and δ * i ; Q t,PV is the joint distribution of δ t and δ * t ; D i,PV and D t,PV are the illumination intensity fuzzy set and surface temperature fuzzy set respectively, Ψ(ξ i ) is the support set ξi The probability distribution of all light intensities on, Ψ(ξ t ) is the probability distribution of all surface temperatures on the support set ξ t , ε i,PV and ε t,PV are the Wasserstein ball radii of the light intensity and the surface temperature, respectively.

[0069] According to the internal physical characteristics of the energy storage battery, an energy storage battery charge and discharge power prediction model is established, expressed as:

[0070]

[0071] Among them, E t SOC and E t-1 SOC are the remaining capacities of the battery at time t and time t-1, respectively. P t BESS,e is the charge and discharge power of the battery at time t. When the power is positive, it represents charging, and when the power is negative, it represents discharging. γ + BESS and γ - BESS are the charge and discharge efficiencies of the battery, respectively. Δt represents the elapsed time.

[0072] It should be noted that according to the description of step S100, for the multi-energy coupling devices existing in the system, an integrated energy system energy conversion model is established. In step 200, on the basis of completing step S100, energy is converted in different forms through multiple coupling devices, and the conversion, regulation, and coupling of various energies such as electric energy, heat energy, and gas existing in the integrated energy system are realized through the energy coupling devices, and the flow and distribution of energy are optimized to improve the efficiency and stability of the entire system.

[0073] In a preferred embodiment, establishing the integrated energy system energy conversion model includes:

[0074] By analyzing the internal physical characteristics of the energy coupling device and establishing the integrated energy system energy conversion model in matrix form, expressed as:

[0075]

[0076] Among them, P t l,e , Q t l,c , Q t l,h and V t l,g represent the electrical load, cooling load, heating load, and gas load per period, respectively. Pt buy,e and V t buy,g represent the electricity purchase volume and gas purchase volume for each time period respectively. P t WT,e 、P t PV,e and P t BESS,e represent the predicted power generation of wind turbines, photovoltaic arrays and the predicted charge and discharge power of energy storage batteries for each time period respectively. v ec 、v eb 、v chp and v gb represent the distribution coefficients of air conditioners, electric boilers, combined heat and power units and gas boilers respectively. η chp,e and η chp,h represent the power generation coefficient and heat supply coefficient of the combined heat and power unit respectively. η eb,h and η gb,h represent the heating coefficients of electric boilers and gas boilers respectively. COP ec and COP ac represent the refrigeration coefficients of air conditioners and absorption chillers respectively. u 1 and u 2 are binary variables representing seasons.

[0077] Specifically, in step S300, according to the description of the energy conversion model in step S200, a multi-cost mathematical model and an optimal revenue objective function for day-ahead scheduling and real-time scheduling of the integrated energy system considering operation and environmental factors are established. Among them, the operation factors may include historical energy supply, energy conversion efficiency and load, etc., and the environmental factors may include historical wind speed, light and temperature, etc. The scheduling strategy proposed in the present invention is divided into two stages: the day-ahead scheduling stage and the real-time scheduling stage. In the day-ahead optimal scheduling plan, in the deterministic scenario composed of predicted data, the integrated energy and the system can rely on various energy conversion devices to maximize the consumption of renewable energy under a specific objective function.

[0078] In a preferred embodiment, the multi-cost mathematical model and the optimal revenue objective function for day-ahead scheduling include:

[0079] Considering the operation factors of historical energy supply, energy conversion efficiency and load, and the environmental factors of historical wind speed, light and temperature, the multi-cost mathematical model and the optimal revenue objective function of the day-ahead scheduling plan are obtained, which are expressed as:

[0080]

