[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

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

CCHP type micro-grid multi-time scale optimization scheduling method and system Download PDF

Info

Publication number
CN109617142B
CN109617142B CN201811522497.8A CN201811522497A CN109617142B CN 109617142 B CN109617142 B CN 109617142B CN 201811522497 A CN201811522497 A CN 201811522497A CN 109617142 B CN109617142 B CN 109617142B
Authority
CN
China
Prior art keywords
load
cchp
scheduling
unit
cost
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811522497.8A
Other languages
Chinese (zh)
Other versions
CN109617142A (en
Inventor
窦春霞
米雪
张博
岳东
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xiamen Torch Xinyuan Electric Power Technology Co ltd
Original Assignee
Yanshan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Yanshan University filed Critical Yanshan University
Priority to CN201811522497.8A priority Critical patent/CN109617142B/en
Publication of CN109617142A publication Critical patent/CN109617142A/en
Application granted granted Critical
Publication of CN109617142B publication Critical patent/CN109617142B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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
    • 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]
    • 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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

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

CCHP type micro-grid multi-time scale optimization scheduling method and system
Technical Field
The invention relates to the field of optimal scheduling of a micro-grid, in particular to a CCHP type micro-grid multi-time scale optimal scheduling method and system.
Background
The basic task of the optimal scheduling of the micro-grid is to reasonably and effectively arrange the output of each distributed power supply and the interaction power with the power distribution network according to a certain control strategy on the premise of meeting the load requirement of the micro-grid system, so that the operation maintenance cost, the emission cost and the like of the whole micro-grid are the lowest.
Compared with a common microgrid, the combined cooling, heating and power (CCHP) type microgrid has the characteristics of various operation modes, high energy utilization rate, flexibility in control, high power supply reliability, small environmental pollution and the like, can meet the requirements of users on various types of energy such as cold, heat and electricity, and has good social and economic benefits. However, the coupling relationship between the energy structure and the equipment in the combined cooling heating and power micro-grid is complex, and particularly, the thermoelectric coupling phenomenon of the combined cooling heating and power system makes the determination of the optimized scheduling scheme very difficult; the operation mode of 'fixing electricity by heat' or 'fixing heat by electricity' is adopted, so that the thermoelectric decoupling effect is achieved to a certain extent, but the unified coordination and scheduling of thermoelectric loads are not facilitated.
In addition, the indirection, volatility and randomness of renewable energy power generation and load in the microgrid seriously affect the stability and economy of the whole microgrid, for example, the traditional power grid scheduling has limitations, so that a serious wind abandon phenomenon occurs in wind power stations, the problem of electric quantity loss is increasingly prominent, and the waste of renewable energy is caused. With the increase of the grid-connected capacity of renewable energy sources and the access of various demand side resources to the power grid, the requirements of the economic dispatching of the micro-grid cannot be met only by utilizing the resources on the power generation side for optimized dispatching.
Disclosure of Invention
The invention aims to provide a CCHP type micro-grid multi-time scale optimal scheduling method and system, and aims to solve the problems of serious wind abandon, electric quantity loss and serious renewable energy waste of wind power plants in various places due to limitation of traditional power grid scheduling.
In order to achieve the purpose, the invention provides the following scheme:
a CCHP type micro-grid multi-time scale optimization scheduling method comprises the following steps:
acquiring a load cutting parameter of a load cutting, an adjustable load parameter of an adjustable load, a CCHP unit parameter, an absorption type lithium bromide refrigerating unit parameter and a phase change energy storage device parameter; the load cutting parameters comprise a scheduling cost parameter, a market price, a cutting rate and a rated power of the cutting load; the adjustable load parameters comprise an adjustable rate and the active power of the adjustable load; the CCHP unit parameters comprise the heat price of the CCHP unit, the cold price of the CCHP unit, the natural gas price, the power generation amount, the hot output of the unit i in the time period t, the cold output of the unit i in the time period t, the used natural gas amount and the hot gas conversion efficiency of the unit i; the parameters of the absorption lithium bromide refrigerating unit comprise the length of cold supply, the heat supply power of the gas turbine and the thermodynamic coefficient of the electric refrigerating efficiency; the parameters of the phase change energy storage device comprise the heat efficiency, the heat load and the cold load of the energy storage device;
determining the power grid compensation cost for cutting off the load-cuttable according to the load-cuttable parameter;
determining the load adjustment cost of the adjustable load according to the adjustable load parameter;
determining the gain of the CCHP unit according to the CCHP unit parameters;
determining the operation benefit of the absorption type lithium bromide refrigerating unit according to the parameters of the absorption type lithium bromide refrigerating unit;
determining the operation income of the phase change energy storage equipment according to the parameters of the phase change energy storage equipment;
establishing a triple co-generation system operation profit model according to the CCHP unit profit, the absorption type lithium bromide refrigerating unit operation profit and the phase change energy storage equipment operation profit;
according to the power grid compensation cost, the load adjustment cost and the operation income model of the triple co-generation system, optimizing and scheduling the CCHP type micro-grid according to a multi-time scale optimizing and scheduling strategy to enable the operation cost of the CCHP type micro-grid to be minimum; the multi-time scale optimization scheduling strategy comprises a day-ahead optimization scheduling stage, an in-day rolling optimization stage, an ultra-short period scheduling stage and an ultra-short period scheduling stage; and in different dispatching stages, the target functions for optimizing and dispatching the CCHP type micro-grid are different.
Optionally, the determining, according to the load shedding parameter, a power grid compensation cost for shedding the load shedding may specifically include:
according to the formula
Figure BDA0001903571280000021
Determining the power grid compensation cost for cutting off the load; wherein, Ccli(t) removing the compensation cost of the electric network with the load cutting function; epsiloncli(t) is the scheduling cost coefficient of the load-shedding; c. Cgrid(t) represents a buying and selling market price; ρ cli (t) is the cuttability;
Figure BDA0001903571280000031
is the rated power of the cutting load i.
Optionally, the determining the load adjustment cost of the adjustable load according to the adjustable load parameter specifically includes:
according to the formula
Figure BDA0001903571280000032
Determining a load adjustment cost for the adjustable load, wherein Calj(t) load adjustment costs for said adjustable load; deltaalj(t) is the adjustable rate; palj(t) is the active power of the adjustable load j; t is the adjustment period.
Optionally, the determining the CCHP unit revenue according to the CCHP unit parameters specifically includes:
according to formula CCCHP(t)＝cgrid(t)Pi(t)+cH(t)Ph(t)+cQ(t)PQ(t)-cF(t)PF(t) determining the gain of the CCHP unit; wherein, CCCHP(t) the revenue of the CCHP unit; c. CH(t) is the heat value of the CCHP unit; c. CQ(t) is the cold price of the CCHP unit; c. CF(t) natural gas prices; pi(t) is the amount of power generation; ph(t) the thermal output of the unit i in the time period t; pQ(t) is the cold output of the unit i in the time period t; pF(t)＝Ph(t)/μHIn terms of the amount of natural gas used, muHThe hot gas conversion efficiency of the unit i.
Optionally, determining the operation benefit of the absorption type lithium bromide refrigeration unit according to the parameters of the absorption type lithium bromide refrigeration unit specifically includes:
according to the formula
Figure BDA0001903571280000033
Determining the operation income of the absorption lithium bromide refrigerating unit; wherein, Cl-b(t) the operating profit of the absorption lithium bromide refrigerating unit; t is tsThe cooling time is the length of time; phPower for heating gas turbine ηCOPeThermodynamic coefficient of electric refrigeration efficiency ηCOPThe refrigeration power of the lithium bromide refrigeration system.
Optionally, determining the operation income of the phase change energy storage device according to the parameters of the phase change energy storage device specifically includes:
according to the formula
Figure BDA0001903571280000034
Determining the operation income of the phase change energy storage equipment; wherein, Cp-c(t) the operating yield of the phase change energy storage device, η the thermal efficiency of the energy storage device, LhIs a thermal load; l iscIs a cold load; t is ton|winterThe time that the heat production of the triple-generation unit is greater than the heat load in winter is provided; t is ton|summerThe heat generation of the unit is more than the time of heat load in summer.
Optionally, the method for establishing a triple co-generation system operation benefit model according to the CCHP unit benefit, the absorption lithium bromide refrigeration unit operation benefit and the phase change energy storage device operation benefit specifically includes:
according to the formula C (t) ═ CCCHP(t)+Cl-b(t)+Cp-c(t) establishing a triple co-generation system operation income model; and C (t) the running benefit of the triple co-generation system.
Optionally, the performing optimized scheduling on the CCHP microgrid according to the power grid compensation cost, the load adjustment cost and the triple co-generation system operation revenue model and a multi-time scale optimized scheduling strategy makes the operating cost of the CCHP microgrid minimum, and specifically includes:
in the day-ahead optimization scheduling stage, according to a formula
Figure BDA0001903571280000041
Performing optimized scheduling on the CCHP type microgrid; NR is the number of controllable distributed power supplies; i is the number of load that can be cut; j is the number of adjustable loads; pi(t) represents the output of the ith distributed power supply at time t, Ci(Pi(t)) represents that the ith distributed power supply has the output of Pi(T) cost, Δ T being the scheduling cycle duration; cDGg(t) the operating and maintenance costs of the controllable distributed power supply; cbat(t) the cost of use of the battery; cgrid(t) interaction cost with a large power grid;
rolling an optimization phase within the dayIn accordance with the formula
Figure BDA0001903571280000042
Performing optimized scheduling on the CCHP type microgrid; wherein, Δ Pi(t) is the power adjustment of the distributed power source i; t is0Is the current time node;
in the ultra-short period scheduling stage, according to a formula
Figure BDA0001903571280000043
Performing optimized scheduling on the CCHP type microgrid; wherein,
Figure BDA0001903571280000044
represents the comprehensive scheduling cost at the moment of the ultra-short scheduling t,
Figure BDA0001903571280000045
representing the cost corresponding to the time period rolling optimization;
in the ultra-short period scheduling stage, according to a formula
Figure BDA0001903571280000046
Performing optimized scheduling on the CCHP type microgrid; wherein,
Figure BDA0001903571280000047
Figure BDA0001903571280000051
Pr(k + n) is an active power output reference value obtained by short-term scale optimization; p (k + n) is a predicted value of the distributed power supply, the large power grid, the energy storage and the switchable load optimized by the ultra-short-term scale; p0(k + n) is an initial value for optimizing active power output of each part in an ultra-short-term scale; Δ u (k + t-1) is predicted [ k + t-1, k + t [ ]]Active power output increment in a time period;
Figure BDA0001903571280000052
the active output reference value is the active output reference value of the controllable distributed power supply;
Figure BDA0001903571280000053
the active output reference value is interactive with a large power grid;
Figure BDA0001903571280000054
the active output reference value is the switchable load;
Figure BDA0001903571280000055
an active power output reference value for an adjustable load;
Figure BDA0001903571280000056
the active output reference value of the energy storage battery.
A CCHP type micro-grid multi-time scale optimization scheduling system comprises:
the parameter acquisition module is used for acquiring the load cutting parameter of the load cutting, the adjustable load parameter of the adjustable load, the CCHP unit parameter, the absorption type lithium bromide refrigerating unit parameter and the phase change energy storage equipment parameter; the load cutting parameters comprise a scheduling cost parameter, a market price, a cutting rate and a rated power of the cutting load; the adjustable load parameters comprise an adjustable rate and the active power of the adjustable load; the CCHP unit parameters comprise the heat price of the CCHP unit, the cold price of the CCHP unit, the natural gas price, the power generation amount, the hot output of the unit i in the time period t, the cold output of the unit i in the time period t, the used natural gas amount and the hot gas conversion efficiency of the unit i; the parameters of the absorption lithium bromide refrigerating unit comprise the length of cold supply, the heat supply power of the gas turbine and the thermodynamic coefficient of the electric refrigerating efficiency; the parameters of the phase change energy storage device comprise the heat efficiency, the heat load and the cold load of the energy storage device;
the power grid compensation charge determining module is used for determining the power grid compensation charge for cutting off the load-cuttable according to the load-cuttable parameter;
the load adjustment fee determining module is used for determining the load adjustment fee of the adjustable load according to the adjustable load parameter;
the CCHP unit income determining module is used for determining the CCHP unit income according to the CCHP unit parameters;
the absorption type lithium bromide refrigerating unit operation income determining module is used for determining the absorption type lithium bromide refrigerating unit operation income according to the absorption type lithium bromide refrigerating unit parameters;
the phase change energy storage equipment operation income determining module is used for determining the phase change energy storage equipment operation income according to the phase change energy storage equipment parameters;
the triple co-generation system operation profit model establishing module is used for establishing a triple co-generation system operation profit model according to the CCHP unit profit, the absorption type lithium bromide refrigerating unit operation profit and the phase change energy storage equipment operation profit;
the optimized scheduling adjustment module is used for performing optimized scheduling on the CCHP type micro-grid according to the power grid compensation cost, the load adjustment cost and the operation income model of the triple co-generation system and a multi-time scale optimized scheduling strategy, so that the operation cost of the CCHP type micro-grid is minimum; the multi-time scale optimization scheduling strategy comprises a day-ahead optimization scheduling stage, an in-day rolling optimization stage, an ultra-short period scheduling stage and an ultra-short period scheduling stage; and in different dispatching stages, the target functions for optimizing and dispatching the CCHP type micro-grid are different.
Optionally, the power grid compensation cost determining module specifically includes:
a grid compensation charge determination unit for determining the compensation charge according to the formula
Figure BDA0001903571280000061
Determining the power grid compensation cost for cutting off the load; wherein, Ccli(t) removing the compensation cost of the electric network with the load cutting function; epsiloncli(t) is the scheduling cost coefficient of the load-shedding; c. Cgrid(t) represents a buying and selling market price; rhocli(t) is the cuttability;
Figure BDA0001903571280000062
is the rated power of the cutting load i.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a CCHP type microgrid multi-time scale optimization scheduling method and a system, based on demand side response (flexible load characteristic is considered, the load that can be cut and the load that can be adjusted are set, and a certain compensation mechanism is established), according to the demand for cold and hot energy, the heat energy cost of a user at a certain moment is partially or completely compensated, the user is stimulated to consume more heat energy at a specific moment, thereby the heat production quantity of CCHP is improved, the income of a CCHP unit is determined, and the uncertainty of renewable energy sources, the CHP type microgrid is optimally scheduled according to a multi-time scale scheduling strategy according to the difference and complementarity of various energy sources such as cold, heat, electricity and the like on price, energy utilization characteristic and energy utilization demand, aiming at different time intervals, the electric power shortage can be effectively relieved, the functional reliability is improved, the maximum utilization of the renewable energy sources can be achieved, and the economic operation of the CCHP type microgrid is realized, the electric quantity loss, the wind abandoning phenomenon of wind power plants in various places and the waste of renewable energy sources are reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a CCHP-type microgrid multi-time scale optimization scheduling method provided by the invention;
fig. 2 is a system diagram of a scheme of cooperative operation of a phase change energy storage device configured in a triple co-generation system provided by the invention;
FIG. 3 is a schematic diagram of the operation of an absorption lithium bromide refrigerator according to the present invention;
FIG. 4 is a diagram of a CCHP type microgrid system provided by the present invention;
fig. 5 is a multi-time scale scheduling block diagram of the CCHP-type microgrid provided by the invention;
fig. 6 is a structural diagram of a CCHP-type microgrid multi-time scale optimization scheduling system provided by the invention;
fig. 7 is a block diagram of a multi-time scale scheduling structure of a CCHP-type microgrid based on demand-side response provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a CCHP type micro-grid multi-time scale optimal scheduling method and system, which are used for effectively relieving power shortage, improving functional reliability, achieving maximum utilization of renewable energy, realizing economic operation of the CCHP type micro-grid, reducing electric quantity loss and reducing wind abandon of wind power plants in various places and waste of the renewable energy.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a CCHP microgrid multi-time scale optimization scheduling method provided by the present invention, and fig. 1 shows a CCHP microgrid multi-time scale optimization scheduling method, which includes:
step 101: acquiring a load cutting parameter of a load cutting, an adjustable load parameter of an adjustable load, a CCHP unit parameter, an absorption type lithium bromide refrigerating unit parameter and a phase change energy storage device parameter; the load cutting parameters comprise a scheduling cost parameter, a market price, a cutting rate and a rated power of the cutting load; the adjustable load parameters comprise an adjustable rate and the active power of the adjustable load; the CCHP unit parameters comprise the heat price of the CCHP unit, the cold price of the CCHP unit, the natural gas price, the power generation amount, the hot output of the unit i in the time period t, the cold output of the unit i in the time period t, the used natural gas amount and the hot gas conversion efficiency of the unit i; the parameters of the absorption lithium bromide refrigerating unit comprise the length of cold supply, the heat supply power of the gas turbine and the thermodynamic coefficient of the electric refrigerating efficiency; the phase change energy storage device parameters include a thermal efficiency, a thermal load, and a cold load of the energy storage device.
Step 102: and determining the power grid compensation cost for cutting off the load-cuttable according to the load-cuttable parameter.
Step 103: and determining the load adjustment cost of the adjustable load according to the adjustable load parameter.
