CN115640902A - Park comprehensive energy system low-carbon optimization scheduling method considering carbon value uncertainty - Google Patents
Park comprehensive energy system low-carbon optimization scheduling method considering carbon value uncertainty Download PDFInfo
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Abstract
The invention discloses a park comprehensive energy system low-carbon optimization scheduling method considering carbon value uncertainty. The method comprises the steps of building a comprehensive energy system model of energy conversion equipment such as an electricity-to-gas and gas cogeneration unit, a gas boiler and the like, and introducing a time-sharing step type carbon transaction mechanism to restrict the carbon emission of the comprehensive energy system of the park. Secondly, uncertainty of carbon value is processed by adopting an information gap decision theory, a low-carbon optimization scheduling model with the lower layer taking the energy purchasing cost, the operation and maintenance cost and the carbon trading cost as the minimum and the upper layer taking the maximization and minimization system capable of bearing fluctuation under a certain expected target is constructed. Finally, the GUROBI solver can be called by the Yalmip software to solve, and simulation results show that the provided scheme can give consideration to economy, low carbon and robustness, and scientific and reliable bases can be provided for reasonable operation of the comprehensive energy system of the park.
Description
Technical Field
The invention belongs to an optimal scheduling method of a park integrated energy system, and particularly relates to a low-carbon optimal scheduling method of the park integrated energy system considering carbon value uncertainty.
Background
Since the industrial revolution in the twentieth century, the greenhouse gases generated by human activities have increased dramatically, causing global warming and causing huge damage to the ecological environment.
The carbon emission right trading is used as a policy tool for controlling greenhouse gas emission by applying a market mechanism, pricing is mainly carried out on the carbon emission right, enterprises are guided to achieve the carbon emission reduction target at the minimum cost through carbon price signals, and therefore energy-saving emission reduction control and operation optimization scheduling of equipment are carried out by taking the carbon emission right trading as a measure.
The carbon trading market is mainly divided into a traditional carbon trading mechanism and a step-type carbon trading mechanism as an important measure for energy conservation and emission reduction. However, the carbon trading mechanism widely adopted at present only considers the carbon emission quota trading of the system under the whole cycle day time scale, and cannot participate in the carbon market trading in real time. The existing literature is mainly focused on the analysis of uncertainty of renewable energy output, uncertainty of load prediction and uncertainty of electricity and gas purchase prices, and the characterization of uncertainty of carbon transaction prices and the research result of modeling are considered rarely.
Disclosure of Invention
The invention aims to provide a low-carbon optimal scheduling method for a park comprehensive energy system considering carbon value uncertainty, which optimizes the comprehensive operation cost of the system, improves the energy utilization rate, reduces carbon emission and considers low carbon, economy and robustness.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows.
A campus integrated energy system low-carbon optimization scheduling method considering carbon price uncertainty comprises the steps of establishing a campus integrated energy system low-carbon optimization scheduling model and an operation scheduling model of energy conversion equipment, converting carbon prices into carbon emission rights to serve as influence factors of optimization scheduling, and comprises the following steps:
(1) Acquiring information of a park comprehensive energy system and constructing a park comprehensive energy system model, wherein the information of the park comprehensive energy system comprises time-of-use electricity price, time-of-use gas price, time-of-use carbon trading base price, parameters of carbon dioxide emission of park energy equipment and load information of a park;
(2) Constructing an energy conversion equipment operation scheduling model including an electricity-to-gas cogeneration unit, a gas-fired cogeneration unit and a gas-fired boiler;
(3) Determining initial quota of carbon emission right and actual carbon emission of the system according to the power purchase of a superior power grid, the output of a gas cogeneration unit, a gas boiler and an electric-to-gas device and gas load;
(4) According to the initial carbon emission quota and the actual carbon emission amount of the system, establishing a carbon emission quota trading mechanism of the system under an hourly scale, wherein the carbon emission quota trading mechanism comprises establishing a carbon emission authority initial quota model, an actual carbon emission amount model and a time-sharing step-type carbon trading cost calculation model;
(5) Constructing a low-carbon optimized scheduling model with the lower layer with the minimum energy purchasing cost, operation and maintenance cost and carbon transaction cost as targets based on the step (4), processing the uncertainty of the carbon transaction base price based on an information gap decision theory, and constructing an opportunity seeking strategy model with the upper layer with the maximum system capable of bearing the fluctuating risk avoiding robustness and the minimum system capable of bearing the fluctuation under the expected target;
(6) And setting operation constraint conditions of the park comprehensive energy system, solving a park comprehensive energy system low-carbon optimization scheduling model considering carbon value uncertainty, and acting on the output of the energy conversion equipment.
