CN116245338A - Low-carbon economic operation optimization method for mine comprehensive energy system - Google Patents
Low-carbon economic operation optimization method for mine comprehensive energy system Download PDFInfo
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Abstract
The invention discloses a low-carbon economic operation optimization method of a mine comprehensive energy system, which comprises the following steps: (1) Introducing the low-carbon requirement of system operation, and establishing an energy flow-carbon flow system architecture of the mine comprehensive energy system; (2) Constructing a low-carbon economic operation optimization model of the mine comprehensive energy system by taking the minimum running cost and carbon emission of the mine comprehensive energy system as objective functions; (3) Modeling analysis is carried out on multiple uncertainty of the system by adopting a robust model under a risk avoidance strategy, and a deterministic conversion method of a low-carbon economic operation optimization model of the system is designed. The low-carbon economic operation optimization method of the mine comprehensive energy system is adopted, the carbon emission of the system and each device is restrained, the low-carbon economic scheduling of the mine comprehensive energy system is realized, the uncertainty of renewable energy sources and derived energy sources at the source end of the mine comprehensive energy system is modeled and analyzed, and a robust model for taking the energy supply uncertainty into account is provided.
Description
Technical Field
The invention relates to the technical field of energy scheduling optimization, in particular to a low-carbon economic operation optimization method of a mine comprehensive energy system.
Background
The exploitation and utilization of energy brings about a series of problems such as global warming and aggravation of greenhouse effect, and the problems are particularly remarkable in the mine comprehensive energy system (Mine integrated energy system, MIES). In this context, the construction of low-carbon, economical, environmentally friendly energy systems has become a research hotspot. The mine comprehensive energy system is used as a typical application of a regional comprehensive energy system (Regional integrated energy system, RIES), has the characteristics of various energy supply and demand and large carbon emission, and is an important carrier for realizing low carbon and environmental protection. Along with the proposal of the Chinese double-carbon target, the low-carbon research on the mine comprehensive energy system becomes a problem to be solved urgently.
The coal mine can generate derivative energy sources such as ventilation air methane, gas, mine water and the like in the exploitation process, the energy sources contain a large amount of low-quality energy, and the direct emptying of the energy sources can not only cause the waste of the energy sources, but also cause serious ecological environment problems. The greenhouse effect of ventilation air and gas is statistically CO 2 The mine water source heat pump can generate far higher heat energy than nearly 3-4kWh when consuming 1kWh of electric energy, and the energy-saving effect is remarkable. The mine MIES is constructed, so that the carbon emission of the whole system can be reduced while the utilization potential of various derived energy sources is effectively excavated, and the urgent requirement of green development of the coal mine is met. On the other hand, similar to renewable energy sources, mine derived energy sources are affected by a plurality of factors such as production flow and geological conditions, and the like, and have large prediction uncertainty. The output of the derived energy device is closely related to the variation of key characteristics of various derived energy, thusThe research on multiple uncertainty of source end energy supply has important significance for safe and stable operation of mine MIES.
Mines associated with coal mining are a typical and unique integrated energy system. Compared with the traditional RIES, the mine MIES energy supply and demand diversity has larger prediction uncertainty, and a large amount of greenhouse gas emission can be generated in the production and use processes of various energy sources. In order to improve the environmental benefits of the integrated energy system, many documents begin to study the influence of carbon emissions on the operation of the integrated energy system.
In the aspect of uncertainty research, the introduction of renewable energy sources such as wind energy, solar energy and the like and the utilization of mine derived energy sources such as ventilation air methane, gusts and low-concentration gas lead to the increasingly complex mine comprehensive energy system with stronger uncertainty. Common methods in the aspect of processing system uncertainty include basic methods such as random planning, fuzzy planning, robust optimization and the like. The research results of the academic world on the uncertainty optimization problem provide a theoretical basis for the operation optimization problem of the comprehensive energy system, and a plurality of students conduct preliminary researches on the uncertainty in the comprehensive energy system at present. The multi-energy complementation and low-carbon operation optimization scheduling strategy oriented to the ecological agriculture IES is used for establishing a two-stage robust optimization model of the ecological agriculture IES by considering uncertainty of photovoltaic, load and methane; aiming at the uncertainty of the source load and the variable working condition characteristic of the equipment in the park comprehensive energy system, the uncertainty of the source load is represented by utilizing the nonlinear interval number, and a park comprehensive energy system collaborative optimization integrated model considering the variable working condition characteristic of the energy conversion equipment is established; the entropy weight self-adaptive information gap decision method is provided for processing the source load uncertainty factors, the uncertainty existing in the source load of the comprehensive energy system is fully considered, and the influence caused by subjective factors in the traditional information gap decision method is eliminated.
It can be seen that the method provides a new thought for uncertainty research in the traditional comprehensive energy system, but for uncertainty factors in the mine comprehensive energy system with typical industry characteristics, the existing research is still less, and in particular, the analysis of uncertainty of the output of mine derived energy is performed. In the aspect of low-carbon optimization of a comprehensive energy system, the existing work also provides a corresponding method for solving the problem of low-carbon scheduling in a general scene, but how to accurately describe the carbon emission level of related links or equipment of the derived energy is still lacking in an effective method. The existing low-carbon scheduling model is expanded, the carbon emission target constraint of the mine comprehensive energy system is established, the special scene of the mine comprehensive energy system is fully considered, the expanded low-carbon model can be better applied to the problem of low-carbon scheduling of mine MIES, and a new method is required to be designed.
Disclosure of Invention
The invention aims to provide a low-carbon economic operation optimization method of a mine comprehensive energy system, and provides an energy flow-carbon flow operation optimization model considering carbon emission target constraint, wherein carbon emission of each link of the system is accurately calculated, carbon emission of the system and each device is constrained when the mine comprehensive energy system is scheduled and optimized, low-carbon economic scheduling of the mine comprehensive energy system is realized, modeling analysis is carried out on uncertainty of renewable energy and derivative energy at a source end of the mine comprehensive energy system, and a robust model considering energy supply uncertainty is provided.
