CN114881296A - Comprehensive energy system peak clipping and valley filling scheduling strategy based on paid distribution - Google Patents
Comprehensive energy system peak clipping and valley filling scheduling strategy based on paid distribution Download PDFInfo
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Abstract
The invention relates to a comprehensive energy system peak clipping and valley filling scheduling strategy based on paid distribution, which belongs to the technical field of electricity-gas comprehensive energy system optimization scheduling and comprises the following steps: acquiring a first initial parameter, a second initial parameter and a third initial parameter; respectively constructing a first optimization model, a second optimization model and a third optimization model; according to the three established optimization models, a total cost objective function is established by using an equal weight method, the total cost objective function is linearized, and the optimal solution of a decision variable is obtained by solving the minimum total cost as an optimization objective, wherein the decision variable comprises an energy purchasing strategy of a superior energy network, an output strategy of energy conversion equipment and a scheduling strategy of peak clipping and valley filling equipment of a system; and respectively bringing the solved corresponding decision variables into a first optimization model, a second optimization model and a third optimization model to obtain corresponding results. The optimized dispatching model comprehensively considers the stability, economy and low-carbon environmental protection of the operation of the energy system.
Description
Technical Field
The invention relates to a comprehensive energy system peak clipping and valley filling scheduling strategy based on paid distribution, and belongs to the technical field of optimization scheduling of an electricity-gas comprehensive energy system.
Background
At present, with the continuous push of market reform and the rapid development of information technology, the traditional monopolized energy supply system, in which only the electric power company meets the corresponding energy demand of the user, is gradually replaced by the comprehensive energy system. The comprehensive energy system can fully exert the advantage complementarity among various forms of energy, effectively improve the flexibility of energy supply, and play an important role in the background that the quantity and the quality of energy demand of people are continuously improved.
The comprehensive energy system inevitably generates carbon dioxide in the process of purchasing energy from a superior energy network and the process of realizing conversion between different energy forms by the energy conversion equipment, and is not beneficial to relieving the problem of global warming. During peak periods of energy use by users, limited energy supply parameters and equipment capacity can bring great challenges to stable operation of the system and normal energy use by users. The operation of the comprehensive energy system aims at good economy, and the above problems put higher requirements on the optimized dispatching operation of the comprehensive energy system.
Disclosure of Invention
Based on the background technology, an optimized scheduling scheme which gives consideration to the operation stability, the operation economy and the low-carbon environmental protection performance of the comprehensive energy system is established. Under the condition of ensuring the economic operation of the energy system, the transfer of the operation load time or the form in the peak period of power utilization is effectively realized, the carbon emission generated by the system is reduced as much as possible, and the green ecological function of the system is fully exerted. In order to verify the functional effectiveness of the scheme, three schemes of non-peak clipping valley filling equipment, an electrolytic cell, a hydrogen fuel cell, an electricity-to-gas and a gas turbine are drawn up as comparison schemes of peak clipping and valley filling performances, and the carbon emission amount of the system under the traditional condition of not considering the carbon emission cost is compared and analyzed with the situation of the scheme.
The invention provides a comprehensive energy system peak clipping and valley filling scheduling strategy based on paid distribution.
In order to achieve the purpose, the invention adopts the technical scheme that:
a peak clipping and valley filling scheduling strategy of a comprehensive energy system based on paid distribution comprises the following steps:
l1, acquiring a first initial parameter, a second initial parameter and a third initial parameter;
l2, establishing an equality constraint and an inequality constraint of the operation of the comprehensive energy system according to the retail electricity price and the retail gas price of the upper-level energy network at each moment in the first initial parameters, taking the energy purchasing strategy and the unit scheduling output of the comprehensive energy system from the upper-level energy network as decision variables, taking the energy purchasing cost and the equipment operation cost as objective functions, taking the upper limit and the lower limit of electric energy purchased from the upper-level power network, the upper limit and the lower limit of natural gas purchased from the upper-level natural gas network, the efficiency and the capacity parameter of a gas boiler unit, the heat generation efficiency, the electricity generation efficiency and the capacity parameter of a cogeneration unit, the electricity load, the heat load, the wind power generator output parameter and the photovoltaic generator output parameter at each moment on the user side, and establishing the equality constraint and the inequality constraint of the comprehensive energy system operation, and establishing a first optimization model;
l3, constructing a second optimization model by taking the carbon emission cost of the comprehensive energy system as a target function and the unit dispatching output as a decision variable according to the second initial parameter;
l4, according to the third initial parameters, taking the sum of the operation cost of the peak clipping and valley filling equipment of the comprehensive energy system and the peak clipping and valley filling effect mapped to the economic dimension as a target function, and taking the electric power purchased from a superior power grid, the charging power and the discharging power of a storage battery as decision variables to construct a third optimization model;
l5, constructing a total cost objective function by using an equal weight method according to three optimization models established by L2-L4, linearizing the total cost objective function, and solving by taking the minimum total cost as an optimization objective to obtain an optimal solution of decision variables, wherein the decision variables comprise an energy purchasing strategy of a system from a superior energy network, an output of energy conversion equipment and a scheduling strategy of peak clipping and valley filling equipment;
l6, respectively bringing the corresponding decision variables obtained by the solution in the step L5 into the first optimization model, the second optimization model and the third optimization model to obtain corresponding results.
The technical scheme of the invention is further improved as follows: the first initial parameters comprise upper and lower limits of electric energy purchased from an upper power network; purchasing upper and lower limits of natural gas from a superior natural gas network; retail electricity prices and retail gas prices of the upper-level energy network at every moment; efficiency, capacity parameters, unit operating cost of the gas boiler; the heat production efficiency, the electricity production efficiency, the capacity parameter and the unit operation cost of the cogeneration unit; the user side uses electric load and heat load at every moment; the output parameters of the wind generating set; photovoltaic generator set output parameters; the second initial parameters comprise a quadratic coefficient, a primary coefficient and a constant coefficient of carbon emission of electric energy purchased from an upper-level power grid; the secondary term coefficient, the primary term coefficient and the constant term coefficient of the carbon emission amount of the energy conversion equipment for supplying energy; the price per carbon emission obtained by NBS bargaining; the third initial parameter comprises an economic conversion coefficient; the charging efficiency, the discharging efficiency and the capacity parameter of the storage battery.
