CN111539584A - User-level comprehensive energy system planning method, system and equipment - Google Patents
User-level comprehensive energy system planning method, system and equipment Download PDFInfo
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Abstract
The invention discloses a user-level comprehensive energy system planning method, a system and equipment, wherein the method comprises the following steps: predicting the load of a target year, selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating and power system by combining with a list of alternative equipment, and calculating the capacity of a gas turbine based on the integral operation efficiency constraint of the natural gas combined cooling heating and power system to obtain performance parameters of the natural gas combined cooling heating and power system; inputting the planning data and the performance parameters of the natural gas combined cooling heating and power system into a user-level comprehensive energy system planning model for optimization solution to obtain an optimization result of equipment model selection and capacity configuration; according to the invention, the constraint conditions are set in the user-level comprehensive energy system planning model for planning, so that the defect of low accuracy of planning effect caused by the fact that the annual load curve cannot be accurately obtained is avoided, and the capacities of the gas turbine, the waste heat boiler and the refrigeration equipment are preselected, so that the solution variables during planning are reduced, and the calculation efficiency is improved.
Description
Technical Field
The invention relates to the technical field of user-level comprehensive energy systems, in particular to a user-level comprehensive energy system planning method, system and equipment.
Background
With the continuous progress of distributed energy technology, the coupling degree of various types of energy such as cold, heat, electricity, gas and the like on the production and demand sides is continuously deepened, and a user-level integrated energy system (UIES) is an effective way for realizing multi-energy complementation and cascade utilization. For users with multi-type energy demands, the energy utilization economy of the users can be improved on the premise of ensuring normal production and life of the users by reasonably selecting energy supply channels and the capacities and models of related equipment.
The combined cooling and heating and power system (CCHP) utilizes the heat energy generated by the combustion of natural gas in a cascade manner, the overall energy utilization efficiency can reach more than 80%, and the output proportion of cold and heat (including hot steam and hot water) can be flexibly configured according to the user requirements, so that the CCHP system is a preferred energy supply device in the planning of a comprehensive energy system.
Relevant researches are carried out by domestic and foreign scholars aiming at a UIES planning method of a combined cooling heating and power system containing natural gas, the general research idea is that the relation between the capacity of each device to be selected and the initial investment and the operation economy of the system and a mathematical model are formed by considering the energy input and output relation of each device to be selected according to a multi-energy load demand curve of a user, and then a proper optimization solving method is selected to obtain an optimal solution. In recent years, artificial intelligence algorithms have also been increasingly applied to integrated energy system planning and operation. From the analysis of the practical application of engineering, the existing research mainly has the following problems: 1) in the existing research, it is mostly assumed that the annual load curves of various energy demands of various users are known, the accuracy or representativeness of the established model on input data is very high, the model can not be completely obtained in actual engineering, and the accuracy of related prediction data is difficult to ensure even if an artificial intelligence algorithm is adopted; 2) during planning, the capacity of equipment to be selected is supposed to be continuously variable, and the selectable capacity of the equipment in actual engineering is a discrete discontinuous variable; 3) the industrial steam and the domestic hot water are summarized into heat loads during planning, and the requirements of users on the thermal and power parameters such as temperature, pressure, flow and the like of different types of heat loads are not considered. The above disadvantages cause poor planning effect and complex calculation process of the user-level comprehensive energy system planning.
In summary, the prior art has technical problems of low planning accuracy and complex calculation process in planning the user-level integrated energy system.
Disclosure of Invention
The invention provides a planning method, a planning system and planning equipment for a user-level comprehensive energy system, which are used for solving the technical problems of low planning accuracy and complex calculation process in the planning of the user-level comprehensive energy system in the prior art.
The invention provides a user-level comprehensive energy system planning method, which is characterized in that a user-level comprehensive energy system planning model is pre-established, and the method comprises the following steps:
acquiring planning data, load historical data and an alternative equipment list of a user-level comprehensive energy system;
predicting the load of the target year according to the load historical data to obtain a load predicted value of the target year;
selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating and power system according to the load predicted value of the target year by combining with the alternative equipment list, and obtaining the capacity of the waste heat boiler and the capacity of the refrigeration equipment;
calculating the capacity of the gas turbine based on the load predicted value of the target year, the capacity of the waste heat boiler, the capacity of the refrigeration equipment and the overall operation efficiency constraint of the natural gas cooling combined heat and power system, and obtaining performance parameters of the natural gas cooling combined heat and power system according to the capacity of the gas turbine, the capacity of the waste heat boiler and the capacity of the refrigeration equipment;
inputting the planning data and the performance parameters of the natural gas combined cooling heating and power system into a user-level comprehensive energy system planning model for optimization solution to obtain an optimization result of equipment model selection and capacity configuration;
and carrying out sensitivity analysis on the optimization results of the equipment model selection and the capacity configuration.
Preferably, the constraints of the user-level integrated energy system planning model include power constraints, energy constraints, equipment operation constraints, and user site constraints.
Preferably, the objective function of the user-level integrated energy system planning model is as follows:
in the formula: c1、C2Respectively representing the annual equivalent investment cost and the annual operation cost of the comprehensive energy system; j. the design is a squareC、 JSAnd JDGRespectively representing a device set, an energy storage device set and a distributed power supply set of the natural gas combined cooling heating and power system;
the method is characterized in that the method respectively represents the power income of the natural gas combined cooling heating power system on the internet, the user power cost equivalently reduced by the energy storage device and the subsidy income of the distributed power supply.
Preferably, the planning data of the user-level comprehensive energy system comprises a planned target year, typical annual utilization hours of wind energy and solar energy at the location of the user-level comprehensive energy system, and various energy purchase prices at the location of the user-level comprehensive energy system; the candidate device list includes a candidate device type, a candidate device type number, and a unit capacity cost of the candidate device.
Preferably, the load prediction value of the target year includes an electric load prediction value and an annual amount, a gas load prediction value and an annual amount, a cold load prediction value and an annual amount, and a heat load prediction value and an annual amount.
Preferably, the specific process of selecting the waste heat boiler and the refrigeration equipment of the natural gas cooling combined heat and power system according to the load predicted value of the target year by combining the alternative equipment list is as follows:
and calculating the efficiency of heat-cold conversion according to the ratio of the cold load predicted value and the heat load predicted value in the load predicted value of the target year, and selecting a waste heat boiler and a refrigerating device of the natural gas combined cooling heating and power system which meet the requirements from the alternative device list according to the cold load predicted value, the heat load predicted value and the efficiency of the heat-cold conversion.
Preferably, the specific process of calculating the capacity of the gas turbine based on the load predicted value of the target year, the capacity of the waste heat boiler, the capacity of the refrigeration equipment and the overall operation efficiency constraint of the combined cooling, heating and power natural gas system is as follows:
calculating annual energy production according to the electric load predicted value in the load predicted values of the target year;
calculating annual heat supply quantity and annual cold supply quantity according to the capacity of the waste heat boiler and the capacity of the refrigeration equipment;
calculating the annual gas consumption based on the annual generated energy, the annual heat supply amount, the annual cold supply amount and the integral operation efficiency constraint of the natural gas combined cooling heating and power system;
and calculating the capacity of the gas turbine according to the annual gas consumption.
