CN116244927A - Zero-carbon island energy system optimization method, device, equipment and storage medium - Google Patents
Zero-carbon island energy system optimization method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a zero-carbon island energy system optimization method, a device, equipment and a storage medium, wherein the method comprises the following steps: building a building energy consumption simulation model based on user energy consumption behaviors, and predicting the energy demand of island residents by adopting the building energy consumption simulation model; taking the predicted energy demands of island residents as input conditions of a island energy system design scheduling optimization stage, and generating a plurality of representative energy demand scenes by adopting a k-means clustering algorithm; and constructing a zero-carbon island energy system by coupling biogas and an electric conversion technology, minimizing the annual total cost and renewable energy waste of the zero-carbon island energy system as targets, constructing a model, and solving the pareto front edge of the model to determine the comprehensive optimal scheme of the zero-carbon island energy system under different energy demand situations. The invention can realize self-sufficiency, self-regulation and stable operation under the condition of no external fossil fuel and electric power supply to provide energy demand prediction, system design and scheduling optimization.
Description
Technical Field
The invention relates to the technical field of energy system optimization, in particular to a zero-carbon island energy system optimization method, a zero-carbon island energy system optimization device, zero-carbon island energy system optimization equipment and a zero-carbon island energy system storage medium.
Background
Islands are limited in energy delivery by geographical conditions. The diversity of island energy requirements results in higher economic costs and environmental pollution problems due to weaker connections to continents and the power grid. The current optimal design tool for zero-carbon energy system planning only considers the supply of electric power, heating and refrigeration, and seldom considers the supply of natural gas and fresh water in islands.
The commonly used energy supply technology of islands includes battery energy storage, renewable energy power generation, sea water desalination equipment and the like. Battery energy storage is one of the most common methods for improving the flexibility and reliability of renewable energy systems, but large-scale application of battery energy storage faces challenges such as low energy density, battery aging, poor thermal stability, and the like. Renewable energy power generation has typical instability characteristics and requires stable and reliable energy storage devices. Although the sea water desalting equipment can produce fresh water to meet the fresh water demands of residents, the electric power demands are greatly increased, higher requirements are put forward on island energy source guarantee, and the sea water desalting demands need to be considered in the island energy source system planning and design stage.
The optimal design and coordinated operation of the zero-carbon island energy system are realized, and the island energy load is required to be predicted. Most of the island energy load data are difficult to acquire, and the condition limiting historical data are mostly missing to a large extent, so that a certain difficulty is brought to load prediction. Meanwhile, the energy demand of resident users depends on the energy consumption behavior of people, the energy consumption habit and behavior mode of people are quite different, and the uncertainty influence of the energy consumption behavior of the resident needs to be fully considered when load prediction is carried out.
Computational simulation can be used to predict energy demands of buildings and areas. The demand prediction model can solve this problem to some extent. The current mainstream demand prediction methods include a unit area index method, a regression statistical method, an artificial neural network method, a numerical simulation method and the like. The above method simulates and predicts the energy load of a single building or a group of buildings, but few methods can give consideration to the influence of uncertainty of the occupant's energy consumption behavior on the building load.
Disclosure of Invention
The invention aims to solve the technical problem of providing a zero-carbon island energy system optimization method, a zero-carbon island energy system optimization device, zero-carbon island energy system optimization equipment and a zero-carbon island energy system storage medium, which can realize self-sufficiency, self-regulation and stable operation under the condition of no external fossil fuel and power supply to provide energy demand prediction, system design, scheduling optimization and system evaluation.
The technical scheme adopted for solving the technical problems is as follows: the method for optimizing the zero-carbon island energy system comprises the following steps:
building a building energy consumption simulation model based on user energy consumption behaviors, and predicting the energy demand of island residents by adopting the building energy consumption simulation model;
taking the predicted energy demands of island residents as input conditions of a island energy system design scheduling optimization stage, and generating a plurality of representative energy demand scenes by adopting a k-means clustering algorithm;
and constructing a zero-carbon island energy system by coupling biogas and an electric conversion technology, minimizing the annual total cost and renewable energy waste of the zero-carbon island energy system as targets, constructing a model, and solving the pareto front edge of the model to determine the comprehensive optimal scheme of the zero-carbon island energy system under different energy demand situations.