The technical scheme of the invention is further improved as follows: the first optimization model is as follows:
wherein N is t For dividing a scheduling period into a number of time segments, F 1 For integrating the energy purchase cost and equipment operation cost functions of the energy system in a dispatching period 1 For unit transportation of cogeneration unitsLine cost, ε 2 Is the unit operating cost of the gas boiler, P CHP,out,e,t 、P CHP,out,h,t Electric and thermal power, P, output by the cogeneration unit at time t GB,out,t For thermal power, lambda, output by gas-fired boilers t And mu t Respectively retail electricity prices for purchasing electric energy from an upper grid and retail gas prices for purchasing natural gas from an upper natural gas grid, P e,in,t Representing the electrical power purchased from the upper grid at time t, P g,in,t Representing the natural gas power purchased from the upper natural gas grid at time t.
The technical scheme of the invention is further improved as follows: the second optimization model is as follows:
F 2 ＝φ * E
wherein, F 2 The purchase cost of carbon emission right for the integrated energy system, E is the total carbon emission amount of the energy system, phi * Is the price per unit carbon emission;
wherein phi is max For the upper limit of the carbon emission trading price, phi min Is the lower limit of the carbon emission right trade price.
The technical scheme of the invention is further improved as follows: the third optimization model is as follows:
wherein, theta 1 For the unit operating cost of the battery installation, omega is the economic conversion factor, P e,in,t-1 Electric power, P, purchased from the upper grid for time t-1 ES,out,t For the electric power output by the accumulator at time t, P ES,in,t The electric power input to the battery at time t.
The technical scheme of the invention is further improved as follows: the total cost objective function in the step L5 is linearized by the expression
Wherein omega K Is a segmented set;
is a segment k 1 Abscissa P corresponding to nodes at both ends e,in A value of (d);
is a segment k 1 Ordinate E corresponding to nodes at both ends 1 A value of (d); o ° o k Representing the segment k as a continuous variable 1 The corresponding position of the upper optimization result; v is k Is a binary variable for guaranteeing the kth 1 K after each segment is filled 1 +1 segment can start filling.
Due to the adoption of the technical scheme, the invention has the following technical effects:
the optimized dispatching model comprehensively considers the stability, economy and low-carbon environmental protection of the operation of the energy system. The invention establishes a paid distribution model aiming at the problem of carbon emission right price, avoids the problem of unfair carbon emission right distribution of the comprehensive energy system adopting energy-saving and emission-reducing measures under the paid distribution model, is beneficial to stimulating the comprehensive energy system to update the scheduling strategy and reduces the carbon emission of the comprehensive energy system in the scheduling period. For the bargaining problem of the carbon emission right, NBS bargaining is adopted to ensure the justice of the bargaining process. Meanwhile, an economic conversion coefficient is adopted to map the electric quantity curve purchased by the comprehensive energy system from a superior power grid to economic dimensionality, and the peak clipping and valley filling effect can be judged according to the smoothness degree of the curve. When the model is solved, the nonlinear objective function which is not beneficial to solving is converted into the linear objective function which is easy to solve, the precision of the objective function and the solving speed are compromised, and the proper number of segments is selected, so that the precision of the objective function is ensured, and the solving speed of the model is also ensured.
Drawings
FIG. 1 is a block diagram and energy flow diagram of the integrated energy system of the present invention;
FIG. 2 illustrates the interaction of the integrated energy system with a superordinate government;
FIG. 3 is a graph of the relationship between the amount of carbon emissions purchased by an integrated energy system and the corresponding social benefits;
FIG. 4 is a schematic diagram of a quadratic function piecewise linearization;
FIG. 5 is a flow chart of the present invention
Detailed Description
The present invention will be described in further detail with reference to the drawings and specific embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
FIG. 1 is a block diagram and energy flow diagram of the integrated energy system of the present invention; the integrated energy system is regarded as an intermediary between the upper-level energy network and the consumer-side loads, and on the one hand plays the role of loads, purchasing electric energy and natural gas energy from the upper-level energy network. On the other hand, the system plays the role of an energy supplier, and the energy is converted into an energy form meeting the requirements of users through cogeneration and a gas boiler unit in the system.
The interactive relationship between the integrated energy system and the superior government is shown in fig. 2, and the integrated energy system and the superior government have bargaining games for the price of the carbon emission right. The generation of carbon dioxide is divided into two parts, one part is the carbon dioxide emission generated by the electric energy purchased by the comprehensive energy system from a superior power grid, and the other part is the carbon dioxide generated by the cogeneration and the energy form conversion of the gas boiler unit. Under paid distribution, the integrated energy system needs to internalize the carbon emission cost and undertake the payment of the corresponding price. The upper government is equivalent to a body issuing a carbon emission right, the integrated energy system is a body purchasing the carbon emission right, and the carbon emission is given to the attributes of the goods.
The comprehensive energy system cooperatively considers the operation stability, the operation economy and the low-carbon environmental protection property to optimize the scheduling model, and the idea is as follows:
1) and establishing a cost model of the comprehensive energy system for purchasing energy from a superior energy network and operating equipment in the comprehensive energy system, and solving the cost model to obtain an energy purchasing strategy of the system from the superior power grid and the output condition of the equipment. And establishing related equality constraint and inequality constraint conditions, including supply and demand balance constraint, the relation between input and output of the unit, the upper output limit of the unit, the upper and lower limits of energy purchased from an energy system and the like, and ensuring that the model carries out a realistic optimization strategy under the condition of meeting the constraint conditions.
2) And establishing a cost model of the carbon emission of the comprehensive energy system. Fig. 3 is a graph illustrating the relationship between the amount of carbon emissions purchased by the integrated energy system and the corresponding social benefits. A bargaining game model is established between the comprehensive energy system and the superior government. By establishing a carbon emission right selling income model of a government department and a social welfare model of an energy system for purchasing the carbon emission right, the feasible set of the price is proved to be a convex set through the derivation of a mathematical formula according to the definition of the convex set, and the conclusion that the cost problem of the carbon emission right is a bargaining problem is obtained. In the actual interaction, the government is equivalent to a seller selling the carbon emission right, the integrated energy system is equivalent to a buyer purchasing the carbon emission right, and the price setting level can influence the positivity of the government and the integrated energy system for bargaining transaction. And establishing a second optimization model according to the definition of the Nash bargaining solution and the bargaining result of the Nash bargaining solution and the mathematical expression of the Nash bargaining solution and the determined optimal price.
