CN111539584B - 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, combining an alternative equipment list, selecting an exhaust-heat boiler and refrigeration equipment of the natural gas combined cooling heating and power system, and calculating the capacity of the gas turbine based on the overall operation efficiency constraint of the natural gas combined cooling heating and power system to obtain the performance parameters of the natural gas combined cooling heating and power system; inputting planning data and performance parameters of the natural gas combined cooling heating 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 condition is set in the user-level comprehensive energy system planning model to plan, the defect of low accuracy of the 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 solving variable in planning is 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 multiple types of energy sources 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 (user-level integrated energy system, UIES) is an effective mode for realizing multi-energy complementation and cascade utilization. For users with multi-type energy requirements, the capacities and the models of the energy supply channels and related equipment are reasonably selected, so that the energy economy of the users can be improved on the premise of ensuring the normal production and life of the users.
The natural gas combined cooling heating power system (combined cooling heating and power system, CCHP) utilizes heat energy generated by natural gas combustion in a cascade way, the overall energy utilization efficiency can reach more than 80%, and the output proportion of cold and heat (comprising hot steam and hot water) can be flexibly configured according to the needs of users, so that the natural gas combined cooling heating power system is the first choice energy supply equipment in comprehensive energy system planning.
The method is characterized in that students at home and abroad develop related researches aiming at a UIES planning method of a combined cooling, heating and power system containing natural gas, the overall research thinking is mostly based on a multi-energy load demand curve of a user, the energy input and output relation of each device to be selected is considered, further, the relation and mathematical model of the capacity of each device to be selected, the initial investment and the running economy of the system are formed, and then an appropriate optimization solving method is selected to obtain an optimal solution. In recent years, artificial intelligence algorithms have also been increasingly applied to comprehensive energy system planning and operation. From the perspective of engineering practical application, the existing research mainly has the following problems: 1) The existing research assumes that various energy demand annual load curves of all users are known, the built model has high accuracy or representative requirements on input data, the input data can be difficult to obtain completely in actual engineering, and the accuracy of relevant prediction data can be difficult to ensure even if an artificial intelligent algorithm is adopted; 2) Part of the research assumes that the capacity of the equipment to be selected can be continuously changed in planning, and the selectable capacity of the equipment in actual engineering is a discrete discontinuous variable; 3) Industrial steam and living hot water are classified into heat loads during planning, and the requirements of users on the heat and power parameters such as temperature, pressure, flow and the like of different types of heat loads are not considered. The above-mentioned shortcomings make the planning effect of user-level comprehensive energy system planning poor, and its calculation process is complex.
In summary, the planning of the user-level comprehensive energy system in the prior art has the technical problems of low planning accuracy and complex calculation process.
Disclosure of Invention
The invention provides a user-level comprehensive energy system planning method, system and equipment, which are used for solving the technical problems of low planning accuracy and complex calculation process in the planning of a user-level comprehensive energy system in the prior art.
The invention provides a user-level comprehensive energy system planning method, which is used for pre-establishing a user-level comprehensive energy system planning model and comprises the following steps:
acquiring planning data, load history data and an alternative equipment list of a user-level comprehensive energy system;
predicting the load of the target year according to the load history data to obtain a load predicted value of the target year;
according to the load predicted value of the target year, selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating power system by combining with an 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 the 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 planning data and performance parameters of the natural gas combined cooling heating 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 performing sensitivity analysis on the optimization result of the equipment type selection and capacity configuration.
Preferably, the constraint conditions of the user-level comprehensive energy system planning model include power constraints, energy constraints, equipment operation constraints and user site constraints.
Preferably, the objective function of the user-level comprehensive energy system planning model is:
wherein: c (C) 1 、C 2 Respectively representing the annual equivalent investment cost and annual operation cost of the comprehensive energy system; j (J) C 、 J S And J DG Respectively representing a device set, a set of energy storage devices and a set of distributed power sources of the natural gas combined cooling heating power system;
the method respectively represents the network electric quantity benefits of the natural gas combined cooling heating power system, the user electricity cost and the distributed power patch benefits of the equivalent reduction of the energy storage equipment.
Preferably, the planning data of the user-level comprehensive energy system comprises planned target years, wind energy and solar model year utilization hours of the 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; the candidate device list includes candidate device types, candidate device type numbers, and unit capacity costs of the candidate devices.
Preferably, the load predicted value for the target year includes an electric load predicted value and an annual usage amount, a load predicted value and an annual usage amount of gas, a cold load predicted value and an annual usage amount, and a heat load predicted value and an annual usage amount.
Preferably, according to the load predicted value of the target year, the specific process of selecting the waste heat boiler and the refrigeration equipment of the natural gas combined cooling heating and power system by combining with 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 the waste heat boiler and the refrigeration equipment of the natural gas combined cooling heating and power system meeting 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.
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 natural gas combined cooling heating and power system is as follows:
calculating annual energy production from electrical load predictions in load predictions for a target year;
calculating annual heat supply and annual cold supply according to the capacity of the waste heat boiler and the capacity of the refrigerating equipment;
Calculating annual energy consumption based on the annual energy production, annual heat supply, annual cold supply and the overall operating efficiency constraints of the natural gas cogeneration system;
the capacity of the gas turbine is calculated from annual gas consumption.
Preferably, the specific process of performing sensitivity analysis on the optimization results of device type selection and 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, gradually increasing to the maximum value by taking the variable scale as an increment, and calculating the annual average investment, the annual average investment of the electric boilers, the annual outsourcing electricity fee and the annual natural gas fee of the corresponding natural gas combined cooling heating and power supply system by increasing each time.
A user-level integrated energy system planning system, 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 history 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 history 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 power system according to a load predicted value of a target year and combining an alternative 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 overall operation efficiency constraint of the natural gas combined cooling heating and power system;
the performance parameter calculation module is used for obtaining the 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 optimization results of equipment type selection and capacity configuration according to planning data and performance parameters of the natural gas combined cooling heating power system;
the sensitivity analysis module is used for performing sensitivity analysis on the optimization result of equipment model selection and capacity configuration.
