CN112085263A - A method and system for optimal configuration of hybrid energy storage in a user-side distributed energy system - Google Patents

A method and system for optimal configuration of hybrid energy storage in a user-side distributed energy system Download PDF

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CN112085263A
CN112085263A CN202010884046.XA CN202010884046A CN112085263A CN 112085263 A CN112085263 A CN 112085263A CN 202010884046 A CN202010884046 A CN 202010884046A CN 112085263 A CN112085263 A CN 112085263A
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吴奎华
蒋德玉
冯亮
李沛东
卢志鹏
魏飞
张恒
王士勇
王龙凯
杨国军
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Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
Linyi Power Supply Co of State Grid Shandong Electric Power Co Ltd
State Grid Corp of China SGCC
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Linyi Power Supply Co of State Grid Shandong Electric Power Co Ltd
State Grid Corp of China SGCC
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Abstract

本公开公开的一种用户侧分布式能源系统混合储能优化配置方法和系统，包括：采集用户侧分布式能源系统的能源费用，及系统内各单元设备的物理参数和经济参数；对构建好的双层优化配置模型求解，获得系统运行方式和容量配置的最佳调度方案；双层优化配置模型，包含内层调度优化模型和外层规划模型，内层调度优化模型以系统日经济运行成本最小为目标,输出系统运行方式的优化调度和日经济运行成本，外层规划模型以系统全寿命周期内等年值成本最小为目标,输出系统容量配置方案和等年值成本。实现对系统运行方式和容量配置的同时优化调度，且该调度方案满足系统的最优经济运行要求。

Figure 202010884046

A method and system for optimizing the configuration of hybrid energy storage in a user-side distributed energy system disclosed in the present disclosure include: collecting energy costs of the user-side distributed energy system, as well as physical parameters and economic parameters of each unit equipment in the system; The optimal scheduling scheme of system operation mode and capacity configuration can be obtained by solving the two-layer optimal configuration model of the The minimum is the goal, and the optimal scheduling of the system operation mode and the daily economic operation cost are output. The outer planning model takes the minimum equivalent annual cost in the whole life cycle of the system as the goal, and outputs the system capacity configuration plan and the equivalent annual cost. It realizes the optimal scheduling of the system operation mode and capacity configuration at the same time, and the scheduling scheme meets the optimal economic operation requirements of the system.

Figure 202010884046

Description

User side distributed energy system hybrid energy storage optimal configuration method and system
Technical Field
The disclosure relates to a hybrid energy storage optimal configuration method and system for a user-side distributed energy system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The user side distributed energy system is connected to the urban power distribution network at different geographical distribution positions, can cooperatively schedule various distributed energy sources, realizes complementary utilization of the energy sources, has important significance for improving the on-site consumption capability of renewable energy sources and improving the comprehensive utilization efficiency of the energy sources, and has attracted extensive attention in related research. However, the renewable energy output has a strong random characteristic, and the mismatch with the load increases the complexity of the system operation, and poses challenges to the stability, safety and reliability of the system operation. The energy storage system can break through the time and space limitation of energy, effectively stabilize the randomness and the fluctuation of the output of renewable energy sources, and is an effective means for solving the problems.
At present, aiming at the research of the optimal configuration of a user side energy storage system, a hybrid energy storage system of a battery and a super capacitor is provided, the super capacitor assists the battery energy storage system to work, and the power fluctuation of the system is compensated in sections according to the frequency characteristic; researches are made to break the constraint of 'fixing the power with the heat' of the cogeneration unit by utilizing the heat storage device, so that the cogeneration unit can operate in a thermoelectric decoupling working state, the power regulation range of the cogeneration unit is effectively expanded, the air volume of the system is reduced, and the consumption level of the system on renewable energy is increased. However, there is no effective method for the cooperative optimal configuration of the electricity/heat storage hybrid energy storage system, so that the renewable energy utilization level of the system is limited, and the system economy is seriously affected.
Disclosure of Invention
In order to solve the problems, the disclosure provides a hybrid energy storage optimal configuration method and a hybrid energy storage optimal configuration system for a user-side distributed energy system.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
one or more embodiments provide a hybrid energy storage optimal configuration method for a user-side distributed energy system, including:
collecting energy cost of a user side distributed energy system and physical parameters and economic parameters of each unit device in the system;
solving the constructed double-layer optimization configuration model to obtain the optimal scheduling scheme of the system operation mode and the capacity configuration;
the double-layer optimization configuration model comprises an inner-layer scheduling optimization model and an outer-layer planning model, wherein the inner-layer scheduling optimization model takes the minimum daily economic operation cost of the system as a target, takes the system energy cost, the physical parameters and the economic parameters of each unit device in the system and the system capacity configuration output by the outer-layer planning model as input and outputs the optimization scheduling and the daily economic operation cost of the system operation mode, the outer-layer planning model takes the minimum equal-year-value cost in the whole life cycle of the system as a target, takes the physical parameters and the economic parameters of each unit device in the system and the output of the inner-layer scheduling optimization model as input and outputs a system capacity configuration scheme and the equal-year-value cost.
One or more embodiments provide a hybrid energy storage optimal configuration system of a user-side distributed energy system, including:
the acquisition module is used for acquiring system energy cost, equipment physical parameters, equipment economic parameters, system parameters and energy storage economic parameters;
the solving module is used for solving the established double-layer optimization configuration model to obtain the optimal scheduling scheme of the system operation mode and the capacity configuration;
the double-layer optimization configuration model comprises an inner-layer scheduling optimization model and an outer-layer planning model, wherein the inner-layer scheduling optimization model takes the minimum daily economic operation cost of the system as a target, takes the system energy cost, the physical parameters and the economic parameters of all equipment in the system and the system capacity configuration output by the outer-layer planning model as input and outputs the optimization scheduling of the system operation mode and the daily economic operation cost, the outer-layer planning model takes the minimum equal-year-value cost in the whole life cycle of the system as a target, and takes the physical parameters and the economic parameters of all equipment in the system and the output of the inner-layer scheduling optimization model as input and outputs a system capacity configuration scheme and the equal-year-value cost.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the above method.