In order to achieve the purpose, the invention provides a low-carbon economic operation optimization method of a mine comprehensive energy system, which comprises the following steps:
(1) On the basis of the energy flow basic framework of the mine comprehensive energy system, introducing the low-carbon requirement of system operation, and establishing the energy flow-carbon flow system framework of the mine comprehensive energy system;
(2) Taking carbon emission target constraint of the system into consideration, and constructing a low-carbon economic operation optimization model of the mine comprehensive energy system by taking the operation cost and the minimum carbon emission of the mine comprehensive energy system as target functions;
(3) And modeling and analyzing multiple uncertainty of the system by adopting a robust model under a risk avoidance strategy, and designing a deterministic conversion method of a low-carbon economic operation optimization model of the mine comprehensive energy system.
Preferably, in the step (1), in the energy flow-carbon flow system architecture of the mine comprehensive energy system, an energy flow layer takes energy flow in the system as a basic flow, and the energy supply and demand balance of the mine comprehensive energy system is met through coupling complementation among different energy flows, and the optimal operation of each device of the system is realized through energy flow balance and economic constraint; the carbon flow layer takes the emissions generated in the operation process of each module device of the energy flow layer as material flow, and the carbon flow layer realizes the low-carbon operation of the system by restraining the carbon emissions of each module device on the premise that the energy flow layer meets the system load requirement.
Preferably, in step (2), the carbon emission objective function is:
in the method, in the process of the invention,
carbon emission cost for mine comprehensive energy system, < ->
For carbon trade base price->
Carbon emission amount ρ of comprehensive energy system for mine k,t For the carbon emission coefficient of the plant k at time t, P k,t For the output of device k at time t, Ω is the set of all devices, +.>
Initial carbon quota for mine integrated energy system.
Preferably, in the step (2), the optimization model includes a carbon transaction mechanism model, an energy flow layer optimization model and a carbon flow layer optimization model;
the carbon transaction mechanism model can effectively reduce carbon emission in the process of utilizing the derived energy by utilizing a carbon transaction mechanism, carbon emission quota allocation adopts a gratuitous allocation mode, and for different types of units in the system, the initial carbon emission quota is related to the unit type and output power, and the related mathematical model is as follows:
in the method, in the process of the invention,
initial carbon emission quota for mine comprehensive energy system, H i,t For the thermal power of the unit i at time t, P i,t For the electric power of the unit i at the moment t, C i,t For the cooling power of the unit i at the moment t i,h 、ε i,e 、ε i,c The carbon emission quota coefficients of unit electricity, heat and cold output of the unit i are respectively represented;
the energy flow layer optimization model is based on energy flow in a system, optimizes the economical efficiency of the system, and achieves the minimum operation cost under the condition that the energy flow operation constraint of the system is met, and the operation cost objective function of the energy flow layer is as follows:
f 1 ＝min(C trade +C op +C penalty )
wherein C is trade 、C op 、C penalty Respectively representing the external energy purchasing cost, the operation and maintenance cost and the energy discarding cost of the mine comprehensive energy system, c G,t 、c gas,t Respectively representing the electricity purchasing price and the gas purchasing price in the period t, P G,t 、P gas,t Respectively representing the electricity purchasing power and the gas purchasing power in the period t, c k For the operation and maintenance cost of the unit capacity of the equipment k, P k,t For the power of the device at time t,
a penalty factor for energy rejection per unit power for the ith energy source, < ->
Predicted power or energy for the ith energy source, P i,t Representing the actual energy of the ith energy source, and specifically comprising wind, light and water source heat pump energy;
the carbon flow layer optimization model comprises a source end carbon emission model, a carbon emission model of energy conversion equipment and a carbon emission model at a load side;
the source end carbon emission model comprises equivalent carbon emission equivalent generated by system purchase energy, carbon emission generated by renewable energy and derived energy utilization process and equivalent carbon emission equivalent which is not fully utilized for direct emptying, and the carbon emission amount at the input side is as follows:
E s ＝E G,t +E g,t +E re,t +E fw,t
wherein E is G,t 、E g,t 、E re,t And E is fw,t Equivalent carbon emission equivalent and ρ in the process of electricity purchasing, gas purchasing, renewable energy source and derived energy source utilization at the moment t respectively G,t 、ρ g,t 、ρ w,t 、ρ pv,t Carbon emission coefficients corresponding to the output of the external network electricity purchasing, gas purchasing, wind purchasing and light renewable energy source at the time t are p fw,t For deriving the carbon emission coefficient, ρ, corresponding to the energy output vam The ventilation air density is 0.716kg/m 3 ，μ vam,t The carbon emission coefficient of the ventilation air methane is taken as a value 21;
the carbon emission model of the energy conversion equipment calculates the carbon emission amount corresponding to the energy conversion process of each equipment by utilizing the carbon emission coefficient, and the carbon emission corresponding to the t-th moment of the single input-single output equipment is as follows:
E i,t ＝P i,t ρ i,t
wherein P is i,t For the output power corresponding to the t time point of the energy conversion equipment i, ρ i,t Carbon emission coefficient corresponding to unit power;
the carbon emissions corresponding to time t of the single input-multiple output device can be expressed as:
in the method, in the process of the invention,
and->
Respectively represent the carbon emission coefficient and P corresponding to the power generation and the heat supply of the combined heat and power (Combined heat and power, CHP) unit at the time t CHP,t And H CHP,t The power and heat output power of the combined heat and power CHP unit at the moment t are respectively;
the carbon emission corresponding to each output energy flow in the carbon emission model at the load side is as follows:
wherein R is Le,t 、R Lh,t And R is Lc,t Carbon emission amounts of electricity, heat and cold output energy flows at time t respectively, L e,t 、L h,t And L c,t And the integrated electric, thermal and cold energy flows are respectively corresponding to the load side.
Preferably, the robust model under the risk avoidance maneuver in step (3) is:
wherein C is 0 Making a reference cost of a scheduling strategy for a scheduling decision maker, wherein delta is a scheduling cost deviation coefficient of a robust model, and H i (X,P i ) And G j (X,P i ) Omega for corresponding inequality constraints and equality constraints in energy flow-carbon flow operation optimization model ineq And omega eq And U is an uncertainty set model for describing multiple uncertainties of a source end, and specifically comprises uncertainties of wind, light and derived energy output.
