CN112103940A - Optimal economic operation method of multi-energy micro-grid with temperature control equipment - Google Patents

Abstract

The invention relates to an optimal economical operation method of a multi-energy microgrid including temperature control equipment, comprising the following steps: S1, establishing a temperature control equipment model; S2, establishing an optimal economic operation model of a multienergy microgrid including temperature control equipment; S3. Set the component parameters in the microgrid, predict the power generation of new energy, the power load curve and the external ambient temperature, and set the temperature control range of the heat load; S4. Substitute the above parameters into the multi-energy microgrid with temperature control equipment. In the optimal economic operation model, the particle swarm algorithm is used to solve the optimal economic operation cost and the output of each micro-source, temperature control equipment and energy storage in the model during the dispatch period. The present invention adds temperature control equipment and temperature control heat load to the microgrid composed of wind power, photovoltaic, micro-gas turbine and electric energy storage (battery), and considers the thermal inertia effect of the temperature control heat load, and establishes a temperature control system with temperature control. Optimal economic operation model of multi-energy microgrid for equipment.

Description

An optimal economical operation method of a multi-energy microgrid with temperature control equipment

technical field

The invention relates to an optimal operation method of a power system, in particular to an optimal economic operation method of a multi-energy microgrid including temperature control equipment.

Background technique

The multi-energy microgrid includes wind power and photovoltaics and other renewable energy power generation forms, and the fluctuation and uncontrollability of its output will have a certain impact on the operation of the microgrid. At the same time, the load forms in the multi-energy microgrid are also diverse. How to improve the consumption rate of new energy in the microgrid while meeting various load demands and realize the economic operation of the multi-energy microgrid is a major issue in the current power system. Hot Issues.

The multi-energy microgrid can realize the production, dispatch, transmission, distribution and consumption of various energy forms. Taking the combined heat and power microgrid as an example, its load forms include electrical load and thermal load. In the multi-energy microgrid, the load demand for a certain type of energy can be met by various forms of energy. This cross and complementary utilization of energy makes the optimal operation mode of the multi-energy microgrid become complication.

At present, in the research on the optimal operation of multi-energy microgrid, the optimal economic operation model of multi-energy microgrid is constructed mainly according to the energy composition of the system and the actual optimization objectives. Most of the models take the optimal total system operating cost as the objective function, and can also use the optimal system total energy consumption, the lowest carbon emission or the optimal operation reliability as the objective function, and an appropriate objective function can be selected according to actual needs. Due to the existence of multiple energy forms, different energy forms have differences in time and space scales, the constraints of the model need to consider more factors, and the difficulty of solving the model is increased.

At present, the optimal operation models of multi-energy microgrids are mostly constructed by constructing different forms of objective functions to study the optimal economic operation of different types of multi-energy systems. In the multi-energy microgrid, various load types have different characteristics, such as the temperature control characteristics of thermal loads have interval and certain inertial response. At present, few literatures comprehensively consider the real physical structure model inside the multi-energy system to analyze the influence of the thermal load temperature control characteristics on the optimal operation of the multi-energy system. Further research work is needed to address this issue.

SUMMARY OF THE INVENTION

In order to solve the above technical problems, the present invention provides an optimal economical operation method of a multi-energy microgrid with temperature control equipment.

When analyzing the optimal operation of a multi-energy system, it is necessary to comprehensively consider the utilization efficiency of energy itself, the conversion efficiency between energy sources, and the differences between different energy forms on the space and time scales. The isolated microgrid needs to achieve self-sufficiency of electrical and thermal energy from production to consumption, in which the thermal load is usually supplied through electric-thermal conversion or direct supply of thermal energy. In the multi-energy microgrid, the user's demand for heat load is usually reflected in the temperature index. Unlike the supply and demand of electric load, which can be matched in real time, such heat load needs to be adjusted by temperature control equipment, so that the temperature of the heat load reaches a satisfactory level for the user. The temperature interval range can be used to improve the operation state of the micro-sources in the micro-grid by using the thermal inertia of the heat load and the properties of the temperature interval. Therefore, considering the thermal load temperature control characteristics in the microgrid, rationally arrange the economic dispatch of various microsources, this issue is worthy of in-depth study.

