CN115378002A - Optimal scheduling model of regional comprehensive energy system based on hybrid energy storage - Google Patents
Optimal scheduling model of regional comprehensive energy system based on hybrid energy storage Download PDFInfo
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Abstract
The invention belongs to the field of energy system scheduling, and discloses a region-level comprehensive energy system optimization scheduling model based on hybrid energy storage. Firstly, a structure diagram of a hybrid energy storage area comprehensive energy system including a ground source heat pump is constructed, then renewable energy output and load fluctuation are described in a scene generation and scene reduction mode, and by taking a wind turbine generator as an example, prediction uncertainty is processed to generate renewable energy output data. And finally, simulating and solving the regional comprehensive energy system optimization scheduling model based on hybrid energy storage in a Matlab environment through a Yalmip, matpower tool box and Cplex solver, so that the target function, namely the system running cost value is minimum. The example proves that the system energy supply form combining short-term energy storage and long-term energy storage can realize the voltage regulation and frequency modulation in the system, keep the power balance of the system, realize seasonal energy transfer and improve the operating economy of the power system.
Description
Technical Field
The invention belongs to the field of energy system scheduling, and particularly relates to a regional comprehensive energy system optimization scheduling model based on hybrid energy storage.
Background
A large amount of new energy power generation is connected to the power grid, so that the problems of environmental pollution and energy use sustainability caused by the large amount of fossil energy are solved to a certain extent, and meanwhile, the problems of complex planning, poor running stability, wind and light abandonment and the like are also caused. Although the light abandoning rate of the abandoned wind is reduced in recent years, the value of the light abandoning amount of the abandoned wind is still considerable. In China, the traditional energy supply systems operate separately, the coordination is poor, and the system is obviously not suitable for the current of large-scale power generation and grid connection of new energy. Therefore, integrated Energy Systems (IES) have been developed, that is, an energy supply system with Integrated coordination and optimization of energy production, transmission, distribution, conversion, storage, consumption and other links is developed. The IES can be divided into three types of trans-regional, regional and user-level comprehensive energy systems according to the geographical factors and the energy supply and marketing links. The regional energy system is a regional energy supply system which adopts clean energy units such as a cogeneration unit, a heat pump unit, a distributed renewable energy unit and the like, and provides power, heat, cold and gas demands for users by using industrial waste heat, geothermal energy, solar energy, wind energy and other energy sources in a region. As the prelude of smart energy cities, regional energy systems are widely used globally due to the characteristics that the regional energy systems can reasonably and efficiently produce, transport, utilize and dissipate various forms and grade energy required by people in a certain specific region.
The energy storage link plays a vital role in breaking through the rigid coupling of the traditional cogeneration for fixing the power by heat, improving the grid-connected rate of renewable energy sources and ensuring the economic and stable operation of a system, but the current research is focused on short-term energy storage, and the cooperative problem of long-term and short-term energy storage is not well solved. The power supply planning problem of the power system needs to carry out overall coordination on different energy storage technologies, and each energy storage technology should not be considered separately.
Aiming at the problems, the invention provides an optimized dispatching model of a regional comprehensive energy system containing long-term and short-term hybrid energy storage.
Disclosure of Invention
The invention provides an optimal scheduling model of a regional comprehensive energy system based on hybrid energy storage. A structure diagram of a hybrid energy storage area comprehensive energy system comprising a ground source heat pump is constructed, and is shown in attached figures 1 and 2. When the regional IES is optimally scheduled, the random fluctuation of the renewable energy output can directly influence the operation condition of equipment in the system, and further influence the economy and feasibility of system planning. In addition, the current load prediction technology is difficult to realize zero error of load prediction and has certain deviation. Therefore, the renewable energy output and the load fluctuation are described by adopting a scene generation and scene reduction mode, and a random planning model of the regional comprehensive energy system considering the renewable energy output and the load fluctuation is established. On the basis, a ground source heat pump and a hybrid energy storage system are introduced into a regional comprehensive energy system, the ground source heat pump system is used for decoupling the 'fixing power by heat' constraint of a CCHP unit, a reasonable long-term and short-term energy storage coordinated operation scheme is formulated, and a regional comprehensive energy system day-ahead scheduling model including energy conversion and storage equipment is established with the aim of minimizing the system operation cost.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
step 3, establishing an optimized dispatching model of the regional comprehensive energy system, wherein the optimized dispatching model comprises a regional comprehensive energy wind turbine model, a regional comprehensive energy photovoltaic unit model, a regional comprehensive energy CCHP unit model and a regional comprehensive energy ground source heat pump system model;
step 4, solving a hybrid energy storage based regional comprehensive energy system optimization scheduling model by constructing constraint conditions and an objective function;
as a supplement to the above technical solution, taking the wind turbine generator set as an example in step 1, the renewable energy source initial scene generation construction process is as follows:
step 1.1, carrying out classified statistics and processing on historical data to obtain the output distribution condition of the wind turbine generator in each hour at all seasons;
and step 1.2, obtaining a wind power probability density curve by using a nonparametric fitting method, generating N random sampling arrays on the basis of the wind power probability density curve of each hour according to a Monte Carlo random simulation method, and further obtaining N x T random sampling arrays corresponding to each season. Wherein T is the time period number of each scene, and the method takes 24;
step 1.3, the wind power of T periods in each scene can be represented by a random sequence, such as
The wind-power output sequence under scenario S may be represented as:
the wind power output sequence at the moment is a random wind power output value, and the uncertainty of random factors is reflected to the maximum extent. By adopting the steps, a corresponding N x T sampling matrix in the dispatching day can be generated, namely N wind power random scene sets, and the method is 1000.