Preferably, the specific process of performing sensitivity analysis on the optimization result of the device model selection and the capacity configuration is as follows:
selecting influence factors, and determining the maximum value, the minimum value and the variation scale of the influence factors;
and taking the minimum value of the influence factors as a starting point, taking the variation scale as increment to gradually increase to the maximum value, and calculating the annual average investment, annual average investment cost of the electric boiler, annual outsourcing electricity charge and annual natural gas charge of the corresponding natural gas combined cooling, heating and power system when the natural gas combined cooling, heating and power system is increased in each increment.
A user-level integrated energy system planning system comprises
The system comprises a data acquisition module, a load prediction module, a capacity pre-configuration module, a performance parameter calculation module, a user-level comprehensive energy system planning model module and a sensitivity analysis module;
the data acquisition module is used for acquiring planning data, load historical data and an alternative equipment list of the user-level comprehensive energy system;
the load prediction module is used for predicting the load of the target year according to the load historical data to obtain a load prediction value of the target year;
the capacity pre-configuration module is used for selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating and power system according to a load predicted value of a target year by combining with a standby equipment list to obtain the capacity of the waste heat boiler and the capacity of the refrigeration equipment; calculating the capacity of the gas turbine based on the load predicted value of the target year, the capacity of the waste heat boiler, the capacity of the refrigeration equipment and the integral operation efficiency constraint of the natural gas combined cooling heating and power system;
the performance parameter calculation module is used for obtaining performance parameters of the natural gas combined cooling heating and power system according to the capacity of the gas turbine, the capacity of the waste heat boiler and the capacity of the refrigerating device;
the user-level comprehensive energy system planning model module is used for outputting an optimization result of equipment model selection and capacity configuration according to planning data and performance parameters of the natural gas cooling combined heat and power system;
and the sensitivity analysis module is used for carrying out sensitivity analysis on the optimization result of the equipment model selection and the capacity configuration.
A user-level integrated energy system planning apparatus comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute a user-level integrated energy system planning method as described above according to instructions in the program code.
According to the technical scheme, the embodiment of the invention has the following advantages:
the embodiment of the invention does not depend on load and a medium-long term hourly power prediction curve of a distributed power supply when planning the user-level comprehensive energy system, plans by setting a constraint condition in a user-level comprehensive energy system planning model, and avoids the defect of low accuracy of planning effect caused by incapability of accurately obtaining an annual load curve.
The embodiment of the invention has the following other advantages:
according to the embodiment of the invention, the capacity can be finely distinguished during capacity configuration according to the load requirements of users on different forms, different pressures and different temperatures, and the capacity of each device to be selected is discretized during device model selection, so that the planning result is more suitable for the engineering practice and higher in accuracy.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method, a system and a device for planning a user-level integrated energy system according to an embodiment of the present invention.
Fig. 2 is a system structure diagram of a user-level integrated energy system planning method, system and device according to an embodiment of the present invention.
Fig. 3 is an apparatus framework diagram of a method, a system, and an apparatus for planning a user-level integrated energy system according to an embodiment of the present invention.
Fig. 4 is a user-level integrated energy system key device and a typical architecture diagram of a user-level integrated energy system planning method, system and device according to an embodiment of the present invention.
Fig. 5 is a graph illustrating sensitivity analysis of hours of utilization of a method, system, and apparatus for planning a user-level integrated energy system according to an embodiment of the present invention.
Fig. 6 is a load ratio sensitivity analysis diagram of a user-level integrated energy system planning method, system and device provided by the embodiment of the present invention.
Fig. 7 is a diagram of analyzing the sensitivity of the price of natural gas for a method, system, and apparatus for planning a user-level integrated energy system according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method, a system and equipment for planning a user-level comprehensive energy system, which are used for solving the technical problems of low planning accuracy and complex calculation process in the prior art for planning the user-level comprehensive energy system.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, fig. 1 is a flowchart of a method, a system and a device for planning a user-level integrated energy system according to an embodiment of the present invention.
The invention provides a user-level comprehensive energy system planning method, which is characterized in that a user-level comprehensive energy system planning model is pre-established, and the method comprises the following steps:
the method comprises the steps of obtaining planning data, load historical data and an alternative equipment list of a user-level comprehensive energy system, wherein the planning data of the user-level comprehensive energy system comprises a planned target year, typical annual utilization hours of wind energy and solar energy of a place where the user-level comprehensive energy system is located and various energy purchase prices of the place where the user-level comprehensive energy system is located, wherein if the energy purchase prices change along with time (such as peak-valley electricity prices), a complete price catalogue needs to be provided, and data used in a subsequent calculation process are obtained in advance, so that subsequent processes can be developed smoothly.
Predicting the load of the target year according to the load historical data to obtain a load predicted value of the target year; the method for accurately predicting the medium-long term hourly load curve of the user is very difficult, and the selection of different types of loads of different types of users is mainly based on the existence of historical data, the characteristics of the life and the energy of the user and the like. In general, for users with complete historical data, a load prediction method based on the historical data, such as regression prediction, is adopted; for newly built projects or users with missing historical data, load can be predicted by adopting prediction models such as a load density method and a unit consumption method as appropriate.
Selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating and power system according to the load predicted value of the target year by combining with the alternative equipment list, and obtaining the capacity of the waste heat boiler and the capacity of the refrigeration equipment; the configuration of the natural gas cooled cogeneration system is influenced by the load demands of the user. For industrial users, the heat load can be classified into hot steam load and hot water load 2. If the user site does not have an external centralized steam heating system or an established steam boiler, the combined cooling, heating and power system of the natural gas becomes a preferred supply device of hot steam due to the high energy utilization efficiency of the combined cooling, heating and power system. Under the condition, the steam output quantity of the natural gas combined cooling heating and power system should meet the requirement of a user on hot steam; and the power supply amount and the cooling amount can be less than or equal to the requirements of users, because the energy can be supplied by other devices. On the other hand, the natural gas combined cooling heating and power system can generate power while supplying heat, the power generation efficiency of the natural gas combined cooling heating and power system depends on the performance of a gas turbine (or a gas internal combustion engine), and the power generation efficiency is generally between 0.3 and 0.4. In the planning stage, the cooling efficiency and the heating efficiency of the natural gas combined cooling heating power system can be flexibly adjusted by configuring waste heat boilers and refrigeration equipment with different models on the premise of the known total waste heat power, and the output proportion of hot water and hot steam can also be adjusted.
Calculating the capacity of a gas turbine based on a load predicted value of a target year, the capacity of a waste heat boiler, the capacity of refrigeration equipment and the integral operation efficiency constraint of a natural gas cooling, heating and power combined supply system, wherein the waste heat boiler, the refrigeration equipment and the gas turbine are constituent units of the natural gas cooling, heating and power combined supply system, and the requirement on the primary energy utilization efficiency of the natural gas cooling, heating and power combined supply system is met besides the requirement on the load; obtaining performance parameters of the natural gas combined cooling heating and power system according to the capacity of the gas turbine, the capacity of the waste heat boiler and the capacity of the refrigeration equipment; the performance parameters comprise input parameters and output parameters, and the input parameters comprise natural gas quantity; the output parameters include power generation, heat supply (divided into steam and hot water) and cold supply.
