CN111523697A - Comprehensive energy service cost allocation and pricing calculation method - Google Patents
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Abstract
The invention discloses a comprehensive energy service cost allocation and pricing calculation method, which comprises the following steps: calculating the cold, heat and electric loads of users in the region by adopting the DEST; establishing various energy supply equipment model constraint conditions, taking the annual minimum operating cost as a target function, and optimizing the capacity of each equipment in the comprehensive energy system by adopting a particle swarm algorithm; performing subsystem division and stock division on the optimized comprehensive energy system
Of a stream
Value calculation, pairThe system lists the heat economic balance equation, and solves each strand by adding proper supplementary equation
The cost of the heat circulation economy, and the pricing of energy products; establishing a real-time electricity price model by taking the determined electricity price as a reference; and establishing a price type power demand response model on the basis of the calculated real-time electricity price, and obtaining the optimal real-time electricity price through iterative calculation. The cost allocation and pricing calculation method provided by the application
And pricing, which considers the quality of energy and the demand response of a user side and promotes the balance of supply and demand while reasonably pricing energy products.
Description
Technical Field
The invention relates to the technical field of cost and pricing of complex energy system energy products based on multi-energy complementation, in particular to a comprehensive energy service cost allocation and pricing method.
Background
The comprehensive energy system adopts electric power and gas as system input, supplies various energy products such as cold, heat, electricity and the like to users at the same time, and obtains certain operation profits on the basis of optimizing regional energy structures. The integrated energy system is a passive recipient of energy prices and needs to make optimal operation strategies by optimizing operation to reduce its operating costs. In order to obtain the best profit, firstly, the system needs to be operated and optimized, secondly, reasonable cost sharing needs to be carried out on energy products, energy product pricing is carried out based on a reasonable pricing strategy, then, supply and demand balance is ensured by formulating a corresponding real-time price mechanism, and peak-valley value of electric load is reduced by establishing comprehensive energy demand side response management.
In an integrated energy system, the optimal operation of the system is mainly considered, since it involves the conversion and transmission between a plurality of different energy sources. Document "liu fangzi, billow, etc. model building and optimization of micro-energy grid multi-energy coupling hub [ J ] power system automation, 2018,42 (06): 1735-. On the basis of the optimized operation of the integrated energy system, since the integrated energy company needs to provide the energy product to the user, the cost of the integrated energy service and the pricing of the energy product must be determined. The existing research mainly aims at carrying out economic analysis on a small-sized combined cooling heating and power system, but the research based on the comprehensive energy cost allocation containing an energy storage device is relatively lacked, and the literature ' Dongjun, gas-steam combined cycle and combined heating, power and cooling combined supply ' is subjected to thermal and economic analysis [ D ]. Wuhan, Wuhan university, 2005. ' the gas-steam combined cycle and combined heating, power and cooling combined supply thereof are subjected to thermodynamic analysis and the product cost is allocated by adopting energy levels. In addition, through patent retrieval, the invention of application No. 201811414810.6 discloses a comprehensive energy service pricing mechanism based on user demand direction, which takes user satisfaction as the center, introduces the concepts of user perception utility, user intention and user preference on the basis of analyzing a comprehensive energy service pricing strategy, and provides a comprehensive energy service product and service package pricing method based on user demand direction. The invention relates to a multi-type energy pricing and energy management method for park energy Internet operators, which is applied to the filed of the application No. 201810645896.7, and is characterized in that a master-slave game model of the park operators and user agents is established, the master-slave game model is converted into a mixed integer linear programming problem and is solved, and the park operators make reasonable electric energy, gas energy and/or heat energy prices based on actual energy supply and energy consumption conditions of a park.
Through analyzing the existing documents and patents, the discovery is that whether the energy can be supplied enough or not is considered in one way in the existing comprehensive energy service, and the benefit of a user and an energy service provider is balanced by lacking the application of a price incentive means and the demand response of the user side; or only considering the energy level, neglecting the influence of the quality of the energy on the cost distribution and the pricing.
Disclosure of Invention
To address the deficiencies of the prior art, the present invention provides a demand side based load price response, and
the comprehensive energy service cost allocation and pricing calculation method of the pricing heat economy method promotes supply and demand interaction by making real-time energy prices while reasonably pricing, and plays a role in peak clipping and valley filling on a user load curve.