A user-level comprehensive energy system planning device comprises a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
The processor is used for executing the user-level comprehensive energy system planning method according to the instructions in the program codes.
From the above technical solutions, the embodiment of the present invention has the following advantages:
the embodiment of the invention does not depend on the load and the medium-long time-by-time power prediction curve of the distributed power supply when planning the user-level comprehensive energy system, but performs planning by setting constraint conditions in the user-level comprehensive energy system planning model, thereby avoiding the defect of low planning effect accuracy caused by the fact that the annual load curve cannot be accurately obtained.
The embodiment of the invention has the following other advantages:
according to the embodiment of the invention, the capacity configuration can be finely distinguished according to the load demands of users on different forms, different pressures and temperatures, and the capacity of each device to be selected is discretized when the device is selected, so that the planning result is more suitable for engineering practice and the accuracy is higher.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
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 configuration diagram of a user-level comprehensive energy system planning method, system and device according to an embodiment of the present invention.
Fig. 3 is an equipment framework diagram of a user-level comprehensive energy system planning method, system and equipment according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of key devices and typical architecture of a user-level integrated energy system planning method, system and device according to an embodiment of the present invention.
Fig. 5 is an analysis chart of the sensitivity of the user-level comprehensive energy system according to the embodiment of the present invention.
Fig. 6 is a load duty ratio sensitivity analysis chart of a user-level comprehensive energy system planning method, system and device according to an embodiment of the present invention.
Fig. 7 is a natural gas price sensitivity analysis chart of a user-level comprehensive energy system planning method, system and equipment according to an embodiment of the invention.
Detailed Description
The embodiment of the invention provides a user-level comprehensive energy system planning method, a system and equipment, which are used for solving the technical problems of low planning accuracy and complex calculation process in the planning of a user-level comprehensive energy system in the prior art.
In order to make the objects, features and advantages of the present invention more comprehensible, the technical solutions in the embodiments of the present invention are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the 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 used for pre-establishing a user-level comprehensive energy system planning model and 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 comprise planned target years, wind energy and solar model year utilization hours of the user-level comprehensive energy system and various energy purchase prices of the user-level comprehensive energy system, and if the energy purchase prices change with time (such as peak Gu Dianjia), a complete price catalog is required to be provided, and data used in a subsequent calculation process are obtained in advance so that a subsequent process can be developed smoothly.
Predicting the load of the target year according to the load history data to obtain a load predicted value of the target year; the method for accurately predicting the medium-and-long time-by-time load curve of the user is very difficult, and the prediction methods for different types of loads of different types of users are different in size and mainly based on the existence of historical data, the characteristics of production and living energy of the users and the like. In general, for users with complete historical data, load prediction methods based on the historical data, such as regression prediction, are adopted; for users with new projects or missing historical data, a load density method, a unit consumption method and other prediction models can be adopted to predict the load as appropriate.
According to the load predicted value of the target year, selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating power system by combining with an 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 cogeneration system is affected by the load demands of the user. For industrial users, the heat loads can be classified into a heat steam load and a heat water load 2. If the user's location does not have an external central steam heating system or an established steam boiler, the natural gas cogeneration system will become the preferred supply of hot steam due to its high energy utilization efficiency. In this case, the steam output of the natural gas cogeneration system should meet the needs of users for hot steam; while the amount of electricity, cooling, may be less than or equal to the user's demand, as this energy may be supplied by other devices. On the other hand, the natural gas combined cooling heating and power system can generate electricity while supplying heat, and the electricity generation efficiency depends on the performance of a gas turbine (or a gas internal combustion engine), and 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 and heating system can be flexibly adjusted by configuring waste heat boilers and refrigeration equipment of different types on the premise of knowing the total waste heat power, and the output proportion of hot water and hot steam can be adjusted.
Calculating the capacity of a 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, wherein the waste heat boiler, the refrigeration equipment and the gas turbine are constituent units of the natural gas combined cooling, heating and power system, and besides meeting the load requirement, the primary energy utilization efficiency requirement of the natural gas combined cooling, heating and power system is also required; according to the capacity of the gas turbine, the capacity of the waste heat boiler and the capacity of the refrigeration equipment, obtaining the performance parameters of the natural gas combined cooling heating power system; the performance parameters comprise input parameters and output parameters, wherein the input parameters comprise natural gas quantity; the output parameters include power generation, heat supply (divided into steam and hot water), and cooling.
And inputting the planning data and the performance parameters of the natural gas combined cooling heating 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 comprehensive energy system planning model in the embodiment is an MILP model, and decision variables in the model include: the number of devices of each type is the total annual outsourcing of each type of energy source.
The key of the reasonable model solving result is the setting of the annual utilization maximum hour number of the energy conversion equipment and the annual charge-discharge cycle number of the energy storage equipment. The rated charge-discharge energy power and capacity of the emergency standby energy storage equipment can be directly determined according to important load conditions required to be supported, and the annual maximum charge-discharge cycle times of the commercial operation energy storage equipment can also be determined according to a local actual energy price catalog. In the user-level integrated energy system architecture shown in fig. 4, the energy conversion devices include a natural gas cogeneration system, an electric boiler, and an air conditioning device, and the above-described types of energy conversion devices are discussed separately.
1) For industrial users with a stable annual load curve, the natural gas combined cooling, heating and power system can have a 24-hour starting operation condition, so that the annual operation hours of the natural gas combined cooling, heating and power system unit can be set as a constant according to actual conditions and user requirements, and the value of the annual operation hours is preferably matched with the annual maximum utilization hours of the load of the users.
2) The electric boiler and the air conditioner are mainly used for adjusting 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 place, can perform rough estimation according to a certain number of hours of daily operation, and can also be set as a constant.