In addition to the above technical solution, the scene reduction construction process for the result by using the backward scene reduction technology in step 2 is as follows:
step 2.1, combining partial scenes to form a limited number of typical scene sets with a certain probability value;
step 2.2, adopting backward scene reduction technology to reduce the N.T sampling matrix of the original data to N s T matrix, corresponding to N in the model s The wind turbine generator output sequence of each scene can be obtained s Probability pi corresponding to each scene s . Similarly, corresponding to the uncertainty of the electric load, the gas load and the heat load predictionThe same applies to the above, and will not be described here.
As a supplement to the above technical solution, the process of establishing the regional integrated energy system optimization planning model mentioned in step 3 is as follows:
step 3.1, establishing a wind turbine generator model: the output power is mainly determined by wind speed and utilization coefficient, and the output power can be obtained by dynamics knowledge as follows:
in the formula: t is the number of the scheduling time segments, and theta is the current air density; p WT 、v t The output power and the wind speed of the fan are respectively in the t period; r is WT And C P The radius of the blade of the fan and the utilization coefficient of wind energy are respectively.
The output power of the fan has the characteristics of uncertainty and intermittence under the influence of wind speed change factors. According to the operating characteristics of the wind power motor, when the wind speed is smaller than the cut-in value, the output power of the wind power motor is zero, and the wind power motor is in a shutdown state; when the wind speed is larger than the cut-in value, the wind motor starts to be started, the excitation torque of the generator is adjusted through the controller, so that the unit operates in a Maximum Power Point Tracking (MPPT) mode, and the wind energy utilization coefficient is highest at the moment; when the wind speed becomes a rated value, the output power of the fan also reaches the maximum; when the wind speed exceeds the rated value, the pitch adjusting device acts to increase the pitch angle so that the output power is maintained near the rated value; when the wind speed is greater than the cut-out value, the wind motor is stopped to ensure the safety of the unit equipment, the output power is zero at the moment, the pitch angle is 90 degrees, and the output power of the wind motor is as follows:
in the formula: n is the number of wind power generation sets of the wind power plant grid-connected wind power generation set P N Rated power v for wind turbine ω (t) the wind turbine generator operates in the period of tWind speed, v rated Rated wind speed (generally 15 m/s), v of wind turbine generator in 、v out And switching in and switching out wind speed for the wind turbine generator.
Step 3.2, building a photovoltaic unit model: the temperature of photovoltaic cell board at a certain moment is related to two factors of ambient temperature, illumination intensity, and the expression of its polar plate temperature is:
in the formula: t is e，t The temperature of the photovoltaic cell panel at the time t; t is amd Is the outside ambient temperature; g PV，t The intensity of the illumination received by the photovoltaic plate is t time period. The output power of the polar plate changes along with the changes of the external temperature and the illumination intensity, the output power is obtained by taking the power value of the standard test condition as a reference, and the expression of the output power is as follows:
in the formula: p PV，t The output power of the photovoltaic cell is the t period; p is STC 、G STC Respectively the output power and the illumination intensity of the photovoltaic cell under the standard test condition, wherein the illumination intensity value is 1000W/m 3 (ii) a k is a power temperature coefficient, and the value is-0.0047/DEG C; t is a unit of r The reference temperature was 25 ℃.
