CN115147014A - Multi-target balanced distribution method of comprehensive energy system - Google Patents
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Abstract
The invention provides a multi-target balanced distribution method of a comprehensive energy system, which comprises the following steps: establishing an equipment model according to equipment in the integrated energy system, wherein the equipment model comprises a conversion equipment model and a storage equipment model; establishing a multi-target scheduling model based on an objective function based on the equipment model, wherein the multi-target scheduling model comprises an economic optimization model, an energy efficiency optimization model and an environmental protection index model; establishing a constraint condition according to the equipment model so as to constrain the multi-target scheduling model; and solving the optimal solution of the multi-target scheduling model under the constraint condition through a particle swarm algorithm, and taking the optimal solution as a multi-target balanced distribution plan of the comprehensive energy system to ensure that an electric energy, heat energy, cold energy and natural gas distribution plan meeting the requirements is formulated for the comprehensive energy system.
Description
Technical Field
The invention relates to the technical field of energy management distribution, in particular to a multi-target balanced distribution method of an integrated energy system.
Background
In recent years, increasing the energy consumption rate and promoting efficient, green, clean, environmental protection and sustainable development of energy have become widely concerned, and in actual production, the energy consumption of disposable energy represented by coal and oil is being tilted to green, clean and renewable energy represented by natural gas and electricity. The comprehensive energy system provides energy supply service for users from the perspective of consumption side according to the requirements of the users on various energy sources.
However, wind power and photovoltaic power generation systems have the characteristic of randomness, so that the comprehensive energy system has higher difficulty in determining the energy distribution proportion. In addition, the structural relationship of the comprehensive energy system is complex, and the prior art is difficult to realize multi-energy coupling complementation and cascade utilization of energy, so that more energy is wasted in the energy distribution process of the comprehensive energy system.
Disclosure of Invention
The problem to be solved by the invention is how to build a reliable mathematical model for the integrated energy system to determine an optimal energy distribution plan.
In order to solve the above problems, the present invention provides a multi-target balanced distribution method for an integrated energy system, comprising:
establishing an equipment model according to equipment in the integrated energy system, wherein the equipment model comprises a conversion equipment model and a storage equipment model; establishing a multi-target scheduling model based on an objective function based on the equipment model, wherein the multi-target scheduling model comprises an economic optimization model, an energy efficiency optimization model and an environmental protection index model; establishing a constraint condition according to the equipment model so as to constrain the multi-target scheduling model; and solving the optimal solution of the multi-target scheduling model under the constraint condition through a particle swarm algorithm, and taking the optimal solution as a multi-target balanced distribution plan of the comprehensive energy system.
Optionally, the building of the equipment model according to the equipment in the integrated energy system includes:
modeling an energy conversion device in the integrated energy system, comprising:
performing mathematical modeling on the combined cooling heating and power system, the solar equipment and the gas-fired boiler respectively to obtain a combined cooling, heating and power system model, a solar equipment model and a gas-fired boiler model; and taking the combined cooling heating and power system model, the solar energy equipment model and the gas-fired boiler model as the conversion equipment model.
Optionally, the building of the equipment model according to the equipment in the integrated energy system further includes:
modeling an energy storage device in the integrated energy system, comprising:
respectively carrying out mathematical modeling on the heat storage equipment, the battery replacement station and the electric automobile to obtain a heat storage equipment model, a battery replacement station model and an electric automobile model; and taking the heat storage equipment model, the battery replacement station model and the electric automobile model as the storage equipment model.
Optionally, the establishing a multi-objective scheduling model based on the device model includes:
establishing a first objective function with the minimum operation cost as a target based on the operation and maintenance cost and the energy cost of equipment in the comprehensive energy system, wherein the first objective function is used as an objective function of the economic optimization model; establishing a second objective function with the maximum efficiency of 15794based on the input and output values of 15794in the comprehensive energy system as an objective function of the energy efficiency optimization model; and establishing a third objective function by taking the minimum emission control cost as a target based on the pollutant emission amount of the comprehensive energy system, wherein the third objective function is used as an objective function of the environment-friendly index model.
Optionally, the constraints include cold, hot, electrical power balance constraints, electric vehicle operating constraints, central air conditioning operating constraints, and photovoltaic/thermal assembly operating constraints.
Optionally, the establishing a constraint condition according to the device model to constrain the multi-objective scheduling model includes:
establishing the cold, heat and electric power balance constraints based on the electricity, heat and cold loads, the electricity output, the heat output, the electric efficiency and the power of each device in the comprehensive energy system; establishing the running constraint of the electric vehicle based on the charge-discharge state, the charge state and the running power of the electric vehicle in the comprehensive energy system; establishing central air conditioner operation constraints based on the power and the power limit of the central air conditioner in the integrated energy system; and establishing the operation constraint of the photovoltaic/photothermal component based on the installation area, the electrothermal conversion efficiency and the illumination intensity of the photovoltaic/photothermal component in the comprehensive energy system.
Optionally, the solving, by a particle swarm algorithm, an optimal solution of the multi-target scheduling model under the constraint condition, and taking the optimal solution as the multi-target balanced distribution plan of the integrated energy system includes:
inputting an original plan, wherein the original plan comprises a preset multi-energy distribution plan of the integrated energy system; initializing the speed and the position of the particle, judging whether the original plan meets the constraint condition, and if not, adjusting the original plan in the constraint condition; if the original plan satisfies the constraints, updating the speed and the position of the particle within the constraints; calculating the fitness of each particle, and updating an optimal value according to a preset global optimal solution updating method, wherein the optimal value comprises a particle optimal value and a particle swarm optimal value; updating an external file by a preset external file updating method according to the optimal value, and fuzzifying the objective function to obtain a more optimal value; and after the optimal value is obtained, updating and iterating the external archive to obtain the iterated optimal value as a final optimal solution.
Optionally, the calculating the fitness of each particle, and updating the optimal value according to a preset global optimal solution updating method includes:
when the external file contains non-inferior solutions suitable for the particles, one of the solutions is selected as an optimal value; and when the external file does not have non-inferior solutions suitable for the particles, randomly selecting one non-inferior solution from the external file as an optimal value.