As a preferred embodiment, the basic principle of equipment model selection and capacity configuration in the planning stage is to minimize the investment and running cost of the system in the planning period on the premise of meeting the energy consumption requirements. For the user-level integrated energy system, the primary investment mainly comes from the investment of the energy conversion equipment, the energy storage equipment and the distributed power supply, and the influence of the line investment on the system investment can be ignored in the planning stage due to the short line of the user-level integrated energy system. The running cost of the system comprises 2 parts, namely the energy purchasing cost of 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 is as follows, and the objective function of the user-level comprehensive energy system planning model is as follows:
wherein: c (C) 1 、C 2 Respectively representing the annual equivalent investment cost and annual operation cost of the comprehensive energy system; j (J) C 、 J S And J DG Respectively representing a device set, a set of energy storage devices and a set of distributed power sources of the natural gas combined cooling heating power system;
the method respectively represents the network electric quantity benefits of the natural gas combined cooling heating power system, the user electricity cost and the distributed power patch benefits of the equivalent reduction of the energy storage equipment.
The main equipment related in the formula (1) comprises a natural gas combined cooling heating and power system, a battery energy storage, ice cold storage and a distributed power supply, and the calculation basis of the key equipment in an objective function is further described.
The generated energy of the natural gas combined cooling heating power system can be directly used by users, and can be purchased by the grid company according to the local online electricity price of the natural gas combined cooling heating power system.
For battery energy storage, in areas with peak-valley electricity prices or time-of-use electricity price policies, the battery energy storage system can earn a gap price by using a low-charge high-discharge operation mode, and the energy consumption cost of a user is reduced equivalently.
For ice cold accumulation, the ice cold accumulation can utilize the peak-to-valley electricity price policy of the place where the project is located, and the energy cost of a user is reduced by means of ice making in the low-valley electricity price period and ice melting and refrigerating in the high-peak electricity price period.
The generated energy of the distributed power supply can reduce the outsourcing electric quantity, the partial income is not calculated repeatedly, and the distributed photovoltaic power generation has the related generated energy subsidy policy in China, so that the energy consumption cost of a user can be reduced equivalently.
The cost and benefit of each item in the formula (1) can be represented by the formulas (2) to (6), respectively.
Wherein: m represents the number of types of equipment to be selected; n is n j The number of the j-class equipment is selected and is a decision variable; c j Representing the investment cost of the equipment j; r represents the equipment depreciation rate; r represents the discount rate; τ j Indicating the age of the device j; lambda (lambda) j Representing the proportion of the annual maintenance cost of the equipment j to the one-time investment; k represents the type of outsourcing energy involved in the user-level integrated energy system;
represents the outsourcing price of ki-type energy; />
Represents the total annual outsourcing amount of ki-type energy; s is(s) j S is constant when the rated power generation capacity of the natural gas combined cooling heating power system equipment j is directly supplied to users j When the power generation capacity of the natural gas combined cooling heating power system is fully on the internet, s is (0) j ＝1；/>
Representing annual energy production of the natural gas combined cooling heating and power system equipment j; n (N) j The annual utilization days of the energy storage equipment j are represented; k (K) j The daily charge and discharge cycle times of the energy storage equipment j; c (C) j Is the actual available capacity of the energy storage device j; mu (mu) j The comprehensive utilization efficiency of the energy storage equipment j; p is p out,i 、p in,i The corresponding user electricity prices are respectively obtained according to the ith cycle of the local peak-valley electricity price catalog; />
The price of the distributed power supply electric patch for the place where the project is located is different in price; / >
Representing the annual energy production of the distributed power source j. />
And performing sensitivity analysis on the optimization result of the equipment type selection and capacity configuration.
As a preferred embodiment, constraints of the user-level integrated energy system planning model include power constraints, energy constraints, plant operating constraints, and user site constraints. Further analysis of each of the bundles is described below.
1) Power constraint
Considering the extreme conditions that the output of the distributed power supply is 0, the energy storage equipment is in a charging state, and the electric load of the user reaches the peak load, considering a certain energy supply margin, the user-level comprehensive energy system is required to meet the energy consumption requirement of the user, and the power constraint shown in the formula (7) is required to be met.
Wherein:
the maximum power of the ki type energy which can be provided by the external energy supply system is represented, and the value of the maximum power is determined by 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 a ki-type energy, +.>
If device j is a device that generates ki-type energy, then
If device j is a device which neither generates nor consumes energy of the ki-type +.>
Representing a ki type energy margin; />
A maximum load forecast representing a user's target annual ki-type energy.
2) Energy constraint
The total supply and demand of various energy sources should be kept substantially balanced for a long period of operation (e.g., 1 year), as shown in formula (8).
Wherein:
representing the total amount of the energy source of the type ki supplied by the external system year, wherein the total amount is a decision variable; />
Indicating the rated power of the device j to produce or consume ki type energy; t (T) j Indicating the annual maximum number of hours of utilization for device j.
It should be noted that, if the natural gas cogeneration system is fully connected to the internet, the power generation power and the power generation amount of the natural gas cogeneration system need to be excluded from the formulas (7) and (8) when calculating the power and the energy balance.
3) Plant operation constraints
Whether the parameters of the device meet the self operation constraint directly determines whether the device can be selected, and for the j-th class of device, if the parameters do not meet the self operation constraint, the corresponding decision variable n j ＝0。
The energy conversion device should satisfy the constraint shown in the formula (9).
Wherein:
representing the total amount of ki-type energy consumed or generated by the energy conversion device j in one year; />
The power of the energy conversion equipment j for consuming or generating ki type energy under the rated working condition is represented; />
The annual maximum use hour number of the energy conversion device j is represented. />
The natural gas combined cooling heating power system is a special energy conversion device, and 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 requirements of a formula (10).
Wherein: k1 and k2 respectively represent two different types of energy in electricity, gas, cold and heat;
representing the efficiency of the energy conversion device j to convert the k1 form of energy into the k2 form of energy; />
A dimensional constant representing the conversion of energy in the form of k1 to energy in the form of k 2; />
Respectively, the power of the device j for consuming the k1 type energy and the power for generating the k2 type energy under the rated working condition.
