CN116341847A - Scheduling method and system for carbon emission of comprehensive energy system under variable working conditions - Google Patents
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Abstract
The invention discloses a method and a system for scheduling carbon emission of a comprehensive energy system under variable working conditions, wherein the method comprises the following steps: acquiring a multi-energy coupling relation of the comprehensive energy system and variable working condition characteristics of energy conversion equipment; establishing an energy hub model according to the multi-energy coupling relation of the comprehensive energy system; based on the variable working condition characteristics of the energy conversion equipment, correcting efficiency parameters in the energy hub model by utilizing a pre-constructed efficiency correction model based on a deep neural network to obtain a dynamic energy hub model; and constructing a scheduling model of the carbon emission of the comprehensive energy system under the variable working condition based on the dynamic energy hub model, and solving to obtain a scheduling scheme of the comprehensive energy system under the variable working condition. The invention focuses on the influence of the variable working condition characteristics of equipment on the carbon emission of the system, can realize the low-carbon operation of the comprehensive energy system, and improves the solving speed and precision of the scheduling model.
Description
Technical Field
The invention relates to a scheduling method and a system for carbon emission of a comprehensive energy system under variable working conditions, and belongs to the technical field of comprehensive energy of electric power systems.
Background
Currently, research on operation optimization of integrated energy systems mainly comprises two aspects of system modeling and scheduling. The former focuses on researching equipment modeling and tide computing, and the latter focuses on optimizing algorithms and source load uncertainty analysis. However, these studies typically ignore the variable operating characteristics of the device, simplifying the efficiency of the energy conversion device to a constant. In practice, the plant is typically operated under variable operating conditions due to the load and the fluctuation of environmental factors. The energy supply deviation caused by neglecting the variable working condition characteristics of the single equipment can be transmitted to the whole system, so that the energy conversion relation of the system is deviated, and the accuracy of the optimized dispatching result is reduced. Therefore, the accurate modeling of the IES under the variable working condition has important significance for guaranteeing the balance of supply and demand of the system and optimizing the operation.
Research on variable working condition characteristics of equipment has been advanced to some extent. Literature: huang Wujin, zhang Ning, wang Yi, et al matrix modeling of Energy hub with variable Energy efficiencies [ J ]. International Journal of Electrical Power & Energy Systems,2020, 119. A piecewise linearization method is used to approximate a variable operating mode efficiency curve, but the accuracy of the method prediction is dependent on the accuracy of the linearization piecewise. Literature: li Hong, du Shiqi comprehensive energy system optimization configuration considering variable operating characteristics of energy hub [ J ]. Modern power, 2021, 39:1-8, a dynamic function model of the variable working condition of the equipment is established, and an IES mixed integer configuration optimization model is introduced aiming at the variable working condition characteristic of the equipment, but larger calculation amount is brought. The above studies have all established functional models of plant efficiency versus load factor, but a number of complex mathematical calculations have limited their practical application.
With the rapid development of emerging technologies such as big data, intelligent methods and the like, the deep neural network (deep neural network, DNN) is widely applied in IES, and provides a new solution for the prediction problem difficult to accurately model.
In order to fully exploit the economic and environmental benefits of IES, carbon emission scheduling of IES has become a research hotspot. Literature: wei Zhenbo, wei Pingan, guo Yi, etc. consider the distributed low-carbon economic dispatch [ J ]. High-voltage technology, 2021, 47 (01) for electric-gas interconnection network for demand side management and carbon transactions: 33-47. CO2 emissions targets were introduced in the constraints of the electric-gas interconnection network scheduling model. Literature: wu Lei comprehensive energy System Multi-timescale Low carbon economic dispatch study [ D ]: university of Shenyang industry 2021 research IES low carbon dispatch optimization method to effectively achieve carbon emission reduction. Literature: han Xiaoqing, li Tingjun, zhang Dongxia, etc. novel power system planning new problem and key technology [ J ]. High voltage technology, 2021, 47 (09): 3036-3046A dynamic carbon emission factor is added to participate in the electricity consumption behavior guidance of the user, and an optimal scheduling method aiming at low carbon property is provided. Literature: li Yaowang, zhang Ning, du Ershun, etc. low carbon demand response mechanism research and benefit analysis of electric power systems based on carbon emission streams [ J ]. Chinese motor engineering journal, 2022, 42 (08): 2830-2842, load adjustment is achieved through demand response on the basis of considering demand side management and carbon trade, and system operation cost and carbon emission are further reduced.