Optionally, the updating the external profile according to the optimal value by a preset external profile updating method includes:
when the external file is lower than a preset value, directly adding a new non-inferior solution into the external file;
discarding a new non-inferior solution when the external profile is higher than the preset value; when the external file is equal to the preset value, judging whether the non-inferior solution can dominate the external file; if the dominant individual can be replaced by the non-inferior individual; if the density value of the non-inferior solution is not dominant, adding the non-inferior solution into the external file, and removing the non-inferior solution with the minimum density value in the external file.
Optionally, after obtaining the better value, performing update iteration on the external archive to obtain an iterated better value, where the obtaining a final optimal solution includes:
judging whether the iteration times are greater than or equal to the preset times; if yes, outputting the optimal solution; if not, re-executing the step of updating the velocity and the position of the particle within the constraint.
Compared with the prior art, the energy conversion equipment and the energy storage equipment in the comprehensive energy system are respectively modeled, so that the comprehensive energy system with a complex structure is ensured to be modeled, and an energy distribution scheme is formulated according to the actual condition of the comprehensive energy system; and finally, solving through a particle swarm algorithm with high convergence rate and strong optimizing capability to obtain a final multi-target balanced distribution plan, and ensuring that an electric energy, heat energy, cold energy and natural gas distribution plan meeting the requirements is formulated for the comprehensive energy system.
Drawings
FIG. 1 is a system framework diagram of a multi-objective balanced distribution method for an integrated energy system according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a multi-target balanced distribution method of the integrated energy system according to the embodiment of the invention;
fig. 3 is a flowchart illustrating a multi-target balanced allocation method of an integrated energy system according to an embodiment of the present invention after step S100 is refined;
fig. 4 is another schematic flow chart of the multi-target balanced allocation method for an integrated energy system according to the embodiment of the present invention after step S100 is refined;
fig. 5 is a flowchart illustrating a multi-target balanced allocation method of an integrated energy system according to an embodiment of the present invention after step S200 is refined;
fig. 6 is a flowchart illustrating a multi-target balanced distribution method of the integrated energy system according to an embodiment of the present invention after step S300 is refined;
fig. 7 is a flowchart illustrating a multi-target balanced allocation method of an integrated energy system according to an embodiment of the present invention after step S400 is refined;
fig. 8 is a partial flowchart of step S400 of the multi-target balanced distribution method for the integrated energy system according to the embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. While certain embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and the embodiments of the invention are for illustration purposes only and are not intended to limit the scope of the invention.
It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "alternative embodiment". Relevant definitions for other terms will be given in the following description. It should be noted that the terms "first", "second", and the like in the present invention are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in the present invention are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
As shown in fig. 1 and fig. 2, an embodiment of the present invention provides a multi-target balanced distribution method for an integrated energy system, including:
and S100, establishing an equipment model according to equipment in the comprehensive energy system, wherein the equipment model comprises a conversion equipment model and a storage equipment model.
The energy system covers various types of energy and can be divided into a conventional energy system and a distributed new energy system according to regulation and control means and development time. The former mainly comprises an electric power subsystem, a gas and coal subsystem, a waste heat boiler subsystem, a combined supply gas turbine set and the like, and the latter is a photovoltaic power generation, wind power generation, geothermal energy heat production, tidal power generation and other subsystems with random output characteristics. The development and fusion of the comprehensive energy system in the links of sending, transmitting, changing, storing, using and the like can realize the comprehensive utilization of various energy sources, improve the utilization rate of the energy sources, reduce the waste of the energy sources and reduce the pollution degree of the coal combustion to the environment; meanwhile, various energy value-added services can be provided for users, and centralized supply of electricity, gas, heat, cold and water is realized, so that composite transmission and utilization of pipelines can be realized, and sustainable development of economy, society and energy is promoted. However, since the integrated energy system has various forms of energy subsystems, energy utilization modes and equipment structures of the systems are different, and functional attributes and operation characteristics of the systems are also greatly different.
As shown in fig. 1 and 2, the integrated energy system of the present invention includes a power grid, a distributed power source, an electric refrigerator, an electric storage device, a CCHP system, a natural gas grid, a gas boiler, a heat storage device, a solar heat collector, a cold storage device, and corresponding electric, gas, cold, and heat loads. The direction indicated by the arrow in the figure indicates the energy flowing direction, for example, the distributed power supply provides electric energy for the electric load, and the electric storage equipment can be charged from the power grid and can also be discharged for the electric load; solar collectors supply thermal energy to thermal loads.
The combined cooling-heating-power system (CCHP) is a system composed of equipment such as a gas turbine, a lithium bromide refrigerator, a waste heat boiler and the like and auxiliary supporting facilities thereof.
The CCHP system produces electrical energy as well as thermal energy, and therefore it has essentially two modes of operation, namely in a thermalisation mode and in an electricalisation mode. In a thermal power-on-demand (FTL) mode, the air inflow of the gas turbine is determined according to the amount of heat energy required by a user, the electric output of the gas turbine is used as an auxiliary to supply energy to users in a park, and the shortage of electric energy in the park is supplied by a power distribution network, photovoltaic and other renewable energy sources for power generation, an electric energy storage device and the like. In the electric heating model (FEL), the air input of the gas turbine is determined by the electric energy demand of the garden users, namely, the power supply is taken as the main task, the thermal output of the gas turbine is taken as the auxiliary energy to provide the heat energy demand for the garden users, and the heat energy shortage of the users is mainly provided by a gas boiler, a heat pump, a higher-level thermal network, a heat energy storage device arranged in the garden and the like.
