CN107482638B - Multi-objective dynamic optimization scheduling method for combined cooling heating and power supply type micro-grid - Google Patents

Multi-objective dynamic optimization scheduling method for combined cooling heating and power supply type micro-grid Download PDF

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CN107482638B
CN107482638B CN201710600001.3A CN201710600001A CN107482638B CN 107482638 B CN107482638 B CN 107482638B CN 201710600001 A CN201710600001 A CN 201710600001A CN 107482638 B CN107482638 B CN 107482638B
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CN107482638A (en
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罗平
孙作潇
章坚民
陈巧勇
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Hangzhou Dianzi University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/383—
    • H02J3/386—
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/388—Islanding, i.e. disconnection of local power supply from the network
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/242—Home appliances
    • Y04S20/244—Home appliances the home appliances being or involving heating ventilating and air conditioning [HVAC] units

Abstract

The invention discloses a multi-target dynamic optimization scheduling method for a combined cooling heating and power type micro-grid; in the optimization process, the characteristic of the level-shift load is considered at first, then the schedulability of the source side and the energy storage system is established and considered, the output of each time period in the three types of controllable units is taken as an optimization variable, the lowest system operation cost and the lowest pollutant emission control cost are taken as optimization scheduling targets, and a mathematical model of the multi-objective optimization scheduling problem in the day-ahead is established; solving the optimization problem by adopting a multi-target particle swarm optimization algorithm guided by 'excellent particles', namely respectively finding two points with the lowest system operation cost and the lowest pollutant discharge treatment cost by using a single-target genetic algorithm, and guiding the optimization direction of the multi-target particle swarm optimization algorithm by using the points as the 'excellent particles'; the invention provides an effective multi-objective dynamic optimization scheduling method, which has certain significance for improving the comprehensive utilization efficiency of the energy of the multi-energy coupling system and promoting the development of renewable energy.

Description

Multi-objective dynamic optimization scheduling method for combined cooling heating and power supply type micro-grid
Technical Field
The invention belongs to the technical field of micro-grids, particularly relates to a cold-heat-power combined supply type micro-grid system for a large commercial complex, and particularly relates to a source-storage-charge coordinated dispatching source-storage-charge combined cooling-heat-power combined supply type micro-grid multi-target dynamic optimization dispatching method.
Background
The combined cooling heating and power supply type micro-grid combines the advantages of a combined cooling-heating-power supply system and a micro-grid system, can recycle waste heat generated by a micro gas turbine in the micro-grid during power generation, and adopts an absorption refrigerator for refrigeration, thereby realizing combined cooling heating and power supply. The combined cooling heating and power supply type micro-grid can be used as a small-scale combined cooling and heating and power supply low-voltage power supply network to supply power or cooling and heating energy for residential areas, industrial parks or commercial areas and the like. The electric load can be provided by the micro gas turbine and the large power grid in a coordinated dispatching mode, and for the cold/heat load, on one hand, the cold/heat load can be ensured by driving the cold/heat generated by the lithium bromide absorption type cold/hot water unit by the waste heat of the smoke exhausted after the power generation of the gas turbine, and on the other hand, the cold/heat load can be satisfied by the refrigerating and heating functions of the air conditioner. From the energy supply of the user side, the combined cooling heating and power type micro-grid can provide double guarantees no matter the electric load or the cooling heating load.
However, because the combined cooling, heating and power micro-grid has a balance relationship among various energies such as cooling, heating and power, the system load demand, interactive power constraint, fuel cost and other constraint conditions need to be considered. In addition, with the development and construction of smart grids, more and more load types capable of participating in system scheduling appear on the load side, such as electric automobiles, heat storage (cold) air conditioning systems, smart water heaters, smart washing machines and the like. The load can be used as a new controllable or translatable load resource to participate in the scheduling of the combined cooling heating and power type micro-grid under the bidirectional interactive power utilization technology, and technical support is provided for the regulation and control and operation of the power system on different time scales. Therefore, how to comprehensively coordinate and schedule the schedulable resources of the cooling-heating-power combined supply type micro-grid power generation side controllable unit, the energy storage system and the load side, and under the condition of meeting the operation constraint and the equipment characteristic constraint, the scheduling scheme meeting different scheduling target requirements is obtained, so that the method has great significance for popularizing the cooling-heating-power combined supply type micro-grid, improving the utilization rate of energy and reducing environmental pollution. At present, the optimization research on the combined cooling heating and power supply type micro-grid at home and abroad mainly focuses on the dispatching of a source at a power generation side and a controllable unit at an energy storage (electricity, heat/cold) side, and the controllability research on a load side is insufficient. Even if a small amount of research work considers the influence of load shifting characteristics on the scheduling result, only the number of different types of loads which can be shifted in/out in each period is given, and the period of specific load shifting in/out is not given, so that the realizability of the scheduling result is limited. In addition, due to the operation characteristics of the energy storage equipment, the constraint of 0-1 variable and nonlinear coupling is introduced into the optimization problem, so that the solution of the optimization problem becomes more complex, and the search of an effective optimization method is another problem to be solved at the present stage.
Disclosure of Invention
Aiming at a combined cooling heating and power supply micro-grid system of a large commercial complex, the invention fully utilizes controllable units at a power generation side and an energy storage side and a load side, takes the lowest system operation cost and the lowest pollutant discharge treatment cost as an optimized scheduling target, preferentially consumes renewable energy, and takes the output of each time period in three controllable units, namely a micro gas turbine, a storage battery and a heat storage (cold) tank as an optimized variable. In order to reduce the complexity of the optimization problem and improve the solving speed, a staged optimization scheduling strategy is adopted to carry out optimization solving on the optimization problem. Firstly, according to the characteristics and the quantity of the translatable load, on the principle of preferentially absorbing renewable energy sources, the quantity of the translatable electric load capable of translating and the time period of the translatable load in/out can be determined by utilizing a particle swarm optimization algorithm and a strategy of 'quotient quantification and allowance degree'. And then, establishing a mathematical model of multi-objective optimization scheduling in the future by using the improved electric load curve under the condition of meeting system operation and model constraint. In order to effectively solve the multi-variable mixed integer nonlinear optimization problem, a multi-objective particle swarm optimization algorithm based on 'excellent particles' guidance is provided to solve the optimized scheduling model. The method is implemented according to the following steps:
step 1, determining a target electrical load curve to obtain the number of the translatable electrical loads and the time period of the translatable in/out.