3) And establishing a peak clipping and valley filling effect of the comprehensive energy system and a cost model of peak clipping and valley filling equipment. The peak clipping and valley filling of the comprehensive energy system are realized by transferring the energy form or the consumed time of the electric energy. The dimensions of the peak clipping and valley filling effects and the economic cost are different, so that a proper economic conversion coefficient is selected and mapped to the economic dimensionality, and a third optimization model is established according to the unit operation cost and the economic conversion coefficient of the peak clipping and valley filling equipment.
After the first optimization model, the second optimization model and the third optimization model are established, the three cost functions are added by adopting an equal weight method to obtain a total cost target function of the comprehensive energy system. Because the discharge amount of carbon dioxide of the comprehensive energy system and the output of electric energy purchased from a power grid, cogeneration and a gas boiler form a quadratic function relationship, the optimization problem is a nonlinear optimization problem, and the traditional solving method is not beneficial to solving, the objective function is subjected to linear processing by adopting a quadratic function linearization method.
FIG. 4 is a diagram illustrating a quadratic function piecewise linearization. By selecting the proper number of the segments, compromise is carried out between the operation speed and the data accuracy of the piecewise linear function after the quadratic function is linearized, and the accuracy of the result is ensured while the operation efficiency is ensured.
Examples
The peak clipping and valley filling scheduling strategy of the comprehensive energy system based on paid distribution, as shown in fig. 5, includes the following steps:
l1: and acquiring a first initial parameter, a second initial parameter and a third initial parameter.
The first initial parameters comprise upper and lower limits of electric energy purchased from an upper power network; purchasing upper and lower limits of natural gas from a superior natural gas network; retail electricity prices and retail gas prices of the upper-level energy network at every moment; efficiency, capacity parameters, unit operating cost of the gas boiler; the heat production efficiency, the electricity production efficiency, the capacity parameter and the unit operation cost of the cogeneration unit; the user side uses electric load and heat load at every moment; the output parameters of the wind generating set; photovoltaic generator set output parameters; the second initial parameters comprise a quadratic coefficient, a primary coefficient and a constant coefficient of carbon emission of electric energy purchased from an upper-level power grid; the secondary term coefficient, the primary term coefficient and the constant term coefficient of the carbon emission amount of the energy conversion equipment for supplying energy; the price per carbon emission obtained by NBS bargaining; the third initial parameter comprises an economic conversion coefficient; the charging efficiency, the discharging efficiency and the capacity parameter of the storage battery.
L2: according to the retail electricity price and retail gas price of the upper-level energy network at each moment in the first initial parameters, the energy purchasing strategy and unit dispatching output of the integrated energy system from the upper-level energy network are used as decision variables, the energy purchasing cost and the equipment operation cost are used as objective functions, the upper limit and the lower limit of electric energy purchased from the upper-level power network, the upper limit and the lower limit of natural gas purchased from the upper-level natural gas network, the efficiency and capacity parameters of a gas boiler unit, the heat generation efficiency, the electricity generation efficiency and the capacity parameters of a cogeneration unit, the electricity load and the heat load of a user side at each moment, the output parameter of a wind driven generator and the output parameter of a photovoltaic generator are used for establishing the equality constraint and the inequality constraint of the operation of the integrated energy system, and a first optimization model is established;
the decision variable in L2 is the electric power P purchased by the integrated energy system from the upper level power grid e,in And electric power P purchased from the natural gas grid g,in Electric energy P output by cogeneration unit CHP,out,e And the heat energy P output CHP,out,h Heat energy P output by gas boiler GB,out . The comprehensive energy system is characterized in that a superior power grid and a natural gas grid provide electric energy and natural gas energy for the comprehensive energy system, the provided electric energy is directly transmitted to users, the provided natural gas energy is uniformly dispatched by a cogeneration unit and a gas boiler, the cogeneration unit converts the natural gas energy into the electric energy and the heat energy, and the gas boiler converts the natural gas energy into the heat energy to be supplied to electric loads and heat loads of the users. The optimization model takes into account the energy purchase costs from the upper power grid and the natural gas grid and the operating costs of the cogeneration unit and the gas boiler. Dividing a scheduling period into N t Integrating the energy purchasing cost and the equipment operation cost function F of the energy system in each time period in the dispatching cycle 1 Comprises the following steps:
wherein epsilon 1 Is the unit running cost of the cogeneration unit, epsilon 2 Is the unit operating cost of the gas boiler, P CHP,out,e,t 、P CHP,out,h,t Electric and thermal power, P, output by the cogeneration unit at time t GB,out,t For thermal power, lambda, output by gas-fired boilers t And mu t Respectively retail electricity prices for purchasing electric energy from an upper grid and retail gas prices for purchasing natural gas from an upper natural gas grid, P e,in,t Representing the electrical power purchased from the upper grid at time t, P g,in,t Representing the natural gas power purchased from the upper natural gas grid at time t.
Meanwhile, the equality constraint and the inequality constraint are satisfied as follows:
(1) and (3) equipment output constraint:
the gas boiler is used for converting the natural gas energy purchased from the upper natural gas network into heat energy to be supplied to users. The relationship between the input, output and conversion efficiency of the device is:
P CHP,out,e ＝P CHP,in η CHP,e
P CHP,out,h ＝P CHP,in η CHP,h
P GB,out ＝P GB,in η GB
wherein eta is CHP,e 、η CHP,h Outputting the conversion efficiency of electric power and thermal power for the cogeneration unit; p CHP,in Natural gas power input for the cogeneration unit; p GB,in The natural gas power input for the gas boiler; eta GB The operation efficiency of the gas boiler.