Step 3.3, establishing a CCHP unit model: the mathematical model of natural gas consumption is:
in the formula: v MT，t The gas consumption of the micro gas turbine is t time period; p is MT，t 、η MT，t The output power and the efficiency of the micro gas turbine are respectively in the period t; l is NG Is natural gas with low heating value. The normal value is 9.97kWh/m 3 . Function fitting using softwareIt can be known that the expression of efficiency and output electric power is:
the relation among the refrigeration power, the heating power and the output electric power of the CCHP system is as follows:
Q MT，c，s，t ＝η rec C MTC Q MT，s，t (8)
Q MT，h，s，t ＝η rec C MTH Q MT，s，t (9)
in the formula: q MT，s，t In the s-th scene, the exhaust waste heat of the micro-combustion engine in the t period; q MT，c，s，t And Q MT，h，s，t The refrigeration power and the heating power of the CCHP system are respectively in a t period; eta L Is the heat dissipation loss coefficient; eta rec The recovery rate of the flue gas is adopted; c MTC And C MTH The refrigeration and heating coefficients of the absorption refrigerator and the waste heat recovery unit are respectively.
Step 3.4, establishing a ground source heat pump system model: taking the refrigeration working condition as an example, the maximum refrigeration capacity of the surface water source heat pump unit is as follows:
Q c，max ＝Q c，c0 ·f(T co ，T ci )＝Q c，c0 ·f cc (10)
in the formula: the rated refrigerating capacity is the rated refrigerating capacity of the heat pump unit under the refrigerating working condition; t is co The water temperature at the outlet of the evaporator; t is ci The water temperature is the inlet water temperature of the condenser; f. of cc And the coefficient is corrected for the refrigerating capacity of the unit. The unit load rate is expressed as:
further, the unit power expression can be obtained:
in the formula: q load，c The demand of the cold load for the user; p c The refrigeration power of the heat pump; p c0 The rated refrigeration power of the heat pump; f. of PLR Correcting the coefficient for the heat pump power load rate;
the coefficient is corrected for the cooling power. According to an empirical formula:
f PLR ＝0.251+0.048PLR+0.701PLR 2 (15)
the water flow in the water storage tank is assumed to flow uniformly without convection. The axial flow of the water flow in the water pool is approximately equal to the fact that a certain water flow layer rises by one layer along with the time step length, and the temperature expression of the water flow of the certain layer is as follows:
in the formula: m i The mass of each layer of water; c is the specific heat capacity of water; λ is the thermal conductivity of water; k is the heat transfer coefficient of the reservoir; a. The c Is the cross-sectional area of the reservoir; a. The s Is the circumferential side surface area of each layer; t is i The water temperature of the ith layer is the water temperature of the ith layer; t is a Is the ambient temperature.
The method adopts a soil source heat pump model used in the literature (Wu Xueqin, ground source heat pump and energy storage composite energy system operation optimization research [ D ]. Southeast university, 2018.) to calculate the power consumption of the soil source heat pump. The model assumes that the soil around the laying position of the buried pipe is homogeneous, and comprises three heat transfer parts, namely fluid in the pipeline and heat exchange of the buried pipe, interaction of a local soil heat exchanger and heat storage volume and global heat exchange of the surrounding soil. The model is verified to be accurate in performance calculation of the ground heat exchanger, the calculation speed is high, and the calculation result is very close to the actual situation.
As a supplement to the above technical solution, the construction process of establishing the constraint conditions and the objective function and solving the optimal scheduling model of the hybrid energy storage based regional-level integrated energy system in step 4 is as follows:
step 4.1, establishing constraint conditions, including short-term energy storage unit constraint, electric power balance constraint, gas quantity balance constraint, thermal power balance constraint, cold power balance constraint, controllable unit (GT, FC) minimum start-stop constraint, controllable unit climbing constraint, tie line and tie line energy flow constraint;
step 4.1.1, short-term energy storage unit constraint: the constraints of the storage battery are mainly the limits of charge and discharge capacity and storage capacity, and can be specifically expressed as:
E ES，i，min ≤E ES，i，s，t ≤E ES，i，max (21)
E ES，i，s，T ＝E ES，i，s，0 (22)
in the formula:
and
the variable is 0-1 and is respectively the charging and discharging states of the ith storage battery in the t time period under the s scene; omega in And omega out The charging and discharging coefficients of the storage battery are respectively; e ES，i，max And E ES，i，min Respectively the upper limit and the lower limit of the ith storage battery capacity. In order to meet the demand of the energy storage system in the next scheduling period. The constraint conditions of the gas storage unit, the hot storage unit, the cold storage unit and the electricity storage unit are the same, and are not repeated here.