Building a building energy consumption simulation model based on user energy consumption behaviors, and predicting the energy demand of island residents by adopting the building energy consumption simulation model, wherein the building energy consumption simulation model specifically comprises the following steps:
dividing the equipment into continuous equipment, standby equipment, refrigeration equipment and active equipment according to the energy utilization characteristics of the household energy consumption equipment;
dividing island resident behavior activities into continuous behaviors and transient behaviors, and constructing probability models of the continuous behaviors and the transient behaviors aiming at different types of household energy consumption equipment;
determining the energy consumption per hour of each household on the island according to the probability models of continuous behaviors and transient behaviors;
the energy demand per island hour is obtained according to the energy consumption per household per hour on the island.
The probability model of the continuous behavior comprises a periodic behavior probability model and an aperiodic behavior probability model, wherein the periodic behavior probability model is that
t represents time, C i Indicated at a fixed point in time t i Probability values of periodic behavior are performed; the aperiodic behavior probability model is p 2 ＝Aτ -α Both a and α are fitting parameters, τ representing the latency of the aperiodic behaviour.
The probability model of the transient behavior comprises a behavior probability model related to an event and a behavior probability model related to an environment, wherein the behavior probability model related to the event is that
C e The probability value representing transient behavior under sustainable event conditions, the behavior probability model related to the environment is represented by a threshold model, and the probability model for air conditioning is represented by a discrete three-parameter weibull cumulative model.
The island energy demand per hour is calculated by:
wherein (1)>
And->
Respectively representing the power consumption of household energy consumption equipment, the energy consumption of natural gas consumption equipment and the energy consumption of water consumption equipment of the f th family per hour, < >>
Indicating the hour power consumption of the heater or air conditioner in the f-th household,
refers to the coefficient of performance of the heater or air conditioner, +.>
And->
The electric load, the heat/cold load, the gas load and the water load per hour of the island are represented respectively, and F represents the number of households on the island.
The zero-carbon island energy system takes renewable energy as a main power supply and combined cooling heating and power as an auxiliary power supply; when the energy generation capacity of the renewable energy sources is overlarge, the residual renewable energy sources are converted into methane by utilizing carbon dioxide in the methane and the flue gas of the combined cooling, heating and power supply through an electric gas conversion system; when renewable energy is in shortage, methane is recycled through combined cooling, heating and power for generating electricity, and the methane is stored in a gas storage tank or provided with fuel gas for combined cooling, heating and power through bottled natural gas for generating electricity to meet the power demands of residents; at low power demands, the excess renewable energy is used to electrolyze water to produce hydrogen and react with the co-generation tail gas and carbon dioxide in the biogas to produce methane, which is stored or used as a renewable fuel.
And when the pareto front of the model is solved, an epsilon-constraint method is introduced to convert the multi-objective problem into a single-objective problem to solve, and meanwhile, the multi-objective decision method is combined to perform optimization on the pareto front, wherein the multi-objective decision method is used for calculating Euclidean distance between each optimal point and an ideal point/non-ideal point so as to identify the pareto optimal solution.
The technical scheme adopted for solving the technical problems is as follows: provided is a zero-carbon island energy system optimizing device, comprising:
the prediction module is used for constructing a building energy consumption simulation model based on the energy consumption behaviors of users and predicting the energy demands of island residents by adopting the building energy consumption simulation model;
the generation module is used for taking the predicted energy demands of island residents as input conditions of a island energy system design scheduling optimization stage and generating a plurality of representative energy demand scenes by adopting a k-means clustering algorithm;
the determining module is used for constructing a zero-carbon island energy system through coupling of methane and electric conversion technology, minimizing annual total cost and renewable energy waste of the zero-carbon island energy system as targets to construct a model, and solving the pareto front edge of the model to determine a comprehensive optimal scheme of the zero-carbon island energy system under different energy demand situations.
The technical scheme adopted for solving the technical problems is as follows: there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above-described zero-carbon island energy system optimization method when executing the computer program.