Further, in the method, the time-sharing step-type carbon emission right quota trading mechanism divides the carbon dioxide emission of the system per hour into a plurality of intervals, and the mathematical model corresponding to the time-sharing step-type carbon emission right quota trading mechanism comprises a carbon emission right initial quota model, an actual carbon emission model and a time-sharing step-type carbon trading cost calculation model, which are specifically as follows:
the initial quota of carbon emission model has the following expression:
in the formula: e PIES,free,t 、E buy,free,t 、E CHP,free,t 、E GB,free,t 、E gload,free,t Carbon emission quota of a system, a superior electricity purchasing and gas heat and power cogeneration unit, a gas boiler and gas load at the time t respectively; lambda [ alpha ] e Carbon emission quota per unit of power supply; lambda [ alpha ] h Carbon emission quota per unit of heat supply; lambda [ alpha ] gload A carbon emissions quota for consumed unit gas load; p buy,e,t Purchasing electric quantity to the upper level in the time period t; p CHP,e,t 、P CHP,h,t Respectively the electric power and the thermal power output by the gas cogeneration unit in the time period t; p GB,h,t The thermal power output by the gas boiler in the time period t; p load,g,t Is the gas load over time t;
the actual carbon emission model has the following expression:
in the formula: e PIES,t 、E buy,t 、E gload,t Actual carbon emission of a system, a superior purchasing power and a gas load in a time period t respectively; e CHP_GB,t For the total actual carbon emission of the gas cogeneration unit, the gas boiler during the period t, E P2G,t For CO actually absorbed by the electric gas-converting equipment in the period of t 2 An amount; p total,t The sum of the output power of the gas cogeneration unit and the output power of the gas boiler is obtained in the time period t; p P2G,g,t The natural gas power output by the electric gas conversion equipment in the time period t; a is 1 、b 1 、c 1 And a 2 、b 2 、c 2 Carbon emission coefficients of a coal-fired unit, a gas cogeneration unit and a gas boiler are respectively set; tau is gload Is the gas load carbon emission coefficient; tau. P2G Absorbing CO for the process of converting electricity into natural gas of an electricity-to-gas device 2 A parameter;
the mathematical expression of the time-sharing step type carbon transaction cost calculation model is as follows:
E PIES,l,t ＝E PIES,t -E PIES,free,t
in the formula: e PIES,l,t Trading amount of system carbon emission right in t period;
cost for carbon transactions over a period of t; zeta t Is the carbon trading base price on the market during the time period t; l t The length of the carbon emission interval in the period t; σ is the magnitude of the increase in carbon trading price. When E is PIES,l,t And when the carbon emission quantity is less than 0, the actual carbon emission quantity is lower than the uncompensated carbon emission right quota in the period t, and the surplus share is sold at the carbon trading base price to obtain the carbon trading income.
Further, in the step (5), the objective function of the lower-layer low-carbon optimized scheduling model is to minimize the comprehensive operation cost of the system, and the expression is as follows:
in the formula, F is the comprehensive operation cost of the system; c buy Cost for purchasing energy; c ope The equipment operation and maintenance cost;
is the carbon transaction cost; wherein, the calculation expression of the energy purchasing cost is as follows:
in the formula: c buy Cost for purchasing energy; c. C buy,e,t 、c buy,g,t Electricity prices and gas prices at the time period t, respectively;
the calculation expression of the equipment operation and maintenance cost is as follows:
in the formula: c ope The operation and maintenance cost; c. C WT 、c PV 、c CHP 、c P2G 、c GB 、c AC 、c EC The unit operation and maintenance costs of a wind turbine generator set, a photovoltaic unit, a gas cogeneration unit, an electric gas conversion device, a gas boiler, an absorption refrigerator and an electric refrigerator in the system are respectively; c. C ES,n The unit operation and maintenance cost of the nth energy storage device is obtained; p WT,t The actual output power of the wind power in the time period t is obtained; p PV,t The actual photovoltaic output power in the period t is obtained; p CHP,g,t Inputting the natural gas power of the gas cogeneration unit at a time t; p P2G,e,t Is the electric power input during the period t; p GB,g,t Inputting natural gas power of a gas boiler in a time period t; p AC,h,t Represents the thermal power input into the absorption refrigerator in the period t; p EC,e,t Indicating t-period input controlThe electric power of the refrigerator;
respectively charging and discharging maximum power of the nth energy storage device for a single time;
the calculated expression for carbon trading cost is as follows:
in the formula: c ope Is the carbon transaction cost.
Further, the upper layer model in the step (5) is a risk avoidance robust and opportunity seeking decision model established based on the uncertainty of carbon price, and the risk avoidance robust model is represented as follows:
in the formula: f 0 The optimal scheduling cost is obtained when the predicted value is taken as the reference value and the uncertain parameter; mu is a risk avoidance coefficient; (1 + μ) F 0 Representing a pessimistic scheduling target cost in the risk avoidance robust model;
a carbon transaction base price prediction value for a time period t;
the opportunity seeking decision model is as follows:
in the formula: ρ is an opportunity seeking coefficient; (1-. Rho) F 0 Representing the optimistic scheduling objective cost in the opportunity seeking decision model.
Further, the operation constraint conditions of the campus integrated energy system low-carbon optimization scheduling model in the step (6) include an energy balance constraint, an energy conversion equipment output constraint and an energy storage constraint, and specifically include the following:
a) Wind power output constraint
In the formula:
predicting power for the wind power output in the t time period;
rated power of the wind turbine generator;
b) Photovoltaic output constraint
In the formula:
predicting power for the photovoltaic output at time t;
rated power for the photovoltaic unit;
c) Operation constraint of gas cogeneration unit
In the formula:
energy conversion efficiency of electricity and thermal power of the gas cogeneration unit is respectively obtained;
the upper limit and the lower limit of the natural gas power input into the gas cogeneration unit are respectively set;
the upper limit and the lower limit of the power ramp of the natural gas input into the gas cogeneration unit are respectively set;
d) Gas boiler operation constraints
In the formula: eta GB The energy conversion efficiency of the gas boiler;
the upper limit and the lower limit of the natural gas power input into the gas boiler are respectively set;
the upper limit and the lower limit of the natural gas power input into the gas boiler are respectively set;
e) Electric gas-to-gas equipment operation constraints
In the formula: eta P2G The energy conversion efficiency of the electric gas conversion equipment;
the upper limit and the lower limit of electric power input into the electric gas conversion equipment respectively;
the upper limit and the lower limit of the electric power input into the electric power conversion equipment during climbing are respectively set;
f) Electric refrigerator operation constraint
In the formula: p EC,c,t Representing the cold power output by the electric refrigerator in the t period; eta EC Representing the energy conversion efficiency of the electric refrigerator;
the upper limit and the lower limit of electric power input into the electric refrigerator respectively;
the upper limit and the lower limit of the electric power input into the electric refrigerator for climbing are respectively set;
g) Absorption chiller operation constraint
In the formula: p AC,h,t Represents the thermal power input into the absorption refrigerator in the period t; p AC,c,t The cold power output by the absorption refrigerator is represented in the t period; eta AC Represents the energy conversion efficiency of the absorption chiller;
the upper limit and the lower limit of the thermal power input into the absorption refrigerator respectively;
the upper limit and the lower limit of the thermal power input into the absorption refrigerator during climbing are respectively.