However, the prior research is less concerned about the influence of the variable working condition characteristics of equipment on the carbon emission of the system.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a scheduling method and a scheduling system for carbon emission of a comprehensive energy system under variable working conditions, which pay attention to the influence of equipment variable working condition characteristics on the carbon emission of the system, can realize low-carbon operation of the comprehensive energy system, and improve the solving speed and the solving precision of a scheduling model. In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides a method for scheduling carbon emissions of a comprehensive energy system under variable working conditions, including:
acquiring a multi-energy coupling relation of the comprehensive energy system and variable working condition characteristics of energy conversion equipment;
establishing an energy hub model according to the multi-energy coupling relation of the comprehensive energy system;
based on the variable working condition characteristics of the energy conversion equipment, correcting efficiency parameters in the energy hub model by utilizing a pre-constructed efficiency correction model based on a deep neural network to obtain a dynamic energy hub model;
and constructing a scheduling model of the carbon emission of the comprehensive energy system under the variable working condition based on the dynamic energy hub model, and solving to obtain a scheduling scheme of the comprehensive energy system under the variable working condition.
With reference to the first aspect, further, the pre-constructed efficiency correction model based on the deep neural network is represented by the following formula:
in the formula (1), i is energy conversion equipment; η (eta) i,t The efficiency of the energy conversion equipment i at the moment t; t (T) i,t The temperature of the operating environment of the energy conversion equipment i at the moment t; f (F) i,t The air pressure of the operating environment of the energy conversion equipment i at the moment t; n (N) i,t The load power of the energy conversion device i at the time t is represented by the following formula:
N i,t ＝P i,t /P i,cap (2)
in the formula (2), P i,t The output power of the energy conversion equipment i at the moment t is kW; p (P) i,cap The plant capacity of the energy conversion plant i, kW.
With reference to the first aspect, further, the matrix form of the energy hinge model is represented by the following formula:
in the formula (3), L e,t For the electric load of the comprehensive energy system at the moment t, L h,t The heat load of the comprehensive energy system at the time t; η (eta) CHP Is the power generation efficiency eta of the CHP of the cogeneration unit CHP,h For the heat supply efficiency eta of the CHP of the cogeneration unit GB The heat supply efficiency of the gas boiler GB is improved; p (P) e,t Supplying power to the electric energy source at time t, P g,t Supplying power for the gas energy source at the moment t; η (eta) C Charge efficiency, eta for battery BAT D Discharging efficiency for battery BAT; w (W) e,t The BAT energy storage capacity of the storage battery at the moment t; v t And the natural gas consumed by the cogeneration unit CHP at the moment t accounts for the proportionality coefficient of the natural gas supply at the moment t.
With reference to the first aspect, further,
with reference to the first aspect, further, the matrix form of the dynamic energy hinge model is represented by the following formula:
in the formula (4), eta CHP,t For the power generation efficiency eta of the CHP at the moment t CHP,h,t For the heat supply efficiency of the CHP of the combined heat and power unit at the moment t, eta GB,t The heating efficiency of the gas boiler GB at the time t is obtained.
In combination with the first aspect, further, the scheduling model of the carbon emission of the comprehensive energy system under the variable working condition uses the lowest energy cost of the comprehensive energy system as an objective function, and is represented by the following formula:
minf c ＝f ope +f env (5)
in the formula (5), f c The energy cost of the comprehensive energy system; f (f) ope To purchase energy cost, including purchase electricity cost f e And the cost of purchasing gas f g Represented by the following formula:
in the formula (6), T is a scheduling period, and Δt is a unit scheduling period; c e,t Electricity price for electricity purchase at time t, c g The price of the gas purchase is the unit; p (P) e,t For purchasing electric power at time t, P g,t The gas purchase power is t time;
in the formula (5), f env For environmental costs, including CO generated from grid purchases 2 Discharge cost
CO generated by CHP (heat-power generation) unit 2 Exhaust costs->
CO generated by gas boiler GB 2 Exhaust costs->
Represented by the formula:
in the formula (7), T is a scheduling period, and Δt is a unit scheduling period;
is CO as emission unit 2 Is a cost of the environment;
carbon emission calculation coefficient for supplying the grid, +.>
Calculating a coefficient for the carbon emission of the CHP unit output of the cogeneration unit, < >>
Calculating a coefficient for carbon emission of the unit output power of the gas boiler GB; p (P) e,t For purchasing electric power at time t, P CHP,t For the output power of the combined heat and power unit CHP at the time t, P GB,t The output power of the gas boiler GB at the time t.