Different electrical load translation targets are formulated according to different operation modes or operation environments of the micro-grid. When the micro-grid system is in a grid-connected mode, according to the electricity price data, more electric loads are arranged when the electricity price is low, and the electric load quantity is reduced as much as possible when the electricity price is high, namely the target load and the electricity price are in an inverse proportional relation. When the micro-grid is in an island operation state, in order to reduce the abandoned wind and light indexes, according to wind and light power generation data, less electric loads are arranged when the wind and light power generation power is less, and more loads are arranged when the wind and light power is more, namely the target electric loads and the wind and light output are in a direct proportional relation. The objective function of the translatable load optimization model may be expressed as:
Figure BDA0001356908960000021
in the formula, T is toneDegree period; pobj,tA target load for a period t; ps,tLoad for time period t after translation; pf,tThe original predicted load for the t period. The load translation should also satisfy: the types of loads before and after translation are unchanged, and the total amount of all the translatable loads in the scheduling period is unchanged. Furthermore, the time allowed for each type of translatable load to move in and out is also constrained.
The key point of solving the optimization scheduling of the microgrid in which the translatable load participates is how to determine the shift-in and shift-out amount and meet the constraint of the system on the translatable load according to the number of units of the translatable load at each moment. The invention provides a method of 'quantitative by quotient and remaining degree' for solving the problems, which can be expressed as:
M÷N＝S......Y (2)
wherein M is a certain component of the optimization variable; n is a constant value, and the size of N can be determined according to the situation; s is the quotient of the equation, which determines the number of cells that can be shifted out of the translational load, i.e., "quotient amount" as described above; y is the remainder of the equation, which determines the translation margin of the translatable load, i.e., "degree of redundancy" as described above. For the translation amount and the translation margin, only the shift-out amount is calculated, and the shift-in amount can be obtained by the corresponding shift-out amount and the corresponding translation margin.
And (3) carrying out optimization solution on the equations (1) and (2) by utilizing a particle swarm optimization algorithm to obtain the number of the translatable electric loads and the translatable in/out time period.
Step 2: and determining the principle of optimizing and scheduling the micro-grid in a combined cooling, heating and power mode.
For the grid-connected cooling, heating and power combined type micro-grid, the wind power and photovoltaic power generation system adopts a maximum tracking mode and preferentially uses the generated energy. In addition, due to the introduction of the heat storage (cold) tank, the cogeneration system does not need to track the change of heat (cold) load at any time, and can be used as a free variable to participate in the dispatching of the system.
And step 3: and establishing a mathematical model of the cooling, heating and power combined type microgrid optimization scheduling problem according to the optimized electrical load curve and the known wind and light data and cold/heat loads and the scheduling principle. The optimized variables of the optimized scheduling problem are the output of the storage battery, the heat storage (cold) tank and the micro gas turbine at each moment in 24 hours a day. One of two objective functions of the combined cooling heating and power supply type micro-grid is the total operation cost of the system, and the other is the system pollutant emission control cost, namely the environmental cost.
The constraints of the optimal scheduling are mainly divided into two categories: one is equipment model constraints, which are usually imposed by physical limits of equipment operation, and otherwise permanent damage may be caused to the equipment itself or even the entire microgrid system. Such constraints typically include charge-discharge power constraints of the battery and state-of-charge constraints of the battery, thermal storage level and discharge depth constraints of the thermal storage (cold) tank, power and ramp rate constraints of the gas turbine, and the like. Another type of constraint is a system operating constraint, i.e., a constraint that the system should meet during operation, which primarily includes power and energy balance constraints at each time and the remaining energy of the energy storage device, including the battery and thermal (cold) storage tank, should remain consistent at the beginning and end times within the scheduling period,
the constraint processing method adopted by the invention is also different for the two types of constraints. And for the equipment model constraint, a constraint hard processing method is adopted to force the operating variables violating the constraint elements to be assigned as boundary values. For the constraint of power balance in the system operation constraint, a dimension reduction processing method is generally adopted, namely N variables in an equation are assumed, and N-1 variables are selected as independent variables. The remaining one is a dependent variable whose value is determined by the values of the other independent variables together with the constraint equation. For dispatching the periodic constraint, the invention adopts a flexible constraint processing method, and adds the condition of violating the constraint as a penalty item to the total running cost by using a penalty function method, thereby forming a new objective function:
and 4, solving the optimized scheduling model by using a multi-objective particle swarm optimization algorithm based on 'excellent particles' guidance.
Firstly, a single-target genetic algorithm is utilized to carry out optimized scheduling on the lowest running cost and environment cost of the combined cooling heating and power type micro-grid system as targets respectively, and an optimized scheduling result is stored. Secondly, after the multi-target particle swarm algorithm is initialized, two scheduling results stored by the genetic algorithm are randomly assigned to two individuals in the particle swarm algorithm. And finally, performing optimal scheduling calculation on the system by using a multi-objective particle swarm optimization algorithm.
And 5, outputting the result of the optimization calculation, namely the Pareto front edge of the total operation cost and the environmental cost of the system and the output of the storage battery, the heat storage tank and the micro gas turbine in each period.
The method of the invention has the advantages and beneficial results that:
1. the combined cooling heating and power supply type micro-grid can solve the problem that a large number of distributed power supplies are connected into a large power grid, and meanwhile, due to the intelligent and flexible control characteristics of the micro-grid, the micro-grid has great potential in the aspects of solving the problems of environmental pollution, energy shortage, improving the power supply reliability and the energy utilization rate and the like. In addition, with the development of smart meters and smart appliances, loads of electric vehicles, smart water heaters, smart washing machines and the like on a user side can be used as translatable load resources to participate in the dispatching of the combined cooling heating and power micro-grid under the bidirectional interaction power utilization technology, so that the operation flexibility, reliability and economy of the micro-grid system are improved. When the optimization scheduling problem of the combined cooling heating and power type microgrid is researched, the schedulability of the source side, the energy storage system and the load side is comprehensively considered, so that the obtained result is more in line with the marketization requirement of the power system. In addition, the result of multi-objective optimization scheduling is given by using pareto frontier, and more choices can be provided for actual operators than the result of processing multi-objective weighting into a single-objective optimization problem.
2. The invention can determine the shifting-in and shifting-out quantity of each type of translatable electric load in each period and the corresponding translation period by adopting a method of 'using the quotient of the fixed quantity and the remaining degree' under the condition of giving the optimized target electric load. That is, it is not only known the number of cells that the load is shifted in and out during this period, but also that the load being shifted out is adjusted to a particular time. For a centralized control combined cooling heating and power micro-grid system such as a large commercial complex, the operability of a scheduling result is greatly improved.