Step 104: and determining the gain of the CCHP unit according to the CCHP unit parameters.
The invention considers the problem of demand side response, and comprises two parts:
① considering the flexible load characteristics, the load that can be cut and the load that can be adjusted are set, and a certain compensation mechanism is established.
The flexible load interactive response scheduling is to consider the load resource with the adjusting capacity as a scheduling object, adopt various demand response measures adapted to the scheduling object, realize the interactive response between the flexible load and the power supply, so as to deal with the problem of renewable energy source intermittency, and achieve the goal of energy source optimal configuration.
It can be classified into three categories according to load characteristics: critical load, cuttable load and adjustable load. The key load is an uncontrollable load, and the system needs to meet the requirements of the load at any moment; the load can be cut, the load grade is low, the micro-grid cannot be adversely affected by cutting, and when the cutting cost is lower than other scheduling cost, a load shedding strategy is selected; the adjustable load can be shifted from peak time to valley time when the operation working time is shifted. The targeted processing is carried out according to different load characteristics, so that the economy of the operation of the micro-grid is better.
Figure BDA0001903571280000081
Figure BDA0001903571280000082
The formula (1) is used for cutting off the compensation cost of the power grid with the load cutting function; epsiloncli(t) is the scheduling cost coefficient of the load-shedding; c. Cgrid(t) represents a buying and selling market price; rhocli(t) is the cuttability;
Figure BDA0001903571280000083
rated power for cutting load i; formula (2) is the load adjustment cost for the adjustable load; deltaalj(t) is the adjustable rate; palj(t) is the active power of the adjustable load j.
Figure BDA0001903571280000091
Figure BDA0001903571280000092
Equation (3) is the power adjustment constraint for load shedding;
Figure BDA0001903571280000093
and
Figure BDA0001903571280000094
the maximum and minimum cutting rates of the cutting load; equation (4) is an operational constraint for adjustable loads that operate only within a given time window and cannot be stopped once operation has been completed before a task is completed.
② the establishment of interaction mechanism of CCHP system is stimulated, according to the demand of cold and hot energy, the heat energy cost of user at a certain time is partially or totally compensated, the user is stimulated to consume more heat energy at a specific time, thereby increasing the heat production of CCHP.
The main way to stimulate the CCHP system to participate in the interaction is to stimulate the user's demand for heat by means of cold and heat compensation. The management center partially or completely compensates the energy cost of the user exceeding the original planned heat load in a certain time period according to the heat and cold demand characteristics of the user, so that the heat and cold loads are increased; because the CCHP unit works in a mode of electrically heating or electrically heating, the heat output is increased, the power generation amount is increased, if the cold compensation cost and the heat compensation cost are lower than the interruptible compensation cost, the CCHP unit is scheduled preferentially, and meanwhile, the total scheduling cost is reduced. In addition, the amount of electricity that is increased as the heat output increases is sold to a large power grid at a uniform purchase price or is used to supply electricity to customers at a contract price. The CCHP unit gains are as follows:
CCCHP(t)＝cgrid(t)Pi(t)+cH(t)Ph(t)+cQ(t)PQ(t)-cF(t)PF(t) (5)
in the formula (5), cH(t) is the heat value of the CCHP unit; c. CQ(t) is the cold price of the CCHP unit; c. CF(t) natural gas prices; pi(t) is the amount of power generation; ph(t) the thermal output of the unit i in the time period t; pQ(t) is the cold output of the unit i in the time period t; pF(t)＝Ph(t)/μHIn terms of the amount of natural gas used, muHThe hot gas conversion efficiency of the unit i is related to the heat production quantity.
Step 105: and determining the operation benefit of the absorption type lithium bromide refrigerating unit according to the parameters of the absorption type lithium bromide refrigerating unit.
Step 106: and determining the operation income of the phase change energy storage equipment according to the parameters of the phase change energy storage equipment.
Step 107: and establishing a triple co-generation system operation profit model according to the CCHP unit profit, the absorption type lithium bromide refrigerating unit operation profit and the phase change energy storage equipment operation profit.
Designing a scheme for the phase change energy storage equipment to cooperate with a combined supply system to operate: the cold/heat energy storage device adopts phase change energy storage, so that the problem of mismatching of energy supply and demand relation in time is solved; carry out the electric heat accumulation through electric boiler at millet electricity period, not only abandon the wind and absorb, play the effect of electric power peak regulation moreover.
The phase change energy storage device uses a phase change material to absorb or release latent heat so as to realize effective storage and utilization of heat energy, and can be used for solving the difference between production and demand of the heat energy and improving the phenomenon of energy waste. The phase change energy storage is a process of generating heat storage and release by utilizing the change of a biological state of a material under the action of heat, and has two advantages of high energy storage density and constant and controllable temperature in the energy storage and release process compared with sensible heat energy storage. The temperature of the phase-change material is continuously increased, the physical state changes when the phase-change temperature is reached, and the temperature of the material is almost unchanged before the phase change is completed; meanwhile, a large amount of phase change heat is absorbed and released, and a wider temperature platform is generated. On the premise of the same heat storage amount and the same temperature difference, the heat storage relative volume of the phase-change material is 4 to 5 times smaller than that of the sensible heat material. In addition, the phase change energy storage also has the advantages of high conversion efficiency, easy realization of high capacity, environmental protection, convenient installation and the like.
For a centralized combined cooling, heating and power system, on one hand, in order to ensure the operating efficiency and energy utilization efficiency of a unit, the unit needs to be operated at full capacity as much as possible, but this may cause a phenomenon that the output of the unit is not matched with the load demand, so that the combined cooling and power system needs to frequently adjust the output according to the demands of a cold load or a heat load, and may cause waste heat. The phase change energy storage equipment can be effectively matched with a triple supply system to operate by replacing the energy storage material, and the problem that the energy supply and demand relationship is not matched in time is solved by storing and releasing cold and heat. The phase-change material has the characteristic of passively storing (releasing) heat of absorbing (or releasing) a large amount of phase-change latent heat under the condition of constant temperature or approximate constant temperature, stores solar energy in the daytime, can release the energy stored in the daytime at night, completely or partially replaces night heating, and achieves the purposes of 'time transfer' and 'peak clipping and valley filling' of solar energy utilization. In addition, the electric boiler can be used for performing electric heat storage in the valley power time, and the electric heat storage is applied to building heating, so that the electric power peak regulation of a power grid and the heating operation cost of users are both high in value. Fig. 2 is a system diagram of a scheme of cooperative operation of a phase change energy storage device configured by a triple co-generation system, as shown in fig. 2.
Absorption lithium bromide refrigerating unit:
the absorption type lithium bromide refrigerating unit is different from the traditional vapor compression type refrigerating cycle, and the function of transferring heat from a low-temperature medium to a high-temperature medium is realized by using an absorbent and a heat source to replace a compressor to consume mechanical energy to do work, so that the effect of consuming heat energy and driving a non-spontaneous process to be carried out is realized. As shown in fig. 3, the whole unit can be divided into 2 parts, the left half part is the circulation process of the absorbent, and the right half part is the circulation process of the refrigerant. In the absorption type lithium bromide refrigerating unit, the absorbent selects a lithium bromide solution, the refrigerant is water, the lithium bromide solution has the characteristic of low saturated vapor pressure, and the water vapor released by the water with the temperature far lower than that of the evaporator can be absorbed in the absorber, so that the requirement of cooling the refrigerant is met; the lithium bromide solution in the generator is provided with heat energy by using a heat source, and then high-temperature and high-pressure water vapor is generated and provided for the condenser so as to release heat to the outside. The whole circulation can effectively utilize waste heat gas of the gas turbine and can provide high-efficiency cold energy for the outside.
The thermal coefficient is used for expressing the refrigerating power of the absorption lithium bromide refrigerating system, and the expression is
Figure BDA0001903571280000111
In the formula (6), Q0The refrigerating capacity; qgFor the purpose of heat consumption, η for lithium bromide-type refrigeration unitsCOPThe value is generally about 0.9 to 1.2, and the operation benefit can be determined as the operation benefit of the absorption type lithium bromide refrigerating unit by considering the electric energy saved by directly supplying cold energy
Figure BDA0001903571280000112
In the formula (7), tsFor the cooling time, h, ηCOPeThe thermal coefficient of the electric refrigeration efficiency is generally between 3.8 and 4.
Phase change energy storage: when considering the economic benefits of the operation of the phase change energy storage device, the heat energy is lost during storage and release, and then the economic benefits of the operation of the phase change energy storage device are as follows:
Figure BDA0001903571280000113
cold/heat energy balance constraints:
LH＝PEB+Pph(9)
Lc＝Pair+Pph(10)
the capacity and the charge-discharge power of the phase change energy storage both have upper and lower limits:
-(Pph)max≤Pph≤(Pph)max(11)
0≤Eph≤max[∫(Ph-Lh)dt](12)
wherein η is the thermal efficiency of the energy storage device, Lh、LcRespectively representing heat load, cold load, MW; t is tonRepresenting the time h when the heat production of the triple co-generation unit is greater than the heat load; pEBRepresenting the heat release power of the electric boiler; pphRepresenting the energy storage or discharge power of the phase change energy storage device; pairIndicating the refrigerating power of the air conditioner; ephAnd the heat energy storage capacity in the phase change energy storage equipment is represented as MW & h.
The running profit objective function of the triple co-generation system is as follows:
C(t)＝CCCHP(t)+Cl-b(t)+Cp-c(t) (13)
an energy storage battery model: the service life of the energy storage battery can be influenced in the charging and discharging processes, and the use cost function, the power, the capacity, the charge state constraint and the energy storage state balance constraint of the energy storage battery are given below.
Figure BDA0001903571280000121
Figure BDA0001903571280000122
Figure BDA0001903571280000123
Figure BDA0001903571280000124
SOCT＝SOC0(18)
Wherein λ isbatFor scheduling cost factor, P, of energy storage cellsbat(t) and Ebat(t) charge and discharge power and capacity of the energy storage battery, respectively, ηcAnd ηdRespectively the charge-discharge efficiency, SOC, of the energy storage cellTAnd SOC0The charge capacity at the final time and the initial time of the day is planned respectively; fig. 4 is a structural diagram of a CCHP-type microgrid system provided by the present invention, as shown in fig. 4.
Step 108: according to the power grid compensation cost, the load adjustment cost and the operation income model of the triple co-generation system, optimizing and scheduling the CCHP type micro-grid according to a multi-time scale optimizing and scheduling strategy to enable the operation cost of the CCHP type micro-grid to be minimum; the multi-time scale optimization scheduling strategy comprises a day-ahead optimization scheduling stage, an in-day rolling optimization stage, an ultra-short period scheduling stage and an ultra-short period scheduling stage; and in different dispatching stages, the target functions for optimizing and dispatching the CCHP type micro-grid are different.
The model prediction control can better solve the optimization control problem containing various uncertain factors based on the ideas of rolling optimization and feedback correction, and has strong anti-interference capability and robustness; meanwhile, the MPC can conveniently account for various constraint conditions, has no specific requirements on the form of a prediction model, and can realize simultaneous tracking of a plurality of optimization targets, so that the MPC is particularly suitable for the problem of optimizing and scheduling of the microgrid with uncertain factors in various aspects such as random fluctuation of output power of renewable energy sources, uncertain load power, fluctuation of market price and the like; on the other hand, in the micro-grid intra-day scheduling, besides the aim of realizing the intra-day plan tracking of the day-ahead plan at the minimum unit adjustment cost, the requirement that the energy storage state of charge (SOC) meets the daily operation energy balance is also considered necessarily and comprehensively to ensure that the energy storage meets the operation requirement of the next scheduling day, and the MPC can effectively realize the simultaneous tracking of a plurality of optimization targets and has good applicability; in addition, the MPC acquires the ultra-short term power prediction information in real time in the day scheduling, and performs rolling optimization scheduling by taking the actual scheduling result and the new prediction information as feedback information, so that the influence of uncertain factors in the microgrid on the optimization operation scheduling scheme can be eliminated to the greatest extent. The invention provides a dynamic optimization scheduling strategy of a cooling, heating and power combined supply type microgrid based on model prediction control, an optimization model performs rolling calculation by taking the minimum running cost in an optimization period in the future as an optimization target, and introduces feedback correction to effectively correct the prediction error and the deviation of an optimization scheduling result generated by random factors in time, so that the system scheduling scheme is corrected, and the optimal scheduling of various energies is realized.
Dynamic optimization scheduling model based on model predictive control
Conditions such as random fluctuation of output power of renewable energy sources, peak-to-valley change of thermoelectric load and the like cause that core combined supply equipment needs to be optimized in the day, so that the system can respond to changes of photovoltaic, wind power and user cooling, heating and power requirements in time. Therefore, the dynamic optimization scheduling based on model prediction control mainly comprises a day-ahead scheduling plan, an intra-day rolling optimization model, an ultra-short-term scheduling model and a real-time feedback correction stage. The day-ahead scheduling plan takes 1h as a time interval and takes the lowest daily operating cost as a target to obtain the optimal scheduling plan value all day. The day rolling optimization is a process of continuously correcting and refreshing a day-ahead plan, 15min is taken as a time interval, the main aim is to correct subsequent renewable energy and load power by utilizing actual operation data of a system through calculation of a prediction model, and the minimum cost of micro-source adjustment is taken as a target function. And in the ultra-short period scheduling stage, 10min is taken as a period, and an energy storage device penalty function is added into the existing objective function to ensure the balance of the energy storage state. The ultra-short-term scheduling time scale is 5min to further absorb the fluctuation of the load and the renewable energy source, and the system scheduling block diagram is shown in fig. 5.
Performing energy optimization management on the microgrid in multiple time scales:
① in the day-ahead scheduling stage, with hours (h) as the time scale, based on renewable energy and load day-ahead prediction and real-time electricity price, on the premise of satisfying the system constraint conditions, an optimal economic scheduling model with the lowest system operation cost as the optimization target is established.
And carrying out optimized dispatching on the CCHP type micro-grid according to the load before the day and the renewable energy source prediction information, and dispatching the adjustable load by taking 1h as a time scale and the lowest daily running cost as a target so that the adjustable load closely follows the renewable energy source to generate power, thereby reducing the dispatching pressure of the adjustable distributed power source and obtaining the optimal dispatching scheme all day.
The aim of day-ahead optimization operation of the combined cooling, heating and power type microgrid is to minimize the operation cost of the system, namely the objective function is
Figure BDA0001903571280000141
Figure BDA0001903571280000142
Wherein NR is the number of controllable distributed power sources, R is the number of renewable energy sources, I is the number of removable loads, J is the number of adjustable loads, ηbatThe charge-discharge efficiency of the energy storage battery is obtained; pi(t) represents the output of the ith distributed power supply at the moment t; ci(Pi(t)) represents that the ith distributed power supply has the output of PiCost at (t); Δ T is the scheduling cycle duration.
Controllable distributed power model: for the controllable distributed power supply, the operation cost is mainly composed of two parts of operation maintenance cost and fuel cost, and the constraint on the controllable distributed power supply mainly considers output power constraint and operation climbing rate constraint.
Figure BDA0001903571280000143
Figure BDA0001903571280000144
|PDGg(t)-PDGg(t-1)|≤ΔPDGg(23)
The formula (21) is an operation cost function of the distributed power supply, and a, b and c are coefficients of a quadratic term, a primary term and a constant term of the operation cost function; equation (22) is the active power constraint of the distributed power supply, and the controllable distributed power supply needs to operate within a certain range; equation (23) for operating ramp rate constraint, Δ PDGgIs the power change of the distributed power supply in the delta t time.
Interaction model of micro-grid and large grid: cgrid(t)＝cgrid(t)Pgrid(t) (24)
Figure BDA0001903571280000151
Figure BDA0001903571280000152
In the formula (26), the reaction mixture is,
Figure BDA0001903571280000153
the maximum active power of the interaction between the micro-grid and the large grid is obtained.
② ultra-short-term scheduling takes 10min as a time scale, ultra-short-term prediction is carried out on renewable energy sources and loads, compared with conventional multi-time scale scheduling, capacity constraint and charge state constraint of an energy storage device reflecting long-term characteristics are added into a target function as penalty functions, balance of energy storage states is guaranteed, and overall economy of a system and local economy of short-term scheduling are optimized due to coordination of long-term scheduling distributed power supplies and energy storage.
Due to the fact that large errors may exist between the forecast of the renewable energy sources and the load in the day and the actual value, a rolling optimization link is added, the deviation between the day-ahead plan and the ultra-short-term scheduling can be reduced, and the influence of uncertainty of the renewable energy sources and the load on the scheduling precision is reduced. The goal of the rolling optimization is to adjust T on the basis of satisfying load balance0The output correction value in the subsequent time period at the moment enables the adjustment cost to be minimum, and the objective function is as follows:
Figure BDA0001903571280000154
in the formula,. DELTA.Pi(T) represents the amount of power adjustment of distributed power source i, T0Representing the current time node.