The technical scheme adopted for solving the technical problems is as follows: there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above-described zero-carbon island energy system optimization method.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects:
the invention can be used for construction planning decisions of a zero-carbon island energy system comprising centralized power supply, heat supply, cold supply, sea water desalination and zero-carbon gas supply, and particularly solves planning design requirements such as load prediction, energy supply scheme design, energy supply guarantee assessment in extreme climate environment and the like in system construction planning.
Aiming at the difficulty that fine historical data of a new project or a part of a modified project is difficult to obtain, adopting an occupant energy behavior simulation method to predict the building energy demand of a planning area and quantifying the uncertainty of the demand, thereby providing load data support for system planning and design. By using the energy behavior modeling method, finer load prediction data and uncertainty data distribution can be provided than prediction methods that typically rely solely on historical data.
In the scheme planning of the energy supply system, the design of the electric, thermal and cold energy supply system of the traditional comprehensive energy service and the special sea water desalination and zero carbon gas supply technology in the zero carbon island scene are integrated and modeled, and the mathematical planning method is utilized and the energy uncertainty simulation result is combined to carry out the optimization calculation of the overall design scheme by adopting the stochastic planning method, so that the system optimization design scheme matched with the construction environment of the zero carbon island specific energy system is provided.
Aiming at the problem that islands are extremely susceptible to the extreme climate environment of the ocean, the method further simulates the change conditions of various energy demands and wind and light resource conditions affected by the extreme weather when different design schemes of the energy system of the planned area experience the extreme weather, thereby realizing the assessment of the energy supply guarantee capability under the extreme climate environment.
Drawings
FIG. 1 is a flow chart of a method of optimizing a zero-carbon island energy system in accordance with a first embodiment of the present invention;
FIG. 2 is a flow chart of simulation of energy demand prediction in a first embodiment of the present invention;
FIG. 3 is a diagram of a randomly planned scene tree structure in a first embodiment of the invention;
FIG. 4 is a schematic diagram of a zero-carbon island energy system in accordance with a first embodiment of the present invention;
FIG. 5 is a schematic representation of island locations and percentages of different types of households in an embodiment of the invention;
FIG. 6 is a graph of outdoor average temperature distribution over time for a typical week of each season in an embodiment of the present invention;
FIG. 7 is a schematic diagram of a supply and demand balance of a representative scenario in a typical weather situation in an embodiment of the present invention;
FIG. 8 is a schematic diagram of energy balance for a representative scenario in typical weather conditions in an embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. Further, it is understood that various changes and modifications may be made by those skilled in the art after reading the teachings of the present invention, and such equivalents are intended to fall within the scope of the claims appended hereto.
The first embodiment of the invention relates to a method for optimizing a zero-carbon island energy system, which is shown in fig. 1 and comprises the following steps:
and step 1, building a building energy consumption simulation model based on user energy consumption behaviors, and predicting the energy demand of island residents by adopting the building energy consumption simulation model.
The step provides a building energy consumption simulation model constructed based on user behavior, which can capture the complex influence of user behavior, and specific energy consumption distribution of residents under different classifications can be obtained by using the simulation model when detailed time-sharing data are lacking.
When the energy demand prediction simulation flow is shown in fig. 2, and the complex influence of the behavior of the resident on the energy consumption is captured by using the energy behavior simulation model to predict the energy demand, the communities of the resident living on the island are classified according to the number, sex, age and the like of family members. By combining input information such as meteorological data, building information, energy utilization equipment parameters, household attributes and the like, the energy load of different types of households can be simulated. Based on the number of households in the demographic information, the total energy demand of the island community can be predicted.
The method is based on an energy consumption behavior mode modeling method, equipment is divided into four types of continuous equipment, standby equipment, refrigeration equipment and active equipment according to the energy consumption characteristics of household energy consumption equipment, and island resident behavior activities are divided into two types of continuous behaviors and transient behaviors.
The continuous behavior includes periodic behavior and aperiodic behavior, and classification of periodic behavior and aperiodic behavior is mainly dependent on whether or not a behavior of a specific person has periodicity at the time of occurrence. The probability of periodic behavior occurring at a given point in time is a fixed value, such as sleeping or cooking:
wherein t represents time, C i Indicated at a fixed point in time t i Probability values for periodic behavior are made.