Step 4.1.2, electric power constraint:
in the formula:
and P EX，s，t Respectively representing the controllable unit in the t time period, the output of the renewable energy and the actual power interacting with the large power grid in the s-th scene; p e，load，s，t 、P HP，s，t And P P2G，s，t 、P EB，s，t Respectively representing the electric load quantity in the t period and the power consumption magnitudes of HP, P2G and EB in the s-th scene; wherein, N and M respectively represent the number of the controllable unit and the renewable energy unit.
Step 4.1.3, gas balance constraint:
in the formula: v P2G，s，t 、
And with
Respectively representing the gas production amount of the electric gas conversion device, the gas output amount of the gas storage device and the gas input amount in a t-period under an s-th scene; v EX，s，t Representing the magnitude of gas quantity interacted with a natural gas pipe network; v g，load，s，t 、V GT，s，t 、V FC，s，t And V GB，s，t And respectively representing the actual gas load size in the t period and the gas consumption of the micro gas turbine, the FC and the FB unit in the s-th scene.
Step 4.1.4, thermal power balance constraint:
in the formula: h CCHP，s，t 、H HP，s，t And with
Respectively representing the heat generation power of a bromine refrigerator and an HP unit in a CCHP unit at a t-time interval and the heat input and output of a heat storage device in an s-th scene; h GB，s，t 、H EB，s，t And H h，load，s，t The heat generation power and the actual heat load of the FB unit and the EB unit in the s-th scene at the t period are respectively.
Step 4.1.5, the minimum start-up and shut-down constraints of the controllable units (GT, FC):
linearizing equations (27) and (28) to yield:
y i，t -z i，t ＝I i，t -I i，t-1 (35)
y i，t +z i，t ≤1 (36)
in the formula: I.C. A i，t The controllable unit i is in a starting and stopping state in a time period t, namely a starting and stopping mark of the controllable unit, the value of the starting and stopping mark is 0 to represent a stopping state, and the value of the starting and stopping mark is 1 to represent a starting state;
respectively the continuous start time and the continuous stop time of the unit i in the time period t;
minimum sustained on and off times required for unit i, respectively. T is Ui And T Di The time periods of the unit which must be started and stopped at the initial stage of the dispatching period are respectively;
the continuous start time and the continuous stop time of the unit i are respectively set; y is i，t 、z i，t Respectively the starting and stopping variables of the controllable unit.
Step 4.1.5, controllable unit climbing restriction:
in the formula:
respectively is the upper limit and the lower limit of the climbing speed of the controllable unit i.
Step 4.1.6, energy flow constraint of tie lines and tie pipes:
-P line，max ≤P EX，s，t ≤P line，max (38)
-V line，max ≤V EX，s，t ≤V line，max (39)
in the formula: p line，max 、V line，max The maximum limit of the tie line power and the tie pipeline airflow is respectively.
Step 4.2, since there are 4 kinds of energy coupling of cold, heat, electricity and gas in the user-level IES, the economic optimization model is more complex compared with the traditional micro-grid. Under the condition of meeting the load balance and related constraint conditions, the scheduling and conversion relation among the energies needs to be coordinated as well as possible so as to improve the energy utilization rate and reduce the total operation cost of the system. Therefore, the invention constructs the day-ahead scheduling model objective function.
Step 4.2.1, unit fuel cost:
step 4.2.2, operating and maintaining cost:
in the formula: p ES，s，t Is the actual capacity of each energy storage device.
Step 4.2.3, start-stop cost:
in the formula: c ST，i The cost is one-time starting and stopping cost of each controllable unit i.
Step 4.2.4, maintenance cost of the energy storage device:
in the formula, C i，main For the unit maintenance cost of each energy storage device.
Step 4.2.5, electric energy interaction cost:
in the formula: c cb，t And C rs，t The prices of electricity purchase and electricity sale from the large power grid are respectively.
Step 4.2.5, purchase cost of natural gas:
in the formula: c gb，t Purchase price for natural gas.
Step 4.2.6, heating (refrigerating) benefits:
C CH，s，t ＝C co Q co，s，t +C he Q he，s，t (46)
in the formula: q co，s，t 、Q he，s，t Respectively the cold and hot loads in the t period in the s scene; c co 、C he Respectively, the unit price of the cooling (heating) gain.
Step 4.2.7, abandoning wind and abandoning light cost:
step 4.2.8, controllable unit pollution emission cost:
in the formula: a is i，t 、b i，t And c i，t Constant term, primary term and secondary term coefficient of pollution emission coefficient of the controllable unit are respectively;
the actual output of each controllable unit.