(2) Supply and demand balance constraint:
for batteries, electrical energy can be stored as a "load" during the off-peak period of electricity usage; the power supply can be used as a power supply to provide electric energy for users in the period of high power utilization, the energy supply pressure in the period of peak power utilization is relieved, and the transfer of the electric energy in time is realized. For the electrolysis cell and the hydrogen fuel cell, the electrolysis cell charges the load during the electricity consumption valley period, and the electric energy is converted into the hydrogen energy to be stored; during the peak period of electricity utilization, the hydrogen fuel cell converts the stored hydrogen energy into electric energy to be released, and the transfer of electric energy time and energy forms is realized. For the electricity-to-gas and gas turbine, when the electricity price is low, the system can purchase more electric energy from a higher-level power grid, the electricity-to-gas is converted into natural gas to be supplied to cogeneration and a gas boiler unit, when the electricity price is high, the system can purchase more natural gas from a natural gas grid, and the gas turbine converts the redundant natural gas into electric energy to be supplied to a user side, so that the economy of the system is ensured.
When the storage battery is adopted to participate in peak clipping and valley filling of the system, the electric power and the thermal power supplied by the comprehensive energy system to the user are consistent with the electric load on the user side, and the thermal load is kept consistent, namely:
P h,load ＝P CHP,in η CHP,h +P GB,in η GB
P g,in ＝P CHP,in +P GB,in
wherein P is E,load 、P h,load Respectively representing the user-side electrical and thermal loads, P WT 、P PV The output of a wind driven generator and a photovoltaic generator set; p ES,in 、P ES,out For charging and discharging the accumulator, eta ES,in 、η ES,out The charging and discharging efficiency of the storage battery. The three formulas respectively represent the supply and demand balance of electricity and heat load at the side of supplying energy to users by the comprehensive energy system and the supply and demand balance of natural gas at the side of purchasing energy from a superior energy network by the energy system.
When no equipment participates in peak clipping and valley filling, the supply and demand balance of the system is constrained as follows:
P E,load ＝P e,in +P WT +P PV +P CHP,in η CHP,e
P h,load ＝P CHP,in η CHP,h +P GB,in η GB
P g,in ＝P CHP,in +P GB,in
when the electrolysis cell and the hydrogen fuel cell are used to participate in peak clipping and valley filling, the supply and demand balance of the system is constrained as follows:
P h,load ＝P CHP,in η CHP,h +P GB,in η GB
P g,in ＝P CHP,in +P GB,in
wherein eta HFC,out 、η ET,in Efficiency of hydrogen fuel cell hydrogen power to electric power and electrolysis cell to hydrogen power, P ET,in 、P HFC,out The electric power input by the electrolytic cell and the hydrogen power released by the hydrogen fuel cell are respectively.
When electric power conversion and gas turbine are used to participate in peak clipping and valley filling, the supply and demand balance constraint of the system is as follows:
P E,load ＝P e,in +P WT +P PV +P GT η GT -P P2G -P CHP,in η CHP,e
P h,load ＝P CHP,in η CHP,h +P GB,in η GB
P g,in ＝P CHP,in +P GB,in -P P2G η P2G +P GT
wherein P is P2G 、P GT Input power, η, of electric-to-gas, gas turbines, respectively P2G 、η GT The energy conversion efficiency of electric gas conversion and gas turbine respectively. The other quantities for the above three cases are consistent with the battery participating in peak clipping and valley filling.
(3) Device-dependent constraints:
for a battery, the capacity reserve at a certain time is related to the previous time as follows:
E ES,t ＝E ES,t-1 +P ES,in,t +P ES,out,t
E ES,Nt ＝E ES,0
wherein E ES,t 、E ES,t-1 Representing the electric power reserved by the storage battery at t and t-1; p ES,in,t And P ES,out,t The input electric power and the output electric power at the moment t of the storage battery are respectively. E ES,0 、E ES,Nt Represents a storage battery inAnd scheduling the energy storage capacity in the initial state and the tail end state of the cycle. At the same time, the charging power and the discharging power of the storage battery should be kept within predetermined parameters, and the stored electric quantity cannot exceed the rated capacity at any moment, namely:
wherein
The rated charging power of the storage battery is set,
is the rated discharge power of the storage battery,
is the rated capacity of the storage battery.
When the electrolytic cell and the hydrogen fuel cell are adopted for peak clipping and valley filling, the hydrogen energy output by the electrolytic cell is stored in the hydrogen storage tank, and the hydrogen fuel cell obtains the hydrogen from the hydrogen storage tank and realizes the conversion of the hydrogen energy and the electric energy. The electric power output by the hydrogen fuel cell and the hydrogen power output by the electrolytic cell, and the hydrogen stored in the hydrogen storage tank satisfy the following conditions:
H HS,t ＝H HS,t-1 +P ET,out,t -P HFC,in,t
H HS,Nt ＝H HS,0
wherein, P ET,out,t Hydrogen power, P, output for the cell at time t HFC,in,t 、P HFC,out,t Hydrogen power input and electric power output for the hydrogen fuel cell at time t, H HS,t 、H HS,t-1 The hydrogen capacity stored in the hydrogen storage tank at t and t-1. H HS,0 、H HS,Nt Representing the hydrogen storage capacity of the hydrogen storage device in the initial state and the terminal state of a scheduling period. In this case, the 4 th equation indicates that the hydrogen storage amount of the hydrogen storage tank is the same in the beginning and end states.
The rated power of the electrolytic cell and the hydrogen fuel cell and the hydrogen storage capacity of the hydrogen storage tank.
When the electric gas conversion and the gas turbine participate in peak clipping and valley filling, the output power of the electric gas conversion and the gas turbine does not exceed the rated power, namely:
wherein P is P2G,out,t 、P GT,out,t Power output at time t for the electric gas and gas turbine;
the rated output power of the electric conversion gas and the gas turbine.
The output power of the cogeneration unit and the gas boiler must not exceed the rated power thereof, namely:
wherein
The rated output power of the cogeneration unit and the gas turbine.
(4) Upper and lower bound constraints for purchasing energy from an energy network
The upper level energy network provides limited energy at every moment, and the energy purchased from the upper level energy network by the energy system at every moment should satisfy the following constraints:
wherein,
the upper limit of energy purchased from an upper-level power grid and an upper-level natural gas grid.
