CN115659651A - Comprehensive energy collaborative optimization scheduling method considering various flexible resources - Google Patents
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Abstract
The invention relates to a comprehensive energy collaborative optimization scheduling method considering various flexible resources, which is characterized by comprising the following steps: establishing a model of flexibility requirements and flexibility resources of the IES system, and carrying out flexibility constraint; establishing an IES double-layer distributed coordination optimization scheduling model, wherein the optimization scheduling model comprises an upper-layer energy supply system and a lower-layer IES park service system; and solving an optimized scheduling model by adopting an improved ATC algorithm, and performing cooperative scheduling. Compared with the prior art, the method has the advantages of fully exploiting the adjusting capability of various flexible resources, improving the economy and low carbon of the system and the like.
Description
Technical Field
The invention relates to the technical field of comprehensive energy collaborative optimization scheduling, in particular to a comprehensive energy collaborative optimization scheduling method considering various flexible resources.
Background
With the proposition of the target of "carbon peak, carbon neutralization" and the large-scale wind and light access Integrated Energy System (IES), the flexibility requirement of the System is increasing. The regional comprehensive Energy system takes a power transmission network and a gas network as net racks and an Energy Hub (EH) as a node, is an effective means for communicating Energy producers and consumers, can utilize various flexibility resources and multi-Energy complementary characteristics of the regional comprehensive Energy system, and meets the flexibility requirement of the system. Secondly, besides the flexibility resources of the system, the proper flexibility modification has important significance for improving the flexibility regulation capability, and the Carbon Capture and sequestration technology (CCS) is taken as an important emission reduction technology, is an important technology selection for practicing the low Carbon development strategy in China, and is a key means for realizing the Carbon neutralization target.
Chinese patent CN112417652A discloses an electricity-gas-heat comprehensive energy system optimization scheduling method and system, the method comprising: constructing an electricity-gas-heat low-carbon economic dispatching model based on a stepped carbon transaction mechanism; constructing an objective function and constraint conditions of a low-carbon optimization model based on an electricity-gas-heat low-carbon economic dispatching model; and solving the low-carbon optimization model to obtain low-carbon optimization parameters so as to perform low-carbon scheduling on the comprehensive energy system according to the low-carbon optimization parameters.
The existing optimization scheduling method does not provide reasonable constraint measures for the fluctuation generated by various energy sources in the integrated energy system IES, the scheduling model constructed by effectively utilizing the prior art for the adjusting capability of various flexible resources only considers the operating cost of each energy hub EH, does not comprehensively consider the energy supply cost and other factors, and has deviation, so that the optimization result of the existing optimization scheduling scheme is unsatisfactory, and has larger deviation from the reality.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a comprehensive energy collaborative optimization scheduling method considering various flexible resources.
The purpose of the invention can be realized by the following technical scheme:
compared with the prior art, the invention has the following beneficial effects:
1) The invention introduces a concept of flexibility margin constraint and establishes a model of flexibility requirements and flexibility resources. After the flexibility margin constraint is considered, the consumption rate of wind power and the utilization rate of stored energy are improved, the adjusting capability of various flexibility resources is fully developed, and the flexibility requirement of the system is met.
2) The invention deduces the flexible operation model of carbon capture by analyzing the characteristics of the carbon capture technology. Based on the method, an IES distributed low-carbon economic scheduling model considering various flexible resources is established. The flexible operation mode of the carbon capture power plant is fully utilized, so that the carbon capture output is reduced during the load peak period, and the carbon capture output is stored by a storage; in the load valley period, the output of the carbon capture equipment is improved, the carbon capture equipment is absorbed into a storage, the flexibility of the unit is improved, and the economical efficiency and the low carbon performance of the system are improved.
3) The invention adopts an ATC algorithm for cooperative scheduling, a target cascade analysis method can quickly solve the problem of coordination of distributed and hierarchical structures, and allows each main body in the hierarchical structure to make a decision independently, and the main bodies make decisions on each sub-main body in a distributed coordination optimization manner to obtain the optimal solution of the whole system. The target cascade method has the advantages of capability of parallel optimization, no limitation of the number of stages, strict convergence certification and the like. And the calculation example of the ATC algorithm is utilized for analysis, so that the upper-layer and lower-layer cooperative scheduling of the energy supplier and the energy service provider is realized.
Drawings
FIG. 1 is a flow chart illustrating an optimized scheduling process according to the present invention.