As shown in FIG. 4, the natural gas combined cooling heating and power system is a combined energy supply system consisting of three main parts of a gas turbine, a waste heat boiler and refrigeration equipment, wherein the gas turbine, the waste heat boiler and the refrigeration equipment are matched with various thermodynamic parameters such as pressure, temperature and the like, and the requirement of the whole energy utilization efficiency of the natural gas combined cooling heating and power system unit is met as shown in a formula (11).
Wherein:
representing annual energy production of natural gas cogeneration system equipment; />
Representing annual heat supply of natural gas-cooling heat-power cogeneration system equipment; />
The annual cooling capacity of the natural gas combined cooling heating and power system equipment is represented;
combined cooling heating and power system for representing natural gasAnnual gas consumption of the device; q (Q) g Representing the lower heating value of the fuel gas.
The formula (11) unifies the dimensions of energy in different forms such as natural gas, cold, heat, electricity and the like, and can be converted into an inequality relation between powers under the rated operation working condition, as shown in the formula (12).
Wherein: t (T) CCHP The annual operation hours of the natural gas combined cooling heating power system unit are represented;
the method comprises the steps of representing the sum of power of various types of energy supplied to a user by a natural gas combined cooling heating power system unit under a rated working condition, and obtaining a unit kW;
and the power consumption of the natural gas combined cooling heating power system unit under the rated working condition is expressed as kW.
The energy storage device should meet the operational constraints of the device itself, and furthermore, its annual maximum number of charges and discharges is related to its operational mode and capacity configuration. The capacity of the energy storage device as emergency standby should be configured to meet the constraint condition shown in the formula (13), and the annual maximum duty cycle should meet the constraint shown in the formula (14).
Wherein:
the rated charge-discharge power, the rated charge-discharge time and the rated capacity of the energy storage equipment for emergency are respectively represented; tsu represents the time that the energy storage device is required to continue to supply power to the critical load; />
Representing the important load power of a user; />
Representing the annual maximum duty cycle of the emergency energy storage device; min (·) is a function taking the minimum value; />
The rated charge/discharge time of the energy storage equipment is represented as the ratio of rated capacity to rated power; nem represents the number of times the system is used to emergency energy storage devices in a year, which can be estimated based on the reliability of the energy supply at the user's site, or can be set to a constant by investigation.
Another mode of operation of the energy storage device is to make use of the difference in energy selling prices at different times of day, earn price differences through charging and discharging at different times of day, and considering the self-operating economy, the maximum number of times of charging and discharging per day of such energy storage device is related to the local energy price list, capacity ratio and battery performance, and the constraints shown in formulas (15) and (16) should be satisfied.
Wherein:
respectively representing rated charge-discharge energy power, rated charge-discharge energy time and capacity of energy storage equipment which should be operated commercially; />
Representing a maximum number of operational cycles of the energy storage device for commercial operation; npr represents the maximum charge and discharge times per day calculated according to the energy price catalog of the place where the project is located; />
Representing the maximum charge-discharge cycle number allowed by the battery device of the energy storage device j; ysys represents the number of years of battery storage operation required by the system.
For the energy storage device with partial capacity as emergency backup, it can be regarded as 2 different energy storage devices according to the emergency backup capacity, and each of the energy storage devices corresponds to satisfying the constraint conditions of equations (13) to (16).
4) User site constraints
In practical engineering, the installation scale of each type of equipment depends on the load level of a user, on the other hand, the installation scale is limited by the available area of an installation place, and the installation conditions of different types of equipment are different, for example, the distributed photovoltaic power generation can be installed on a roof with a bearing structure meeting the relevant requirements, and the equipment such as a natural gas combined cooling heating and power system and the like needs to be arranged in a separate factory building or an indoor space, and the relation between the capacity of each type of equipment and the available area resource of the user is shown as a formula (17).
Wherein: m represents a collection of 3 types of energy conversion devices, energy storage devices, and distributed power sources; p (P) j Representing the capacity of device j;
representing the installable capacity per unit area of the plant j, kW/m2 or kWh/m2; />
Representing the area available to the user for construction of class j devices.
In summary, the constraint conditions of the device model selection can be summarized as shown in the formula (18).
As a preferred embodiment, the planning data of the user-level comprehensive energy system comprises planned target years, and wind energy and solar energy model year utilization hours of the 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. If the energy purchase price changes with time (e.g. peak Gu Dianjia), a complete price list is provided. The alternative equipment list is shown as a variant 5, comprises alternative equipment types, alternative equipment models and unit capacity manufacturing cost of the alternative equipment, and is clear and convenient to calculate when the equipment is selected through various data of Luo Liechu alternative equipment.
As a preferred embodiment, according to the load predicted value of the target year, the specific process of selecting the waste heat boiler and the refrigeration equipment of the natural gas combined cooling heating and power system is as follows:
The efficiency of the heat-to-cold conversion is calculated from the ratio of the predicted cold load value to the predicted hot load value among the predicted load values of the target year by the following formula,
wherein: k1 and k2 respectively represent two different types of energy in cold and hot;
representing the efficiency of the energy conversion device j to convert the k1 form of energy into the k2 form of energy; />
A dimensional constant representing the conversion of energy in the form of k1 to energy in the form of k 2; />
Respectively, the power consumed by the device j under the rated working condition and the power generated by the k2 type energy.
And selecting the waste heat boiler and the 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 heat-cold conversion efficiency.
As a preferred embodiment, 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 natural gas cogeneration system is as follows:
calculating annual energy production from electrical load predictions in load predictions for a target year;
calculating annual heat supply and annual cold supply according to the capacity of the waste heat boiler and the capacity of the refrigerating equipment;
Annual energy consumption is calculated based on the constraint of the whole operation efficiency of the annual energy production, annual heat supply, annual cold supply and natural gas combined cooling heating power system, and the specific process is as follows:
wherein:
representing annual energy production of natural gas cogeneration system equipment; />
Representing annual heat supply of natural gas-cooling heat-power cogeneration system equipment; />
The annual cooling capacity of the natural gas combined cooling heating and power system equipment is represented;
the annual gas consumption of the natural gas combined cooling heating power system equipment is represented; q (Q) g Representing the lower heating value of the fuel gas.