The occurrence of non-periodic behavior, such as watching television or surfing the internet, is random, depending on the nature of human activity, with a probabilistic model of:
p 2 ＝Aτ -α
where a and α are both fitting parameters, τ represents the latency of the aperiodic behaviour, i.e. the difference between the start time of an event and the end time of the same event.
Transient behavior includes event-related behavior and environmental-related behavior, the event-related behavior being affected by a sustained behavior event signal, such as a habit of boiling water while watching television:
wherein C is e Probability values representing transient behavior under sustainable event conditions.
The environment-related behavior may be affected by environmental signals, such as turning on an air conditioner when hot or turning on a lamp when dark. When the environmental parameters deviate from the comfort zone, the occupant typically has some action to adjust the current state. The probability function of the environment-related behavior is mainly represented by a threshold model, e.g. describing the lighting behavior, which can be represented by the following equation:
or (b)
Where x represents an environmental parameter. The threshold value threshold is used to control the behavior pattern.
Furthermore, the probability function for air conditioning is represented by a discrete three-parameter weibull cumulative model:
or (b)
Where T represents the indoor temperature and L, u and k represent parameters describing the range of the function variable, the control discomfort threshold, and the function shape, respectively.
The energy consumption per hour per household of each island resident can be summarized by the energy consumption of various household devices as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
and->
Respectively representing the power consumption of household energy consumption equipment, the energy consumption of natural gas consumption equipment and the energy consumption of water consumption equipment of the f th family per hour, < >>
And->
Representing the power consumption of the continuous device, the standby device, the refrigeration device and the active device respectively, which are calculated by the probability behavior model. />
And
representing the energy consumption of the natural gas consumption equipment and the energy consumption of the water consumption equipment respectively.
The energy demand per hour of a full island can be calculated by the following equation:
wherein F represents the number of households on the island,
indicating the hour power consumption of the heater or air conditioner in each home,
refers to the coefficient of performance of a heater or air conditioner, wherein the amount of power consumed for the hour does not include the amount of power consumed for providing cooling and heating.
Uncertainty caused by island resident activity behavior on energy demand can be quantified by using an energy behavior simulation model. The load curve obtained by the energy behavior simulation is different each time under the same input conditions. According to different typical week scenes (summer, transitional season and winter), the random energy demands of island residents can be obtained through repeatedly running the model.
And 2, taking the predicted energy demands of island residents as input conditions of a island energy system design scheduling optimization stage, and generating a plurality of representative energy demand scenes by adopting a k-means clustering algorithm.
In the step, island resident community energy demands based on energy utilization behavior prediction are used as input conditions of a island energy system design scheduling optimization stage, and a k-means clustering algorithm is adopted to generate a representative energy demand scene. The method can divide the energy demand sample set into k clusters, so that points in the clusters are connected as closely as possible, and the generated scene can be displayed in a scene tree mode. Scene trees can be used to capture the uncertainty of seasons and various energy demands. In order to ensure accurate and rapid solution of the zero-carbon island energy system model, redundant random input data are required to be removed. Thus, two clusters are considered for each energy demand type in the scene tree. Fig. 3 is a scene tree overview of 40 scenes in total, and the probability of each scene is obtained by multiplying the scene probabilities of season, power demand, cooling and heating demand, gas demand and fresh water demand.
And 3, constructing a zero-carbon island energy system through coupling of methane and an electric conversion technology, minimizing annual total cost and renewable energy waste of the zero-carbon island energy system as targets, constructing a model, and solving the pareto front edge of the model to determine the comprehensive optimal scheme of the zero-carbon island energy system under different energy demand situations.
In order to realize the investment construction of the zero-carbon island energy system and the optimal design of the operation scheme, the system optimal design and the scheduling model are further developed. Potential technologies considered in the system include photovoltaic, wind power generators, cogeneration of cooling, heating and power, sea water desalination, gas storage, water storage, electricity to gas conversion and biogas.