Step 4.2.9, total cost:
step 4.3, solving a day-ahead scheduling model objective function:
a0-1 mixed integer linear programming model is established, and the standard form is as follows:
the optimization variables x comprise input and output quantities of energy conversion equipment, electricity, heat and cold output quantities of a CCHP unit, electricity consumption quantity of an HP system, heat and cold output quantities and energy stored and released by an energy storage device; the equality constraint is the power balance constraint of electricity, heat and gas; the inequality constraint is the operation constraint of each device. Aiming at the model, the simulation and solution are carried out by the Yalmip tool box, the Matpower tool box and the Cplex solver in the Matlab environment.
Drawings
FIG. 1 is a block diagram of a regional level integrated energy system;
fig. 2 is a structure diagram of a ground source heat pump system;
FIG. 3 is a NewEngland10 machine 39 node system diagram;
FIG. 4 is a diagram of a summer cooling state of a regional integrated energy system;
FIG. 5 is a winter heating state diagram of a regional integrated energy system;
FIG. 6 is a relationship of electric quantity balance of the regional comprehensive energy system;
FIG. 7 is a gas quantity balance relationship of the regional comprehensive energy system;
FIG. 8 is a drawing of the abstract of the specification;
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings, but the embodiments of the present invention are not limited thereto.
The invention relates to a regional comprehensive energy system optimization scheduling model based on hybrid energy storage, which is characterized in that an IEEE39 node system is used as a research object for simulation, the system comprises PV, WT,1 EB used for supplying insufficient heat supply, 2 GSS units, 2 CCHP units which are adjacently arranged, 2 ground source heat pump systems A and B which are distributed on two sides of the edge of a region, 1P 2G device used for producing natural gas and reducing the natural gas purchase amount when the electric quantity is excessive, and1 ESS, wherein the minimum startup time of a host in the ground source heat pump system A is 2h, the minimum shutdown time is 2h, the minimum startup time of the host in the heat pump system B is 2h, and the minimum shutdown time is 1h. The length of the scheduling period is 24h, and the scheduling step Δ t is 1h. The operation conditions of the regional comprehensive energy system in the typical days in summer and winter are simulated respectively, and the operation characteristics of the system in the two seasons of summer and winter can be obtained.
Setting a simulation process, wherein in the summer cold supply period, when the surface water source heat pump is in a cold accumulation working condition, the water temperature of an inlet and an outlet of an evaporator is 12/4 ℃, and the water temperature of an inlet and an outlet of a condenser is 30/35 ℃; under the direct supply working condition, the water temperature at the inlet and the outlet of the evaporator is 13/6 ℃, and the water temperature at the inlet and the outlet of the condenser is 30/35 ℃. Under the working condition of heat storage in the heat supply period in winter, the water temperature of an inlet and an outlet of the evaporator is 5/10 ℃, and the water temperature of an inlet and an outlet of the condenser is 41/53 ℃; under the direct supply working condition, the water temperature at the inlet and the outlet of the evaporator is 5/10 ℃, and the water temperature at the inlet and the outlet of the condenser is 40/45 ℃. The method comprises the following steps that (1) a soil source heat pump is used as an auxiliary system, namely when the output of a surface water source heat pump system is insufficient, the soil source heat pump system is started to supply energy, the soil temperature is set not to change along with the change of external climate, and when a host machine directly supplies water in summer, the water temperature of an inlet and an outlet of an evaporator is 12/4 ℃, and the water temperature of an inlet and an outlet of a condenser is 30/35 ℃; under the working condition of heat storage in winter, the water temperature at the inlet and the outlet of the evaporator is 26/21 ℃, and the water temperature at the inlet and the outlet of the condenser is 41/53 ℃; under the direct supply working condition, the water temperature at the inlet and the outlet of the evaporator is 26/21 ℃, and the water temperature at the inlet and the outlet of the condenser is 40/45 ℃. The performance parameters of each heat pump are shown in the table 1, and the operation and maintenance cost of the unit is 0.028 yuan/kW. The upper and lower limits of the gas turbine power, the upper and lower limits of the ramp rate, and the minimum start and stop parameters in the CCHP units 1 and 2 are shown in table 2. The cold energy efficiency coefficient of the two bromine coolers is 1.36, the heat energy efficiency coefficient is 1.2, and the waste heat recovery rate is 90%. The maintenance cost of the unit is 0.025 yuan/kw. The low heating value of the natural gas is 9.7kWh/m < 3 >, and the price of buying and selling electricity to the power grid is shown in the table 3, and the unit is yuan/kWh. The important load in the region is 3MW, and the important load is ensured to be capable of continuously running for 2 hours after the user system is disconnected with the large power grid. The ambient temperature of typical day in summer is 35 ℃ at most and 26 ℃ at least, and the ambient temperature of typical day in winter is 2 ℃ at most and 8 ℃ at least.