L3: according to the second initial parameter, taking the carbon emission cost of the comprehensive energy system as a target function and the unit dispatching output as a decision variable, and constructing a second optimization model;
the comprehensive energy system and the superior government have a bargaining game for the price of the carbon emission right. The generation of the carbon dioxide is divided into two parts, one part is the carbon dioxide emission generated by the comprehensive energy system purchasing electric energy from a superior power grid, and the other part is the carbon dioxide generated by the cogeneration and the gas boiler unit when the energy form is converted. In the distribution of compensationNext, energy systems need to internalize the carbon emission costs, undertaking the payment of the corresponding consideration. The government corresponds to the body issuing the carbon emission rights, the energy system is the body purchasing the carbon emission rights, and the carbon emission is given to the attributes of the goods. The decision variable is the electric energy P output by the cogeneration unit CHP,out,e Heat energy P output by cogeneration unit CHP,out,h Heat energy P output from gas boiler GB,out Electric power P purchased from the upper-level grid e,in And establishing a second optimization model as follows:
carbon emission rights purchase cost F of system 2 Comprises the following steps:
F 2 ＝φ * E
wherein E is the total carbon emission of the energy system, phi * Is the price per unit carbon emission.
The carbon emission of the comprehensive energy system is mainly divided into two departments, wherein one part is carbon emission generated by purchasing electric energy from a superior power grid, and the other part is carbon emission generated by supplying energy to energy conversion equipment. According to the second initial parameter, the secondary term coefficient, the primary term coefficient and the constant term coefficient of the carbon emission of the electric energy purchased from the superior power grid; the secondary term coefficient, the primary term coefficient and the constant term coefficient of the carbon emission of the energy conversion equipment for supplying energy establish an expression of the carbon emission of two parts as follows:
E 1 ＝a 1 (P e,in ) 2 +b 1 P e,in +c 1
E 2 ＝a 2 (P CHP,out,e ) 2 +b 2 P CHP,out,e +c 2
+a 2 (P CHP,out,h ) 2 +b 2 P CHP,out,h +c 2
+a 2 (P GB,out ) 2 +b 2 P GB,out +c 2
wherein a is 1 、b 1 、c 1 Obtaining a quadratic coefficient, a primary coefficient and a constant coefficient for purchasing energy from a superior power grid to generate carbon emission; a is 2 、b 2 、c 2 For energy conversion equipmentThe secondary term coefficient, the primary term coefficient and the constant term coefficient of carbon emission caused by energy conversion are carried out; the sum of the discharge amount of the two parts is as follows:
E＝E 1 +E 2
for an integrated energy system, it is necessary to purchase a certain amount of carbon emissions to maintain proper operation of the system. When the carbon emission rights purchased by enterprises are less, the department can be used for a more core industrial department, and the social welfare of the system is increased quickly; when the amount of purchased carbon emission is increased to a certain value, the normal operation and maintenance of the energy system can be basically met, and the social benefit value of the system is slowly increased. Therefore, as the carbon emission increases, the social welfare of the enterprise increases first and then slowly, and can be approximately fitted to the following function:
U＝wlogE t
where w is the scaling factor. The integrated energy system purchases the revenue function U of the carbon emission right at the moment t 1 Can be expressed as:
U 1 ＝wlogE t -φE t
where φ is the price per carbon emission right, E t Is the carbon emission of the system at time t. Revenue function U of government department 2 The profit of selling carbon emission rights minus the cost of environmental pollution caused by energy system carbon emission is:
U 2 ＝φE t -[β 1 (E t ) 2 +β 2 E t +β 3 ]
when the energy system is gaming with government authorities, the revenue function for the energy system and government is:
the price of the unit carbon emission right can be respectively calculated according to the income function as follows:
by phi min Represents the upper price limit of carbon emission per unit, phi min Representing the lower price limit of the carbon emission unit, the set pi of feasible carbon emission unit for the carbon emission unit trading market can be represented as:
π＝{φ＝(φ 1 ,φ 2 )|φ min ≤φ≤φ max }
by using
Represents the upper limit of the revenue of the integrated energy system,
represents the lower revenue limit of the integrated energy system,
represents the upper limit of revenue for the government department,
representing a lower revenue limit for the government, the revenue set for the integrated energy system and the government may be represented as:
introduction 1: for collections
If any point X in the set, Y belongs to D and the real number delta belongs to 0,1]And if the values all satisfy delta X + (1-delta) Y ∈ D, the set D is called a convex set.
Theorem 1: set of feasible solutions pi atR 2 The upper side is closed and strictly convex.
And (3) proving that: assuming the presence of two elements phi a ，φ b And each is
Then it can be obtained:
what needs to be demonstrated next is: (delta phi) is satisfied for all 0 & ltoreq.delta & ltoreq.1 a +(1-δ)φ b ) E.g. phi. According to the above results, can be derived
And
the weighted sum expression of (a) is:
due to the fact that
And is
Then there are:
for the
And
the upper price limit and the lower price limit are respectively:
the following steps can be obtained:
the same can be obtained:
in summary, the following results can be obtained:
φ min ≤δφ+(1-δ)φ≤φ max
thus, it can be demonstrated that the set π is at R 2 Convex in the sense that the set pi is at R 2 The above is closed and strictly convex, so that the negotiation between government departments and N integrated energy systems about the carbon emission right price is a bargaining problem and is solved by adopting an NBS bargaining strategy.
Definition 1: g → R satisfies the following axiom:
(1) independent rationality: f (Z, phi) min )≥φ min ；
(2) Feasibility: f (Z, phi) min )∈Z；
(3) Pareto optimal: f (Z, phi) min ) Is pareto optimal;
(4) linear axiom: it is assumed that a linear transformation function xi is present,
Ξ(f(Z,φ min ))＝f(Ξ(Z),Ξ(φ min ))；
(5) the unrelated theorem: if feasible profit allocation set
Then for any f (Z', phi) min ) E.g. Z', all have f (Z, phi) min )＝f(Z′,φ min )。
Then the nash bargained solution is the optimal solution of the following expression:
in the formula, V i Can be expressed as:
wherein U is i The benefit of the participant i is represented,
representing the minimum benefit of participant i.