③ because the time interval between the day-ahead plan and the ultra-short scheduling is large and the deviation of the day-ahead plan is large in the actual micro-grid operation, a rolling optimization link is added, the latest system state information is used for correcting the subsequent renewable energy sources and the load predicted power by taking 15min as a time scale, and the day-ahead plan is continuously refreshed and corrected.
And in the ultra-short period scheduling stage, ultra-short period prediction based on similar days is adopted for renewable energy sources and loads in a period of 10 min. According to renewable energy and load side power change obtained by ultra-short term prediction, at a certain time t, the output of each unit is finely adjusted to enable the ultra-short term scheduling cost to be closest to the comprehensive cost corresponding to rolling scheduling, and the objective function is as follows:
Figure BDA0001903571280000161
in the formula,
Figure BDA0001903571280000162
represents the comprehensive scheduling cost at the moment of the ultra-short scheduling t,
Figure BDA0001903571280000163
indicating the cost corresponding to the time period rolling optimization.
Because the ultra-short-period scheduling period is short, the local economy of the scheduling result is highest, but the balance of the energy storage state cannot be guaranteed in the whole day time, in order to enable the scheduling result to integrate the optimal characteristics of the global economy of a system and the local economy of short-period scheduling, which are generated by reasonably matching a distributed power supply with energy storage in long-period scheduling, reduce the charging and discharging times and prolong the service life of a storage battery in order to avoid overcharging and overdischarging of the storage battery, the capacity constraint, the charge state constraint and the consistent constraint of the initial and final states of the period of the storage device are added into the original ultra-short-period scheduling objective function as a penalty function, and:
Figure BDA0001903571280000164
where σ is a penalty factor, g is an inequality constraint, and h is an equality constraint.
④ because there is a large time span between the ultra-short period scheduling and the real-time control, the ultra-short period scheduling link is added between the ultra-short period scheduling and the real-time control, the power fluctuation of renewable energy sources and loads is absorbed, the adjusting pressure of the controllable distributed power supplies in the real-time control link is reduced, and the target function is that the output of each distributed power supply and the load deviation adjustment quantity are minimum.
The scheduling time period is short, 5min is taken as a time scale, and load and renewable energy fluctuation (namely net load fluctuation amount) are further absorbed. The aim is that the output and load adjustment deviation of each distributed power supply at the current moment is minimum, so that the stability of the system for dealing with the fluctuation of the renewable energy sources is ensured. The objective function is:
Figure BDA0001903571280000165
Figure BDA0001903571280000166
Figure BDA0001903571280000171
constraint conditions are as follows:
Pmin(k+n)≤P(k+n)≤Pmax(k+n) (33)
Figure BDA0001903571280000172
Figure BDA0001903571280000173
Figure BDA0001903571280000174
wherein: pr(k + n) is an active power output reference value obtained by short-term scale optimization; p (k + n) is a predicted value of the distributed power supply, the large power grid, the energy storage and the switchable load optimized by the ultra-short-term scale; p0(k + n) is an initial value of active power output of each part optimized by the ultra-short-term scale and is obtained by actual measurement; Δ u (k + t-1) is predicted [ k + t-1, k + t [ ]]The active power output increment in the time period is an optimized control variable; deltaaljAnd (k + n) is a control variable of the adjustable load.
The traditional multi-time scale framework is simpler, and the span between time scales is large, so that the ultra-short-period scheduling and ultra-short-period scheduling stages are added on the basis of the traditional scheduling mode, the step-by-step refinement of micro-grid energy management is realized, and the power unbalance caused by the uncertainty of wind power is balanced step by step; and a multi-time scale scheduling strategy based on model prediction is adopted, and when rolling optimization is executed each time, the updated short-term predicted power value is compared with an actual measured value, and closed-loop control is formed for feedback correction so as to ensure that the rolling strategy has better stability and robustness. Conventional multi-time scale scheduling is open loop control; meanwhile, in ultra-short-term scheduling, an energy storage device penalty function reflecting long-term optimization characteristics is considered, so that global and local economic optimization of the system is coordinated.
Fig. 6 is a structural diagram of a CCHP-type microgrid multi-time scale optimization scheduling system provided by the present invention, and as shown in fig. 6, a CCHP-type microgrid multi-time scale optimization scheduling system includes:
the parameter acquisition module 601 is used for acquiring a load cutting parameter of a load cutting, an adjustable load parameter of an adjustable load, a CCHP unit parameter, an absorption lithium bromide refrigerating unit parameter and a phase change energy storage device parameter; the load cutting parameters comprise a scheduling cost parameter, a market price, a cutting rate and a rated power of the cutting load; the adjustable load parameters comprise an adjustable rate and the active power of the adjustable load; the CCHP unit parameters comprise the heat price of the CCHP unit, the cold price of the CCHP unit, the natural gas price, the power generation amount, the hot output of the unit i in the time period t, the cold output of the unit i in the time period t, the used natural gas amount and the hot gas conversion efficiency of the unit i; the parameters of the absorption lithium bromide refrigerating unit comprise the length of cold supply, the heat supply power of the gas turbine and the thermodynamic coefficient of the electric refrigerating efficiency; the phase change energy storage device parameters include a thermal efficiency, a thermal load, and a cold load of the energy storage device.
A grid compensation charge determining module 602, configured to determine, according to the load-cuttable parameter, a grid compensation charge for cutting off the load-cuttable.
A load adjustment fee determining module 603, configured to determine a load adjustment fee of the adjustable load according to the adjustable load parameter.
And a CCHP unit revenue determining module 604, configured to determine a CCHP unit revenue according to the CCHP unit parameter.
And the absorption type lithium bromide refrigerating unit operation income determining module 605 is used for determining the absorption type lithium bromide refrigerating unit operation income according to the absorption type lithium bromide refrigerating unit parameters.
And the phase change energy storage device operation profit determining module 606 is configured to determine the phase change energy storage device operation profit according to the phase change energy storage device parameter.
And a triple co-generation system operation profit model establishing module 607 for establishing a triple co-generation system operation profit model according to the CCHP unit profit, the absorption type lithium bromide refrigerating unit operation profit and the phase change energy storage device operation profit.
The optimized scheduling adjustment module 608 is configured to perform optimized scheduling on the CCHP micro-grid according to the power grid compensation cost, the load adjustment cost, and the triple co-generation system operation revenue model and according to a multi-time scale optimized scheduling policy, so that the operation cost of the CCHP micro-grid is minimum; the multi-time scale optimization scheduling strategy comprises a day-ahead optimization scheduling stage, an in-day rolling optimization stage, an ultra-short period scheduling stage and an ultra-short period scheduling stage; and in different dispatching stages, the target functions for optimizing and dispatching the CCHP type micro-grid are different.
Fig. 7 is a block diagram of a multi-time scale scheduling structure of a CCHP-type microgrid based on demand side response provided by the present invention, and as shown in fig. 7, the multi-time scale optimized energy management scheme of the CCHP-type microgrid based on demand side response provided by the present invention has the following beneficial effects compared with the prior art:
1) improving the demand side response strategy: flexible load is considered, a CCHP system interaction mechanism is established on the basis of traditional demand side response, and multi-energy source complementation of cold, heat, electricity and the like is achieved.
2) The improved energy storage device comprises: the initial investment cost of the cold storage and heat storage equipment is high, the equipment volume is large, and the service life loss cost is too high. And phase change energy storage equipment utilizes phase change latent heat of phase change material to carry out energy storage and release, and its unit calorific value is big, and operating temperature is stable, and the heat conductivity is good, can compensate the difference between supply and the load, avoids the electric energy and the heat energy surplus of trigeminy confession unit output to cause the waste, compares in ice cold storage and heat storage water tank equipment and is more applicable to trigeminy confession system.
3) Improving a microgrid optimization scheduling strategy: because a large time span exists between ultra-short-period scheduling and real-time control, an ultra-short-period scheduling model is added on the basis of a conventional multi-time scale energy management framework, power fluctuation of renewable energy sources and loads is reduced, and the adjusting pressure of a controllable distributed power supply in a real-time control link is reduced.
4) Improving the objective function: adding an energy storage device penalty function into an ultra-short-term scheduling target to avoid the damage to the service life of a battery due to excessive charging and discharging times, and coordinating the coordination of long-term scheduling of a distributed power supply and energy storage to ensure that the overall economy of the system and the local economy of short-term scheduling are optimal; in addition, lithium bromide operation income, phase change energy storage income and CCHP operation income are added into the total expense, and the total operation cost can be calculated more conveniently and accurately.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (9)