The invention provides a comprehensive energy service cost allocation and pricing calculation method, which adopts the technical scheme that:
firstly, calculating time-by-time cold, heat and electric loads of regional users all year round by adopting energy consumption simulation software DEST, and further comprising the following specific steps: setting the location of the calculated area, and determining the outdoor meteorological parameters of the typical year through location selection; drawing a geometric model of the building, and establishing a digital model of the building through commands of newly building in the DEST, newly building a floor, setting floor height, inserting windows, doors and the like; setting parameters of a building enclosure structure, including an outer wall, an outer window, an outer door, a roof, the ground, a floor slab and the like; the building digital model is preprocessed, requirements such as thermal disturbance and temperature and humidity are set according to functions of each room, when the air conditioner is started, thermal disturbance of light of personnel equipment is considered, ventilation problems are not considered in the room, when heating is started, the thermal disturbance of the personnel light equipment is not considered, and the ventilation times of the room need to be set; and performing simulation calculation on the regional building model, and deriving the calculation results of the cold, heat and electric loads of the regional building.
Further, establishing various energy supply equipment model constraint conditions:
electric load balance constraint:
Le+Pehp+Pec＝ηT×Ein+ηchpe×Gchp；
thermal load balancing constraints:
Lh+Hhs+Hab＝ηchph×Gchp+ηgb×Ggb+ηehp×Pehp；
cold load balancing constraint:
Lc+Hcs＝ηab×Hab+ηec×Pec；
energy storage element restraint:
Et+1 x＝Et x+(ex.c×Px,c-Pt x,d/ex.d)×dt；
0≤Px,c≤ax×Pmax x,c；
0≤Px,d≤(1-ax)×Pmax x,d；
Emin x≤Et x≤Emax x；
ET x＝E0 x；
and power constraint of each component:
Pmin≤η×P≤Pmax；
the objective function of the comprehensive energy system operation optimization and capacity optimization is that the annual operation cost of the system is the lowest, and the function is as follows: min { Sigma (p)ebEin+pg(Gchp+Ggb))}；
In the formula: l ise、Lh、LcThe power of electric load, heat load and cold load at the moment t respectively; pehp、Pec、 Gchp、Hab、GgbThe input power of an electric heat pump, an electric refrigerator, a cogeneration unit, an absorption refrigerator and a gas boiler is respectively; einPurchasing electric power from a power grid; hhsDifference between heat storage and heat release; hcsη is the difference between stored and released coldT、ηgb、ηchpe、ηchph、ηehp、ηab、ηecThe efficiency of the transformer, the efficiency of the boiler, the power generation efficiency of the cogeneration unit, the heat production efficiency of the cogeneration unit, the efficiency of the electric heat pump, the efficiency of the absorption refrigerator and the efficiency of the electric refrigerator are respectively; et xThe energy storage capacity of the energy storage element x at the moment t is represented, and x can be represented as heat storage or cold storage; px,c、 Px,dRespectively charging energy power and discharging energy power for the energy storage element x; pmax x,c、Pmax x,dThe maximum energy charging power and the maximum energy discharging power of the energy storage element x are respectively set; a isxThe variable of 0-1 is used for limiting the charging and discharging energy not to be carried out simultaneously; emin xAnd Emax xThe minimum stored energy and the maximum stored energy of the energy storage element x; eT x＝E0 XRepresenting the charge-discharge energy balance of the energy storage element in one period; pmin、PmaxVector formed by the lowest operating power and the highest operating power of each energy-using device respectively, η vector formed by the operating efficiency of each energy-using device, and P vector formed by the operating input power of each energy-using device； peb、pgThe unit price of electricity and the unit price of natural gas are respectively per kilowatt hour.
And further, carrying out subsystem division on the optimized comprehensive energy system, wherein the principle of the subsystem division is as follows: the equipment using different energy sources is divided into a subsystem, and the conversion device between the two energy sources is defined as a subsystem, regardless of the number of the equipment contained in the set and the sharing property among the equipment. And drawing a production structure diagram of the comprehensive energy system.