Compared with the prior art, the beneficial effect of this disclosure is:
the method establishes an inner-layer scheduling optimization model taking the minimum daily economic operation cost of the system as a target and an outer-layer planning model taking the minimum equal-annual-value cost in the whole life cycle of the system as a target, realizes the simultaneous optimization scheduling of the operation mode and the capacity configuration of the system by solving the established double-layer planning configuration model, and has the optimal economic index under the optimized scheduling scheme.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a block diagram of a typical universal bus for a customer-side distributed energy system;
FIG. 2 is a diagram of a two-layer optimized configuration model architecture according to the present disclosure;
FIG. 3 is a specific flow chart of the genetic algorithm of the present disclosure;
fig. 4 is a user-side distributed energy system hybrid energy storage planning case system of the present disclosure;
FIG. 5 is a block diagram of a bus bar according to an embodiment of the disclosure;
FIG. 6 is a graph of various load conditions and photovoltaic output conditions in summer for the case of the present disclosure;
FIG. 7 is a graph of various load conditions and photovoltaic output conditions during winter for the case of the present disclosure; (ii) a
FIG. 8 is a graph of various load conditions and photovoltaic output conditions in spring/autumn for the case of the present disclosure; (ii) a
FIGS. 9(a) - (d) are power balance diagrams before and after energy storage for a winter working day for the case of the present disclosure;
fig. 10(a) - (d) are power balance diagrams before and after energy storage for summer working days for the disclosed case.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
Example 1
In the embodiment, a user-side distributed energy system is established, including an energy production unit model, an energy conversion unit model and a hybrid energy storage unit model, and the user-side distributed energy system is integrally described based on a universal bus model.
The established energy production unit model comprises the following steps: gas boiler model, combined cooling heating and power unit model and photovoltaic power generation device model specifically do:
1) gas Boiler (GB)
The gas boiler uses natural gas as fuel and liquid as medium to transmit heat energy, and is a common heating device in a distributed energy system, and a model of the gas boiler can be represented by formula (1):
Ht GBΔt＝Ft GBηGB (1)
wherein Ht GBIndicating the thermal power provided to the load by the gas boiler at the time t; ft GBRepresenting the fuel heat energy consumed by the gas boiler burning natural gas; etaGBIs the working efficiency of the gas boiler. Meanwhile, the gas boiler output power level limit may be expressed as formula (2), where HR GBIs the rated power of the gas boiler:
Figure BDA0002655001630000041
2) combined cooling, heating and power units (CCHP)
The CCHP unit mainly comprises a generator and a bromine cooling machine. Firstly, natural gas is combusted in a micro gas turbine to do work, the released high-grade energy is used for producing electric energy, and simultaneously, waste heat recovery and utilization are carried out on the discharged high-temperature flue gas in a bromine refrigerator to supply cold and heat load requirements of a user side. The cold/heat coupling relation of the combined cold and heat and power supply unit can be expressed as a mathematical function relation of the power supply quantity and the heat supply/cold quantity of the unit with respect to the fuel consumption quantity. To simplify the problem, the present disclosure uses a simplified linear model, assuming that the amount of cooling and heating is constant for each amount of power produced by the cogeneration unit, using αCCHP1Expressing the heating coefficient of the bromine refrigerator by using alphaCCHP2Expressing the heating coefficient of the bromine refrigerator, and using etaCCHPRepresents a gas-to-electric conversion ratio:
Figure BDA0002655001630000042
meanwhile, the output power level limit of the unit during operation can be expressed as formula (4):
Figure BDA0002655001630000043
wherein, Pmin CCHPRepresenting the lower limit of the output power of the combined supply unit; pmax CCHPRepresents the upper limit of the output power of the cogeneration unit.
3) Photovoltaic power generation (PV)
The photovoltaic output is mainly determined by the comprehensive intensity of the light irradiated on the photovoltaic surface, the system operation condition, the photovoltaic physical parameters and the like. In general, the output characteristic of a photovoltaic device can be described by a P-G curve, where P represents the output power of the photovoltaic device and G represents the illumination intensity.
Figure BDA0002655001630000044
In the formula (f)PVThe power derating factor of the photovoltaic is used for representing the reduction of the output power caused by the factors such as dust and dirt on the surface of the photovoltaic, aging and the like, and is generally 0.9; pPV,RIs the photovoltaic peak capacity (kWp), GTIs the actual illuminance (kW/m)2)，GT,STCThe illuminance under standard test conditions is generally 1kW/m2；αpAs power temperature coefficient (%/deg.C), TcellSurface temperature (. degree. C.) for the current photovoltaic system, Tcell,STCFor photovoltaic temperature under standard test conditions, 25 ℃ is typically taken.
As can be seen from the formula (5), the photovoltaic surface temperature has a certain influence on the photovoltaic output, and under the normal condition, the photovoltaic operation efficiency can be increased along with the photovoltaic surface temperatureHigh and low. Photovoltaic surface temperature TcellDepending on the ambient temperature, it can be calculated by:
Figure BDA0002655001630000051
in the formula, TaIs the ambient temperature (. degree. C.), Tcell,NOCTThe photovoltaic surface temperature under the standard operation condition is generally 45-48 ℃, wherein the standard operation condition refers to standard illuminance GT,NOCT(generally 0.8kW/m is taken2) Standard ambient temperature Tα,NOCTThe temperature is 20 ℃, and the wind speed is 1 m/s; etaMPP,STCIs the photovoltaic efficiency under standard test conditions, τ being the solar energy transmittance of the photovoltaic covering, αSIs the solar absorptance of the photovoltaic, i.e. the ratio of the photovoltaic surface capable of absorbing solar energy,. tau.alpha.SIs 0.9.
