CN110188492B - Combined cooling heating and power micro-grid optimized scheduling method considering heat supply network characteristics - Google Patents
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Abstract
The invention discloses a cooling, heating and power combined supply micro-grid optimized scheduling method considering heat supply network characteristics, which is suitable for a cooling, heating and power combined supply micro-grid system of a building group. On the basis of considering the transmission delay and the temperature loss of the heat supply network, the invention establishes a combined cooling heating and power micro-network optimization scheduling model considering the load balance constraint, the equipment safety constraint and the heat supply network characteristic constraint by taking the minimum daily operation cost of the system as an optimization target. The model is a mixed integer nonlinear programming model, and optimizes the operation conditions of all energy supply, energy storage and energy conversion equipment in the combined cooling heating and power micro-grid at each scheduling period according to given time-of-use electricity price, gas price and cold, heat and electricity load prediction information. Due to the fact that the characteristics of the heat supply network are considered, the optimal scheduling method of the combined cooling heating and power micro-network can fully explore the heat storage capacity of the heat supply network, change the heat load curve, optimize the operation condition of the micro-network, and further give a more economical and reasonable scheduling plan of the combined cooling heating and power micro-network.
Description
Technical Field
The invention relates to a combined cooling heating and power microgrid optimal scheduling technology, in particular to a combined cooling heating and power microgrid optimal scheduling method considering heat supply network characteristics.
Background
With the rapid development of global industry and economy, the problems of energy crisis and environmental pollution become increasingly serious, the traditional energy structure and utilization mode are difficult to continue, and the promotion of energy structure transformation and the improvement of energy utilization efficiency are imperative. The energy comprehensive cascade utilization mode realizes the cooperative supply of multiple energy forms such as electric energy, heat energy, cold energy and the like, and combined cooling, heating and power (CCHP) can improve the energy utilization efficiency, reduce the energy use cost and reduce the pollutant discharge. Therefore, combined cooling, heating and power is becoming one of the important development trends in the energy field. For modern building groups, besides the power demand, a large amount of cold and heat loads exist, and the CCHP is one of important application scenes.
The CCHP system applied to the building group can be called as a CCHP type microgrid, and at present, a large amount of literature is available for researching the optimal scheduling problem of the CCHP type microgrid. In the literature, "day-ahead cooling, heating and power combined economic optimization scheduling of a micro energy grid based on a Hessian interior point method" (power grid technology, 2016, volume 40, phase 6, pages 1657 to 1665), the electricity, heat and cooling loads are subdivided into 5 types, and a CCHP type microgrid optimization scheduling model based on the interior point method of Hessian matrix iteration is provided. A bus-type structure describing the composition and energy flow of the CCHP-type microgrid is proposed in the document "universal modeling method for optimizing and scheduling of combined cooling, heating and power microgrid" (report of electrical engineering science in china, 2013, volume 33, period 31, pages 26 to 33), and a universal modeling method for scheduling the CCHP-type microgrid is established around the bus-type structure. The technical method provided by the above documents can realize the optimized scheduling of the CCHP type microgrid, and has a strong engineering application prospect. However, the methods only consider simple electrical and thermal balance constraints, neglect the influence of the heat supply network characteristics on the CCHP type microgrid optimization scheduling, and influence the engineering practical value and the economical efficiency of the scheduling result to a certain extent.
Transmission delay and temperature loss are two main expression forms of heat supply network characteristics, and modeling the heat supply network characteristics is the basis for considering the heat supply network characteristics in CCHP type micro-network optimization scheduling. At present, there is a great deal of literature modeling heat supply network characteristics. The three Combined analysis of electric and heat networks (Energy Procedia 2014, 61 st, 155 th to 159 th) documents study the heat transfer model of the heat supply network in detail, define the basic variables of the heat supply network, and give a basic equation to lay a foundation for modeling the characteristics of the heat supply network. The document four (IEEE Transactions on stable Energy 2015, volume 7, phase 1, pages 12 to 22) models the heat grid characteristics and analyzes the influence of the heat grid characteristics on wind power absorption. However, up to now, a CCHP-type microgrid optimization scheduling method considering characteristics of a heat supply network in detail has not appeared.
The characteristics of the heat supply network have a significant influence on the optimal scheduling of the CCHP-type microgrid, and therefore, the characteristics of the heat supply network must be considered in the scheduling. However, in the prior art method, only the thermal power balance is considered and the heat supply network characteristic is ignored, so that the engineering practical value and the economical efficiency of the scheduling result are reduced to a certain extent.