h) Restraint of stored energy
The method is used for uniformly modeling the electricity, heat, gas and cold energy storage equipment, and the constraint conditions are as follows:
in the formula: e n,t The capacity of the nth energy storage device in the t period;
charging and discharging efficiencies of the nth energy storage device are respectively obtained;
respectively charging and discharging 0-1 state variables of the nth energy storage device;
the upper limit and the lower limit of the capacity of the nth energy storage device are respectively set;
i) Electric power balance constraints
In the formula: p buy,e,t Purchasing electric quantity to the upper level in the time period t; p load,e,t Is the electrical load during the time period t;
the limit value of purchasing electricity to the upper-level power grid;
j) Thermal power balance constraint
In the formula: p load,h,t Is the thermal load during time t;
k) Natural gas power balance constraints.
In the formula:
the limit value of gas purchasing to the upper-level gas network;
l) Cold Power balance constraints
In the formula: p load,c,t At time period tA cold load.
Has the advantages that: compared with the prior art, the campus comprehensive energy system low-carbon optimization scheduling method considering the uncertainty of the carbon price can effectively reduce the comprehensive operation cost of a campus, reduce the pollution degree to the environment and reduce the emission of carbon dioxide through the constructed time-sharing stepped carbon trading mechanism; the invention also considers the influence of carbon value uncertainty on the low-carbon optimized scheduling of the system, the information gap decision theory scheduling model can better describe uncertain information, and simultaneously can provide different scheduling strategies for decision makers with different risk preferences, thereby overcoming the problems of large calculated amount and conservative decision and providing a new idea for processing uncertainty factors. Finally, when the comprehensive energy system of the park is optimally scheduled, the carbon emission right quota transaction of the system under the hourly scale is considered, the characteristics of the carbon transaction price are identified, and a proper characterization method is selected to carry out fine modeling on the uncertainty of the carbon transaction price, so that the method has important practical significance.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a block diagram of an exemplary embodiment of a park energy integration system;
FIG. 3 is a graph of power predicted by wind power, photovoltaic and multivariate load in a park;
FIG. 4 is a plot of electricity price per time of day, gas price per time of day, carbon trading base price per time of day;
FIG. 5 is a length curve of a carbon emission interval under different scenes;
FIG. 6 is a plot of scenario 4 and scenario 5 carbon traffic and carbon emissions;
fig. 7 is a carbon trading base price curve under different scenes.
Detailed Description
The technical solution of the present invention is described in detail below with reference to the drawings and the specific embodiments, but the scope of the present invention is not limited to the embodiments.
The invention provides a low-carbon optimal scheduling method considering carbon value uncertainty. Secondly, uncertainty of carbon value is processed by adopting an information gap decision theory, a low-carbon optimization scheduling model with the lower layer taking the energy purchasing cost, the operation and maintenance cost and the carbon trading cost as the minimum and the upper layer taking the maximization and minimization system capable of bearing fluctuation under a certain expected target is constructed. Finally, a GUROBI business solver is called by Yalmip software to carry out solving, and as an embodiment, a simulation result of the method shows that compared with the traditional method in which the time scale carbon emission right quota transaction of the whole cycle day is considered, the proposed time-sharing step-type carbon transaction mechanism in which the time scale carbon emission right quota transaction is considered can restrict the carbon emission of the system more strictly, the operation economy of the system is considered, the low-carbon optimal scheduling method in which the uncertainty of the carbon price is considered can take the economy, the low carbon and the robustness into consideration, and a scientific and reliable basis can be provided for the reasonable operation of the comprehensive energy system of the park.