The problems that the output of green clean power sources such as wind and light is intermittent and unstable, the power supply of a power grid, a heat supply network and a gas network is interrupted briefly due to disaster accidents, the load fluctuates and the like may exist in the integrated energy system for various reasons, and an energy storage device needs to be configured in the Integrated Energy System (IES). Energy memory can stabilize under the normal condition and fluctuate and absorb green clean power and exert oneself, when the system can not maintain supply and demand balance because of reasons such as calamity energy supply interruption or normal operating appearance energy shortage, energy memory can regard as temporary power supply, heat source to the system energy supply in order to maintain energy balance. The energy supply and demand relationship can be flexibly adjusted by configuring the energy storage device, the energy supply pressure of the upper-layer energy network is relieved by peak clipping and valley filling, and the comprehensive benefit is better.
Specifically, the devices in the integrated energy system are classified, and the energy conversion device and the energy storage device are mathematically modeled, respectively. By mathematically modeling the energy conversion devices, the power, output and operating efficiency of each energy conversion device can be obtained; the energy storage devices are subjected to mathematical modeling, and the charging and discharging speed and the charging and discharging efficiency of each energy storage device can be obtained.
And S200, establishing a multi-target scheduling model based on an objective function based on the equipment model, wherein the multi-target scheduling model comprises an economic optimization model, an energy efficiency optimization model and an environmental protection index model.
The load requirements of the comprehensive energy system are more and more diversified, so that the traditional power demand response needs to be improved and converted into electricity, heat, cold and gas comprehensive demand response, and as the system is more and more complex, a multi-target scheduling model needs to be established, and a corresponding energy distribution plan is made to meet the complex load requirements.
Analyzing an energy supply structure of the comprehensive energy system, judging an energy circulation mode and a circulation direction, and then establishing an economic optimization model about operation and maintenance cost according to the energy supply structure; establishing an energy efficiency optimization model related to energy consumption according to the energy consumption value and the energy consumption rate; and establishing an environment-friendly index model based on the emission of the polluted gas according to the operation parameters of the equipment. And then solving the minimum operation cost according to the economic optimization model, solving the maximum energy utilization rate according to the energy efficiency optimization model, solving the minimum pollution gas emission treatment cost according to the environmental protection index model, and further obtaining an energy distribution plan with the most economy, the highest efficiency and the least pollution.
And step S300, establishing constraint conditions according to the equipment model so as to constrain the multi-target scheduling model.
The comprehensive energy system designs various different types of equipment, and the operation conditions, the operation capabilities and the operation principles of the different equipment are different, so that constraint conditions need to be formulated according to the capacity of the equipment when an energy distribution plan is formulated, actual requirements can be met when the plan is formulated, and the load capacity of the equipment can also be met.
And S400, solving the optimal solution of the multi-target scheduling model under the constraint condition through a particle swarm optimization, and taking the optimal solution as a multi-target balanced distribution plan of the comprehensive energy system.
Due to the complex structure of the comprehensive energy system, the equipment and the operation parameters of various energy sources need to be coordinated, so that the scheduling problem can be described as a problem of dynamic multidimensional nonlinear function optimization. For multi-objective optimization, the general algorithm has poor function convergence capacity, the speed of searching for the global optimal point is low, the optimal solution needs to be searched for in a long time, and the global optimal solution can be obtained through strong optimization capability in a short time through the particle swarm optimization.
Optionally, as shown in fig. 3, the building of the equipment model according to the equipment in the integrated energy system includes:
modeling an energy conversion device in the integrated energy system, comprising:
step S110, performing mathematical modeling on the combined cooling heating and power system, the solar equipment and the gas boiler respectively to obtain a combined cooling heating and power system model, a solar equipment model and a gas boiler model;
and step S111, taking the combined cooling heating and power system model, the solar energy equipment model and the gas boiler model as the conversion equipment model.
The combined cooling heating and power system (CCHP) is used as an important energy conversion module in a park comprehensive energy system, and a mathematical model of the combined cooling and heating and power system can be expressed as follows:
wherein P, H and C respectively represent electric power, hot power and cold power,
the electrical output of the GT is shown,
the gas consumption of the GT is expressed,
the GT power generation efficiency is shown as a result,
which represents the heating value of the natural gas,
the thermal output of the HRB is shown,
the efficiency of the recovery of the waste heat is shown,
the thermal output of the CCHP system is shown,
the cold output of the CCHP system is shown,
the efficiency of the lithium bromide refrigerator is shown,
indicating the input of the lithium bromide refrigerator power,
represents the charging power of the thermal storage device,
represents the heat-releasing power of the heat storage device,
representing a time interval.
Wherein HRB represents a waste heat boiler, GB represents a gas boiler, GT represents a gas turbine, and LBR represents a lithium bromide refrigerator.
The solar energy equipment is a solar photovoltaic photo-thermal integrated component (PVT), which mainly comprises a solar cell panel (PVP), a light heat collector, a water conveying pipeline and related accessories, wherein a copper absorption layer part in the light heat collector heats cold water by using solar energy and conveys the cold water to a heating pipe network through a copper pipe or directly conveys the cold water to a user, and the solar photovoltaic photo-thermal integrated component (PVT) can utilize the cold water to cool a photovoltaic cell while utilizing solar energy to generate power, so that the energy conversion efficiency of the solar cell is improved to a certain extent.
The electric and thermal output of the PVT component have certain correlation, namely are related to the intensity of illumination radiation. In one day, the illumination radiation intensity has uncertainty, so that the electrical and heat output values of the PVT component also have certain uncertainty, related researches show that the illumination radiation intensity approximately obeys Beta distribution, and a probability density function can be expressed as follows:
wherein,
which is indicative of the intensity of the illuminating radiation,
represents an upper limit value of the intensity of the illumination radiation,
and
respectively the parameters of the Beta distribution function,
is a Gamma function.
The mathematical model of photovoltaic and photo-thermal output can be obtained by the probability density function, and the solar equipment model can be expressed as follows:
wherein,
the electrical output of the PVT is represented,
the thermal output of the PVT is shown,
the PVT installation area is represented by,
to representThe electrical conversion efficiency of the PVT is,
the thermal conversion efficiency of the PVT is expressed,
representing the intensity of the illumination.
A Gas Boiler (GB) obtains hot water or high-temperature steam by burning natural gas, which is a commonly used auxiliary heating apparatus in a CCHP system. When the heat energy produced by the CCHP system is insufficient to meet the user's heat energy demand, the gas fired boiler provides a portion of the heat energy to maintain the system heat energy supply and demand balance.