For dividing and calculating sub-systems for integrated energy systems
A stream; calculating each fuel
A value; list system events matrix a: defining an event matrix A as A (m, n), wherein the elements in the matrix are represented by aijWhen is coming into contact with
When the flow flows into subsystem i, aij1 is ═ 1; when in use
When the flow goes out of the subsystem i, aij-1; when in use
When the flow neither flows into nor out of the system, aij＝0。
Establishing a thermal economic cost balance matrix equation for each subsystem by adopting a matrix mode:
A×ED×C+Z＝0
wherein EDFor each strand
Diagonal matrix of flows (n × n), C is the unit thermal economic cost vector (n × 1), Z is the individual system non-energy cost vector (m × 1)
It is necessary to establish (n-m) complementary equations to make the momentsThe array equation is full rank, and the establishment principle of the complementary equation is as follows: external part of energy system consumption
Being fuel
A value; if the energy system outputs more than one type of energy product, various energy product units are assumed
The cost is equal; for a multiple-input multiple-output subsystem, the output
Flow unit
Cost equals input
Flow unit
A weighted average of the costs.
The system supplement equation is:
θ×ED×C＝W
wherein θ is a matrix comprising (n-m) rows and n columns; w is an (n-m) dimensional column vector
Calculating to obtain each share
Flow of
The cost equation is as follows:
C＝-(ED)-1×A1-1×Z1
wherein EDFor each strand
Diagonal matrix of streams, a1 extended event matrix, Z1 non-energy cost matrix.
Further, with the electricity price Pe as a reference electricity price, the time-by-time electricity load curve d of the users passing through the region0(t) and daily average load davCalculating floating factor of electricity price, calculating real-time electricity price p0(t)。
Calculating the total daily electricity load:
Wd＝Σd0(t)；
calculating the average electric load of the whole day:
dav＝Wd/24；
calculating a real-time electricity price floating factor:
β(t)＝d0(t)/dav；
real-time electricity price constraint function:
pemin≤p(t)≤pemax；
the real-time electricity price calculation formula is as follows:
p(t)＝β(t)×pr；
wherein WdTypical daily total electricity load; davAverage electrical load, β (t) real-time price fluctuation factor, peminAnd pemaxThe real-time electricity price is the lowest and highest constraint; pr is a benchmark reference electricity selling price.
Still further, the real-time electricity price is applied to the electric load d before response0(t) obtaining a user electric load curve d after demand response1(t)；
Calculating the electric load after the user response under the action of the dynamic electricity price:
d(t)＝d0(t)[1+Σ{E(t,t0)×(p(t0)-p0(t0))/p0(t0)}]；
wherein E (t, t)0) For t time to t in a calculation period0Required elastic coefficient of moment, d0(t) and d (t) are the respective pre-and post-response load demands at time t, p0And (t) and p (t) are the reference electricity price and the real-time electricity price at the time t respectively.
Given precision, assumed priceLoad difference k before and after grid response1＝Abs[d1(t)-d0(t)]Given precision, decision k1The size of and;
if k is1Less than or equal to the real-time price, the load on the demand side does not respond with the real-time price any more, and the real-time price p is output1(t)；
If k is1If the current time is more than or equal to the preset time, the iteration is not converged, and the real-time electricity price and the load after response need to be recalculated;
assuming the ith iteration convergence, calculating the ith real-time electricity price floating factor, the real-time electricity price and the responded electric load, and finally outputting the real-time price pi(t)。
Drawings
FIG. 1 is a calculation flow of a method for cost sharing and pricing of integrated energy service
FIG. 2 is a graph of the annual hourly cooling load of a forecast area
FIG. 3 is a graph of annual hourly thermal load for a forecast area
FIG. 4 is a graph of the annual hourly electrical load for a forecast area
FIG. 5 is a diagram of the production structure of the integrated energy system
FIG. 6 is an event matrix A
FIG. 7 is a graph of real-time electricity prices in a first iteration of a typical day
FIG. 8 is a graph of response load in a first iteration of a typical day
FIG. 9 is a typical day convergence real-time electricity price
Detailed Description
In a typical embodiment of the application, the invention provides a comprehensive energy cost allocation and pricing calculation method, and specifically, firstly, a state space method-based DEST software is adopted to predict the cold load Lc, the heat load Lh and the electric load Le of a regional user; secondly, establishing various energy supply equipment model constraint conditions, taking the annual minimum operating cost of the comprehensive energy system as a target function, optimizing the operating conditions and equipment capacity of each equipment by adopting a particle swarm algorithm, then performing subsystem division on the optimized comprehensive energy system, and adopting a method based on the principle of energy supply equipment model constraint conditions
Modeling the analyzed thermal economic cost model and calculating each strand