The capacity of the gas turbine is calculated from annual gas consumption.
As a preferred embodiment, in order to more comprehensively analyze the influence of the influence factors on the planning result, the embodiment further performs sensitivity analysis on the influence factors, and the influence factors are classified into single influence factors and double influence factors according to the number of the influence factors, which are specifically as follows:
1) Single influence factor analysis
The influence of a single influencing factor (such as natural gas price, equipment parameters and load conditions) on a planning result can be analyzed according to the following steps:
step 1: determining the maximum and minimum values of the influence factors;
step 2: setting a change scale of the influencing factors;
step 3: and gradually increasing to the maximum value by taking the minimum value of the influence factors as a starting point and taking the variable scale as an increment, and calculating the annual average investment, the annual average investment of the electric boiler, the annual outsourcing electricity charge and the annual natural gas charge of the corresponding natural gas combined cooling heating and power supply system.
2) Dual influencing factor analysis
Because there may be a correlation between the two influencing factors, to more fully analyze the influence of the double influencing factors on the planning result, the specific steps of the double influencing 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, and taking different values;
step 3: and gradually increasing to the maximum value by taking the minimum value of the influence factors as a starting point and taking the variable scale as an increment, and calculating the annual average investment, the annual average investment of the electric boiler, the annual outsourcing electricity fee and the annual natural gas purchase fee of the corresponding natural gas combined cooling heating and power supply system.
As shown in fig. 2, a user-level integrated energy system planning system includes:
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 an alternative device list of the user-level integrated energy system;
the load prediction module 202 is configured to predict a load of a target year according to load history data, so as to obtain a load prediction value of the target year;
The capacity pre-configuration module 203 is configured to select a waste heat boiler and a refrigeration device of the natural gas combined cooling heating power system according to a load predicted value of a target year and in combination with an alternative device list, so as to obtain the capacity of the waste heat boiler and the capacity of the refrigeration device; 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;
the performance parameter calculation module 204 is configured to obtain performance parameters of the natural gas cogeneration 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 205 is configured to output an optimization result of device type selection and capacity configuration according to planning data and performance parameters of the natural gas cogeneration system.
The sensitivity analysis module 206 is configured to perform sensitivity analysis on the optimization result of device model selection and 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 user-level integrated energy system planning method embodiments described above in accordance with 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 complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 302 in the terminal device 30.
The terminal device 30 may be a computing device such as a desktop computer, a notebook computer, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor 300, a memory 301. It will be appreciated by those skilled in the art that fig. 6 is merely an example of the terminal device 30 and is not meant to be limiting as to the terminal device 30, and may include more or fewer components than shown, or may combine certain components, or may be different, e.g., the terminal device may also include input and output devices, network access devices, buses, etc.
The processor 300 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 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) or 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
To further illustrate the invention, 2 embodiments are provided for detailed description. The first embodiment takes a hospital user-level comprehensive energy system as an example, and the second embodiment takes an actual industrial user-level comprehensive energy system in a south area of China as an example. The energy types and the demands of the user loads in the two specific schemes are different, and the external energy supply environments are different. Both specific schemes can be planned by the method of the invention, and the feasibility and the universality of the invention are illustrated. The natural gas cogeneration system in this embodiment is replaced by the english abbreviation CCHP.
1) Hospital user-level comprehensive energy system
(1) 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 certain hospital are taken as an example for verification. The load demand types of the users comprise electric, hot water and cold 3 types, wherein the maximum electric load is 165kW, the annual electricity consumption is about 1161MWh, and the maximum load utilization hours are 7039h; the maximum heat load is 820kW, the annual heat consumption is about 3095 MWh, and the maximum load utilization hours are 3775h; the maximum cooling load is 350kW, the annual cooling capacity is about 280MWh, and the maximum load utilization hour is 799h. The natural gas price in the region where the user is located is converted into RMB to be 0.145 yuan/kWh; according to a typical daily time-of-use electricity price curve, the local electricity price can be roughly divided into 3 electricity price periods of peak, flat and valley for the convenience of calculation: peak segment electricity price is 0.8 yuan/kWh, duration is 6 h/day, flat segment electricity price is 0.6 yuan/kWh, duration is 10 h/day, valley segment electricity price is 0.3 yuan/kWh, duration is 8 h/day. The average value of electricity price in the local area is about 0.55 yuan/kWh, and the user is not provided with a central heating system or a central cooling system and is not provided with a distributed power patch price.
The type of the equipment to be selected in planning is shown in table 1, and the comprehensive energy utilization rate of the CCHP unit is over 75%. The schemes and corresponding parameters that can be combined into a CCHP set according to the candidate devices listed in table 1 are shown in table 2.
Table 1 list of device parameters to be selected
Tab.1 List of Devices to be Selected
TABLE 2 CCHP preselected configuration
Tab.2 CCHP Devices to be Selected
According to the cold and hot load demand conditions of users, the following parameter settings are made before planning:
1) Reserving a margin of 10% according to the maximum load of various types of energy sources during planning;
2) Setting the power generation amount of the CCHP to be directly supplied to users, setting the annual maximum operation hour number of the users to be 3095h, and setting the power generation efficiency to be 35% (according to the power generation efficiency, the air consumption of the CCHP unit can be calculated, and under the condition of neglecting heat loss of a heating system and a cooling system, the comprehensive utilization efficiency under different configurations can be calculated);
3) The annual maximum operating hours of the electric boiler equipment is 3095h, the heating efficiency is 100%, the annual maximum operating hours of the electric refrigeration equipment is 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 capacity is shown in the table 1, the energy is charged in the valley electricity price period every day, the energy is discharged in the peak electricity price period, and the energy is circularly operated for 1 time every day at most, namely the maximum utilization days of the energy storage year are 365 days;
5) Setting the annual maximum utilization hour number of wind power as 2100h and setting the annual maximum utilization hour number of photovoltaic as 1500h;
6) The equipment depreciation rate r=0, and the discount rate r=6%;
7) The life cycle of the planning system is set to be 20 years, the operation and maintenance cost of the equipment accounts for 5% of the investment cost of each year, and the user has a large enough area to arrange various kinds of equipment to be selected.