Preferably, in the step (3), the low-carbon economic operation optimization model of the mine comprehensive energy system is as follows:
f 1 ＝min(C trade +C op +C penalty )
wherein F is the total cost of low-carbon economic operation of the mine comprehensive energy system, and F 1 The operation cost of the mine comprehensive energy system is high,
carbon emission cost for mine comprehensive energy system, C trade 、C op 、C penalty Respectively represents the external energy purchasing cost, the operation and maintenance cost and the energy discarding cost of the mine comprehensive energy system,/->
For carbon trade base price->
Carbon emission for mine integrated energy system, < ->
Initial carbon quota for mine integrated energy system.
Preferably, in the step (3), the low-carbon economic operation optimization model of the mine comprehensive energy system is solved by calling a gurobi business solver by using a Yalmip tool box, and the specific solving process comprises the following steps:
(1) Based on the predicted values of wind, light and derived energy output, comprehensively considering the energy flow-carbon flow operation optimization model to obtain a target optimal value of the deterministic model, and setting the target optimal value as a reference value;
(2) An uncertain set of wind, light and derived energy output is constructed, the actual values of wind, light and derived energy output are used for replacing predicted values in a deterministic model, then a target deviation coefficient is set, an acceptable expected target value of a decision maker is determined, and a robust model under a risk avoidance strategy is written;
(3) And calling a gurobi business solver to solve the robust model to obtain uncertainty, scheduling cost and each unit output plan.
The low-carbon economic operation optimization method for the mine comprehensive energy system has the advantages and positive effects that:
(1) The low-carbon requirement of the mine comprehensive energy system is introduced, and the energy flow-carbon flow system framework of the mine comprehensive energy system is constructed; the problems of carbon constraint such as source end derived energy utilization, external network electricity and gas purchase, hidden carbon emission of related energy conversion equipment and the like are solved, and accurate accounting is carried out on the carbon emission of each link and each equipment of the mine comprehensive energy system.
(2) On the basis of the energy flow operation optimization model of the mine comprehensive energy system, an existing system economic scheduling model is expanded, carbon emission target constraint in the operation of the mine comprehensive energy system is considered, and low-carbon scheduling of the mine comprehensive energy system is studied by taking the total operation cost and the minimum carbon emission of the system as target functions.
(3) Aiming at the uncertainty of mine wind, light and derived energy supply, a robust model analysis method based on an information gap decision theory risk avoidance strategy is provided, modeling analysis is carried out on the system uncertainty based on the information gap decision theory, and adverse effects caused by the system energy supply uncertainty are reduced.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a diagram of an energy flow-carbon flow model of an integrated energy system for mines in an embodiment of the invention;
FIG. 2 is a schematic diagram of model solving in an embodiment of the invention;
FIG. 3 is a graph of renewable energy and electric, thermal and cold load predictions for a mine integrated energy system in a verification example of the present invention;
FIG. 4 is a graph of comparative results of system electricity purchasing and total output of each unit in three scenarios in an verification example of the present invention;
FIG. 5 is a diagram of the results of optimizing the system operation in scenario 1 and scenario 2 in the verification example of the present invention, wherein A is a diagram of the results of optimizing the power in scenario 1, B is a diagram of the results of optimizing the power in scenario 1, C is a diagram of the results of optimizing the power in scenario 1, D is a diagram of the results of optimizing the power in scenario 2, E is a diagram of the results of optimizing the power in scenario 2, and F is a diagram of the results of optimizing the power in scenario 2;
FIG. 6 is a graph of the carbon emissions of system 24h under scenario 1 and scenario 2 in a verification example of the present invention;
FIG. 7 is a graph showing the correspondence between the total cost of the system and the overall uncertainty radius for different deviation coefficients in an example of verification of the present invention;
fig. 8 shows the system operation optimization results under the scenario 2 and the scenario 3 in the verification example of the present invention, wherein a is the electric energy optimization result diagram in the scenario 2, b is the electric energy optimization result diagram in the scenario 2, c is the electric energy optimization result diagram in the scenario 2, d is the electric energy optimization result diagram in the scenario 3, e is the electric energy optimization result diagram in the scenario 3, and f is the electric energy optimization result diagram in the scenario 3.
Detailed Description
The technical scheme of the invention is further described below through the attached drawings and the embodiments.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs.
Examples
A low-carbon economic operation optimization method of a mine comprehensive energy system comprises the following steps:
(1) On the basis of the energy flow basic framework of the mine comprehensive energy system, introducing the low-carbon requirement of system operation, and establishing the energy flow-carbon flow system framework of the mine comprehensive energy system;
(2) Taking carbon emission target constraint of the system into consideration, and constructing a low-carbon economic operation optimization model of the mine comprehensive energy system by taking the operation cost and the minimum carbon emission of the mine comprehensive energy system as target functions;
(3) Modeling analysis is carried out on multiple uncertainty of the system by adopting a robust model under a risk avoidance strategy, and a deterministic conversion method of a low-carbon economic operation optimization model of the system is designed.
1. Energy flow-carbon flow system architecture of mine comprehensive energy system
In the energy flow-carbon flow model framework of the mine comprehensive energy system, energy flows of electricity, heat, cold, gas and the like in the system are taken as basic flows by an energy flow layer, the energy supply and demand balance of the mine comprehensive energy system is met through coupling complementation among different energy flows, and the optimal operation of each device of the system is realized through energy flow balance and economic constraint. Carbon flow layer is formed by CO generated in the operation process of each module device of energy flow layer 2 And the emissions are material flows, and the carbon flow layer realizes the low-carbon operation of the system by restraining the carbon emissions of the module equipment on the premise that the energy flow layer meets the load demand of the system.
2. Energy flow-carbon flow optimization model based on carbon emission target constraint
(1) Carbon trade mechanism model
The specific process of carbon transaction is: the related departments allocate a certain amount of initial carbon emission allowance to each carbon emission source, when the actual carbon emission of enterprises exceeds the initial allocation allowance, the enterprises need to purchase the carbon emission allowance of the exceeding part in the carbon trade market, which increases the running cost of the enterprises and guides the enterprises to reduce the carbon emission; conversely, when the actual carbon emissions of the enterprise is lower than the initial allocation credit, the enterprise may sell excess carbon emissions credits to obtain corresponding carbon trade economic benefits. In the mine comprehensive energy system, the renewable energy unit has low carbon emission, and the carbon emission in the process of utilizing the derived energy can be effectively reduced by utilizing a carbon transaction mechanism.