Fig. 2 is a schematic diagram of an IES system framework of the present invention;
FIG. 3 is a schematic view of a load flexibility analysis;
FIG. 4 is a schematic diagram of a flexible operational framework of a carbon capture plant;
FIG. 5 is a schematic diagram of an energy hub EH;
FIG. 6 is a flow chart of the ATC algorithm solution used in the present invention;
FIG. 7 is a graph of load data in accordance with an embodiment of the present invention;
FIG. 8 is a graph of tie line power data in accordance with an embodiment of the present invention;
fig. 9 is a diagram of a flexibility supply-demand relationship in scenario 1 according to an embodiment of the present invention, where (a) is an uplink flexibility requirement and (b) is a downlink flexibility requirement;
fig. 10 is a diagram of a flexibility supply and demand relationship in scenario 2 according to an embodiment of the present invention, where (c) is an uplink flexibility requirement, and (d) is a downlink flexibility requirement;
FIG. 11 is a graphical illustration of flexibility safety margin versus total cost in an embodiment of the present invention;
FIG. 12 is a schematic diagram of the energy consumption of a carbon capture plant in an embodiment of the invention;
FIG. 13 is a schematic view of the liquid volume in the storage of the carbon capture apparatus in an embodiment of the invention;
FIG. 14 is a graph illustrating sensitivity of carbon transaction prices in accordance with an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
In order to improve the flexibility and low carbon performance of the IES in multiple parks and improve the autonomy of each main body, the invention provides an IES distributed low carbon economic dispatching model considering various flexible resources. The main work is as follows: 1) Modeling is carried out on each flexible resource, IES flexibility margin constraint is put forward, and an IES double-layer optimization model considering flexibility is established. 2) And introducing a flexible operation model and a stepped carbon transaction model of the carbon capture unit, and analyzing the effectiveness of the carbon capture technology on flexibility. 3) And the ATC algorithm is adopted to realize the balance and cooperative optimization of the energy suppliers and the energy operators. The technical scheme of the invention comprises the following specific steps as shown in figure 1:
s1, establishing a model of flexibility requirements and flexibility resources of the IES system, and carrying out flexibility constraint;
s2, establishing an IES double-layer distributed coordination optimization scheduling model, wherein the optimization scheduling model comprises an upper-layer energy supply system and a lower-layer IES park service system;
and S3, solving an optimized scheduling model by adopting an improved ATC algorithm, and performing collaborative scheduling.
IES flexibility requirement and flexibility resource modeling
According to the IES operator body structure, the typical IES considered by the present invention is shown in fig. 2, and consists of an IES energy supplier and an IES energy operator. Energy suppliers include multi-energy transmission networks consisting of power and gas networks, focusing on meeting load demands, reducing energy supply costs, and meeting flexibility regulations. The energy operator consists of a multi-energy hub, focuses on serving the multi-energy load of the user, improving the energy utilization benefit, meeting the energy conversion requirement and the like. Since the flexibility requirement of the system is mostly generated by the load side of the energy hub, and the flexibility resource is mostly provided by the energy supply system, the flexibility requirement can be moved up to the energy supplier through the contact line.
1.1 flexibility requirement
The fluctuation of renewable energy sources such as wind power, photovoltaic and the like, IES electric heating load prediction error, the efficiency of the multi-energy conversion equipment and the like all influence the flexibility adjusting capability of the system, and the method analyzes the flexibility requirement, as shown in FIG. 3: l is 1 And L 2 Are respectively t 1 And t 2 The load at the moment of time is,
and
are respectively t 2 Upper and lower load fluctuation limits at time. Can be found out that 1 The uplink flexibility requirement of the time is
t 1 The downstream flexibility requirement of the time is
Thus, a flexibility requirement model for the system can be derived:
in the formula:
and
uplink and downlink flexibility requirements, P, at t respectively load,t Load at t, Y t up And Y t down Uplink flexibility safety margin and downlink at t respectivelyFlexibility safety margins.
1.2 Flexible resources
In order to ensure that the flexibility requirements of the system are met and sufficient adjustment capability is provided, the invention analyzes the flexibility from a carbon capture unit, wind photovoltaic, energy storage and gas network.
In the formula: f t up And F t down And uplink flexibility and downlink flexibility are respectively provided for the system at the time t.
Respectively provides the upward flexible capability for the carbon capture unit, the wind power, the photovoltaic, the energy storage and the air network at the moment t,
and
the down-going flexibility capability is respectively provided for the carbon capture unit, the wind power, the photovoltaic, the energy storage and the air network at the moment t.
Considering the economy of the system, the flexibility constraint of the invention is that the flexibility resource is larger than the flexibility requirement to evaluate the flexibility of the system, as shown in the formula:
each flexible resource will be modeled below.