With reference to fig. 1 and based on the above technical solution, the specific implementation steps of the method of the present invention can be implemented step by step according to the following processes:
(1) Collecting park comprehensive energy system information, including system network architecture information, park time-of-use electricity price, time-of-use gas price, time-of-use carbon transaction base price information, equipment capacity, energy conversion efficiency, carbon dioxide emission, climbing rate upper and lower limit parameter information in the park, and electricity, gas, heat and cold multi-load information in the park;
(2) Building a park comprehensive energy system model comprising a wind turbine generator, a photovoltaic generator, an electric-to-gas and gas-fired cogeneration generator, a gas boiler, an absorption refrigerator, an electric refrigerator energy conversion device and an electric, gas, heat and cold multi-element energy storage device;
(3) Determining an initial quota of the carbon emission right according to the power purchase of a superior power grid, a gas cogeneration unit, the output of a gas boiler and gas load, and constructing a carbon emission right initial quota model according to the following formula:
in the formula: e PIES,free,t 、E buy,free,t 、E CHP,free,t 、E GB,free,t 、E gload,free,t Carbon emission quota of a system, a superior electricity purchasing and gas heat and power cogeneration unit, a gas boiler and gas load at the time t respectively; lambda e Carbon emission quota per unit of power supply; lambda [ alpha ] h Carbon emission quota per unit of heat supply; lambda [ alpha ] gload A carbon emissions quota for consumed unit gas load; p is buy,e,t Purchasing electric quantity to the upper level in the time period t; p CHP,e,t 、P CHP,h,t Respectively the electric power and the thermal power output by the gas cogeneration unit in the time period t; p GB,h,t The thermal power output by the gas boiler in the time period t; p load,g,t Is the air load over the period t.
(4) Determining the actual carbon emission of the system according to the power purchase of a superior power grid, the output of a gas cogeneration unit, a gas boiler and an electric-to-gas device and the gas load, and constructing an actual carbon emission model according to the following formula:
in the formula: e PIES,t 、E buy,t 、E gload,t Actual carbon emission of a system, a superior purchasing power and a gas load in a time period t respectively; e CHP_GB,t For the total actual carbon emission of the gas cogeneration unit, the gas boiler during the period t, E P2G,t For CO actually absorbed by the electric gas-converting equipment in the period of t 2 An amount; p total,t The sum of the output power of the gas cogeneration unit and the output power of the gas boiler is obtained in the time period t; p P2G,g,t The natural gas power output by the electric gas conversion equipment in the time period t; a is 1 、b 1 、c 1 And a 2 、b 2 、c 2 The carbon emission of a coal-fired unit, a gas cogeneration unit and a gas boiler.
(5) According to the initial quota of the carbon emission rights of the system and the actual carbon emission amount, the carbon emission rights quota transaction of the system under the hourly scale is considered, the carbon dioxide emission amount of the system per hour is divided into a plurality of intervals, and the more the carbon emission is, the higher the transaction (carbon transaction) price of the unit carbon emission rights is, the more the cost required by the system is. Therefore, the step establishes a time-sharing ladder type carbon transaction cost calculation model as follows:
E PIES,l,t ＝E PIES,t -E PIES,free,t
in the formula: e PIES,l,t Trading amount of system carbon emission right in t period;
carbon transaction cost for time period t; zeta t Is the carbon trading base price on the market during the time period t; l t The length of the carbon emission interval in the period t; σ is the magnitude of the increase in carbon trading price. When E is PIES,l,t And when the carbon emission quantity is less than 0, the actual carbon emission quantity is lower than the gratuitous carbon emission right quota in the period of t, and the surplus quota can be sold at the carbon trading base price to obtain the carbon trading income.
(6) According to the established time-sharing step-type carbon transaction cost calculation model, constructing a lower-layer low-carbon optimization scheduling model with the lowest energy purchase cost, operation and maintenance cost and carbon transaction cost as targets:
in the formula: f is the comprehensive operation cost of the system; c buy Cost for purchasing energy; c ope The equipment operation and maintenance cost;
is the carbon transaction cost;
A. energy purchase cost:
in the formula: c. C buy,e,t 、c buy,g,t Respectively, electricity prices and gas prices at the time period t.
B. The operation and maintenance cost is as follows:
in the formula: c. C WT 、c PV 、c CHP 、c P2G 、c GB 、c AC 、c EC The unit operation and maintenance costs of a wind turbine generator set, a photovoltaic unit, a gas cogeneration unit, an electric gas conversion device, a gas boiler, an absorption refrigerator and an electric refrigerator in the system are respectively; c. C ES,n The unit operation and maintenance cost of the nth energy storage device is obtained; p WT,t The actual output power of the wind power in the time period t is obtained; p PV,t The actual photovoltaic output power in the period t is obtained; p is CHP,g,t Inputting the natural gas power of the gas cogeneration unit at a time t; p P2G,e,t Is the electric power input during the period t; p GB,g,t Inputting natural gas power of a gas boiler in a time period t; p AC,h,t Represents the thermal power input into the absorption refrigerator in the period t; p is EC,e,t Represents the electric power input into the electric refrigerator for a period t;
respectively charging and discharging the maximum power of the nth energy storage device.
C. Carbon transaction cost:
(7) The uncertainty of the carbon trading base price is processed by adopting an information gap decision theory,
modeling by adopting an envelope constraint uncertainty model:
in the formula:
the predicted value of the uncertain parameter is alpha, and the fluctuation range of the uncertain parameter is alpha;
is the fluctuation range of the uncertain parameters.
According to the envelope constraint uncertain model, the fluctuation range of the carbon number is as follows:
in the formula:
a carbon trading base price predicted value in a period t; and xi is the fluctuation amplitude of the carbon valence.
(8) In the upper layer model, uncertainty of carbon number is considered, and a risk avoidance robust and opportunity seeking decision model is established. A conservative decision maker tends to accept higher operation cost to avoid risks brought by uncertainty, and a risk avoidance robust model provided by the information gap decision theory method can meet decision requirements of the conservative decision maker. When the uncertain parameters change towards the direction which is unfavorable for the optimization target, on the premise that the running cost of the system is not more than the expected (pessimistic) scheduling target cost, the fluctuation amplitude xi of the uncertain parameters is maximized, and the larger the fluctuation amplitude xi of the uncertain parameters is, the better the robustness of the system is. The constructed risk avoidance robust model for the upper layer to maximize the fluctuation that the system can bear under a certain expected target is represented as follows:
in the formula: f 0 The optimal scheduling cost is obtained when the predicted value is taken as the reference value and the uncertain parameter; mu is a risk avoidance coefficient; (1 + μ) F 0 Representing a pessimistic scheduling target cost in the risk avoidance robust model;
is a carbon trading base price predicted value in the t period.
