CN109617142B - CCHP type micro-grid multi-time scale optimization scheduling method and system - Google Patents

Abstract

The invention discloses a CCHP type micro-grid multi-time scale optimization scheduling method and system. The multi-time scale optimization scheduling method comprises the following steps: firstly, starting from the response of a demand side, determining the compensation cost of a power grid according to the load-cuttable and adjustable load parameters; secondly, respectively establishing operation benefits according to parameters of the CCHP unit, the lithium bromide absorption type refrigerating unit and the phase change energy storage device, so that operation benefits of a triple co-generation system are established; and finally, establishing a system operation cost and a revenue function, and performing optimized scheduling on the CCHP type micro-grid by adopting a multi-time scale optimized scheduling strategy to minimize the total cost. By adopting the multi-time scale optimal scheduling method and system provided by the invention, the power shortage can be effectively relieved, the reliability is improved, and the maximum utilization of renewable energy sources is achieved.

Description

A multi-time-scale optimal scheduling method and system for CCHP-type microgrid

technical field

The invention relates to the field of optimization and scheduling of microgrids, in particular to a multi-time scale optimal scheduling method and system for CCHP-type microgrids. According to the cooling, heating and electric load demand within the microgrid system, the power generation plan of the microgrid system in the future period of time is formulated, so that the microgrid system can obtain the best economic benefits and power supply reliability.

Background technique

The basic task of microgrid optimal scheduling is to reasonably and effectively arrange the output of each distributed power source and the interactive power with the distribution network according to a certain control strategy on the premise of meeting the load demand of the microgrid system, so that the entire microgrid can be controlled. The operation and maintenance cost and emission cost of the power grid are the lowest.

Compared with ordinary microgrids, combined cooling heating and power (CCHP) microgrids have the characteristics of diverse operation modes, high energy utilization, flexible control, high power supply reliability and low environmental pollution. The user's demand for various types of energy sources such as cold, heat and electricity has good social and economic benefits. However, the coupling relationship between the energy structure and equipment inside the CCHP microgrid is complex, especially the thermoelectric coupling phenomenon of CCHP system, which makes it very difficult to determine the optimal dispatch plan; To a certain extent, the operation mode of “fixing electricity by heat” or “fixing heat by electricity” plays the role of thermal and electrolytic coupling, but it is not conducive to the unified coordination and dispatch of thermal and electrical loads.

In addition, the indirectness, volatility and randomness of renewable energy generation and load in the microgrid have seriously affected the stability and economy of the entire microgrid. For example, the traditional grid scheduling has limitations, resulting in serious wind curtailment in wind farms around the world. phenomenon, the problem of power loss has become increasingly prominent, resulting in waste of renewable energy. With the increase of the grid-connected capacity of renewable energy and the connection of various demand-side resources to the power grid, only using the resources on the power generation side for optimal scheduling can no longer meet the requirements of microgrid economic scheduling.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a multi-time scale optimal scheduling method and system for CCHP type microgrid, so as to solve the problem of severe wind abandonment, power loss and serious waste of renewable energy in wind farms around the world due to the limitations of traditional grid scheduling. The problem.

For achieving the above object, the present invention provides the following scheme:

A CCHP-type microgrid multi-time scale optimal scheduling method, comprising:

Obtain shedable load shedding parameters, adjustable load adjustable load parameters, CCHP unit parameters, absorption lithium bromide refrigeration unit parameters and phase change energy storage equipment parameters; the shedable load parameters include dispatch cost parameters, market electricity prices , the shaving rate and the rated power of the shaving load; the adjustable load parameters include the adjustable rate and the active power of the adjustable load; the CCHP unit parameters include the heating price of the CCHP unit, the cooling price of the CCHP unit, Natural gas price, power generation, heat output of unit i in time period t, cooling output of unit i in time period t, the amount of natural gas used, and the hot gas conversion efficiency of unit i; the parameters of the absorption lithium bromide refrigeration unit include cooling duration, gas turbine The thermodynamic coefficient of the heating power and the electric cooling efficiency; the parameters of the phase change energy storage device include the thermal efficiency, heat load and cooling load of the energy storage device;

Determine the grid compensation fee for shedding the shedable load according to the shedable load parameter;

determining a load adjustment fee for the adjustable load according to the adjustable load parameter;

Determine the CCHP unit revenue according to the CCHP unit parameters;

Determine the operating income of the absorption lithium bromide refrigeration unit according to the parameters of the absorption lithium bromide refrigeration unit;

Determine the operating income of the phase change energy storage device according to the parameters of the phase change energy storage device;

According to the income of the CCHP unit, the operation income of the absorption lithium bromide refrigeration unit and the operation income of the phase change energy storage device, an operation income model of the triple supply system is established;

According to the power grid compensation fee, the load adjustment fee and the operation benefit model of the triple supply system, the CCHP type microgrid is optimally scheduled according to the multi-time-scale optimal scheduling strategy, so that the operation cost of the CCHP type microgrid is minimized; The multi-time-scale optimal scheduling strategy includes a day-ahead optimal scheduling stage, an intra-day rolling optimization stage, an ultra-short-term scheduling stage, and an ultra-ultra-short-term scheduling stage; in different scheduling stages, the objective functions for optimal scheduling of the CCHP-type microgrid are different.

Optionally, the determining of the grid compensation fee for shedding the shedable load according to the shedable load parameter specifically includes:

确定切除所述可切负荷的电网补偿费用；其中，C_cli(t)为切除所述可切负荷的电网补偿费用；ε_cli(t)为可切负荷的调度成本系数；c_grid(t)表示买卖市场电价；ρcli(t)为可切率；

为可切负荷i的额定功率。According to the formula

Determine the grid compensation cost for removing the shedable load; wherein, C _cli (t) is the grid compensation cost for removing the shedable load; ε _cli (t) is the dispatch cost coefficient of the shedable load; c _grid (t) represents the electricity price in the buying and selling market; ρcli(t) is the cut rate;

is the rated power of the load i can be cut.

Optionally, the determining the load adjustment fee of the adjustable load according to the adjustable load parameter specifically includes:

确定所述可调负荷的负荷调整费用，其中，C_alj(t)为所述可调负荷的负荷调整费用；δ_alj(t)为可调率；P_alj(t)为可调整负荷j的有功功率；T为调整周期。According to the formula

Determine the load adjustment cost of the adjustable load, wherein C _alj (t) is the load adjustment cost of the adjustable load; δ _alj (t) is the adjustable rate; P _alj (t) is the adjustable load j Active power; T is the adjustment period.

Optionally, the determining of the CCHP unit revenue according to the CCHP unit parameters specifically includes:

According to the formula C _CCHP (t)=c _grid (t)P _i (t)+c _H (t)P _h (t)+c _Q (t)P _Q (t)-c _F (t)P _F (t ) to determine the revenue of CCHP units; where C _CCHP (t) is the revenue of CCHP units; c _H (t) is the heat price of CCHP units; c _Q (t) is the cooling price of CCHP units; c _F (t) is the price of natural gas; P _i (t) is the power generation; P _h (t) is the heat output of unit i in time period t; P _Q (t) is the cooling output of unit i in time period t; P _F (t)=P _h (t)/ μ _H is the amount of natural gas used, and μ _H is the hot gas conversion efficiency of unit i.