3. Because the optimization scheduling problem of the combined cooling heating and power supply type microgrid system not only relates to conversion and coupling of various energy source flows, but also relates to an electric energy and cold/heat energy storage system, the optimization scheduling problem belongs to a multi-variable mixed integer nonlinear programming problem from the mathematical perspective, so that the conventional algorithms such as a traditional interior point method and the like are invalid, and the calculation efficiency of the methods such as a mixed integer, a penalty function, a smooth function and the like is poor. The optimization problem is solved by adopting a multi-target particle swarm optimization algorithm guided by 'excellent particles', and the advantages of the particle swarm optimization algorithm and the genetic algorithm are combined, so that the calculation speed and the global search capability of the algorithm are improved.
Drawings
FIG. 1 is a flow chart of a multi-objective particle swarm optimization algorithm based on "excellent particles" guidance provided by the present invention;
FIG. 2 is a schematic diagram of a basic CCHP microgrid architecture according to an embodiment of the present invention;
FIG. 3 is a summer typical solar, thermal/cold load predicted effort and real time electricity price curve for a specific example of the present invention;
FIG. 4 is a plot of power demand per hour for the translatable load sanitizer, washer, and water heater in one embodiment of the present invention;
FIG. 5 is a target and optimized load curve for an CCHP microgrid according to an embodiment of the present invention;
FIG. 6 is a Pareto frontier graph of system optimization scheduling in an embodiment of the present invention;
FIG. 7 is a graph of the output of the power unit at the lowest operating cost of the system in accordance with an embodiment of the present invention;
FIG. 8 is a graph of the output of the cold energy unit at the lowest operating cost of the system in one embodiment of the invention;
FIG. 9 is a graph of the output of the power unit at the lowest environmental remediation cost for an embodiment of the invention;
FIG. 10 is a graph of the output of the cold energy unit at the lowest environmental remediation cost in one embodiment of the invention;
fig. 11 shows the result of the scheduling of the storage battery and the heat storage (cold) tank SOC in one embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific embodiments.
The invention provides a multi-objective dynamic optimization scheduling method for a combined cooling heating and power type microgrid, which is implemented according to the following steps.
Step 1, determining a target electrical load curve to obtain the number of the translatable electrical loads and the time period of the translatable in/out. The specific steps of the electric load translation implementation algorithm are as follows:
(1) basic data is input.
The unit number of each type of translatable electric load at each moment, the electricity utilization characteristics of each type of translatable electric load, electric load data predicted by day-ahead scheduling, wind power generation and photovoltaic power generation prediction data and real-time electricity price are included.
(2) And determining the target electric load according to the corresponding mechanism.
When the micro-grid system is in a grid-connected mode, according to the electricity price data, more electric loads are arranged when the electricity price is low, and the load is reduced as much as possible when the electricity price is high, namely, the target electric loads and the electricity price are in an inverse proportional relation, as shown in formula (1):
Figure BDA0001356908960000051
in the formula DtFor electricity prices at t-th time in the scheduling period, Pobj,tTarget load for time period t, Pf,tAnd T is the original predicted load of the T period, and T is the scheduling period.
When the micro-grid is in an island operation state, in order to reduce the drop of wind and light indexes, according to wind and light power generation data, less electric loads are arranged when the wind and light power generation power is less, and more electric loads are arranged when the wind and light power is more, that is, the target electric load and the wind and light output are in a direct proportional relation, as shown in formula (2):
Figure BDA0001356908960000052
in the formula, WPtAnd predicting the power of wind power and photovoltaic power generation at the t moment in the scheduling period.
(3) And solving the translatable load model.
Substituting the formula (1) or the formula (2) into the formula (3), obtaining the number of units which can be shifted out of the electric load in a translation mode and the translation margin of the electric load which can be shifted out by adopting a particle swarm optimization algorithm and utilizing a method of 'quantitative quotient and remaining degree' defined by the formula (4), thereby obtaining the number of units which are shifted in and out of the load at the moment and the specific time to which the shifted-out load is adjusted;
wherein the formula (3) is
Figure BDA0001356908960000061
In the formula, T is a scheduling period; pobj,tA target load for a period t; ps,tLoad for time period t after translation; pf,tThe original predicted load for the t period; wherein the load translation satisfies: the types of the loads before and after translation are unchanged, and the total amount of all the translatable loads in the scheduling period is unchanged; each type of translatable load has a time margin allowing translation, and can be constrained by Y in equation (4).
Formula (4) is
M÷N＝S......Y (4)
Wherein M is a certain component of the optimization variable; n is a fixed value; s is the quotient of the equation, which determines the number of units from which the load can be shifted; y is the remainder of the equation, which determines the translation margin of the translatable load; for the translation amount and the translation margin, only the shift-out amount is calculated, and the shift-in amount can be calculated according to the corresponding shift-out amount and the corresponding translation margin.
And 2, establishing a mathematical model of the cooling, heating and power combined type microgrid optimization scheduling problem according to the principle of the cooling, heating and power combined type microgrid optimization scheduling problem, the optimized electrical load curve, the known wind and light data and the known cold/heat load and the scheduling principle. One of the objective functions is the total operating cost of the system:
f1(X)＝JE(X)+JO(X)+JF(X)+JB(X) (5)
in the formula, JE(X) is the energy interaction cost of the micro-grid system and the large power grid; j. the design is a squareO(X) the operating maintenance cost of the equipment; j. the design is a squareF(X) is the fuel cost of the gas turbine; j. the design is a squareB(X) is the depreciation cost of the battery and the heat storage/cold storage tank.
Another objective function is the system pollutant emission abatement cost, i.e. the environmental cost:
Figure BDA0001356908960000062
wherein n is the type of contaminant; viThe discharge and treatment cost of the ith pollutant; qi(X) is the amount of the pollutant discharged in the item i.
Therefore, the objective function of the combined cooling heating and power micro-grid can be expressed as follows:
F(X)＝min([f1(X),f2(X)]T) (7)
the constraints of the optimal scheduling are divided into two categories: one is the equipment model constraints, including:
(1) storage battery
The accumulator satisfies the charge-discharge power constraint, i.e.
-PES_ch_max≤PES≤PES_dis_max(8) In the formula, PES_ch_maxAnd PES_dis_maxMaximum allowable charging power and discharging power, PESThe battery power is negative during charging and positive during discharging.