The established energy conversion unit model comprises the following steps: the heat pump model, the electric refrigerator model and the absorption refrigerator model are specifically as follows:
1) heat Pump (HP)
The ground source heat pump exchanges heat with soil, so that the exchange efficiency is higher than that of air exchange equipment such as an air conditioner. The heat pump can be driven to work by a small amount of electric energy, and the heat pump utilizes the shallow geothermal heat energy to transfer the heat energy from the low-temperature source to the high-temperature source, so that the energy utilization efficiency is high. The input-output relationship can be expressed as:
Figure BDA0002655001630000052
wherein, Pt HPThe electric power is input to the ground source heat pump at the moment t; ht HPThe heat power provided by the ground source heat pump to the load; etaHPThe heating coefficient of the ground source heat pump. Meanwhile, the limitation of the input electric power of the ground source heat pump can be expressed as formula (8):
Figure BDA0002655001630000053
2) electric refrigerator (EC)
The electric refrigerator uses electric energy to refrigerate and provides cold load for users of the system, and the energy exchange medium is air, so the efficiency of the electric refrigerator is lower than that of a ground source heat pump. The input-output relationship can be expressed as:
Figure BDA0002655001630000054
wherein, Ct ECCold energy provided to the load by the electric refrigerator; etaECIs the refrigeration coefficient; pt ECThe electric refrigerators input electric energy respectively. Meanwhile, the limit of the input electric power of the electric refrigerator can be expressed as formula (10):
Figure BDA0002655001630000061
wherein,
Figure BDA0002655001630000062
the rated input electric power of the electric refrigerator.
3) Absorption refrigerator (EC)
The absorption refrigerator provides cold energy by consuming heat energy, and the input and output energy relation of the absorption refrigerator is as follows:
Figure BDA0002655001630000063
Figure BDA0002655001630000064
wherein, Ct AC,outRepresents the cold power supplied to the load by the absorption chiller; ht AC,inIs the input thermal power; hR AC,inRepresenting the upper limit of the heat input power of the absorption refrigerator; etaACSystem for indicating absorption refrigeratorAnd (4) cooling efficiency.
The established hybrid energy storage unit model comprises the following steps: storage battery energy storage unit model and heat accumulation unit model specifically do:
1) storage battery energy storage unit model
In this embodiment, the lead-acid battery is used, and the function of the battery is similar to that of a container capable of storing energy, the charging process of the battery, namely the process of injecting electric energy into the container, and the discharging process of the battery, namely the process of outputting electric energy from the container, are reversible. The interior of the battery is not ideal, ohmic internal resistance and polarization internal resistance exist, and side reactions occur near the electrode along with the progress of the chemical reaction of the battery in the charging and discharging processes of the battery, which causes the energy loss of the battery in the charging and discharging processes, and how much energy can be output without inputting how much energy, in the embodiment, eta is adoptedech、ηedisThe process is expressed by respectively representing the charge and discharge efficiency of the battery.
The storage battery can generate energy loss when not put into use and still stand, because a small amount of electrons exist in the battery, partial short circuit is formed as a result of electrotransport operation, the phenomenon is the self-discharge phenomenon of the battery, and the self-discharge phenomenon of the battery mainly occurs in the cathode of the battery and the PbSO of the anode of the lead-acid battery because the active substance of the cathode material of the battery is active4Is a strong oxidant, so that the self-discharge phenomenon of the anode is very little. If metal impurities exist on the surface of the negative electrode of the storage battery, when the potential of the metal impurities is lower than that of hydrogen, a corrosive microbattery is formed at the negative electrode of the storage battery, the negative electrode material is dissolved for a long time, and the capacity of the lead-acid battery is attenuated for a long timeESThis process is shown.
In this embodiment, a linear model is used to process a storage battery energy storage unit model, the energy loss of the energy storage device is considered in the model, and the model expression is obtained as formula (13) when the charging and discharging energy loss is in use:
Figure BDA0002655001630000071
wherein E ist ESRepresenting the stored electrical energy at time t; pt ES,chRepresents the charging power of the power storage device at time t; pt ES,disRepresents the discharge power of the power storage device at time t; etaechIndicating the charging efficiency of the power storage device; etaedisIndicating a discharge efficiency of the electrical storage device; mu.sESRepresents the electric energy dissipation rate of the electric storage device, i.e., the self-discharge rate of the battery; Δ t represents the time period of charge and discharge. The upper and lower limits of the charge and discharge power of the battery can be expressed by equation (14):
Figure BDA0002655001630000072
wherein, Pmax ES,chRepresents a maximum value of the energy storage device charging power; pmax ES,disThe maximum value of the discharge power of the energy storage device is shown, except that the energy storage device is constrained in output, and the energy storage state, namely the charge state, is correspondingly constrained. State of charge σt ESIs defined as follows, QESThe rated capacity, i.e., the mounting capacity, of the battery is expressed:
Figure BDA0002655001630000073
there is a state of charge constraint:
Figure BDA0002655001630000074
wherein σES min、σES maxThe minimum and maximum state of charge of the storage battery. For convenience of description, the ratio of the maximum charge/discharge power to the rated energy storage capacity is defined as the charge/discharge rate (h-1)。ξES,ch maxIndicating storage batteryMaximum charge and discharge rate (h-1)，ξES,dis maxRepresents the minimum charge-discharge rate (h-1) Namely:
Figure BDA0002655001630000075
the capacity fading phenomenon can occur in the process of recycling the battery, and the capacity fading caused by frequent charging and discharging is particularly obvious. In order to avoid this, the number of times the battery is charged and discharged must be limited, and the battery is not allowed to be charged and discharged frequently during the use period:
Figure BDA0002655001630000081
in summary, the constraints of the battery model are as follows:
Figure BDA0002655001630000082
because the charging and discharging states of the energy storage equipment can not occur simultaneously, the parameter chi is usedES,disHexix-ES,chIndicating the operating state of the battery. Chi shape ES,dis1 represents the discharge state of the battery, χES,chA 1 indicates that the battery is in a charged state, and both cannot be 1 at the same time. Parameter YES,disAnd YES,chRepresenting the battery discharge transition state variable and the charge transition state variable. A value of 1 indicates that the battery state is switched, and both cannot be 1 at the same time. N is the total number of charging interconversion in the planning period.