Disclosure of Invention
The technical problem to be solved by the invention is that aiming at the problem of optimal scheduling of the combined cooling heating and power microgrid, an optimal scheduling model of the combined cooling heating and power microgrid considering the transmission characteristic of a heat pipe network can be established, load balance constraint, equipment safety constraint and heat network characteristic constraint are comprehensively considered, coordinated optimization of three energy forms of cooling, heating and power in the combined cooling heating and power microgrid is carried out, and an optimal scheduling scheme is finally specified.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention provides a cooling, heating and power combined supply micro-grid optimal scheduling method considering heat supply network characteristics, which is characterized by comprising the following steps:
s1: inputting electricity price information and gas price information according to the selected combined cooling heating and power microgrid, reading cold, heat and electricity load prediction information, and inputting parameter information of combined cooling heating and power microgrid equipment;
s2: the method for establishing the combined cooling heating and power micro-grid optimization scheduling model considering the heat supply network characteristics comprises the following steps: setting the minimum daily operation cost of the combined cooling heating and power micro-grid as a target function, and respectively considering load balance constraint, equipment safety constraint and heat supply network characteristic constraint;
s3: calling a related nonlinear solver to solve the cooling, heating and power combined microgrid optimization scheduling model obtained in the step S2;
s4: and determining the optimal scheduling scheme of the combined cooling heating and power microgrid according to the solving result of the combined cooling heating and power microgrid optimal scheduling model in the step S3. The scheduling scheme specifically comprises an energy supply main body start-stop instruction, operation conditions of each energy supply device, each energy storage device, each energy conversion device and the like.
Preferably, the objective function in step S2 is specifically expressed by the following formula:
C＝min(CE+CG)
in the above formula: c is the daily operating cost of the system; cEFor the total purchase of electricity, ce,tFor the purchase price of electricity at time t, Pgrid,tThe power purchasing power at the moment t is shown, and delta t is the length of a scheduling period; cGFor total gas charge, cgasIs the natural gas unit heat value price, PGT,tFor the power generated by the gas turbine at time t, QGB,tIs the heat production power, eta, of the gas boiler at the moment tGTFor the efficiency of the power generation of the gas turbine, ηGBThe heat production efficiency of the gas boiler.
Preferably, the load balancing constraints in step S2 include an electrical load balancing constraint, a thermal load balancing constraint, and a cold load balancing constraint:
the electrical load balancing constraint is specifically represented by the following equation:
in the above formula, PESC,t、PESD,tRespectively the charging and discharging power, eta, of the accumulator at time tESC、ηESDRespectively the charging efficiency and the discharging efficiency of the storage battery; u. ofC,tAnd uD,tBinary variables of the storage battery in the charging and discharging states at the moment t respectively; pL,tThe electrical load requirement, P, of the combined cooling heating and power microgrid at the moment tEC,tThe power consumption of the electric refrigerator at the moment t;
the thermal load balancing constraint is specifically represented by the following formula:
in the above formula, QWH,tFor the heat recovery power of the waste heat boiler at the time t,
is the hot water power, eta, of the gas boiler at time tWHFor the heat-generating efficiency of waste-heat boilers, QH,tThe heat load demand, Q, of the combined cooling heating and power microgrid at the moment tAC,tTo absorb the heat recovery power of the refrigerator at time t;
the cold load balancing constraint is specifically represented by the following formula:
PEC,tCOPEC+QAC,tηAC-QC,t＝0
in the above formula, COPECIs the energy efficiency ratio of the electric refrigeratorACTo suckCooling efficiency of the absorption refrigerator, QC,tAnd the cold load requirement of the combined cooling heating and power micro-grid at the moment t is met.
Preferably, the device safety constraints in step S2 include an operation constraint, a hill climbing constraint, a start/stop constraint, a battery state of charge constraint, and a battery charging/discharging power constraint;
wherein the operating constraints are represented by:
in the above formula: u. ofGT,t、uGB,tRespectively representing binary variables of the gas turbine and the gas boiler in the on-off state at the moment t;
the minimum value and the maximum value of the power generation power of the gas turbine are obtained;
the minimum value and the maximum value of the heat production power of the gas-fired boiler are obtained;
the minimum value and the maximum value of the heat generation power of the waste heat boiler are obtained; qAC,N、PEC,NThe rated power of the absorption refrigerator and the electric refrigerator;
the minimum value and the maximum value of the power purchased from the cold-heat-electricity combined supply micro-grid to the large power grid are obtained;
the hill climbing constraint is represented by the following formula:
in the above formula:
the lower climbing speed and the upper climbing speed which are respectively the power generation power of the gas turbine;
outputting a downward climbing speed and an upward climbing speed of thermal power for the gas boiler;
the start-stop constraint is represented by the following formula:
in the above formula:
respectively minimum on-off time of the gas turbine;
minimum start-up and shut-down time of the gas boiler respectively;
the battery state of charge constraint is represented by the following equation:
WES,t＝96＝WES,t＝0
in the above formula:
minimum and maximum allowable value of state of charge, W, of the storage batteryES,tIs the state of charge of the battery at time t, CBESSIs the battery capacity; wES,t-1Is a storage battery in timeThe state of charge at t-1; sigmaESIs the self-discharge rate of the battery.