Furthermore, the power constraint of the battery is expressed as a constraint of the SOC amplitude variation at two adjacent times, that is:
SOCi+1-SOCi≤δ (9)
in the formula, δ has different values in different operating states, i.e., in the charged or discharged state.
In addition to this, the state of charge constraints of the battery need to be considered:
SOCmin≤SOCi≤SOCmax(10)
in the formula, SOCmax、SOCminRespectively representing the upper and lower limit requirements of the state of charge of the storage battery.
(2) Heat/cold storage tank
The heat storage/cold tank adopts the same method as the storage battery in the constraint processing, wherein the heat storage level of the heat storage/cold tank corresponds to the rated capacity of the storage battery, and the energy discharge depth corresponds to the discharge depth of the storage battery;
(3) gas turbine
Power P generated by gas turbinegen(t) the upper and lower limits of power should be met:
Figure BDA0001356908960000071
in the formula
Figure BDA0001356908960000072
Is the minimum starting power of the generator;
Figure BDA0001356908960000073
the maximum generated power.
In addition, the gas turbine should also meet the ramp rate constraint:
Figure BDA0001356908960000074
in the formula, Pup、PdownThe upper and lower limit climbing rate limit values of the generator are respectively.
Another type of constraint is a system operation constraint, i.e., a constraint that the system should meet in operation, such constraints including:
(1) power and energy balance constraints
The system should meet power balance in operation, and therefore, at various time periods:
Pload(t)＝Pgen(t)+PES(t)+PPV(t)+PWT(t) (13)
in the formula, Pload(t)、PPV(t)、PWT(t) predicting the generating power for the optimized electrical load power, photovoltaic and wind power respectively; pES(t) represents the charging/discharging power of the battery, and the battery is discharged in a positive state and charged in a negative state.
And simultaneously, the cold/heat energy balance of the system is required to be met:
Qload(t)＝Qair(t)+Qgt+Qhs(14)
in the formula, Qload(t)、Qair(t) heat/cold load, air-conditioning heating/cooling capacity, QhsHeat/cold released from the heat storage tank; for releasing heat/cold at positive time and for storing heat/cold at negative time, QgtHeating/cooling capacity is provided for the high-temperature tail gas waste heat of the micro gas turbine.
(2) The initial time and the end time of the SOC value of the energy storage equipment are the same
Because the combined cooling heating and power type microgrid optimization scheduling presents periodicity, the initial and termination time of the state of the energy storage equipment including the storage battery and the heat storage/cold storage tank in the scheduling period should be kept consistent, namely:
SOCstart_b＝SOCend_b(15)
SOCstart_q＝SOCend_q(16)
in the formula, SOCstart_b、SOCend_b、SOCstart_qAnd SOCend_qThe charge states of the storage battery at the initial time and the termination time in the dispatching cycle and the heat storage levels of the heat storage/cold storage tank at the initial time and the termination time are respectively.
And aiming at equipment model constraints, namely the power of a storage battery, the starting power and the maximum power of a gas turbine generator, a constraint hard processing method is adopted, namely the operation variables of elements violating the constraints are assigned to boundary values in a forced mode. I.e., as shown by the battery constraint of equation (9):
if SOCi+1-SOCi|＞δ (17)
Then
Figure BDA0001356908960000081
For the constraint of power balance in the system operation constraint, a dimension reduction processing method is generally adopted, namely N variables in an equation are assumed, and N-1 variables are selected as independent variables. The other variable is a dependent variable, and the value of the dependent variable is determined by the values of other independent variables and a constraint equation; will Pgrid(t) as a dependent variable.
For the energy storage unit in the system operation period, the constraint that the initial and end time SOC needs to be consistent is combined with the equipment model constraint to form a hybrid constraint with time coupling; and (3) adopting a flexible constraint processing method, namely adding the constraints of violations (15) and (16) as penalty items into the total operation cost, thereby forming a new objective function:
F'(X)＝F(X)+β·|SOCi+1-SOCi| (19)
wherein β is SOC constraint penalty factor F (X) is a multi-objective function, i.e. a penalty term is added to each sub-objective function.
Step 3, solving the optimized scheduling model by using a multi-objective particle swarm optimization algorithm based on 'excellent particles' guidance; the method comprises the following specific steps:
(1) and respectively carrying out optimized scheduling on the lowest running cost and environment cost of the combined cooling heating and power type micro-grid system by using a single-target genetic algorithm as targets, and storing optimized scheduling results.
(2) Particle swarm initialization
Initializing the multi-target particle swarm optimization algorithm, wherein the initialization comprises the steps of determining the number of the populations, the total iteration times, the setting of inertia weight values and learning factors. And randomly assigning the two scheduling results stored by the genetic algorithm in the step (1) to two individuals in the particle swarm algorithm population.
(3) Calculating the fitness value of each particle
And determining a fitness function and calculating a fitness value corresponding to each particle. Determining a non-domination solution of the population according to the domination relation of each particle in the population, and putting the non-domination solution into an external archive set;
(4) sorting by congestion distance and removing particles that are out of scale
Without limiting the size of the external archive set, the new non-dominated solution will continuously enter the external archive set to cause explosive growth of its internal particles, thereby reducing the computational performance of the entire algorithm. In addition, in order to maintain the diversity of the entire non-dominant solution, the particles in the external archive set need to be sorted in descending order of the congestion distance. Therefore, the calculation performance of the algorithm is guaranteed, and the diversity of the whole population can be kept.
(5) Updating individual suboptimal and global optimal positions of particles
And updating the individual optimal position and the global optimal solution of the particle. The method for determining the global optimal solution is greatly different from the single-target particle swarm algorithm, when the optimal selection of the target is determined through a Pareto dominance relation, the external archive set stores the found non-dominance solution, but the whole set does not have the absolute optimal solution. And selecting the first ten percent of particles from the external file set sorted by the crowding distance, and then randomly selecting one of the particles as the global optimal solution of the iteration.