The heat economic cost of the flow, and the basic prices of cold, heat and electric energy products are respectively determined as pc, ph and pe; then, a real-time electricity price model is established, the electricity price pe is taken as the reference electricity price, and a time-by-time electricity load curve d is formed by regional users0(t) and daily average load davCalculating floating factor of electricity price, calculating real-time electricity price p0(t) on the basis, establishing a price type power demand response model, and applying the real-time electricity price to the electric load d before response0(t) obtaining a user electric load curve d after demand response1(t); and finally, setting iteration precision, judging the size of a load difference value k and precision before and after user demand response, if k is less than or equal to iteration convergence, outputting the real-time electricity price, if k is greater than k, continuously performing iteration calculation on the real-time electricity price and the user demand response, continuously judging the convergence until iteration convergence, and outputting the optimal real-time electricity price. A flowchart of a specific calculation procedure is shown in fig. 1.
The method adopts energy consumption simulation software DEST to calculate the annual hourly cold, hot and electric loads of regional users, and comprises the following specific steps: setting the location of the calculated area, and determining the outdoor meteorological parameters of the typical year through location selection; drawing a geometric model of the building, and establishing a digital model of the building through commands of newly building in the DEST, newly building a floor, setting floor height, inserting windows, doors and the like; setting parameters of a building enclosure structure, including an outer wall, an outer window, an outer door, a roof, the ground, a floor slab and the like; the building digital model is preprocessed, requirements such as thermal disturbance and temperature and humidity are set according to functions of each room, when the air conditioner is started, thermal disturbance of light of personnel equipment is considered, ventilation problems are not considered in the room, when heating is started, the thermal disturbance of the personnel light equipment is not considered, and the ventilation times of the room need to be set; and performing simulation calculation on the regional building model, and deriving the calculation results of the cold, heat and electric loads of the regional building. The annual hourly cold and heat load results using the DeST simulation are shown in fig. 2 and 3, and the electrical load is shown in fig. 4.
Solving the linear integer programming problem by adopting a particle swarm algorithm, wherein the constraint function of each energy supply device comprises the following steps:
electric load balance constraint:
Le+Pehp+Pec＝ηT×Ein+ηchpe×Gchp；
thermal load balancing constraints:
Lh+Hhs+Hab＝ηchph×Gchp+ηgb×Ggb+ηehp×Pehp；
cold load balancing constraint:
Lc+Hcs＝ηab×Hab+ηec×Pec；
energy storage element restraint:
Et+1 x＝Et x+(ex.c×Px,c-Pt x,d/ex.d)×dt；
0≤Px,c≤ax×Pmax x,c；
0≤Px,d≤(1-ax)×Pmax x,d；
Emin x≤Et x≤Emax x；
ET x＝E0 x；
and power constraint of each component:
Pmin≤η×P≤Pmax；
the objective function of the comprehensive energy system operation optimization and capacity optimization is that the annual operation cost of the system is the lowest, and the function is as follows: min { Sigma (p)ebEin+pg(Gchp+Ggb))}；
In the formula: l ise、Lh、LcThe power of electric load, heat load and cold load at the moment t respectively; pehp、Pec、 Gchp、Hab、GgbRespectively an electric heat pump, an electric refrigerator, a cogeneration unit,The input power of the absorption refrigerator and the gas boiler; einPurchasing electric power from a power grid; hhsDifference between heat storage and heat release; hcsη is the difference between stored and released coldT、ηgb、ηchpe、ηchph、ηehp、ηab、ηecThe efficiency of the transformer, the efficiency of the boiler, the power generation efficiency of the cogeneration unit, the heat production efficiency of the cogeneration unit, the efficiency of the electric heat pump, the efficiency of the absorption refrigerator and the efficiency of the electric refrigerator are respectively; et xThe energy storage capacity of the energy storage element x at the moment t is represented, and x can be represented as heat storage or cold storage; px,c、 Px,dRespectively charging energy power and discharging energy power for the energy storage element x; pmax x,c、Pmax x,dThe maximum energy charging power and the maximum energy discharging power of the energy storage element x are respectively set; a isxThe variable of 0-1 is used for limiting the charging and discharging energy not to be carried out simultaneously; emin xAnd Emax xThe minimum stored energy and the maximum stored energy of the energy storage element x; eT x＝E0 XRepresenting the charge-discharge energy balance of the energy storage element in one period; pmin、PmaxRespectively a vector formed by the lowest operating power and the highest operating power of each energy utilization device, η a vector formed by the operating efficiency of each energy utilization device, P a vector formed by the operating input power of each energy utilization device, and Peb、pgThe unit price of electricity and the unit price of natural gas are respectively per kilowatt hour.