Optionally, determining the operating income of the absorption lithium bromide refrigeration unit according to the parameters of the absorption lithium bromide refrigeration unit specifically includes:

确定吸收式溴化锂制冷机组运行收益；其中，C_l-b(t)为吸收式溴化锂制冷机组运行收益；t_s为供冷时长；P_h为燃气轮机的供热功率；η_COPe为电制冷效率的热力系数；η_COP为溴化锂制冷系统的制冷功率。According to the formula

Determine the operating income of the absorption lithium bromide refrigeration unit; wherein, C _lb (t) is the operating income of the absorption lithium bromide refrigeration unit; t _s is the cooling time; P _h is the heating power of the gas turbine; η _COPe is the thermodynamic coefficient of the electric cooling efficiency ; η _COP is the refrigeration power of the lithium bromide refrigeration system.

Optionally, the determining the operating profit of the phase change energy storage device according to the parameters of the phase change energy storage device specifically includes:

确定相变储能设备运行收益；其中，C_p-c(t)为相变储能设备运行收益；η为储能设备的热效率；L_h为热负荷；L_c为冷负荷；t_on|winter为在冬季三联供机组产热大于热负荷的时间；t_on|summer为在夏季三联供机组产热大于热负荷的时间。According to the formula

Determine the operating income of the phase change energy storage device; where C _pc (t) is the operating income of the phase change energy storage device; η is the thermal efficiency of the energy storage device; L _h is the heat load; L _c is the cooling load; t _on|winter is In winter, the heat generation time of the triple supply unit is greater than the heat load; t _on|summer is the time when the heat generation of the triple supply unit is greater than the heat load in summer.

Optionally, establishing a triple supply system operating benefit model according to the CCHP unit revenue, the absorption lithium bromide refrigeration unit operating revenue, and the phase-change energy storage device operating revenue, specifically includes:

According to the formula C(t)=C _CCHP (t)+C _lb (t)+C _pc (t), the operation income model of the triple supply system is established; C(t) is the operation income of the triple supply system.

Optionally, according to the power grid compensation fee, the load adjustment fee, and the operation benefit model of the triple supply system, the CCHP-type microgrid is optimally scheduled according to a multi-time-scale optimal scheduling strategy, so that the CCHP-type microgrid can be optimally scheduled. The operating costs of the grid are minimal, including:

In the day-ahead optimization scheduling phase, according to the formula

Perform optimal scheduling on the CCHP-type microgrid; wherein, NR is the number of controllable distributed power sources; I is the number of shedable loads; J is the number of adjustable loads; P _i (t) represents the i-th distribution C _i (P _i (t)) represents the cost of the i-th distributed power source when the output is P _i (t), ΔT is the duration of the scheduling cycle; C _DGg (t) is the controllable distribution C _bat (t) is the use cost of the battery; C _grid (t) is the interaction cost with the large power grid;

对所述CCHP型微电网进行优化调度；其中，ΔP_i(t)为分布式电源i的功率调整量；T₀为当前时间节点；During the intraday rolling optimization phase, according to the formula

Perform optimal scheduling on the CCHP type microgrid; wherein, ΔP _i (t) is the power adjustment amount of the distributed power source i; T ₀ is the current time node;

对所述CCHP型微电网进行优化调度；其中，

表示超短期调度t时刻综合调度成本，

表示该时段滚动优化对应成本；In the ultra-short-term scheduling phase, according to the formula

Perform optimal scheduling on the CCHP-type microgrid; wherein,

represents the comprehensive scheduling cost of ultra-short-term scheduling at time t,

Indicates the corresponding cost of rolling optimization in this period;

对所述CCHP型微电网进行优化调度；其中，

In the ultra-ultra-short-term scheduling phase, according to the formula

Perform optimal scheduling on the CCHP-type microgrid; wherein,

P_r(k+n)为有功出力参考值，由短期尺度优化得到；P(k+n)为超短期尺度优化的分布式电源、大电网、储能及可切负荷的预测值；P₀(k+n)为超短期尺度优化各部分有功出力的初始值；Δu(k+t-1)为预测的[k+t-1，k+t]时段内的有功出力增量；

为可控分布式电源的有功出力参考值；

为大电网交互的有功出力参考值；

为可切负荷的有功出力参考值；

为可调负荷的有功出力参考值；

为储能电池的有功出力参考值。

P _r (k+n) is the reference value of active power output, which is obtained by short-term scale optimization; P(k+n) is the predicted value of distributed power generation, large power grid, energy storage and load shedding optimized by ultra-short-term scale; P ₀ (k+n) is the initial value of the active power output of each part of the ultra-short-term scale optimization; Δu(k+t-1) is the predicted active power output increment in the period of [k+t-1, k+t];

is the active power output reference value of the controllable distributed power source;

Active power output reference value for large grid interaction;

is the reference value of active power output for the shedable load;

is the active output reference value of the adjustable load;

It is the active power output reference value of the energy storage battery.

A CCHP-type microgrid multi-time scale optimal dispatching system, comprising:

The parameter acquisition module is used to acquire the load shedding parameters of the shedable load, the adjustable load parameters of the adjustable load, the parameters of the CCHP unit, the parameters of the absorption lithium bromide refrigeration unit, and the parameters of the phase change energy storage device; the load shedding parameters include: Dispatching cost parameters, market electricity price, shaving rate, and rated power of the shaving load; the adjustable load parameters include the adjustable rate and the active power of the adjustable load; the CCHP unit parameters include the CCHP unit heat price , CCHP unit cooling price, natural gas price, power generation, the heat output of unit i in time period t, the cooling output of unit i in time period t, the amount of natural gas used and the hot gas conversion efficiency of unit i; the parameters of the absorption lithium bromide refrigeration unit Including the cooling time, the heating power of the gas turbine and the thermodynamic coefficient of the electric cooling efficiency; the parameters of the phase change energy storage device include the thermal efficiency, heating load and cooling load of the energy storage device;

a power grid compensation cost determination module, configured to determine the power grid compensation cost for removing the shedable load according to the shedable load parameter;

a load adjustment fee determination module, configured to determine the load adjustment fee of the adjustable load according to the adjustable load parameter;

The CCHP unit revenue determination module is used to determine the CCHP unit revenue according to the CCHP unit parameters;

a module for determining the operating income of the absorption-type lithium bromide refrigeration unit, used for determining the operation income of the absorption-type lithium bromide refrigeration unit according to the parameters of the absorption-type lithium bromide refrigeration unit;

a phase change energy storage device operating income determination module, configured to determine the phase change energy storage device operating income according to the phase change energy storage device parameters;

A module for establishing an operation benefit model of the triple supply system is used to establish an operation benefit model of the triple supply system according to the income of the CCHP unit, the operation income of the absorption lithium bromide refrigeration unit, and the operation income of the phase change energy storage device;

The optimal scheduling adjustment module is configured to perform optimal scheduling on the CCHP type microgrid according to the multi-time scale optimal scheduling strategy according to the power grid compensation fee, the load adjustment fee and the triple supply system operation revenue model, so that the CCHP type microgrid can be optimally scheduled. The operating cost of the microgrid is the smallest; the multi-time scale optimal scheduling strategy includes a day-ahead optimal scheduling phase, an intraday rolling optimization phase, an ultra-short-term scheduling phase, and an ultra-ultra-short-term scheduling phase; in different scheduling phases, the CCHP-type microgrid is carried out. The objective function of optimal scheduling is different.