8) Hospitals have adequate power and hot water supplies in the area, but no centralized cooling equipment.
(2) Capacity allocation results
The device model selection and capacity configuration are carried out according to the method of the invention, and the detailed device configuration of the system is shown in table 3. According to the method for calculating the cost and the equivalent reduced cost in the formula (5), the annual average construction operation total cost of the user-level comprehensive energy system is 132.89 ten thousand yuan, wherein the annual average equipment investment cost is 54.19 ten thousand yuan, the annual equipment maintenance cost is 2.71 ten thousand yuan, the annual outsourcing electricity quantity is 32.5MWh, and the annual purchasing electricity 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 equivalent reduced running cost of ice storage is 3.26 ten thousand yuan. From the capacity allocation results, the annual average construction operation total cost of the user-level comprehensive energy system is mainly derived from the investment of the CCHP and the outsourcing natural gas cost.
TABLE 3 device selection results
Tab.3 Equipment selection results
(3) Sensitivity analysis
In order to further study the influence of equipment parameters, load conditions and natural gas prices on planning results, the maximum annual utilization hours of equipment and the electric/cold/heat load ratio of users are taken as targets, and the sensitivity analysis is carried out on the annual construction operation cost of the system, and the results are respectively shown in fig. 5 and 6.
Fig. 5 shows the results of the annual construction running costs of the system as a function of the number of hours of utilization of the plant. In theory, similar sensitivity analysis can be performed on the number of utilization hours of all the equipment to be selected, and for convenience of description, two types of equipment, namely a CCHP unit and an electric boiler, are selected for analysis, wherein the annual maximum utilization hour number change range of the two types of equipment is 1000-8000 h, and the interval is 500h. The two types of equipment can supply heat, and under the precondition that other conditions are not changed, the annual construction operation cost of the system is 115.95 ten thousand yuan, and the annual maximum utilization hours of the CCHP and the electric boiler are 6000 hours and 1000 hours respectively. It can be seen that the number of utilization hours of the device is increased and the construction and operation costs of the whole system cannot be effectively reduced, and the number of utilization hours of the device should be matched with the number of load utilization hours of the type of energy supplied.
When analyzing the influence of the electric/cold/heat load ratio on the annual construction operation cost of the system, the electric load ratio coefficient shown in the definition formula (19) represents the proportion of the cold/heat and electric load demands. The sum of the cold, hot and electric loads is kept to be a fixed value when the sensitivity analysis is carried out, the proportion of the cold and hot loads is constant, the annual maximum utilization hours of various loads are also unchanged, and the re is changed within the range of 0.1-0.9, and the result is shown in figure 6.
Wherein: r is (r) e Representing the electrical load duty cycle;
representing the user maximum electrical load, maximum thermal load and maximum cooling load, respectively. />
As can be seen from fig. 6, under the boundary conditions set in the present embodiment, as the electrical load duty ratio is continuously increased, the capacity configuration of the CCHP is not increased, and the system satisfies the power demand by increasing the outsourcing power. Therefore, in the user-level comprehensive energy system, the main functions of the CCHP still meet the requirements of cold and heat loads because the electricity price of an external power grid is not high; for satisfying the cold and hot loads, the comprehensive economic benefit is better than that of an electric boiler and an electric refrigerating device due to the energy cascade utilization of the CCHP, so that the cold and hot loads are mainly satisfied by the CCHP, and the initial purpose of planning is also met.
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 and 1 yuan/kWh, and the planning economy result is shown in figure 7. When the price of the natural gas is lower than 0.2 yuan/kWh, the user selects CCHP preferentially, and the electric energy of the user can be basically completely supplied by the CCHP unit; but if the price of natural gas continues to rise, the user will prefer the way to outsource electricity. When the natural gas price reaches about 0.24 yuan/kWh, the user will not configure the CCHP any more, but the energy requirements are met by means of outsourcing electricity, configuring an electric boiler, electric refrigeration equipment and the like.
2) Industrial user-level comprehensive energy system
(1) Description of boundary conditions
Major energy requirements for certain tire manufacturing enterprises in Guangzhou city include electricity (110 kV access), hot steam, live hot 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 kWh; the enterprise is provided with a diesel generator as a standby power supply, when a fault and power failure occur, the power is supplied to an important load, the rated power of the important load is 5MW, and the starting time of the diesel engine is 30s.
The inlet pressure of hot steam required by enterprise production is 2.4Mpa, the inlet temperature is about 230 ℃, the outlet pressure is 0.5Mpa, the outlet temperature is 120 ℃, the flow is 20t/h, and the steam consumption in 2017 is 65520t. The maximum thermal steam load of the industrial user can be measured to be 12.9MW, and the annual steam consumption is 4229 kWh.
In the tire rolling process, industrial circulating water at 80-105 ℃ is needed, the living water of staff is needed in enterprises, the hot water load of the user in 2017 is 1.2MW, and the annual hot water consumption is 254 kWh.
Meteorological data display [23] The minimum air temperature of 2017 Guangzhou city is 1.8 ℃, the maximum air temperature is 39.7 ℃ and the average air temperature is 22.8 ℃. The tire shaping requires chilled water with inlet and outlet temperatures of 7 ℃ and 14 ℃ to cool. In addition, the production workshop has high temperature requirement, and the annual temperature is required to be kept at about 20-25 ℃. The total cooling load requirement of the industrial user is 15MW, and the annual cooling capacity is 7664 kWh.