An optimal economical operation method for a multi-energy microgrid including temperature control equipment, comprising the following steps:

S1. Establish a temperature control equipment model;

S2. Establish an optimal economic operation model of a multi-energy microgrid with temperature control equipment;

S3. Set the component parameters in the microgrid, predict the power generation of new energy, the power curve of the electric load and the external ambient temperature, and set the temperature control range of the heat load;

S4. Substitute the above parameters into the optimal economic operation model of the multi-energy microgrid with temperature control equipment, and use the particle swarm algorithm to solve the optimal economic operation cost in the model during the dispatch period and the various micro sources, temperature control equipment and storage. able output.

Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention adds temperature control equipment and temperature control heat load to the microgrid composed of wind power, photovoltaic, micro-gas turbine and electric energy storage (battery), etc. Based on the thermal inertia effect of thermal load control, an optimal economic operation model of a multi-energy microgrid with temperature control equipment is established. The model takes into account equipment operation cost, start-stop cost, energy storage operation loss cost, loss of load penalty cost and penalty cost of wind and light abandonment, etc. The objective function is to minimize the total operating cost in the scheduling period, and the particle swarm algorithm is used to solve the problem. The optimal operation mode of energy microgrid.

Description of drawings

FIG. 1 is a schematic diagram of the energy transfer mode of the multi-energy microgrid according to the present invention.

FIG. 2 is a schematic diagram of the thermal load temperature control behavior process of the present invention.

FIG. 3 is an equivalent circuit diagram of the residential heating and ventilation system of the present invention.

FIG. 4 is a diagram showing the steps for solving the particle swarm algorithm of the present invention.

FIG. 5 is a diagram of the total predicted power of the new energy power generation and the predicted power of the electric load according to the present invention.

FIG. 6 is a data diagram of typical daily ambient temperature in winter according to the present invention.

FIG. 7 is a schematic diagram of the output level of components in Case 1 of the present invention.

FIG. 8 is a schematic diagram of the output level of components in Case 2 of the present invention.

FIG. 9 is a schematic diagram of the output level of the microgrid element without considering the temperature control characteristics of the present invention.

Figure 10 shows the battery SOC curves in different scenarios.

Detailed ways

In order to deepen the understanding of the present invention, the following detailed description is given in conjunction with the specific embodiments. The embodiments described in this specification are only used to explain the present invention, and are not used to limit the present invention.

The energy types in the multi-energy microgrid involved in the present invention include wind, light, electricity, heat and other energy forms, and this combination form can realize the complementarity between energy sources, improve the utilization rate of renewable energy, the flexibility of the system and the load reliability of supply. In the operation stage, the operation state of multi-energy microgrid can be divided into two types: grid-connected and isolated. The grid-connected microgrid is connected to the large grid through tie lines to realize the interactive transfer of energy, while the isolated microgrid only relies on its own system. The internal power supply and energy storage device realize the balance of energy and power.

The present invention focuses on the research on the operation of an isolated multi-energy micro-grid. The structural composition and energy flow form of the multi-energy micro-grid are shown in Figure 1. The components in the micro-grid include: wind turbines, photovoltaic units, micro-gas turbines, and storage batteries. and heat pumps and other temperature control equipment, among which wind turbines and photovoltaic units are renewable energy generation units, micro-gas turbines are controllable output units in the system, and batteries as electric energy storage units can smooth the power fluctuations in the system and improve system stability. , as an energy conversion device, the heat pump realizes the coupling connection of the energy of the electric-heat system, and realizes the supply control of the heat load through the electric-to-heat mode. The load types include two types of loads: electric load and thermal load.

It can be seen from Figure 1 that the electrical load provides energy through photovoltaics, wind power and micro-gas turbines and other power sources, the electrical energy storage smoothes the imbalance between the power supply side and the electrical load side, and the thermal load converts electrical energy into The thermal energy meets the corresponding needs. The present invention uses a heat pump as a temperature control device. From the perspective of energy conversion, the electric energy storage stores the remaining renewable energy power generation by charging, and the electric heat pump uses the electric heat conversion method to generate electricity from the remaining new energy. Power is converted into heat energy, both of which can achieve the role of new energy consumption to a certain extent. In summary, electric energy storage and temperature control equipment (heat pump) play a role in coupling various energy networks in multi-energy microgrids, and at the same time, they can enhance the adaptability of multi-energy microgrids to renewable energy generation and improve the flexibility of the system. When considering optimal operation scheduling in multi-energy microgrids, it is important to utilize the properties of different energy sources efficiently and reasonably.