For a carbon capture unit, due to uncertainty of wind and light, the unit needs to improve output when the uplink flexibility is insufficient, and reduce output when the downlink flexibility is insufficient, and a comprehensive flexible operation mode of a carbon capture power plant is an effective means for improving the flexibility of a system. The schematic diagram of the flexible operation framework of the carbon capture power plant is shown in fig. 4, the total output of the carbon capture power plant consists of net output and carbon capture energy consumption, absorbed carbon dioxide needs to be sealed and stored after being processed by an absorption tower, a regeneration tower and a compressor, and in addition, the carbon dioxide can be reserved in a storage after being absorbed. On one hand, in a high-load period, the carbon capture power plant can store the absorbed carbon dioxide into a storage, and transfers high energy consumption links such as regeneration and compression to a low-load period; on the other hand, during a low load period, the carbon capture power plant can absorb wind power by increasing the carbon capture output. Through the nimble operation mode, to the time shift of the carbon dioxide in the memory, improved the flexibility and the low carbon nature of system, the carbon capture power plant model is as follows:
in the formula: p Gi,t 、P out,Gi,t And P ccs,i,t Respectively the total output, the net output and the carbon capture energy consumption of the carbon capture unit i at t, P ccsy,i,t And P ccsg,i,t Respectively the carbon capture operation energy consumption and the fixed energy consumption of the carbon capture unit i at t, E Gi,t 、E ccs,Gi,t And E sGi,t CO of carbon capture unit i at t 2 Total yield, CO 2 Trapping volume and in-memory CO 2 Amount used for trapping. Alpha is alpha i And alpha, lambda and eta are respectively the maximum working state coefficient, unit trapping energy consumption and carbon trapping efficiency of the regeneration tower and the compressor. e.g. of a cylinder Gi Is the carbon emission intensity of the carbon capture unit i. The memory model is as follows:
in the formula: v s,i,t Releasing CO for unit i at time t 2 Volume of (V) m,i,t Volume of liquid in reservoir, V, at t for unit i m,max,i,t And the maximum capacity of the storage of the unit i at the time t. k is a radical of s Is CO 2 Volume to mass conversion factor. According to the carbon capture model, the output range of the carbon capture power plant is deduced as follows:
in the formula: p Gi,max And P Gi,min Upper and lower limits, P, of the total output of the carbon capture power plant unit i at t out,Gi,t,max And P out,Gi,t,min Respectively is the upper limit and the lower limit of net output of the carbon capture power plant unit I at t, and I is the number of the units. It can be seen that the net output range of the carbon capture plant is greatly improved compared to conventional units. The flexibility model of the carbon capture unit is as follows:
in the formula: delta P ccs,i The climbing rate of the unit i is set;
certain uncertainty exists in wind power and photovoltaic, and wind and solar energy are determined through a fluctuation coefficient. When the wind and light output is increased, uplink flexibility can be provided, and when the wind and light output is reduced, downlink flexibility can be provided.
In the formula: lambda [ alpha ] wind And λ pv The fluctuation coefficients of wind power and photovoltaic power, P w,t+1 And P w,t The output of wind power w at t +1 and t, P v,t+1 And P v,t The output of the photovoltaic V at t +1 and t is provided, and the W and V are respectively the number of the fan and the photovoltaic.
The up flexibility is provided during energy storage discharging, and the down flexibility is provided during energy storage charging.
In the formula: e soc,t To store the amount of charge at time t,
and
minimum and maximum amounts of charge, P, respectively, of stored energy s soc,s,t For the charging and discharging power of the stored energy s at the time t,
and
the maximum power of charging and discharging of the energy storage s at the moment t respectively,
and
the efficiency of charging and discharging of stored energy is respectively, and S is the stored energy quantity.
The flexibility of the gas network is mainly reflected in the output of the gas turbine, the output of the gas turbine needs to be improved when the flexibility of the gas turbine is insufficient, and the output of the gas turbine needs to be reduced when the flexibility of the gas turbine is insufficient.
In the formula: p GT,n,max And P GT,n,min Maximum and minimum output, P, of gas turbine n at time t GT,n,t The output of gas turbine N at time t, and N is the number of gas turbines.
IES double-layer distributed coordination optimization scheduling model
The invention considers the multi-main-body operation characteristics of the IES system and the information communication and safety requirements of the main bodies, provides an IES double-layer distributed coordination optimization scheduling model, an upper-layer energy supply system takes the minimum energy supply cost as an objective function, a lower-layer IES park service system takes the minimum running cost of each EH as an objective function, considers the distributed coordination optimization and adopts an improved ATC algorithm to solve.
2.1IES energy supply system optimization model
The upper energy supply system takes the minimum energy supply cost as an objective function, and comprises the total fuel cost of the carbon capture power plant, the natural gas exploitation cost, the wind abandoning cost, the carbon transaction cost and the CO capture 2 And selling the revenue.
In the formula: f. of e 、f g 、f wind 、f ccs 、
And f ccs The total fuel cost, the natural gas exploitation cost, the wind abandoning cost, the carbon transaction cost and the CO capture of the carbon capture power plant are respectively 2 And selling the revenue. a is i 、b i And c i Is the power generation coefficient of the unit i,
is the production of the gas well j at time t, ρ j Is the unit price of natural gas, J is the number of gas wells, P wp,w,t And the predicted value is the wind power predicted value.