2) Heat storage system (HS)
Sensible heat energy storage is typical cold/heat energy storage mode, and sensible heat energy storage low cost, the operation is maintained simply, and the vertical cylindrical storage tank of a tape button head that the embodiment chose the heat storage tank to be sold as hot energy storage equipment on the market, and the water tank adopts 20 centimetres flexible polyurethane foam insulation, and coefficient of heat conductivity is 0.04W/m K. The external working characteristics of the heat storage tank are similar to those of the storage battery, and the energy dissipation of the energy storage device and the charging and discharging processes in the using process also need to be considered. The model can be represented as:
Figure BDA0002655001630000083
the heat storage tank model is constrained as follows:
Figure BDA0002655001630000084
Figure BDA0002655001630000085
Figure BDA0002655001630000086
wherein S ist HSRepresenting the thermal energy stored at time t; ht HS,chRepresenting the charging power of the heat storage equipment at the moment t;
Figure BDA0002655001630000091
represents the heat release power of the heat storage device at time t; etahchRepresents the charging efficiency of the thermal storage device; etahdisIndicating the heat release efficiency of the thermal storage device; mu.sHSRepresents the dissipation rate of the thermal energy, i.e. the self-heat release rate; Δ t represents the length of time of heat charge and discharge; sigmaHS min、σHS maxRepresenting the minimum and maximum energy storage states of the heat storage tank; xiHS,ch maxIndicates the maximum heat charge and discharge rate (h) of the heat storage tank-1)；ξHS,dis maxIndicates the minimum heat charging and discharging rate (h) of the heat storage tank-1)。
The established universal bus model comprises the following steps: the electric bus power balance model, the hot bus power balance model, the cold bus power balance model and the flue gas bus power balance model specifically are as follows:
the distributed energy system is described by using a general bus type structure, and buses can be divided into the following parts according to energy transfer media: types of electricity, flue gas, steam, hot water, air, etc.; the devices can be divided into 4 types according to the functions of the devices in the energy conversion process of the system: source, conversion device, energy storage and load. Fig. 1 is a typical bus-type structure diagram of a user-side distributed energy system, and it can be seen from the diagram that the application of the universal bus-type structure clearly shows the connection mode of each device, and visually reflects the energy flow direction in the whole system and the coupling relationship of various types of energy, thereby effectively ensuring the universality and flexibility of system modeling and being helpful for analyzing the energy balance constraint of the system. The intra-system energy flow balance relationship is expressed as follows:
1) electric bus power balance equation:
Pt grid+Pt CHP+Pt PV+Pt ES,dis＝Pt EL+Pt ES,ch+Pt EC (24)
wherein, each item in the equation is sequentially power grid power, output of the combined supply unit, photovoltaic power, battery discharge power, electric load, battery charging power and electric refrigerator power consumption power from left to right.
2) Thermal bus power balance equation:
Figure BDA0002655001630000094
wherein, each item in the equation is boiler heat production power, heat storage tank heat release power, exhaust-heat boiler heat release power, heat load power, heat storage tank heat absorption power from left to right in proper order.
3) Cold bus power balance equation:
Figure BDA0002655001630000092
wherein, each item in the equation is the refrigeration power of the electric refrigerator, the cold discharge power of the ice storage device and the cold load power from left to right in sequence.
4) Flue gas bus power balance equation:
Figure BDA0002655001630000093
wherein, each item in the equation is the flue gas power of the CHP unit, the flue gas power of the absorption refrigerator and the flue gas power of the waste heat recoverer from left to right in sequence.
In order to realize simultaneous optimal scheduling of the operation mode and the capacity configuration of a user-side distributed energy system, a hybrid energy storage optimal configuration method of the user-side distributed energy system is provided, which specifically comprises the following steps:
collecting energy cost of a user side distributed energy system and physical parameters and economic parameters of each unit device in the system;
solving the constructed double-layer optimization configuration model to obtain the optimal scheduling scheme of the system operation mode and the capacity configuration;
the double-layer optimization configuration model comprises an inner-layer scheduling optimization model and an outer-layer planning model, wherein the inner-layer scheduling optimization model takes the minimum daily economic operation cost of the system as a target, takes the system energy cost, the physical parameters and the economic parameters of each unit device in the system and the system capacity configuration output by the outer-layer planning model as input and outputs the optimization scheduling and the daily economic operation cost of the system operation mode, the outer-layer planning model takes the minimum equal-year-value cost in the whole life cycle of the system as a target, takes the physical parameters and the economic parameters of each unit device in the system and the output of the inner-layer scheduling optimization model as input and outputs a system capacity configuration scheme and the equal-year-value cost.
The double-layer optimization configuration model comprises an inner-layer scheduling optimization model and an outer-layer planning model, and specifically comprises the following steps:
and aiming at the economic index of the system, and determining the optimal hybrid energy storage configuration of the system under the condition of comprehensively considering various constraints of the system. In the process of optimization, since the scheduling strategy will have an important influence on the optimization result, the economy of the full life cycle of the device needs to be considered, and the mathematical expression is obtained as follows:
Figure BDA0002655001630000101
wherein, F is an objective function of the double-layer model, X is a scheduling optimization vector, Y is a planning optimization vector, A is an equality constraint set, B is an inequality constraint set, and the main constraints considered by the distributed energy system are as follows:
1) and (3) bus power balance constraint: formulas (24) - (27);
2) and (3) constraint of the equipment model: formulas (1) - (23);
decomposing the planning operation double-layer optimization problem into a combination of two optimization problems, and regarding the operation optimization problem as a sub-problem of the planning optimization problem to obtain a double-layer optimization configuration model comprising an inner-layer scheduling optimization model and an outer-layer planning model, wherein the mathematical expression of the model is as follows:
Figure BDA0002655001630000111
the objective function of the inner-layer scheduling optimization model is that the daily economic operation cost of the system is minimum, and the mathematical form of the objective function is expressed as follows:
Figure BDA0002655001630000112
wherein, X represents a scheduling decision vector, F represents an objective function, omega represents a value range of the scheduling decision vector, A is an equality constraint set, and B is an inequality constraint set, and the inequality constraint set comprises various constraints of system equipment and a system. Te e {1,2,3, …,24} in the following equation.