Various raw materials on the island can produce biogas including seaweed, grass, agricultural sludge and food waste. The biogas technology and the electric conversion technology are coupled, so that the stable operation of a zero-carbon island energy system is facilitated, and the agricultural industry local to islands and the waste recovery treatment and environment improvement of the area are facilitated. Biogas technology has a triple role in treating residential waste on islands, supplying carbon dioxide required for electrical conversion, and supplying methane. In the zero-carbon island energy system, renewable energy sources such as wind energy, solar energy and the like are main power sources, and combined cooling, heating and power is an auxiliary power source. When the energy generation capacity of the renewable energy sources is overlarge, the electric conversion gas system can convert the residual renewable energy sources into methane by utilizing carbon dioxide in methane and flue gas of combined cooling, heating and power, so that the uncertainty risk brought by intermittent wind energy and solar energy is reduced, and reliable energy storage selection is provided for the energy source system. When renewable energy is in shortage, methane is recycled through the combined cooling, heating and power system for generating electricity, and the methane is stored in a gas storage tank or provided with fuel gas for combined cooling, heating and power through bottled natural gas for generating electricity to meet the power demands of residents. The system can promote the consumption of renewable energy sources and reduce the emission of greenhouse gases through the coupling of biogas and an electric conversion technology, and the structure is shown in figure 4.
At low power demand, excess renewable power will be used to electrolyze water to produce hydrogen and react with co-generation tail gas and carbon dioxide in biogas to produce methane for storage or use as renewable fuel. As the carbon dioxide is converted into methane, the methane output of the system is greatly improved compared with the original methane in the methane. In general, the system can promote the full utilization of renewable energy sources through the coupling of methane and electricity to reduce the emission of greenhouse gases.
After the zero-carbon island energy system is built, minimizing the annual total cost and renewable energy waste of the zero-carbon island energy system as targets to build a model, and solving the pareto front edge of the model to determine the comprehensive optimal scheme of the zero-carbon island energy system under different energy demand situations. When solving, an epsilon-constraint method can be introduced to convert the multi-objective problem into a single-objective problem for solving, and meanwhile, the multi-objective decision method is combined to perform optimization on the pareto front edge. The Euclidean distance method can be adopted to further identify the pareto optimal solution when making decisions, namely, the Euclidean distance between each optimal point and an ideal point/non-ideal point is calculated, namely, the LINMAP and TOPSIS two decision methods.
Taking a resident community of Huang Longxiang island in Zhejiang province as an example, the comprehensive planning decision optimization method provided by the embodiment is adopted to carry out system optimization design on the island energy system combined with the electric conversion technology. The Huanglongxiang island is positioned at the eastern part of the east sea, is hot in summer, cold in winter and cool in spring and autumn. Meanwhile, in order to reduce the calculation cost of the zero-carbon island energy system model, one year is divided into three groups of scenes of summer, transitional seasons and winter.
First, users are used to simulate different types of households residing in different types of apartments with energy behavior to predict energy needs of a case study area. Island locations and percentages of different types of households such as shown in fig. 5, 6621 resident residents on the yellow-long rural island, the residence space ratio being assumed to be 10%, and six different types of residents being considered to reside in corresponding apartments. The main technical parameters related to the zero-carbon island energy system proposed in this embodiment are shown in table 1, and the main economic parameters are shown in table 2.
TABLE 1 main technical parameters
TABLE 2 economic parameters of the Main technology
Load prediction is carried out by using an energy behavior simulation model by a user to obtain prediction curves of five energy demands in a typical week, a shadow area of the curves represents uncertainty of energy consumption of resident users, and two curves in the shadow represent two representative clustering centroid curves generated by a k-means clustering method, as shown in fig. 6. It can be seen from the demand curves that the demand of residents for electricity, natural gas and fresh water is substantially similar during the same typical week of different seasons. However, during a typical week of the same season, the energy demands on weekdays and weekends are quite different, since weekend residents mostly consume more energy at home.
After the solved prediction results of island electricity, cold, hot, natural gas, fresh water demands and the like and the energy demand statistical data are carried out, the calculated relative error is found to be within 5%, and the base load simulation integrity method provided by the embodiment is proved to be capable of realizing reliable prediction of the energy demands of island resident communities.