Optionally, the power grid compensation fee determination module specifically includes:

确定切除所述可切负荷的电网补偿费用；其中，C_cli(t)为切除所述可切负荷的电网补偿费用；ε_cli(t)为可切负荷的调度成本系数；c_grid(t)表示买卖市场电价；ρ_cli(t)为可切率；

为可切负荷i的额定功率。Grid compensation fee determination unit, which is used according to the formula

Determine the grid compensation cost for removing the shedable load; wherein, C _cli (t) is the grid compensation cost for removing the shedable load; ε _cli (t) is the dispatch cost coefficient of the shedable load; c _grid (t) represents the electricity price in the buying and selling market; ρ _cli (t) is the cut rate;

is the rated power of the load i can be cut.

According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the present invention provides a CCHP-type microgrid multi-time-scale optimal scheduling method and system, based on demand-side response (considering flexible load characteristics, for load shedding According to the demand for cold and heat energy, the user will be partially or fully compensated for the thermal energy cost at a certain time, and the user will be encouraged to consume more heat energy at a specific time, thereby improving CCHP. heat production, determine the income of CCHP units, and the uncertainty of renewable energy, through the differences and complementarity of prices, energy characteristics, and energy demand of various energy sources such as cooling, heat, and electricity, for different time periods, According to the multi-time-scale scheduling strategy, the optimal scheduling of the CHP-type microgrid can not only effectively alleviate the power shortage, improve the functional reliability, but also maximize the utilization of renewable energy, realize the economical operation of the CCHP-type microgrid, and reduce the amount of electricity. losses, the occurrence of wind curtailment in local wind farms, and the waste of renewable energy.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings required in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some of the present invention. In the embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative labor.

Fig. 1 is the flow chart of CCHP type microgrid multi-time scale optimal scheduling method provided by the present invention;

FIG. 2 is a system diagram of a coordinated operation scheme of phase-change energy storage equipment configured in a triple supply system provided by the present invention;

Fig. 3 is the schematic diagram of the working principle of the absorption lithium bromide refrigerator provided by the present invention;

Fig. 4 is the CCHP type microgrid system structure diagram provided by the present invention;

Fig. 5 is the multi-time scale scheduling block diagram of CCHP type microgrid provided by the present invention;

6 is a structural diagram of a CCHP-type microgrid multi-time-scale optimal dispatching system provided by the present invention;

FIG. 7 is a block diagram of the multi-time scale scheduling structure of CCHP type microgrid based on demand side response provided by the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The purpose of the present invention is to provide a multi-time-scale optimal scheduling method and system for CCHP-type microgrid, so as to effectively alleviate power shortage, improve functional reliability, maximize the utilization of renewable energy, and realize the economical operation of CCHP-type microgrid , reduce power loss, reduce the occurrence of wind abandonment and waste of renewable energy in various wind farms.

In order to make the above objects, features and advantages of the present invention more clearly understood, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

FIG. 1 is a flowchart of a CCHP type microgrid multi-time scale optimal scheduling method provided by the present invention. As shown in FIG. 1, a CCHP type microgrid multi-time scale optimal scheduling method includes:

Step 101: Acquire shedable load shedding parameters, adjustable load shedding parameters, CCHP unit parameters, absorption lithium bromide refrigeration unit parameters, and phase change energy storage device parameters; the shedable load parameters include scheduling cost parameters , market electricity price, shaving rate and rated power of the shaving load; the adjustable load parameters include the adjustable rate and the active power of the adjustable load; the CCHP unit parameters include the heat price of the CCHP unit, the CCHP unit Cooling price, natural gas price, power generation, heat output of unit i in time period t, cooling output of unit i in time period t, amount of natural gas used, and hot gas conversion efficiency of unit i; the parameters of the absorption lithium bromide refrigeration unit include cooling supply Duration, heating power of gas turbine and thermodynamic coefficient of electric cooling efficiency; the parameters of the phase change energy storage device include thermal efficiency, heat load and cooling load of the energy storage device.

Step 102: Determine the grid compensation fee for shedding the shedable load according to the shedable load parameter.

Step 103: Determine the load adjustment fee of the adjustable load according to the adjustable load parameter.

Step 104: Determine CCHP unit revenue according to the CCHP unit parameters.

The present invention considers the problem of demand side response, including two parts:

①Consider flexible load characteristics: Set the cuttable load and adjustable load, and establish a certain compensation mechanism.

The so-called flexible load interactive response scheduling is to consider the load resource with adjustment ability as the scheduling object, and adopt various demand response measures suitable for it to realize the interactive response between the flexible load and the power source, so as to deal with the intermittent problem of renewable energy. , to achieve the goal of optimal allocation of energy resources.

According to the load characteristics, it can be divided into three categories: critical load, shearable load and adjustable load. The key load is the uncontrollable load, and the system needs to meet the demand of this type of load at all times; the load can be shed, its load level is low, and the removal will not adversely affect the microgrid, and when the removal cost is lower than other dispatch costs, Choose a load shedding strategy; an adjustable load whose operating hours can be shifted from peak to valley hours. Targeted processing according to different load characteristics is more conducive to the economy of microgrid operation.

为可切负荷i的额定功率；式(2)为可调负荷的负荷调整费用；δ_alj(t)为可调率；P_alj(t)为可调整负荷j的有功功率。Equation (1) is the grid compensation cost for shedding shedable loads; ε _cli (t) is the dispatching cost coefficient of shedable loads; c _grid (t) represents the electricity price in the buying and selling market; ρ _cli (t) is the shedding rate;

is the rated power of the cuttable load i; formula (2) is the load adjustment cost of the adjustable load; δ _alj (t) is the adjustable rate; P _alj (t) is the active power of the adjustable load j.

和

为可切负荷的最大、最小可切率；式(4)为可调负荷的运行约束，可调负荷只有在给定时间窗口内运行，且一旦运行在未完成任务之前不能停止。Equation (3) is the power adjustment constraint of the load shedding;

and

is the maximum and minimum severable rate of the shaving load; Equation (4) is the operation constraint of the adjustable load, the adjustable load can only run within a given time window, and cannot stop once the operation is not completed before the task.

②Encourage the formulation of the interaction mechanism of the CCHP system: According to the demand for cold and heat energy, partially or fully compensate the user's thermal energy cost at a certain time, and encourage the user to consume more heat energy at a specific time, thereby increasing the heat production of CCHP.