1) Objective function
The scheduling optimization model takes the lowest daily economic operation cost as an objective function of the model:
min F(x)＝cfuel+com+cgrid (31)
wherein, cfuelRepresents the daily fuel cost; c. ComRepresents daily operating maintenance costs; c. CgridShowing dayAnd (5) the electricity purchasing cost. The units are all yuan.
The daily fuel cost refers to the daily consumption natural gas cost of the CHP combined supply unit and the gas boiler, wherein CfFor the price of natural gas:
Figure BDA0002655001630000113
the daily operation and maintenance cost relates to all equipment in the system, including not only the newly installed energy storage equipment, but also the existing equipment operation and maintenance cost in the system, wherein omegai,t omThe output at the moment t of the ith device; omegai omThe unit output operation and maintenance cost of the ith device is as follows:
Figure BDA0002655001630000121
when the local energy of the system is not enough to meet the energy demand of the system load, the commercial power needs to be bought to the power distribution network, and the electricity purchasing cost is positive at the moment; when the distributed energy of the system is sufficient and has surplus, the electric energy can be sold through the connecting line of the commercial power grid to obtain income, the electricity purchasing cost at the moment is negative, and the cost is expressed as:
Figure BDA0002655001630000122
wherein
Figure BDA0002655001630000123
Respectively the electricity purchasing quantity and the electricity price to the power grid at the moment t.
2) Equality constraint
The equality constraint conditions of the distributed energy system inner-layer scheduling optimization model based on the general bus model comprise power balance constraints of buses of the formulas (24) to (27) and equality constraints in the equipment model. In addition, the periodicity and the time interval coupling characteristic of the energy storage of the hybrid energy storage device are ensured, and the addition of an equation constraint (35) indicates that the energy storage state of the energy storage device is unchanged at the beginning and the end of the scheduling cycle:
Figure BDA0002655001630000124
wherein, E, S, t0、tNRespectively representing the electricity storage energy, the heat storage energy, the initial time of the scheduling period and the end time of the scheduling period.
3) Inequality constraint condition
The inequality constraint conditions of the distributed energy system inner-layer scheduling optimization model based on the universal bus model comprise inequality constraints of equipment models in formulas (1) to (23), and specifically comprise the following steps: the output of the energy production unit is within the upper and lower limit ranges of the output; the input power of the energy conversion unit does not exceed the upper limit of the allowable input power; the stored energy of the hybrid energy storage unit is between the maximum and minimum allowed stored energy, and the charge and discharge power or the heat and discharge power does not exceed the maximum allowed charge and discharge power or the heat and discharge power. In addition, power constraints of the system and the tie lines of the distribution network need to be considered, and the constraints are caused by line construction and other reasons:
Figure BDA0002655001630000125
wherein,
Figure BDA0002655001630000126
represents the maximum value of tie-line let-through power;
Figure BDA0002655001630000127
and the minimum value of the tie line power is represented, wherein the minimum value can be negative, and the positive and negative of the tie line power respectively represent the purchase of electricity and the sale of electricity.
4) Optimizing variables
Optimization variable X of scheduling period tnThe method comprises the following steps of purchasing and selling electric quantity for the output of various devices in the system and the power grid:
Figure BDA0002655001630000131
and carrying out dimension reduction simplification on the variables by using independent equality constraints in the equality constraint set. Considering the device model, the input and output variables of the energy conversion device may be eliminated, and then the optimal variable at the final scheduling time t is:
Figure BDA0002655001630000132
in conclusion, an inner-layer scheduling optimization model can be obtained, and the specific expression of the scheduling optimization of the single-target distributed energy system is as follows:
Figure BDA0002655001630000133
the above problem is expressed as a general form of a 0-1 mixed integer linear programming model:
Figure BDA0002655001630000141
in optimizing vector XnIn (1), the energy storage amount [ E ] in the t period is increasedt ES，St HS]With the convenient model programming, the final optimization variables can be obtained as:
Figure BDA0002655001630000142
the objective function of the outer layer planning model is a single-objective optimization model constructed with minimum equal-annual-value cost in the whole life cycle of the system.
The objective function with the minimum equal-year-number cost in the whole life cycle of the system is as follows:
min F(z)＝Ccap+Cfuel+Com+Cgrid (42)
wherein, CcapRepresenting the equal annual value investment cost of the energy storage equipment; cfuelIndicating annual fuel compositionThen, the process is carried out; comRepresents the annual operating maintenance cost; cgridThe unit represents the annual electricity purchasing cost.
The equal-year-number investment cost is the cost for purchasing and installing the storage battery and the heat storage tank in the system, and the investment cost can be changed into the equal-year-number according to the service life of the system, wherein the storage battery investment cost comprises the cost of the converter. The investment cost is a function of the installation capacity and the cost per unit capacity, which can be expressed as:
Figure BDA0002655001630000143
wherein psiiA capital recovery rate for the energy storage device; gamma is the discount rate; liExpected value of the operating life of the energy storage device is year; qi is the installation capacity of the energy storage device; omegai capIs the cost per unit capacity of the energy storage device.
The annual fuel costs are expressed as follows:
Figure BDA0002655001630000144
the annual operating maintenance costs are as follows:
Figure BDA0002655001630000145
the annual electricity purchase cost is expressed as:
Figure BDA0002655001630000146
the constraints of the outer planning model include:
(1) the daily economic operation cost of the system obtained by the inner-layer scheduling optimization model is the minimum.
(2) There is a constraint on energy storage investment capacity, limited by the system site, where Q represents the installed capacity of the energy storage:
Figure BDA0002655001630000151
the inner-layer scheduling optimization model and the outer-layer planning model are combined to form a double-layer optimization configuration model, the relation between the inner-layer scheduling optimization model and the outer-layer planning model is shown in fig. 2, the optimized scheduling and daily economic operation cost of the system operation mode output by the inner-layer scheduling optimization model serves as the input of the outer-layer planning model, and the system capacity configuration output by the outer-layer planning model serves as the input of the inner-layer scheduling optimization model.