The gas boiler model can be expressed as:
wherein,
the thermal output of the gas boiler is shown,
which indicates the amount of intake air of the gas boiler,
represents the thermal efficiency under the working condition operation of the gas boiler,
indicating the heating value of natural gas.
A central Air Conditioner (AC) is generally adopted in a building to adjust the room temperature, and mathematical modeling can be carried out on the central air conditioner according to an equivalent thermal parameter model.
The central air-conditioning model may be expressed as:
wherein,
represents the room temperature of the ith station AC at time t,
represents the outdoor temperature of the ith AC at time t,
an upper limit value of the indoor temperature is represented,
a lower limit value indicating the indoor temperature is set,
the value of 0-1 of the working state of the ith AC at the time t is shown (the working time is 1, the non-working time is 0),
represents the total power consumption of AC at time t,
represents the rated power consumption of AC at time t,
represents the total cooling power of the AC at time t,
represents the nominal coefficient of refrigeration performance of AC at time t,
represents the total number of AC units, and R, C and Q represent equivalent thermal resistance, equivalent thermal capacitance and equivalent thermal ratio, respectively.
Alternatively, the equivalent thermal resistance is taken as 0.1208 deg.C/W, the equivalent thermal capacitance is taken as 3599.3J/deg.C, and the equivalent thermal ratio is taken as 400W.
Optionally, as shown in fig. 4, the building of the equipment model according to the equipment in the integrated energy system further includes:
modeling an energy storage device in the integrated energy system, comprising:
step S120, mathematical modeling is respectively carried out on the heat storage equipment, the battery replacement station and the electric automobile, and a heat storage equipment model, a battery replacement station model and an electric automobile model are obtained;
step S121, using the thermal storage device model, the battery replacement station model, and the electric vehicle model as the storage device model.
At present, devices for storing energy in an integrated energy system can be divided into a gas energy storage device, an electric energy storage device, a heat energy storage device, a cold energy storage device and the like from the energy perspective. The electric energy storage devices in the power grid mainly comprise energy storage battery packs and super capacitors, and a pumped storage power station can also be used as a generalized electric energy storage device in a regional power system. The thermal energy storage device can be classified into a sensible heat storage device using liquid water as a representative medium and a latent heat storage device using a phase change material such as a high-temperature molten salt as a representative medium. Cold energy storage devices are commonly used for cold water energy storage, ice cold storage, eutectic salt cold storage and gas hydrate cold storage. For the gas energy storage device, compressed air energy storage and compressed hydrogen and natural gas energy storage based on an electric gas conversion technology are mainly used. The energy storage device in the invention refers to electric and thermal energy storage equipment.
The heat storage device (HS) mainly comprises a hot water heat storage tank, a heat storage tank and a heat storage electric boiler. A hot water heat storage tank is used as a heat storage device, and the purpose of configuring the heat storage device is to ensure the balance of supply and demand of heat energy in the system. The hot water heat accumulation tank can be matched with a CCHP system to store redundant electric energy in the form of heat energy. When the heat energy supply in the system is not enough to maintain the heat load demand, the hot water heat accumulation tank supplements heat energy to the park heating power pipe network through energy release so as to maintain the balance of the heat energy supply and demand.
The mathematical model of the thermal storage device may be expressed as:
wherein,
indicates the stored heat amount of the thermal storage device k,
represents the charging power of the thermal storage device,
represents the heat-releasing power of the heat storage device,
indicating the charging efficiency of the thermal storage device,
indicating the heat-releasing efficiency of the thermal storage device,
indicating the rate of heat loss from the thermal storage device.
At present, the power battery of the electric bus mainly has three charging modes, namely conventional charging, quick charging and quick battery replacement. The conventional charging time generally needs hours, the quick charging mode is an emergency charging mode, and the charging time is generally dozens of minutes. The quick battery replacement is a more convenient battery replacement means, when the electric quantity of a power battery of the electric bus is not enough to maintain normal running, the power battery can continue to run by replacing the battery, and the replaced power battery is charged in a battery replacement station (BSS) implementing a battery networking technology. The quick battery replacement means does not influence the normal operation of the electric bus and can ensure that the electric quantity of the standby power battery pack is sufficient.
A plug-and-charge operation mode is often adopted in a charging station (BSS), the operation mode can exert the charging capability of the charging station to the maximum extent, but peak-valley electricity price cannot be fully utilized, and the BSS is an unordered charging mode. A sufficient number of power battery packs are generally configured in the battery replacement station, and can be used as an electric energy storage device to flexibly interact with a power distribution network.
An Electric Vehicle (EV) implementing the Internet of vehicles technology is charged/discharged by connecting an intelligent bidirectional charging pile. The power battery packs in the electric automobile and the electric bus power exchange station are used as the electric energy storage devices of the comprehensive energy system, and are batteries in nature, so that the electric automobile and the electric bus power exchange station have similar mathematical models.
Optionally, the establishing a multi-objective scheduling model based on the device model includes:
and S210, establishing a first objective function with the minimum operation cost as a target based on the operation and maintenance cost and the energy cost of the equipment in the comprehensive energy system, and taking the minimum operation cost as the objective function of the economic optimization model.
The objective function of the economic optimization part in the multi-objective optimization scheduling model of the comprehensive energy system is the minimum operation cost
Including electricity purchase fees in parks
Gas purchase cost
Equipment operating and maintenance costs
And integrated demand response cost
The formula can be expressed as:
wherein,
which represents the total operating cost of the plant,
which represents the cost of purchasing the natural gas,
the cost of the electricity purchase is shown,
represents the cost of the overall demand response and,
which represents the cost of the operation and maintenance of the equipment,
represents the time-of-use electricity price of a common commercial product,
representing an IDR compensation price, the compensation mode compensating the cost due to EV, BSS discharge and AC transition supply modes for the integrated energy system operator, wherein IDR is the integrated demand response.