Aiming at the situation that temperature control equipment and thermal inertia of heat load are rarely considered in the economic operation model of the existing microgrid, the present invention adds temperature control to the microgrid composed of wind power, photovoltaic, micro-gas turbine and electric energy storage (battery), etc. equipment and temperature-controlled heat load, considering the thermal inertia effect of the temperature-controlled heat load, an optimal economic operation model of a multi-energy microgrid with temperature-controlled equipment is established. The model takes into account equipment operation cost, start-stop cost, energy storage operation loss cost, loss of load penalty cost and penalty cost of wind and light abandonment, etc. The objective function is to minimize the total operating cost in the scheduling period, and the particle swarm algorithm is used to solve the problem. The optimal operation mode of energy microgrid.

An optimal economical operation method for a multi-energy microgrid containing temperature control equipment of the present invention includes the following steps:

S1. Establish a temperature control equipment model;

S2. Considering the thermal inertia of the heat load, establish the optimal economic operation model of the multi-energy microgrid with temperature control equipment;

S3. Set the component parameters in the microgrid, predict the power generation of new energy, the power curve of the electric load and the external ambient temperature, and set the temperature control range of the heat load;

S4. Substitute the above parameters into the optimal economic operation model of the multi-energy microgrid with temperature control equipment, and use the particle swarm algorithm to solve the optimal economic operation cost in the model during the dispatch period and the various micro sources, temperature control equipment and storage. able output.

While establishing the temperature control equipment model, it is necessary to establish the multi-energy micro-grid micro-gas turbine model, the multi-energy micro-grid electric energy storage charging and discharging model, the multi-energy micro-grid micro-gas turbine model, the multi-energy micro grid electric energy storage charging and discharging model, and the multi-energy micro grid electric energy storage charging and discharging model. The temperature control equipment model together constitute the optimal economic operation model of the multi-energy microgrid.

The above micro-gas turbine models include:

Micro gas turbines usually refer to small gas turbines that use methane and natural gas as fuel. The working principle is that the chemical heat energy generated by the combustion of methane, natural gas and other fuels drives the rotating blades to rotate and do work, thereby converting mechanical energy into electrical energy. Due to the high combustion efficiency of natural gas, methane and other fuels, the pollutant emissions are less than other traditional energy sources, so micro-gas turbines can be widely used in micro-grids. The output power of the micro gas turbine has a linear exponential relationship with the natural gas fuel consumption:

The output power of the micro gas turbine has a linear exponential relationship with the natural gas fuel consumption:

(1)

(1)

(2)

(2)

In the formula, P _MGT ( t ) represents the output of the micro-gas turbine at time t ; G _MGT ( t ) represents the consumption of natural gas at time t ; η _gas represents the thermoelectric conversion efficiency; q represents the calorific value of natural gas; C _MGT ( t ) represents the Gas turbine fuel cost; C _gas represents the unit price of natural gas per unit capacity.

The above-mentioned multi-energy microgrid electric energy storage charging and discharging models include:

The state of charge is used to describe the state of energy storage in the electric energy storage operation stage. The state of charge is a continuous sequential process, which is related to the state of charge, charge and discharge power, and charge and discharge efficiency of the previous period of operation, as shown in equations (3)-(4 ) as shown:

Electric energy storage charging process:

(3)

(3)

Electric energy storage and discharge process:

(4)

(4)

In the formula, SOC ( t ) and SOC ( t -1) represent the state of charge of the energy storage system at the current moment and the state of charge at the previous moment, respectively; P _charge ( t ) and P _discharge ( t ) are the electrical energy storage, respectively The charging power and discharging power of the system; η _c and η _d represent the charging efficiency and discharging efficiency of the energy storage system, respectively; δ represents the self-discharge rate of the electrical energy storage system; E _ESS is the rated capacity of the energy storage system; Δ t represents the electrical energy storage system A single charge and discharge cycle is possible.