A total of 40 representative demand scenarios for two types of electricity, cold, heat, natural gas, and fresh water can be generated by k-means clustering, respectively. The energy requirements of residents in different seasons are different, for example, the residents only need to be cooled in summer, and only need to be heated in winter. Thus, the scenario tree generated by clustering needs to contain different seasonal scenarios (summer, winter and transition seasons) and two types of representative demand scenarios of electricity, cold, heat, natural gas and fresh water.
And respectively solving the pareto fronts under typical weather conditions and extreme weather conditions, and carrying out economic analysis, environmental protection analysis, system design analysis, system operation strategy analysis, standard regression coefficient analysis and sensitivity analysis on the results.
The environmental protection analysis is carried out on the system, and the zero-carbon island energy system provided by the embodiment is a 100% renewable energy system, so that no pollutant emission exists in the energy supply process, and the environmental protection performance is good. The redundant power generated by a fan and photovoltaic in the system is converted into methane by an electric conversion technology, and CO can be further utilized 2 Methanation is carried out, so that the emission reduction effect is achieved to the greatest extent. As can be seen from the stoichiometric relationship, about 1Nm is required in the methanation of the electric shift gas 3 CO of (c) 2 Can generate 1Nm3 of CH 4 . According to the optimal design result, the electric conversion gas system needs to convert about 1533052Nm each year 3 CO of (c) 2 To meet the residential natural gas demand in typical weather conditions, meaning that 3012 tons of CO can be recycled per year from the system 2 。
And a coupling system of electricity-gas conversion and biogas technology is introduced into the zero-carbon island energy system, so that domestic sewage, kitchen waste or animal manure can be methanized in a biogas digester to produce biogas, and the recycling of waste is realized. Besides biogas production, the anaerobic digestion of fermentation raw materials such as animal manure, kitchen waste and the like can also effectively treat organic waste on islands. In addition, methane can replace part of electric energy to be used as fuel for energy supply, and digestate is used for replacing mineral fertilizers and controlling the degradation of feces, so that the emission of greenhouse gases can be further reduced.
After determining the optimal installed capacity of the various energy devices, a system operation strategy analysis is performed. The operation strategy of the zero-carbon island energy system mainly relates to the scheduling strategies of power supply, heating, cooling, natural gas supply and fresh water supply. The optimal operation strategy of the system in typical weather conditions is shown in fig. 7 and 8, in fig. 7, (a) summer power (scenario # 1), (b) summer cooling (scenario # 1), (c) winter power (scenario # 25), (d) winter heating (scenario # 25), (e) transition season power (scenario # 17), (f) wind speed, and (g) solar radiation; in fig. 8, (a) summer natural gas (scenario # 1), (b) summer fresh water (scenario # 1), (c) winter natural gas (scenario # 25), (d) winter fresh water (scenario # 25), (e) transition season natural gas (scenario # 17), (f) transition season fresh water (scenario # 17).
The natural gas is mainly generated by electric conversion gas and methane technology, and the redundant natural gas can be stored for standby. As can be seen from the graph, most of the natural gas in the zero-carbon island energy system is supplied by electric conversion gas, and 82.61%, 78.49% and 70.76% of the natural gas are supplied to the zero-carbon island energy system in summer, winter and transitional seasons respectively. Most of the redundant natural gas generated by the electric conversion gas and methane technology is stored by a gas storage tank, and about 47% of natural gas needs to be stored by the gas storage tank and then supplied.
According to the analysis, the optimized 100% renewable energy zero-carbon island energy system adopts the comprehensive system configuration and the operation scheduling scheme after the optimization design of the method provided by the embodiment, and has good technical feasibility and comprehensive benefits.
A second embodiment of the present invention is directed to a zero-carbon island energy system optimization apparatus comprising:
the prediction module is used for constructing a building energy consumption simulation model based on the energy consumption behaviors of users and predicting the energy demands of island residents by adopting the building energy consumption simulation model;
the generation module is used for taking the predicted energy demands of island residents as input conditions of a island energy system design scheduling optimization stage and generating a plurality of representative energy demand scenes by adopting a k-means clustering algorithm;
the determining module is used for constructing a zero-carbon island energy system through coupling of methane and electric conversion technology, minimizing annual total cost and renewable energy waste of the zero-carbon island energy system as targets to construct a model, and solving the pareto front edge of the model to determine a comprehensive optimal scheme of the zero-carbon island energy system under different energy demand situations.