The price of the natural gas is shown,
represents the cost of operating and maintaining the equipment per unit power,
represents the output power of the jth device, j is the number of devices,
which represents the power of the electricity purchased,
the electrical output of the GT is shown,
the thermal output of the GB is expressed,
indicates the discharge power of the kth EV,
represents the kth ESS discharge power,
indicating the change of the power consumption of the ith AC response IDR,
the electrical efficiency of the GT is shown,
the thermal efficiency of the GB is expressed,
indicating a low heating value of natural gas.
Alternatively, as shown in FIG. 5, the natural gas has a lower heating value of 9.97kWh/m3.
Step S220, establishing a second objective function with the maximum efficiency of 15794as the target based on the value of 15794input and output in the comprehensive energy system, wherein the second objective function is used as the objective function of the energy efficiency optimization model;
the method comprises the steps of taking a park comprehensive energy system as a research object, constructing two mathematical models of energy utilization rate, and then establishing an efficiency (EE) optimization model 15794based on a second law of thermodynamics. And finally establishing a detailed efficiency mathematical model 15794for energy efficiency optimization.
The comprehensive energy system can realize complementary utilization and efficient coupling of energy in various forms such as cold, heat, electricity and gas, and the comprehensive energy system is modeled and optimized by establishing appropriate objective functions such as economy, energy efficiency and environment, so that after the energy efficiency optimization model is established, an energy efficiency optimization mathematical model is established from the aspect of energy utilization efficiency, and the high-quality utilization conditions of cold, heat, electricity and gas in the comprehensive energy system are evaluated.
When the system reversibly changes from an arbitrary state to a state of equilibrium with a given environment, the portion of energy that can theoretically be converted in its entirety to any other energy form is called \15794 (Exergy), also called usable energy.
To assess high quality utilization of energy, utilization efficiency is defined as the ratio of total output energy, 15794to total input energy, 15794:
wherein,
representing the efficiency of energy utilization of 15794,
represents the total output of the integrated energy system,
represents the integrated energy system input of 15794.
When the above formula is combined with an integrated energy system, it can be expressed as:
indicating the partial cold demand that the LBR assumes due to the AC participating in the IDR.
A power limit indicating that each AC participates in the IDR.
Wherein,
represents the value of energy input into the integrated energy system, \ 15794,
represents the value of output integrated energy system energy \15794,
a value of 15794representing electrical load (PL),
a value of 15794representing basic Heat Load (HL)/Domestic Hot Water (DHW),
a value of 15794representing the Cooling Load (CL),
a value of \15794representingelectrical energy,
represents a value of 15794for natural gas,
electrical/thermal values representing PVT 15794.
The power versus premium power value of 15794may be expressed as:
wherein,
represents a value of electrical energy of 15794,
which represents the power purchased by the utility,
representing a loss coefficient of 15794during the power generation process of a coal-fired thermal power plant,
represents the value of electrical load of 15794,
which is indicative of the electrical load,
electrical output representing PVT value of 15794
Representing the electrical power of the PVT.
Optionally, the loss factor of the coal-fired thermal power plant during power generation is 0.34 in 15794.
For Hot Load (HL)/Domestic Hot Water (DHW), cold Load (CL) and photo-thermal (PVTh) output, the value 15794is positively correlated with temperature. Thus, the relationship between energy \ 15794values and temperature can be expressed as:
wherein,
a value of \ 15794representing k,
and represents the thermal power of the heat source k,
denotes a cold load, where k is a set of a Heat Load (HL), a Domestic Hot Water (DHW), and a photo-thermal load (PVTh),
which indicates the set temperature of the CL,
represents the ambient temperature of CL.
The value expression for natural gas can be expressed as:
wherein,
represents a value of natural gas of 15794,
the value coefficient of v 15794for natural gas,
representing the gas consumption of the Gas Boiler (GB),
representing the gas consumption of the Gas Turbine (GT),
indicating the heating value of natural gas.
Alternatively, the value coefficient for natural gas is taken to be 1.04.
And step S230, establishing a third objective function with the minimum emission control cost as the target based on the pollutant emission amount of the comprehensive energy system, and taking the third objective function as the objective function of the environmental protection index model.
The environmental protection evaluation index of the integrated energy system mainly takes pollutant discharge amount as a main index, and a large amount of greenhouse gases (such as carbon dioxide) are generated when natural gas is combusted by a distributed power supply and a heat source in IES (energy generation system) such as a gas turbine and a gas boiler.
In one embodiment, the pollution gas cost generated by the comprehensive energy system and the pollution gas treatment cost indirectly generated by the system from the power purchase of a superior power grid are constructed into an environmental protection index model together. Because some of the purchased electric energy comes from a clean energy power plant, and the clean energy power plant does not generate polluting gas, the proportion of the IES outsourcing electric energy coming from a green clean energy power plant is represented by a proportionality coefficient.
The environmental index model can be expressed as:
wherein,
the cost for the discharge and treatment of the polluted gas is shown,
represents the discharge and treatment cost of the ith pollutant gas,
indicates the discharge amount of the i-th item of the polluted gas,
indicating the i-th pollutant gas emission amount generated by burning natural gas,
representing the proportion of electricity purchased by the IES from green clean energy plants,
represents the i-th pollutant gas emission amount indirectly generated by the integrated energy system from the external power grid power purchase,
indicating the gas consumption of the Gas Boiler (GB),
represents the gas consumption of the Gas Turbine (GT).
Alternatively, the IES purchases a proportion of electricity from a green clean energy plant of 0.25.
Optionally, the constraints include cold, hot, electrical power balance constraints, electric vehicle operating constraints, central air conditioning operating constraints, and photovoltaic/thermal assembly operating constraints.
Because the structure of the comprehensive energy system is complex, one-to-one corresponding constraint conditions need to be established for each established model so as to correspond to the operation capacity of each device in the actual situation.
Optionally, as shown in fig. 6, the establishing a constraint condition according to the device model to constrain the multi-objective scheduling model includes:
step S310, establishing the cold, heat and electric power balance constraint based on the electricity, heat and cold loads, the electricity output, the heat output, the electric efficiency and the power of each device in the comprehensive energy system.