And inputting the planning data and the performance parameters of the natural gas combined cooling heating and power system into a user-level comprehensive energy system planning model for optimization solution to obtain an optimization result of equipment model selection and capacity configuration. The user-level integrated energy system planning model in this embodiment is an MILP model, and decision variables in the model include: the number of each type of equipment is the total annual outsourcing amount of each type of energy.
The key of whether the model solving result is reasonable is the setting of the maximum annual utilization hours of the energy conversion equipment and the annual charge-discharge cycle times of the energy storage equipment. The rated charging and discharging power and capacity of the emergency standby energy storage equipment can be directly determined according to the important load condition required to be supported, and the annual maximum charging and discharging cycle number of the commercial operation energy storage equipment can also be determined according to a local actual energy price catalogue. In the user-level integrated energy system architecture shown in fig. 4, the energy conversion devices include a combined natural gas cooling, heating and power system, an electric boiler, and an air conditioning device, and the types of energy conversion devices are discussed separately.
1) For industrial users with stable annual load curves, the natural gas combined cooling heating power system may have 24h startup operation conditions, so the annual operation hours of the natural gas combined cooling heating power system unit can be set as a constant according to actual conditions and user requirements, and the value of the constant is suitable for being matched with the annual maximum utilization hours of the user load.
2) The electric boiler and the air conditioner are mainly used for regulating indoor temperature, can extract days with the highest daily temperature lower than 18 ℃ and higher than 26 ℃ according to the annual air temperature curve of the location, perform rough estimation according to certain hours of operation every day, and can also be set as a constant.
As a preferred embodiment, in the planning stage, the basic principle of the equipment model selection and capacity configuration is to minimize the investment and operation cost of the system in the planning period on the premise of meeting the energy demand. For the user-level comprehensive energy system, one-time investment mainly comes from investment of energy conversion equipment, energy storage equipment and a distributed power supply, and the influence of the line investment on the system investment can be ignored in a planning stage due to the fact that a line of the user-level comprehensive energy system is short. The operation cost of the system comprises 2 parts, namely the energy purchasing cost from an external energy system, such as electricity purchasing cost, gas purchasing cost, heat purchasing cost and the like; secondly, the operation and maintenance cost of the selected equipment, and the objective function of the user-level comprehensive energy system planning model is as follows:
in the formula: c1、C2Respectively representing the annual equivalent investment cost and the annual operation cost of the comprehensive energy system; j. the design is a squareC、 JSAnd JDGRespectively representing a device set, an energy storage device set and a distributed power supply set of the natural gas combined cooling heating and power system;
the method is characterized in that the method respectively represents the power income of the natural gas combined cooling heating power system on the internet, the user power cost equivalently reduced by the energy storage device and the subsidy income of the distributed power supply.
The main devices related to the formula (1) include a natural gas combined cooling heating and power system, a battery energy storage system, an ice cold storage system and a distributed power supply, and the basis for calculating the key devices in the objective function is further described.
The generated energy of the natural gas combined cooling heating and power system can be directly used by users, and the natural gas combined cooling heating and power system can be purchased by a power grid company according to the net electricity price of the natural gas combined cooling heating and power system on the ground, so that when the generated energy of the natural gas combined cooling heating and power system is bought on the net in full amount, a part of generated energy benefits can be brought, and the annual operation cost is made up.
For battery energy storage, in areas with peak-valley electricity price or time-of-use electricity price policies, the battery energy storage system can utilize a low-charge high-discharge running mode to earn a difference price, and the user energy cost is equivalently reduced.
For ice storage, the ice storage can utilize a peak-valley electricity price policy of the place where the project is located, and the energy cost of a user is reduced by a mode of ice making at a low-valley electricity price time and ice melting and refrigeration at a high-peak electricity price time.
The generated energy of the distributed power supply can reduce outsourcing electric quantity, the income is not repeatedly calculated, and the country has a related generated energy subsidy policy for the distributed photovoltaic power generation, so that the user energy cost can be equivalently reduced.
The cost and yield of each item in the formula (1) can be represented by the formulas (2) to (6), respectively.
In the formula: m represents the number of the types of the equipment to be selected; n isjThe number of the selected j-type devices is represented and is a decision variable; c. CjRepresents the investment cost of equipment j; r represents the equipment depreciation rate; r represents the discount rate; tau isjRepresents the age of device j; lambda [ alpha ]jRepresents the proportion of the annual maintenance cost of the equipment j to the one-time investment; k represents the type of outsourced energy involved in the user-level integrated energy system;
represents the outsourcing price of ki-type energy;
represents the total annual outsourcing of ki-type energy; sjIs constant, s is when the amount of power generated by the natural gas combined cooling heating and power system device j is directly supplied to the userjWhen the generated energy of the natural gas combined cooling heating and power system is completely on line, s is 0j＝1；
The annual power generation amount of the natural gas combined cooling heating and power system equipment j is represented; n is a radical ofjRepresents the number of days of annual use of the energy storage device j; kjThe number of charging and discharging cycles of the energy storage device j every day; cjIs the actual available capacity of the energy storage device j; mu.sjThe comprehensive utilization efficiency of the energy storage device j is obtained; p is a radical ofout,i、pin,iRespectively corresponding user electricity prices in the ith circulation according to the local peak valley electricity price catalogue;
the subsidy price of the distributed power supply electricity at the location of the project is possibly different;
representing the annual energy production of the distributed power source j.
And carrying out sensitivity analysis on the optimization results of the equipment model selection and the capacity configuration.
As a preferred embodiment, the constraints of the user-level integrated energy system planning model include power constraints, energy constraints, equipment operating constraints, and user site constraints. Each constraint is further analyzed and explained below.
1) Power constraint
Considering that the output of the distributed power supply is 0, the energy storage device is in the energy charging state, and the electric load of the user reaches the extreme condition of peak load, considering a certain energy supply margin, the user-level comprehensive energy system needs to meet the energy utilization requirement of the user and should meet the power constraint shown in the formula (7).
In the formula:
represents the maximum power of ki-type energy available from the external energy supply system, the value of which depends on the capacity of the user gateway equipment; j1 and J2 respectively represent a set of energy conversion devices and a set of energy storage devices;
is a constant, if device j is a device consuming ki-type energy, then
If device j is a device that generates energy of the ki type, then
If device j is a device that neither generates nor consumes ki-type energy, then
Representing a ki-type energy margin;
and representing the maximum load predicted value of the ki type energy of the user target year.
2) Energy confinement
In a longer operation period (such as 1 year), the supply and demand of various energy sources should be basically balanced, as shown in formula (8).
In the formula:
representing the total amount of ki type energy source supplied by an external system in year, and taking the total amount as a decision variable;
represents the rated power of the plant j to produce or consume the ki-type energy; t isjRepresenting the maximum number of hours of annual utilization of device j.
It should be noted that, if the natural gas combined cooling heating power system is fully on line, the generated power and the generated energy of the natural gas combined cooling heating power system need to be excluded when the power and the energy balance are calculated by the formula (7) and the formula (8).