The main way to motivate the CCHP system to participate in the interaction is to stimulate the user's demand for heat by means of cold and heat compensation. According to the user's demand for heat and cooling, the management center will partially or fully compensate the energy cost of the user for the excess of the original planned heat load during a certain period of time, thereby increasing the heat and cooling load; Working in the way of constant heat and electricity, while increasing the heat output, it also increases the power generation. If the cooling and heat compensation costs are lower than the interruptible compensation costs, the CCHP units will be dispatched first, and the total dispatch cost will be reduced. In addition, the additional electricity generated with the increase of thermal output is sold to the large power grid at a unified purchase price or directly reduced to supply power to users at the contract price. The benefits of CCHP units are:

C _CCHP (t) = c _grid (t) P _i (t) + c _H (t) P _h (t) + c _Q (t) P _Q (t) - c _F (t) P _F (t) ( 5)

In formula (5), c _H (t) is the heat price of the CCHP unit; c _Q (t) is the cooling price of the CCHP unit; c _F (t) is the natural gas price; P _i (t) is the power generation; P _h (t) ) is the heat output of unit i in time period t; P _Q (t) is the cooling output of unit i in time period t; P _F (t)=P _h (t)/μ _H is the amount of natural gas used, μ _H is the unit i The hot gas conversion efficiency is related to the heat production.

Step 105: Determine the operating income of the absorption lithium bromide refrigeration unit according to the parameters of the absorption lithium bromide refrigeration unit.

Step 106: Determine the operating income of the phase change energy storage device according to the parameters of the phase change energy storage device.

Step 107 : Establish an operation income model of the triple supply system according to the income of the CCHP unit, the operation income of the absorption lithium bromide refrigeration unit, and the operation income of the phase change energy storage device.

The scheme of designing phase-change energy storage equipment to cooperate with the operation of the co-generation system: the cold storage/heat energy storage device adopts phase-change energy storage to solve the problem of time mismatch between energy supply and demand; It not only abandons wind for consumption, but also plays a role in power peak regulation.

Phase change energy storage equipment refers to the use of phase change materials to absorb or release latent heat to achieve effective storage and utilization of thermal energy, which can be used to solve the gap between thermal energy production and demand and improve the phenomenon of energy waste. Phase change energy storage is a process in which the physical state of materials changes under the action of heat to generate heat storage and release. Compared with sensible heat energy storage, it has two advantages of high energy storage density and constant and controllable temperature during energy storage and release. . The temperature of the phase change material continues to rise, and the physical state changes when the phase change temperature is reached, and the temperature of the material itself is almost unchanged before the phase change is completed; at the same time, a large amount of phase change heat is absorbed and released, resulting in a relatively wide temperature. platform. Under the premise of the same heat storage and the same temperature difference, the relative volume of heat storage of the phase change material is more than 4 to 5 times smaller than that of the sensible heat material. In addition, phase change energy storage also has the advantages of high conversion efficiency, easy realization of large capacity, green environmental protection, and convenient installation.

For the centralized cooling, heating and power supply system, on the one hand, in order to ensure the operating efficiency and energy utilization efficiency of the unit, it is necessary to run the unit at full capacity as much as possible, but this will cause the phenomenon that the output of the unit does not match the load demand. The power supply system needs to frequently adjust the output according to the demand of the cooling load or the heating load, and may cause waste heat to be wasted. The phase change energy storage device can effectively cooperate with the triple power supply system by replacing the energy storage material, and solve the problem of time mismatch between energy supply and demand by storing and releasing cold and heat. Phase change materials have passive heat storage (release) characteristics of absorbing (or releasing) a large amount of latent heat of phase change under constant temperature or near constant temperature conditions, storing solar energy during the day, and releasing the energy stored during the day at night, completely or partially replacing nighttime heating, To achieve the purpose of "time shift" and "shaving peaks and filling valleys" in solar energy utilization. In addition, the electric boiler can also be used for electric heat storage during the valley electricity time, and the electric heat storage can be applied to building heating, which has good value for the power peak regulation of the power grid and the user's heating operation cost. FIG. 2 is a system diagram of a coordinated operation scheme of phase-change energy storage devices configured in a triple power supply system provided by the present invention, as shown in FIG. 2 .

Absorption lithium bromide refrigeration unit:

The absorption lithium bromide refrigeration unit is different from the traditional vapor compression refrigeration cycle. It uses an absorbent and a heat source instead of a compressor to consume mechanical energy to perform work to realize the function of heat conduction from a low temperature medium to a high temperature medium, so as to achieve the effect of consuming heat energy and driving the non-spontaneous process. . As shown in Figure 3, the whole unit can be divided into two parts, the left half is the cycle process of the absorbent, and the right half is the cycle process of the refrigerant. In the absorption lithium bromide refrigeration unit, lithium bromide solution is used as the absorbent, and water is used as the refrigerant. The lithium bromide solution has the characteristics of low saturated water vapor pressure, and the absorber can absorb the water released by the water in the evaporator whose temperature is much lower than its steam, so as to complete the cooling requirements of the refrigerant; use a heat source to provide heat energy for the lithium bromide solution in the generator, and then generate high-temperature and high-pressure water vapor, which is provided to the condenser to release heat to the outside world. The overall cycle can complete the effective utilization of the waste heat gas of the gas turbine, and can provide high-efficiency cold energy to the outside world.

The thermodynamic coefficient is used to express the cooling power of the absorption lithium bromide refrigeration system, and its expression is

In formula (6), Q ₀ is the cooling capacity; Q _g is the heat consumption; for lithium bromide refrigeration units, the value of η _COP is generally about 0.9 to 1.2, and the electric energy saved by direct supply of cold energy is considered as the absorption type. The operating income of the lithium bromide refrigeration unit can be determined as

In formula (7), t _s is the cooling time, _h ;

Phase change energy storage: When considering the operating economic benefits of phase change energy storage devices, thermal energy is lost during storage and release, then the operating benefits of phase change energy storage devices are:

Cold/Heat Balance Constraints:

L _H = P _EB + P _ph (9)

L _c =P _air +P _ph (10)

There are upper and lower limits for the capacity and charge and discharge power of phase change energy storage:

-(P _ph ) _max ≤P _ph ≤(P _ph ) _max (11)

0≤E _ph ≤max[∫(P _h -L _h )dt] (12)

Among them, η is the thermal efficiency of the energy storage device; L _h and L _c represent the heating load and cooling load, respectively, MW; t _on represents the time when the heat generated by the triple power supply unit is greater than the thermal load, h; P _EB represents the heat release power of the electric boiler ; P _ph represents the energy storage or discharge power of the phase change energy storage device; P _air represents the cooling power of the air conditioner; E _ph represents the thermal energy storage in the phase change energy storage device, MW·h.

Then the objective function of the operating income of the triple supply system is:

C(t)=C _CCHP (t)+C _lb (t)+C _pc (t) (13)

Energy storage battery model: The energy storage battery will affect its service life during the charging and discharging process. The use cost function and power, capacity, state of charge constraints and energy storage state balance constraints of the energy storage battery are given below.