During the operation phase of the electric energy storage, after a large number of charge-discharge cycles are repeated, its lifespan will gradually decrease. The life of the energy storage is affected by the number of charges and discharges, the depth of each charge and discharge, and other factors. In the existing related research, the rainflow method is a counting method that is widely used to calculate fatigue damage. The rainflow counting method can calculate the number of charges and discharges and the depth of charge and discharge of the electrical energy storage during the entire operation stage. Among them, According to the relationship between the number of cycles and the depth of charge and discharge of a lead-acid battery in the design life cycle, equation (5) is obtained by fitting, and the relationship between battery life loss and the depth of charge and discharge and the number of equivalent cycles of the battery is shown in equation (6) -(7) shows:

(5)

(5)

(6)

(6)

(7)

(7)

为储能等效循环次数、

为放电深度、

为储能寿命损耗、

为储能测试运行年、

表示储能的运行寿命。In the formula,

is the equivalent number of cycles of energy storage,

is the depth of discharge,

For energy storage life loss,

Years of operation for energy storage tests,

Indicates the operating life of the energy storage.

The above temperature control equipment models include:

The supply form of heat load is mostly represented by temperature index. Electricity and heat conversion can be carried out through temperature control equipment (heat pump, heating and ventilation, air conditioning system and water heater), and the temperature index of heat load can be controlled to achieve user satisfaction. At the same time, the configuration of temperature control equipment in the microgrid can not only achieve the supply of heat load to users, but also play a role in absorbing new energy to a certain extent, and the investment cost of temperature control equipment is low and has high energy Conversion efficiency, configuration is more economical to use.

The thermal load temperature control behavior process is shown in Figure 2, where Upper limit is the upper limit of thermal load temperature regulation, and Lower limit is the lower limit of thermal load temperature regulation. It can be seen that in the process of thermal load temperature control thermal behavior, the load temperature change is not instantaneous, and has a certain inertial response. With the help of this thermal inertia effect, the temperature control equipment can participate in the power balance adjustment of the system.

Considering the first law of thermodynamics, taking a typical residential heating and ventilation system as an example, the equivalent circuit of the equivalent thermodynamic model of the temperature control equipment is simplified and established, as shown in Figure 3:

为供暖通风系统吸收的热功率；

为空气的比热容；

为物质比热容；

为室内热负荷温度；

为外界环境温度；

为室内物质温度；

为备用热损失系数，等效阻抗

、

分别为

、

。In Figure 3,

Thermal power absorbed for heating and ventilation systems;

is the specific heat capacity of air;

is the specific heat capacity of the material;

is the indoor heat load temperature;

is the ambient temperature;

is the indoor material temperature;

is the standby heat loss coefficient, the equivalent impedance

,

respectively

,

.

The state space model of the equivalent thermodynamic model of the system is shown in equation (8).

(8)

(8)

in:

(9)

(9)

(10)

(10)

(11)

(11)

(12)

(12)

转化为一阶微分方程形式，用以模拟热负荷温控行为过程，通过T _load(t)表征热负荷温度，该模型较好的保留了热力学变化的主要特征，热泵作为一种电热转换装置，能够高效的输出热能，从而供给微电网中的热负荷，热泵装置性能一般用制热性能系数（Coefficient of Performance，COP）来评价，其温控模型可以用式(13)-(15)表示：As shown in Figure 4, in step S1, the present invention selects a heat pump as the temperature control device, and by simplifying the equivalent thermodynamic model, the heat load temperature

It is converted into a first-order differential equation form to simulate the thermal load temperature control behavior process, and the thermal load temperature is represented by T _load ( t ). This model better retains the main characteristics of thermodynamic changes. It can efficiently output heat energy to supply the heat load in the microgrid. The performance of the heat pump device is generally evaluated by the coefficient of performance (COP), and its temperature control model can be expressed by equations (13)-(15):

(13)

(13)

In the formula, Q _HP ( t ) is the equivalent thermal power output by the heat pump at time t ; P _HP ( t ) is the electrical power consumed by the heat pump at time t ; COP is the heating performance coefficient.

When the heat pump stops working, the heat load temperature changes naturally with the outside temperature:

T _load (t+1)=T _o (t+1)-(T _o (t+1)-T _load (t))e-Δt ^/RC (14)

When the heat pump is on:

T _load (t+1)=T _o (t+1)+Q _HP (t)R-(T _o (t+1)+Q _HP (t)RT _load (t))e-Δt ^/RC (15 )

In the formula, T _load (t+1) and T _load (t) are the temperatures at a moment after the thermal load and at the current moment, respectively; Q _HP (t) is the equivalent thermal power of the temperature control equipment; T _o (t) is the external temperature. Ambient temperature; R is the equivalent thermal resistance; C is the equivalent thermal capacitance; Δt is the simulation step size.