[0081] Among them, C 1 obj represents the optimized revenue objective function in the day-ahead scheduling stage. Cope Denotes the operating cost of the integrated energy system, C 1 e Denotes the electricity purchase cost in the day-ahead scheduling stage, C g Denotes the gas purchase cost, C 1 env Denotes the environmental protection cost in the day-ahead scheduling stage, α 1 and α 2 Denotes the weight coefficients of economic and environmental benefits, C 1 chp Denotes the cost of the combined heat and power unit in the day-ahead scheduling stage, C eb 、C ec 、C gb and C ac Denote the costs of the electric boiler, air conditioner, gas boiler, and chiller respectively, C t e Denotes the time-of-use electricity price of the upper power grid, Δt denotes the cycle duration, c g Denotes the natural gas price, c CO2 Denotes the carbon emission cost, η CO2 Denotes the carbon emission coefficient of natural gas, V t CHP,g and V t GB,g Denote the gas consumption of the combined heat and power unit and the gas boiler in each period respectively.

[0082] It should be noted that on the basis of the day-ahead optimal plan, by adjusting the power generation of the combined heat and power unit, the power purchased from the grid, and the electricity consumption of the electric boiler or air conditioner, the power balance is achieved, and the change in the heating power caused by the adjustment of the electric power must also be balanced to ensure the overall heating balance.

[0083] In a preferred embodiment, the multi-cost mathematical model and the optimal revenue objective function for real-time scheduling include:

[0084] Taking into account the operating factors of real-time energy supply, energy conversion efficiency, and load, as well as the environmental factors of real-time wind speed, light, and temperature, the multi-cost mathematical model and the optimal revenue objective function for the real-time scheduling plan are obtained, expressed as:

[0085]

[0086] Among them, C 2 obj Denotes the optimal revenue objective function in the real-time scheduling stage, ΔC chp 、ΔC e and ΔC env Denote C chp 、C e and C envThe change amount between the day-ahead scheduling stage and the real-time scheduling stage, C 2 e Represents the electricity purchase cost in the real-time scheduling stage, C 2 env Represents the environmental protection cost in the real-time scheduling stage, C 2 chp Represents the cost of the combined heat and power unit in the real-time scheduling stage.

[0087] In a preferred embodiment, the total power balance constraint condition and the load rate constraint condition of the day-ahead scheduling scheme are expressed as:

[0088]

[0089] Wherein, P t chp,e Represents the output power of the combined heat and power unit at time t, P max chp,e Represents the maximum output of the combined heat and power unit, P max buy,e And V max buy,e Respectively represent the maximum electricity purchase amount and the maximum gas purchase amount, P t eb,e And P t ec,e Respectively represent the loads of the electric boiler and the air conditioner in each time period, P min BESS Is the minimum output power of the energy storage battery, P max BESS Is the maximum output power of the energy storage battery, SOC min Is the minimum capacity of the energy storage battery, SOC max Is the maximum capacity of the energy storage battery.

[0090] In a preferred embodiment, the constraint conditions of the real-time scheduling scheme are expressed as:

[0091]

[0092] Wherein, ΔP t buy,e 、ΔP t chp,e 、ΔP t eb,e And ΔP t ec,e Are respectively the change amounts of P t buy,e 、P t chp,e 、P t eb,e And P t ec,e The change amount of; Pt WT,a , P t PV,a and P t BESS,a are the actual outputs of the wind turbine, the photovoltaic array, and the energy storage battery, respectively.

[0093] Specifically, in step S500, the above model constructed for the integrated energy system is solved by the multi-objective artificial hummingbird optimization algorithm. The multi-objective artificial hummingbird algorithm is a swarm-based multi-objective optimization algorithm. This algorithm is based on the foraging habits of hummingbirds in nature. During the optimization process, it simulates three foraging behaviors: directional foraging, territorial foraging, and migratory foraging. At the same time, the foraging behaviors simulate three flight abilities, including axial flight, diagonal flight, and omnidirectional flight. By applying a fixed-numbered external archive, the obtained optimal Pareto solutions are saved. In the day-ahead scheduling stage, without considering the uncertainty of renewable energy, the day-ahead prediction data of renewable energy is used as the only input data for the power prediction model. Therefore, it is assumed that the day-ahead power prediction data is the actual output of renewable energy, and the scheduling model is regarded as a deterministic scheduling model.