With reference to the first aspect, further, constraint conditions of the scheduling model of carbon emission of the comprehensive energy system under the variable working condition include: power balance constraints, tie-line power constraints, energy conversion device operating constraints and energy storage device operating constraints,
the power balance constraint is that the multi-energy coupling relation described by the dynamic energy hub model is satisfied, and the efficiency of energy conversion equipment in the comprehensive energy system is calculated by a pre-constructed efficiency correction model based on a deep neural network;
the tie line power constraint is that the interaction power of the comprehensive energy system and the upper power grid is within a safe range, and is represented by the following formula:
0≤P grid,t ≤P grid,cap (8)
in the formula (8), P grid,t For purchasing electric power at time t, P grid,cap Is the upper tie line power limit;
the energy conversion equipment operation constraint comprises an input power relation constraint, an output power relation constraint and an energy conversion equipment output upper limit constraint and an energy conversion equipment output lower limit constraint, and the energy conversion equipment operation constraint is expressed by the following formula:
in the formula (9), P CHP,t For the output power of the combined heat and power unit CHP at the moment t, eta CHP,t For the power generation efficiency of the CHP of the T-moment cogeneration unit, P in,CHP,t For the input power of the CHP of the T-moment cogeneration unit, P CHP,cap The upper limit of the output power of the combined heat and power unit CHP is set; p (P) GB,t Is the output power of the gas boiler GB at the time t, eta GB,t For the heat supply efficiency of the gas boiler GB at the moment of t, P in,GB,t For the input power of the gas boiler GB at the moment of t, P GB,cap The upper limit of the output power of the gas boiler GB;
the energy storage device operating constraints include limitations on charge and discharge energy power and device capacity, expressed by the following formula:
in the formula (10), W e,t Energy storage capacity W before charging and discharging BAT of storage battery e,t+1 The energy storage energy after the battery BAT is charged and discharged, sigma is the self-discharging energy rate of the battery BAT, and P C,t For the BAT energy charging power at the moment t, eta C Charge efficiency for battery BAT, P D,t For the BAT energy release power at the moment t, eta D For battery BAT discharge efficiency, deltat is a unit scheduling period; w (W) cap Rated capacity for battery BAT;
maximum charge power for battery BAT; />
Maximum power of battery BAT; w (W) start Energy storage energy of the storage battery BAT at the beginning time of a dispatching cycle is stored; w (W) end For storing energy of the battery BAT at the end of the scheduling period.
In a second aspect, the present invention provides a system for scheduling carbon emissions from a comprehensive energy system under variable operating conditions, comprising:
acquiring a multi-energy coupling relation of the comprehensive energy system and variable working condition characteristics of energy conversion equipment;
establishing an energy hub model according to the multi-energy coupling relation of the comprehensive energy system;
based on the variable working condition characteristics of the energy conversion equipment, correcting efficiency parameters in the energy hub model by utilizing a pre-constructed efficiency correction model based on a deep neural network to obtain a dynamic energy hub model;
and constructing a scheduling model of the carbon emission of the comprehensive energy system under the variable working condition based on the dynamic energy hub model, and solving to obtain a scheduling scheme of the comprehensive energy system under the variable working condition.
In a third aspect, the present invention provides a computing device comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method of the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor implements the steps of the method according to the first aspect.
Compared with the prior art, the scheduling method and the scheduling system for the carbon emission of the comprehensive energy system under the variable working condition provided by the embodiment of the invention have the beneficial effects that:
the invention obtains the multi-energy coupling relation of the comprehensive energy system and the variable working condition characteristic of the energy conversion equipment; establishing an energy hub model according to the multi-energy coupling relation of the comprehensive energy system; based on the variable working condition characteristics of the energy conversion equipment, correcting efficiency parameters in the energy hub model by utilizing a pre-constructed efficiency correction model based on a deep neural network to obtain a dynamic energy hub model; according to the invention, the efficiency correction model based on the deep neural network is adopted to model the relation between the efficiency of the energy conversion equipment and the load rate, the air temperature and the air pressure, so that the problems of large calculated amount and low accuracy of the traditional method are solved;
the method is characterized in that a scheduling model of carbon emission of the comprehensive energy system under variable working conditions is built based on a dynamic energy hub model, and a scheduling scheme of the comprehensive energy system under the variable working conditions is obtained through solving; the invention considers the strong nonlinearity of the equipment efficiency change, improves the accuracy of the equipment model, can realize the low-carbon operation of the comprehensive energy system, and improves the solving speed and the accuracy of the scheduling model.
Drawings
FIG. 1 is a flow chart of a method for scheduling carbon emissions of a comprehensive energy system under variable working conditions according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a typical integrated energy system in a method for scheduling carbon emissions of an integrated energy system under variable operating conditions according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an efficiency correction model based on a deep neural network in a scheduling method of carbon emission of a comprehensive energy system under a variable working condition according to a second embodiment of the present invention;
FIG. 4 is a schematic diagram of a dynamic energy hub model in a method for scheduling carbon emission of a comprehensive energy system under variable working conditions according to a second embodiment of the present invention;
FIG. 5 is a schematic diagram of electrical and thermal loads of a typical integrated energy system under typical days in a method for scheduling carbon emissions of an integrated energy system under variable operating conditions according to a second embodiment of the present invention;
FIG. 6 is a schematic diagram of air temperature and air pressure of an exemplary integrated energy system operating environment in a method for scheduling carbon emissions of an integrated energy system under variable operating conditions according to a second embodiment of the present invention;
fig. 7 is a schematic diagram of scheduling results of different scenarios provided in the second embodiment of the present invention, fig. 7a is a schematic diagram of scheduling results of scenario 1, fig. 7b is a schematic diagram of scheduling results of scenario 2, and fig. 7c is a schematic diagram of scheduling results of scenario 3.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Embodiment one:
as shown in fig. 1, the embodiment of the invention provides a method for scheduling carbon emission of a comprehensive energy system under variable working conditions, which comprises the following steps:
acquiring a multi-energy coupling relation of the comprehensive energy system and variable working condition characteristics of energy conversion equipment;
establishing an energy hub model according to the multi-energy coupling relation of the comprehensive energy system;
based on the variable working condition characteristics of the energy conversion equipment, correcting efficiency parameters in the energy hub model by utilizing a pre-constructed efficiency correction model based on a deep neural network to obtain a dynamic energy hub model;
and constructing a scheduling model of the carbon emission of the comprehensive energy system under the variable working condition based on the dynamic energy hub model, and solving to obtain a scheduling scheme of the comprehensive energy system under the variable working condition.