And (3) based on the predicted annual hourly cooling and heating loads, performing annual hourly optimization of the operation of the comprehensive energy system by adopting a particle swarm algorithm, wherein the annual maximum operation capacity of each device is taken as the optimal capacity, and the capacity model selection result of each device is shown in table 1.
TABLE 1 optimization selection table for main equipment capacity of comprehensive energy system
Before establishing the thermal economics cost model of the integrated energy system, a production structure diagram of the integrated energy system needs to be drawn, as shown in fig. 5. Then, according to the rated parameters of each device and the production structure chart of the comprehensive energy system, each energy flow is carried out by adopting a corresponding formula
The results of the calculation of the values are shown in Table 2.
TABLE 2 Integrated energy System
Flow calculation table
List system events matrix a: defining an event matrix A as A (m, n), wherein the elements in the matrix are represented by aijWhen is coming into contact with
When the flow flows into subsystem i, aij1 is ═ 1; when in use
When the flow goes out of the subsystem i, aij-1; when in use
When the flow neither flows into nor out of the system, aij＝0。
Establishing (n-m) supplementary equations to enable the matrix equation to be full-rank, wherein the establishment principle of the supplementary equations is as follows: external part of energy system consumption
Being fuel
A value; if the energy system outputs more than one type of energy product, various energy product units are assumed
The cost is equal; for a multiple-input multiple-output subsystem, the output
Flow unit
Cost equals input
Flow unit
A weighted average of the costs.
The complementary equation of the system is theta × ED×C＝W
Wherein θ is a matrix comprising (n-m) rows and n columns; w is an (n-m) dimensional column vector
An expanded event matrix a is obtained according to the above calculation formula, as shown in fig. 6.
The flow vector diagonal matrix ED is: diag { E1, E2, E3, …, E20 }.
The total investment of the project is 2717 ten thousand yuan, and the main equipment comprises: the electric power is 1500kW gas internal combustion engine (including a generator), two cooling capacity is 1800kW flue gas hot water type lithium bromide units, one heat is 4200kW gas vacuum boiler, one cooling capacity is 5627kW refrigerating unit, one cooling capacity is 1420kW, the heating capacity is 1505kW water source heat pump, the purchase cost of each equipment, the installation cost, the construction engineering cost and the like are counted as the following table, wherein the heat power pipe network cost is the total cost of building a comprehensive energy source station to a user side water system, the labor cost is the management cost of the energy source station, 3 people are total, 2 ten thousand are per year for each person, 15 years of operation are total 450 ten thousand.
TABLE 3 cost and investment cost calculation table for comprehensive energy system
Adopting a uniform depreciation method for equipment of the comprehensive energy system according to a formula: d ═ I (I)0-IL)/L
Wherein D represents depreciation cost, I0 represents initial investment value of the equipment, and L represents economic life or age limit of the equipment: IL represents the residual value by L years, assuming over fifteen years the equipment residual value is zero. Then there are: d ═ I0/L
In the project, L is 15 years, assuming that the project operates for 365 days every year, the annual depreciation cost of each subsystem can be calculated, and then the heat power pipe network cost and the labor cost are distributed into each subsystem to obtain the non-energy cost of each subsystem, which is shown in Table 4.