(6) Updating the velocity and position of particles
The particle swarm optimization algorithm is derived from the research on the predation behavior of a bird swarm, abstracts birds in the swarm into individual particles, updates the position of each particle by following the searched individual optimal value of the particle and the global optimal value of the particle swarm through information sharing and mutual cooperation among the particles, and finally determines the global optimal value through multiple iterations. While the algorithm is in progress, PbestiOptimal refers to the optimal value that one particle has sought so far; gbestRefers to the optimum value found so far for the entire population of particles. The update speed and position formula of all particles in the population is as follows:
Figure BDA0001356908960000091
Figure BDA0001356908960000092
in which k is the iterationThe number of times; vi kIs the flight velocity of particle i; vi k+1The speed of the flight of the particle i in the (k + 1) th iteration is obtained; c. C1、c2The value is 2 for the learning factor; r is1、r2Is located at [0, 1 ]]A random number in between; and omega is an inertia weight coefficient and is used for balancing the searching capacity of local optimum and global optimum. In algorithms where ω generally requires dynamic adjustment, the inertial weight ω is calculated as a function of the linear decrease in the number of iterations in equation (22):
Figure BDA0001356908960000101
in the formula, ωmaxThe value is generally 0.9; omegaminIs 0.4; k is the current iteration number; k is a radical ofmaxIs the set maximum number of iterations.
The velocity and position of the entire population of particles are updated according to equations (20) and (21). The updated particle velocity and position may exceed a given search space range, where the particle that exceeds the search space is assigned a boundary value and its velocity is reversed. In order to prevent the particles from falling into local optimum, the particles in the population are selected according to a certain probability and then subjected to position disturbance. The probability value should be selected to decrease as the number of iterations increases, so as to facilitate rapid convergence later in population evolution.
(7) Whether or not a termination condition is satisfied
And (4) judging whether the algorithm meets the termination condition, if so, outputting a related result, and otherwise, turning to the step (3) to continue executing.
And 4, outputting a final calculation result, namely a Pareto frontier between two targets of the total operation cost and the environmental governance cost of the system. On the basis, the output of the micro gas turbine, the storage battery, the heat accumulation/cold storage tank and the air conditioner in each period and the interactive electric power with a large power grid are obtained.
Examples
The combined cooling heating and power type microgrid shown in fig. 2 is selected, the microgrid operates in a grid-connected state, the scheduling period is one day, and the unit scheduling time delta t is one hour. Typical summer dayThe predicted power for photovoltaic generation, wind power generation, electrical load, thermal (cold) load, and real-time electricity prices are shown in fig. 3. In order to ensure the high-efficiency utilization of energy, the waste heat and smoke of the micro gas turbine are all supplied to the lithium bromide absorption type cold and hot water unit. The rated output power of the micro gas turbine is 60kW, the minimum starting power is 18kW, the power generation efficiency is 0.3, the heat dissipation loss coefficient is 0.16, the used fuel is natural gas, and the heat value is 9.7 kW.h/m3The price is 3.3 yuan/m3(ii) a The refrigerating performance coefficient of the lithium bromide absorption type refrigerating and heating unit is 1.2, and the heating performance coefficient is 0.9; the coefficient of the refrigerating and heating performance of the electric air conditioner is 2.7; the charge-discharge rate of the storage battery and the heat storage tank is 0.9, the depreciation cost is 0.05 yuan/(kWh), the capacity is 200AH, and the maximum SOC change value at two adjacent moments is 0.3; the pollutant emission coefficients and corresponding costs of the microgrid system and the large power grid are shown in table 1:
TABLE 1 pollutant emission coefficient and treatment cost
Figure BDA0001356908960000102
TABLE 2 initial distribution of translatable loads
Figure BDA0001356908960000111
1. Translatable loads primarily include disinfection cabinets, washing machines, and electric water heaters. The continuous working time of the disinfection cabinet is one hour, the continuous working time of the washing machine is two hours, the continuous working time of the electric water heater is three hours, and the power required by the translatable load in each hour in the continuous working time period is different. The proportion of translatable loads in the total load is approximately 30% or so, and the specific power demand parameter per hour is shown in fig. 4. The initial distribution of the number of three types of translatable load units within a scheduling period is shown in table 2.
And (3) determining a target electrical load curve by using the formula (3), selecting the quantity of each type of translatable load in each scheduling time period as an optimization variable, and setting the particle dimension to be 72. The number of times that the electrical load can be translated and the time period of translation in/out are obtained by using a classical particle swarm optimization algorithm. The particle population scale is set to be 200, the iteration times are set to be 1000, and the optimization problem with the constraint condition described in the formulas (1) and (3) is solved by using a single-target particle swarm optimization algorithm. The strategy to obtain the optimized translational load is shown in table 3.
TABLE 3 translatable load movement strategy
Figure BDA0001356908960000112
Taking the first-time-period cabinet movement strategy of 68 in table 3, it can be seen from equation (2) that when N is 7, 68 is divided by 7, the quotient is 9, and the remainder is 5. "in quotient quantity": the number of cabinets is now 5, so that the number of cabinet moves is 5 × 9/10 — 4.5, and according to the four-house five-in principle, the number of cabinets removed is then 5, i.e. all. "with a degree of redundancy": the remainder is 5, and the mobile strategy is solved after the translatable load in the time period needs to be moved for five hours. It follows that by changing the value of the divisor N, the maximum value of the shift margin can be changed. The distribution of the number of the three types of translatable load cells after the movement is shown in table 4:
TABLE 4 translatable load cell number distribution after translation
Figure BDA0001356908960000121
The load translation result of the combined cooling heating and power supply type microgrid with the predicted load in the typical summer day is shown in fig. 5. As can be seen from fig. 5, although the translated load curve does not coincide with the target load curve, it is already approaching the target load direction compared to the predicted load curve. From the fluctuation and fluctuation of the load curve after translation, the load curve is smoother than the predicted load curve. In addition, the peak-valley power of the original predicted load is 121.4kW and 40.2kW respectively, the peak-valley power of the load after translation is 110.8kW and 68.2kW respectively, and the peak-valley difference is reduced, so that the peak clipping and valley filling functions are realized.
2. And (4) establishing a mathematical model of the multi-objective optimization problem of the grid-connected combined cooling heating and power supply type micro-grid by using formulas (5) - (19) according to the specific data and the translated electric load curve.
3. The particle population scale of the multi-target particle swarm optimization algorithm is set to be 200, the iteration times are set to be 1000, the initial SOC value of a storage battery and the heat storage level of a heat storage (cold) tank at the initial moment are set to be 0.4, the minimum value point of the total operation cost of the system and the minimum value point of the environmental governance cost obtained by using the single-target genetic algorithm are used as two initial particles of the multi-target particle swarm optimization algorithm, and the multi-target particle swarm optimization algorithm is used for optimization solution. The specific solving step is shown in step 3 in the embodiment. The obtained optimized scheduling result includes Pareto frontier of multi-objective optimized scheduling, the output of each device of the electric energy unit and the thermal energy unit when the lowest operation cost is taken as a target, the output of each device of the electric energy unit and the thermal (cold) energy unit when the lowest operation cost is taken as a target, and the value of the energy storage unit SOC at each moment in the scheduling cycle, as shown in fig. 6-11.