3) Plant operating constraints
Whether the parameters of the equipment meet the self operation constraint directly determines whether the equipment can be selected, and for the j-th class of equipment, if the parameters do not meet the self operation constraint, the corresponding decision variable nj＝0。
The energy conversion device should satisfy the constraint shown in equation (9).
In the formula:
represents the total amount of ki-type energy consumed or generated by energy conversion device j over the year;
represents the power consumed or generated by energy conversion device j at rated operating conditions;
representing the maximum number of hours of annual use of the energy conversion device j.
The natural gas cooling, heating and power combined supply system is special energy conversion equipment, can convert natural gas into energy in three forms of electricity, heat and cold, and the corresponding gas-electricity, gas-heat and heat-cold conversion relations respectively meet the requirement of the formula (10).
In the formula: k1 and k2 respectively represent two different types of energy in electricity, gas, cold and heat;
represents the efficiency of the energy conversion device j for converting energy in the form of k1 into energy in the form of k 2;
a dimensional constant representing the conversion of energy in the form k1 to energy in the form k 2;
respectively, the power consumed by the device j under the rated working condition is represented by k1 type energy, and the power generated by the device j under the rated working condition is represented by k2 type energy.
As shown in fig. 4, the natural gas combined cooling heating and power system is a combined energy supply system composed of three main parts, i.e., a gas turbine, a waste heat boiler and a refrigeration device, and the gas turbine, the waste heat boiler and the refrigeration device need to satisfy not only the matching of various thermodynamic parameters, such as pressure and temperature, but also the requirement of the overall energy utilization efficiency of the natural gas combined cooling heating and power system unit, as shown in formula (11).
In the formula:
the annual generating capacity of the natural gas combined cooling heating and power system equipment is represented;
the annual heat supply amount of the natural gas combined cooling heating power system equipment is represented;
the annual cooling capacity of the natural gas combined cooling heating and power system equipment is represented;
representing the annual gas consumption of the natural gas combined cooling heating and power system equipment; qgIndicating the low calorific value of the fuel gas.
The formula (11) unifies the dimensions of different forms of energy such as natural gas, cold, heat, electricity and the like, and under the rated operation working condition, the above formula can be converted into an inequality relation between powers, as shown in the formula (12).
In the formula: t isCCHPThe annual running hours of the natural gas combined cooling heating and power system unit are represented;
the power sum of various types of energy supplied to users by the natural gas combined cooling heating and power supply system unit under the rated working condition is expressed in kW;
the unit kW represents the gas consumption power of the natural gas combined cooling heating and power system unit under the rated working condition.
The energy storage device should meet the operating constraints of the device itself, and in addition, its maximum number of annual charges and discharges is related to its operating mode and capacity configuration. The capacity configuration of the emergency backup energy storage device should satisfy the constraint condition shown in equation (13), and the annual maximum number of operating cycles should satisfy the constraint shown in equation (14).
In the formula:
respectively representing rated charging and discharging energy power, rated charging and discharging energy time and capacity of the emergency energy storage equipment; tsu represents the time that the energy storage device is required to continuously supply power to the important load;
representing the important load power of the user;
representing the annual maximum number of working cycles of the emergency energy storage device; min (-) is a function of taking the minimum value;
the rated charging/discharging time of the energy storage equipment is represented and is the ratio of rated capacity to rated power; nem, the number of times the system is used in the year, which can be estimated according to the reliability of the energy supply at the user's location, or can be set as a constant through research.
Another operation mode of the energy storage device is to utilize the difference between the selling prices of the energy sources in different time periods every day, earn the price difference through the charging and discharging in different time periods, and consider the operation economy of the energy storage device, and the maximum daily charging and discharging times of the energy storage device are related to the local energy price catalog, the capacity ratio and the battery performance and should satisfy the constraints shown in the formulas (15) and (16).
In the formula:
respectively representing rated charging and discharging energy power, rated charging and discharging energy time and capacity of energy storage equipment to be operated commercially;
represents the annual maximum number of duty cycles of a commercial operated energy storage device; npr represents the maximum daily charge and discharge times calculated according to the energy price catalog of the place of the project;
representing the maximum allowable charge-discharge cycle number of the battery equipment of the energy storage equipment j; ysys represents the number of battery energy storage operating years required by the system.
For the energy storage device with partial capacity as emergency backup, the energy storage device can be regarded as 2 different energy storage devices according to the capacity of the emergency backup, and the energy storage devices respectively meet the constraint conditions of the expressions (13) to (16).
4) User site constraints
In actual engineering, the installation scale of each type of equipment depends on the load level of users on one hand, and on the other hand, is limited by the available area of installation sites, and the installation conditions of different types of equipment are different, for example, distributed photovoltaic power generation can be installed on a roof with a load-bearing structure meeting relevant requirements, while equipment such as a natural gas combined cooling heating and power system needs to be installed in a separate factory building or an indoor space, and each type of equipment is shown in formula (17) according to the relation between capacity and available area resources of users.
In the formula: m represents a set of 3 types of energy conversion devices, energy storage devices, and distributed power sources; pjRepresents the capacity of device j;
representing the unit area available capacity of plant j, kW/m2 or kWh/m 2;
representing the area available to the user to build the class j device.
In summary, the constraint conditions of the device model selection can be summarized as shown in equation (18).
In a preferred embodiment, the planning data of the user-level integrated energy system includes a planned target year, typical annual hours of use of wind energy and solar energy at the location of the user-level integrated energy system, and various purchase prices of energy at the location of the user-level integrated energy system. In which, if the energy purchase price changes with time (such as peak-valley electricity price), a complete price list is provided. The alternative equipment list is shown as change 5 and comprises alternative equipment types, alternative equipment models and unit capacity cost of the alternative equipment, and various data of the alternative equipment are listed, so that the equipment is clear when the equipment is selected, and calculation is convenient.
As a preferred embodiment, according to the predicted load value of the target year, in combination with the list of alternative devices, the specific process of selecting the waste heat boiler and the refrigeration device of the combined cooling heating and power system of natural gas comprises the following steps:
calculating the efficiency of heat-cold conversion according to the ratio of the cold load predicted value and the heat load predicted value in the load predicted value of the target year by the following formula,
in the formula: k1 and k2 respectively represent the difference between cold and hotA type of energy;
represents the efficiency of the energy conversion device j for converting the energy in the form of k1 into the energy in the form of k 2;
a dimensional constant representing the conversion of energy in the form k1 to energy in the form k 2;
respectively representing the power consumed by the device j under the rated working condition for energy of type k1 and the power generated by the device j under the rated working condition for energy of type k 2.
And selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating and power system which meet the requirements from the alternative equipment list according to the cold load predicted value, the heat load predicted value and the efficiency of heat-cold conversion.
As a preferred embodiment, the specific process of calculating the capacity of the gas turbine based on the load prediction value of the target year, the capacity of the waste heat boiler, the capacity of the refrigeration equipment and the overall operation efficiency constraint of the combined cooling, heating and power system of the natural gas is as follows:
calculating annual energy production according to the electric load predicted value in the load predicted values of the target year;
calculating annual heat supply quantity and annual cold supply quantity according to the capacity of the waste heat boiler and the capacity of the refrigeration equipment;
the annual gas consumption is calculated based on the annual energy production, the annual heat supply, the annual cold supply and the integral operation efficiency constraint of the natural gas combined cooling heating and power system, and the specific process is as follows:
in the formula:
the annual generating capacity of the natural gas combined cooling heating and power system equipment is represented;
the annual heat supply amount of the natural gas combined cooling heating power system equipment is represented;
the annual cooling capacity of the natural gas combined cooling heating and power system equipment is represented;
representing the annual gas consumption of the natural gas combined cooling heating and power system equipment; qgIndicating the low calorific value of the fuel gas.