(9) The aggressive decision maker pursues more extra income possibly brought by uncertainty, and when the decision maker holds optimistic attitude to uncertain parameters and gives optimistic scheduling target cost (1-rho) F 0 Then, the available opportunity seeking decision model is as follows:
in the formula: ρ is an opportunity seeking coefficient; (1-. Rho.) F 0 Representing the optimistic scheduling objective cost in the opportunity seeking decision model.
(10) Setting operation constraint conditions of the park comprehensive energy system, including an electric power balance constraint condition, a thermal power balance constraint condition, a cold power balance constraint condition, a gas power balance constraint condition, an output constraint of energy conversion equipment and an energy storage constraint, wherein the constraint conditions are as follows:
electric power balance constraint:
in the formula: p buy,e,t Purchasing electric quantity to the upper level in the time period t; p load,e,t Is the electrical load during time t;
the limit value of purchasing power to the upper-level power grid.
And thermal power balance constraint:
in the formula: p load,h,t Is the heat load during the period t.
Natural gas power balance constraint:
in the formula:
is the limit for purchasing gas to the upper-stage gas network.
Cold power balance constraint:
in the formula: p is load,c,t Is the cooling load during the period t.
And (3) output constraint of energy conversion equipment:
1) Wind power output constraint
In the formula:
predicting power for the wind power output in the t time period;
the rated power of the wind turbine generator is obtained.
2) Photovoltaic output constraint
In the formula:
predicting power for the photovoltaic output at time t;
the rated power of the photovoltaic unit.
3) Operation constraint of gas cogeneration unit
In the formula:
energy conversion efficiency of electricity and thermal power of the gas cogeneration unit is respectively obtained;
the upper limit and the lower limit of the natural gas power input into the gas cogeneration unit are respectively set;
the upper limit and the lower limit of the natural gas power climbing input into the gas cogeneration unit are respectively.
4) Gas boiler operation constraints
In the formula: eta GB The energy conversion efficiency of the gas boiler;
the upper limit and the lower limit of the power of the natural gas input into the gas boiler are respectively set;
the upper limit and the lower limit of the natural gas power input into the gas boiler during climbing are respectively.
5) Electric gas-to-gas equipment operation constraints
In the formula: eta P2G The energy conversion efficiency of the electric gas conversion equipment;
the upper limit and the lower limit of electric power input into the electric gas conversion equipment respectively;
the upper limit and the lower limit of the electric power input into the electric gas conversion equipment during climbing are respectively.
6) Electric refrigerator operation constraint
In the formula: p EC,c,t Representing the cold power output by the electric refrigerator in the t period; eta EC Representing the energy conversion efficiency of the electric refrigerator;
the upper limit and the lower limit of electric power input into the electric refrigerator respectively;
the upper limit and the lower limit of the electric power input into the electric refrigerator during climbing are respectively.
7) Absorption chiller operation constraint
In the formula: p is AC,h,t Represents the thermal power input into the absorption refrigerator in the period t; p AC,c,t Representing the output of the absorption refrigerator during the period tThe cold power of (c); eta AC Represents the energy conversion efficiency of the absorption chiller;
the upper limit and the lower limit of the thermal power input into the absorption refrigerator respectively;
the upper limit and the lower limit of the thermal power input into the absorption refrigerator for climbing are respectively.
Energy storage restraint:
the invention carries out unified modeling on the electricity, heat, gas and cold energy storage equipment, and for this, the following constraint conditions exist:
in the formula: e n,t The capacity of the nth energy storage device in the t period;
charging and discharging efficiencies of the nth energy storage device are respectively obtained;
respectively charging and discharging 0-1 state variables of the nth energy storage device;
the upper limit and the lower limit of the capacity of the nth energy storage device are respectively set.
Based on the implementation steps, it is pointed out that in the low-carbon optimized scheduling model of the campus comprehensive energy system considering the carbon price uncertainty, the model of the campus comprehensive energy system built in the step (2), the initial quota model of the carbon emission right built in the step (3), the model of the actual carbon emission right built in the step (4), the time-sharing step-type carbon trading cost calculation model built in the step (5), the economic target set in the step (6), the uncertainty of the carbon trading base price in the step (7), the risk evasion robust model in the step (8), the machine in the step (9) finds a policy model, the constraint conditions set in the step (10) are combined to solve the low-carbon optimized scheduling model of the campus comprehensive energy system considering the carbon price uncertainty to obtain an optimized result, and the output result based on the model acts on the medium-energy conversion equipment or the electric power output equipment of the campus comprehensive energy system, so as to realize the optimized scheduling of the electric power equipment and energy conversion.
Based on the above method and steps, an application example is given below to fully illustrate the implementation process of the present invention and the significant technical effects thereof.