Can be expressed as:
wherein,
the electrical load of the park is represented,
representing the base heat load/domestic hot water load,
the cold load is indicated and is,
the electrical output of the PVT is represented,
the thermal output of the heat collector is shown,
which represents the electrical output of the gas turbine,
which represents the power of the electricity purchased,
indicating the power consumption change amount of the ith central air conditioner responding to the IDR,
representing the electrical efficiency of the gas turbine,
which represents the thermal output of the gas-fired boiler,
represents the power consumption of the ith central air conditioner,
the refrigerating capacity of the LBR is represented,
the cooling power of the ith central air conditioner is shown,
the total power of the k-th vehicle EV is indicated,
which represents the total power of the BSS,
the input power of the LBR is represented,
the HS charging power is represented by the HS charging power,
the heat-release power of the HS is shown,
showing the working state of the ith central air conditioner (1 when working and 0 when not working),
representing the HRB efficiency.
And S320, establishing the running constraint of the electric vehicle based on the charge-discharge state, the charge state and the running power of the electric vehicle in the comprehensive energy system.
Wherein,
a 0-1 variable representing the state of charge of the kth electric vehicle,
a 0-1 variable representing the discharge state of the kth electric vehicle,
represents a state of charge (SOC) of an Electric Vehicle (EV) for an initial period,
representing a state of charge (SOC) of an end-of-time Electric Vehicle (EV),
represents the lower limit of the state of charge (SOC),
represents the upper limit of the state of charge (SOC),
it is shown that the process of the present invention,
which is indicative of the time of arrival of the vehicle,
indicating the time at which the vehicle left the campus,
electric automobile with displayThe state of charge on leaving the park zone,
which represents the travel distance of the vehicle,
and the maximum driving range of the vehicle is represented.
Alternatively, the lower limit of the state of charge is 20% and the upper limit of the state of charge is 90%.
And step S330, establishing the operation constraint of the central air conditioner based on the power and the power limit value of the central air conditioner in the comprehensive energy system.
The central air conditioning operating constraints may be expressed as:
wherein,
the LBR cooling power is represented by,
represents the power consumption change amount of the ith AC response IDR,
which is indicative of the coefficient of refrigeration performance,
indicating the ith AC operating state 0-1 value,
which represents the electrical power of the AC,
indicating that the LBR is assumed due to AC participating in IDRCold demand, power limit per AC participating in IDR.
And S340, establishing the operation constraint of the photovoltaic/photothermal component based on the installation area, the electrothermal conversion efficiency and the illumination intensity of the photovoltaic/photothermal component in the comprehensive energy system.
The photovoltaic/photothermal assembly operating constraints can be expressed as:
wherein,
the values of PVT, mounting area,
the electrical conversion efficiency of the PVT is expressed,
the thermal conversion efficiency of the PVT is shown,
representing the illumination intensity.
Optionally, as shown in fig. 7 and fig. 8, the solving, by a particle swarm algorithm, an optimal solution of the multi-target scheduling model under the constraint condition, and taking the optimal solution as a multi-target balanced distribution plan of the integrated energy system includes:
step S410, inputting an original plan, wherein the original plan comprises a preset multi-energy distribution plan of the comprehensive energy system.
In one embodiment, the original plan comprises an original distribution plan of the integrated energy system, and the original plan is optimized through a multi-target particle swarm optimization.
Step S420, initializing the speed and the position of the particle, judging whether the original plan meets the constraint condition, and if not, adjusting the original plan within the constraint condition.
The multi-target particle swarm algorithm needs to ensure that particles move in a solution space, so the positions of the particles need to be constrained, the upper limit and the lower limit of power are set at the speed and the positions of initialized particles, after the setting is finished, whether an original plan is in a constraint range of a constraint condition needs to be judged, if the original plan is not in the constraint range, the original plan needs to be preliminarily adjusted to meet the constraint condition, and the effectiveness of optimization is ensured.
Step S430, if the original plan satisfies the constraint condition, updating the speed and the position of the particle within the constraint condition.
After the constraint condition is met, initializing the iteration times, namely making the iteration times be 0, continuously updating the speed and the position of the particle, and adjusting the speed and the position of the particle when the speed and the position of the particle do not meet the constraint condition so as to ensure that the speed and the position of the particle are always in the constraint condition.
Step S440, calculating the fitness of each particle, and updating an optimal value according to a preset global optimal solution updating method, wherein the optimal value comprises a particle optimal value and a particle swarm optimal value.
Optionally, the calculating the fitness of each particle, and updating the optimal value according to a preset global optimal solution updating method includes:
when the external file contains non-inferior solutions suitable for the particles, one of the solutions is selected as an optimal value;
and when the external file does not have non-inferior solutions suitable for the particles, randomly selecting one non-inferior solution from the external file as an optimal value.
And step S450, updating an external file through a preset external file updating method according to the optimal value, and fuzzifying the objective function to obtain a better value.
Optionally, the updating the external profile according to the optimal value by a preset external profile updating method includes:
when the external file is lower than a preset value, directly adding a new non-inferior solution into the external file;
discarding a new non-inferior solution when the external profile is higher than the preset value;
when the external file is equal to the preset value, judging whether the non-inferior solution can dominate the external file;
if the dominant individual can be replaced by the non-inferior individual;
if the density value of the non-inferior solution is not dominant, adding the non-inferior solution into the external file, and removing the non-inferior solution with the minimum density value in the external file.
And step S460, after the optimal value is obtained, updating and iterating the external archive to obtain the iterated optimal value as a final optimal solution.
Partial non-inferior solutions obtained by the target particle swarm algorithm under continuous calculation are stored in an external archive. The non-inferior solution set for each time segment is the final external profile. And (3) obtaining a final scheme from the solution set by using a fuzzy decision method, wherein the first objective function and the third objective function are fuzzified, and membership functions of the first objective function and the third objective function are as follows:
the objective function value of the ith non-inferior solution is f; the fuzzified objective function value is
(ii) a The non-inferior solution set has an objective function value with an upper and lower bound of
And
。
optionally, after obtaining the better value, performing update iteration on the external archive to obtain an iterated better value, where the obtaining a final optimal solution includes:
judging whether the iteration times are greater than or equal to the preset times;
if yes, outputting the optimal solution;
if not, re-executing the step of updating the speed and the position of the particle in the constraint condition.