TABLE 1 parameter table of main equipment of ground source heat pump system
TABLE 2 CCHP Unit operation-related parameters
TABLE 3 Interactive Power price of grid within 24 hours
The invention adopts a K-means hard clustering partitioning algorithm to cluster an initially generated random scene set, reduces a clustering result by utilizing a backward scene reduction technology, reduces a 1000 x 24 sampling matrix of original data into a 10 x 24 matrix, and correspondingly obtains a basic probability value pi of the ith scene (i = 1.., 10.) i And the sum of the probability densities of the 10 scenes is 1, and the predicted probability density average value of the electricity, gas, cold and heat loads and the renewable energy output is defined as the total scene probability average value. The total probability density values and corresponding expected values for 10 scenes are shown in table 4.
TABLE 4 target expectation for different renewable energy output and load prediction probability densities
As can be seen from the above table, the expected minimum operating cost values of the 10 different probabilistic models obtained by one stochastic programming have large volatility, and it is proved that consideration of the influence of the renewable energy output and the load uncertainty on the final system operating cost has a certain practical significance, but there is no obvious numerical relationship between the two. The random planning is carried out for a plurality of times in Matlab, and the cost expectation values of 10 scenes obtained each time are approximately stabilized within a certain range, and the numerical range represents the average cost expectation when the integrated energy system in the region operates.
(embodiment 1) summer system operation condition analysis
Firstly, simulation analysis is carried out aiming at the operating condition of a ground source heat pump system, and 0 of each unit of a ground source water source heat pump and 0 of each unit of a ground source heat pump can be obtained: 00-24:00 Total Cooling Capacity and Main Engine Power in each time interval are shown in Table 5.
TABLE 5
As can be seen from the data in the table, 1:00-7: when 00 hours, the surface water source heat pump unit and the soil source heat pump unit are in a night cold accumulation working condition, and the surface water temperature is lower than the soil temperature at night, so that the surface water source heat pump unit bears the main cold accumulation output for improving the cold accumulation energy efficiency of the system; 8:00-10: 00. when the system is in use, the cold load demand of the system is gradually increased, and the heat stored in the water storage tank and the ground surface water source heat pump unit are combined for cooling; 11:00-17: the 00 hours are the peak hours of electricity and cold load demands all day long, and simultaneously are the peak hours of electricity selling and purchasing all day long, and the soil temperature is higher than the lake water temperature after the direct sunlight in the morning hours. At the moment, the ground source heat pump system is in a cold storage water pool, a ground surface water source heat pump unit and a soil source heat pump unit combined cooling working condition, and 6 soil source heat pump units of the two sets of ground source heat pump systems are in a full-open state, so that the cooling energy efficiency is improved, and the power consumption of the system is reduced. In order to evaluate the ground source heat pump system, the comprehensive performance coefficient COP of the system is introduced, and the definition formula is as follows:
in the formula: q Σ The heat pump system is fully supplied with cold energy; p is Σ The full power consumption of the heat pump system. The comprehensive performance coefficient of the ground source heat pump system in summer operation obtained at the moment is 5.37, and is lower than the design parameters of the system, because the power consumption of the water pump and the heat dissipation loss of the plate heat exchanger cannot be ignored in the operation process, and the energy efficiency of the system can be reduced to a certain extent. The system cooling load supply relationship is shown in fig. 4.
[ embodiment 2 ] analysis of operating conditions of winter system
The load of the system in winter is greatly different from the load of the system in summer, the running condition of the system in winter is simulated, and each unit of the surface water source heat pump and each unit of the soil source heat pump are obtained by the following steps: 00-24:00 Total Heat Generation and Main machine Power at each time interval are shown in Table 6.
TABLE 6
According to data in the meter, the cold supply capacity of users in winter is mainly provided by the cold storage capacity of each unit of the surface water source heat pump at night, and the soil source heat pump is used as an auxiliary system to absorb the surplus output power of renewable energy at night. The comprehensive performance coefficient of the ground source heat pump system in winter is 5.99 and is lower than the design coefficient of the system by 6.4. The main causes of this phenomenon are: (1) In practical situations, each unit of the heat pump cannot operate under a designed working condition, and the temperature difference between the inlet and the outlet of the condenser is small; (2) The water pump and the plate heat exchanger have certain power consumption and heat dissipation loss, and the unit energy efficiency is reduced to a certain extent. In typical winter days, the heating relationship of the regional integrated energy system is shown in the attached figure 5.