[0094] Proposing a day-ahead optimal scheduling plan one day before the actual operation of the integrated energy system can effectively reduce the preparatory work before scheduling. However, the forecast data based on historical data and weather data may deviate from the actual scenario. If no adjustment is made during the actual operation of the integrated energy system and the outputs of each unit of the integrated energy system are allocated according to the day-ahead optimal plan, a large amount of energy waste will be caused. Therefore, it is necessary to adjust the original scheduling plan based on the day-ahead plan and perform real-time scheduling to achieve a better renewable energy consumption effect.

[0095] Real-time scheduling is implemented in parallel with the actual operation of the integrated energy system and is performed when there is a deviation between the day-ahead prediction data and the actual scenario. Therefore, both the output of the day-ahead scheduling stage and the deviation between the prediction data and the actual scenario are inputs to the real-time scheduling stage.

[0096] In summary, according to the integrated energy system optimal scheduling method based on the multi-objective artificial hummingbird algorithm provided by the present invention, first, output power prediction models for wind power generation, photovoltaic power generation, and energy storage batteries are established. Subsequently, a mathematical model for energy conversion in the integrated energy system is established to achieve reasonable distribution of energy through flexible energy conversion. In addition, a two-stage scheduling strategy is proposed, which includes a day-ahead scheduling stage and a real-time scheduling stage. The day-ahead scheduling stage can output the day-ahead optimal scheduling plan one day in advance to provide preliminary guidance for the operation preparation of the integrated energy system. The real-time scheduling stage is synchronized with the actual operation of the integrated energy system, and adjustments are made based on the day-ahead scheduling plan to output the real-time scheduling plan. Finally, by introducing an external archive set to save a fixed number of non-dominated solutions, a multi-objective artificial hummingbird algorithm is proposed as the solution algorithm for the scheduling model by using the crowding distance method based on dynamic elimination and the non-dominated sorting solution update mechanism.

[0097] The above is a schematic solution of an integrated energy system optimal scheduling method considering environment and cost in this embodiment. It should be noted that the technical solution of the integrated energy system optimal scheduling device considering environment and cost belongs to the same concept as the technical solution of the above-mentioned integrated energy system optimal scheduling method considering environment and cost. For the details not described in detail in the technical solution of the integrated energy system optimal scheduling device considering environment and cost in this embodiment, reference can be made to the description of the technical solution of the above-mentioned integrated energy system optimal scheduling method considering environment and cost.

[0098] The integrated energy system optimal scheduling device considering environment and cost in this embodiment includes:

[0099] The first construction module is used to establish a day-ahead prediction integrated energy system model composed of multiple energies and multiple energy coupling devices according to the uncertainty of renewable energy.

[0100] The second construction module is used to establish an energy conversion model of the integrated energy system for multiple energy coupling devices by using a multi-energy collaborative hybrid modeling method based on the day-ahead prediction integrated energy system model.

[0101] The third construction module is used to establish a multi-cost mathematical model and an optimal revenue objective function for the day-ahead scheduling and real-time scheduling of the integrated energy system based on the energy conversion model of the integrated energy system, considering energy operation factors and environmental factors.

[0102] The fourth construction module is used to establish two-stage thermal and electrical power balance constraints and thermal and electrical load rate constraints of the integrated energy system based on the energy conversion model of the integrated energy system and the optimal revenue objective function.

[0103] An output module, configured to solve the integrated energy system by means of a multi-objective artificial hummingbird optimization algorithm based on the best benefits and constraint conditions of daily scheduling and real-time scheduling, so as to obtain a two-stage optimal scheduling scheme.

[0104] This embodiment further provides an electronic device, which is applicable to the optimization scheduling of an integrated energy system considering the environment and cost, and includes:

[0105] A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for optimizing the scheduling of an integrated energy system considering the environment and cost as proposed in the above embodiment.

[0106] This embodiment further provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for optimizing the scheduling of an integrated energy system considering the environment and cost as proposed in the above embodiment.