The method comprises the following specific steps:
step 1: an efficiency correction model based on a deep neural network is constructed and expressed by the following formula:
in the formula (1), i is energy conversion equipment; η (eta) i,t The efficiency of the energy conversion equipment i at the moment t; t (T) i,t The temperature of the operating environment of the energy conversion equipment i at the moment t; f (F) i,t The air pressure of the operating environment of the energy conversion equipment i at the moment t; n (N) i,t The load power of the energy conversion device i at the time t is represented by the following formula:
N i,t ＝P i,t /P i,cap (2)
in the formula (2), P i,t The output power of the energy conversion equipment i at the moment t is kW; p (P) i,cap The plant capacity of the energy conversion plant i, kW.
Step 2: and acquiring the multi-energy coupling relation of the comprehensive energy system and the variable working condition characteristic of the energy conversion equipment. And building an energy hub model according to the multi-energy coupling relation of the comprehensive energy system.
The matrix form of the energy pivot model is represented by the following formula:
in the formula (3), L e,t For the electric load of the comprehensive energy system at the moment t, L h,t The heat load of the comprehensive energy system at the time t; η (eta) CHP Is the power generation efficiency eta of the CHP of the cogeneration unit CHP,h For the heat supply efficiency eta of the CHP of the cogeneration unit GB The heat supply efficiency of the gas boiler GB is improved; p (P) e,t Supplying power to the electric energy source at time t, P g,t Supplying power for the gas energy source at the moment t; η (eta) C Charge efficiency, eta for battery BAT D Discharging efficiency for battery BAT; w (W) e,t The BAT energy storage capacity of the storage battery at the moment t; v t And the natural gas consumed by the cogeneration unit CHP at the moment t accounts for the proportionality coefficient of the natural gas supply at the moment t.
Step 3: based on the variable working condition characteristics of the energy conversion equipment, the efficiency parameters in the energy hub model are corrected by utilizing a pre-constructed efficiency correction model based on the deep neural network, so that a dynamic energy hub model is obtained.
The matrix form of the dynamic energy hinge model is represented by the following formula:
in the formula (4), eta CHP,t For the power generation efficiency eta of the CHP at the moment t CHP,h,t For the heat supply efficiency of the CHP of the combined heat and power unit at the moment t, eta GB,t The heating efficiency of the gas boiler GB at the time t is obtained.
Step 4: and constructing a scheduling model of the carbon emission of the comprehensive energy system under the variable working condition based on the dynamic energy hub model, and solving to obtain a scheduling scheme of the comprehensive energy system under the variable working condition.
And on the premise of meeting the operation constraint of the comprehensive energy system, the low-carbon operation is realized. The scheduling model of the carbon emission of the comprehensive energy system under the variable working condition takes the lowest energy cost of the comprehensive energy system as an objective function, and is expressed by the following formula:
minf c ＝f ope +f env (5)
in the formula (5), f c The energy cost of the comprehensive energy system; f (f) ope To purchase energy cost, including purchase electricity cost f e And the cost of purchasing gas f g Represented by the following formula:
in the formula (6), T is a scheduling period, and Δt is a unit scheduling period; c e,t Electricity price for electricity purchase at time t, c g The price of the gas purchase is the unit; p (P) e,t For purchasing electric power at time t, P g,t The gas purchase power is t time;
in the formula (5), f env For environmental costs, including CO generated from grid purchases 2 Discharge cost
CO generated by CHP (heat-power generation) unit 2 Exhaust costs->
CO generated by gas boiler GB 2 Exhaust costs->
Represented by the formula:
in the formula (7), T is a scheduling period, and Δt is a unit scheduling period;
is CO as emission unit 2 Is a cost of the environment;
carbon emission calculation coefficient for supplying the grid, +.>
Calculating a coefficient for the carbon emission of the CHP unit output of the cogeneration unit, < >>
Calculating a coefficient for carbon emission of the unit output power of the gas boiler GB; p (P) e,t For purchasing electric power at time t, P CHP,t For the output power of the combined heat and power unit CHP at the time t, P GB,t The output power of the gas boiler GB at the time t.