And calculating the capacity of the gas turbine according to the annual gas consumption.
As a preferred embodiment, in order to analyze the influence of the influencing factors on the planning result more comprehensively, the embodiment further performs sensitivity analysis on the influencing factors, and divides the influencing factors into a single influencing factor and a double influencing factor according to the number of the influencing factors, specifically as follows:
1) single influence factor analysis
Aiming at the influence of a single influence factor (such as natural gas price, equipment parameters and load conditions) on a planning result, the method can be used for analyzing according to the following steps:
step 1: determining the maximum and minimum values of the influence factors;
step 2: setting a variation scale of the influencing factors;
and step 3: and taking the minimum value of the influence factors as a starting point, gradually increasing the variation scale as increment to the maximum value, and calculating the annual average investment, annual average investment of the electric boiler, annual outsourcing electricity charge and annual natural gas charge of the corresponding natural gas combined cooling, heating and power system when the variation scale is increased each time.
2) Dual impact factor analysis
Because the two influence factors may have correlation, in order to more comprehensively analyze the influence of the double influence factors on the planning result, the specific steps of the double influence factor analysis are as follows:
step 1: determining the maximum and minimum values of each influence factor;
step 2: determining the change step length of each influence factor, wherein different values can be taken;
and step 3: and taking the minimum value of the influence factors as a starting point, gradually increasing the variation scale as increment to the maximum value, and calculating the annual average investment, annual average investment of an electric boiler, annual outsourcing electricity charge and annual natural gas charge of the corresponding natural gas combined cooling, heating and power system when the variation scale is increased each time.
As shown in fig. 2, a user-level integrated energy system planning system includes:
the system comprises a data acquisition module 201, a load prediction module 202, a capacity pre-configuration module 203, a performance parameter calculation module 204, a user-level comprehensive energy system planning model module 205 and a sensitivity analysis module 206;
the data acquisition module 201 is configured to acquire planning data, load history data, and a list of alternative devices of the user-level integrated energy system;
the load prediction module 202 is configured to predict a load of a target year according to the load historical data to obtain a load prediction value of the target year;
the capacity pre-configuration module 203 is used for selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating and power system according to a load predicted value of a target year by combining with a standby equipment list to obtain the capacity of the waste heat boiler and the capacity of the refrigeration equipment; calculating the capacity of the gas turbine based on the load predicted value of the target year, the capacity of the waste heat boiler, the capacity of the refrigeration equipment and the integral operation efficiency constraint of the natural gas combined cooling heating and power system;
the performance parameter calculation module 204 is configured to obtain a performance parameter of the natural gas combined cooling heating and power system according to the capacity of the gas turbine, the capacity of the waste heat boiler, and the capacity of the refrigeration equipment;
the user-level integrated energy system planning model module 205 is configured to output an optimization result of the equipment model selection and the capacity configuration according to the planning data and the performance parameters of the natural gas cooling combined heat and power system.
The sensitivity analysis module 206 is configured to perform sensitivity analysis on the optimization result of the device model selection and the capacity configuration.
As shown in fig. 3, a user-level integrated energy system planning apparatus 30 includes a processor 300 and a memory 301;
the memory 301 is used for storing a program code 302 and transmitting the program code 302 to the processor;
the processor 300 is configured to perform the steps of one of the embodiments of the method for user-level integrated energy system planning described above according to the instructions in the program code 302.
Illustratively, the computer program 302 may be partitioned into one or more modules/units that are stored in the memory 301 and executed by the processor 300 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 302 in the terminal device 30.
The terminal device 30 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 300, a memory 301. Those skilled in the art will appreciate that fig. 6 is merely an example of a terminal device 30, and does not constitute a limitation of terminal device 30, and may include more or fewer components than shown, or some components may be combined, or different components, for example, the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 300 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 301 may be an internal storage unit of the terminal device 30, such as a hard disk or a memory of the terminal device 30. The memory 301 may also be an external storage device of the terminal device 30, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 30. Further, the memory 301 may also include both an internal storage unit and an external storage device of the terminal device 30. The memory 301 is used for storing the computer program and other programs and data required by the terminal device. The memory 301 may also be used to temporarily store data that has been output or is to be output.
Example 2
In order to further illustrate the content of the invention, 2 specific schemes are provided for detailed description. The first specific scheme is taken as an example of a hospital user-level comprehensive energy system, and the second specific scheme is taken as an example of a certain actual industrial user-level comprehensive energy system in a southern region of China. In the two specific schemes, the energy types and the required quantity of user loads are different, and the external energy supply environment is also different. Two specific schemes can be planned by the method, and feasibility and universality of the method are demonstrated. The natural gas combined cooling heating and power system in this embodiment is replaced by CCHP, an acronym of english.
1) Hospital user-level comprehensive energy system
Description of boundary conditions
In order to verify the effectiveness of the user-level comprehensive energy system planning method provided by the invention, the user-level comprehensive energy system parameters of a hospital are taken as an example for verification. The load demand types of the user comprise 3 types of electricity, hot water and cold, wherein the maximum electricity load is 165kW, the annual electricity consumption is about 1161MWh, and the maximum load utilization hours is 7039 h; the maximum heat load is 820kW, the annual heat consumption is about 3095 MWh, and the maximum load utilization hours is 3775 h; the maximum cooling load is 350kW, the annual cold energy consumption is about 280MWh, and the maximum load utilization hours is 799 h. The price of the natural gas in the area where the user is located is converted into RMB which is 0.145 yuan/kWh; according to a typical time-of-day electricity price curve, the electricity price of the current ground can be roughly divided into 3 electricity price periods of peak, flat and valley for calculation: the peak section electricity price is 0.8 yuan/kWh, the duration is 6 h/day, the flat section electricity price is 0.6 yuan/kWh, the duration is 10 h/day, the valley section electricity price is 0.3 yuan/kWh, and the duration is 8 h/day. Therefore, the average value of the local electricity price is about 0.55 yuan/kWh, and the user is located in a place without a central heating system or a central cooling system and a subsidy price of a distributed power supply.
The types of the devices to be selected during planning are shown in table 1, and the comprehensive utilization efficiency of the energy of the formed CCHP unit is over 75 percent. The scheme and corresponding parameters that make up the CCHP train according to the candidate devices listed in table 1 are shown in table 2.