[0107] The storage medium proposed in this embodiment and the method for optimizing the scheduling of an integrated energy system considering the environment and cost proposed in the above embodiment belong to the same inventive concept. For technical details not described in detail in this embodiment, reference can be made to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0108] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FLASH), a hard disk, or an optical disc of a computer, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for optimizing the dispatching of an integrated energy system taking into account the environment and costs, characterized in that: include: According to the uncertainty of renewable energy, a comprehensive energy system model consisting of multiple energy sources and multiple energy coupling devices is established; Based on the day-ahead forecast integrated energy system model, an integrated energy system energy conversion model is established for the multi-energy coupling device through a multi-energy collaborative hybrid modeling method; Based on the energy conversion model of the integrated energy system, taking into account energy operation factors and environmental factors, a multi-cost mathematical model of day-ahead scheduling and real-time scheduling of the integrated energy system and an optimal benefit objective function are established; Based on the energy conversion model of the integrated energy system and the optimal benefit objective function, two-stage thermal and electric power balance constraints and thermal and electric load rate constraints of the integrated energy system are established; Based on the optimal benefits and constraints of day-ahead scheduling and real-time scheduling, the integrated energy system is solved by the multi-objective artificial hummingbird optimization algorithm to obtain a two-stage optimal scheduling scheme.

2. The method for optimizing and dispatching an integrated energy system taking into account the environment and costs as claimed in claim 1, characterized in that: The day-ahead prediction integrated energy system model includes a wind power generation output power prediction model, a photovoltaic power generation output power prediction model, and an energy storage battery charging and discharging power prediction model; By using the past real-time wind speed and output power, the uncertainty response fuzzy set is constructed, and combined with the wind power generation equation, a wind power generation output power prediction model taking into account wind speed uncertainty is constructed; By using the past real-time illumination and output power, the uncertainty fuzzy set is constructed, and combined with the photovoltaic power generation equation, a photovoltaic power generation output power prediction model taking into account illumination uncertainty is constructed; According to the internal physical characteristics of the energy storage battery, a charging and discharging power prediction model of the energy storage battery is established.

3. The method for optimizing and dispatching an integrated energy system taking into account the environment and costs as claimed in claim 1 or 2, characterized in that: Establishing an energy conversion model for an integrated energy system includes: By analyzing the physical characteristics inside the energy coupling device, an energy conversion model of the integrated energy system is established in matrix form, which is expressed as: Among them, P t l,e , Q t l,c , Q t l,h and V t l,g Represents the electric load, cooling load, heating load and gas load in each period, P t buy,e and V t buy,g Respectively represent the amount of electricity and gas purchased in each period, P t WT,e , P t PV,e and P t BESS,e They represent the predicted power generation of wind turbines and photovoltaic arrays and the predicted charging and discharging power of energy storage batteries in each period, respectively. ec 、v eb 、v chp and v gb are the distribution coefficients of air conditioners, electric boilers, cogeneration units and gas boilers, respectively, chp,e and η chp,h They represent the power generation coefficient and heating coefficient of the cogeneration unit, η eb,h and η gb,h Represents the heating coefficient of electric boiler and gas boiler, COP ec and COP ac They represent the refrigeration coefficients of the air conditioner and absorption chiller respectively, and u1 and u2 are binary variables representing the seasons.

4. The method for optimizing and dispatching an integrated energy system taking into account the environment and costs as claimed in claim 3, characterized in that: The multi-cost mathematical model of day-ahead scheduling and the optimal revenue objective function include: Taking into account the operating factors of historical energy supply, energy conversion efficiency and load, as well as the environmental factors of historical wind speed, light and temperature, the multi-cost mathematical model of the day-ahead scheduling scheme and the optimal benefit objective function are obtained, which are expressed as: Among them, C 1 obj represents the optimization revenue objective function in the day-ahead scheduling phase, C ope represents the operating cost of the integrated energy system, C 1 e represents the electricity purchase cost in the day-ahead dispatch stage, C g represents the gas purchase cost, C 1 env represents the environmental protection cost in the day-ahead dispatching stage, α1 and α2 represent the weight coefficients of economic and environmental benefits, and C 1 chp represents the cost of the CHP unit in the day-ahead dispatch stage, C eb , C ec , C gb and C ac Represent the cost of electric boiler, air conditioner, gas boiler and refrigerator respectively, C t e represents the time-sharing electricity price of the upper power grid, Δt represents the cycle length, c g represents the natural gas price, c CO2 represents the carbon emission cost, η CO2 Represents the carbon emission coefficient of natural gas, V t CHP,g and V t GB,g Respectively represent the gas consumption of cogeneration units and gas boilers in each period.