Solving the outer layer planning model by adopting a genetic algorithm, wherein the genetic algorithm is an intelligent algorithm, and the specific flow is shown in figure 3:
the method comprises the following steps: data initialization: the method comprises the input of system composition and structure parameters, equipment model parameters and genetic algorithm parameters.
Step two: population initialization: an initial population is randomly generated, wherein each individual corresponds to a dispatch plan within a dispatch period.
Step three: and calculating the fitness of the population individuals according to various groups of individuals, wherein the fitness of each individual corresponds to the economic cost of one scheduling period.
Step four: and transferring the individual fitness of the population to an optimization module, and obtaining the offspring population through championship selection, single-point crossing and uniform variation operation.
Step five: and returning to the step three until the requirement of the maximum genetic algebra is met. And outputting a final scheduling optimization scheme and the optimal economic cost.
In the embodiment, a user-side distributed energy system model is established, and comprises an energy production unit, a conversion unit and a hybrid energy storage unit model; the hybrid energy storage optimization configuration method for the user-side distributed energy system for the renewable energy consumption is provided, the electricity/heat hybrid energy storage system is optimally configured in the whole life cycle, under the condition of high-proportion renewable energy access, the electricity/heat hybrid energy storage of the user-side distributed energy system is optimally configured, the hybrid energy storage capacity and the system operation mode are simultaneously optimized by using a double-layer optimization configuration method, the electricity/heat hybrid energy storage cooperative optimization configuration is realized, and the important application value is realized for improving the system economy and the renewable energy consumption level.
The optimal configuration method for the hybrid energy storage of the user-side distributed energy system is applied to the planning of the hybrid energy storage of the user-side distributed energy system shown in fig. 4, load data is obtained from a medium-scale user distributed energy system, the system adopts an operation mode of cooling in summer and heating in winter, and the load types comprise three loads of cold, heat and pure electricity. Wherein, the pure electric load comprises the loads of laboratory electricity, fire-fighting electricity, office building illumination and the like; the heat load is a heating load in winter; the cooling load mainly includes the summer system building refrigeration. The bus-type structure of the system is shown in FIG. 5. The CCHP combined supply unit supplies cold and power in summer; heat supply and power supply in winter. The photovoltaic system, the CCHP system, the heat pump system and the electric refrigerator system are existing devices of the system, the capacity of the existing devices is known, the heat storage tank and the storage battery are devices which need to be newly installed and configured, and the capacity of the existing devices is unknown. The heat storage tank can store heat in winter and cool in summer.
For the convenience of system analysis, the project year is selected to be 10 years, and the annual interest rate is selected to be 0.06. The maximum photovoltaic output can reach 500kW, and the system has high renewable energy permeability. The population size of the genetic algorithm is set to 40 individuals, the maximum iteration number is set to 20 generations, and the crossover and mutation probabilities of genetic operators are set to 0.9 and 0.4 respectively. The example divides a year into three quarters: summer, winter, spring/autumn. Each quarter is divided into a working day and a rest day, each typical day takes one hour as the data sampling length, 24-6 groups of data represent the data conditions of the whole year, and fig. 6 to 8 show the load conditions and photovoltaic output conditions of three quarters. The economic and physical parameters of the equipment in the system are shown in tables 1 and 2, respectively.
TABLE 1 economic parameters of the plant
Figure BDA0002655001630000161
TABLE 2 physical parameters of the devices
Figure BDA0002655001630000162
Figure BDA0002655001630000171
TABLE 3 time of use price
Figure BDA0002655001630000172
In this case, the connection power of the distributed energy system and the power grid is set to be incapable of being delivered reversely, namely, the system is not allowed to sell electricity to the power grid, and the price of natural gas is 3.45 yuan/m3The price per heating value is 0.349 (yuan/kWh), and the electricity rate scheme adopts time-of-use electricity rates, as shown in table 3.
The outer layer of the constructed model is used for planning the capacity of the system for installing the electricity/heat mixed energy storage, the optimization variable is the capacity of the electricity storage and the heat storage, the time scale is the full life cycle, and the objective function is the minimum equal-year-value total cost of the distributed energy system after the energy storage equipment is installed. The inner-layer model is combined scheduling and is used for optimizing scheduling values of the energy storage equipment and other equipment in a typical scene of the system, the optimization variables are output of the stored energy of the electric power storage and the scheduling values of the other equipment, the time scale is a typical day, and the objective function is to minimize daily scheduling cost of the system after the energy storage equipment is installed. i ∈ {1,2,3,4,5,6} respectively 6 devices in the system, the device type is shown in fig. 4; d e {1,2,3} represents three quarters, respectively, m e {1,2} represents weekday, respectively, Nd,m,Days representing a typical season:
Figure BDA0002655001630000181
in order to research the internal relation and influence factors of the electricity/heat energy storage configuration, various scene analyses are carried out, and different scenes represent different original configuration conditions of the system. The CCHP unit selects an internal combustion engine with the rated capacity of 250 kW. The capacities of the CCHP, the heat pump and the electric refrigerator in the data table are the existing data of the system, and the capacities of the storage battery and the heat storage tank are the energy storage capacity optimization configuration results. The economics of the configuration scheme were evaluated using "cost savings" as an indicator. It is defined as: the cost saving is initial energy storage investment + total operation cost of the system after energy storage investment for 10 years-total operation cost of the system before energy storage investment for 10 years. As can be seen from table 4, light abandoning phenomena occur in summer, spring and autumn before the system is configured to store energy, and the renewable energy consumption capability of the system under the configuration scheme is evaluated by using the light abandoning rate as an index, which is defined as: the light rejection rate is (photovoltaic power generation amount-load actually consumed photovoltaic power amount)/photovoltaic power generation amount. The energy storage configuration results under different scenarios are shown in table 4.