The carbon emission quota distribution adopts a gratuitous distribution mode. For different types of units in the system, the initial carbon emission quota is related to the unit type and the output power, and the related mathematical model is as follows:
in the method, in the process of the invention,
to the initial carbon emission quota of the system, H i,t For the thermal power of the unit i at time t, P i,t For the electric power of the unit i at the moment t, C i,t For the cooling power of the unit i at the moment t i,h 、ε i,e 、ε i,c And the carbon emission quota coefficients of unit electricity, heat and cold output of the unit i are respectively represented.
Considering a carbon trade mechanism with initial carbon emission allowance, CO 2 The emissions cost model may be expressed as:
in the method, in the process of the invention,
for the carbon emission of the system, +.>
The value of the carbon trading basic price is 0.252 yuan/kg.
(2) Energy flow layer optimization model
The energy flow layer optimization model is based on energy flow in the system, and mainly optimizes the economy of the system, and the minimum operation cost is realized under the condition that the energy flow operation constraint of the system is met. The running cost objective function of the energy flow layer is as follows:
f 1 ＝min(C trade +C op +C penalty )
wherein C is trade 、C op 、C penalty Respectively representing the external energy purchasing cost, the operation and maintenance cost and the energy discarding cost of the mine comprehensive energy system, c G,t 、c gas,t Respectively representing the electricity purchasing price and the gas purchasing price in the period t, P G,t 、P gas,t Respectively representing the electricity purchasing power and the gas purchasing power in the period t, c k For the operation and maintenance cost of the unit capacity of the equipment k, P k,t For the power of the device at time t,
a penalty factor for energy rejection per unit power for the ith energy source, < ->
Predicted power or energy for the ith energy source, P i,t The i-th energy source practical energy is represented, and the i-th energy source practical energy specifically comprises wind, light and water source heat pump energy.
Constraint conditions of energy flow layer operation optimization need to be satisfied:
the energy-saving system is characterized in that the formula (1) is energy-saving layer power balance constraint, the formula (2) is mine comprehensive energy system electricity purchase quantity and air purchase quantity upper and lower limit constraint, the formula (3) is operation constraint of each device of the mine comprehensive energy system, and the formula (4) is energy storage device constraint.
(3) Carbon flow layer optimization model
In order to establish a carbon flow layer optimization model of a mine comprehensive energy system, carbon emission constraints of a system source end, internal devices and a load side are comprehensively considered when the system operates, carbon emission amounts of energy sources in production, conversion and consumption links are respectively calculated, carbon emission of the source end, an energy source conversion process and the load side are respectively limited by utilizing carbon emission target constraints, and carbon emission of each main body of an energy flow layer is limited by formulating reasonable carbon emission constraints.
(1) Source end carbon emission model
The energy flow input of the mine comprehensive energy system comprises electric energy purchased by an external network, natural gas, wind-solar renewable energy and various derived energy generated in the coal production process. The carbon emission of the mine comprehensive energy system source end mainly comprises two parts: one part is equivalent carbon emission equivalent generated by system purchase energy, and the other part is equivalent carbon emission equivalent generated by renewable energy and derived energy utilization process and underutilized direct emptying. According to the carbon emission definition, the carbon emission amount at the input side is:
E s ＝E G,t +E g,t +E re,t +E fw,t
wherein E is G,t 、E g,t 、E re,t And E is fw,t Equivalent carbon emission equivalent and ρ in the process of electricity purchasing, gas purchasing, renewable energy source and derived energy source utilization at the moment t respectively G,t 、ρ g,t 、ρ w,t 、ρ pv,t Carbon emission coefficients (representing carbon emission amount released by unit output of a unit) corresponding to output of renewable energy sources such as electricity purchase, gas purchase, wind, light and the like are respectively at t moments, ρ fw,t For deriving the carbon emission coefficient, ρ, corresponding to the energy output vam The ventilation air density is 0.716kg/m 3 ，μ vam,t The carbon emission coefficient of the ventilation air methane is 21.
(2) Carbon emission model of energy conversion equipment
The energy conversion devices of the mine comprehensive energy system are divided into single-input-single-output devices and single-input-multiple-output devices, and carbon emissions of the respective energy conversion devices can be expressed as follows:
single input-single output device
Calculating the carbon emission amount corresponding to the energy conversion process of each device by using the carbon emission coefficient, wherein the carbon emission corresponding to the t-th moment of the single input-single output device is as follows:
E i,t ＝P i,t ρ i,t
wherein P is i,t For the output power corresponding to the t time point of the energy conversion equipment i, ρ i,t Carbon emission coefficient corresponding to unit power.
Single input-multiple output device
The single input-multiple output equipment in the mine comprehensive energy system mainly refers to a Combined Heat and Power (CHP) unit, and the carbon emission corresponding to the t time can be expressed as follows:
in the method, in the process of the invention,
and->
Respectively representing carbon emission coefficients corresponding to power generation and heat supply of the CHP unit at the moment t, P CHP,t And H CHP,t The power output by the CHP unit at the time t and the power output by the heat are respectively.
(3) Carbon emission model on load side
The output energy flow of the mine comprehensive energy system comprises electric, thermal and cold loads, and the carbon emission corresponding to each load is influenced by the converged energy flow, so that the carbon emission of each output load energy flow is limited. The carbon emissions corresponding to each output energy stream are:
wherein R is Le,t 、R Lh,t And R is Lc,t Carbon emission amounts of electricity, heat and cold output energy flows at time t respectively, L e,t 、L h,t And L c,t And the integrated electric, thermal and cold energy flows are respectively corresponding to the load side.
The carbon flow layer optimization model of the mine comprehensive energy system mainly optimizes the low carbon property of the system, and realizes the low carbon operation of the system by taking the lowest carbon emission of the mine comprehensive energy system as an objective function under the condition of meeting the carbon emission objective constraint condition of the system. Specifically, the carbon emission objective function is:
in the method, in the process of the invention,
for the carbon emission costs of the system, +.>
For carbon trade base price->
ρ is the carbon emission of the system k,t For the carbon emission coefficient of the plant k at time t, P k,t For the output of device k at time t, Ω is the set of all devices, +.>
Is the initial carbon quota for the system.