Another embodiment of the present invention provides an electronic device, including a memory and a processor; the memory for storing a computer program; the processor is used for realizing the multi-target balanced distribution method of the integrated energy system when executing the computer program.
A further embodiment of the present invention provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the multi-objective balanced distribution method for an integrated energy system as described above.
An electronic device that can be a server or a client of the present invention, which is an example of a hardware device that can be applied to aspects of the present invention, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
The electronic device includes a computing unit that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) or a computer program loaded from a storage unit into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, the ROM, and the RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a computer to implement the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like. In this application, the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be made by those skilled in the art without departing from the spirit and scope of the disclosure, and these changes and modifications are intended to fall within the scope of the invention.
Claims (10)
1. A multi-target balanced distribution method of an integrated energy system is characterized by comprising the following steps:
establishing an equipment model according to equipment in the integrated energy system, wherein the equipment model comprises a conversion equipment model and a storage equipment model;
establishing a multi-target scheduling model based on an objective function based on the equipment model, wherein the multi-target scheduling model comprises an economic optimization model, an energy efficiency optimization model and an environmental protection index model;
establishing a constraint condition according to the equipment model so as to constrain the multi-target scheduling model;
and solving the optimal solution of the multi-target scheduling model under the constraint condition through a particle swarm algorithm, and taking the optimal solution as a multi-target balanced distribution plan of the comprehensive energy system.
2. The multi-target balanced distribution method of the integrated energy system according to claim 1, wherein the establishing of the equipment model according to the equipment in the integrated energy system comprises:
modeling an energy conversion device in the integrated energy system, comprising:
performing mathematical modeling on the combined cooling, heating and power system, the solar equipment and the gas boiler respectively to obtain a combined cooling, heating and power system model, a solar equipment model and a gas boiler model;
and taking the combined cooling heating and power system model, the solar energy equipment model and the gas boiler model as the conversion equipment model.
3. The multi-objective balanced allocation method for the integrated energy system according to claim 1, wherein the step of establishing the equipment model according to the equipment in the integrated energy system further comprises:
modeling an energy storage device in the integrated energy system, comprising:
mathematical modeling is carried out on the heat storage equipment, the battery replacement station and the electric automobile respectively to obtain a heat storage equipment model, a battery replacement station model and an electric automobile model;
and taking the heat storage equipment model, the battery replacement station model and the electric automobile model as the storage equipment model.
4. The multi-objective balanced distribution method of the integrated energy system according to claim 1, wherein the establishing of the multi-objective scheduling model based on the objective function based on the equipment model comprises:
establishing a first objective function with the minimum operation cost as a target based on the operation and maintenance cost and the energy cost of equipment in the comprehensive energy system, wherein the first objective function is used as an objective function of the economic optimization model;
establishing a second objective function with the maximum efficiency of 15794based on the input and output values of 15794in the comprehensive energy system as an objective function of the energy efficiency optimization model;
and establishing a third objective function by taking the minimum emission control cost as a target based on the pollutant emission amount of the comprehensive energy system, wherein the third objective function is used as an objective function of the environment-friendly index model.
5. The method for multi-objective equal distribution of an integrated energy system according to claim 1, wherein the constraints include cold, hot, electric power balance constraints, electric vehicle operation constraints, central air conditioning operation constraints, and photovoltaic/thermal component operation constraints.
6. The method for multi-target balanced distribution of an integrated energy system according to claim 5, wherein the establishing of the constraint condition according to the equipment model to constrain the multi-target scheduling model comprises:
establishing the cold, heat and electric power balance constraints based on the electricity, heat and cold loads, the electricity output, the heat output, the electric efficiency and the power of each device in the comprehensive energy system;
establishing electric vehicle operation constraints based on the charge-discharge state, the charge state and the operation power of the electric vehicle in the comprehensive energy system;
establishing a central air conditioner operation constraint based on power and power limits of a central air conditioner in the integrated energy system;
and establishing the operation constraint of the photovoltaic/photothermal component based on the installation area, the electrothermal conversion efficiency and the illumination intensity of the photovoltaic/photothermal component in the comprehensive energy system.
7. The multi-target balanced distribution method of the integrated energy system according to claim 1, wherein the solving of the optimal solution of the multi-target scheduling model under the constraint condition through a particle swarm optimization comprises:
inputting an original plan, wherein the original plan comprises a preset multi-energy distribution plan of the integrated energy system;
initializing the speed and the position of the particle, judging whether the original plan meets the constraint condition, and if not, adjusting the original plan in the constraint condition;
if the original plan satisfies the constraint condition, updating the speed and the position of the particle in the constraint condition;
calculating the fitness of each particle, and updating an optimal value according to a preset global optimal solution updating method, wherein the optimal value comprises a particle optimal value and a particle swarm optimal value;
updating an external file by a preset external file updating method according to the optimal value, and fuzzifying the objective function to obtain a more optimal value;
and after the optimal value is obtained, updating and iterating the external archive to obtain the iterated optimal value as a final optimal solution.
8. The multi-target balanced distribution method of the integrated energy system according to claim 7, wherein the calculating the fitness of each particle and the updating the optimal value according to the preset global optimal solution updating method comprises:
when the external file contains non-inferior solutions suitable for the particles, one of the solutions is selected as an optimal value;
and when the external file does not have non-inferior solutions suitable for the particles, randomly selecting one non-inferior solution from the external file as an optimal value.