Cost of carbon transaction, rho, for unit i ccs Selling high concentration CO 2 Monovalent of (2), m i,ccs CO captured for Unit i 2 And (4) quality.
The carbon transaction mechanism consists of three parts: carbon trading price, carbon emission quota, and carbon emissions. The invention adopts a step-type carbon transaction model, which is shown as the following formula:
in the formula:
carbon emission of unit i, phi 1 、φ 2 And phi 3 Three-stage quota, λ, for carbon transactions, respectively 1 、λ 2 And λ 3 Respectively, carbon trade unit price.
The invention adopts direct current power flow constraint as power network constraint, and the constraint conditions comprise node power balance constraint, phase angle constraint, generator output constraint and transmission line constraint. As shown in the following formula.
In the formula (f) l,t For the power flow of line l at t, P cl,c,t The tie power at t for tie C, and L and C are the line and tie number. Delta theta l,t The phase angle difference of the first and last section voltages of the line l at t, x hj Is the reactance of line i.
The natural gas network consists primarily of gas wells, pipelines, gas turbines, and tie line gas flow. The constraint mainly considers node supply and demand balance constraint, node pressure constraint, air network pipeline constraint and air source constraint, and is as follows:
in the formula (I), the compound is shown in the specification,
for gas well natural gas
Output flow at t, Q GT,n,t The gas consumption of the gas turbine t. Q, Q p,t Flow rate of pipe p at t, Q cl,c,t The flow rate of gas in the case of the connecting line t, P r,p,t Is the pressure of the natural gas pipeline P at time t, P r，min,i And P r，max,i The upper and lower limits of the pressure of the natural gas pipeline p,
and
respectively the minimum and maximum values of the output force of the air source.
The problem is a non-convex programming problem, the conventional solving is difficult, and the problem is converted into a linear programming problem by adopting an incremental piecewise linearization method.
Will Q pi,t |Q pi,t And l, carrying out piecewise linearization on the increment. a is n N is the number of segments. (x) n ,y n ) And (x) n+1 ,y n+1 ) To describe the segmentation point of the function y = x | x |, the segmentation position quantity a can be utilized n To describe (x, y) and can ensure that the segment intervals are filled in sequence.
The energy storage constraints are as follows:
the gas turbine is constrained as follows:
P GT,n,t ＝η GT Q GT,n,t (2.8)
P GT,min ≤P GT,n,t ≤P GT,max (2.9)
in the formula: eta GT Is the gas turbine efficiency. P GT,min And P GT,max Minimum and maximum output of the gas turbine, respectively.
In addition, the constraint of the carbon capture unit is shown as formula (1.4), and the constraint of the flexibility margin is shown as formula (1.3).
2.2 IES park service system optimization model
The IES park service system takes the minimum electricity and gas purchasing cost of each energy hub as an objective function.
min F 2 ＝f EH,e +f EH,g (2.10)
In the formula: i is EH Is the number of system EHs. f. of EH,e For the total EH electricity purchase cost, f EH,g In order to solve the problem of the total gas purchase cost of the EH,
for the total cost of carbon transactions, λ e,t And λ g,t Respectively the electricity purchase price and the gas purchase price at the moment of t. P buy,c,t And Q buy,c,t Respectively the electricity purchase and the gas purchase quantity of the EH at the time t.
The energy hub EH model considered by the present invention is shown in fig. 5, with equipment including photovoltaic, electric to gas, heat pump, cogeneration and gas boilers, and load considering electric load, thermal load and gas load.
The power balance equations for the electrical, gas and thermal buses are as follows:
in the formula: p is buy,c,t 、
And
the power purchasing power at t, the EH photovoltaic output power, the cogeneration electric power, the electric-to-gas electric power and the heat pump electric power are respectively. Q buy,c,t 、
And
respectively the gas purchasing quantity at t, the gas consumption of electricity-to-gas, the gas consumption of cogeneration and the gas consumption of a gas boiler.
And
respectively the thermal power of the cogeneration, the thermal power of the heat pump and the thermal power of the gas boiler at t. P load,c,t 、Q load,c,t And H load,c,t The electrical, gas and thermal loads at t.
The device transition constraints are as follows:
in the formula: eta CHP,P 、η CHP,H 、η P2G 、η HP And η GB Respectively the cogeneration efficiency, the cogeneration thermal efficiency, the electricity-to-gas efficiency, the heat pump efficiency and the gas boiler efficiency.
The upper and lower power limits of each device are constrained as follows:
S i,min ≤S i ≤S i,max (2.17)
in the formula: s i For the power of the respective device, S i,min As a lower power limit, S, of the respective device i,max The upper power limit of each device.