And in the aspect of carbon flow layer operation optimization constraint conditions, the carbon emission of each energy conversion device, the carbon emission of the whole system and the carbon emission of each output energy flow at the load side are constrained. The corresponding constraint conditions of the carbon flow layer need to be satisfied:
(1) Carbon emission constraints at each scheduling instant for each device:
(2) Carbon emission constraints for each device complete scheduling cycle:
(3) Carbon emission constraints at various moments of the system:
(4) Total carbon emission limit of complete scheduling period of system:
(5) Carbon emission constraints for each energy stream on the load side:
0＜R Le,t /L e,t ＜ρ Le,max
0＜R Lh,t /L h,t ＜ρ Lh,max
0＜R Lc,t /L c,t ＜ρ Lc,max
wherein ρ is Le,max 、ρ Lh,max 、ρ Lc,max Carbon emission upper limit values of unit electric, heat and cold output energy flows respectively.
3. Robust model for risk avoidance
The information gap decision theory (Information gap decision theory, IGDT) is an effective method for processing uncertainty, and it does not need to obtain probability density and membership function of an uncertainty variable in advance, and directly analyzes the influence of the uncertainty factor on the system according to the difference between the predicted value and the actual value of the uncertainty variable on the premise of meeting the preset requirement.
Robust scheduling model based on IGDT
The information gap decision makes an IGDT scheduling under a risk avoidance strategy for a scheduling decision maker with a conservation decision intention through a robust functionAnd (5) a model. The strategy finds the corresponding uncertainty if the expected cost deviation is met. The optimal target value obtained by the energy flow-carbon flow operation optimization model is taken as the base scheduling cost and is marked as C 0 . In the IGDT scheduling model, C 0 As a reference cost for a scheduling decision maker to formulate a scheduling strategy, delta is a scheduling cost deviation coefficient of the IGDT robust model. The IGDT robust scheduling model under the risk avoidance strategy is:
wherein H is i (X,P i ) And G j (X,P i ) Omega for corresponding inequality constraints and equality constraints in energy flow-carbon flow operation optimization model ineq And omega eq And U is an uncertainty set model for describing multiple uncertainties of a source end, and specifically comprises uncertainties of wind, light and derived energy output.
The robust model belongs to a double-layer model under the risk avoidance strategy, the lower-layer objective of the model is to calculate the maximum value of the total cost of the system, and because the output cost of renewable energy sources and derived energy sources is far lower than the running cost of the power purchasing system and the CHP unit of the system, when the output of the renewable energy sources and the derived energy sources reaches the minimum value, the total cost of the system is maximum, the double-layer optimization model can be converted into a single-layer optimization model, and the optimization is carried out only on the comprehensive uncertain radius of the upper-layer objective.
4. Optimization goals and constraints
And comprehensively considering two aspects of economy and environment, and constructing a low-carbon economy running optimization model of the mine comprehensive energy system. Specifically, the objective function considers the carbon emissions of the system operation in addition to the cost of the system operation. In order to convert a plurality of targets into a single target function, converting the carbon emission of the system into carbon emission cost through a carbon transaction mechanism model, and superposing the carbon emission cost and the energy layer system operation cost to obtain the total operation cost of the system; meanwhile, the operation constraint condition of the system energy flow layer and the carbon emission constraint condition of the carbon flow layer are simultaneously considered in the model. Thus, the overall optimization objective of the system is:
f 1 ＝min(C trade +C op +C penalty )
wherein F is the total cost of low carbon economic operation of the system, F 1 For the cost of operation of the system,
for the carbon emission cost of the system, C trade 、C op 、C penalty Respectively represents the external energy purchasing cost, the operation and maintenance cost and the energy discarding cost of the mine comprehensive energy system,/->
For carbon trade base price->
For the carbon emission of the system, +.>
Is the initial carbon quota for the system.
Aiming at a constructed mine comprehensive energy system low-carbon economic operation optimization model with uncertain energy supply, a Yalmip toolbox is utilized to call a gurobi business solver for solving, and a specific solving flow comprises the following steps:
step one: based on the predicted values of wind, light and derived energy output, comprehensively considering the energy flow-carbon flow operation optimization model to obtain a target optimal value of the deterministic model, and setting the target optimal value as a reference value;
step two: an uncertain set of wind, light and derived energy output is constructed, the actual values of wind, light and derived energy output are used for replacing predicted values in a deterministic model, then a target deviation coefficient is set, an acceptable expected target value of a decision maker is determined, and an IGDT robust model under a risk avoidance strategy is written;
step three: and calling a gurobi business solver to solve the robust model to obtain uncertainty, scheduling cost and each unit output plan.
Verification example
And (5) performing simulation solution on the energy flow-carbon flow operation optimization model shown in fig. 1. The operation parameters of the energy flow layer related equipment are shown in table 2, the wind, solar, electric, thermal and cold load prediction curves are shown in fig. 3, the electricity purchasing of the mine comprehensive energy system and the carbon emission parameter values of the energy conversion equipment are shown in table 3, and the real-time average carbon price of the carbon transaction is 0.252 yuan/kg. The time-of-use electricity prices are shown in Table 1, and the natural gas price is 0.27 yuan/kWh. The calculation example takes 24 hours before the day as a complete dispatching cycle, and the dispatching time length is 1 hour. The upper limit of each period of carbon emission of the system is 2400kg, and the upper limit of the total carbon emission of the complete dispatching cycle of the system is 35t.
TABLE 1 time of day electricity price data
| Time period of | Time | Electricity price/(Yuan/kWh) |
| Cereal section | 23:00-next day 07:00 | 0.2916 |
| Flat section | 07:00-08:00，12:00-18:00 | 0.5030 |
| Peak segment | 09:00-11:00，19:00-23:00 | 0.7303 |
Table 2 main equipment parameters of mine integrated energy system
TABLE 3 carbon emission parameters of energy supply end, coupling end devices
In order to verify the effectiveness of the established low-carbon economic operation optimization model of the mine comprehensive energy system, the following three scenes are set for comparative analysis of the calculation examples:
scene 1: and the operation optimization model of the mine comprehensive energy system is not considered, and the objective function only considers the operation cost of mine MIES.
Scene 2: considering the system carbon target constraint, comprehensively considering the economy and low carbon of the mine comprehensive energy system by the target function, and adopting an energy flow-carbon flow low carbon economic dispatching optimization model.
Scene 3: and taking multiple uncertainty of source end energy supply into consideration, and adopting an energy flow-carbon flow low-carbon economic dispatching optimization model.