Table 1 list of parameters of devices to be selected
Tab.1 List of Devices to be Selected
TABLE 2 CCHP preselection configuration
Tab.2 CCHP Devices to be Selected
According to the cold and heat load demand condition of the user, the following parameters are set before planning:
1) reserving 10% margin according to the maximum load of each type of energy during planning;
2) setting the generated energy of the CCHP to be directly supplied to users, setting the maximum annual operating hours to be 3095h, and setting the generating efficiency to be 35% (so that the gas consumption of the CCHP unit can be calculated, and the comprehensive utilization efficiency under different configurations can be calculated under the condition of neglecting the heat loss of a heating system and a cooling system);
3) the maximum annual operating hours of the electric boiler equipment are 3095h, the heating efficiency is 100%, the maximum annual operating hours of the electric refrigeration equipment are 799h, and the COP value is 3;
4) the working efficiencies of the battery energy storage, the heat accumulator and the ice cold storage are respectively 0.75, 0.9 and 0.65, the selectable capacities are shown in table 1, the energy is charged every day in the valley period of electricity price, the energy is discharged in the peak period of electricity price, the battery energy storage, the heat accumulator and the ice cold storage work for 1 time at most in a circulating mode every day, namely the maximum utilization days in the energy storage year is 365 days;
5) the annual maximum utilization hours of wind power are set as 2100h, and the annual maximum utilization hours of photovoltaic are set as 1500 h;
6) the equipment depreciation rate R is 0, and the pasting rate R is 6%;
7) the life cycle of the planning system is set to be 20 years, the equipment operation and maintenance cost accounts for 5% of the annual investment cost of each system, and a user has a large enough area to place various types of equipment to be selected.
8) The hospital area has enough power supply and hot water supply, but has no centralized cooling equipment.
(ii) capacity allocation result
The method of the invention is used for equipment type selection and capacity configuration, and the detailed equipment configuration of the system is shown in Table 3. According to the calculation method of each cost and equivalent reduced cost in the formula (5), the total annual construction and operation cost of the user-level comprehensive energy system is 132.89 ten thousand yuan, wherein the annual equipment investment cost is 54.19 ten thousand yuan, the annual equipment maintenance cost is 2.71 ten thousand yuan, the annual outsourcing electric quantity is 32.5MWh, and the annual electricity purchase cost is 1.79 ten thousand yuan; the annual gas purchase amount is 5305.7MWh, the annual gas purchase cost is 77.46 ten thousand yuan, and the running cost of the ice storage equivalent reduction is 3.26 ten thousand yuan. According to the capacity configuration result, the annual construction and operation total cost of the user-level comprehensive energy system mainly comes from the investment of CCHP and the cost of outsourcing natural gas.
TABLE 3 device model selection results
Tab.3 Equipment selection results
Analysis of sensitivity
In order to further study the influence of equipment parameters, load conditions and natural gas prices on the planning result, the maximum utilization hours of the equipment per year and the electricity/cold/heat load ratio of the user are taken as objects respectively, sensitivity analysis is performed on the operating cost established in each system year, and the results are respectively shown in fig. 5 and fig. 6.
Figure 5 shows the results of the annual average construction and operation costs of the system as a function of the number of hours of equipment utilization. Theoretically, similar sensitivity analysis can be performed on the utilization hours of all the devices to be selected, for convenience of description, two types of devices, namely a CCHP unit and an electric boiler, are selected for analysis in the embodiment, the annual maximum utilization hours of the two types of devices vary from 1000 to 8000 hours, and the interval is 500 hours. The two types of equipment can supply heat, the minimum value of the construction and operation cost of the system is 115.95 ten thousand yuan each year on the premise that other conditions are not changed, and the annual maximum utilization hours of the CCHP and the electric boiler are 6000h and 1000h respectively. It is obvious that the construction and operation cost of the whole system cannot be effectively reduced by increasing the utilization hours of the equipment, and the utilization hours of the equipment should be matched with the load utilization hours of the type of the supplied energy.
When analyzing the influence of the electric/cold/heat load ratio on the annual average construction and operation cost of the system, the electric load ratio coefficient shown in the definition formula (19) represents the ratio of the cold/heat and electric load demands. In the sensitivity analysis, the sum of the cold load, the heat load and the electric load is kept constant, the ratio of the cold load and the heat load is constant, the annual maximum utilization hours of each type of load is also constant, and re is changed within the range of 0.1-0.9, and the result is shown in fig. 6.
In the formula: r iseRepresenting an electrical load ratio coefficient;
representing the customer maximum electrical load, maximum thermal load and maximum cold load, respectively.
As can be seen from fig. 6, under the boundary conditions set in this embodiment, as the electric load ratio is increased, the capacity configuration of the CCHP is not increased, and the system meets the electric power demand by increasing the outsourcing electric quantity. Therefore, in the user-level comprehensive energy system, the main function of the CCHP meets the requirements of cold and heat loads because the price of the external power grid is not high; and for meeting the cold and heat loads, due to the energy cascade utilization of the CCHP, the comprehensive economic benefit is superior to that of an electric boiler and electric refrigeration equipment, so that the cold and heat loads are mainly met by the CCHP, and the requirement is in accordance with the original purpose of planning.
In order to further study the influence of the natural gas price change on the system planning result, the natural gas price is changed between 0.1-1 yuan/kWh, and the planning economy result is shown in fig. 7. When the price of the natural gas is lower than 0.2 yuan/kWh, the user preferentially selects the CCHP, and the electric energy of the user can be basically and completely supplied by the CCHP unit; however, if the price of the natural gas is continuously increased, the user will prefer to purchase the electricity. When the price of the natural gas reaches about 0.24 yuan/kWh, the user does not configure CCHP any more, and the energy demand is met by purchasing electricity from outside, configuring an electric boiler, electric refrigeration equipment and the like.
2) Industrial user level comprehensive energy system
Description of boundary conditions
Major energy requirements for a tire manufacturing enterprise in Guangzhou city include electricity (110kV access), hot steam, hot live water, and refrigeration.
(1) User load demand
The maximum power consumption of the enterprise in 2017 is 27.8MW, and the power consumption is 15088 ten thousand kWh; the enterprise is provided with a diesel generator as a standby power supply, and supplies power to important loads when power failure occurs, the rated power of the important loads is 5MW, and the starting time of a diesel engine is 30 s.
Saturated steam with inlet pressure of 2.4Mpa, inlet temperature of about 230 ℃, outlet pressure of 0.5Mpa and outlet temperature of 120 ℃ required by enterprise production has flow of 20t/h and steam consumption of 65520t in 2017. The maximum heat steam load for the industrial user can be estimated to be 12.9MW and the annual steam usage to be 4229 kWh.
In the tire calendering process, industrial circulating water at 80-105 ℃ is needed, the domestic water requirements of workers exist in enterprises, the hot water load of a user in 2017 is 1.2MW, and the annual hot water consumption is 254 ten thousand kWh.
Meteorological data display[23]The lowest temperature of Guangzhou city in 2017 is 1.8 ℃, the highest temperature is 39.7 ℃ and the average temperature is 22.8 ℃. The tire shaping needs cooling by chilled water with inlet and outlet temperatures of 7 ℃ and 14 ℃ respectively. In addition, the production workshop has higher requirements on temperature, and the temperature needs to be kept at about 20-25 ℃ all the year round. The total cooling load demand of the industrial user is 15MW, and the annual cooling capacity is 7664 ten thousand kWh.