Constraint conditions of the scheduling model of the comprehensive energy system carbon emission under the variable working conditions comprise: power balance constraints, tie-line power constraints, energy conversion device operating constraints, and energy storage device operating constraints.
(1) The comprehensive energy system comprises electric, thermal and air energy flows, the power balance constraint is that the energy conversion equipment in the comprehensive energy system meets the multi-energy coupling relation described by the dynamic energy hub model, and the efficiency of the energy conversion equipment in the comprehensive energy system is calculated by a pre-constructed efficiency correction model based on a deep neural network, as shown in formulas (1) and (4).
(2) Tie line power constraint: in order to ensure the safe operation of the comprehensive energy system, the interactive power between the comprehensive energy system and the upper power grid is in a safe range, and is expressed by the following formula:
0≤P grid,t ≤P grid,cap (8)
in the formula (8), P grid,t For purchasing electric power at time t, P grid,cap Is the upper link power limit.
(3) The energy conversion device operation constraint comprises an input power relation constraint, an output power relation constraint and an energy conversion device output upper limit constraint and a lower limit constraint, and the energy conversion device operation constraint is expressed by the following formula:
in the formula (9), P CHP,t For the output power of the combined heat and power unit CHP at the moment t, eta CHP,t For the power generation efficiency of the CHP of the T-moment cogeneration unit, P in,CHP,t For the input power of the CHP of the T-moment cogeneration unit, P CHP,cap The upper limit of the output power of the combined heat and power unit CHP is set; p (P) GB,t Is the output power of the gas boiler GB at the time t, eta GB,t For the heat supply efficiency of the gas boiler GB at the moment of t, P in,GB,t For the input power of the gas boiler GB at the moment of t, P GB,cap Is the upper limit of the output power of the gas boiler GB.
(4) The energy storage device operating constraints include limitations on charge-discharge power and device capacity, expressed by the following equation:
in the formula (10), W e,t Energy storage capacity W before charging and discharging BAT of storage battery e,t+1 The energy storage energy after the battery BAT is charged and discharged, sigma is the self-discharging energy rate of the battery BAT, and P C,t For the BAT energy charging power at the moment t, eta C Charge efficiency for battery BAT, P D,t For the BAT energy release power at the moment t, eta D For battery BAT discharge efficiency, deltat is a unit scheduling period; w (W) cap Rated capacity for battery BAT;
maximum charge power for battery BAT; />
Maximum power of battery BAT; w (W) start Energy storage energy of the storage battery BAT at the beginning time of a dispatching cycle is stored; w (W) end For storing energy of the battery BAT at the end of the scheduling period.
According to the invention, the efficiency correction model based on the deep neural network is adopted to model the relation between the efficiency of the energy conversion equipment and the load rate, the air temperature and the air pressure, so that the problems of large calculated amount and low accuracy of the traditional method are solved; the strong nonlinearity of the equipment efficiency change is considered, the accuracy of the equipment model is improved, the low-carbon operation of the comprehensive energy system can be realized, and the solving speed and the solving accuracy of the scheduling model are improved.
Embodiment two:
the embodiment selects the typical comprehensive energy system in the south of China as shown in fig. 2 for carbon emission scheduling based on the comprehensive energy system carbon emission scheduling method under the variable working condition provided in the first embodiment.
Fig. 5 and 6 are schematic diagrams of electric and thermal loads and air temperature and air pressure of an operating environment under typical days of a typical south integrated energy system in this embodiment.
The electricity purchase price, the CO2 emission cost and the upper limit of the interactive power of the connecting line are shown in table 1, and the rated parameters of the equipment are shown in table 2.
TABLE 1 purchase price, CO2 emission cost and tie line interaction Power Limit
Table 2 rated parameters of the apparatus
In order to verify the effectiveness of the method, the following three scenes are constructed for comparison:
scene 1: based on the EH model with constant efficiency, the variable working condition characteristic of the equipment is not considered.
Scene 2: a DEH model based on a polynomial fitting method.
Scene 3: the proposed DNN-based DEH model. In addition, different iteration times (E) are set when training the neural network, and a plurality of sub-scenes are set accordingly. When E is sufficiently large (e.g., 500), the deep neural network-based efficiency correction model is fully converged, with higher prediction accuracy, with a network loss function value of approximately 0. The prediction result can be regarded as an accurate value at this time and used as a reference for other scene analysis comparison.
And solving a scheduling model of the carbon emission of the comprehensive energy system under three scenes, wherein the obtained energy purchasing cost, the environment cost, the running cost, the relative error and the calculation time are shown in a table 3.