Comparing the operation results of 3 scenes, the total cost of the system, the operation cost, the carbon emission cost, the electricity purchasing cost, the gas purchasing cost and the carbon emission amount in the corresponding scenes are shown in table 4.
Table 4 System operation optimization results under three scenarios
Comparing the scheduling results of the mine comprehensive energy system model under 3 scenes in table 4, it can be seen that:
(1) Only the economy of the operation of the mine comprehensive energy system is considered in the scene 1, the constraint of carbon emission targets is not involved in the model, the total operation cost of the system is high, and the carbon emission is highest.
(2) Scene 2 objective function comprehensively considers economy and low carbon of system operation, and carbon emission objective constraint is considered in the model. Compared with the scene 1, the running cost of the system in the scene 2 is increased from 50784 yuan to 51855 yuan, and the running cost is increased by 2.1 percent; however, the corresponding carbon emissions were reduced from 49846 kg to 34811kg by 30%. Compared with the scene 1, the system electricity purchasing cost in the scene 2 is reduced from 16837 yuan to 12300 yuan, the gas purchasing cost is reduced by 2.7%, the gas purchasing cost is increased from 18061 yuan to 22545 yuan, and the gas purchasing cost is increased by 2.5%. This is due to the system's lower carbon emission coefficient with natural gas supply than with direct off-grid electricity. Further analysis shows that the main reason for the reduction of carbon emissions in scenario 2 is that the system in turn uses the energy required by the CHP unit supply system with relatively low carbon emissions due to the reduction in electricity purchase.
(3) On the basis of the scene 2, the scene 3 further considers multiple uncertainties of energy supply of the system, and the running cost, electricity purchasing cost and gas purchasing cost of the system are all improved. Compared with scene 2, the total running cost of the system is increased from 60627 yuan to 63658 yuan, but the robustness of the system is improved. When the power supply side wind and light and derived energy source output fluctuates, the obtained scheduling scheme can meet the requirement that the total running cost of the system is not higher than 63658 yuan, and is within the range of expected cost acceptable by scheduling decision makers.
In general, compared with a single mine comprehensive energy system economic dispatch model, the energy flow-carbon flow low-carbon economic dispatch optimization model reduces 30% of carbon emission of the system on the premise of only improving 2.1% of system operation cost, and further reduces the total cost of the system from 63340 yuan to 60627 yuan, and reduces 4.3%. The simulation results in the 3 scenes show that the carbon target constraint and uncertainty are considered in the mine comprehensive energy system optimization scheduling, so that the carbon emission of the system can be reduced, the running reliability of the system is improved, and the effectiveness of the running optimization model is fully proved.
Fig. 4 is a comparison result of total output of the external power purchase, the renewable energy unit, the derivative energy unit and the CHP unit in 3 scenarios. As can be seen from fig. 4, the output of renewable energy and derived energy increases in scenario 2, the external electricity purchase decreases, and the CHP unit output increases compared to scenario 1. This is because, considering the carbon emission target constraint, the system tends to meet the load demand of the whole system by using the unit with low carbon emission coefficient, thereby improving the capacity of the system for the renewable energy and the derived energy.
1. Scene 2 and scene 1 scheduling results are compared after carbon emission target constraint is considered
Firstly, an electricity, heat and cold energy optimization scheduling scheme of a mine comprehensive energy system under analysis scenes 1 and 2 is shown in fig. 5, wherein A in fig. 5 is an electricity energy optimization result diagram in scene 1, B in fig. 5 is an electricity energy optimization result diagram in scene 1, C in fig. 5 is an electricity energy optimization result diagram in scene 1, D in fig. 5 is an electricity energy optimization result diagram in scene 2, E in fig. 5 is an electricity energy optimization result diagram in scene 2, and F in fig. 5 is an electricity energy optimization result diagram in scene 2. It can be seen that the generated power of renewable energy sources such as wind and light is obviously improved, the external electricity purchasing power is reduced, the output power of the CHP unit is improved, and the output of mine derived energy utilization equipment is increased under the influence of the constraint of a carbon target.
(1) For the optimal scheduling of electric energy, the carbon emission coefficient of the CHP unit is lower than that of the external network purchase electricity, so that the purchase electricity quantity in the scheduling period of the system is obviously reduced, and the power supply quantity of the CHP unit is increased. In particular, this contrast is more pronounced during the level period. In the electricity price valley period, because the coefficient of the wind power carbon bank of the system is negative, the wind power output is obviously increased in the period, and partial system outsourcing electric quantity is counteracted. Because the charge and discharge process of the electricity storage equipment in the model also relates to carbon emission, the charge and discharge process in the scene 2 is obviously reduced compared with that in the scene 1.
(2) For optimal scheduling of heat energy, the electric output of the CHP unit is increased in the scheduling period, so that the heat output of the CHP unit is correspondingly increased. The heat pump unit in the system consumes electric energy when heating, the output of the heat pump unit in the scheduling period of the scene 2 is reduced compared with that of the scene 1, and the carbon emission coefficient of the air source heat pump is lower than that of the water source heat pump, so that the output of the water source heat pump in the scene 2 is obviously reduced, and the output of the air source heat pump in the scene 2 is obviously higher than that of the water source heat pump. For the ventilation air methane heat storage oxidation unit, the output of the heat storage oxidation device in the scene 2 is obviously higher than that of the scene 1, because the direct exhaust of ventilation air methane can cause obvious carbon emission effect, and the scene 2 improves the heat output of the heat storage oxidation unit in order to reduce the carbon emission of the system. The thermal storage device in the model also involves carbon emissions, so the thermal storage process for scenario 2 is significantly lower than that for scenario 1.
(3) For optimal scheduling of cold energy, the carbon emission coefficient of the absorption refrigerator is slightly higher than that of the electric refrigerator, so that the output of the electric refrigerator is slightly increased and the output of the absorption refrigerator is correspondingly reduced in the scheduling period of the scene 2 system.
And secondly, analyzing the carbon emission condition of the mine comprehensive energy system and each device under the scenes 1 and 2. Fig. 6 shows a 24h carbon emission comparison of the system under scenarios 1 and 2, and it can be seen that the carbon emission of the system is significantly reduced at various periods, taking into account the carbon emission target constraint, which verifies the effectiveness of the mine MIES low carbon economy optimization scheduling strategy.