The battery charge and discharge power constraint is expressed by the following formula:
in the above formula, the first and second carbon atoms are,
maximum charging and discharging power u of the storage battery respectivelyC,tAnd uD,tThe binary variables of the charging and discharging states of the storage battery at the moment t are respectively.
Preferably, the heat supply network characteristic constraints in step S2 include heat source supply, return water temperature and heat exchange constraints, pipeline heat delay and temperature drop constraints, and heat supply pipeline temperature constraints;
the heat source supply and return water temperature and heat exchange constraint is specifically represented by the following formula:
in the above formula: m is the number of heat sources in the combined cooling heating and power micro-grid; qS,j,tThe heating power of the jth heat source in the combined cooling heating and power micro-grid at the moment t is provided; gj,tThe hot water flow of the branch where the heat source j is located at the moment t; t issg,j,t、Tsh,j,tRespectively the supply water temperature and the return water temperature of the heat source j at the moment t.
The thermal delay and temperature drop constraints of the pipeline are specifically expressed as follows:
in the formula: in the above formula, Te,i(t+τi) Temperature of the end of the pipe i at time t, τiFor the propagation delay time of the pipe i, TsI (t) is the temperature of the head end of the pipeline i at the time t, i is the index of the heat supply pipeline, ρ is the density of hot water, AiIs the cross-sectional area of the conduit i, LiIs the length of the pipe i, v is the average flow velocity of the hot water, miIs the running flow of the pipeline i, delta t is the scheduling time interval, lambda is the heat transfer efficiency per unit length of the pipeline, CPSpecific heat capacity of hot water, Ts,i(T) is the temperature of the head end of the pipeline i at time T, ToThe ambient temperature outside the pipeline;
the heat supply pipeline temperature constraint is specifically represented by the following formula:
in the above formula:
is the upper limit of the temperature of the water supply pipeline;
is the lower limit of the temperature of the water return pipeline.
Compared with the prior art, the combined cooling heating and power microgrid optimization scheduling method considering the characteristics of the heat supply network provided by the invention considers the characteristics of the heat supply network, such as transmission delay and temperature loss of the heat supply network, and establishes the combined cooling heating and power microgrid optimization scheduling model with the minimum system daily operation cost as the optimization target. The model simultaneously considers load balance constraint, equipment safety constraint and heat supply network characteristic constraint. The model is a mixed integer nonlinear programming model, and optimizes the operation conditions of all energy supply, energy storage and energy conversion equipment in the combined cooling heating and power micro-grid at each scheduling period according to given time-of-use electricity price, gas price and cold, heat and electricity load prediction information. Due to the fact that the characteristics of the heat supply network are considered, the optimal scheduling method of the combined cooling heating and power micro-network can fully explore the heat storage capacity of the heat supply network, change the heat load curve, optimize the operation condition of the micro-network, and further give a more economical and reasonable scheduling plan of the combined cooling heating and power micro-network.
Drawings
Fig. 1 is a flowchart of an optimal scheduling method of a combined cooling heating and power micro-grid in consideration of heat supply network characteristics;
fig. 2 is a structural diagram of a combined cooling heating and power microgrid in an embodiment of the invention;
FIG. 3 is a diagram of a heat network configuration in an embodiment of the present invention;
FIG. 4 is a time-of-use electricity price chart according to an embodiment of the present invention;
FIG. 5 is a graph comparing the total power of the heat source with the total heating power of the load according to the embodiment of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention are 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 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.
Referring to fig. 1, fig. 1 is a flowchart of a combined cooling heating and power microgrid optimal scheduling method considering heat supply network characteristics and considering heat supply network characteristics, disclosed in an embodiment of the present invention, and the method includes:
s1: inputting electricity price information and gas price information according to the selected combined cooling heating and power microgrid, reading cold, heat and electricity load prediction information, and inputting parameter information of combined cooling heating and power microgrid equipment; in this embodiment, the electricity price refers to the time-of-use electricity price, and the gas price refers to the unit heating value price of natural gas.