The following 8 scenes are set for carrying out benefit analysis of a time-sharing step-type carbon trading mechanism, effectiveness of the time-sharing step-type carbon trading mechanism in aspects of reducing system operation cost and carbon emission is analyzed, and influence of uncertainty of carbon value on low-carbon optimization scheduling of the system is analyzed:
scene 1: under the environment of a ladder-type carbon trading market, the target function does not consider the carbon trading cost;
scene 2: in the traditional carbon trading market environment, the objective function considers the carbon trading cost and the fixed carbon price;
scene 3: under the environment of a ladder-type carbon trading market, the objective function considers the carbon trading cost and fixes the carbon trading base price;
scene 4: under the time-sharing ladder type carbon trading market environment, the target function considers the carbon trading cost, the interval length is self-adaptive, and the carbon trading base price is fixed;
scene 5: under the time-sharing ladder type carbon trading market environment, the target function considers the carbon trading cost, the fixed interval length and the fixed carbon trading base price;
scene 6: under the time-sharing ladder type carbon trading market environment, the target function considers the carbon trading cost, the fixed interval length and the time-sharing carbon trading base price;
scene 7: under the time-sharing ladder type carbon trading market environment, the target function considers the carbon trading cost, the fixed interval length, the time-sharing carbon trading base price and the uncertainty of the carbon trading base price, and the risk avoidance coefficient mu =0.05;
scene 8: under the time-sharing ladder type carbon trading market environment, the target function considers the carbon trading cost, the fixed interval length and the time-sharing carbon trading base price, considers the uncertainty of the carbon trading base price, and the opportunity seeking coefficient rho =0.05.
The optimized scheduling is carried out by taking 24h a day as a scheduling period, the prediction results of the multivariate load, the wind power and the photovoltaic output in the system are shown in fig. 3, the time-of-use electricity price, the time-of-use gas price and the time-of-use carbon trading base price are shown in fig. 4, the installation capacity and the operation parameters of each device are shown in a graph 1, the installation capacity and the parameters of each energy storage are shown in a graph 2, the carbon emission right quota of unit power supply is 0.798 t/(MW & h), and the carbon emission right quota of unit heat supply is 0.385 t/(MW & h); the incremental price of the ladder type carbon trading is increased by 25 percent.
TABLE 1 Equipment parameters
TABLE 2 energy storage parameters
The embodiment of the invention writes a model and an algorithm program based on a Matlab2020a software platform. The comprehensive operation cost, the electricity purchasing cost, the gas purchasing cost, the operation and maintenance cost, the carbon transaction cost, the carbon emission and the carbon value fluctuation range of the system corresponding to 8 scenes are solved by calling a GUROBI business solver through Yalmip software in Matlab2020a, and are shown in Table 3.
TABLE 3 carbon trading mechanism benefit comparison
The optimization utility of different carbon trading mechanism parameters is comparatively analyzed in scenarios 4, 5, 6. As shown in fig. 5, the length of the adaptive interval of each time period of the scene 4 is the same as the length of the fixed interval of each time period of the scenes 5, 6, 7, 8, compared with fig. 5 and 6, the length of the adaptive interval of each time period of the scene 4 is consistent with the variation trend of the carbon trading volume of each time period, because the system takes the optimal comprehensive operation cost as the objective function, the system economy is the dominant factor, and the system tends to reduce the comprehensive operation cost by reducing the carbon trading cost through the interval length. The fixed interval lengths of the time intervals in the scenes 5, 6, 7 and 8 are set according to the simulation result of the scene 4, and the lengths of each time interval and the whole period interval are kept equal, so that the carbon emission of the system is further restricted by enabling the interval length of the system to be larger when the carbon transaction amount is low and the interval length of the system to be smaller when the carbon transaction amount is high. As can be readily seen from table 3 and fig. 6, the carbon traffic and carbon emissions for scenario 5 are 18: compared with a scene 4, the time interval 00-20 is reduced, the carbon emission in a scene 5 is reduced by 133kg compared with the scene 4, namely the emission is reduced by 0.64%, the comprehensive operation is increased by 438.4 yuan compared with the scene 4 and increased by 2.06%, and a decision maker can flexibly select preference and adjust the length of each time interval on the premise that the length of the whole period interval is equal to the length of the whole period interval. As shown in fig. 7, the fixed carbon trading base price in each time period of scenes 4 and 5 and the time-sharing carbon trading base price in each time period of scenes 6, 7 and 8 are shown in table 3, it can be seen from table 3 that the carbon emission in scene 6 is reduced by 44.3kg compared with scene 5, that is, the emission is reduced by 0.21%, the comprehensive operation ratio scene 5 is increased by 301.5 yuan and increased by 1.39%, and a decision maker can flexibly select preference and adjust the carbon trading base price in each time period on the premise that the carbon trading base price in the whole period is equal to the carbon trading base price in the whole period.