TABLE 3 running cost, relative error and computation time for three scenarios
| Scene(s) | Cost of purchase/yuan | Environmental cost/element | Running cost/element | Relative error | Calculating time/ |
| 1 | 51012.45 | 2873.94 | 53886.39 | 4.1824％ | 0.5312 |
| 2 | 52830.56 | 3051.77 | 55882.33 | 0.6334％ | 4.1786 |
| 3(E＝100) | 52919.14 | 3062.45 | 55958.59 | 0.4978％ | 0.5540 |
| 3(E＝200) | 53085.62 | 3083.06 | 56168.68 | 0.1242％ | 0.5527 |
| 3(E＝300) | 53129.75 | 3090.16 | 56219.91 | 0.0331％ | 0.5507 |
| 3(E＝400) | 53142.86 | 3093.93 | 56236.79 | 0.0031％ | 0.5534 |
| 3(E＝500) | 53143.95 | 3094.57 | 56238.52 | - | 0.5516 |
In scenario 1, the relative error of the running cost reaches 4.1824%, which indicates that the energy hub model with constant efficiency is difficult to realize accurate modeling of the comprehensive energy system, and influences the accuracy of optimized scheduling.
In scenario 2, the polynomial fitting method reduces the relative error of the running cost from 4.1824% to 0.6334%, and it is still difficult to accurately describe the actual running condition of the integrated energy system. However, this method is computationally intensive, complex to solve, and the computation time is about 8 times that of scene 1.
In scene 3 (e=100 to 500), as E increases, the prediction accuracy of the deep neural network increases, the prediction accuracy of the efficiency correction model based on the deep neural network increases, and the relative error of each sub-scene gradually decreases. Scenario 3 (e=500) has the highest accuracy, and its scheduling scheme best fits the actual operation of the integrated energy system. The method for scheduling carbon emission of the comprehensive energy system under the variable working condition provided by the embodiment can more accurately describe nonlinear changes of equipment efficiency and has more excellent prediction performance compared with the traditional polynomial fitting method in the scene 2. Because the calculation efficiency of the deep neural network is higher, the model solving time in the scene 3 is slightly increased compared with that in the scene 1, and the model solving time is not more than 0.03 seconds. In addition, both the purchase cost and the environmental cost are higher in scenario 3 than in scenarios 1 and 2. The changes in equipment load rate, air temperature and air pressure can cause the actual operating conditions of the equipment to deviate from the rated operating conditions, and the operating efficiency of CHP and GB is lower than the rated efficiency, so that the energy purchasing cost is increased. In order to meet the load demand, the power supply amount of the power grid is increased, namely the output of the thermal power generating unit with higher carbon emission intensity is increased, and the output of the gas generating unit with low carbon emission intensity is reduced, so that the environmental cost is increased. Therefore, the accuracy of the scheduling scheme and the carbon emission research can be effectively improved by considering the variable working condition characteristics of the equipment.
The low-carbon economic dispatch results for two scenarios 1, 2 and 3 (e=500) are shown in fig. 7.
In scenario 3 (e=500), as shown in fig. 7c, the electrical and thermal loads are mostly supplied by the grid and GB, respectively, CHP being the supplemental energy supply device. During peak electricity prices (9:00-10:00 and 18:00-20:00), CHP acts as the primary heating device to maintain its high efficiency operation, with the grid supplying the remaining electrical load. Furthermore, when the heat load level is low (4:00-5:00), the GB rated capacity is large resulting in lower efficiency, so the heat load is now fully supplied by the CHP. When the heat load level exceeds the GB rated capacity (12:00-13:00), CHP acts as the primary heating device to ensure its efficient operation.
The scheduling result of scenario 1 has a large difference from the result in scenario 3 (e=500), as shown in fig. 7 a. Scenario 1 ignores the variable operating characteristics of the device, most of the load being met by CHP operating at low load levels. GB only participates in heating (12:00-13:00) when the heat load exceeds the CHP capacity, the grid compensating for the CHP electrical supply deficiency. Therefore, the influence of the variable working condition characteristics of the equipment is not ignored, and a constant-efficiency scheduling model can lead to an unreasonable scheduling scheme.
Compared with the traditional polynomial fitting method used in the scene 2, the scheduling method for the carbon emission of the comprehensive energy system under the variable working condition provided by the embodiment I has higher accuracy, and as shown in the figure 7b, although the scheduling schemes obtained by the two scenes are similar, some errors still exist in the scheduling scheme in the scene 2. In scenario 2, the thermal load is met by CHP, not GB, for a period of 3:30-4:00. The on/off states of CHP and GB in scenes 2 and 3 (e=500) are also quite different in the 4:30-5:00 period. The above results indicate that the polynomial fitting method is less accurate in predicting device efficiency than neural networks, which can lead to significant deviations in IES scheduling and even potentially changing the on/off state of the device.
In summary, the invention provides a method for scheduling carbon emission of a comprehensive energy system under variable working conditions, which has the following conclusion:
1) The energy hub model with constant efficiency can shift the energy conversion relation of the system, and the accuracy of the comprehensive energy system scheduling scheme is affected.