S2: the method for establishing the combined cooling heating and power micro-grid optimization scheduling model considering the heat supply network characteristics comprises the following steps: setting the minimum daily operation cost of the combined cooling heating and power micro-grid as a target function, and respectively considering load balance constraint, equipment safety constraint and heat supply network characteristic constraint;
the method is characterized in that a combined cooling heating and power micro-grid optimization scheduling model considering load balance constraint, equipment safety constraint and heat supply network characteristic constraint is established by taking the minimum daily operation cost of the system as an optimization target.
The optimal scheduling objective is that the system daily operation cost is minimal, and is specifically as follows:
C＝min(CE+CG) (1)
in the formula: c is the daily operating cost of the system; cEThe total electricity purchasing cost is Yuan; cGThe total gas cost is Yuan. Wherein, the gas cost formula of calculation is:
in the formula: c. CgasThe unit heat value price of the natural gas is yuan/(kW.h); pGT,tIs the power generation power of the gas turbine at the moment t, kW; qGB,tThe power is kW which is the heat production power of the gas boiler at the moment t; etaGT、ηGBThe power generation efficiency of the gas turbine and the heat generation efficiency of the gas boiler are respectively.
The electricity purchasing cost calculation formula is as follows:
in the formula: c. Ce,tThe electricity purchase price at the moment t is yuan/(kW & h); pgrid,tThe power purchasing power at the moment t is kW; Δ t is the scheduling period length, min.
For the combined cooling heating and power microgrid, the constraints considered in operation include: load balancing constraints, equipment safety constraints and heat supply network characteristic constraints.
The load balancing constraints include electrical load balancing constraints, thermal load balancing constraints, and cold load balancing constraints.
1) Electric load balance constraint:
the discharge time period of the storage battery:
PGT,t+Pgrid,t+PESD,tηESD-PL,t-PEC,t＝0 (4)
the charging time period of the storage battery is as follows:
the electrical load balancing constraint is specifically represented by the following equation:
in the above formula, PESC,t、PESD,tRespectively the charging and discharging power, eta, of the accumulator at time tESC、ηESDRespectively the charging efficiency and the discharging efficiency of the storage battery; u. ofC,tAnd uD,tBinary variables of the storage battery in the charging and discharging states at the moment t respectively; pL,tThe electrical load requirement, P, of the combined cooling heating and power microgrid at the moment tEC,tThe power consumption of the electric refrigerator at the moment t;
in the formula: pESC,t、PESD,tThe charging power and the discharging power of the storage battery at the moment t are respectively kW; etaESC、ηESDRespectively the charging efficiency and the discharging efficiency of the storage battery; pL,tThe power load demand of the combined cooling heating and power micro-grid at the moment t is kW; pEC,tIs the power consumption of the electric refrigerator at the moment t, kW.
2) Thermal load balancing constraints:
in the formula: qWH,tThe heat recovery power of the waste heat boiler at the moment t is kW;
the power of hot water at the moment t of the gas boiler is kW; etaWHThe heat production efficiency of the waste heat boiler is obtained; qH,tThe heat load demand of the combined cooling heating and power micro-grid at the moment t is kW; qAC,tIn order to absorb the heat recovery power, kW, of the refrigerator at time t.
3) Cold load balancing constraint:
PEC,tCOPEC+QAC,tηAC-QC,t＝0 (8)
in the formula: COPECThe energy efficiency ratio of the electric refrigerator is the ratio of refrigerating capacity (kW) to input power (kW); etaACThe refrigeration efficiency of the absorption refrigerator; qC,tThe cooling load demand, kW, of the combined cooling heating and power micro-grid at the moment t.
The equipment safety restraint comprises operation restraint, climbing restraint, start-stop restraint and storage battery restraint.
1) And (4) operation constraint:
in the formula: u. ofGT,t、uGB,tBinary variables respectively representing the startup and shutdown states of the gas turbine and the gas boiler at the moment t, wherein '1' represents startup and '0' represents shutdown;
the power generation power is the minimum value and the maximum value, kW, of the power generation power of the gas turbine;
the power is the minimum value and the maximum value of the heat production power of the gas boiler, namely kW;
the power is the minimum value and the maximum value of the heat production power of the waste heat boiler, namely kW; qAC,N、PEC,NIs the rated power of the absorption refrigerator and the electric refrigerator, kW;
the minimum value and the maximum value of the power purchased from the cold-heat-electricity combined supply micro-grid to the large power grid are kW.
2) And (3) climbing restraint:
in the formula:
the lower climbing speed and the upper climbing speed which are respectively the power generation power of the gas turbine;
and outputting the downward climbing speed and the upward climbing speed of the thermal power for the gas boiler.