2.3 ATC algorithm solution
The target cascade analysis method is an effective method for rapidly solving the coordination problem of a distributed type and a hierarchical structure, and allows each main body in the hierarchical structure to make a decision independently, and when each main body makes a decision on each sub-main body, the distributed coordination optimization is carried out to obtain the overall optimal solution of the system. Compared with other optimization methods, the target cascade method has the advantages of parallel optimization, unlimited series, strict convergence certification and the like.
Firstly, decoupling a connecting line coupling variable, and adding Lagrange penalty function primary and secondary terms in each main body objective function, as follows:
in the formula:
and
in order to be the target function after the modification,
and
the first order multipliers of the lagrangian penalty functions of the power grid and the air grid at the time t respectively,
and
and (4) multiplying the quadratic term of the Lagrange penalty function of the power grid and the air grid at the time t respectively. Convergence conditions of the inner loop and the outer loop are shown as formulas (2.20) and (2.21), respectively.
In the formula: epsilon 1 、ε 2 And ε 3 And the convergence accuracy of the power grid coupling variable, the gas grid coupling variable and the target function difference value is respectively. Lagrange multiplier update formula:
fig. 6 shows a solving flowchart, which includes the following steps:
initializing an IES service system;
entering an inner loop to solve an optimized scheduling model;
judging whether the inner loop is converged, if not, returning to the inner loop, and if so, entering the outer loop;
judging whether the outer loop is converged, if not, updating the Lagrange multiplier and then returning to the inner loop, and if so, ending;
the inner loop solution optimization scheduling model comprises the following steps:
respectively solving the economic dispatch of each EH in the energy supply system;
communicating the coupling variable to the IES energy supply system;
initializing an IES functional system;
and calculating the economic dispatch of the energy supply system.
3. Example analysis
3.1 basic data
The invention utilizes an IEEE30 node power grid and a Belgian 20 node air network to form an IES energy supply system, and 3 EHs form an IES park service system and are interconnected with the energy supply system. Wherein, the IEEE30 node power grid comprises 3 carbon capture power plants, 1 wind power plant, 1 photovoltaic system and 1 gas turbine; the gas network system contains 2 gas sources, and the load data in the EH is shown in fig. 7.
The invention utilizes CPLEX to carry out optimization solution, and the system takes 24h as a period and 1h as a step length to carry out simulation. In order to analyze the impact of flexibility constraints, carbon capture equipment and carbon transactions on the system, the invention sets 4 scenarios:
scene 1: flexibility constraints are considered;
scene 2: flexibility constraints are not considered;
scene 3: considering flexibility constraints and not considering a carbon capture unit;
scene 4: flexibility constraints are considered, carbon trade is not considered.
3.2 scheduling results analysis
TABLE 1 scheduling results
Table 1 is data of total cost, carbon trading cost, carbon capture revenue and carbon emissions for four scenarios, where the flexibility safety margin values for scenarios 1, 3 and 4 are 250MW. The total cost of scenario 1 is improved by 0.47% compared with scenario 2, the carbon trading cost is improved by 2.98%, meanwhile, the carbon capture yield is also improved by 2.88%, and the carbon emission is reduced by 4.35%. This shows that, after considering the flexible resources, the total cost rises slightly, the system exerts the adjusting capability of the flexible resources, reduces the air abandon amount, improves the effect of carbon transaction, and effectively controls the rise of the cost through the carbon capture technology. Compared with scenario 3, in scenario 1, after the carbon capture technology is considered, the total cost of the system is reduced by 7.57%, and the carbon emission is greatly reduced, which shows that the carbon capture technology is beneficial to improving the economy and low carbon of the system and greatly reduces the cost of carbon transaction. After carbon transaction is considered, compared with scenario 4, in scenario 1, the total cost is increased by 1.88%, and the carbon emission is reduced by 3.33%, which shows that the carbon emission can be greatly reduced, the economy of the system is improved, and the system can meet the flexibility requirement under the condition that the total cost is slightly increased by the stepped carbon transaction model.
Fig. 8 is the tie line power under scenario one, and it can be seen from the figure that, in two time periods of 2; and 9.
3.3 flexibility analysis
Fig. 9 and 10 show the supply-demand relationship between uplink flexibility and downlink flexibility in scenario 1 and scenario 2, respectively. As can be seen from fig. 9, the flexibility requirement of the system changes with the load requirement of the EH, and the flexibility constraint enables the system to utilize various flexibility resources and invoke various devices such as a carbon capture unit and an energy storage unit, so as to meet the flexibility requirement of the system. As can be seen from the graph of the uplink flexibility requirement, the flexibility resource of the system is mainly provided by the carbon capture unit, which is due to the large output and wide operation range of the unit, and the relatively low output of wind, light and the like. At 0-4 and 9-00, the load demand and fluctuation of the system are smaller, so the uplink flexibility demand is smaller at this time, and the uplink flexibility resource is much larger than the flexibility demand, which indicates that the system has a larger flexibility margin to satisfy the flexibility, and similarly, at 5-11 and 16-00 in the following flexibility demand diagram, the downlink flexibility resource is much larger than the flexibility demand.