The scheduling results show that the operating cost is in the range of [ 891.1-1663.0 yuan ], and the environmental cost is in the range of [ 297.0-827.7 yuan ]. Compared with 1051.1 yuan of the lowest operation cost in the system scheduling result before load translation, 60 yuan is saved, and therefore the translation effect is obvious. The main reason for this is due to the shifting of the translatable loads at high electricity prices to low electricity prices, thereby reducing the cost of the system to purchase electricity from the large grid. Since the target load is set according to the fluctuation of the electricity price, and the electricity price is in close economic relation with the system operation, 300.7 yuan is not greatly improved relative to the lowest environmental cost before shifting the load.
The scheduling result when the system running cost is the lowest is taken for analysis, as shown in fig. 7 and 8. As can be seen in fig. 7, it can be seen that the battery has undergone mainly two charge and discharge processes throughout the scheduling period. Wherein, in the morning at 4 and 5 o' clock, the price of electricity of the large power grid is lower, and the storage battery is charged. During the period from 12 am to 15 pm, the battery is mostly discharged and is operated at maximum power when the price of electricity is selected to be relatively high in a targeted manner during these several periods. As can be seen from fig. 11, after the 15-point battery discharge is completed, the SOC value reaches the lower limit, and the entire amount of electricity is discharged, thereby making a sufficient margin for the system. The micro gas turbine starts between 11 pm and 16 pm, mainly due to the high grid price during this time. As can be seen from the price of natural gas and the power generation efficiency of the micro gas turbine, the price per one-hour power generation is equivalent to 0.63 yuan, and the power prices in this period are all higher than 0.63 yuan. At the times 12, 14, 15 and 16, the electric charge generated by the micro gas turbine and discharged by the storage battery together meet the requirement of electric load, and the residual electric charge is sold to a large power grid. Therefore, in this time period, the accumulator earns a difference price by charging at a low electricity price and discharging at a high electricity price, and the micro gas turbine earns a profit with the economic advantage of its power generation. When the electricity price is lower than 18 and 19 points, the storage battery is charged, and the SOC of the storage battery changes, so that the storage battery operates in a storage state at the maximum power at the time, and the storage battery discharges at high electricity price at night to be used as energy storage. The large power grid at this time period is required to provide the electric quantity required by the electric load, and the storage battery electric capacity and the electric quantity required by the electric air conditioner are borne by the large power grid, so that the power of the large power grid at the two time periods is higher and reaches more than 200 kW. And the storage battery discharges in the period of 21 to 22 high electricity price at night, and the micro gas turbine is started again when the electricity price is higher than 0.63 yuan.
The electric air conditioner is used as a variable load, converts electric energy into cold or heat energy, and jointly regulates the supply and demand balance of the cold/heat load in the system with the heat storage (cold) tank and the flue gas waste heat generated by the power generation of the micro gas turbine. The scheduling result of the hot (cold) energy unit in the whole scheduling period is shown in fig. 8. As can be seen from fig. 8, the heat storage tank is in the energy storage state at points 5, 18 and 19, and in the energy release state at points 12, 14, 21 and 22; the working state of the lithium bromide absorption refrigerator driven by the waste heat of the flue gas generated by the power generation of the micro gas turbine is consistent with the running state of the micro gas turbine, namely the lithium bromide absorption refrigerator is in the working state at 11, 12, 13, 14, 15, 16, 20 and 21. The operation state of the heat (cold) storage tank is similar to that of the storage battery, but the total charge and discharge amount is relatively small. On the one hand, compared with a storage battery, the energy transfer and storage benefits of the heat storage (cold) tank, namely the difference earned by high-price energy storage and low-price discharge, are not obvious. For example, the cooling (thermal) coefficient of performance of an air conditioner is 2.7, which means that an electric air conditioner consumes 1kW h of electricity and can "carry" 2.7kW h of cooling (thermal) energy. This makes the heat storage tank relatively inexpensive in transferring cold (heat) energy. On the other hand, the depreciation cost of the thermal storage (cold) tank also has a part of influence on the cost, and if profit is to be earned, the benefit of the discharged energy is greater than the sum of the low-cost stored energy and the depreciation cost. So in the case where the spread itself is relatively small, the space in which it earns profit is correspondingly reduced. When the electricity price is higher, because of the start of the micro gas turbine, most of the cold load in the system is absorbed by the lithium bromide absorption refrigerator to bear the cold energy of the rest heat conversion, and part of the deficient cold load on the basis is borne by the electric air conditioner and the heat storage (cold) tank together; when the electricity price is relatively low, the cooling load may be satisfied by the cooling capacity of the electric air conditioner, for example, at 1, 2, 3, 4, 6, 7, 8, 9, 17, and 23 points, and the electric air conditioner may also bear the cooling load of the heat storage (cooling) tank, for example, at 5, 18, and 19 points.
And (5) analyzing the scheduling result of the system when the environment cost is the lowest. As shown in fig. 9-11. As can be seen from fig. 11, the micro gas turbine is in a start-up state during the whole scheduling period due to the environmental advantage of the micro gas turbine power generation over the large power grid. In the time period from 1 point to 13 points, the exchange power of the system and the large power grid is almost zero. During this period, the net load is less than the rated power of the micro gas turbine, and the power deficit of the system can be fully assumed by the latter. Because the cold load is relatively high, the cold energy generated by driving the lithium bromide refrigerator by the waste heat of the flue gas generated by the power generation of the micro gas turbine cannot meet the cold load, the residual cold load caused by the cold load cannot be met by the cold energy provided by the electric air conditioner, and the electric quantity required by the electric air conditioner is sourced from the generated energy of the micro gas turbine in the period. After 13, the power shortage of the entire system cannot be met even if the micro gas turbine is operated at rated power, due to the increase in the net load, and the surplus power can only be provided by the large grid. When the net load is less than the rated power of the micro gas turbine, the storage battery is always in a charging state, and the part of the electric quantity charged into the storage battery is provided by the micro gas turbine, and the main reason is that the power exchange between the system and a large power grid can be reduced as much as possible. When the net load is larger than the rated power of the micro gas turbine, partial shortage power can be provided by the storage battery to avoid being provided by a large power grid, namely, the electric quantity meeting the part shortage power is generated by the micro gas turbine, the storage battery only plays a role in carrying, and the environmental cost of power generation of the micro gas turbine is lower than that of the large power grid, so that the environmental cost of the system is reduced. As can be seen from fig. 10, the air conditioner is in a low power operation state due to the start of the micro gas turbine. The function of the heat storage (cold) tank in the whole system can improve the economy of the whole system by discharging heat storage (cold) high electricity price at low electricity price on one hand, and decouple the electric heating load sent by the micro gas turbine on the other hand, so that electric energy and heat (cold) energy are independent from each other, and the control flexibility of the system is improved. The thermal (cold) storage tank serves a second primary function when the system is operated with the goal of minimizing environmental costs.