3) Start-stop restraint:
the gas turbine and the gas boiler can not be started and stopped frequently, and the following start-stop constraints exist:
in the formula:
respectively the minimum on-off time, min, of the gas turbine;
the minimum start-up and shut-down time of the gas boiler is min.
4) And (3) battery restraint:
the battery constraint comprises a charge state constraint part and a charge-discharge power constraint part, and specifically comprises the following steps:
for batteries, overcharging and overdischarging will significantly shorten their cycle life, so there are battery state of charge constraints as follows:
in the formula:
the minimum and maximum allowable values of the state of charge of the storage battery can be determined according to the maximum flow principle of the full life cycle of the battery, WES,tFor the state of charge of the battery at time t, it can be calculated according to the following formula:
in the formula: cBESSThe capacity of the storage battery is kW.h; wES,t-1The state of charge of the storage battery at the time t-1; sigmaESIs the self-discharge rate of the battery.
In order to facilitate the comparison of the cooling, heating and power combined supply microgrid scheduling plan, the state of charge of the storage battery should be kept balanced in the whole scheduling period, namely:
WES,t＝96＝WES,t＝0 (15)
for a battery, too fast charge and discharge rates will also damage the battery, so there are the following charge and discharge power constraints:
in the formula: u. ofC,tAnd uD,tAre binary variables, u, representing the charge and discharge states of the accumulator at time tC,tLet "1" denote that the battery is in a charged state at time t, uD,tTaking "1" indicates that the storage battery is in a discharge state at time t;
the maximum charging and discharging power of the storage battery is kW.
The heat supply network characteristic constraints comprise heat source supply and return water temperature and heat exchange constraints, pipeline heat delay and temperature drop constraints and heat supply pipeline temperature constraints.
1) Heat source supply and return water temperature and heat exchange restraint:
in the formula: m is the number of heat sources in the combined cooling heating and power micro-grid; qS,j,tHeating power, kW, of the jth heat source in the combined cooling heating and power micro-grid at the moment t; gj,tThe hot water flow of the branch where the heat source j is located at the moment t is kg/s; t issg,j,t、Tsh,j,tRespectively the supply water temperature and the return water temperature of the heat source j at the moment t.
2) Pipeline thermal delay and temperature drop constraints:
the characteristics of the heat supply network are mainly expressed in two aspects of hot water transmission delay and temperature loss. Because the flowing speed of the hot water in the heat supply pipeline is slow, the time delay exists in the temperature drop at the water inlet compared with the temperature drop at the water outlet, and therefore, the transmission delay time of the heat supply pipeline is as follows:
in the formula: i is a heat supply pipeline index; tau isiIs the transmission delay time of the pipeline, s; rho is hot water density, kg/m3；AiIs the cross-sectional area of the pipe, m2；LiIs the length of the pipe, m; v is the average flow velocity of hot water, m/s; m isiThe operation flow of the pipeline i is kg/s; Δ t is the scheduling period interval, s.
Because of the difference with the ambient temperature of the outer wall of the heat supply pipeline, the hot water has temperature loss in transmission, and for the pipeline i, the hot water temperature loss is as follows:
in the formula: delta TL,i(t) is the temperature drop, DEG C, of the pipeline i at the time t; lambda is the heat transfer efficiency per unit length of the pipeline, and W/m ℃; cPThe specific heat capacity of hot water is J/kg ℃; t iss,i(t) is the temperature, DEG C, of the head end of the pipeline i at the time t; t isoIs the ambient temperature outside the pipeline, DEG C.
Te,i(t+τi)＝Ts,i(t)-ΔTL,i(t) (20)
In the formula: t ise,i(t+τi) Is the temperature at the end of the pipe i at time t, deg.C.
From equations (18), (19) and (20), it can be calculated:
in the formula: t ise,i(t+τi) Is the temperature, deg.C, of the end of the pipe i at time t; lambda is the heat transfer efficiency per unit length of the pipeline, and W/m ℃; cPThe specific heat capacity of hot water is J/kg ℃; t iss,i(t) is the temperature, DEG C, of the head end of the pipeline i at the moment t; t isoThe temperature of the external environment of the pipeline is DEG C.
3) Heat supply pipeline temperature restraint:
in the formula:
the upper temperature limit of a water supply pipeline is DEG C;
the lower limit of the temperature of the water return pipe is DEG C.