Upper revenue limit for integrated energy systems
Lower revenue limit for integrated energy systems
Upper limit of revenue for government department
Lower revenue limit for government department
The expression is as follows:
then there are:
and then, pushing out:
when the carbon emission of each integrated energy system
At a given time, the expression substituted into the Nash bargaine solution is:
φ * ＝argmax[(φ max -φ)(φ-φ min )]＝argmax[-φ 2 +φ(φ max +φ min )-φ min φ max ]
the globally optimal solution to the above problem is then:
and determining a global optimal solution after the price upper limit and the price lower limit of the unit carbon emission weight are determined.
L4: and constructing a third optimization model by taking the sum of the operation cost of the peak clipping and valley filling equipment of the comprehensive energy system and the effect mapped to the economic cost as a target function and the electric power purchased from a superior power grid and the charging power and the discharging power of the storage battery as decision variables according to the third initial parameter.
The third optimization model in the third model construction module is:
wherein, theta 1 The unit operating cost of the battery device.
L5: and constructing a total cost objective function by using an equal weight method according to three optimization models established by L2-L4, linearizing the total cost objective function, and solving by using the minimum total cost as an optimization objective to obtain an optimal solution of decision variables, wherein the decision variables comprise an energy purchasing strategy of a system from a superior energy network, an output of energy conversion equipment and a scheduling strategy of peak load shifting equipment.
According to three optimization models established by L2-L4, an equal weight method is utilized to establish a comprehensive operation cost objective function of the system, the comprehensive operation cost is composed of the above 3 operation costs, and the objective function relation is as follows:
min F＝F 1 +F 2 +F 3
and under the condition of minimum comprehensive operation cost, the output of the energy conversion equipment and the peak clipping and valley filling equipment is the optimal scheduling result.
For the carbon emission of the comprehensive energy system, the carbon emission and the related variables are in a quadratic function relationship, and the objective function is a nonlinear model, so that the carbon emission needs to be linearized and converted into a linear problem so as to be convenient to solve. The linearization process is that the definition domain of the quadratic function is divided into a plurality of sections with the same length, the left end point and the right end point of each section are linearly connected and linearized into a plurality of piecewise functions, and the expression is as follows:
wherein omega K Is a segmented set;
is a segment k 1 Abscissa P corresponding to nodes at both ends e,in A value of (d);
is a segment k 1 Ordinate E corresponding to nodes at both ends 1 A value of (d); o ° o k Representing the segment k as a continuous variable 1 The corresponding position of the upper optimization result;
is a binary variable for guaranteeing the kth 1 After the segment intervals are filled. Kth 1 +1 segment can start filling.
The function value after the secondary function linearization has a certain deviation with the function value of the original secondary function, and the absolute error of the function value on the middle point of each section interval is the largest.
And (3) proving that: let f (x) be ax 2 +bx+c(a≠0)。A(x 1 ,y 1 ),B(x 2 ,y 2 ) The two endpoints are left and right ends of a certain section. The slope k of the straight line at which the two points are linearly connected AB Comprises the following steps:
according to the slope expression and the point A (x) 1 ,y 1 ) The function expression corresponding to the segment interval can be obtained as follows:
f 1 (x)＝[a(x 1 +x 2 )+b]x+c-ax 1 x 2
the error value between the linearized function and the original function is:
for a given x 1 ,x 2 When the error is maximum, satisfy
The abscissa x corresponding to this time is:
the absolute error of the midpoint in each segment interval is selected to reflect the absolute error of the independent variable in different intervals, that is, the absolute error of the independent variable in each segment interval can be expressed as:
where N is the number of segment intervals. When the quadratic function is linearized and the number of the selected segmented intervals is small, the calculated amount is small, the operation speed is high, but the error between the linearized function value and the original function value is large, and the data accuracy is low; when the number of the selected subsection intervals is large, the error between the linearized function value and the original function value is small, the data accuracy is high, but the calculated amount is large, and the operation speed is low. Therefore, when the quadratic function is determined, it is necessary to select an appropriate value of the number of segments to make a compromise between the operation speed and the data accuracy.
L6: and (4) respectively bringing the corresponding decision variables obtained by the solution in the step L5 into the first optimization model, the second optimization model and the third optimization model to obtain corresponding results. The optimal scheduling result comprises the following steps: the comprehensive energy system is used for obtaining electric energy purchased from a superior power grid and natural gas energy purchased from a superior natural gas grid at each moment in a scheduling period; scheduling output of the cogeneration unit and the gas boiler at each moment in a scheduling period, and input and output power of a storage battery at each moment in the scheduling period; the method comprises the steps of integrating the carbon emission cost of an energy system, the sum of the cost of purchasing energy from an upper-level energy network and the operation cost of an energy conversion device, and the sum of the operation cost of a peak clipping and valley filling device and a peak clipping and valley filling effect mapped to economic dimensions.
The invention takes the running economy, running stability and low-carbon environmental protection of the comprehensive energy system into consideration cooperatively, and has the following advantages:
most of the prior art changes the energy consumption behavior of users through demand response when optimizing and scheduling the comprehensive energy system, thereby achieving the purposes of relieving the power supply pressure in the peak period of power consumption and improving the resource utilization rate in the valley period of power consumption, and neglecting the influence of the energy system on the environment when modeling the total cost of the comprehensive energy system.