Without considering the flexibility constraint, fig. 10 can see that the uplink flexibility requirements at 7-00 and 18-00. This shows that the flexibility constraint can improve the flexibility margin at each moment, and effectively meet the flexibility requirement of the system.
Fig. 11 is a relationship between the flexibility safety margin and the total cost, in scenario 1, after the flexibility safety margin is increased from 50MW to 200MW, the total cost is reduced by 5.89%, after the flexibility demand is increased to 250MW, the total cost of the system starts to be increased, which means that after a certain flexibility safety margin is increased, the consumption rate of the wind power can be effectively increased, the efficiency of the carbon capture device is improved, the capture income is improved, the carbon transaction cost is reduced, however, along with the continuous improvement of the flexibility demand, the flexibility adjusting capability of the system also disappears completely, the operation cost of the system will start to be increased, and after the flexibility safety margin is increased to 300MW, the system will not meet the demand. Since the carbon capture unit is not considered in scenario 3, the total cost is greatly increased after the system provides the flexibility requirement, which also indicates that the flexible operation mode of the carbon capture technology can effectively relieve the economic cost pressure caused by the increase of the flexibility requirement of the system.
3.4 carbon Capture analysis
Fig. 11 is a graph of the carbon capture power consumption in the scenario 1, and fig. 13 is a graph of the capacity of the solution in the carbon capture memory in the scenario 1. As can be seen from fig. 12, at 1-00-5 and 11-00, the carbon capture plant consumes more power, which illustrates that during this time the system load is lower and the upstream flexibility requirement is less, so the plant output is relatively abundant, and the output of the carbon capture plant can be increased, at 6.
3.5 carbon transaction analysis
Fig. 14 shows the influence of the change of the carbon trading price on the carbon emission of the system, and it can be seen from the graph that when the carbon trading price is increased within 40%, the carbon emission of the system is greatly reduced, and after the carbon trading price is increased to 50%, the change of the carbon emission is not obvious any more, which indicates that the reasonable carbon trading price can effectively balance the economic requirement and the systematic requirement of the system.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations can be devised by those skilled in the art in light of the above teachings. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A comprehensive energy collaborative optimization scheduling method considering various flexible resources is characterized by comprising the following steps:
establishing a model of flexibility requirements and flexibility resources of the IES system, and carrying out flexibility constraint;
establishing an IES double-layer distributed coordination optimization scheduling model, wherein the optimization scheduling model comprises an upper-layer energy supply system and a lower-layer IES park service system;
and solving an optimized scheduling model by adopting an improved ATC algorithm, and performing cooperative scheduling.
2. The method according to claim 1, wherein the flexibility requirements and flexibility resources of the IES system are modeled with flexibility constraints as follows:
the flexibility requirement model of the system is as follows:
in the formula:
and
uplink and downlink flexibility requirements, P, at t respectively load,t Load at time t, P load,t+1 Load at time t +1, Y t up And Y t down An uplink flexibility safety margin and a downlink flexibility safety margin at t are respectively set;
the flexible resource considers a carbon capture unit, wind power, photovoltaic, energy storage and a gas network, and the model is established as follows:
in the formula: f t up And F t down Respectively provides uplink flexibility and downlink flexibility for the system at the time t,
respectively provides the upward flexible capability for the carbon capture unit, the wind power, the photovoltaic, the energy storage and the air network at the moment t,
and
downlink flexibility capability is respectively provided for the carbon capture unit, the wind power, the photovoltaic, the energy storage and the air network at the moment t;
the flexibility constraint is that the flexibility resource is greater than the flexibility requirement, as shown in the formula:
3. the method of claim 2, wherein the co-optimization scheduling of energy for the carbon capture unit takes into account multiple flexibility resources
And downlink flexibility
Comprises the following steps: establishing a carbon capture power plant and a storage model;
deducing the output range of the carbon capture power plant according to the carbon capture model;
flexibility model for calculating carbon capture unit
The carbon capture power plant model is as follows:
in the formula: p is Gi,t 、P out,Gi,t And P ccs,i,t Respectively the total output, net output and carbon capture energy consumption of the carbon capture unit i at t, P ccsy,i,t And P ccsg,i,t Respectively the carbon capture running energy consumption and the fixed energy consumption of the carbon capture unit i at t, E Gi,t 、E ccs,Gi,t And E sGi,t CO of carbon capture unit i at t 2 Total yield, CO 2 Trapping volume and in-memory CO 2 Amount for trapping, α i The flue gas split ratio of the carbon capture unit i, alpha, lambda and eta are respectively the maximum working state coefficient, unit capture energy consumption and carbon capture efficiency of the regeneration tower and the compressor, and e Gi The carbon emission intensity of the carbon capture unit i;
the output range of the carbon capture power plant is as follows:
in the formula: p is Gi,max And P Gi,min Upper and lower limits, P, of the total output of the carbon capture power plant unit i at t out,Gi,t,max And P out,Gi,t,min Respectively the upper limit and the lower limit of net output of the carbon capture power plant unit I at t, wherein I is the number of the units;
the flexibility model of the carbon capture unit is as follows:
in the formula: delta P ccs,i The ramp rate of unit i.