TABLE 4 non-energy cost calculation table for subsystem of integrated energy system
The non-energy cost vector Z is: z ═ o [0.03288,0.00434,0.011,0004,0.007, 0.005,0.00265,0.00328,0.00265,0.6375,0.375,0,0,0,0,0,0]T
According to the formula: c ═ ED)-1×A-1× Z determines the cost price of unit electric power, cold power and heat power as 0.69, 0.29 and 0.33 (yuan/kw.h).
After the cooling, heating and power costs are calculated, a reasonable pricing strategy needs to be adopted. The predicted annual total cooling, heating and power load Lc is 8928.4MWh, Lh is 3915.7MWh, Le is 9065.6MWh, the unit kW. h electricity, heat and cold unit price is pe, ph and pc respectively, the project recovery period is planned to be 10 years, namely the net present value of the project is zero within 10 years: NPV ═ Σ (CI-CO)/(1+ i) ^ t ═ 0; the investment is the initial investment of the comprehensive energy system equipment, the operation cost is the fuel cost and the labor management cost, and the income is the product of year-by-year cold, heat and electricity load and the difference between price and cost, namely: pL ═ (pe-0.69) × Le + (pc-0.29) × Lc + (ph-0.33) × Lh; setting the benchmark yield i to be 5% in 10 years, then the net present value NPV calculation formula is as follows:
NPV＝(0-15000000)/(1+0.05)+(0-15070000)/(1+0.05)^2+[(Pe-0.69)×9065599+(Pc-0.29)×8928422+(Ph-0.33)×3915715-600000]×[1/(1+0.05)^3+1/(1+0.05)^4+1/(1+0.05)^5+ 1/(1+0.05)^6+1/(1+0.05)^7+1/(1+0.05)^8+1/(1+0.05)^9+ 1/(1+0.05)^10]
setting the selling price constraint functions of unit electricity, cold and heat as follows:
0.69≤Pe≤0.9
0.29≤Pc≤0.6
0.33≤Ph≤0.6
under the constraint function, with NPV2The minimum is an objective function, and the price of the electricity, the cold and the heat energy sources obtained by the solution is 0.88, 0.53 and 0.49 (yuan/kw.h) respectively.
Taking typical daily electricity loads of users as an example, a real-time electricity price model is established, and the typical daily electricity loads are shown in table 5.
TABLE 5 typical daily Electrical load
Taking the electricity price pe optimally determined through cost allocation and recovery period as a reference electricity price, and passing through a regional user time-by-time electricity load curve d0(t) and daily average load davCalculating floating factor of electricity price, calculating real-time electricity price p0(t)。
Calculating the total daily electricity load:
Wd＝Σd0(t)；
calculating the average electric load of the whole day:
dav＝Wd/24；
calculating a real-time electricity price floating factor:
β(t)＝d0(t)/dav；
real-time electricity price constraint function:
pemin≤p(t)≤pemax；
the real-time electricity price calculation formula is as follows:
p(t)＝β(t)×pr；
wherein WdTypical daily total electricity load; davAverage electrical load, β (t) real-time price fluctuation factor, peminAnd pemaxThe real-time electricity price is the lowest and highest constraint; pr is a benchmark reference electricity selling price.
According to the electric load size, peak time periods (11-14,19-20), ordinary time periods (8-10,15-18,21-22) and low valleys (1-7,23-24) are defined for making real-time electricity prices, as shown in figure 7.
Still further, the real-time electricity price is applied to the electric load d before response0(t) calculating to obtain a user electrical load curve d after the demand response by adopting the elastic coefficient table provided by the table 10 and an electrical load response formula1(t) as shown in FIG. 8.
And calculating the electric load after the user responds under the action of the dynamic electricity price:
d(t)＝d0(t)[1+Σ{E(t,t0)×(p(t0)-p0(t0))/p0(t0)}]
wherein E (t, t)0) For t time to t in a calculation period0Required spring rate at time (Table 6), d0(t) and d (t) are the respective pre-and post-response load demands at time t, p0And (t) and p (t) are the reference electricity price and the real-time electricity price at the time t respectively.