S3: calling a related nonlinear solver to solve the cooling, heating and power combined microgrid optimization scheduling model obtained in the step S2;
the nonlinear term of multiplication of integer variables and continuous variables occurs in the device operation constraint, so that the optimized scheduling model is a mixed integer nonlinear programming model and can be solved by adopting a relevant solver, such as CPLEX, LINGO and the like.
S4: and determining the optimal scheduling scheme of the combined cooling heating and power microgrid according to the solving result of the combined cooling heating and power microgrid optimal scheduling model in the step S3.
After the model is solved, the start-stop instruction of the energy supply main body and the operation conditions of all energy supply, energy storage and energy conversion devices in the combined cooling heating and power micro-grid at each scheduling time period can be determined according to the solving result. The functional equipment comprises a gas turbine and a gas boiler; the energy storage device comprises a storage battery; the energy conversion equipment comprises an electric refrigerator, an absorption refrigerator and a waste heat boiler. Due to the fact that the characteristics of the heat supply network are considered, the optimal scheduling method of the combined cooling heating and power micro-network can fully explore the heat storage capacity of the heat supply network, change the heat load curve, optimize the operation condition of the micro-network, and further give a more economical and reasonable scheduling plan of the combined cooling heating and power micro-network.
The technical scheme provided by the embodiment of the invention is explained in detail by combining with an actual application scene, firstly, cold, heat and electric load prediction data, time-of-use electricity price and natural gas unit heat value price information in a scheduling period of a system are input; then inputting variables or initial values of the parameters such as system equipment composition, equipment operation parameters, heat supply network parameters, system scheduling interval, heat medium parameters, upper and lower water temperature limits of a pipe network outlet and the like. In the system, the gas turbine, the power grid and the storage battery meet the power demand; the heat supply requirements are met by the gas turbine, the gas boiler and the waste heat boiler; the electric refrigerator and the absorption refrigerator meet the system cold load requirement.
As shown in fig. 2, the main devices in the combined cooling heating and power microgrid are a gas turbine, a gas boiler, a waste heat boiler, an electric refrigerator, an absorption refrigerator, and a storage battery. As shown in fig. 3, the heat network structure in the example contains 2 heat sources, 6 nodes, and 15 pipe segments. Wherein, the numbers 1, 2, 3, 4, 5, 6, 7 and 8 of the pipe sections are water supply pipelines, and the numbers 9, 10, 11, 12, 13, 14 and 15 of the pipe sections are water return pipelines.
It should be noted that in this embodiment, one scheduling cycle is one day, one scheduling period is 15 minutes in length, and there are 96 scheduling periods in total in one scheduling cycle.
The main equipment of the system comprises a gas turbine, a gas boiler, a waste heat boiler, an electric refrigerator, an absorption refrigerator and a storage battery. The specific parameters of the equipment are shown in table 1:
TABLE 1 Main plant parameters
As shown in fig. 3, the heat network structure in the example contains 2 heat sources, 6 nodes, and 15 pipe segments. Specific heat supply network parameters are shown in table 2.
TABLE 2 Heat supply network parameters
Table 3 shows the system day-to-day thermal and electrical load prediction data. Fig. 4 shows the time-of-use electricity price of a certain place, while assuming that the price per heating value of natural gas is 0.381 yuan/(kW · h). In addition, the system scheduling time interval is 15 min; the density of the hot water is 983.24kg/m 3; the heat transfer efficiency of the pipeline per unit length is 0.2W/m ℃; the specific heat capacity of the hot water is 4182J/kg ℃; the external environment temperature of the pipeline is 0 ℃; the upper and lower limits of the water temperature at the outlet of the pipe network are respectively 120 ℃ and 75 ℃.
TABLE 3 System in-day cooling, heating and power load prediction data
The computer hardware environment for executing the optimization calculation is Intel (R) core (TM) i5-8265U, the dominant frequency is 1.60GHz, and the memory is 8 GB; the software environment is a Windows 10 operating system.
The above data are used for simulation, and the daily scheduling results of the electric power, the hot power and the cold power in the system are respectively given in tables 4 to 6. As can be seen from tables 4-6: the system can completely meet the requirements of electricity, heat and cold loads, and the condition of abandoning electricity/heat/cold energy does not occur. As can be seen from Table 4, the gas turbine has low output in the valley and flat time periods (00:00-14:00, 17:00-19:00, 22:00-24:00), and the electric load in the system is mainly satisfied by the power purchase of the power grid. During the peak period of the electricity price (14:00-17:00, 19:00-22:00), the output of the gas turbine is gradually increased and becomes the main power supply body in the system. For thermal power balance, it can be seen from table 5 that gas boiler heating dominates, but decreases relatively as the gas turbine output increases. In the aspect of refrigeration, the cold load power in the system is provided by the electric refrigerator and the absorption refrigerator, and when the electric refrigerator is not enough to provide the system cold load requirement, the absorption refrigerator carries out cold supplement. Thus, the absorption chiller only has capacity during periods 8:30-10:15, 13:15-19: 45.