4. The method as claimed in claim 2, wherein the wind power uplink flexibility is considered in the method for scheduling energy resource integration optimization in cooperation with multiple flexibility resources
And downlink flexibility
Uplink flexibility with photovoltaics
And downlink flexibility
Comprises the following steps:
in the formula: lambda wind And λ pv The fluctuation coefficients of wind power and photovoltaic power, P w,t+1 And P w,t The output of wind power w at t +1 and t, P v,t+1 And P v,t The output of the photovoltaic V at t +1 and t is provided, and the W and V are respectively the number of the fan and the photovoltaic.
5. The method according to claim 2, wherein the energy storage and discharge provides uplink flexibility
Providing downlink flexibility during energy storage charging
Comprises the following steps:
in the formula: e soc,t To store the amount of charge at time t,
and
minimum and maximum charge quantities, P, of stored energy s, respectively soc,s,t For the charging and discharging power of the stored energy s at the time t,
and
respectively the maximum power of the stored energy s charged and discharged at the moment t,
and
the efficiency of charging and discharging of stored energy is respectively, and S is the stored energy quantity.
6. The method according to claim 2, wherein the flexibility of the gas grid is mainly expressed by the output of the gas turbine, the output of the gas turbine needs to be increased when the uplink flexibility of the gas turbine is insufficient, the output is reduced when the downlink flexibility of the gas turbine is insufficient, and the uplink flexibility of the gas grid is increased
And downlink flexibility
Comprises the following steps:
in the formula: p GT,n,max And P GT,n,min Maximum and minimum power, P, respectively, of the gas turbine n at time t GT,n,t The output of gas turbine N at time t, and N is the number of gas turbines.
7. The method of claim 1, wherein the upper energy supply system has a minimum energy supply cost as an objective function, and the objective function comprises total fuel cost of the carbon capture plant, natural gas exploitation cost, wind curtailment cost, carbon transaction cost and CO capture cost 2 The sales revenue is as follows:
in the formula: f. of e 、f g 、f wind 、f ccs 、
And f ccs The total fuel cost, natural gas exploitation cost, wind abandoning cost, carbon transaction cost and CO capture of the carbon capture power plant 2 Sales revenue a i 、b i And c i Is the power generation coefficient of the unit i,
is the production of the gas well j at time t, ρ j Is the natural gas unit price, J is the gas well number, P wp,w,t The wind power is used as a predicted value of the wind power,
cost of carbon transaction, rho, for unit i ccs Selling high concentration CO 2 Monovalent of (m) i,ccs CO captured for Unit i 2 Quality;
the carbon transaction mechanism consists of three parts: the carbon trading model adopts the steps of:
in the formula:
carbon emission of unit i, phi 1 、φ 2 And phi 3 Three-stage quota, λ, for carbon transactions, respectively 1 、λ 2 And λ 3 Respectively, the unit price of the carbon transaction,
the method comprises the following steps of adopting direct current power flow constraint as power network constraint, wherein the constraint conditions comprise node power balance constraint, phase angle constraint, generator output constraint and transmission line constraint, and are shown as the following formula:
in the formula (f) l,t For the power flow of line l at t, P cl,c,t For the tie power at t for tie C, L and C are the number of lines and ties, Δ θ l,t The phase angle difference of the first and last section voltages of the line l at t, x hj Is the reactance of line l;
the natural gas network is mainly composed of gas wells, pipelines, gas turbines and tie line gas flow, and the natural gas network constraints mainly consider node supply and demand balance constraints, node pressure constraints, gas network pipeline constraints and gas source constraints as follows:
in the formula (I), the compound is shown in the specification,
for gas well natural gas
Output flow at t, Q GT,n,t Is the gas consumption, Q, of the gas turbine t p,t Flow rate of pipe p at t, Q cl,c,t The amount of airflow in the connecting line t, P r,p,t Is the pressure of the natural gas pipeline P at time t, P r，min,i And P r，max,i The upper and lower limits of the pressure of the natural gas pipeline p,
and
respectively the minimum and maximum values of the air source output;
the energy storage constraints are as follows:
gas turbines are constrained as follows:
P GT,n,t ＝η GT Q GT,n,t
P GT,min ≤P GT,n,t ≤P GT,max
in the formula: eta GT For gas turbine efficiency, P GT,min And P GT,max Minimum and maximum output of the gas turbine, respectively.