In step S3, equipment parameters such as the micro-combustion unit, heat pump and electric energy storage are set, the new energy generation power, the electric load power curve and the external ambient temperature are predicted, and the temperature control range of the heat load is set.

In step S4, the above parameters are substituted into the optimal economic operation model of the multi-energy microgrid with temperature control equipment, and the particle swarm algorithm is used to solve the optimal economic operation cost in the model within the dispatch period and the various micro sources and temperature control equipment. and energy storage output.

The calculation steps of the particle swarm algorithm are as follows:

S4-1. Set the parameters of the particle swarm algorithm to generate an initial population, the number of particles is 50, and the maximum number of iterations is 500.

S4-2. According to the new energy generation power and load power curve, adjust the output of the micro-gas turbine, heat pump and electric energy storage to meet the supply of electric load and heat load, and calculate the fitness value under this mode, that is, this mode The total running cost under .

S4-3, update the speed and position of the particle, and update the individual extremum and the global extremum with the optimal economic operating cost as the fitness value.

S4-4: Determine whether the number of iterations is reached, and if so, output the optimal operating cost and the optimal operating output of the micro-gas turbine, heat pump and electric energy storage; if not, return to step S4-2 for iteration.

In the particle swarm optimization model, the optimal economic operation model of the multi-energy microgrid with temperature control equipment is:

①Objective function

，设备启停费用

，储能损耗费用

，失负荷惩罚费用

和弃风、弃光惩罚费用

，如式(16)所示。The optimal economic operation model of multi-energy microgrid with temperature control equipment takes the minimum total operating cost in the dispatch period as the objective function, including equipment operating cost

, equipment start and stop costs

, the cost of energy storage loss

, the loss of load penalty fee

And the penalty fee for abandoning wind and abandoning light

, as shown in formula (16).

(16)

(16)

The above cost calculation method is shown in formulas (42)-(46).

(17)

(17)

为第i种类型设备在t时段的出力；

为第i种类型设备的单位运行成本；N为调度周期总时段数。In the formula,

is the output of the i -th type of equipment in time period t ;

is the unit operating cost of the i -th type of equipment; N is the total number of time periods in the scheduling cycle.

(18)

(18)

为微燃机组状态变量，

表示微燃机开机，

表示微燃机停机；

、

、

为微燃机的开机费用系数。In the formula,

is the state variable of the micro-combustion unit,

Indicates that the micro-combustion engine is turned on,

Indicates that the micro-gas turbine is shut down;

,

,

is the startup cost factor of the micro-gas turbine.

(19)

(19)

表示调度周期内储能系统充放电次数；

表示蓄电池第k次充放电时对应的最大充放电循环次数；

表示微电网中蓄电池的投资费用。In the formula,

Indicates the number of charges and discharges of the energy storage system in the dispatch period;

Indicates the maximum number of charge and discharge cycles corresponding to the kth charge and discharge of the battery;

Represents the investment cost of batteries in the microgrid.

(20)

(20)

(21)

(twenty one)

为新能源发电总功率；

为新能源实际消纳功率；

为单位弃风、弃光惩罚成本；

为t时段平均失负荷功率；

为单位失负荷惩罚成本。In the formula,

Total power for new energy generation;

Actual power consumption for new energy;

It is the penalty cost of abandoning wind and light per unit;

is the average loss of load power in period t ;

is the unit load loss penalty cost.

②Constraints

1) Electric power balance constraints

(22)

(twenty two)

为新能源发电总功率；

为微燃机发电功率；

为热泵数量，

为热泵耗电功率；

、

为电储能充、放电功率；

为电负荷功率。In the formula,

Total power for new energy generation;

Generating power for the micro gas turbine;

is the number of heat pumps,

Power consumption for heat pump;

,

Charge and discharge power for electric energy storage;

is the electrical load power.

2) Constraints of micro-combustion units

(23)

(twenty three)

(24)

(twenty four)

受其最大出力

和最小出力

限制，同时，微燃机出力增加的速率要小于最大向上爬坡率

；反之，出力降低的速率要小于最大向下爬坡率

。In the formula, the output of the micro-combustion unit

subject to its maximum output

and minimum output

limit, and at the same time, the rate of increase in the output of the micro-combustion engine is less than the maximum upward slope rate

; On the contrary, the rate of output reduction is less than the maximum downhill rate

.