As can be seen from fig. 11, the 1-point time is the same as the 25-point time (to distinguish the time values at the start and end of the scheduling cycle), that is, the 1-point time at the end of the scheduling. Therefore, the system scheduling result meets the requirement that the SOC value of the energy storage unit is kept consistent at the beginning and the end of scheduling.

Claims (1)

1. The cold-heat-power combined supply type micro-grid multi-objective dynamic optimization scheduling method is characterized by comprising the following steps of: the method specifically comprises the following steps:
step 1, determining a target electrical load curve to obtain the number of translatable electrical loads and the time period of translatable in/out; the specific steps of the electric load translation implementation algorithm are as follows:
(1) inputting basic data;
the unit number of each type of translatable electric load at each moment and the electricity utilization characteristics of the translatable electric load, electric load data predicted by day-ahead scheduling, wind power generation and photovoltaic power generation prediction data and real-time electricity price;
(2) determining a target electrical load according to a corresponding mechanism;
when the micro-grid system is in a grid-connected mode, according to the electricity price data, more electric loads are arranged when the electricity price is low, and the load is reduced as much as possible when the electricity price is high, namely, the target electric loads and the electricity price are in an inverse proportional relation, as shown in formula (1):
Figure FDA0002267353790000011
in the formula DtFor electricity prices at t-th time in the scheduling period, Pobj,tTarget load for time period t, Pf,tThe original predicted load in the period of T is T, and T is a scheduling period;
when the micro-grid is in an island operation state, in order to reduce the drop of wind and light indexes, according to wind and light power generation data, less electric loads are arranged when the wind and light power generation power is less, and more electric loads are arranged when the wind and light power is more, that is, the target electric loads and the wind and light output are in a direct proportional relation, as shown in formula (2):
Figure FDA0002267353790000012
in the formula, WPtThe predicted power of wind power and photovoltaic power generation at the t moment in the scheduling period;
(3) solving a translatable load model;
substituting the formula (1) or the formula (2) into the formula (3), obtaining the number of units which can be shifted out of the electric load in a translation mode and the translation margin of the electric load which can be shifted out by adopting a particle swarm optimization algorithm and utilizing a method of 'quantitative quotient and remaining degree' defined by the formula (4), thereby obtaining the number of units which are shifted in and out of the load at the moment and the specific time to which the shifted-out load is adjusted;
wherein the formula (3) is
Figure FDA0002267353790000021
In the formula, T is a scheduling period; pobj,tA target load for a period t; ps,tLoad for time period t after translation; pf,tThe original predicted load for the t period; wherein the load translation satisfies: the load types before and after translation are not changed, and the total amount of all the translatable loads in the scheduling periodThe change is not changed; each type of translatable load has a time margin for allowing translation, and constraint is carried out through Y in the formula (4);
formula (4) is
M÷N＝S......Y (4)
Wherein M is a certain component of the optimization variable; n is a fixed value; s is the quotient of the equation, which determines the number of units from which the load can be shifted; y is the remainder of the equation, which determines the translation margin of the translatable load; for the translation amount and the translation margin, only the shift-out amount is calculated, and the shift-in amount is calculated according to the corresponding shift-out amount and the corresponding translation margin;
step 2, establishing a mathematical model of the cooling, heating and power combined type microgrid optimization scheduling problem according to the principle of the cooling, heating and power combined type microgrid optimization scheduling problem, the optimized electrical load curve, the known wind and light data and the known cooling/heating load and the scheduling principle; one of the objective functions is the total operating cost of the system:
f1(X)＝JE(X)+JO(X)+JF(X)+JB(X) (5)
in the formula, JE(X) is the energy interaction cost of the micro-grid system and the large power grid; j. the design is a squareO(X) the operating maintenance cost of the equipment; j. the design is a squareF(X) is the fuel cost of the gas turbine; j. the design is a squareB(X) depreciation costs for batteries and thermal/cold storage tanks;
another objective function is the system pollutant emission abatement cost, i.e. the environmental cost:
Figure FDA0002267353790000022
wherein n is the type of contaminant; viThe discharge and treatment cost of the ith pollutant; qi(X) is the amount of the pollutant discharged in item i;
therefore, the objective function of the combined cooling heating and power micro-grid is expressed as:
F(X)＝min([f1(X),f2(X)]T) (7)
the constraints of the optimal scheduling are divided into two categories: one is the equipment model constraints, including:
(1) storage battery
The accumulator satisfies the charge-discharge power constraint, i.e.