The prediction module specifically comprises:
the equipment dividing unit is used for dividing the equipment into continuous equipment, standby equipment, refrigeration equipment and active equipment according to the energy consumption characteristics of the household energy consumption equipment;
the behavior model construction unit is used for dividing island resident behavior activities into continuous behaviors and transient behaviors and constructing probability models of the continuous behaviors and the transient behaviors aiming at different types of household energy consumption equipment;
the energy consumption determining unit is used for determining the energy consumption per hour of each household on the island according to the probability models of the continuous behavior and the transient behavior;
and the computing unit is used for obtaining the energy demand of each island per hour according to the energy consumption of each household per hour on the island.
The probability model of the continuous behavior comprises a periodic behavior probability model and an aperiodic behavior probability model, wherein the periodic behavior probability model is that
t represents time, C i Indicated at a fixed point in time t i Probability values of periodic behavior are performed; the aperiodic behavior probability model is p 2 ＝Aτ -α Both a and α are fitting parameters, τ representing the latency of the aperiodic behaviour.
The probability model of the transient behavior comprises a behavior probability model related to an event and a behavior probability model related to an environment, wherein the behavior probability model related to the event is that
C e Representing transient behavior under sustainable event conditionsThe probability model for the air conditioner is represented by a discrete three-parameter weibull cumulative model.
The calculation unit is calculated by the following method:
wherein, the liquid crystal display device comprises a liquid crystal display device,
and->
Respectively representing the power consumption of household energy consumption equipment, the energy consumption of natural gas consumption equipment and the energy consumption of water consumption equipment of the f th family per hour, < >>
Indicating the hour power consumption of the heater or air conditioner in the f-th household,/->
Refers to the coefficient of performance of the heater or air conditioner, +.>
And->
The electric load, the heat/cold load, the gas load and the water load per hour of the island are represented respectively, and F represents the number of households on the island.
The zero-carbon island energy system constructed by the determining module takes renewable energy as a main power supply and combined cooling heating and power as an auxiliary power supply; when the energy generation capacity of the renewable energy sources is overlarge, the residual renewable energy sources are converted into methane by utilizing carbon dioxide in the methane and the flue gas of the combined cooling, heating and power supply through an electric gas conversion system; when renewable energy is in shortage, methane is recycled through combined cooling, heating and power for generating electricity, and the methane is stored in a gas storage tank or provided with fuel gas for combined cooling, heating and power through bottled natural gas for generating electricity to meet the power demands of residents; at low power demands, the excess renewable energy is used to electrolyze water to produce hydrogen and react with the co-generation tail gas and carbon dioxide in the biogas to produce methane, which is stored or used as a renewable fuel.
When the determining module solves the pareto front of the model, an epsilon-constraint method is introduced to convert the multi-objective problem into a single-objective problem to solve, and meanwhile, the multi-objective decision method is combined to perform optimization on the pareto front, wherein the multi-objective decision method is used for calculating Euclidean distance between each optimal point and an ideal point/non-ideal point so as to identify the pareto optimal solution.
A third embodiment of the invention is directed to an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the zero-carbon island energy system optimization method of the first embodiment when the computer program is executed.
A fourth embodiment of the invention is directed to a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the zero-carbon island energy system optimization method of the first embodiment.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the invention can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The optimization method of the zero-carbon island energy system is characterized by comprising the following steps of:
building a building energy consumption simulation model based on user energy consumption behaviors, and predicting the energy demand of island residents by adopting the building energy consumption simulation model;
taking the predicted energy demands of island residents as input conditions of a island energy system design scheduling optimization stage, and generating a plurality of representative energy demand scenes by adopting a k-means clustering algorithm;
and constructing a zero-carbon island energy system by coupling biogas and an electric conversion technology, minimizing the annual total cost and renewable energy waste of the zero-carbon island energy system as targets, constructing a model, and solving the pareto front edge of the model to determine the comprehensive optimal scheme of the zero-carbon island energy system under different energy demand situations.