Table 4 hybrid energy storage configuration results
Figure BDA0002655001630000191
As can be seen from fig. 9 and 10:
1. the whole hybrid energy storage works in a mode guided by electricity price, namely, the charging and discharging states of electricity price valley period, peak period and flat period are determined by the electricity price and the load condition at the front moment and the back moment, so that the effect of reducing the difference between the peak period and the valley period of the electric heating load is achieved, and the hybrid energy storage participates in the joint cooperative scheduling of the system. The system adopts an electric power storage and heat storage coordination energy storage strategy, so that the system can work in a thermoelectric decoupling state, and the flexibility of system scheduling is improved. The coordinated electrical/thermal configuration also facilitates the complementary energy flow of the system. Thus, the configuration of hybrid energy storage can effectively improve the ability of the system to consume renewable energy.
2. The surplus photovoltaic electric quantity in summer is preferentially utilized by the electric refrigerator for cold accumulation, and the surplus photovoltaic electric quantity is stored by the storage battery according to the load and the electricity price. The energy conversion device in the distributed energy system facilitates the cooperative configuration of electricity storage and heat storage/cold storage. The heat pump/refrigerator electric heating/cooling realizes energy conversion, and the heat storage tank can store heat energy. Since the heat/cold storage cost is lower than the electricity storage cost, the heat/cold storage uses renewable energy and low-priced electricity in preference to electricity storage. However, the process is a process of converting high-quality energy into low-quality energy, and cannot be performed without limitation, the heat/cold self-loss is larger than the power storage self-loss, and the system can avoid the situation that the stored energy is kept still after the energy storage equipment is charged as far as possible, and selects a mode of charging and discharging, so that the stored energy needs to be comprehensively determined according to the conditions such as the electricity price and the load condition at the previous moment and the next moment. The storage battery can directly store redundant photovoltaic power generation, but the storage capacity is mainly limited by economy due to high cost of electric energy storage.
3. The energy stored in the heat storage tank comes from low price purchase and electric heating/cold storage by utilizing photovoltaic power generation and CCHP heat/cold direct storage. In summer, when the photovoltaic output is large and the photovoltaic electric quantity is residual, the electric refrigerator works to preferentially utilize the photoelectricity to perform electric refrigeration, and the renewable energy is consumed to store energy.
4. The storage battery energy storage comes from direct storage of low-price electricity and photovoltaic output elimination, and the storage battery is charged in a time period of 5:00-7:00 and 16:00-18:00 with lower electricity price and then discharged in a time period of 7:00-9:00 and 18:00-20:00 with higher electricity price and higher load. In summer and spring/autumn, the storage battery can absorb surplus photovoltaic electric quantity for storage in the middle and middle time period of the rest photovoltaic electric quantity. The output of the storage battery in summer is higher than that of the storage battery in other seasons.
Example 2
In this embodiment, a hybrid energy storage optimal configuration system of a user-side distributed energy system is disclosed, which includes:
the acquisition module is used for acquiring system energy cost, equipment physical parameters, equipment economic parameters, system parameters and energy storage economic parameters;
the model establishing module is used for establishing a double-layer optimization configuration model comprising an inner-layer scheduling optimization model and an outer-layer planning model, the inner-layer scheduling optimization model takes the minimum daily economic operation cost of the system as a target and outputs the optimized scheduling and daily economic operation cost of the system operation mode, and the outer-layer planning model takes the minimum equal-year-value cost in the whole life cycle of the system as a target and outputs a system capacity configuration scheme and the equal-year-value cost;
and the solving module is used for solving the established double-layer optimization configuration model to obtain the optimal scheduling scheme of the system operation mode and the capacity configuration and the optimal economic operation cost of the system.
Example 3
In this embodiment, a computer readable storage medium is disclosed for storing computer instructions which, when executed by a processor, perform the steps of the configuration method disclosed in embodiment 1.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.

Claims (10)

1.一种用户侧分布式能源系统混合储能优化配置方法，其特征在于，包括：1. A user-side distributed energy system hybrid energy storage optimization configuration method, characterized in that, comprising: 采集用户侧分布式能源系统的能源费用，及系统内各单元设备的物理参数和经济参数；Collect the energy cost of the distributed energy system on the user side, as well as the physical parameters and economic parameters of each unit equipment in the system; 对构建好的双层优化配置模型求解，获得系统运行方式和容量配置的最佳调度方案；Solve the constructed two-layer optimal configuration model to obtain the optimal scheduling scheme for system operation mode and capacity configuration; 双层优化配置模型，包含内层调度优化模型和外层规划模型，内层调度优化模型以系统日经济运行成本最小为目标,以系统能源费用、系统内各单元设备的物理参数和经济参数，及外层规划模型输出的系统容量配置为输入，输出系统运行方式的优化调度和日经济运行成本，外层规划模型以系统全寿命周期内等年值成本最小为目标,以系统内各单元设备的物理参数和经济参数，及内层调度优化模型的输出为输入，输出系统容量配置方案和等年值成本。The two-layer optimal configuration model includes an inner-layer scheduling optimization model and an outer-layer planning model. The inner-layer scheduling optimization model aims to minimize the daily economic operation cost of the system, and takes the system energy cost, the physical parameters and economic parameters of each unit in the system, And the system capacity configuration output by the outer planning model is the input, and the optimal scheduling of the output system operation mode and the daily economic operation cost. The outer planning model aims to minimize the equivalent annual cost in the whole life cycle of the system. The physical parameters and economic parameters, and the output of the internal scheduling optimization model are input, and the output system capacity configuration plan and the equivalent annual cost. 2.