SOC _T = SOC ₀ (18)

Among them, λ _bat is the dispatching cost coefficient of the energy storage battery, P _bat (t) and E _bat (t) are the charging and discharging power and capacity of the energy storage battery, respectively, η _c and η _d are the charging and discharging efficiency of the energy storage battery, respectively , SOC _T and SOC ₀ are the amount of charge at the final time and the initial time of the plan ahead of time, respectively; FIG. 4 is a structural diagram of the CCHP type microgrid system provided by the present invention, as shown in FIG. 4 .

Step 108: According to the power grid compensation fee, the load adjustment fee, and the operation revenue model of the triple supply system, the CCHP type microgrid is optimally scheduled according to the multi-time-scale optimal scheduling strategy, so that the operation of the CCHP type microgrid can be achieved. The cost is the smallest; the multi-time-scale optimal scheduling strategy includes a day-ahead optimal scheduling phase, an intra-day rolling optimization phase, an ultra-short-term scheduling phase, and an ultra-ultra-short-term scheduling phase; in different scheduling phases, the objective of optimal scheduling for the CCHP-type microgrid function is different.

Based on the idea of rolling optimization and feedback correction, model predictive control can better solve the optimal control problem with many uncertain factors, and has strong anti-interference ability and robustness; at the same time, MPC can also easily account for many It has no specific requirements for the form of the prediction model. In addition, MPC can also track multiple optimization objectives at the same time, so it is especially suitable for the random fluctuation of renewable energy output power, the uncertainty of load power and the market price of electricity. On the other hand, in the intraday dispatch of microgrid, in addition to the goal of achieving the goal of tracking the day-ahead plan at the minimum unit adjustment cost, it is also necessary to take into account the energy storage load. The state of charge (SOC) meets the requirements of daily operating energy balance to ensure that the energy storage can meet the operating requirements of the next dispatch day, while MPC can effectively achieve the simultaneous tracking of these multiple optimization goals, and has good applicability; In addition, MPC obtains ultra-short-term power forecast information in real time during intraday scheduling, and uses actual scheduling results and new forecast information as feedback information for rolling optimization scheduling, which can eliminate uncertainties in the microgrid to the greatest extent possible for optimal operation scheduling. impact of the program. The invention proposes a dynamic optimal scheduling strategy for a combined cooling, heating and power supply type microgrid based on model predictive control. The optimization model takes the minimum operating cost in a future optimization period as the optimization goal to perform rolling calculation, and introduces feedback correction to effectively correct prediction errors and errors in a timely manner. The deviation of the optimal scheduling result caused by random factors can realize the correction of the system scheduling scheme and realize the optimal scheduling of various energies.

Dynamic Optimal Scheduling Model Based on Model Predictive Control

Random fluctuations in the output power of renewable energy, peak-to-valley changes in thermal and power loads, etc. lead to the need to optimize the core co-supply equipment during the day, so that the system can respond to changes in photovoltaic, wind power, and user demand for cooling, heating and power in a timely manner. Therefore, the dynamic optimal scheduling based on model predictive control proposed by the present invention mainly includes a day-ahead scheduling plan, an intraday rolling optimization model, an ultra-short-term scheduling model, an ultra-ultra-short-term scheduling model and a real-time feedback correction stage. The day-ahead scheduling plan takes 1h as the time interval and takes the lowest daily operating cost as the goal, and obtains the optimal scheduling plan value throughout the day. Intra-day rolling optimization is a process of continuous revision and refresh of the previous plan, with a time interval of 15 minutes. Its main goal is to use the actual operation data of the system, and calculate by the prediction model to correct the follow-up renewable energy and load power, and adjust it with micro-sources. The minimum cost is the objective function. In the ultra-short-term scheduling stage, the period is 10 minutes, and the energy storage device penalty function is added to the existing objective function to ensure the balance of the energy storage state. The ultra-ultra-short-term scheduling time scale is 5 minutes to further absorb the fluctuation of load and renewable energy. The system scheduling block diagram is shown in Figure 5.

Energy-optimized management of microgrids at multiple time scales:

① In the day-ahead scheduling stage, with the hour (h) as the time scale, based on the day-ahead forecast and real-time electricity price of renewable energy and load, and under the premise of satisfying the system constraints, establish the optimal economic dispatch with the lowest system operating cost as the optimization goal Model. Due to the randomness of renewable energy and load power, day-ahead forecasts often have large errors, so it is necessary to add ultra-short-term scheduling links with better real-time performance to revise day-ahead plans.

According to the day-ahead load and renewable energy forecast information, the optimal scheduling of CCHP-type microgrid is carried out. With 1 hour as the time scale and the lowest daily operating cost as the goal, the adjustable load is dispatched so that it closely follows the renewable energy generation and reduces the adjustable load. Based on the scheduling pressure of distributed power sources, the optimal scheduling scheme for the whole day is obtained.

The goal of the day-ahead optimal operation of the combined cooling, heating and power microgrid is to minimize the operating cost of the system, that is, the objective function is

Among them, NR is the number of controllable distributed power sources; R is the number of renewable energy sources; I is the number of shedable loads; J is the number of adjustable loads; _ηbat is the charge and discharge efficiency of the energy storage battery; P _i (t) represents the output of the i-th distributed power source at time t; C _i (P _i (t)) represents the cost of the i-th distributed power source when the output is P _i (t); ΔT is the duration of the scheduling cycle.

Controllable distributed power model: For controllable distributed power, its operating cost is mainly composed of operation and maintenance costs and fuel costs, while the constraints of controllable distributed power mainly consider output power constraints, operating ramps rate constraints.

|P _DGg (t)-P _DGg (t-1)|≤ΔP _DGg (23)

Equation (21) is the operating cost function of the distributed power generation, a, b, c are the quadratic, first-order and constant term coefficients of the operating cost function; Equation (22) is the active power constraint of the distributed power generation, the controllable distribution The power supply must operate within a certain range; formula (23) is the constraint of the operating ramp rate, and ΔP _DGg is the power change of the distributed power supply within the time Δt.

Interaction model between microgrid and large grid: C _grid (t)=c _grid (t)P _grid (t) (24)

为微电网与大电网交互的最大有功功率。In formula (26),

It is the maximum active power of the interaction between the microgrid and the large grid.

②Ultra-short-term scheduling takes 10 minutes as the time scale to make ultra-short-term forecasts for renewable energy and load. Compared with conventional multi-time-scale scheduling, this paper uses energy storage device capacity constraints and state-of-charge constraints that reflect long-term characteristics as penalties. The function is added to the objective function to ensure the balance of the energy storage state, and to coordinate the global economy of the system and the local economic optimization of short-term dispatch brought about by long-term dispatch of distributed power sources and energy storage coordination.

Since there may be a large error between the day-ahead forecast and the actual value of renewable energy and load, adding rolling optimization links can reduce the deviation between day-ahead planning and ultra-short-term scheduling, and reduce the impact of renewable energy and load uncertainty on scheduling accuracy. The goal of rolling optimization is to adjust the output correction value of the subsequent period at time T ₀ to minimize the adjustment cost on the basis of satisfying the load balance, and its objective function is:

In the formula, ΔP _i (t) represents the power adjustment amount of the distributed power source i, and T ₀ represents the current time node.