5. The method for optimizing and dispatching an integrated energy system taking into account the environment and costs as claimed in claim 3, characterized in that: The multi-cost mathematical model of real-time scheduling and the optimal revenue objective function include: Taking into account the operational factors of real-time energy supply, energy conversion efficiency and load, as well as the environmental factors of real-time wind speed, light and temperature, the multi-cost mathematical model of the real-time scheduling scheme and the optimal benefit objective function are obtained, which are expressed as: Among them, C 2 obj represents the objective function of optimizing the profit in the real-time scheduling stage, ΔC chp , ΔC e and ΔC env Respectively represent C chp , C e and C env The change between the day-ahead scheduling stage and the real-time scheduling stage, C 2 e represents the electricity purchase cost in the real-time dispatch stage, C 2 env represents the environmental protection cost in the real-time scheduling stage, C 2 chp Represents the cost of the cogeneration unit in the real-time scheduling stage.

6. The method for optimizing and dispatching an integrated energy system taking into account the environment and costs as claimed in claim 4, characterized in that: The total power balance constraint and load rate constraint of the day-ahead dispatching scheme are expressed as: SOC min ≤SOC t ≤SOC max Among them, P t chp,e represents the output power of the cogeneration unit at time t, P max chp,e Indicates the maximum output of the cogeneration unit, P max buy,e and V max buy,e They are respectively expressed as the maximum electricity purchase and the maximum gas purchase, P t eb,e and P t ec,e They are respectively represented as the load of electric boiler and air conditioner in each period, P min BESS is the minimum output power of the energy storage battery, P max BESS is the maximum output power of the energy storage battery, SOC min is the minimum capacity of the energy storage battery, SOC max The maximum capacity of the energy storage battery.

7. The method for optimizing and dispatching an integrated energy system taking into account the environment and costs as claimed in claim 5, characterized in that: The constraints of the real-time scheduling scheme are expressed as: Among them, ΔP t buy,e , ΔP t chp,e , ΔP t eb,e and ΔP t ec,e P t buy,e , P t chp,e , P t eb,e and P t ec,e The change in P t WT,a , P t PV,a and P t BESS,a They are the actual outputs of wind turbines, photovoltaic arrays and energy storage batteries respectively.

8. An integrated energy system optimization and scheduling device taking into account the environment and costs, characterized in that: include, The first building module is used to establish a day-ahead forecasting integrated energy system model consisting of multiple energy sources and multiple energy coupling devices according to the uncertainty of renewable energy; The second building module is used to establish an energy conversion model of the integrated energy system for the multi-energy coupling device through a multi-energy collaborative hybrid modeling method based on the day-ahead forecast integrated energy system model; The third building module is used to establish a multi-cost mathematical model of day-ahead scheduling and real-time scheduling of the integrated energy system and an optimal benefit objective function based on the energy conversion model of the integrated energy system and taking into account energy operation factors and environmental factors; The fourth building module is used to establish two-stage thermal and electric power balance constraints and thermal and electric load rate constraints of the integrated energy system based on the energy conversion model of the integrated energy system and the optimal benefit objective function; The output module is used to solve the integrated energy system based on the optimal benefits and constraints of day-ahead scheduling and real-time scheduling through the multi-objective artificial hummingbird optimization algorithm to obtain a two-stage optimal scheduling plan.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for optimizing and scheduling an integrated energy system taking into account the environment and costs as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, can implement the steps of the method for optimizing and scheduling an integrated energy system taking into account the environment and costs as described in any one of claims 1 to 7.

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