1. A CCHP type micro-grid multi-time scale optimization scheduling method is characterized by comprising the following steps:
acquiring a load cutting parameter of a load cutting, an adjustable load parameter of an adjustable load, a CCHP unit parameter, an absorption type lithium bromide refrigerating unit parameter and a phase change energy storage device parameter; the load cutting parameters comprise a scheduling cost parameter, a market price, a cutting rate and a rated power of the cutting load; the adjustable load parameters comprise an adjustable rate and the active power of the adjustable load; the CCHP unit parameters comprise the heat price of the CCHP unit, the cold price of the CCHP unit, the natural gas price, the power generation amount, the hot output of the unit i in the time period t, the cold output of the unit i in the time period t, the used natural gas amount and the hot gas conversion efficiency of the unit i; the parameters of the absorption lithium bromide refrigerating unit comprise the length of cold supply, the heat supply power of the gas turbine and the thermodynamic coefficient of the electric refrigerating efficiency; the parameters of the phase change energy storage device comprise the heat efficiency, the heat load and the cold load of the energy storage device;
determining the power grid compensation cost for cutting off the load-cuttable according to the load-cuttable parameter;
determining the load adjustment cost of the adjustable load according to the adjustable load parameter;
determining the gain of the CCHP unit according to the CCHP unit parameters;
determining the operation benefit of the absorption type lithium bromide refrigerating unit according to the parameters of the absorption type lithium bromide refrigerating unit;
determining the operation income of the phase change energy storage equipment according to the parameters of the phase change energy storage equipment;
establishing a triple co-generation system operation profit model according to the CCHP unit profit, the absorption type lithium bromide refrigerating unit operation profit and the phase change energy storage equipment operation profit;
according to the power grid compensation cost, the load adjustment cost and the operation income model of the triple co-generation system, optimizing and scheduling the CCHP type micro-grid according to a multi-time scale optimizing and scheduling strategy to enable the operation cost of the CCHP type micro-grid to be minimum; the multi-time scale optimization scheduling strategy comprises a day-ahead optimization scheduling stage, an in-day rolling optimization stage, an ultra-short period scheduling stage and an ultra-short period scheduling stage; in different scheduling stages, the target functions for performing optimized scheduling on the CCHP type microgrid are different; the optimizing and scheduling of the CCHP type microgrid is performed according to the power grid compensation cost, the load adjustment cost and the operation income model of the triple co-generation system and a multi-time scale optimizing and scheduling strategy, so that the operation cost of the CCHP type microgrid is the minimum, and the optimizing and scheduling method specifically comprises the following steps:
in the day-ahead optimization scheduling stage, according to a formula
Figure FDA0002461498520000021
Performing optimized scheduling on the CCHP type microgrid; NR is the number of controllable distributed power supplies; i is the number of load that can be cut; j is the number of adjustable loads; pi(t) represents the output of the ith distributed power supply at time t, Ci(Pi(t)) represents that the ith distributed power supply has the output of Pi(T) cost, Δ T being the scheduling cycle duration; cDGg(t) the operating and maintenance costs of the controllable distributed power supply; cbat(t) the cost of use of the battery; cgrid(t) interaction cost with a large power grid; ccli(t) removing the compensation cost of the electric network with the load cutting function; calj(t) load adjustment costs for said adjustable load; c (t) is the running income of the triple co-generation system; t is an adjustment period;
in the day rolling optimization stage, according to a formula
Figure FDA0002461498520000022
Performing optimized scheduling on the CCHP type microgrid; wherein, Δ Pi(t) is the power adjustment of the distributed power source i; t is0Is the current time node;
in the ultra-short period scheduling stage, according to a formula
Figure FDA0002461498520000023
Performing optimized scheduling on the CCHP type microgrid; wherein,
Figure FDA0002461498520000024
represents the comprehensive scheduling cost at the moment of the ultra-short scheduling t,
Figure FDA0002461498520000025
representing the cost corresponding to the time period rolling optimization;
in the ultra-short period scheduling stage, according to a formula
Figure FDA0002461498520000026
Performing optimized scheduling on the CCHP type microgrid; wherein,
Figure FDA0002461498520000027
；
Figure FDA0002461498520000028
Pr(k + n) is an active power output reference value obtained by short-term scale optimization; p (k + n) is a predicted value of the distributed power supply, the large power grid, the energy storage and the switchable load optimized by the ultra-short-term scale; p0(k + n) is an initial value for optimizing active power output of each part in an ultra-short-term scale; Δ u (k + t-1) is predicted [ k + t-1, k + t [ ]]Active power output increment in a time period;
Figure FDA0002461498520000031
the active output reference value is the active output reference value of the controllable distributed power supply;
Figure FDA0002461498520000032
the active output reference value is interactive with a large power grid;
Figure FDA0002461498520000033
the active output reference value is the switchable load;
Figure FDA0002461498520000034
an active power output reference value for an adjustable load;
Figure FDA0002461498520000035
the active output reference value of the energy storage battery.
2. The CCHP-type microgrid multi-time scale optimized scheduling method according to claim 1, wherein the determining of grid compensation cost for cutting off the cuttable load according to the cuttable load parameter specifically includes:
according to the formula
Figure FDA0002461498520000036
Determining the power grid compensation cost for cutting off the load; wherein, Ccli(t) removing the compensation cost of the electric network with the load cutting function; epsiloncli(t) is the scheduling cost coefficient of the load-shedding; c. Cgrid(t) represents a buying and selling market price; rhocli(t) is the cuttability;
Figure FDA0002461498520000037
is the rated power of the cutting load i.
3. The CCHP-type microgrid multi-time scale optimization scheduling method according to claim 2, wherein the determining of the load adjustment cost of the adjustable load according to the adjustable load parameter specifically comprises:
according to the formula
Figure FDA0002461498520000038
Determining a load adjustment cost for the adjustable load, wherein Calj(t) load adjustment costs for said adjustable load; deltaalj(t) is the adjustable rate; palj(t) is the active power of the adjustable load j; t is the adjustment period.
4. The CCHP type microgrid multi-time scale optimization scheduling method according to claim 3, wherein the determining CCHP unit revenue according to the CCHP unit parameters specifically comprises:
according to formula CCCHP(t)＝cgrid(t)Pi(t)+cH(t)Ph(t)+cQ(t)PQ(t)-cF(t)PF(t) determining the gain of the CCHP unit; wherein, CCCHP(t) the revenue of the CCHP unit; c. CH(t) is the heat value of the CCHP unit; c. CQ(t) is the cold price of the CCHP unit; c. CF(t) natural gas prices; pi(t) is the amount of power generation; ph(t) the thermal output of the unit i in the time period t; pQ(t) is the cold output of the unit i in the time period t; pF(t)＝Ph(t)/μHIn terms of the amount of natural gas used, muHThe hot gas conversion efficiency of the unit i.
5. The CCHP type microgrid multi-time scale optimization scheduling method according to claim 4, wherein the determining of the operation income of the absorption lithium bromide refrigeration unit according to the absorption lithium bromide refrigeration unit parameters specifically comprises:
according to the formula
Figure FDA0002461498520000041
Determining the operation income of the absorption lithium bromide refrigerating unit; wherein, Cl-b(t) the operating profit of the absorption lithium bromide refrigerating unit; t is tsThe cooling time is the length of time; phPower for heating gas turbine ηCOPeThermodynamic coefficient of electric refrigeration efficiency ηCOPThe refrigeration power of the lithium bromide refrigeration system.
6. The CCHP type microgrid multi-time scale optimization scheduling method according to claim 5, wherein the determining of the operation income of the phase change energy storage equipment according to the phase change energy storage equipment parameters specifically comprises:
according to the formula
Figure FDA0002461498520000042
Determining the operation income of the phase change energy storage equipment; wherein, Cp-c(t) the operating yield of the phase change energy storage device, η the thermal efficiency of the energy storage device, LhIs a thermal load; l iscIs a cold load; t is ton|winterThe time that the heat production of the triple-generation unit is greater than the heat load in winter is provided; t is ton|summerThe heat generation of the unit is more than the time of heat load in summer.
7. The CCHP type microgrid multi-time scale optimization scheduling method according to claim 6, wherein the establishing of a triple co-generation system operation profit model according to the CCHP unit profit, the absorption type lithium bromide refrigeration unit operation profit and the phase change energy storage device operation profit specifically comprises:
according to the formula C (t) ═ CCCHP(t)+Cl-b(t)+Cp-c(t) establishing a triple co-generation system operation income model; and C (t) the running benefit of the triple co-generation system.
8. A CCHP type micro-grid multi-time scale optimization scheduling system is characterized by comprising:
the parameter acquisition module is used for acquiring the load cutting parameter of the load cutting, the adjustable load parameter of the adjustable load, the CCHP unit parameter, the absorption type lithium bromide refrigerating unit parameter and the phase change energy storage equipment parameter; the load cutting parameters comprise a scheduling cost parameter, a market price, a cutting rate and a rated power of the cutting load; the adjustable load parameters comprise an adjustable rate and the active power of the adjustable load; the CCHP unit parameters comprise the heat price of the CCHP unit, the cold price of the CCHP unit, the natural gas price, the power generation amount, the hot output of the unit i in the time period t, the cold output of the unit i in the time period t, the used natural gas amount and the hot gas conversion efficiency of the unit i; the parameters of the absorption lithium bromide refrigerating unit comprise the length of cold supply, the heat supply power of the gas turbine and the thermodynamic coefficient of the electric refrigerating efficiency; the parameters of the phase change energy storage device comprise the heat efficiency, the heat load and the cold load of the energy storage device;
the power grid compensation charge determining module is used for determining the power grid compensation charge for cutting off the load-cuttable according to the load-cuttable parameter;
the load adjustment fee determining module is used for determining the load adjustment fee of the adjustable load according to the adjustable load parameter;
the CCHP unit income determining module is used for determining the CCHP unit income according to the CCHP unit parameters;
the absorption type lithium bromide refrigerating unit operation income determining module is used for determining the absorption type lithium bromide refrigerating unit operation income according to the absorption type lithium bromide refrigerating unit parameters;
the phase change energy storage equipment operation income determining module is used for determining the phase change energy storage equipment operation income according to the phase change energy storage equipment parameters;
the triple co-generation system operation profit model establishing module is used for establishing a triple co-generation system operation profit model according to the CCHP unit profit, the absorption type lithium bromide refrigerating unit operation profit and the phase change energy storage equipment operation profit;
the optimized scheduling adjustment module is used for performing optimized scheduling on the CCHP type micro-grid according to the power grid compensation cost, the load adjustment cost and the operation income model of the triple co-generation system and a multi-time scale optimized scheduling strategy, so that the operation cost of the CCHP type micro-grid is minimum; the multi-time scale optimization scheduling strategy comprises a day-ahead optimization scheduling stage, an in-day rolling optimization stage, an ultra-short period scheduling stage and an ultra-short period scheduling stage; in different scheduling stages, the target functions for performing optimized scheduling on the CCHP type microgrid are different; the optimizing and scheduling of the CCHP type microgrid is performed according to the power grid compensation cost, the load adjustment cost and the operation income model of the triple co-generation system and a multi-time scale optimizing and scheduling strategy, so that the operation cost of the CCHP type microgrid is the minimum, and the optimizing and scheduling method specifically comprises the following steps:
in the day-ahead optimization scheduling stage, according to a formula
Figure FDA0002461498520000061
Performing optimized scheduling on the CCHP type microgrid; NR is the number of controllable distributed power supplies; i is the number of load that can be cut; j is the number of adjustable loads; pi(t) represents the output of the ith distributed power supply at time t, Ci(Pi(t)) represents that the ith distributed power supply has the output of Pi(T) cost, Δ T being the scheduling cycle duration; cDGg(t) the operating and maintenance costs of the controllable distributed power supply; cbat(t) the cost of use of the battery; cgrid(t) interaction cost with a large power grid; ccli(t) removing the compensation cost of the electric network with the load cutting function; calj(t) load adjustment costs for said adjustable load; c (t) is the running income of the triple co-generation system; t is an adjustment period;
in the day rolling optimization stage, according to a formula
Figure FDA0002461498520000062
Performing optimized scheduling on the CCHP type microgrid; wherein, Δ Pi(t) is the power adjustment of the distributed power source i; t is0Is the current time node;
in the ultra-short period scheduling stage, according to a formula
Figure FDA0002461498520000063
Performing optimized scheduling on the CCHP type microgrid; wherein,
Figure FDA0002461498520000064
represents the comprehensive scheduling cost at the moment of the ultra-short scheduling t,
Figure FDA0002461498520000065
indicating that the period has rolled wellCorresponding cost is changed;
in the ultra-short period scheduling stage, according to a formula
Figure FDA0002461498520000066
Performing optimized scheduling on the CCHP type microgrid; wherein,
Figure FDA0002461498520000067
；
Figure FDA0002461498520000068
Pr(k + n) is an active power output reference value obtained by short-term scale optimization; p (k + n) is a predicted value of the distributed power supply, the large power grid, the energy storage and the switchable load optimized by the ultra-short-term scale; p0(k + n) is an initial value for optimizing active power output of each part in an ultra-short-term scale; Δ u (k + t-1) is predicted [ k + t-1, k + t [ ]]Active power output increment in a time period;
Figure FDA0002461498520000071
the active output reference value is the active output reference value of the controllable distributed power supply;
Figure FDA0002461498520000072
the active output reference value is interactive with a large power grid;
Figure FDA0002461498520000073
the active output reference value is the switchable load;
Figure FDA0002461498520000074
an active power output reference value for an adjustable load;
Figure FDA0002461498520000075
the active output reference value of the energy storage battery.
9. The CCHP-type microgrid multi-time scale optimization scheduling system according to claim 8, wherein the grid compensation cost determination module specifically comprises:
a grid compensation charge determination unit for determining the compensation charge according to the formula
Figure FDA0002461498520000076
Determining the power grid compensation cost for cutting off the load; wherein, Ccli(t) removing the compensation cost of the electric network with the load cutting function; epsiloncli(t) is the scheduling cost coefficient of the load-shedding; c. Cgrid(t) represents a buying and selling market price; rhocli(t) is the cuttability;
Figure FDA0002461498520000077
is the rated power of the cutting load i.
CN201811522497.8A 2018-12-13 2018-12-13 CCHP type micro-grid multi-time scale optimization scheduling method and system Active CN109617142B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811522497.8A CN109617142B (en) 2018-12-13 2018-12-13 CCHP type micro-grid multi-time scale optimization scheduling method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811522497.8A CN109617142B (en) 2018-12-13 2018-12-13 CCHP type micro-grid multi-time scale optimization scheduling method and system