2. The method for optimizing the zero-carbon island energy system according to claim 1, wherein the building energy consumption simulation model is constructed based on the user energy consumption behavior, and the energy demand of island residents is predicted by adopting the building energy consumption simulation model, specifically comprising:
dividing the equipment into continuous equipment, standby equipment, refrigeration equipment and active equipment according to the energy utilization characteristics of the household energy consumption equipment; dividing island resident behavior activities into continuous behaviors and transient behaviors, and constructing probability models of the continuous behaviors and the transient behaviors aiming at different types of household energy consumption equipment;
determining the energy consumption per hour of each household on the island according to the probability models of continuous behaviors and transient behaviors;
the energy demand per island hour is obtained according to the energy consumption per household per hour on the island.
3. The method of optimizing a zero-carbon island energy system of claim 2, wherein the probabilistic model of continuous behavior comprises a periodA periodic behavior probability model and an aperiodic behavior probability model, wherein the periodic behavior probability model is
t represents time, C i Indicated at a fixed point in time t i Probability values of periodic behavior are performed; the aperiodic behavior probability model is p 2 ＝Aτ -α Both a and α are fitting parameters, τ representing the latency of the aperiodic behaviour.
4. The method of claim 2, wherein the probabilistic model of transient behavior comprises an event-related behavioral probability model and an environmental-related behavioral probability model, wherein the event-related behavioral probability model is
C e The probability value representing transient behavior under sustainable event conditions, the behavior probability model related to the environment is represented by a threshold model, and the probability model for air conditioning is represented by a discrete three-parameter weibull cumulative model.
5. The method of optimizing a zero-carbon island energy system of claim 2, wherein the island energy demand per hour is calculated by:
wherein (1)>
And
respectively representing the power consumption of household energy consumption equipment, the energy consumption of natural gas consumption equipment and the energy consumption of water consumption equipment of the f th family per hour, < >>
Indicating the hour power consumption of the heater or air conditioner in the f-th household,/->
Refers to the coefficient of performance of the heater or air conditioner, +.>
And->
Respectively representing the electric load, the heat/cold load, the gas load and the water load of islands per hour; f represents the number of households on the island.
6. The method for optimizing a zero-carbon island energy system according to claim 1, wherein the zero-carbon island energy system uses renewable energy as a main power source and co-generation as an auxiliary power source; when the energy generation capacity of the renewable energy sources is overlarge, the residual renewable energy sources are converted into methane by utilizing carbon dioxide in the methane and the flue gas of the combined cooling, heating and power supply through an electric gas conversion system; when renewable energy is in shortage, methane is recycled through combined cooling, heating and power for generating electricity, and the methane is stored in a gas storage tank or provided with fuel gas for combined cooling, heating and power through bottled natural gas for generating electricity to meet the power demands of residents; at low power demands, the excess renewable energy is used to electrolyze water to produce hydrogen and react with the co-generation tail gas and carbon dioxide in the biogas to produce methane, which is stored or used as a renewable fuel.
7. The optimization method of the zero-carbon island energy system according to claim 1, wherein when the pareto front of the solution model is solved, an epsilon-constraint method is introduced to convert a multi-objective problem into a single-objective problem solution, and meanwhile, optimization is performed on the pareto front by combining a multi-objective decision method, wherein the multi-objective decision method is used for calculating Euclidean distance between each optimal point and an ideal point/non-ideal point so as to identify the pareto optimal solution.
8. A zero-carbon island energy system optimization device, comprising:
the prediction module is used for constructing a building energy consumption simulation model based on the energy consumption behaviors of users and predicting the energy demands of island residents by adopting the building energy consumption simulation model;
the generation module is used for taking the predicted energy demands of island residents as input conditions of a island energy system design scheduling optimization stage and generating a plurality of representative energy demand scenes by adopting a k-means clustering algorithm;
the determining module is used for constructing a zero-carbon island energy system through coupling of methane and electric conversion technology, minimizing annual total cost and renewable energy waste of the zero-carbon island energy system as targets to construct a model, and solving the pareto front edge of the model to determine a comprehensive optimal scheme of the zero-carbon island energy system under different energy demand situations.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the zero-carbon island energy system optimization method of any one of claims 1-7 when the computer program is executed.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the zero-carbon island energy system optimization method of any of claims 1-7.
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