2. Scene 3 and scene 2 scheduling result comparison analysis considering source terminal multiple uncertainties
Table 5 shows the corresponding values of the system scheduling cost and the comprehensive uncertainty radius under different deviation coefficients when the information gap decision theory risk avoidance strategy is adopted. Setting the deviation coefficient range to be 0.005-0.05, calculating the critical cost and the comprehensive uncertainty radius corresponding to the system under different deviation coefficients, and displaying the corresponding relation between the total cost and the uncertainty radius of the system under different deviation coefficients in FIG. 7. Under the condition of multiple uncertainty of energy supply of a source end, determining a power plan of each unit of the system and a corresponding electric energy, heat energy and cold energy dispatching optimization scheme, wherein specific results are shown in fig. 8, a is an electric energy optimization result diagram in a scene 2, b is a heat energy optimization result diagram in the scene 2, c is a cold energy optimization result diagram in the scene 2, d is an electric energy optimization result diagram in the scene 3, e is a heat energy optimization result diagram in the scene 3, and f is a cold energy optimization result diagram in the scene 3 in fig. 8.
TABLE 5 comprehensive uncertainty radius and total scheduling cost for systems under different bias coefficients
| Coefficient of deviation | 0.005 | 0.01 | 0.02 | 0.03 | 0.04 | 0.05 |
| Scheduling costs/primitives | 60930 | 61233 | 61840 | 62446 | 63052 | 63658 |
| Uncertainty radius | 0.064 | 0.074 | 0.093 | 0.122 | 0.126 | 0.136 |
As can be seen from fig. 7, with the help of the risk avoidance maneuver, as the coefficient of deviation of the system cost (i.e., the expected cost of the system acceptable to the decision maker) increases, so does the overall uncertainty that the system can tolerate, and the robustness of the system. From the aspect of system operation reliability, the total cost of system operation is lower than the expected scheduling cost acceptable to the system decision maker in the corresponding uncertain fluctuation range of renewable energy sources and derived energy sources.
Fig. 8 shows the optimal scheduling scheme of electric energy, heat energy and cold energy obtained by the mine comprehensive energy system under scenes 2 and 3. Analysis shows that after multiple uncertainties of energy supply at the source end of the mine comprehensive energy system are considered, the scheduling schemes of electric energy, heat energy and cold energy obtained by the model are slightly changed, and mainly, the renewable energy sources such as uncertain wind and light and the mine derivative energy output are changed. From the system operation perspective, after considering the uncertainty of the output of renewable energy sources such as wind and light and derived energy sources, the system has to increase the output of other definite units to compensate the shortage of the energy supply load under the condition that the output of the related equipment unit is less than the predicted output of the unit, so as to balance the load demand of the system. This will result in an increase in system power purchases or other equipment set output, which increases the overall cost of the system, but is still within the acceptable range of the scheduling decision maker given the system integration uncertainty.
Therefore, the invention adopts the low-carbon economic operation optimization method of the mine comprehensive energy system, provides an energy flow-carbon flow operation optimization model considering the constraint of the carbon emission target aiming at the defects of the existing mine comprehensive energy system model, accurately calculates the carbon emission of each link of the system and constrains the carbon emission of the system and each device when the mine comprehensive energy system is scheduled and optimized, realizes the low-carbon economic scheduling of the mine comprehensive energy system, carries out modeling analysis on the uncertainty of renewable energy and derivative energy at the source end of the mine comprehensive energy system, and provides a robust model for accounting for energy supply uncertainty.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting it, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that: the technical scheme of the invention can be modified or replaced by the same, and the modified technical scheme cannot deviate from the spirit and scope of the technical scheme of the invention.
Claims (7)
1. A low-carbon economic operation optimization method of a mine comprehensive energy system is characterized by comprising the following steps of: the method comprises the following steps:
(1) On the basis of the energy flow basic framework of the mine comprehensive energy system, introducing the low-carbon requirement of system operation, and establishing the energy flow-carbon flow system framework of the mine comprehensive energy system;
(2) Taking carbon emission target constraint of the system into consideration, and constructing a low-carbon economic operation optimization model of the mine comprehensive energy system by taking the operation cost and the minimum carbon emission of the mine comprehensive energy system as target functions;
(3) And modeling and analyzing multiple uncertainty of the system by adopting a robust model under a risk avoidance strategy, and designing a deterministic conversion method of a low-carbon economic operation optimization model of the mine comprehensive energy system.
2. The method for optimizing low-carbon economic operation of the mine integrated energy system according to claim 1, which is characterized in that: in the step (1), in the energy flow-carbon flow system architecture of the mine comprehensive energy system, an energy flow layer takes energy flow in the system as a basic flow, the energy supply and demand balance of the mine comprehensive energy system is met through coupling complementation among different energy flows, and the optimal operation of each device of the system is realized through energy flow balance and economic constraint; the carbon flow layer takes the emissions generated in the operation process of each module device of the energy flow layer as material flow, and the carbon flow layer realizes the low-carbon operation of the system by restraining the carbon emissions of each module device on the premise that the energy flow layer meets the system load requirement.
3. The method for optimizing low-carbon economic operation of the mine integrated energy system according to claim 1, which is characterized in that: in step (2), the carbon emission objective function is:
in the method, in the process of the invention,
carbon emission cost for mine comprehensive energy system, < ->
For carbon trade base price->
Carbon emission amount ρ of comprehensive energy system for mine k,t For the carbon emission coefficient of the plant k at time t, P k,t For the output of device k at time t, Ω is the set of all devices, +.>
Initial carbon quota for mine integrated energy system.