如权利要求1所述的一种用户侧分布式能源系统混合储能优化配置方法，其特征在于，用户侧分布式能源系统，包括能源生产单元、能源转换单元及混合储能单元，并基于通用母线模型对用户侧分布式能源系统进行整体描述。2. The method for optimizing the configuration of hybrid energy storage in a user-side distributed energy system according to claim 1, wherein the user-side distributed energy system comprises an energy production unit, an energy conversion unit and a hybrid energy storage unit, and Based on the general busbar model, the user-side distributed energy system is described as a whole. 3.如权利要求1所述的一种用户侧分布式能源系统混合储能优化配置方法，其特征在于，日经济运行成本，包括，日燃料成本、日运行维护成本和日购电成本。3 . The method for optimizing the configuration of hybrid energy storage in a user-side distributed energy system according to claim 1 , wherein the daily economic operation cost includes the daily fuel cost, the daily operation and maintenance cost and the daily electricity purchase cost. 4 . 4.如权利要求2所述的一种用户侧分布式能源系统混合储能优化配置方法，其特征在于，内层调度优化模型的约束条件，包括等式约束条件和不等式约束条件。4 . The method for optimizing the configuration of hybrid energy storage in a user-side distributed energy system according to claim 2 , wherein the constraints of the inner-layer scheduling optimization model include equality constraints and inequality constraints. 5 . 5.如权利要求4所述的一种用户侧分布式能源系统混合储能优化配置方法，其特征在于，等式约束条件，包括，母线功率平衡和混合储能单元的储能状态在调度周期始末不变。5 . The method for optimizing the configuration of hybrid energy storage in a user-side distributed energy system according to claim 4 , wherein the equation constraints include, the bus power balance and the energy storage state of the hybrid energy storage unit in the dispatch period. 6 . The same from beginning to end. 6.如权利要求4所述的一种用户侧分布式能源系统混合储能优化配置方法，其特征在于，不等式约束条件，包括，6. The method for optimizing the configuration of hybrid energy storage in a user-side distributed energy system according to claim 4, wherein the inequality constraints include: 能源生产单元出力在出力的上下限范围内；The output of the energy production unit is within the upper and lower limits of the output; 能源转换单元的输入功率不超过允许输入功率上限；The input power of the energy conversion unit does not exceed the upper limit of the allowable input power; 混合储能单元的存储能量处于允许的最大最小存储能量之间，充放电功率或充放热功率不超过允许的充放电功率或充放热功率最大值；The stored energy of the hybrid energy storage unit is between the allowable maximum and minimum stored energy, and the charge-discharge power or charge-discharge heat power does not exceed the allowable maximum charge-discharge power or charge-discharge heat power; 联络线功率处于联络线允许通过的最大最小值之间。The tie line power is between the maximum and minimum values that the tie line is allowed to pass through. 7.如权利要求2所述的一种用户侧分布式能源系统混合储能优化配置方法，其特征在于，外层规划模型的约束条件，包括：7. The method for optimizing the configuration of hybrid energy storage in a user-side distributed energy system according to claim 2, wherein the constraints of the outer planning model include: 混合储能单元的安装容量不超过允许的最大安装容量，The installed capacity of the hybrid energy storage unit does not exceed the maximum allowable installed capacity, 系统的日经济运行成本最小。The daily economic operating cost of the system is minimal. 8.如权利要求1所述的一种用户侧分布式能源系统混合储能优化配置方法，其特征在于，采用遗传算法求解外层规划模型，具体过程为：8. The method for optimizing the configuration of hybrid energy storage in a user-side distributed energy system according to claim 1, wherein a genetic algorithm is used to solve the outer planning model, and the specific process is: 步骤一：数据初始化：包括系统组成与结构参数、设备模型参数、遗传算法参数的输入；Step 1: Data initialization: including the input of system composition and structural parameters, equipment model parameters, and genetic algorithm parameters; 步骤二：种群初始化：随机地产生一个初始种群，其中每个个体对应调度周期内的一个调度计划；Step 2: Population initialization: randomly generate an initial population, in which each individual corresponds to a scheduling plan in the scheduling period; 步骤三：根据各种群个体计算种群个体的适应度，其中每个个体的适应度对应一个调度周期的经济成本；Step 3: Calculate the fitness of individual populations according to various groups of individuals, wherein the fitness of each individual corresponds to the economic cost of a scheduling cycle; 步骤四：将种群个体适应度传递给优化模块，通过锦标赛选择、单点交叉、均匀变异操作，得到子代种群；Step 4: Pass the individual fitness of the population to the optimization module, and obtain the offspring population through tournament selection, single-point crossover, and uniform mutation operations; 步骤五：返回步骤三，直至满足最大遗传代数的要求，输出最终的调度优化方案和最优经济成本。Step 5: Return to Step 3 until the requirement of the maximum genetic algebra is met, and output the final scheduling optimization scheme and the optimal economic cost. 9.一种用户侧分布式能源系统混合储能优化配置系统，其特征在于，包括：9. A user-side distributed energy system hybrid energy storage optimal configuration system, characterized in that, comprising: 采集模块，用于采集系统能源费用、设备物理参数、设备经济参数、系统参数和储能经济参数；The acquisition module is used to collect system energy costs, equipment physical parameters, equipment economic parameters, system parameters and energy storage economic parameters; 模型建立模块，用于建立包含内层调度优化模型和外层规划模型的双层优化配置模型,内层调度优化模型以系统日经济运行成本最小为目标,输出系统运行方式的优化调度和日经济运行成本，外层规划模型以系统全寿命周期内等年值成本最小为目标,输出系统容量配置方案和等年值成本；The model building module is used to establish a two-layer optimal configuration model including an inner-layer scheduling optimization model and an outer-layer planning model. The inner-layer scheduling optimization model aims to minimize the daily economic operation cost of the system, and outputs the optimal scheduling and daily economic operation of the system. Operating cost, the outer planning model aims to minimize the equivalent annual cost in the whole life cycle of the system, and outputs the system capacity configuration plan and the equivalent annual cost; 求解模块，对建立的双层优化配置模型求解，获得系统运行方式和容量配置的最佳调度方案，及系统的最优经济运行成本。The solution module solves the established two-layer optimal configuration model, and obtains the optimal scheduling scheme of the system operation mode and capacity configuration, as well as the optimal economic operation cost of the system. 10.一种计算机可读存储介质，其特征在于，用于存储计算机指令，所述计算机指令被处理器执行时，完成权利要求1-8任一项所述方法的步骤。10. A computer-readable storage medium, characterized in that it is used for storing computer instructions, and when the computer instructions are executed by a processor, the steps of the method according to any one of claims 1-8 are completed.
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