③ In the actual microgrid operation, due to the large time interval between the day-ahead plan and the ultra-short-term scheduling, and the large deviation of the day-ahead plan, a rolling optimization link is added, and the latest system status information is used to correct the follow-up renewable energy and Load forecast power, and constantly refresh and revise the previous plan.

In the ultra-short-term scheduling stage, the ultra-short-term forecast based on similar days is used for renewable energy and load in a period of 10 minutes. According to the renewable energy and load-side power changes obtained from the ultra-short-term forecast, at a certain time t, fine-tuning the output of each unit makes the ultra-short-term dispatch cost and the comprehensive cost corresponding to the rolling dispatch the closest. The objective function is:

表示超短期调度t时刻综合调度成本，

表示该时段滚动优化对应成本。In the formula,

represents the comprehensive scheduling cost of ultra-short-term scheduling at time t,

Indicates the corresponding cost of rolling optimization in this period.

Due to the short scheduling period of ultra-short-term, the local economy of the scheduling result is the highest, but the balance of the energy storage state cannot be guaranteed during the whole day. In order to make the scheduling result can integrate the global economy of the system generated by the reasonable coordination of distributed power and energy storage in the long-term scheduling In addition, in order to avoid overcharging and overdischarging of the battery, reduce the number of charging and discharging and prolong its service life, the capacity constraints of energy storage devices and state of charge constraints are added to the original ultra-short-term dispatching objective function. And the consistent constraint of the state of the energy storage device at the beginning and end of the cycle is used as a penalty function, as shown in the following formula:

where σ is the penalty factor, g is the inequality constraint, and h is the equality constraint.

④ Since there is a large time span between ultra-short-term scheduling and real-time control, this paper adds an ultra-ultra-short-term scheduling link between the two to absorb the power fluctuations of renewable energy and loads, and reduce the controllable distributed power supply in the real-time control link. The objective function is to minimize the output and load deviation adjustment of each distributed power source.

The scheduling time period is short, with 5min as the time scale, to further absorb the fluctuation of load and renewable energy (ie, the fluctuation of net load). Its goal is to minimize the output and load adjustment deviation of each distributed power source at the current moment, so as to ensure the stability of the system against the fluctuation of renewable energy. Its objective function is:

Restrictions:

P _min (k+n)≤P(k+n)≤P _max (k+n) (33)

Among them: P _r (k+n) is the reference value of active power output, obtained by short-term scale optimization; P(k+n) is the predicted value of distributed power generation, large power grid, energy storage and load shedding optimized by ultra-short-term scale; P ₀ (k+n) is the initial value of the active power output of each part of the ultra-short-term scale optimization, obtained from actual measurement; Δu(k+t-1) is the predicted value in the [k+t-1,k+t] period The active power output increment is the optimized control variable; δ _alj (k+n) is the control variable of the adjustable load.

The traditional multi-time scale framework is relatively simple, and the span between each time scale is large. The present invention adds ultra-short-term scheduling and ultra-ultra-short-term scheduling stages on the basis of the traditional scheduling mode, and realizes the step-by-step refinement of the energy management of the microgrid. The power unbalance caused by the uncertainty of wind power at different stages is balanced; the multi-time scale scheduling strategy based on model prediction is adopted, and each time rolling optimization is performed, the updated short-term predicted power value is compared with the actual measured value, and a closed-loop control is formed. Feedback corrections are made to ensure better stability and robustness of the scrolling strategy. The conventional multi-time-scale scheduling is open-loop control; meanwhile, in the ultra-short-term scheduling, the penalty function of the energy storage device reflecting the long-term optimization characteristics is considered, so as to coordinate the global and local economic optimization of the system.

FIG. 6 is a structural diagram of a CCHP type microgrid multi-time scale optimal dispatching system provided by the present invention. As shown in FIG. 6 , a CCHP type microgrid multi-time scale optimal dispatching system includes:

The parameter acquisition module 601 is used to acquire the shedable load parameters of the shedable load, the adjustable load parameters of the adjustable load, the parameters of the CCHP unit, the parameters of the absorption lithium bromide refrigeration unit and the parameters of the phase change energy storage device; Including dispatching cost parameters, market electricity price, shaving rate and rated power of said shaving load; said adjustable load parameters including adjustable rate and active power of said adjustable load; said CCHP unit parameters including CCHP unit heat price, cooling price of CCHP units, natural gas price, power generation, heat output of unit i in time period t, cooling output of unit i in time period t, amount of natural gas used and hot gas conversion efficiency of unit i; the absorption lithium bromide refrigeration unit The parameters include the cooling duration, the heating power of the gas turbine and the thermodynamic coefficient of the electric cooling efficiency; the parameters of the phase change energy storage device include the thermal efficiency, heating load and cooling load of the energy storage device.

The grid compensation fee determination module 602 is configured to determine the grid compensation fee for shedding the shedable load according to the shedable load parameter.

The load adjustment fee determination module 603 is configured to determine the load adjustment fee of the adjustable load according to the adjustable load parameter.

The CCHP unit benefit determination module 604 is configured to determine the CCHP unit benefit according to the CCHP unit parameter.

The operation benefit determination module 605 of the absorption type lithium bromide refrigeration unit is configured to determine the operation income of the absorption type lithium bromide refrigeration unit according to the parameters of the absorption type lithium bromide refrigeration unit.

The phase change energy storage device operation benefit determination module 606 is configured to determine the phase change energy storage device operation benefit according to the phase change energy storage device parameters.

A triple supply system operation benefit model establishment module 607 is configured to establish a triple supply system operation benefit model according to the CCHP unit revenue, the absorption lithium bromide refrigeration unit operation revenue, and the phase change energy storage device operation revenue.

The optimal scheduling adjustment module 608 is configured to perform optimal scheduling on the CCHP-type microgrid according to the multi-time-scale optimal scheduling strategy according to the power grid compensation fee, the load adjustment fee and the triple supply system operation benefit model, so that the CCHP The operating cost of the CCHP-type microgrid is the smallest; the multi-time-scale optimal scheduling strategy includes a day-ahead optimal scheduling phase, an intraday rolling optimization phase, an ultra-short-term scheduling phase, and an ultra-ultra-short-term scheduling phase; in different scheduling phases, the CCHP-type microgrid The objective functions for optimal scheduling are different.

FIG. 7 is a block diagram of the multi-time scale scheduling structure of CCHP type microgrid based on demand side response provided by the present invention. As shown in FIG. 7 , the multi-time scale optimal energy management scheme of CCHP type microgrid based on demand side response provided by the present invention , compared with the prior art, it has the following beneficial effects:

1) Improve the demand-side response strategy: Consider flexible loads, and establish a CCHP system interaction mechanism on the basis of traditional demand-side response to achieve multi-energy complementation such as cold, heat, and electricity.

2) Improve the energy storage device: the initial investment cost of cold storage and thermal storage equipment is high, and the equipment is large in size, and the cost of life loss is too high. The phase change energy storage device uses the phase change latent heat of the phase change material to store and release energy. Its unit calorific value is large, the working temperature is stable, and the thermal conductivity is good, which can compensate for the gap between the supply and the load and avoid the output of the triple supply unit. Compared with the ice storage and hot water storage tank equipment, it is more suitable for the triple supply system.