3) Heat pump constraints

(25)

(25)

受其最大允许工作功率

限制。In the formula, the working power of the heat pump

Subject to its maximum allowable working power

limit.

4) Electric energy storage system constraints

(26)

(26)

(27)

(27)

(28)

(28)

(29)

(29)

、

，同时，为避免储能过充和过放对其寿命造成的伤害，应严格控制荷电状态

的范围，

为0-1变量，电储能充电时

为1，放电时

为0。In the formula, the charging and discharging power of the electric energy storage cannot exceed its rated charging and discharging power.

,

At the same time, in order to avoid the damage to its life caused by overcharge and overdischarge of energy storage, the state of charge should be strictly controlled

range,

It is a 0-1 variable, when the battery is charged

is 1, when discharging

is 0.

5) Thermal load temperature constraints

(30)

(30)

需控制在上限

和下限

范围内。In the formula, the heat load temperature

To be controlled at the upper limit

and lower bound

within the range.

Taking a multi-energy micro-grid including wind turbines, photovoltaic units, micro-gas turbines, heat pumps, electric energy storage and other devices as an example, the benefits brought by the present invention are illustrated.

The total predicted power of new energy power generation and the predicted power of electric load in typical winter days are collected as shown in Figure 5, the sampling interval is 1h, and the external ambient temperature is shown in Figure 6. For equipment parameters such as micro-combustion units, heat pumps and electric energy storage, see Table 1-Table 3, and the initial state of charge of the battery dispatch cycle is set to 0.5. There are 100 heat loads in the microgrid (a single heat pump supplies a single heat load), the set value of the heat load temperature is 24 °C, the allowable temperature adjustment range is °C, the heat load sampling time is 1min, and the equivalent thermal resistance of the temperature control equipment is 0.1208°C/W, and the equivalent thermal capacitance is 3599.3J/°C. The penalty fee for abandoning wind and light per unit of the system is 2$/kWh, and the penalty fee for unit loss of load is 5$/kWh. The particle swarm algorithm is set as follows: the number of particles is 50, and the maximum number of iterations is 500.

Table 1 Micro-gas turbine parameters

Table 2 Parameters of heat pump device

Table 3 Battery Parameters

The following two scenarios are listed for analysis:

Case 1: Typical winter day microgrid with heat pump

Case 2: No heat pump in the microgrid on a typical day in winter

In a typical winter day microgrid, the scheduling results of each component in the scenario with the heat pump are shown in Figure 7, and the scheduling results of each component in the scenario without the heat pump are shown in Figure 8. Comparing the output of each component in the two scenarios, it is not difficult to see that in Case 1 :

1. When the new energy output is not enough to supply the electrical load, the micro-gas turbine and the battery work together to stabilize the remaining electrical load in the system.

2. During the period of high output of new energy, the battery is charged and the heat pump is turned on to adjust the temperature.

3. In some specific time periods (such as 1h, 20h), the heat load temperature is around the lower limit of the allowable adjustment temperature. At this time, the temperature control equipment needs to be turned on to adjust the heat load temperature, and the new energy power generation is insufficient at this time, and it needs to be supplied by battery discharge. Insufficient electrical power.

In Case 2, the battery can only participate in the regulation during the period of high new energy output. Due to the high price of the battery and the limited capacity of the battery configured in the microgrid, the remaining new energy generation power cannot be fully absorbed, and the new energy utilization rate of the system is relatively high. Low.

Table 4 shows the influence of the temperature control equipment heat pump on the optimal economic operation of the microgrid. It can be seen that after adding the temperature control equipment heat pump, the heat pump can consume excess new energy power to adjust the heat load temperature, and the system will be punished for abandoning wind and light. The cost is reduced by 19.3%, and the total operating cost of the system is reduced by 259.1$.

According to the analysis in the previous section, the addition of the heat pump of the temperature control equipment will have an impact on the optimal operation of the microgrid, and the temperature control characteristics of the thermal load will inevitably have a certain impact on the microgrid. This section analyzes the influence of the thermal load temperature control range on the optimal operation. When the thermal load temperature control characteristics are not considered, the optimal scheduling plan of each component in the microgrid is shown in Figure 9. It can be seen that the thermal load temperature control is not considered. At this time, the heat load does not have an interval property, and the heat load temperature is set to be constant at 24°C. At this time, the output level of the heat pump is maintained at about 22kW, and the flexibility for new energy consumption is low. Reaching $4362, the total cost of running the system is $6780.