(2) Local relative energy prices
Guangzhou city implements peak-valley electricity price mechanism for large industrial users[24]The peak, flat and valley electricity prices of 110kV users are 96.26 min/kWh, 58.34 min/kWh and 29.17 min/kWh respectively, and the duration of the peak, flat and valley electricity prices are 6 h/day, 10 h/day and 8 h/day respectively. The user location has an external centralized heat supply source, provides steam with corresponding pressure, and the price is 325 yuan/t, namely 0.414 yuan/kWh; the local natural gas supply price is 2.08 yuan/Nm3I.e. 0.206 yuan/kWh (36 MJ// Nm for lower heating value of natural gas)3) (ii) a The local CCHP internet electricity price is 0.715 yuan/kWh; bringing in the financial subsidy scale of 2019 according to the existing new energy subsidy policy, adopting an industrial and commercial distributed photovoltaic power generation project in a 'spontaneous self-use and surplus internet surfing' mode, and adjusting the full power generation subsidy standard to be 0.10 yuan per kilowatt-hour; wind power has no relevant subsidy policy temporarily.
(3) Other boundary conditions
Assuming that the planning age is 20 years, the industrial user does not have an idle site to build a wind turbine generator, and in idle roof resources, the length of 5000m meeting the requirements of photovoltaic power generation construction load bearing and the like is about2The annual photovoltaic utilization hours of the project site is about 1000 h; the industrial user has 10000m2The reserved area of the energy conversion device can be provided with an energy storage device.
The power supply, air supply and hot water supply capacity of the area where the industrial user is located is sufficient, but a centralized heating steam system and a centralized cooling system are not available. The parameters of the selectable equipment during planning are detailed in appendix 1, and each type of equipment gives 3 alternative models. Wherein CCHP has been pre-configured according to the pre-selected principles described above.
During planning, 2 planning scenes are divided according to a CCHP power supply mode: the CCHP power generation in the scene 1 is directly supplied to the user, and the CCHP power generation in the scene 2 is completely on line. The rest of the parameter settings were the same as in example 1.
② arrangement result
According to the boundary conditions set in the upper section, the power generation amount of the CCHP is directly provided to the user, and the configuration results of each device are shown in table 4.
Table 4 device model selection results for scenarios 1 and 2
Tab.4 Equipment selection results of 2 Scenarios
In a scene 1, the annual average investment and operation cost of a system is 11092 ten thousand yuan, wherein the annual average equipment investment cost is 1323 ten thousand yuan, the annual average maintenance cost of the system is 66 ten thousand yuan, the annual average external electricity purchase cost is 6688 ten thousand yuan, the annual average external hot steam purchase cost is 128 ten thousand yuan, the annual average natural gas purchase cost is 2892 ten thousand yuan, and the photovoltaic power generation amount subsidy cost is 5 ten thousand yuan; in scene 2, the construction and operation cost of the system is 10363 ten thousand yuan per year, the equipment investment cost is 1716 ten thousand yuan per year, the maintenance cost of the system is 86 ten thousand yuan per year, the electricity purchasing cost is 9394 ten thousand yuan per year, the natural gas purchasing cost is 3856 ten thousand yuan per year, the photovoltaic power generation amount subsidy cost is 5 ten thousand yuan per year, and the CCHP internet electricity amount income is 4685 ten thousand yuan per year. Because the total hot steam supply capacity of the two CCHP units reaches 14.7MW, the hot steam load requirement of a user can be met, and therefore, outsourcing hot steam is not needed, and the cost of the outsourcing hot steam is 0. In both scenarios, the battery energy storage is used only as a backup power source.
Comparing the two scenes, under the existing energy price policy conditions, the mode of direct internet access of the CCHP is beneficial to reducing the energy cost of the user, but the initial investment amount of the user is greatly increased.
In order to further study the influence of the price of outsourced steam and the price change of natural gas on the configuration result, sensitivity analysis is respectively carried out by taking scene 1 as an object. When the price of the outsourcing steam is lower than 0.222 yuan/kWh (namely 157 yuan)Per ton) or natural gas prices higher than 0.297 yuan/kWh (i.e. 3 yuan/Nm)3) When the system is used, the user can buy electricity and steam directly from the outside more economically.
TABLE 5 list of alternative devices
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and in actual implementation, there may be other divisions, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
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.
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 can be realized in a hardware form, and can also be realized in a software functional unit form.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A user-level integrated energy system planning method is characterized in that a user-level integrated energy system planning model is established in advance, and the method comprises the following steps:
acquiring planning data, load historical data and an alternative equipment list of a user-level comprehensive energy system;
predicting the load of the target year according to the load historical data to obtain a load predicted value of the target year;
selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating and power system according to the load predicted value of the target year by combining with the alternative equipment list, and obtaining the capacity of the waste heat boiler and the capacity of the refrigeration equipment;
calculating the capacity of the gas turbine based on the load predicted value of the target year, the capacity of the waste heat boiler, the capacity of the refrigeration equipment and the overall operation efficiency constraint of the natural gas combined cooling heating and power system, and obtaining performance parameters of the natural gas combined cooling heating and power system according to the capacity of the gas turbine, the capacity of the waste heat boiler and the capacity of the refrigeration equipment;
inputting the planning data and the performance parameters of the natural gas combined cooling heating and power system into a user-level comprehensive energy system planning model for optimization solution to obtain an optimization result of equipment model selection and capacity configuration;
and carrying out sensitivity analysis on the optimization results of the equipment model selection and the capacity configuration.
2. The method of claim 1, wherein the constraints of the user-level integrated energy system planning model include power constraints, energy constraints, equipment operation constraints, and user site constraints.
3. The method according to claim 2, wherein the objective function of the user-level integrated energy system planning model is:
in the formula: c1、C2Respectively representing the annual equivalent investment cost and the annual operation cost of the comprehensive energy system; j. the design is a squareC、JSAnd JDGRespectively representing equipment set, energy storage equipment set and distributed power supply set of natural gas combined cooling heating and power systemCombining;
the method respectively represents the net surfing electric quantity benefit of the natural gas combined cooling heating power system, the user electricity cost equivalently reduced by the energy storage device and the subsidy benefit of the distributed power supply.
4. The method of claim 3, wherein the planning data of the user-level integrated energy system comprises a planned target year, typical annual hours of use of wind energy and solar energy at the location of the user-level integrated energy system, and various purchase prices of energy at the location of the user-level integrated energy system; the alternative equipment list comprises alternative equipment types, alternative equipment models and unit capacity cost of the alternative equipment.
5. The method of claim 4, wherein the load forecast values for the target year comprise an electrical load forecast value and an annual amount, a gas load forecast value and an annual amount, a cold load forecast value and an annual amount, and a heat load forecast value and an annual amount.
6. The method for planning the user-level integrated energy system according to claim 5, wherein the specific process of selecting the waste heat boiler and the refrigeration equipment of the combined natural gas cooling, heating and power system according to the predicted load value of the target year and by combining the list of the alternative equipment comprises the following steps:
and calculating the efficiency of heat-cold conversion according to the ratio of the cold load predicted value to the heat load predicted value in the load predicted value of the target year, and selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating and power system which meet the requirements from the alternative equipment list according to the cold load predicted value, the heat load predicted value and the efficiency of heat-cold conversion.