Example 3 analysis of overall System operating State
Taking the system operation state in typical days in summer as an example, the balance relationship between the electric quantity and the air quantity of the regional comprehensive energy system is respectively shown in attached figures 6 and 7. As can be seen from fig. 6, the electrical load demand in the system deviates from the renewable energy output rule by a certain amount, from 5: when the power is 00 hours, the electric load demand is increased, the output of the wind turbine generator is reduced, the output of the CCHP unit and the photovoltaic unit is limited, and the charge quantity of the electricity storage device is gradually reduced. 5:00-7: and in the 00 period, the purchase price of the power grid is low, and the power supply quantity mainly comes from power grid interaction. 8:00-10: and in the 00 time period, the electricity price is raised, and the electric quantity purchased by the power grid is reduced in order to ensure the economic operation of the system. 11:00-14: and in the period of 00 hours, the electricity price reaches the highest all day, the electricity purchasing from the power grid is stopped, the load power supply is mainly provided by the electricity storage device, then the electricity price is reduced, the capacity of the electricity storage device is insufficient, and the electricity load and the charge quantity of the electricity storage device are continuously provided for the electricity purchasing of the power grid. 20: and after 00, the illumination quantity is zero, the output of the photovoltaic unit is zero, the power load is gradually reduced, the output of the CCHP unit is increased to some extent, and the CCHP unit and the wind turbine unit bear the power load together.
The gas load balance relationship in the system is shown in figure 7, the effect of the gas storage tank and the gas boiler can be ignored, and the gas network gas purchasing quantity is mainly used for supplying gas load and the gas consumption of the CCHP unit. From 7: and from 00, the output of the P2G unit is gradually increased along with the fluctuation of the load so as to make up the difference of the gas supply. The P2G unit has higher operating cost, occupies a larger proportion in the system operating cost components, but plays an important role in the consumption and conversion of the redundant electric quantity of the system.
Example 4 System operation economics analysis
In order to verify that the energy supply system combining long-term energy storage and short-term energy storage has certain economic advantages, the following 2 summer system operation modes are compared, and a certain scene under the average cost expectation is selected from the renewable energy output and load prediction curve.
Mode 1: the heat and power cogeneration mode of 'fixing the power with heat' is adopted. The cold load and the heat load in the system are provided by a CCHP unit, the electric load is provided by a GT, a renewable energy source unit, power interacting with a large power grid and an ESS device, and the gas load is provided by a natural gas pipe network interaction and a P2G device. This strategy is more common in regional integrated energy system energy supply mode.
Mode 2: and the underground water resource is used as a long-term heat/cold storage carrier to store the cold energy in winter underground. The heat load is supplied by the ground source heat pump unit and the CCHP unit, and the shortage part is complemented by the electric boiler and the gas boiler. The electrical load is provided by the renewable energy source unit, the GT, the amount of electricity interacting with the large power grid and the ESS device. The gas load is provided by natural gas pipe network interaction, a P2G device and a GSS device. The minimum operating costs of the regional integrated energy system for both modes of operation are shown in table 7.
TABLE 7 comparison of operating costs of the system under two operating modes
It can be seen from the data in the table that under the operating condition of the mode 1, the cold and heat loads in the system are mainly supplied by the CCHP unit, and the gas consumption is large, so that the gas network gas purchase cost is obviously large, and the mode 2 utilizes the ground source heat pump system to absorb the indoor heat and store the indoor heat in the underground water, and utilizes a small amount of low-grade energy to realize the seasonal transfer of the cold load supply and the energy. In the mode 1, the electric energy production of the CCHP unit is strongly coupled with the cold and heat energy production, so that in order to meet the cold and heat load requirements of users, a large amount of electric energy production is delivered to a large power grid because the electric energy production cannot be absorbed, although the electricity selling income is obviously increased compared with the mode 2, the overall operation economy of the system is obviously reduced from the final result, particularly when the system is in an island operation state. In terms of system maintenance, although the unit energy consumption maintenance cost of the CCHP unit is slightly lower than that of the HP unit, the total maintenance cost is higher due to the larger energy consumption. As can be seen from the data in the table, due to the addition of the ground source heat pump system, the cost of wind abandoning and light abandoning of the system is obviously reduced, and the system effectively absorbs surplus wind and light output and converts the surplus wind and light output into heat energy which is stored in a ground water source to supply the demands of users.