Figure 10 shows the change of the SOC curve of the battery under the scenarios of different thermal load temperature control ranges. It can be seen that after taking into account the thermal load temperature control characteristics, the number of battery charge and discharge cycles is reduced, and the depth of charge and discharge is also significantly reduced. It has dropped by 11.5%. With the increase of the thermal load temperature control range, the flexibility of the temperature control equipment is improved, the battery charge and discharge depth is gradually reduced, and the battery loss cost is gradually reduced.

When the thermal load temperature control characteristics are not considered, the penalty fees for wind abandonment, light abandonment and the total operation cost of the microgrid are the highest. The reason is that with the increase of the temperature control range, the flexibility of the output of the temperature control equipment increases. Before the heat load temperature is higher than its lower threshold, the heat pump can be turned off to adjust the system power; when the heat load temperature is lower than its upper limit Before the threshold, the heat pump can be turned on to absorb the remaining new energy power generation in the system. However, the user's heat load temperature control range cannot be expanded indefinitely, and needs to be adjusted within a certain range. Therefore, considering the user's requirements for the heat load temperature control range, the heat load temperature control range can be reasonably set to achieve user satisfaction. At the same time, the system can be optimized operating economy.

In summary, the calculation results show that:

1. Adding temperature control equipment to the multi-energy microgrid can improve the consumption rate of wind power and photovoltaics, improve the comprehensive energy utilization rate, reduce the system load loss to a certain extent, improve the system operation reliability, and optimize the system operation cost.

2. Rational use of thermal load temperature control characteristics and setting a suitable thermal load temperature control range can slow down the life loss of energy storage, improve wind power and photovoltaic consumption rates, and optimize the economy of system operation.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, and substitutions can be made in these embodiments without departing from the principle and spirit of the invention and modifications, the scope of the present invention is defined by the appended claims and their equivalents.

Claims (5)

1. An optimal economic operation method of a multi-energy micro-grid with temperature control equipment is characterized by comprising the following steps:

s1, establishing a temperature control equipment model;

s2, establishing an optimal economic operation model of the multi-energy micro-grid with the temperature control equipment;

s3, setting element parameters in the microgrid, predicting the power generation power of the new energy, the power curve of the electric load and the external environment temperature, and setting the temperature control range of the heat load;

and S4, substituting the parameters into the optimal economic operation model of the multi-energy micro-grid with the temperature control equipment, and solving the optimal economic operation cost and the output of each micro-source, the temperature control equipment and the stored energy in the model in the dispatching cycle by adopting a particle swarm algorithm.

2. The optimal economic operation method of the multi-energy micro-grid with the temperature control equipment as claimed in claim 1, wherein the method comprises the following steps: in step S1, a heat pump is selected as the temperature control device, and the temperature control model can be represented by equations (13) to (15):

Q_HP(t)＝P_HP(t)COP (13)

in the formula, Q_HP(t) is equivalent thermal power output by the heat pump at the time t；P_HP(t) is the electric power consumed by the heat pump at time t; COP is the coefficient of heating performance;

when the heat pump stops working, the temperature of the heat load naturally changes along with the outside temperature:

T_load(t+1)＝T_o(t+1)-(T_o(t+1)-T_load(t))e^-Δt/RC (14)

when the heat pump is turned on:

T_load(t+1)＝T_o(t+1)+Q_HP(t)R-(T_o(t+1)+Q_HP(t)R-T_load(t))e^-Δt/RC (15)

in the formula, T_load(T +1) and T_load(t) temperatures at a later time and a current time of the thermal load, respectively; q_HP(t) is equivalent thermal power of the temperature control equipment; t is_o(t) is the external ambient temperature; r is equivalent thermal resistance; c is equivalent thermal capacitance; Δ t is the simulation step size.