4. The method for optimizing low-carbon economic operation of the mine integrated energy system according to claim 1, which is characterized in that: in the step (2), the optimization model comprises a carbon transaction mechanism model, an energy flow layer optimization model and a carbon flow layer optimization model;
the carbon transaction mechanism model can effectively reduce carbon emission in the process of utilizing the derived energy by utilizing a carbon transaction mechanism, carbon emission quota allocation adopts a gratuitous allocation mode, and for different types of units in the system, the initial carbon emission quota is related to the unit type and output power, and the related mathematical model is as follows:
in the method, in the process of the invention,
initial carbon emission quota for mine comprehensive energy system, H i,t For the thermal power of the unit i at time t, P i,t For the electric power of the unit i at the moment t, C i,t For the cooling power of the unit i at the moment t i,h 、ε i,e 、ε i,c The carbon emission quota coefficients of unit electricity, heat and cold output of the unit i are respectively represented;
the energy flow layer optimization model is based on energy flow in a system, optimizes the economical efficiency of the system, and achieves the minimum operation cost under the condition that the energy flow operation constraint of the system is met, and the operation cost objective function of the energy flow layer is as follows:
f 1 ＝min(C trade +C op +C penalty )
wherein C is trade 、C op 、C penalty Respectively representing the external energy purchasing cost, the operation and maintenance cost and the energy discarding cost of the mine comprehensive energy system, c G,t 、c gas,t Respectively representing the electricity purchasing price and the gas purchasing price in the period t, P G,t 、P gas,t Respectively representing the electricity purchasing power and the gas purchasing power in the period t, c k For the operation and maintenance cost of the unit capacity of the equipment k, P k,t For the power of the device at time t,
a penalty factor for energy rejection per unit power for the ith energy source, < ->
Predicted power or energy for the ith energy source, P i,t Representing the actual energy of the ith energy source;
the carbon flow layer optimization model comprises a source end carbon emission model, a carbon emission model of energy conversion equipment and a carbon emission model at a load side;
the source end carbon emission model comprises equivalent carbon emission equivalent generated by the purchase energy of a mine comprehensive energy system, carbon emission generated in the utilization process of renewable energy and derived energy, and equivalent carbon emission equivalent of direct emptying which is not fully utilized, and the carbon emission amount of the input side is as follows:
E s ＝E G,t +E g,t +E re,t +E fw,t
wherein E is G,t 、E g,t 、E re,t And E is fw,t Equivalent carbon emission equivalent and ρ in the process of electricity purchasing, gas purchasing, renewable energy source and derived energy source utilization at the moment t respectively G,t 、ρ g,t 、ρ w,t 、ρ pv,t Carbon emission coefficients corresponding to the output of the external network electricity purchasing, gas purchasing, wind purchasing and light renewable energy source at the time t are p fw,t For deriving the carbon emission coefficient, ρ, corresponding to the energy output vam Density of ventilation air methane, mu vam,t Carbon emission coefficient of ventilation air methane;
the carbon emission model of the energy conversion equipment calculates the carbon emission amount corresponding to the energy conversion process of each equipment by utilizing the carbon emission coefficient, and the carbon emission corresponding to the t-th moment of the single input-single output equipment is as follows:
E i,t ＝P i,t ρ i,t
wherein P is i,t For the output power corresponding to the t time point of the energy conversion equipment i, ρ i,t Carbon emission coefficient corresponding to unit power;
the carbon emissions corresponding to time t of the single input-multiple output device can be expressed as:
in the method, in the process of the invention,
and->
Respectively represent the carbon emission systems corresponding to the power generation and the heat supply of the cogeneration unit at the time tNumber, P CHP,t And H CHP,t The power output and the heat output of the cogeneration unit at the moment t are respectively;
the carbon emission corresponding to each output energy flow in the carbon emission model at the load side is as follows:
wherein R is Le,t 、R Lh,t And R is Lc,t Carbon emission amounts of electricity, heat and cold output energy flows at time t respectively, L e,t 、L h,t And L c,t And the integrated electric, thermal and cold energy flows are respectively corresponding to the load side.
5. The method for optimizing low-carbon economic operation of the mine integrated energy system according to claim 1, which is characterized in that: in the step (3), the robust model under the risk avoidance maneuver is:
wherein C is 0 Making a reference cost of a scheduling strategy for a scheduling decision maker, wherein delta is a scheduling cost deviation coefficient of a robust model, and H i (X,P i ) And G j (X,P i ) Omega for corresponding inequality constraints and equality constraints in energy flow-carbon flow operation optimization model ineq And omega eq And U is an uncertainty set model for describing multiple uncertainties of a source end.
6. The method for optimizing low-carbon economic operation of the mine integrated energy system according to claim 1, which is characterized in that: in the step (3), the low-carbon economic operation optimization model of the mine comprehensive energy system is as follows:
f 1 ＝min(C trade +C op +C penalty )
wherein F is the total cost of low-carbon economic operation of the mine comprehensive energy system, and F 1 The operation cost of the mine comprehensive energy system is high,
carbon emission cost for mine comprehensive energy system, C trade 、C op 、C penalty Respectively represents the external energy purchasing cost, the operation and maintenance cost and the energy discarding cost of the mine comprehensive energy system,/->
For carbon trade base price->
Carbon emission for mine integrated energy system, < ->
Initial carbon quota for mine integrated energy system.
7. The method for optimizing low-carbon economic operation of the mine integrated energy system according to claim 1, which is characterized in that: in the step (3), the low-carbon economic operation optimization model of the mine comprehensive energy system is solved by calling a gurobi business solver by using a Yalmip toolbox, and a specific solving flow comprises the following steps:
(1) Based on the predicted values of wind, light and derived energy output, comprehensively considering the energy flow-carbon flow operation optimization model to obtain a target optimal value of the deterministic model, and setting the target optimal value as a reference value;
(2) An uncertain set of wind, light and derived energy output is constructed, the actual values of wind, light and derived energy output are used for replacing predicted values in a deterministic model, then a target deviation coefficient is set, an acceptable expected target value of a decision maker is determined, and a robust model under a risk avoidance strategy is written;
(3) And calling a gurobi business solver to solve the robust model to obtain uncertainty, scheduling cost and each unit output plan.
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| CN117094745B (en) * | 2023-09-06 | 2024-03-12 | 天津大学 | Comprehensive energy system optimization control method and device based on IGDT-utility entropy |
| CN117172389A (en) * | 2023-11-01 | 2023-12-05 | 山东建筑大学 | Regional comprehensive energy optimization operation method and system considering wind-light uncertainty |
| CN117172389B (en) * | 2023-11-01 | 2024-02-02 | 山东建筑大学 | Regional comprehensive energy optimization operation method and system considering wind-light uncertainty |
| CN117494909A (en) * | 2023-12-29 | 2024-02-02 | 国网浙江省电力有限公司营销服务中心 | Electricity purchasing optimization method, device and medium based on entropy weight self-adaptive IGDT |
| CN117494909B (en) * | 2023-12-29 | 2024-05-28 | 国网浙江省电力有限公司营销服务中心 | Electricity purchasing optimization method, device and medium based on entropy weight self-adaptive IGDT |
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