The operation state of the typical day system in summer and winter is simulated, and the operation state results of the ground source heat pump system and the regional comprehensive energy system are analyzed, so that the following results can be obtained: the ground source heat pump system can effectively absorb the surplus output of wind and light during the load valley period, the cost of the abandoned wind is reduced to 47.2%, the cost of the abandoned light is reduced to 42.9%, and the consumption of renewable energy sources is increased. And the ground source heat pump system can utilize a small amount of low-grade energy to produce high-grade energy, the operation and maintenance cost is low, and the system operation cost can be greatly reduced. The system energy supply form combining short-term energy storage and long-term energy storage can realize pressure and frequency regulation in the system and keep the power balance of the system, and can also realize seasonal energy transfer, namely, heat in summer is transferred to a surface water source and soil to meet the heat energy demand of users in winter, so that primary energy consumption is reduced.
According to the simulation results of the above examples, wind, light and geothermal energy in the regional system are reasonably called, and a hybrid energy storage coordination scheduling form combining long-term energy storage and short-term energy storage is adopted, so that seasonal energy transfer can be realized, economic advantages are brought to the operation of a regional comprehensive energy system, and the energy utilization rate is improved.
Claims (5)
1. A regional comprehensive energy system optimization scheduling model based on hybrid energy storage is characterized by mainly comprising the following specific steps:
step 1, taking a wind turbine generator as an example, generating an initial scene of output of renewable energy. Acquiring N groups of T-dimensional renewable energy output initial scene sets according to wind power distribution issued by a dispatching center;
step 2, adopting backward scene reduction technology to carry out scene reduction on the result;
step 3, establishing an optimized dispatching model of the regional comprehensive energy system, wherein the optimized dispatching model comprises a regional comprehensive energy wind turbine generator model, a regional comprehensive energy photovoltaic unit model, a regional comprehensive energy CCHP unit model and a regional comprehensive energy ground source heat pump system model;
and 4, solving the optimal scheduling model of the hybrid energy storage based regional comprehensive energy system by constructing constraint conditions and an objective function.
2. The model of claim 1, wherein the step 1 construction process comprises the following steps:
step 1.1, carrying out classified statistics and processing on historical data to obtain the output distribution condition of the wind turbine generator in every hour at all seasons;
and step 1.2, obtaining a wind power probability density curve by using a nonparametric fitting method, generating N random sampling arrays on the basis of the wind power probability density curve of each hour according to a Monte Carlo random simulation method, and further obtaining N x T random sampling arrays corresponding to each season. Wherein T is the time period number of each scene, and the method takes 24;
step 1.3, the wind power of T time periods in each scene can be represented by a random sequence, such as
The wind-power output sequence under scenario S may be represented as:
the wind power output sequence at the moment is a random wind power output value, and the uncertainty of random factors is reflected to the maximum extent. By adopting the steps, a corresponding N x T sampling matrix in a dispatching day can be generated, namely N wind power random scene sets, and the method is 1000.
3. The model of claim 1, wherein the step 2 construction process comprises the following steps:
step 2.1, combining partial scenes to form a typical scene set with a limited number of certain probability values;
step 2.2, adopting backward scene reduction technology to reduce the N.T sampling matrix of the original data to N s T matrix, corresponding to N in the model s The wind turbine generator output sequence of each scene can be obtained s Probability pi corresponding to each scene s . Similarly, the way of handling the prediction uncertainty of the corresponding electrical load, gas load and thermal load can be the same as above, and will not be described here.
4. The optimal scheduling model of the regional comprehensive energy system based on hybrid energy storage of claim 1, wherein the construction process in step 3 comprises a regional comprehensive energy wind turbine model, a regional comprehensive energy photovoltaic unit model, a regional comprehensive energy CCHP unit model and a regional comprehensive energy ground source heat pump system model.
5. The model of claim 1, wherein the step 4 construction process comprises the following steps:
and 4.1, establishing constraint conditions including short-term energy storage unit constraint, electric power balance constraint, air quantity balance constraint, thermal power balance constraint, cold power balance constraint, controllable unit (GT, FC) minimum start-up and shut-down constraint, controllable unit climbing constraint, tie lines and tie pipeline energy flow constraint.
And 4.2, establishing a day-ahead scheduling model objective function, wherein the total cost comprises unit fuel cost, operation maintenance cost, start-stop cost, energy storage device maintenance cost, electric energy interaction cost, natural gas purchase cost, heating (refrigerating) income and controllable unit pollution emission cost.
And 4.3, establishing a 0-1 mixed integer linear programming model, simulating in a Matlab environment through a Yalmip tool box, a Matpower tool box and a Cplex solver, and solving a day-ahead scheduling model objective function.
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