And comparing and analyzing the influence of the carbon value uncertainty on the low-carbon optimal scheduling of the system by using scenes 6, 7 and 8. As can be seen from Table 3, when the comprehensive operation cost of the system is increased from 22034.6 yuan to 23136.3 yuan, the comprehensive operation cost of the system is increased by 5%, but the fluctuation range xi of the carbon number which can be accommodated by the system is increased by 39.06%. When the carbon price of the system is reduced to 38.65%, the comprehensive operation cost of the system is reduced from 22034.6 yuan to 20932.9 yuan, and is only reduced by 5%, and when a comprehensive energy system decision maker makes an optimized scheduling scheme, different risk preferences are flexibly selected according to an IGDT robust model and an opportunity model by combining system historical data and prediction data. And reasonably adjusting the investment budget to schedule the operation development of the system in the range of meeting the expected scheduling target cost and the maximum/minimum fluctuation range of the carbon value.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (5)
1. A campus comprehensive energy system low-carbon optimization scheduling method considering carbon price uncertainty comprises the steps of establishing a campus comprehensive energy system low-carbon optimization scheduling model and an operation scheduling model of energy conversion equipment, and is characterized in that: the method comprises the following steps:
(1) Acquiring information of a park comprehensive energy system and constructing a park comprehensive energy system model, wherein the information of the park comprehensive energy system comprises time-of-use electricity price, time-of-use gas price, time-of-use carbon trading base price, parameters of carbon dioxide emission of park energy equipment and load information of a park;
(2) Constructing an energy conversion equipment operation scheduling model including an electricity-to-gas cogeneration unit, a gas-fired cogeneration unit and a gas-fired boiler;
(3) Determining initial quota of carbon emission power and actual carbon emission of the system according to the power purchase of a superior power grid, the output of a gas cogeneration unit, a gas boiler and an electric-to-gas device and gas load;
(4) According to the initial carbon emission quota and the actual carbon emission of the system, establishing a carbon emission quota trading mechanism of the system under an hourly scale, wherein the carbon emission quota trading mechanism comprises establishing a carbon emission authority initial quota model, an actual carbon emission model and a time-sharing step-type carbon trading cost calculation model;
(5) Constructing a low-carbon optimized scheduling model with the lower layer aiming at the minimum energy purchasing cost, operation and maintenance cost and carbon trading cost based on the step (4), processing the uncertainty of the carbon trading base price based on an information gap decision theory, and constructing a risk avoidance robust model with the maximum system capable of bearing fluctuation and an opportunity seeking strategy model with the minimum system capable of bearing fluctuation on the upper layer under an expected target;
(6) And setting operation constraint conditions of the park comprehensive energy system, solving a park comprehensive energy system low-carbon optimization scheduling model considering carbon value uncertainty, and acting on the output of the energy conversion equipment.
2. The campus integrated energy system low-carbon optimal scheduling method of claim 1, wherein: in the method, a time-sharing step type carbon emission right quota trading mechanism divides the carbon dioxide emission of a system per hour into a plurality of intervals, and mathematical models corresponding to the time-sharing step type carbon emission right quota trading mechanism comprise a carbon emission right initial quota model, an actual carbon emission model and a time-sharing step type carbon trading cost calculation model, and are specifically as follows:
the carbon emission weight initial quota model has the following expression:
in the formula: e PIES,free,t 、E buy,free,t 、E CHP,free,t 、E GB,free,t 、E gload,free,t Carbon emission quota of a system, a superior electricity purchasing and gas heat and power cogeneration unit, a gas boiler and gas load at the time t respectively; lambda e Carbon emission quota per unit of power supply; lambda [ alpha ] h Carbon emission quota per unit of heat supply; lambda gload Carbon emission quota for consumption of unit gas load; p buy,e,t Purchasing electric quantity to the upper level in the time period t; p is CHP,e,t 、P CHP,h,t Respectively the electric power and the thermal power output by the gas cogeneration unit in the time period t; p GB,h,t The thermal power output by the gas boiler in the time period t; p load,g,t Is the gas load over time t;
the actual carbon emission model has the following expression:
in the formula: e PIES,t 、E buy,t 、E gload,t Actual carbon emission of a system, a superior purchasing power and a gas load in a time period t respectively; e CHP_GB,t For the total actual carbon emission of the gas cogeneration unit, the gas boiler during the period t, E P2G,t For CO actually absorbed by the electric gas-converting equipment in the period of t 2 An amount; p total,t The sum of the output power of the gas cogeneration unit and the gas boiler is obtained in the t period; p P2G,g,t The natural gas power output by the electric gas conversion equipment in the time period t; a is 1 、b 1 、c 1 And a 2 、b 2 、c 2 Carbon emission coefficients of a coal-fired unit, a gas cogeneration unit and a gas boiler are respectively set; tau is gload Is the gas load carbon emission coefficient; tau is P2G Absorbing CO for the process of converting electricity into natural gas of an electricity-to-gas device 2 A parameter;
the mathematical expression of the time-sharing step type carbon transaction cost calculation model is as follows:
E PIES,l,t ＝E PIES,t -E PIES,free,t
in the formula: e PIES,l,t Trading amount of system carbon emission right in t period;
carbon transaction cost for time period t; zeta t Is the carbon trading base price on the market during the time period t; l t The length of the carbon emission interval in the period t; σ is the magnitude of the increase in carbon trading price. When E is PIES,l,t And when the carbon emission quantity is less than 0, the actual carbon emission quantity is lower than the uncompensated carbon emission right quota in the period t, and the surplus share is sold at the carbon trading base price to obtain the carbon trading income.