3. The optimal economic operation method of the multi-energy micro-grid with the temperature control equipment as claimed in claim 1, wherein the method comprises the following steps: in step S2, the optimal economic operation model of the multi-energy microgrid including the temperature control device is as follows:

objective function

The optimal economic operation model of the multi-energy micro-grid considering the temperature control equipment takes the minimum total operation cost in a scheduling period as an objective function and comprises the equipment operation cost

Equipment start and stop fee

Cost of energy storage loss

Penalty cost for lost load

Wind and light abandoning punishment cost

As shown in the formula (16),

(16)

the above-mentioned charge calculation method is shown in formulas (17) to (21),

(17)

in the formula (I), the compound is shown in the specification,

is as followsiIs of the typetThe output of the time period;

is as followsiUnit operating cost of type equipment;Nthe total time period number of the scheduling period;

(18)

in the formula (I), the compound is shown in the specification,

is a state variable of the micro-combustion engine set,

it means that the micro combustion engine is started,

indicating shutdown of the micro-combustion engine;

、

、

the starting cost coefficient of the micro-combustion engine is obtained;

(19)

in the formula (I), the compound is shown in the specification,

representing the charging and discharging times of the energy storage system in a scheduling period;

indicates the batterykMaximum charge-discharge cycle times corresponding to the times of charge-discharge;

representing the investment cost of a storage battery in the microgrid;

(20)

(21)

in the formula (I), the compound is shown in the specification,

generating total power for new energy;

actually consuming power for the new energy;

punishing cost for wind abandonment and light abandonment of a unit;

is composed oftAverage loss of load power over a period of time;

penalty cost for unit load loss;

constraint conditions

1) Electric power balance constraint

(22)

In the formula (I), the compound is shown in the specification,

generating total power for new energy;

generating power for the micro gas turbine;

in order to be able to count the number of heat pumps,

the power consumption of the heat pump is;

、

charging and discharging power for the electric energy storage;

is the electrical load power;

2) micro-combustion engine set constraint

(23)

(24)

In the formula, micro-combustion engine group output

Subject to its maximum output

And minimum output

Limiting and at the same time increasing the output of the micro-combustion engine at a rate less than the maximum upward ramp rate

(ii) a Conversely, the rate of decrease in output is less than the maximum downhill rate

；

3) Heat pump constraints

(25)

In the formula, the working power of the heat pump

Subject to its maximum allowable operating power

Limiting;

4) electrical energy storage system restraint

(26)

(27)

(28)

(29)

In the formula, the charge-discharge power of the electric energy storage cannot exceed the rated charge-discharge power

、

Meanwhile, in order to avoid the damage of the over-charge and over-discharge of the stored energy to the service life of the energy storage device, the state of charge should be strictly controlled

In the range of (a) to (b),

is variable 0-1, and is charged when the electric energy is stored

Is 1, at the time of discharge

Is 0;

5) thermal load temperature restraint

(30)

In the formula, heat load temperature

Need to be controlled at the upper limit

And lower limit

Within the range.

4. The optimal economic operation method of the multi-energy micro-grid with the temperature control equipment as claimed in claim 1, wherein the method comprises the following steps: the micro-grid medium element comprises a wind power generator set, a photovoltaic set, a micro-combustion engine, a storage battery and a heat pump, wherein the wind power generator set and the photovoltaic set are renewable energy power generation units, the micro-combustion engine is a controllable output unit in the system, the storage battery is used as an electric energy storage unit and can smooth power fluctuation in the system, the heat pump is used as an energy conversion device to realize coupling connection of electric-thermal system energy, supply control of thermal load is realized through an electric-thermal conversion mode, and the load types comprise two load types of electric load and thermal load.

5. The optimal economic operation method of the multi-energy micro-grid with the temperature control equipment as claimed in claim 1, wherein the method comprises the following steps: the particle swarm algorithm comprises the following calculation steps:

s4-1, setting particle swarm algorithm parameters, generating an initial population, wherein the number of particles is 50, and the maximum iteration number is 500;

s4-2, adjusting the micro-combustion engine, the heat pump and the electric energy storage output force according to the new energy power generation power and the load power curve to meet the supply of the electric load and the heat load, and calculating the fitness value in the mode, namely the total operation cost in the mode;

s4-3, updating the speed and the position of the particles, and updating the individual extreme value and the global extreme value by taking the optimal economic operation cost as a fitness value;

s4-4, judging whether the iteration times are reached, and if so, outputting the optimal operation cost and the optimal operation output of the micro-combustion engine, the heat pump and the electric energy storage; if not, returning to the step S4-2 for iteration.

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