The invention aims at energy conversion equipment in the comprehensive energy system, transfers electric energy in time, realizes peak clipping and valley filling through the equipment of the comprehensive energy system, selects proper economic conversion coefficients for peak clipping and valley filling effects under different dimensions, maps the economic conversion coefficients into a target function of the comprehensive energy system, and evaluates the peak clipping and valley filling effects by economic dimensions. CO emissions for system operation 2 And establishing a reasonable carbon emission cost model according to the influence on the external environment, establishing a game model aiming at the price problem of the carbon emission right, and proving that the problem is an optimization problem through a mathematical formula and obtaining a mathematical expression of an optimal solution. For CO 2 The discharge amount and the decision variable are in a quadratic nonlinear relation, the quadratic function is linearized, and proper segment number is selected to take account of the calculation time and the result accuracy. According to the first initial parameter: the retail electricity price and the retail gas price of the upper-level energy network at every moment are determined by taking the energy purchasing strategy and the unit dispatching output of the comprehensive energy system from the upper-level energy network as decision variables and taking the lowest energy purchasing cost and equipment running cost as targets to construct a first optimization model; according to the second initial parameter: the secondary term coefficient, the primary term coefficient and the constant term coefficient of the carbon emission of the electric energy purchased from the superior power grid are obtained; the secondary term coefficient, the primary term coefficient and the constant term coefficient of the carbon emission amount of the energy conversion equipment for supplying energy; the obtained sheetConstructing a second optimization model by using a Nash Bargaining Solution (NBS) of carbon emission, taking the cost of the carbon emission of an energy system as a target function and the scheduling output of a unit as a decision variable; according to the third initial parameter: and the economic conversion coefficient, the charging efficiency, the discharging efficiency and the capacity parameter of the storage battery are used as target functions by taking the sum of the operation cost of peak clipping and valley filling equipment of the system and the effect mapped to the economic cost, and a third optimization model is constructed by taking the electric power purchased from a superior power grid, the charging power and the discharging power of the storage battery as decision variables.
The peak clipping and valley filling can relieve the energy supply pressure during the peak period of electricity utilization and improve the operation economy of the comprehensive energy system by the utilization rate of energy during the valley period of electricity utilization. Aiming at peak clipping and valley filling, three groups of devices without peak clipping and valley filling are adopted, an electrolytic cell and a hydrogen fuel cell participate in peak clipping and valley filling, and an electric gas conversion and a gas turbine participate in peak clipping and valley filling for comparison. The electrolysis bath converts the electric energy into hydrogen energy, and the hydrogen fuel cell converts the hydrogen energy into the electric energy so as to realize the change of the energy form. The electricity is converted into gas energy, and the gas turbine converts the gas energy into the electric energy, so that the energy conversion is realized. Compared with other three groups of comparison experiments, the optimization model can well promote the running economy, stable running performance and environmental protection performance of the comprehensive energy system.
When no equipment participates in peak clipping and valley filling, the supply and demand balance of the system is constrained as follows:
P E,load ＝P e,in +P WT +P PV +P CHP,in η CHP,e
P h,load ＝P CHP,in η CHP,h +P GB,in η GB
P g,in ＝P CHP,in +P GB,in
under the scene, the operation cost of the peak clipping and valley filling equipment is 0, and the cost of mapping the peak clipping and valley filling effect to the economic dimension is as follows:
when the electrolysis cell and the hydrogen fuel cell are used to participate in peak clipping and valley filling, the supply and demand balance of the system is constrained as follows:
P h,load ＝P CHP,in η CHP,h +P GB,in η GB
P g,in ＝P CHP,in +P GB,in
wherein eta HFC,out 、η ET,in Efficiency of hydrogen fuel cell hydrogen power to electric power and electrolysis cell to hydrogen power, P ET,in 、P HFC,out The electric power input by the electrolytic cell and the hydrogen power released by the hydrogen fuel cell are respectively. The electric power output by the hydrogen fuel cell and the hydrogen power output by the electrolytic cell, and the hydrogen stored in the hydrogen storage tank in the relevant constraint of the equipment should satisfy the following conditions:
H HS,t ＝H HS,t-1 +P ET,out,t -P HFC,in,t
H HS,Nt ＝H HS,0
wherein, P ET,out,t Hydrogen power, P, output for the cell at time t HFC,in,t 、P HFC,out,t Hydrogen power input and electric power output for the hydrogen fuel cell at time t, H HS,t 、H HS,t-1 The hydrogen capacities of the hydrogen storage tank at the time t and the time t-1 are respectively. H HS,0 、H HS,Nt Respectively representing the hydrogen storage capacity of the hydrogen storage device in the initial state and the terminal state of a scheduling period. The 4 th equation of the equipment-related constraint indicates that the hydrogen storage amount of the hydrogen storage tank in the beginning and end states is the same.
The rated power of the electrolytic cell, the rated power of the hydrogen fuel cell and the hydrogen storage capacity of the hydrogen storage tank. The operation cost of the peak clipping and valley filling equipment and the economic cost of peak clipping and valley filling are as follows:
wherein theta is 2 Is the unit operating cost of the electrolyzer and the hydrogen fuel cell. P ET,in,t The electric energy input by the electrolytic cell at the time t is provided.
When electric power conversion and gas turbine are used to participate in peak clipping and valley filling, the supply and demand balance constraint of the system is as follows:
P E,load ＝P e,in +P WT +P PV +P GT η GT -P P2G -P CHP,in η CHP,e
P h,load ＝P CHP,in η CHP,h +P GB,in η GB
P g,in ＝P CHP,in +P GB,in -P P2G η P2G +P GT
wherein P is P2G 、P GT Input power, η, of electric-to-gas, gas turbines, respectively P2G 、η GT The energy conversion efficiencies of the electric gas conversion and the gas turbine, respectively. The other quantities for the above three cases are consistent with the battery participating in peak clipping and valley filling. Meanwhile, the output power of the electric gas conversion and the gas turbine does not exceed the rated power thereof, namely:
wherein P is P2G,out,t 、P GT,out,t The power output by the electric gas conversion and the gas turbine at the moment t.
The rated output power of the electric conversion gas and the gas turbine. The operation cost of the peak clipping and valley filling equipment and the economic cost of peak clipping and valley filling are as follows:
wherein theta is 3 Is the unit operating cost of the electric gas conversion and gas turbine.
In the above 3 pairs of comparative examples, the other constraints, objective function models and other variables have the same meanings as those in the main cases of the present invention.
Compared with other three groups, the operation efficiency of the storage battery is higher, the loss in the energy conversion process is avoided to a greater extent, and the operation economy of the comprehensive energy system is improved. Under the same conditions. The comprehensive energy system has less energy purchased from a superior energy network, and further reduces the carbon emission of the comprehensive energy system. The simulation result can show that the optimization model can well promote the operation economy, stable operation and environmental protection of the comprehensive energy system.