3. The campus integrated energy system low-carbon optimal scheduling method of claim 1, wherein: in the step (5), the objective function of the lower-layer low-carbon optimized scheduling model is the comprehensive operation cost of the minimized system, and the expression is as follows:
in the formula, F is the comprehensive operation cost of the system; c buy Cost for purchasing energy; c ope The equipment operation and maintenance cost;
cost for carbon transactions; wherein, the calculation expression of the energy purchasing cost is as follows:
in the formula: c buy Cost for purchasing energy; c. C buy,e,t 、c buy,g,t Electricity prices and gas prices at the time period t, respectively;
the calculation expression of the equipment operation and maintenance cost is as follows:
in the formula: c ope The operation and maintenance cost; c. C WT 、c PV 、c CHP 、c P2G 、c GB 、c AC 、c EC The unit operation and maintenance costs of a wind turbine generator set, a photovoltaic unit, a gas cogeneration unit, an electric gas conversion device, a gas boiler, an absorption refrigerator and an electric refrigerator in the system are respectively; c. C ES,n The unit operation and maintenance cost of the nth energy storage device is calculated; p WT,t The actual output power of the wind power in the time period t is obtained; p PV,t The actual photovoltaic output power in the time period t is obtained; p CHP,g,t Inputting the natural gas power of the gas cogeneration unit at a time t; p P2G,e,t Is the electric power input during the period t; p GB,g,t Inputting natural gas power of a gas boiler in a time period t; p AC,h,t Represents the thermal power input into the absorption refrigerator in the period t; p EC,e,t Represents the electric power input into the electric refrigerator for a period t;
respectively charging and discharging maximum power of the nth energy storage device for a single time;
the calculated expression for carbon trading cost is as follows:
in the formula: c ope Is the carbon transaction cost.
4. The campus integrated energy system low-carbon optimal scheduling method of claim 1, wherein: in the step (5), the upper layer model is a risk avoidance robust and opportunity seeking decision model established based on the uncertainty of carbon number, and the risk avoidance robust model is represented as follows:
in the formula: f 0 The optimal scheduling cost is obtained when the predicted value is taken as the reference value and the uncertain parameter; mu is a risk avoidance coefficient; (1 + μ) F 0 Representing a pessimistic scheduling target cost in the risk avoidance robust model;
a carbon trading base price predicted value in a period t;
the opportunity seeking decision model is as follows:
in the formula: ρ is an opportunity seeking coefficient; (1-. Rho) F 0 In presentation of opportunistic seek decision modelsOptimistic scheduling objective costs.
5. The campus integrated energy system low-carbon optimal scheduling method of claim 1, wherein: the operation constraint conditions of the park comprehensive energy system low-carbon optimization scheduling model in the step (6) comprise energy balance constraint, energy conversion equipment output constraint and energy storage constraint, and specifically comprise the following steps:
a) Wind power output constraint
In the formula (I), the compound is shown in the specification,
predicting power for the wind power output in the t time period;
rated power of the wind turbine generator;
b) Photovoltaic output constraint
In the formula (I), the compound is shown in the specification,
predicting power for the photovoltaic output at time t;
rated power for the photovoltaic unit;
c) Gas cogeneration unit operation constraint
In the formula (I), the compound is shown in the specification,
energy conversion efficiency of electricity and thermal power of the gas cogeneration unit is respectively obtained;
the upper limit and the lower limit of the natural gas power input into the gas cogeneration unit are respectively set;
the upper limit and the lower limit of the power ramp of the natural gas input into the gas cogeneration unit are respectively set;
d) Gas boiler operation constraints
In the formula eta GB The energy conversion efficiency of the gas boiler;
the upper limit and the lower limit of the natural gas power input into the gas boiler are respectively set;
the upper limit and the lower limit of the natural gas power input into the gas boiler are respectively set;
e) Electric gas-to-gas equipment operation constraints
In the formula eta P2G The energy conversion efficiency of the electric gas conversion equipment;
the upper limit and the lower limit of electric power input into the electric gas conversion equipment respectively;
the upper limit and the lower limit of the electric power input into the electric-to-gas conversion equipment during climbing are respectively set;
f) Electric refrigerator operation constraint
In the formula, P EC,c,t The cold power output by the electric refrigerator is represented in the t period; eta EC Representing the energy conversion efficiency of the electric refrigerator;
the upper limit and the lower limit of electric power input into the electric refrigerator respectively;
the upper limit and the lower limit of the electric power input into the electric refrigerator during climbing respectively;
g) Absorption chiller operation constraint
In the formula: p AC,h,t Represents the thermal power input to the absorption chiller during the period t; p AC,c,t The cold power output by the absorption refrigerator is represented in the t period; eta AC Represents the energy conversion efficiency of the absorption chiller;
the upper limit and the lower limit of the thermal power input into the absorption refrigerator respectively;
respectively of the input absorption typeThe thermal power of the cold machine climbs the upper limit and the lower limit of the slope;
h) Restraint of stored energy
The method is used for uniformly modeling the electricity, heat, gas and cold energy storage equipment, and the constraint conditions are as follows:
in the formula, E n,t The capacity of the nth energy storage device in the t period;
charging and discharging efficiencies of the nth energy storage device are respectively obtained;
respectively charging and discharging 0-1 state variables of the nth energy storage device;
the upper limit and the lower limit of the capacity of the nth energy storage device are respectively set;
i) Electric power balance constraint
In the formula, P buy,e,t Purchasing electric quantity to the upper level in the time period t; p is load,e,t Is the electrical load during the time period t;
the limit value of purchasing electricity to the upper-level power grid;
j) Thermal power balance constraint
In the formula, P load,h,t At time period tA thermal load;
k) Natural gas power balance constraints
In the formula (I), the compound is shown in the specification,
a limit value for purchasing gas to an upper-stage gas network;
l) Cold Power balance constraints
In the formula, P load,c,t Is the cooling load during the period t.
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| CN116757307A (en) * | 2023-04-28 | 2023-09-15 | 生态环境部环境规划院 | Set-based quota reference value measurement and optimization method for carbon market power industry |
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| CN116757307A (en) * | 2023-04-28 | 2023-09-15 | 生态环境部环境规划院 | Set-based quota reference value measurement and optimization method for carbon market power industry |
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