2) Based on the deep neural network, an efficiency correction model is established, and nonlinear change of equipment efficiency can be accurately and rapidly predicted. And combining the model with an energy hub model to establish a dynamic energy hub model with variable efficiency, so as to realize accurate modeling of the comprehensive energy system under variable working conditions.
3) The scheduling method for the carbon emission of the comprehensive energy system under the variable working condition provided by the embodiment can effectively relieve the problem of insufficient accuracy of a constant-efficiency scheduling scheme, realize low-carbon operation of the comprehensive energy system, improve the solving speed and accuracy of a scheduling model, and help a decision maker to accurately analyze the economy of low-carbon scheduling of the comprehensive energy system.
Embodiment III:
the embodiment of the invention provides a scheduling system for carbon emission of a comprehensive energy system under variable working conditions, which comprises the following components:
acquiring a multi-energy coupling relation of the comprehensive energy system and variable working condition characteristics of energy conversion equipment;
establishing an energy hub model according to the multi-energy coupling relation of the comprehensive energy system;
based on the variable working condition characteristics of the energy conversion equipment, correcting efficiency parameters in the energy hub model by utilizing a pre-constructed efficiency correction model based on a deep neural network to obtain a dynamic energy hub model;
and constructing a scheduling model of the carbon emission of the comprehensive energy system under the variable working condition based on the dynamic energy hub model, and solving to obtain a scheduling scheme of the comprehensive energy system under the variable working condition.
Embodiment four:
the embodiment of the invention provides a computing device, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method of embodiment one.
Fifth embodiment:
the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method according to the embodiment.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (9)
1. The method for scheduling the carbon emission of the comprehensive energy system under the variable working condition is characterized by comprising the following steps of:
acquiring a multi-energy coupling relation of the comprehensive energy system and variable working condition characteristics of energy conversion equipment;
establishing an energy hub model according to the multi-energy coupling relation of the comprehensive energy system;
based on the variable working condition characteristics of the energy conversion equipment, correcting efficiency parameters in the energy hub model by utilizing a pre-constructed efficiency correction model based on a deep neural network to obtain a dynamic energy hub model;
and constructing a scheduling model of the carbon emission of the comprehensive energy system under the variable working condition based on the dynamic energy hub model, and solving to obtain a scheduling scheme of the comprehensive energy system under the variable working condition.
2. The method for scheduling carbon emission of a comprehensive energy system under variable working conditions according to claim 1, wherein the pre-constructed efficiency correction model based on the deep neural network is represented by the following formula:
in the formula (1), i is energy conversion equipment; η (eta) i,t The efficiency of the energy conversion equipment i at the moment t; t (T) i,t The temperature of the operating environment of the energy conversion equipment i at the moment t; f (F) i,t The air pressure of the operating environment of the energy conversion equipment i at the moment t; n (N) i,t The load power of the energy conversion device i at the time t is represented by the following formula:
N i,t ＝P i,t P i,cap (2)
in the formula (2), P i,t The output power of the energy conversion equipment i at the moment t is kW; p (P) i,cap The plant capacity of the energy conversion plant i, kW.
3. The method for scheduling carbon emission of a comprehensive energy system under variable working conditions according to claim 1, wherein the matrix form of the energy hub model is represented by the following formula:
in the formula (3), L e,t For the electric load of the comprehensive energy system at the moment t, L h,t The heat load of the comprehensive energy system at the time t; η (eta) CHP Is the power generation efficiency eta of the CHP of the cogeneration unit CHP,h For the heat supply efficiency eta of the CHP of the cogeneration unit GB The heat supply efficiency of the gas boiler GB is improved; p (P) e,t Supplying power to the electric energy source at time t, P g,t Supplying power for the gas energy source at the moment t; η (eta) C Charge efficiency, eta for battery BAT D Discharging efficiency for battery BAT; w (W) e,t The BAT energy storage capacity of the storage battery at the moment t; v t And the natural gas consumed by the cogeneration unit CHP at the moment t accounts for the proportionality coefficient of the natural gas supply at the moment t.
4. The method for scheduling carbon emission of a comprehensive energy system under variable working conditions according to claim 3, wherein the matrix form of the dynamic energy hub model is represented by the following formula:
in the formula (4), eta CHP,t For the power generation efficiency eta of the CHP at the moment t CHP,h,t For the heat supply efficiency of the CHP of the combined heat and power unit at the moment t, eta GB,t The heating efficiency of the gas boiler GB at the time t is obtained.