9. The multi-target balanced distribution method of the integrated energy system according to claim 8, wherein the updating the external profile according to the optimal value by a preset external profile updating method comprises:
when the external file is lower than a preset value, directly adding a new non-inferior solution into the external file;
discarding a new non-inferior solution when the external profile is higher than the preset value;
when the external archive is equal to the preset value, judging whether the non-inferior solution can dominate the external archive;
if the dominant individual can be replaced by the non-inferior individual;
if the density value of the non-inferior solution is not dominant, adding the non-inferior solution into the external file, and removing the non-inferior solution with the minimum density value in the external file.
10. The multi-target balanced distribution method for the integrated energy system according to claim 7, wherein after obtaining the optimal value, the updating iteration is performed on the external archive, and the obtained optimal value after the iteration is used as a final optimal solution, which includes:
judging whether the iteration times are greater than or equal to the preset times;
if yes, outputting the optimal solution;
if not, re-executing the step of updating the velocity and the position of the particle within the constraint.
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| Publication number | Priority date | Publication date | Assignee | Title |
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105740977A (en) * | 2016-01-28 | 2016-07-06 | 福州大学 | Multi-target particle swarm-based power outage management optimization method |
| CN106786603A (en) * | 2017-02-22 | 2017-05-31 | 天津大学 | A kind of regional complex energy resource system multiobjective optimization mixed current algorithm |
| CN106815657A (en) * | 2017-01-05 | 2017-06-09 | 国网福建省电力有限公司 | A kind of power distribution network bi-level programming method for considering timing and reliability |
| CN108053069A (en) * | 2017-12-15 | 2018-05-18 | 南京工程学院 | A kind of integrated energy system traffic control method suitable for multiple-objection optimization scene |
| CN108229865A (en) * | 2018-03-30 | 2018-06-29 | 天津大学 | A kind of electric heating gas integrated energy system low-carbon economy dispatching method based on carbon transaction |
| WO2019134532A1 (en) * | 2018-01-02 | 2019-07-11 | 佛山科学技术学院 | Operating method of combined heat and power system containing wind and light renewable energy |
| CN112784484A (en) * | 2021-01-19 | 2021-05-11 | 国网江苏省电力有限公司经济技术研究院 | Multi-objective optimization method and optimization system for regional comprehensive energy system |
| CN113239607A (en) * | 2021-06-16 | 2021-08-10 | 国网浙江省电力有限公司杭州供电公司 | Economic dispatching optimization method, system, equipment and storage medium for comprehensive energy system |
| CN113987734A (en) * | 2021-07-22 | 2022-01-28 | 太原理工大学 | Multi-objective optimization scheduling method for park comprehensive energy system under opportunity constraint condition |
| CN114707289A (en) * | 2021-12-28 | 2022-07-05 | 国网江苏省电力有限公司经济技术研究院 | Opportunity constraint-based multi-objective optimization method for electrothermal coupling comprehensive energy system |
| WO2022160705A1 (en) * | 2021-01-26 | 2022-08-04 | 中国电力科学研究院有限公司 | Method and apparatus for constructing dispatching model of integrated energy system, medium, and electronic device |
-
2022
- 2022-08-31 CN CN202211059445.8A patent/CN115147014A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105740977A (en) * | 2016-01-28 | 2016-07-06 | 福州大学 | Multi-target particle swarm-based power outage management optimization method |
| CN106815657A (en) * | 2017-01-05 | 2017-06-09 | 国网福建省电力有限公司 | A kind of power distribution network bi-level programming method for considering timing and reliability |
| CN106786603A (en) * | 2017-02-22 | 2017-05-31 | 天津大学 | A kind of regional complex energy resource system multiobjective optimization mixed current algorithm |
| CN108053069A (en) * | 2017-12-15 | 2018-05-18 | 南京工程学院 | A kind of integrated energy system traffic control method suitable for multiple-objection optimization scene |
| WO2019134532A1 (en) * | 2018-01-02 | 2019-07-11 | 佛山科学技术学院 | Operating method of combined heat and power system containing wind and light renewable energy |
| CN108229865A (en) * | 2018-03-30 | 2018-06-29 | 天津大学 | A kind of electric heating gas integrated energy system low-carbon economy dispatching method based on carbon transaction |
| CN112784484A (en) * | 2021-01-19 | 2021-05-11 | 国网江苏省电力有限公司经济技术研究院 | Multi-objective optimization method and optimization system for regional comprehensive energy system |
| WO2022160705A1 (en) * | 2021-01-26 | 2022-08-04 | 中国电力科学研究院有限公司 | Method and apparatus for constructing dispatching model of integrated energy system, medium, and electronic device |
| CN113239607A (en) * | 2021-06-16 | 2021-08-10 | 国网浙江省电力有限公司杭州供电公司 | Economic dispatching optimization method, system, equipment and storage medium for comprehensive energy system |
| CN113987734A (en) * | 2021-07-22 | 2022-01-28 | 太原理工大学 | Multi-objective optimization scheduling method for park comprehensive energy system under opportunity constraint condition |
| CN114707289A (en) * | 2021-12-28 | 2022-07-05 | 国网江苏省电力有限公司经济技术研究院 | Opportunity constraint-based multi-objective optimization method for electrothermal coupling comprehensive energy system |
Non-Patent Citations (6)
| Title |
|---|
| QINGSHENG ZHAO 等: "Research on Multi-objective Optimization of Integrated Energy System Considering Integrated Demand Response", 《2021 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (I&CPS ASIA)》 * |
| 刘敦楠等: "考虑多类型能源转换与存储的综合能源微网优化运行研究", 《南方电网技术》 * |
| 曾红 等: "含电转气设备的气电互联综合能源系统多目标优化", 《电测与仪表》 * |
| 王贵龙 等: "计及机会约束规划的园区能量枢纽经济调度", 《电力系统保护与控制》 * |
| 王贵龙: "面向园区的综合能源系统多目标优化调度研究", 《中国优秀硕士学位论文全文数据库（电子期刊） 工程科技II辑》 * |
| 臧宝志等: "考虑环保与可靠性成本的园区综合能源系统经济运行技术", 《分布式能源》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118469190A (en) * | 2024-05-06 | 2024-08-09 | 国能江苏新能源科技开发有限公司 | Park comprehensive energy system optimal scheduling method considering Yong economy |
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