TABLE 6 electric load elastic coefficient table
Given precision, assumed priceLoad difference k before and after response1＝Abs[d1(t)-d0(t)]Given precision, decision k1The size of and;
if k is1Less than or equal to the real-time price, the load on the demand side does not respond with the real-time price any more, and the real-time price p is output1(t)；
If k is1If the current time is more than or equal to the preset time, the iteration is not converged, and the real-time electricity price and the load after response need to be recalculated;
assuming the ith iteration convergence, calculating the ith real-time electricity price floating factor, the real-time electricity price and the responded electric load, and finally outputting the real-time price pi(t) as shown in FIG. 9.
Claims (7)
1. A comprehensive energy cost allocation and pricing calculation method is characterized by comprising the following steps:
step 1, adopting DesT software based on a state space method to predict the cold load Lc, the heat load Lh and the electric load Le of a regional user;
step 2: establishing various energy supply equipment model constraint conditions, taking the annual minimum running cost of the comprehensive energy system as a target function, and optimizing the running working condition and the equipment capacity of each equipment by adopting a particle swarm algorithm;
and step 3: the optimized comprehensive energy system is divided into subsystems based on
Modeling the analyzed thermal economic cost model and calculating each strand
The heat economic cost of the flow, the basic price of the cold, heat and electric energy products are respectively determined as Pc, Ph and Pe;
and 4, step 4: establishing a real-time electricity price model, taking the electricity price Pe as a reference electricity price, and passing through a regional user time-by-time electricity load curve d0(t) and daily average load davCalculating floating factor of electricity price, calculating real-time electricity price p0(t)；
And 5: establishing a price patternA power demand response model for applying real-time electricity price to the electric load d before response0(t) obtaining a user electric load curve d after demand response1(t)；
Step 6: and setting iteration precision, judging the size of a load difference value k and precision before and after user demand response, if k is less than or equal to iteration convergence, outputting real-time electricity price, if k is greater than k, continuously performing iteration calculation on the real-time electricity price and the user demand response, and continuously judging the convergence until iteration convergence, and outputting the optimal real-time electricity price.
2. The integrated energy service cost sharing and pricing calculation method according to claim 1, wherein: in the step 1, energy consumption simulation software DEST is adopted to calculate the annual hourly cooling, heating and electric loads of regional users, and the specific steps further comprise: setting the location of the calculated area, and determining the outdoor meteorological parameters of the typical year through location selection; drawing a geometric model of the building, and establishing a digital model of the building through commands of newly building in the DEST, newly building a floor, setting floor height, inserting windows, doors and the like; setting parameters of a building enclosure structure, including an outer wall, an outer window, an outer door, a roof, the ground, a floor slab and the like; the building digital model is preprocessed, requirements such as thermal disturbance and temperature and humidity are set according to functions of each room, when the air conditioner is started, thermal disturbance of light of personnel equipment is considered, ventilation problems are not considered in the room, when heating is started, the thermal disturbance of the personnel light equipment is not considered, and the ventilation times of the room need to be set; and performing simulation calculation on the regional building model, and deriving the calculation results of the cold, heat and electric loads of the regional building.
3. The integrated energy service cost sharing and pricing calculation method according to claim 1, wherein: in step 2, the constraint function of each energy supply device includes:
electric load balance constraint:
Le+Pehp+Pec＝ηT×Ein+ηchpe×Gchp；
thermal load balancing constraints:
Lh+Hhs+Hab＝ηchph×Gchp+ηgb×Ggb+ηehp×Pehp；
cold load balancing constraint:
Lc+Hcs＝ηab×Hab+ηec×Pec；
energy storage element restraint:
Et+1 x＝Et x+(ex.c×Px,c-Pt x,d/ex.d)×dt；
0≤Px,c≤ax×Pmax x,c；
0≤Px,d≤(1-ax)×Pmax x,d；
Emin x≤Et x≤Emax x；
ET x＝E0 x；
and power constraint of each component:
Pmin≤η×P≤Pmax；
the objective function of the comprehensive energy system operation optimization and capacity optimization is that the annual operation cost of the system is the lowest, and the function is as follows: min { Sigma (p)ebEin+pg(Gchp+Ggb))}；