Publications (2)

Publication Number Publication Date
CN109617142A CN109617142A (en) 2019-04-12
CN109617142B true CN109617142B (en) 2020-06-09

Family

ID=66007355

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811522497.8A Active CN109617142B (en) 2018-12-13 2018-12-13 CCHP type micro-grid multi-time scale optimization scheduling method and system

Country Status (1)

Country Link
CN (1) CN109617142B (en)

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110110904A (en) * 2019-04-17 2019-08-09 华北电力大学 Consider the integrated energy system optimization method of economy, independence and carbon emission
CN110264051A (en) * 2019-06-06 2019-09-20 新奥数能科技有限公司 Determine the method and device of the Optimized Operation scheme of microgrid
CN110361969B (en) * 2019-06-17 2021-01-05 清华大学 Optimized operation method of cooling, heating and power comprehensive energy system
CN110311421B (en) * 2019-07-12 2021-05-07 燕山大学 Micro-grid multi-time scale energy management method based on demand side response
CN111445107B (en) * 2020-03-02 2023-06-13 山东大学 Multi-objective optimal configuration method for combined cooling heating power type micro-grid
CN112116490A (en) * 2020-07-31 2020-12-22 西安交通大学 Operation control method and device for combined cooling heating and power system of fuel cell
CN112186755B (en) * 2020-09-25 2022-11-22 东南大学 Flexible load energy storage modeling method for regional comprehensive energy system
CN112902273A (en) * 2020-11-18 2021-06-04 国网新疆电力有限公司经济技术研究院 Regulation and control method of heat accumulating type photovoltaic power generation heating system
CN112258096B (en) * 2020-12-22 2021-05-14 国网江西省电力有限公司电力科学研究院 Multi-energy-flow strong-coupling ecological breeding overall-process combined heat and power supply method
CN112785068B (en) * 2021-01-29 2023-08-22 西安峰频能源科技有限公司 Method for establishing optimal window model for ultra-short-term electric quantity prediction
CN114626616B (en) * 2022-03-21 2023-11-21 特斯联科技集团有限公司 Optimal operation method and system for recycling CCHP (CCHP) from high-temperature tail gas waste heat in park

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107506851A (en) * 2017-07-26 2017-12-22 河海大学 A kind of multizone virtual plant comprehensive energy coordinated scheduling Optimized model
CN107887933A (en) * 2017-11-17 2018-04-06 燕山大学 A kind of Multiple Time Scales rolling optimization microgrid energy optimum management method

Also Published As

Publication number Publication date
CN109617142A (en) 2019-04-12

Similar Documents

Publication Publication Date Title
CN109617142B (en) CCHP type micro-grid multi-time scale optimization scheduling method and system
CN113344736B (en) Park-level comprehensive energy system and control method thereof
CN111738497B (en) Virtual power plant double-layer optimal scheduling method considering demand side response
CN107958300A (en) A kind of more microgrid interconnected operation coordinated scheduling optimization methods for considering interactive response
CN109752953B (en) Building energy supply system model prediction regulation and control method of integrated electric refrigerator
Teng et al. A model of electro-thermal hybrid energy storage system for autonomous control capability enhancement of multi-energy microgrid
CN111737884A (en) Multi-target random planning method for micro-energy network containing multiple clean energy sources
Zhang et al. Energy optimal management of microgrid with high photovoltaic penetration
Liu et al. Research on optimization of energy storage regulation model considering wind–solar and multi-energy complementary intermittent energy interconnection
CN110829408A (en) Multi-domain scheduling method considering energy storage power system based on power generation cost constraint
CN105375501A (en) Multi-time scale micro-grid layered stability control method
CN117559526A (en) Router-simulated energy regulation and control method based on optical storage and charging integrated charging station
Guo et al. Energy storage technology and its typical application in new energy grid connection system
CN109361236A (en) A kind of new energy consumption method based on the interaction adjustment of source lotus
CN116599148A (en) Hydrogen-electricity hybrid energy storage two-stage collaborative planning method for new energy consumption
CN116050637A (en) Comprehensive energy virtual power plant optimal scheduling method and system based on time-of-use electricity price
CN115912419A (en) Heat storage-electricity storage cooperative scheduling method based on load aggregation quotient
Yan et al. Practical flexibility analysis on europe power system with high penetration of variable renewable energy
CN113919676A (en) Virtual power plant operation effect evaluation method considering demand response and electric hydrogen production system
CN113627720A (en) Comprehensive park wind-solar energy storage system configuration method based on electric energy substitution
Yiru et al. A modified particle swarm optimization based algorithm for the energy management strategy of a regional integrated energy system
CN113962438A (en) Combined optimization system and method for cooling, heating and power hybrid energy
Jiarui et al. Research on Demand Response Strategy of Electricity Market Based on Intelligent Power Consumption
Li et al. Centralized Control Strategy Considering Decentralized Energy Storage participate in Optimal Power System Dispatching
Li et al. An Optimal Operation Method of Hybrid AC/DC Distribution Network Under the Goal of Peaking Carbon Dioxide Emissions and Carbon Neutrality

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
TR01 Transfer of patent right
TR01 Transfer of patent right

Effective date of registration: 20240618

Address after: 073000 West 200m northbound at the intersection of Dingzhou commercial street and Xingding Road, Baoding City, Hebei Province (No. 1910, 19th floor, building 3, jueshishan community)

Patentee after: Hebei Kaitong Information Technology Service Co.,Ltd.

Country or region after: China

Address before: 066000 No. 438, Hebei Avenue, Qinhuangdao, Hebei

Patentee before: Yanshan University

Country or region before: China

TR01 Transfer of patent right
TR01 Transfer of patent right

Effective date of registration: 20240624

Address after: Room 403, No. 47 Chuangxin Second Road, Torch Park, Torch High tech Zone, Xiamen City, Fujian Province, 361000

Patentee after: Xiamen Torch Xinyuan Electric Power Technology Co.,Ltd.

Country or region after: China

Address before: 073000 West 200m northbound at the intersection of Dingzhou commercial street and Xingding Road, Baoding City, Hebei Province (No. 1910, 19th floor, building 3, jueshishan community)

Patentee before: Hebei Kaitong Information Technology Service Co.,Ltd.

Country or region before: China