7. The user-level comprehensive energy system planning method according to claim 6, wherein the specific process of calculating the capacity of the gas turbine based on the load predicted value of the target year, the capacity of the waste heat boiler, the capacity of the refrigeration equipment and the overall operating efficiency constraint of the combined cooling, heating and power natural gas system is as follows:
calculating annual energy production according to the electric load predicted value in the load predicted values of the target year;
calculating annual heat supply quantity and annual cold supply quantity according to the capacity of the waste heat boiler and the capacity of the refrigeration equipment;
calculating the annual gas consumption based on the annual generated energy, the annual heat supply amount, the annual cold supply amount and the integral operation efficiency constraint of the natural gas combined cooling heating and power system;
and calculating the capacity of the gas turbine according to the annual gas consumption.
8. The method according to claim 7, wherein the sensitivity analysis of the optimization results of the equipment model selection and capacity configuration comprises the following steps:
selecting influence factors, and determining the maximum value, the minimum value and the variation scale of the influence factors;
and taking the minimum value of the influence factors as a starting point, gradually increasing the variation scale as increment to the maximum value, and calculating the annual average investment, annual average investment of an electric boiler, annual outsourcing electricity charge and annual natural gas charge of the corresponding natural gas combined cooling, heating and power system when the variation scale is increased each time.
9. A user-level integrated energy system planning system is characterized by comprising
The system comprises a data acquisition module, a load prediction module, a capacity pre-configuration module, a performance parameter calculation module, a user-level comprehensive energy system planning model module and a sensitivity analysis module;
the data acquisition module is used for acquiring planning data, load historical data and an alternative equipment list of the user-level comprehensive energy system;
the load prediction module is used for predicting the load of the target year according to the load historical data to obtain a load prediction value of the target year;
the capacity pre-configuration module is used for selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating and power system according to a load predicted value of a target year by combining with a standby equipment list to obtain the capacity of the waste heat boiler and the capacity of the refrigeration equipment; calculating the capacity of the gas turbine based on the load predicted value of the target year, the capacity of the waste heat boiler, the capacity of the refrigeration equipment and the integral operation efficiency constraint of the natural gas combined cooling heating and power system;
the performance parameter calculation module is used for obtaining performance parameters of the natural gas combined cooling heating and power system according to the capacity of the gas turbine, the capacity of the waste heat boiler and the capacity of the refrigeration equipment;
the user-level comprehensive energy system planning model module is used for outputting an optimization result of equipment model selection and capacity configuration according to planning data and performance parameters of the natural gas combined cooling heating and power system;
and the sensitivity analysis module is used for carrying out sensitivity analysis on the optimization result of the equipment model selection and the capacity configuration.
10. A user-level integrated energy system planning apparatus comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute a method for user-level integrated energy system planning according to any one of claims 1 to 8 according to instructions in the program code.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112036646A (en) * | 2020-09-02 | 2020-12-04 | 南方电网科学研究院有限责任公司 | Comprehensive energy system planning method and device considering multi-type energy storage configuration |
| CN112561228A (en) * | 2020-10-30 | 2021-03-26 | 西安交通大学 | Constant-temperature water supply planning method and device for multi-energy water circulation system |
| CN112926201A (en) * | 2021-02-23 | 2021-06-08 | 南方电网科学研究院有限责任公司 | Heat supply planning method, system and device for comprehensive energy system |
| CN112990686A (en) * | 2021-03-10 | 2021-06-18 | 国网电力科学研究院武汉能效测评有限公司 | Comprehensive energy system equipment type selection and capacity configuration method |
| CN113031447A (en) * | 2021-03-15 | 2021-06-25 | 上海电气集团股份有限公司 | Configuration method, system, equipment and medium for equipment in combined cooling heating and power system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103400042A (en) * | 2013-08-07 | 2013-11-20 | 东南大学 | Cool-heat-electricity cogeneration type microgrid optimal configuration method |
| WO2016061741A1 (en) * | 2014-10-21 | 2016-04-28 | Accenture Global Services Limited | System, method, and apparatus for capacity determination for micro grid, and tangible computer readable medium |
| CN105869075A (en) * | 2016-04-19 | 2016-08-17 | 东南大学 | Economic optimization scheduling method for cold, heat and electricity combined supply type miniature energy grid |
| CN108964098A (en) * | 2018-06-13 | 2018-12-07 | 燕山大学 | A kind of independent micro-grid system capacity configuration optimizing method |
| CN109784564A (en) * | 2019-01-22 | 2019-05-21 | 天津大学 | Consider the garden integrated energy system energy source station planing method of renewable energy access |
-
2020
- 2020-05-21 CN CN202010435660.8A patent/CN111539584B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103400042A (en) * | 2013-08-07 | 2013-11-20 | 东南大学 | Cool-heat-electricity cogeneration type microgrid optimal configuration method |
| WO2016061741A1 (en) * | 2014-10-21 | 2016-04-28 | Accenture Global Services Limited | System, method, and apparatus for capacity determination for micro grid, and tangible computer readable medium |
| CN105869075A (en) * | 2016-04-19 | 2016-08-17 | 东南大学 | Economic optimization scheduling method for cold, heat and electricity combined supply type miniature energy grid |
| CN108964098A (en) * | 2018-06-13 | 2018-12-07 | 燕山大学 | A kind of independent micro-grid system capacity configuration optimizing method |
| CN109784564A (en) * | 2019-01-22 | 2019-05-21 | 天津大学 | Consider the garden integrated energy system energy source station planing method of renewable energy access |
Non-Patent Citations (2)
| Title |
|---|
| 柳川 等: "含冷热电联供系统微电网优化运行研究", 《电测与仪表》 * |
| 苏福永，赵志南 编著: "《能源工程管理与评估》", 30 June 2019, 北京：冶金工业出版社 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112036646A (en) * | 2020-09-02 | 2020-12-04 | 南方电网科学研究院有限责任公司 | Comprehensive energy system planning method and device considering multi-type energy storage configuration |
| CN112036646B (en) * | 2020-09-02 | 2023-08-08 | 南方电网科学研究院有限责任公司 | Comprehensive energy system planning method and device considering multi-type energy storage configuration |
| CN112561228A (en) * | 2020-10-30 | 2021-03-26 | 西安交通大学 | Constant-temperature water supply planning method and device for multi-energy water circulation system |
| CN112561228B (en) * | 2020-10-30 | 2023-06-27 | 西安交通大学 | Constant-temperature water supply planning method and planning device for multi-energy water circulation system |
| CN112926201A (en) * | 2021-02-23 | 2021-06-08 | 南方电网科学研究院有限责任公司 | Heat supply planning method, system and device for comprehensive energy system |
| CN112926201B (en) * | 2021-02-23 | 2023-08-08 | 南方电网科学研究院有限责任公司 | Comprehensive energy system heat supply planning method, system and device |
| CN112990686A (en) * | 2021-03-10 | 2021-06-18 | 国网电力科学研究院武汉能效测评有限公司 | Comprehensive energy system equipment type selection and capacity configuration method |
| CN113031447A (en) * | 2021-03-15 | 2021-06-25 | 上海电气集团股份有限公司 | Configuration method, system, equipment and medium for equipment in combined cooling heating and power system |
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