In the formula: l ise、Lh、LcThe power of electric load, heat load and cold load at the moment t respectively; pehp、Pec、Gchp、Hab、GgbThe input power of an electric heat pump, an electric refrigerator, a cogeneration unit, an absorption refrigerator and a gas boiler is respectively; einPurchasing electric power from a power grid; hhsDifference between heat storage and heat release; hcsη is the difference between stored and released coldT、ηgb、ηchpe、ηchph、ηehp、ηab、ηecThe efficiency of the transformer, the efficiency of the boiler, the power generation efficiency of the cogeneration unit, and the cogeneration unitHeat generation efficiency, electric heat pump efficiency, absorption refrigerator efficiency, electric refrigerator efficiency; et xThe energy storage capacity of the energy storage element x at the moment t is represented, and x can be represented as heat storage or cold storage; px,c、Px,dRespectively charging energy power and discharging energy power for the energy storage element x; pmax x,c、Pmax x,dThe maximum energy charging power and the maximum energy discharging power of the energy storage element x are respectively set; a isxThe variable of 0-1 is used for limiting the charging and discharging energy not to be carried out simultaneously; emin xAnd Emax xThe minimum stored energy and the maximum stored energy of the energy storage element x; eT x＝E0 XRepresenting the charge-discharge energy balance of the energy storage element in one period; pmin、PmaxRespectively a vector formed by the lowest operating power and the highest operating power of each energy utilization device, η a vector formed by the operating efficiency of each energy utilization device, P a vector formed by the operating input power of each energy utilization device, and Peb、pgThe unit price of electricity and the unit price of natural gas are respectively per kilowatt hour.
4. The integrated energy service cost sharing and pricing calculation method according to claim 1, wherein: in the step 3, the step of constructing the thermal economic cost model further includes: drawing a production structure diagram of the comprehensive energy system; for dividing and calculating sub-systems for integrated energy systems
A stream; calculating each fuel
A value; listing a system event matrix A; establishing a thermal economic cost balance matrix equation for each subsystem by adopting a matrix mode; giving a supplementary equation according to the establishment principle of the supplementary equation, and enabling the thermal economic cost balance matrix to be full-rank; solving the matrix equation to obtain each strand
Cost of heat economy.
5. The integrated energy service cost sharing and pricing calculation method according to claim 1, wherein: in the step 4, the real-time electricity price model specifically comprises:
calculating the total daily electricity load:
Wd＝Σd0(t)；
calculating the average electric load of the whole day:
dav＝Wd/24；
calculating a real-time electricity price floating factor:
β(t)＝d0(t)/dav；
real-time electricity price constraint function:
pemin≤p(t)≤pemax；
the real-time electricity price calculation formula is as follows:
p(t)＝β(t)×pr；
wherein WdTypical daily total electricity load; davAverage electrical load, β (t) real-time price fluctuation factor, peminAnd pemaxThe real-time electricity price is the lowest and highest constraint; pr is a benchmark reference electricity selling price.
6. The integrated energy service cost sharing and pricing method of claim 1, wherein: in step 5, the price type power demand response model specifically includes:
calculating the electric load after the user response under the action of the dynamic electricity price:
d(t)＝d0(t)[1+Σ{E(t,t0)×(p(t0)-p0(t0))/p0(t0)}]；
wherein E (t, t)0) For t time to t in a calculation period0Required elastic coefficient of moment, d0(t) and d (t) are the respective pre-and post-response load demands at time t, p0And (t) and p (t) are the reference electricity price and the real-time electricity price at the time t respectively.
7. The integrated energy service cost sharing and pricing calculation method according to claim 1, wherein: the iteration step in the step 6 is as follows:
setting iteration precision, recording di(t) load of ith response, pi(t) is the real-time electricity price obtained by the calculation of the ith time, and the load difference k before and after the response of the ith time and the (i-1) th time is determined to be | | d under the assumption that the iteration is performed for i timesi(t)-di-1(t) I and the precision, if k is less than or equal to the precision, iterative convergence is carried out, and real-time electricity price p is outputi(t); if k > then the ith real-time electricity price p is utilizedi(t) as a reference electricity price and a load d after responsei(t) calculating the (i + 1) th user demand response to obtain pi+1(t) and di+1(t) continuing convergence determination, assuming convergence for n iterations, outputting pn(t)。
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| CN114580122A (en) * | 2022-01-17 | 2022-06-03 | 华南理工大学 | Energy quality matching optimization method of building heating ventilation air-conditioning system based on exergy economy |
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