The above description of the embodiments is only intended to facilitate the understanding of the method of the invention and its core ideas; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (6)
1. The comprehensive energy system peak clipping and valley filling scheduling strategy based on paid distribution is characterized by comprising the following steps of:
l1, acquiring a first initial parameter, a second initial parameter and a third initial parameter;
l2, according to the retail electricity price and retail gas price of the upper-level energy network at each moment in the first initial parameters, taking the energy purchasing strategy and unit dispatching output of the integrated energy system from the upper-level energy network as decision variables, taking the sum of the energy purchasing cost and the energy conversion equipment operation cost as a target function, taking the upper and lower limits of electric energy purchased from the upper-level power network, the upper and lower limits of natural gas purchased from the upper-level natural gas network, the efficiency and capacity parameters of a gas boiler unit, the heat production efficiency, the electricity production efficiency and the capacity parameters of a cogeneration unit, the electricity load, the heat load and the wind driven generator output parameter at each moment on the user side, and the photovoltaic generator output parameter, the equality constraint and the inequality constraint of the operation of the integrated energy network are established, and a first optimization model is established;
l3, constructing a second optimization model by taking the carbon emission cost of the comprehensive energy system as a target function and the unit dispatching output as a decision variable according to the second initial parameter;
l4, according to the third initial parameters, taking the sum of the operation cost of the peak clipping and valley filling equipment of the comprehensive energy system and the peak clipping and valley filling effect mapped to the economic dimension as a target function, and taking the electric power purchased from a superior power grid, the charging power and the discharging power of a storage battery as decision variables to construct a third optimization model;
l5, constructing a total cost objective function by using an equal weight method according to three optimization models established by L2-L4, linearizing the total cost objective function, and solving by taking the minimum total cost as an optimization objective to obtain an optimal solution of decision variables, wherein the decision variables comprise an energy purchasing strategy of a system from a superior energy network, an output of energy conversion equipment and a scheduling strategy of peak clipping and valley filling equipment;
l6, respectively bringing the corresponding decision variables obtained by the solution in the step L5 into the first optimization model, the second optimization model and the third optimization model to obtain corresponding results.
2. The integrated energy system peak clipping and valley filling scheduling strategy based on paid distribution according to claim 1, wherein: the first initial parameters comprise upper and lower limits of electric energy purchased from an upper power network; purchasing upper and lower limits of natural gas from a superior natural gas network; retail electricity prices and retail gas prices of the upper-level energy network at every moment; efficiency, capacity parameters, unit operating cost of the gas boiler; the heat production efficiency, the electricity production efficiency, the capacity parameter and the unit operation cost of the cogeneration unit; the user side uses electric load and heat load at every moment; the output parameters of the wind generating set; photovoltaic generator set output parameters; the second initial parameters comprise a quadratic coefficient, a primary coefficient and a constant coefficient of carbon emission of electric energy purchased from an upper-level power grid; the secondary term coefficient, the primary term coefficient and the constant term coefficient of the carbon emission amount of the energy conversion equipment for supplying energy; the price per carbon emission obtained by NBS bargaining; the third initial parameter comprises an economic conversion coefficient; the charging efficiency, the discharging efficiency and the capacity parameter of the storage battery.
3. The integrated energy system peak clipping and valley filling scheduling strategy based on paid distribution according to claim 1, wherein: the first optimization model is as follows:
wherein N is t For dividing a scheduling period into a number of time segments, F 1 For integrating the energy purchase cost and equipment operation cost functions of the energy system in a dispatching period 1 Is the unit running cost of the cogeneration unit, epsilon 2 Is the unit operating cost of the gas boiler, P CHP,out,e,t 、P CHP,out,h,t Electric and thermal power, P, output by the cogeneration unit at time t GB,out,t For thermal power, lambda, output by gas-fired boilers t And mu t Respectively retail electricity prices for purchasing electric energy from an upper grid and retail gas prices for purchasing natural gas from an upper natural gas grid, P e,in,t Representing the electrical power purchased from the upper grid at time t, P g,in,t Representing the natural gas power purchased from the upper natural gas grid at time t.
4. The integrated energy system peak clipping and valley filling scheduling strategy based on paid distribution according to claim 1, wherein: the second optimization model is as follows:
F 2 ＝φ * E
wherein, F 2 The purchase cost of carbon emission right for the integrated energy system, E is the total carbon emission amount of the energy system, phi * Is the price per unit carbon emission;
wherein phi is max For the upper limit of the carbon emission trading price, phi min Is the lower limit of the carbon emission right trade price.
5. The integrated energy system peak clipping and valley filling scheduling strategy based on paid distribution according to claim 1, wherein: the third optimization model is as follows:
wherein, theta 1 For the unit operating cost of the battery installation, omega is the economic conversion factor, P e,in,t-1 Electric power, P, purchased from the upper grid for time t-1 ES,out,t For the electric power output by the accumulator at time t, P ES,in,t The electric power input to the battery at time t.
6. The integrated energy system peak clipping and valley filling scheduling strategy based on paid distribution according to claim 1, wherein: the total cost objective function in the step L5 is linearized by the expression
Wherein omega K Is a segmented set;
is a segment k 1 Abscissa P corresponding to nodes at both ends e,in A value of (d);
is a segment k 1 Ordinate E corresponding to nodes at both ends 1 A value of (d); o ° o k Representing the segment k as a continuous variable 1 The corresponding position of the upper optimization result; v is k Is a binary variable for guaranteeing the kth 1 K after each segment is filled 1 +1 segment can start filling.
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| CN117273240A (en) * | 2023-11-17 | 2023-12-22 | 国网安徽省电力有限公司经济技术研究院 | Decision optimization method for carbon emission cost |
| CN118281946A (en) * | 2024-06-04 | 2024-07-02 | 国网浙江省电力有限公司营销服务中心 | New energy data processing method and platform considering demand response |
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2022
- 2022-04-20 CN CN202210413189.1A patent/CN114881296A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117273240A (en) * | 2023-11-17 | 2023-12-22 | 国网安徽省电力有限公司经济技术研究院 | Decision optimization method for carbon emission cost |
| CN117273240B (en) * | 2023-11-17 | 2024-02-02 | 国网安徽省电力有限公司经济技术研究院 | Decision optimization method for carbon emission cost |
| CN118281946A (en) * | 2024-06-04 | 2024-07-02 | 国网浙江省电力有限公司营销服务中心 | New energy data processing method and platform considering demand response |
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