(2) Local related energy price
Guangzhou city implements peak-to-valley electricity price mechanism for large industrial users [24] Peak, flat, valley electricity prices for 110kV users were 96.26 min/kWh, 58.34 min/kWh, and 29.17 min/kWh, respectively, and peak, flat, valley electricity price durations were 6 h/day, 10 h/day, and 8 h/day, respectively. The user is provided with an external concentrated heat supply source, and steam with corresponding pressure is provided, wherein the price is 325 yuan/t, namely 0.414 yuan/kWh; the local natural gas supply price is 2.08 yuan/Nm 3 I.e. 0.206 yuan/kWh (natural gas lower heating value of 36MJ// Nm) 3 )；The local CCHP internet power price is 0.715 yuan/kWh; according to the current new energy subsidy policy, bringing in the financial subsidy scale in 2019, adopting the industrial and commercial distributed photovoltaic power generation project of 'self-service and allowance surfing' mode, and adjusting the total power generation amount subsidy standard to be 0.10 yuan per kilowatt hour; wind power is temporarily free of related subsidy policies.
(3) Other boundary conditions
Assuming that the planning period is 20 years, the industrial user does not have a free site to build a wind turbine generator, and the free roof resources meet the requirements of photovoltaic power generation, construction, bearing and the like of about 5000m 2 The photovoltaic annual utilization hours of the place where the project is located is about 1000 hours; the industrial user has 10000m 2 Can be used for arranging energy conversion equipment and energy storage equipment.
The power supply, air supply and hot water supply capability of the region where the industrial user is located is sufficient, but a central heating steam system and a central cooling system are not provided. The parameters of selectable equipment during planning are detailed in annex 1, and each type of equipment gives 3 alternative models. Wherein CCHP has been preconfigured according to the pre-selection principles described above.
During planning, the method is divided into 2 planning scenes according to a CCHP power supply mode: and the CCHP generating capacity in the scene 1 is directly supplied to users, and the CCHP generating capacity in the scene 2 is fully connected to the Internet. The remaining parameter settings were the same as in example 1.
(2) Configuration results
And according to the boundary conditions set in the upper section, the generated energy of the CCHP is directly supplied to users, and the configuration results of the devices are shown in a table 4.
Table 4 device selection results for scenarios 1 and 2
Tab.4 Equipment selection results of 2 Scenarios
In the scene 1, the annual average investment operation cost of the 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 outsourcing electricity cost is 6688 ten thousand yuan, the annual average outsourcing hot steam cost is 128 ten thousand yuan, the annual average natural gas cost is 2892 ten thousand yuan, and the photovoltaic power generation amount subsidy cost is 5 ten thousand yuan; in the scene 2, the annual average construction operation cost of the system is 10363 ten thousand yuan, the annual average equipment investment cost is 1716 ten thousand yuan, the annual average maintenance cost of the system is 86 ten thousand yuan, the annual average electricity purchasing cost is 9394 ten thousand yuan, the annual average natural gas purchasing cost is 3856 ten thousand yuan, the photovoltaic generating capacity subsidy cost is 5 ten thousand yuan, and the CCHP (universal power supply) online electric quantity benefit is 4685 ten thousand yuan. Because the total heat steam supply capacity of the two CCHP units reaches 14.7MW, the heat steam load demands of users can be met, and therefore the extra-purchased heat steam is not needed, and the extra-purchased heat steam cost is 0. In both scenarios, the battery energy storage is used only as a backup power source.
Comparing the two scenes can show that under the existing energy price policy, the CCHP direct internet surfing mode is beneficial to reducing the energy cost of the user, but the initial investment amount of the user can be greatly increased.
In order to further study the influence of outsourcing steam price and natural gas price change on configuration results, sensitivity analysis is performed by taking scene 1 as an object. When the outsourcing steam price is lower than 0.222 yuan/kWh (i.e. 157 yuan/ton) or the natural gas price is higher than 0.297 yuan/kWh (i.e. 3 yuan/Nm) 3 ) When the user purchases electricity and steam directly from outside, it is more economical.
Table 5 list of alternative devices
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, apparatuses and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not described in detail herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform 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, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The user-level comprehensive energy system planning method is characterized by pre-establishing a user-level comprehensive energy system planning model, and comprises the following steps of:
acquiring planning data, load history data and an alternative equipment list of a user-level comprehensive energy system, wherein constraint conditions of a user-level comprehensive energy system planning model comprise power constraint, energy constraint, equipment operation constraint and user site constraint;
predicting the load of the target year according to the load history data to obtain a load predicted value of the target year;
according to the load predicted value of the target year, selecting a waste heat boiler and refrigeration equipment of the natural gas combined cooling heating power system by combining with an 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 the 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 planning data and performance parameters of the natural gas combined cooling heating 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;
performing sensitivity analysis on the optimization results of equipment type selection and capacity configuration;
the power constraint meets the conditions that the output of the distributed power supply is 0, the energy storage equipment is in a charging state and the electric load of a user reaches the extreme condition of peak load;
the power constraint is:
wherein:
indicating availability of an external energy supply systemkiThe maximum power of the type of energy, the value of which depends on the capacity of the user gateway device;J1、J2respectively representing a set of energy conversion devices and a set of energy storage devices; />
Is constant if the devicejIs consumption ofkiA device of type energy, then->
If the devicejIs to generatekiA device of type energy 
If the devicejIs neither produced nor consumedkiA device of type energy, then->
；/>
Representation ofkiType energy margin; />
Representing user goalsYear of lifekiMaximum load prediction value of type energy;
the equipment operation constraint is specifically as follows:
a first constraint for an emergency standby energy storage device;
a second constraint for an energy storage device of the commercially operated energy storage device; the first constraint condition is:
wherein:
、/>
、/>
the rated charge-discharge power, the rated charge-discharge time and the rated capacity of the energy storage equipment for emergency are respectively represented; />
Indicating the time required for the energy storage device to continue to supply power to the important load; />
Representing the important load power of the user; />
Representing the annual maximum duty cycle of the emergency energy storage device;min(·)to take the minimum function; />
Indicating nominal charge/discharge of an energy storage deviceThe energy time is the ratio of rated capacity to rated power;Nemrepresenting the number of times the system is used to emergency energy storage devices in a year;
the second constraint condition is:
wherein:
、/>
、/>
respectively representing rated charge-discharge energy power, rated charge-discharge energy time and capacity of energy storage equipment which should be operated commercially; />
Representing a maximum number of operational cycles of the energy storage device for commercial operation;Nprrepresenting the maximum charge and discharge times per day calculated according to the energy price list of the place where the project is located; / >
Representing energy storage devicesjAllowing a maximum number of charge and discharge cycles;Ysysrepresenting the number of years of battery energy storage operation required by the system.