TABLE 4 System electric Power optimized scheduling results
The charging and discharging power of the storage battery in table 4 is divided into positive and negative values, when the power is a positive value, the storage battery is in a discharging state, and when the power is a negative value, the storage battery is in a charging state; the electric refrigerator belongs to an electric device, so that the power of the electric refrigerator is actually negative.
TABLE 5 thermal power optimized scheduling results for the system
TABLE 6 System Cold Power optimized Schedule results
Fig. 5 shows a real-time comparison relationship between the total output power at the heat source end and the total heating power at the load end. Therefore, the transmission delay in the thermal characteristics of the heat supply network causes that the thermal power in the system cannot meet the real-time supply and demand matching, and the pipe section power attenuation caused by temperature loss causes that the supply and demand of the heat source end and the load end have obvious difference. On the micro-grid level, the heat supply network can be regarded as heat storage equipment when in operation, and can store and release heat according to the economic optimal principle.
Table 7 shows a comparison of the economics of combined cooling, heating and power microgrid operation without consideration of heat supply network characteristics. Therefore, the daily operation cost considering the heat supply network characteristics is 124300 yuan, the daily operation cost not considering the heat supply network characteristics is 135910 yuan, the cost is reduced by 11610 yuan, and the economic benefit is obviously improved.
TABLE 7 comparison of operating economics
The analysis of the above results shows that under the condition of considering the coordinated operation of electricity, cold and heat, the heat storage capacity of the heat supply network can be fully explored by considering the characteristics of the heat supply network, the heat load curve is changed, the output of equipment is optimized, and a more practical and economic dispatching scheme is provided to reduce the daily operation cost of the microgrid. Therefore, the heat supply network characteristic is a considerable consideration factor in the optimization scheduling of the combined cooling heating and power micro-grid, and has important significance in reducing the operation cost and improving the operation economy.
The cooling, heating and power combined supply microgrid optimization scheduling method considering the characteristics of the heat supply network is described in detail above. The principle and the implementation of the present application are explained by applying specific examples in the present invention, and the above description of the examples is only used to help understanding the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Claims (1)
1. A combined cooling heating and power microgrid optimization scheduling method considering heat supply network characteristics is characterized by comprising the following steps:
s1: inputting electricity price information and gas price information according to the selected combined cooling heating and power microgrid, reading cold, heat and electricity load prediction information, and inputting parameter information of combined cooling heating and power microgrid equipment;
s2: the method for establishing the combined cooling heating and power micro-grid optimization scheduling model considering the heat supply network characteristics comprises the following steps: setting the minimum daily operation cost of the combined cooling heating and power micro-grid as a target function, and respectively considering load balance constraint, equipment safety constraint and heat supply network characteristic constraint;
s3: calling a related nonlinear solver to solve the cooling, heating and power combined microgrid optimization scheduling model obtained in the step S2;
s4: determining an optimal scheduling scheme of the combined cooling heating and power microgrid according to the solving result of the combined cooling heating and power microgrid optimal scheduling model in the step S3;
in the step S2, in the step S,
the objective function is represented by the following formula:
C＝min(CE+CG)
in the above formula: c is the daily operating cost of the system; cEFor the total purchase of electricity, ce,tFor the purchase price of electricity at time t, Pgrid,tThe power purchasing power at the moment t is shown, and delta t is the length of a scheduling period; cGFor total gas charge, cgasIs the natural gas unit heat value price, PGT,tFor the power generated by the gas turbine at time t, QGB,tIs the heat production power, eta, of the gas boiler at the moment tGTFor the efficiency of the power generation of the gas turbine, ηGBThe heat production efficiency of the gas boiler;
the load balancing constraints comprise an electrical load balancing constraint, a thermal load balancing constraint and a cold load balancing constraint;
the electrical load balancing constraint is specifically represented by the following equation:
in the above formula, PESC,t、PESD,tRespectively the charging and discharging power, eta, of the accumulator at time tESC、ηESDRespectively the charging efficiency and the discharging efficiency of the storage battery; u. ofC,tAnd uD,tBinary variables of the storage battery in the charging and discharging states at the moment t respectively; pL,tThe electrical load requirement, P, of the combined cooling heating and power microgrid at the moment tEC,tThe power consumption of the electric refrigerator at the moment t;