3) Improve the optimal scheduling strategy of microgrid: Due to the large time span between ultra-short-term scheduling and real-time control, this paper adds an ultra-ultra-short-term scheduling model on the basis of the conventional multi-time-scale energy management framework to reduce renewable energy and The power fluctuation of the load reduces the adjustment pressure of the controllable distributed power supply in the real-time control link.

4) Improve the objective function: add the penalty function of the energy storage device to the ultra-short-term scheduling objective to avoid excessive charging and discharging times and damage the battery life, and use this to coordinate the global economy of the system brought about by the coordination of long-term scheduling of distributed power sources and energy storage. In addition, lithium bromide operating income, phase change energy storage income and CCHP operating income are added to the total cost, which is more convenient to accurately calculate the total operating cost.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method.

The principles and implementations of the present invention are described herein using specific examples. The descriptions of the above embodiments are only used to help understand the method and the core idea of the present invention; meanwhile, for those skilled in the art, according to the present invention There will be changes in the specific implementation and application scope. In conclusion, the contents of this specification should not be construed as limiting the present invention.

Claims (9)

1. a CCHP type microgrid multi-time scale optimal scheduling method, is characterized in that, comprises: Obtain shedable load shedding parameters, adjustable load adjustable load parameters, CCHP unit parameters, absorption lithium bromide refrigeration unit parameters and phase change energy storage equipment parameters; the shedable load parameters include dispatch cost parameters, market electricity prices , the shaving rate and the rated power of the shaving load; the adjustable load parameters include the adjustable rate and the active power of the adjustable load; the CCHP unit parameters include the heating price of the CCHP unit, the cooling price of the CCHP unit, Natural gas price, power generation, heat output of unit i in time period t, cooling output of unit i in time period t, the amount of natural gas used, and the hot gas conversion efficiency of unit i; the parameters of the absorption lithium bromide refrigeration unit include cooling duration, gas turbine The thermodynamic coefficient of the heating power and the electric cooling efficiency; the parameters of the phase change energy storage device include the thermal efficiency, heat load and cooling load of the energy storage device; Determine the grid compensation fee for shedding the shedable load according to the shedable load parameter; determining a load adjustment fee for the adjustable load according to the adjustable load parameter; Determine the CCHP unit revenue according to the CCHP unit parameters; Determine the operating income of the absorption lithium bromide refrigeration unit according to the parameters of the absorption lithium bromide refrigeration unit; Determine the operating income of the phase change energy storage device according to the parameters of the phase change energy storage device; According to the income of the CCHP unit, the operation income of the absorption lithium bromide refrigeration unit and the operation income of the phase change energy storage device, an operation income model of the triple supply system is established; According to the power grid compensation fee, the load adjustment fee and the operation benefit model of the triple supply system, the CCHP type microgrid is optimally scheduled according to the multi-time-scale optimal scheduling strategy, so that the operation cost of the CCHP type microgrid is minimized; The multi-time-scale optimal scheduling strategy includes a day-ahead optimal scheduling phase, an intra-day rolling optimization phase, an ultra-short-term scheduling phase, and an ultra-ultra-short-term scheduling phase; in different scheduling phases, the objective functions for optimal scheduling of the CCHP-type microgrid are different; According to the power grid compensation fee, the load adjustment fee and the operation benefit model of the triple supply system, the CCHP type microgrid is optimally scheduled according to the multi-time-scale optimal scheduling strategy, so that the operating cost of the CCHP type microgrid is reduced. Minimum, specifically including: In the day-ahead optimization scheduling phase, according to the formula

Perform optimal scheduling on the CCHP-type microgrid; wherein, NR is the number of controllable distributed power sources; I is the number of shedable loads; J is the number of adjustable loads; P _i (t) represents the i-th distribution C _i (P _i (t)) represents the cost of the i-th distributed power source when the output is P _i (t), ΔT is the duration of the scheduling cycle; C _DGg (t) is the controllable distribution C _bat (t) is the use cost of the battery; C _grid (t) is the interaction cost with the large power grid; C _cli (t) is the grid compensation cost for removing the shedable load; C _alj (t) is the load adjustment cost of the adjustable load; C(t) is the operating income of the triple supply system; T is the adjustment period; During the intraday rolling optimization phase, according to the formula

Perform optimal scheduling on the CCHP type microgrid; wherein, ΔP _i (t) is the power adjustment amount of the distributed power source i; T ₀ is the current time node; In the ultra-short-term scheduling phase, according to the formula

Perform optimal scheduling on the CCHP-type microgrid; wherein,

represents the comprehensive scheduling cost of ultra-short-term scheduling at time t,

Indicates the corresponding cost of rolling optimization in this period; In the ultra-ultra-short-term scheduling phase, according to the formula

Perform optimal scheduling on the CCHP-type microgrid; wherein,

;

P _r (k+n) is the reference value of active power output, which is obtained by short-term scale optimization; P(k+n) is the predicted value of distributed power generation, large power grid, energy storage and load shedding optimized by ultra-short-term scale; P ₀ (k+n) is the initial value of the active power output of each part of the ultra-short-term scale optimization; Δu(k+t-1) is the predicted active power output increment in the period of [k+t-1, k+t];

is the active power output reference value of the controllable distributed power source;

Active power output reference value for large grid interaction;

is the reference value of active power output for the shedable load;

is the active output reference value of the adjustable load;

It is the active power output reference value of the energy storage battery. 2 . The multi-time-scale optimal scheduling method for CCHP type microgrid according to claim 1 , wherein the determining the grid compensation fee for removing the shedable load according to the shedable load parameter specifically includes: 2 . According to the formula

Determine the grid compensation cost for removing the shedable load; wherein, C _cli (t) is the grid compensation cost for removing the shedable load; ε _cli (t) is the dispatch cost coefficient of the shedable load; c _grid (t) represents the electricity price in the buying and selling market; ρ _cli (t) is the cut rate;

is the rated power of the load i can be cut. 3. The CCHP-type microgrid multi-time scale optimal scheduling method according to claim 2, wherein the determining the load adjustment cost of the adjustable load according to the adjustable load parameter specifically includes: According to the formula

Determine the load adjustment cost of the adjustable load, wherein C _alj (t) is the load adjustment cost of the adjustable load; δ _alj (t) is the adjustable rate; P _alj (t) is the adjustable load j Active power; T is the adjustment period. 4. The CCHP type microgrid multi-time-scale optimal scheduling method according to claim 3, wherein the determining the CCHP generating unit revenue according to the CCHP generating unit parameter specifically includes: According to the formula C _CCHP (t)=c _grid (t)P _i (t)+c _H (t)P _h (t)+c _Q (t)P _Q (t)-c _F (t)P _F (t ) to determine the revenue of CCHP units; where C _CCHP (t) is the revenue of CCHP units; c _H (t) is the heat price of CCHP units; c _Q (t) is the cooling price of CCHP units; c _F (t) is the price of natural gas; P _i (t) is the power generation; P _h (t) is the heat output of unit i in time period t; P _Q (t) is the cooling output of unit i in time period t; P _F (t)=P _h (t)/ μ _H is the amount of natural gas used, and μ _H is the hot gas conversion efficiency of unit i. 5. The multi-time-scale optimal scheduling method for CCHP type microgrid according to claim 4, characterized in that, determining the operation benefit of the absorption lithium bromide refrigeration unit according to the parameters of the absorption lithium bromide refrigeration unit, specifically comprising: According to the formula