8. The method of claim 1, wherein the IES campus service system is configured to perform the objective function of minimizing the electricity and gas purchase cost at each energy hub:
min F 2 ＝f EH,e +f EH,g
in the formula: i is EH As the number of EHs in the system, f EH,e For the total purchase cost of EH, f EH,g In order to solve the problem of the total gas purchase cost of the EH,
lambda is the total cost of carbon transaction e,t And λ g,t Respectively the electricity purchasing price EH and the gas purchasing price P at the time t buy,c,t And Q buy,c,t Respectively the electricity purchase amount and the gas purchase amount of the EH at the time t;
the equipment of the energy hub model comprises photovoltaic, electricity-to-gas, a heat pump, cogeneration and a gas boiler, and the load considers the electric load, the heat load and the gas load; the power balance equations for the electrical, gas and thermal buses are as follows:
in the formula: p is buy,c,t 、
And
the purchase electric power, the EH photovoltaic output power, the combined heat and power electric power, the electric-to-gas electric power and the heat pump electric power at t are Q buy,c,t 、
And
respectively the gas purchasing quantity at t, the gas consumption of electricity-to-gas, the gas consumption of cogeneration and the gas consumption of a gas boiler,
and
respectively the thermal power of cogeneration at t, the thermal power of the heat pump and the thermal power of the gas boiler, P load,c,t 、Q load,c,t And H load,c,t Electrical, gas, thermal load at t, respectively;
the device transition constraints are as follows:
in the formula: eta CHP,P 、η CHP,H 、η P2G 、η HP And η GB Respectively the cogeneration efficiency, the cogeneration thermal efficiency, the electricity-to-gas efficiency, the heat pump efficiency and the gas boiler efficiency;
the upper and lower power limits of each device are constrained as follows:
S i,min ≤S i ≤S i,max
in the formula: s. the i For the power of the respective device, S i,min As a lower power limit, S, of the respective device i,max The upper power limit of each device.
9. The method for integrated energy collaborative optimal scheduling considering diverse flexible resources according to claim 1, wherein the collaborative scheduling using the ATC algorithm comprises the steps of:
initializing an IES service system;
entering an inner loop to solve an optimized scheduling model;
judging whether the inner loop is converged, if not, returning to the inner loop, and if so, entering the outer loop;
judging whether the outer loop is converged, if not, updating the Lagrange multiplier and then returning to the inner loop, and if so, ending;
the inner loop solving optimization scheduling model comprises the following steps:
respectively solving the economic dispatch of each EH in the energy supply system;
communicating the coupling variable to the IES energy supply system;
initializing an IES functional system;
and calculating the economic dispatch of the energy supply system.
10. The method according to claim 9, wherein the scheduling of energy resources by ATC further comprises:
firstly, decoupling a connecting line coupling variable, and adding Lagrange penalty function primary and secondary terms into each main body objective function, wherein the following steps are as follows:
in the formula:
and
in order to be the target function after the modification,
and
the first order multipliers of the lagrangian penalty functions of the power grid and the air grid at the time t respectively,
and
quadratic multiplier of lagrangian penalty function for time t, P, for electric and gas networks respectively buy,c,t Power purchase at t, Q buy,c,t The gas purchase amount at t, P cl,c,t For the tie line power of tie line c at t, Q cl,c,t The air flow rate at the time of the tie line t;
the convergence conditions of the inner loop and the outer loop are respectively as shown in the following formulas:
in the formula: epsilon 1 、ε 2 And epsilon 3 Convergence accuracy of the power grid coupling variable, the air grid coupling variable and the target function difference value is respectively obtained;
the Lagrange multiplier updating formula is as follows:
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| CN117291315A (en) * | 2023-11-24 | 2023-12-26 | 湖南大学 | Carbon recycling electric-gas-thermal multi-energy combined supply network cooperative operation method |
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| CN116911533B (en) * | 2023-06-27 | 2024-05-14 | 西安理工大学 | Multi-microgrid energy sharing method for regional comprehensive energy system |
| CN117291315A (en) * | 2023-11-24 | 2023-12-26 | 湖南大学 | Carbon recycling electric-gas-thermal multi-energy combined supply network cooperative operation method |
| CN117291315B (en) * | 2023-11-24 | 2024-02-20 | 湖南大学 | Carbon recycling electric-gas-thermal multi-energy combined supply network cooperative operation method |
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