the thermal load balancing constraint is specifically represented by the following formula:
in the above formula, QWH,tFor the heat recovery power of the waste heat boiler at the time t,
is the hot water power, eta, of the gas boiler at time tWHFor the heat-generating efficiency of waste-heat boilers, QH,tThe heat load demand, Q, of the combined cooling heating and power microgrid at the moment tAC,tTo absorb the heat recovery power of the refrigerator at time t;
the cold load balancing constraint is specifically represented by the following formula:
PEC,tCOPEC+QAC,tηAC-QC,t＝0
in the above formula, COPECIs the energy efficiency ratio of the electric refrigeratorACFor absorption refrigeration efficiency, QC,tThe cold load requirement of the combined cooling heating and power micro-grid at the moment t is met;
the equipment safety constraints comprise operation constraints, climbing constraints, start-stop constraints, storage battery charge state constraints and storage battery charge and discharge power constraints;
wherein the operating constraints are represented by:
in the above formula: u. ofGT,t、uGB,tRespectively representing binary variables of the gas turbine and the gas boiler in the on-off state at the moment t;
the minimum value and the maximum value of the power generation power of the gas turbine are obtained;
the minimum value and the maximum value of the heat production power of the gas-fired boiler are obtained;
the minimum value and the maximum value of the heat generation power of the waste heat boiler are obtained; qAC,N、PEC,NThe rated power of the absorption refrigerator and the electric refrigerator;
the minimum value and the maximum value of the power purchased from the cold-heat-electricity combined supply micro-grid to the large power grid are obtained;
the hill climbing constraint is represented by the following formula:
in the above formula:
the lower climbing speed and the upper climbing speed which are respectively the power generation power of the gas turbine;
outputting a downward climbing speed and an upward climbing speed of thermal power for the gas boiler;
the start-stop constraint is represented by the following formula:
in the above formula:
respectively minimum on-off time of the gas turbine;
minimum start-up and shut-down time of the gas boiler respectively;
the battery state of charge constraint is represented by the following equation:
WES,t＝96＝WES,t＝0
in the above formula:
minimum and maximum allowable value of state of charge, W, of the storage batteryES,tIs the state of charge of the battery at time t, CBESSIs the battery capacity; wES,t-1The state of charge of the storage battery at the time t-1; sigmaESIs the self-discharge rate of the battery;
the battery charge and discharge power constraint is expressed by the following formula:
in the above formula, the first and second carbon atoms are,
maximum charging and discharging power u of the storage battery respectivelyC,tAnd uD,tBinary variables of the storage battery in the charging and discharging states at the moment t respectively;
the heat supply network characteristic constraints comprise heat source supply and return water temperature and heat exchange constraints, pipeline heat delay and temperature drop constraints and heat supply pipeline temperature constraints;
the heat source supply and return water temperature and heat exchange constraint is specifically represented by the following formula:
in the above formula: qGB,tThe heat production power of the gas boiler at the moment t; qWH,tThe heat recovery power of the waste heat boiler at the moment t; etaWHThe heat production efficiency of the waste heat boiler is obtained;
the hot water power of the gas boiler at the moment t; cPIs the specific heat capacity of the hot water; m is the number of heat sources in the combined cooling heating and power micro-grid; qS,j,tThe heating power of the jth heat source in the combined cooling heating and power micro-grid at the moment t is provided; gj,tThe hot water flow of the branch where the heat source j is located at the moment t; t issg,j,t、Tsh,j,tRespectively the supply water temperature and the return water temperature of the heat source j at the moment t;
the thermal delay and temperature drop constraints of the pipeline are specifically expressed as follows:
in the above formula, Te,i(t+τi) Temperature of the end of the pipe i at time t, τiFor the propagation delay time of the pipe i, Ts,i(t) is the temperature of the head end of the pipeline i at the time t, i is the index of the heat supply pipeline, rho is the density of hot water, AiIs the cross-sectional area of the conduit i, LiIs the length of the pipe i, v is the average flow velocity of the hot water, miIs the running flow of the pipeline i, delta t is the scheduling time interval, lambda is the heat transfer efficiency per unit length of the pipeline, CPSpecific heat capacity of hot water, Ts,i(T) is the temperature of the head end of the pipeline i at time T, ToThe ambient temperature outside the pipeline;
the heat supply pipeline temperature constraint is specifically represented by the following formula:
in the above formula: t issg,j,t、Tsh,j,tRespectively the supply water temperature and the return water temperature of the heat source j at the moment t; t isg maxIs the upper limit of the temperature of the water supply pipeline; t ish minIs the lower limit of the temperature of the water return pipeline.
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