Determine the operating income of the absorption lithium bromide refrigeration unit; wherein, C _lb (t) is the operating income of the absorption lithium bromide refrigeration unit; t _s is the cooling time; P _h is the heating power of the gas turbine; η _COPe is the thermodynamic coefficient of the electric cooling efficiency ; η _COP is the refrigeration power of the lithium bromide refrigeration system. 6. The CCHP type microgrid multi-time scale optimal scheduling method according to claim 5, wherein the determining the operating income of the phase change energy storage device according to the phase change energy storage device parameters specifically includes: According to the formula

Determine the operating income of the phase change energy storage device; where C _pc (t) is the operating income of the phase change energy storage device; η is the thermal efficiency of the energy storage device; L _h is the heat load; L _c is the cooling load; t _on|winter is In winter, the heat generation time of the triple supply unit is greater than the heat load; t _on|summer is the time when the heat generation of the triple supply unit is greater than the heat load in summer. 7. The CCHP-type microgrid multi-time-scale optimal scheduling method according to claim 6, characterized in that, according to the CCHP unit revenue, the absorption lithium bromide refrigeration unit operating revenue and the phase change energy storage device Operational income Establish a model of operation income of the triple supply system, which includes: According to the formula C(t)=C _CCHP (t)+C _lb (t)+C _pc (t), the operation income model of the triple supply system is established; C(t) is the operation income of the triple supply system. 8. A CCHP type microgrid multi-time scale optimal dispatching system is characterized in that, comprising: The parameter acquisition module is used to acquire the load shedding parameters of the shedable load, the adjustable load parameters of the adjustable load, the parameters of the CCHP unit, the parameters of the absorption lithium bromide refrigeration unit, and the parameters of the phase change energy storage device; the load shedding parameters include: Dispatching cost parameters, market electricity price, shaving rate, and rated power of the shaving load; the adjustable load parameters include the adjustable rate and the active power of the adjustable load; the CCHP unit parameters include the CCHP unit heat price , CCHP unit cooling price, natural gas price, power generation, the heat output of unit i in time period t, the cooling output of unit i in time period t, the amount of natural gas used and the hot gas conversion efficiency of unit i; the parameters of the absorption lithium bromide refrigeration unit Including the cooling time, the heating power of the gas turbine and the thermodynamic coefficient of the electric cooling efficiency; the parameters of the phase change energy storage device include the thermal efficiency, heating load and cooling load of the energy storage device; a power grid compensation cost determination module, configured to determine the power grid compensation cost for removing the shedable load according to the shedable load parameter; a load adjustment fee determination module, configured to determine the load adjustment fee of the adjustable load according to the adjustable load parameter; The CCHP unit revenue determination module is used to determine the CCHP unit revenue according to the CCHP unit parameters; an absorption-type lithium bromide refrigeration unit operating income determination module, configured to determine the absorption-type lithium bromide refrigeration unit operating income according to the parameters of the absorption-type lithium bromide refrigeration unit; a phase-change energy storage device operating benefit determination module, configured to determine the phase-change energy storage device operating revenue according to the phase-change energy storage device parameters; A module for establishing an operation benefit model of the triple supply system is used to establish an operation benefit model of the triple supply system according to the income of the CCHP unit, the operation income of the absorption lithium bromide refrigeration unit, and the operation income of the phase change energy storage device; The optimal scheduling adjustment module is configured to perform optimal scheduling on the CCHP type microgrid according to the multi-time scale optimal scheduling strategy according to the power grid compensation fee, the load adjustment fee and the triple supply system operation revenue model, so that the CCHP type microgrid can be optimally scheduled. The operating cost of the microgrid is the smallest; the multi-time scale optimal scheduling strategy includes a day-ahead optimal scheduling phase, an intraday rolling optimization phase, an ultra-short-term scheduling phase, and an ultra-ultra-short-term scheduling phase; in different scheduling phases, the CCHP-type microgrid is carried out. The objective functions of the optimal scheduling are different; the CCHP-type microgrid is optimally scheduled according to the multi-time-scale optimal scheduling strategy according to the power grid compensation fee, the load adjustment fee, and the triple-supply system operating profit model, so that CCHP-type microgrids have minimal operating costs, including: In the day-ahead optimization scheduling phase, according to the formula

Perform optimal scheduling on the CCHP-type microgrid; wherein, NR is the number of controllable distributed power sources; I is the number of shedable loads; J is the number of adjustable loads; P _i (t) represents the i-th distribution C _i (P _i (t)) represents the cost of the i-th distributed power source when the output is P _i (t), ΔT is the duration of the scheduling cycle; C _DGg (t) is the controllable distribution C _bat (t) is the use cost of the battery; C _grid (t) is the interaction cost with the large power grid; C _cli (t) is the grid compensation cost for removing the shedable load; C _alj (t) is the load adjustment cost of the adjustable load; C(t) is the operating income of the triple supply system; T is the adjustment period; During the intraday rolling optimization phase, according to the formula

Perform optimal scheduling on the CCHP type microgrid; wherein, ΔP _i (t) is the power adjustment amount of the distributed power source i; T ₀ is the current time node; In the ultra-short-term scheduling phase, according to the formula

Perform optimal scheduling on the CCHP-type microgrid; wherein,

represents the comprehensive scheduling cost of ultra-short-term scheduling at time t,

Indicates the corresponding cost of rolling optimization in this period; In the ultra-ultra-short-term scheduling phase, according to the formula

Perform optimal scheduling on the CCHP-type microgrid; wherein,

;

P _r (k+n) is the reference value of active power output, obtained by short-term scale optimization; P(k+n) is the predicted value of distributed power generation, large power grid, energy storage and load shedding optimized by ultra-short-term scale; P ₀ (k+n) is the initial value of the active power output of each part of the ultra-short-term scale optimization; Δu(k+t-1) is the predicted active power output increment in the period of [k+t-1, k+t];

is the active power output reference value of the controllable distributed power source;

Active power output reference value for large grid interaction;

is the reference value of active power output for the shedable load;

is the active output reference value of the adjustable load;

It is the reference value of the active power output of the energy storage battery. 9. The CCHP type microgrid multi-time scale optimal dispatching system according to claim 8, wherein the power grid compensation fee determination module specifically comprises: Grid compensation fee determination unit, which is used according to the formula

Determine the grid compensation cost for removing the shedable load; wherein, C _cli (t) is the grid compensation cost for removing the shedable load; ε _cli (t) is the dispatch cost coefficient of the shedable load; c _grid (t) represents the electricity price in the buying and selling market; ρ _cli (t) is the cut rate;

is the rated power of the load i can be cut.

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