5. The method for scheduling carbon emission of a comprehensive energy system under variable working conditions according to claim 1, wherein the scheduling model of carbon emission of the comprehensive energy system under variable working conditions uses the lowest energy cost of the comprehensive energy system as an objective function, and is represented by the following formula:
minf c ＝f ope +f env (5)
in the formula (5), f c The energy cost of the comprehensive energy system; f (f) ope To purchase energy cost, including purchase electricity cost f e And the cost of purchasing gas f g Represented by the following formula:
in the formula (6), T is a scheduling period, and Δt is a unit scheduling period; c e,t Electricity price for electricity purchase at time t, c g The price of the gas purchase is the unit; p (P) e,t For purchasing electric power at time t, P g,t The gas purchase power is t time;
in the formula (5), f env For environmental costs, including CO generated from grid purchases 2 Discharge cost
CO generated by CHP (heat-power generation) unit 2 Exhaust costs->
CO generated by gas boiler GB 2 Exhaust costs->
Represented by the formula:
in the formula (7), T is a scheduling period, and Δt is a unitScheduling a time period;
is CO as emission unit 2 Is a cost of the environment; />
Carbon emission calculation coefficient for supplying the grid, +.>
Calculating a coefficient for the carbon emission of the CHP unit output power of the cogeneration unit,
calculating a coefficient for carbon emission of the unit output power of the gas boiler GB; p (P) e,t For purchasing electric power at time t, P CHP,t For the output power of the combined heat and power unit CHP at the time t, P GB,t The output power of the gas boiler GB at the time t.
6. The method for scheduling carbon emission of a comprehensive energy system under variable working conditions according to claim 5, wherein the constraint conditions of the scheduling model of carbon emission of the comprehensive energy system under variable working conditions comprise: power balance constraints, tie-line power constraints, energy conversion device operating constraints and energy storage device operating constraints,
the power balance constraint is that the multi-energy coupling relation described by the dynamic energy hub model is satisfied, and the efficiency of energy conversion equipment in the comprehensive energy system is calculated by a pre-constructed efficiency correction model based on a deep neural network;
the tie line power constraint is that the interaction power of the comprehensive energy system and the upper power grid is within a safe range, and is represented by the following formula:
0≤P grid,t ≤P grid,cap (8) In the formula (8), P grid,t For purchasing electric power at time t, P grid,cap Is the upper tie line power limit;
the energy conversion equipment operation constraint comprises an input power relation constraint, an output power relation constraint and an energy conversion equipment output upper limit constraint and an energy conversion equipment output lower limit constraint, and the energy conversion equipment operation constraint is expressed by the following formula:
in the formula (9), P CHP,t For the output power of the combined heat and power unit CHP at the moment t, eta CHP,t For the power generation efficiency of the CHP of the T-moment cogeneration unit, P in,CHP,t For the input power of the CHP of the T-moment cogeneration unit, P CHP,cap The upper limit of the output power of the combined heat and power unit CHP is set; p (P) GB,t Is the output power of the gas boiler GB at the time t, eta GB,t For the heat supply efficiency of the gas boiler GB at the moment of t, P in,GB,t For the input power of the gas boiler GB at the moment of t, P GB,cap The upper limit of the output power of the gas boiler GB;
the energy storage device operating constraints include limitations on charge and discharge energy power and device capacity, expressed by the following formula:
in the formula (10), W e,t Energy storage capacity W before charging and discharging BAT of storage battery e,t+1 The energy storage energy after the battery BAT is charged and discharged, sigma is the self-discharging energy rate of the battery BAT, and P C,t For the BAT energy charging power at the moment t, eta C Charge efficiency for battery BAT, P D,t For the BAT energy release power at the moment t, eta D For battery BAT discharge efficiency, deltat is a unit scheduling period; w (W) cap Rated capacity for battery BAT;
maximum charge power for battery BAT; />
Maximum power of battery BAT; w (W) start Energy storage energy of the storage battery BAT at the beginning time of a dispatching cycle is stored; w (W) end For storing energy of the battery BAT at the end of the scheduling period.
7. The utility model provides a comprehensive energy system carbon emission's dispatch system under variable operating mode which characterized in that includes:
acquiring a multi-energy coupling relation of the comprehensive energy system and variable working condition characteristics of energy conversion equipment;
establishing an energy hub model according to the multi-energy coupling relation of the comprehensive energy system;
based on the variable working condition characteristics of the energy conversion equipment, correcting efficiency parameters in the energy hub model by utilizing a pre-constructed efficiency correction model based on a deep neural network to obtain a dynamic energy hub model;
and constructing a scheduling model of the carbon emission of the comprehensive energy system under the variable working condition based on the dynamic energy hub model, and solving to obtain a scheduling scheme of the comprehensive energy system under the variable working condition.
8. A computing device comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method of any one of claims 1 to 6.
9. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 6.
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| CN117829863A (en) * | 2024-03-06 | 2024-04-05 | 山东石油化工学院 | Carbon emission metering method and device for petrochemical industry |
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| CN117494910B (en) * | 2024-01-02 | 2024-03-22 | 国网山东省电力公司电力科学研究院 | Multi-energy coordination optimization control system and method based on carbon emission reduction |
| CN117829863A (en) * | 2024-03-06 | 2024-04-05 | 山东石油化工学院 | Carbon emission metering method and device for petrochemical industry |
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