-PES_ch_max≤PES≤PES_dis_max(8)
In the formula, PES_ch_maxAnd PES_dis_maxMaximum allowable charging power and discharging power, PESThe power of the storage battery is adopted, and the power is negative during charging and positive during discharging;
furthermore, the power constraint of the battery is expressed as a constraint of the SOC amplitude variation at two adjacent times, that is:
SOCi+1-SOCi≤δ (9)
in the formula, δ has different values in different operating states, i.e., in the charged or discharged state;
in addition to this, the state of charge constraints of the battery need to be considered:
SOCmin≤SOCi≤SOCmax(10)
in the formula, SOCmax、SOCminRespectively representing the upper and lower limit requirements of the state of charge of the storage battery;
(2) heat/cold storage tank
The heat storage/cold tank adopts the same method as the storage battery in the constraint processing, wherein the heat storage level of the heat storage/cold tank corresponds to the rated capacity of the storage battery, and the energy discharge depth corresponds to the discharge depth of the storage battery;
(3) gas turbine
Power P generated by gas turbinegen(t) the upper and lower limits of power should be met:
Figure FDA0002267353790000031
in the formula
Figure FDA0002267353790000032
Is the minimum starting power of the generator;
Figure FDA0002267353790000033
the maximum generated power;
in addition, the gas turbine should also meet the ramp rate constraint:
Figure FDA0002267353790000034
in the formula, Pup、PdownRespectively the upper and lower limit climbing rate limit values of the generator;
another type of constraint is a system operation constraint, i.e., a constraint that the system should meet in operation, such constraints including:
(1) power and energy balance constraints
The system should meet power balance in operation, and therefore, at various time periods:
Pload(t)＝Pgrid(t)+PES(t)+PPV(t)+PWT(t) (13)
in the formula, Pload(t)、PPV(t)、PWT(t) predicting the generated power for the optimized electrical load power, photovoltaic and wind power, respectively; pES(t) is the charge-discharge power of the storage battery, and the storage battery is discharged in positive time and charged in negative time;
and simultaneously, the cold/heat energy balance of the system is required to be met:
Qload(t)＝Qair(t)+Qgt+Qhs(14)
in the formula, Qload(t)、Qair(t) heat/cold load, air-conditioning heating/cooling capacity, QhsHeat/cold released from the heat storage tank; for releasing heat/cold at positive time and for storing heat/cold at negative time, QgtHeating/cooling capacity is provided for the waste heat of the high-temperature tail gas of the micro gas turbine;
(2) the initial time and the end time of the SOC value of the energy storage equipment are the same
Because the combined cooling heating and power type microgrid optimization scheduling presents periodicity, the initial and termination time of the state of the energy storage equipment including the storage battery and the heat storage/cold storage tank in the scheduling period should be kept consistent, namely:
SOCstart_b＝SOCend_b(15)
SOCstart_q＝SOCend_q(16)
in the formula, SOCstart_b、SOCend_b、SOCstart_qAnd SOCend_qThe charge states of the storage battery at the initial time and the termination time in the scheduling period and the heat storage levels of the heat storage/cold storage tank at the initial time and the termination time are respectively;
aiming at equipment model constraint, the power of a storage battery, and the starting power and the maximum power of a gas turbine generator, a constraint hard processing method is adopted, namely, the operation variables of elements violating the constraint are assigned to boundary values in a forced mode; i.e., as shown by the battery constraint of equation (9):
if SOCi+1-SOCi|＞δ (17)
Then
Figure FDA0002267353790000041
For the constraint of power balance in the system operation constraint, a dimension reduction processing method is adopted, namely N variables are assumed in an equation, and N-1 variables are selected as independent variables; the other variable is a dependent variable, and the value of the dependent variable is determined by the values of other independent variables and a constraint equation; will Pgrid(t) as a dependent variable;
for the energy storage units in the system operation period, the constraint that the initial and termination time SOC needs to be kept consistent and the equipment model constraint form a hybrid constraint with time coupling; and (3) adopting a flexible constraint processing method, namely adding the constraints of violations (15) and (16) as penalty items into the total operation cost, thereby forming a new objective function:
F'(X)＝F(X)+β·|SOCi+1-SOCi| (19)
wherein β is SOC constraint penalty factor, F (X) is multi-objective function, i.e. penalty term is added to each sub-objective function;
step 3, solving the optimized scheduling model by using a multi-objective particle swarm optimization algorithm based on 'excellent particles' guidance; the method comprises the following specific steps:
(1) respectively performing optimized scheduling on the lowest running cost and environment cost of the combined cooling heating and power type micro-grid system by using a single-target genetic algorithm as targets, and storing optimized scheduling results;
(2) particle swarm initialization
Initializing a multi-target particle swarm optimization algorithm, wherein the multi-target particle swarm optimization algorithm comprises the steps of determining the number of a population, the total iteration times, the setting of an inertia weight value and a learning factor; randomly assigning two scheduling results stored in the genetic algorithm in the step (1) to two individuals in the particle swarm algorithm population;
(3) calculating the fitness value of each particle
Determining a fitness function, and calculating a fitness value corresponding to each particle; determining a non-domination solution of the population according to the domination relation of each particle in the population, and putting the non-domination solution into an external archive set;
(4) sorting by congestion distance and removing particles that are out of scale
(5) Updating individual suboptimal and global optimal positions of particles
Updating the individual optimal position and the global optimal solution of the particle; the method for determining the global optimal solution is greatly different from the single-target particle swarm algorithm, when the optimal selection of the target is determined through a Pareto domination relation, an external archive set stores the found non-domination solution, but the whole set does not have an 'absolute' optimal solution; selecting the first ten percent of particles from the external file set which is sorted by the crowding distance, and then randomly selecting one of the particles as the global optimal solution of the iteration;
(6) updating the velocity and position of particles
The particle swarm optimization algorithm is derived from the research on the predation behavior of a bird swarm, abstracts birds in the swarm into individual particles, updates the position of each particle by following the searched individual optimal value of the particle and the global optimal value of the particle swarm through information sharing and mutual cooperation among the particles, and finally determines the global optimal value through multiple iterations; while the algorithm is in progress, PbestiOptimal refers to the optimal value of a particle found so far; gbestRefers to the whole granuleThe optimal value that the subgroup found so far; the update speed and position formula of all particles in the population is as follows:
Figure FDA0002267353790000051
Figure FDA0002267353790000052
in the formula, k is iteration times; vi kIs the flight velocity of particle i; vi k+1The speed of the particle i in the k +1 th iterative flight is taken as the speed; c. C1、c2The value is 2 for the learning factor; r is1、r2Is located at [0, 1 ]]A random number in between; omega is an inertia weight coefficient and is used for balancing the search capability of local optimum and global optimum; in algorithms where ω generally requires dynamic adjustment, the inertial weight ω is calculated as a function of the linear decrease in the number of iterations in equation (22):
Figure FDA0002267353790000061
in the formula, ωmaxThe value is 0.9; omegaminIs 0.4; k is the current iteration number; k is a radical ofmaxIs the set maximum number of iterations;
updating the speed and position of the whole population of particles according to equations (20) and (21); the updated velocity and position of the particle may exceed the given search space range, where the particle exceeding the search space is assigned a boundary value and its velocity is reversed; in order to prevent the particles from falling into local optimum, the particles in the population are selected according to a certain probability and then subjected to position disturbance; the selection of the probability value is reduced along with the increase of the iteration times so as to facilitate the rapid convergence of the later period of population evolution;
(7) whether or not a termination condition is satisfied
Judging whether the algorithm meets a termination condition, if so, outputting a related result, and otherwise, turning to the step (3) to continue execution;
step 4, outputting a final calculation result, namely a Pareto frontier between two targets of the total operation cost and the environmental governance cost of the system; on the basis, the output of the micro gas turbine, the storage battery, the heat storage/cold storage tank and the air conditioner in each period and the interactive electric power with a large power grid are obtained.
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