2. The method for planning a user-level integrated energy system according to claim 1, wherein the objective function of the user-level integrated energy system planning model is:
wherein:C 1 、C 2 respectively representing the annual equivalent investment cost and annual operation cost of the comprehensive energy system;J C 、J S andJ DG respectively representing a device set, a set of energy storage devices and a set of distributed power sources of the natural gas combined cooling heating power system;
、/>
、/>
the method respectively represents the network electric quantity benefits of the natural gas combined cooling heating power system, the user electricity cost and the distributed power patch benefits of the equivalent reduction of the energy storage equipment.
3. The method for planning a user-level integrated energy system according to claim 2, wherein the planning data of the user-level integrated energy system comprises planned target years, wind energy and solar model year utilization hours of the place where the user-level integrated energy system is located and various energy purchase prices of the place where the user-level integrated energy system is located; the candidate device list includes candidate device types, candidate device models, and unit capacity costs of the candidate devices.
4. A method of planning a consumer-level integrated energy system according to claim 3, wherein the load forecast values for a target year include an electric load forecast value and an annual usage amount, a gas load forecast value and an annual usage amount, a cold load forecast value and an annual usage amount, and a heat load forecast value and an annual usage amount.
5. The user-level comprehensive energy system planning method according to claim 4, wherein the specific process of selecting the waste heat boiler and the refrigeration equipment of the natural gas combined cooling heating power system according to the load predicted value of the target year by combining with 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 the waste heat boiler and the refrigeration equipment of the natural gas combined cooling heating and power system meeting 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.
6. The method for planning a user-level integrated energy system according to claim 5, 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 operation efficiency constraint of the natural gas cogeneration system is as follows:
Calculating annual energy production from electrical load predictions in load predictions for a target year;
calculating annual heat supply and annual cold supply according to the capacity of the waste heat boiler and the capacity of the refrigerating equipment;
calculating annual energy consumption based on the annual energy production, annual heat supply, annual cold supply and the overall operating efficiency constraints of the natural gas cogeneration system;
the capacity of the gas turbine is calculated from annual gas consumption.
7. The method for planning a user-level integrated energy system according to claim 6, wherein the specific process of performing sensitivity analysis on the optimization results of device model selection and 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 gradually increasing to the maximum value by taking the minimum value of the influence factors as a starting point and taking the variable scale as an increment, and calculating the annual average investment, the annual average investment of the electric boiler, the annual outsourcing electricity charge and the annual natural gas charge of the corresponding natural gas combined cooling heating and power system.
8. A user-level integrated energy system planning system, 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 history 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 history 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 power system according to a load predicted value of a target year and combining an alternative 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 overall operation efficiency constraint of the natural gas combined cooling heating and power system;
the performance parameter calculation module is used for obtaining the 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 optimization results of equipment type selection and capacity configuration according to planning data and performance parameters of the natural gas combined cooling heating power system;
the sensitivity analysis module is used for carrying out sensitivity analysis on the optimization result of equipment model selection and capacity configuration;
The power constraint meets the conditions that the output of the distributed power supply is 0, the energy storage equipment is in a charging state and the electric load of the user reaches the extreme condition of peak load;
the power constraint is:
wherein:
indicating availability of an external energy supply systemkiThe maximum power of the type of energy, the value of which depends on the capacity of the user gateway device;J1、J2respectively representing a set of energy conversion devices and a set of energy storage devices; />
Is constant if the devicejIs consumption ofkiA device of type energy, then->
If the devicejIs to generatekiA device of type energy
If the devicejIs neither produced nor consumedkiA device of type energy, then->
；/>
Representation ofkiType energy margin; />
Representing a user's target yearkiMaximum load prediction value of type energy;
the equipment operation constraint is specifically as follows:
a first constraint for an emergency standby energy storage device;
a second constraint for an energy storage device of the commercially operated energy storage device; the first constraint condition is:
wherein:
、/>
、/>
the rated charge-discharge power, the rated charge-discharge time and the rated capacity of the energy storage equipment for emergency are respectively represented; />
Indicating the time required for the energy storage device to continue to supply power to the important load; />
Representing the important load power of the user; / >
Representing the annual maximum duty cycle of the emergency energy storage device;min(·)to take the minimum function; />
The rated charge/discharge time of the energy storage equipment is represented as the ratio of rated capacity to rated power;Nemrepresenting the number of times the system is used to emergency energy storage devices in a year;
the second constraint condition is:
wherein:
、/>
、/>
respectively representing rated charge-discharge energy power, rated charge-discharge energy time and capacity of energy storage equipment which should be operated commercially; />
Representing a maximum number of operational cycles of the energy storage device for commercial operation;Nprrepresenting the maximum charge and discharge times per day calculated according to the energy price list of the place where the project is located; />
Representing energy storage devicesjAllowing a maximum number of charge and discharge cycles;Ysysrepresenting the number of years of battery energy storage operation required by the system.
9. The user-level comprehensive energy system planning device is characterized by 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 the user-level comprehensive energy system planning method according to any one of claims 1 to 7 according to the instructions in the program code.
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