Virtual power plant scheduling optimization method and equipment

Abstract

The invention provides a virtual power plant scheduling optimization method and equipment, and the method comprises the steps: building a virtual power plant income model taking carbon transaction and green certificate transaction into consideration, and enabling the target function of the virtual power plant income model to maximize the net income of a virtual power plant during the scheduling period; setting constraint conditions for the virtual power plant income model; solving the virtual power plant income model based on a VPSO particle swarm optimization algorithm to obtain output plans of various power generation energy sources; wherein the various power generation energy sources comprise at least one of gas energy sources, wind power energy sources and photovoltaic energy sources. According to the virtual power plant scheduling optimization method based on the multiple transaction mechanisms and the VPSO, the multiple transaction mechanisms are considered, the operation profit of the virtual power plant VPP is further improved, the carbon emission of the system is reduced, and the utilization rate of renewable energy sources is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a virtual power plant dispatching optimization method and device. Background Art

[0002] As my country accelerates its pace of low-carbon energy transformation, the proportion of installed capacity and power generation of distributed energy continues to increase. The problem of renewable energy consumption has gradually become prominent, mainly due to low production capacity, scattered distribution, large quantity, and output fluctuations. In contrast, virtual power plants (VPPs) can gather different types of data collectors through advanced control, metering and communication technologies, which can participate in the operation of power grids and power markets as a whole. At present, the research on VPP optimization scheduling models mainly focuses on how to determine the optimization objectives and establish optimization models. Most studies target the highest income or lowest cost of VPP systems. The difference lies in the different internal power generation resources aggregated by VPPs, which produce different economic benefits and technical indicators. However, the above studies only consider economic indicators and have no significant impact on reducing carbon emissions of VPP systems and improving the utilization rate of renewable energy. Carbon trading and green certificate trading securitize the shortage part of the quota system and establish a market-based supply and demand mechanism and market trading system. However, few studies consider VPPs participating in carbon trading and green certificate trading at the same time. In addition, the volatility of renewable energy and the operating characteristics of VPP system equipment lead to the establishment of operation scheduling models that are usually high-dimensional and nonlinear, which are not easy to solve. Summary of the invention

[0003] To this end, the present invention provides a virtual power plant scheduling optimization method and device, aiming to solve the technical problem in the prior art of lacking consideration of power scheduling optimization when VPP participates in carbon trading and green certificate trading at the same time.

[0004] To achieve the above objectives, the present invention adopts the following technical solutions:

[0005] According to a first aspect of the present invention, the present invention provides a virtual power plant scheduling optimization method, the method comprising:

[0006] Establishing a virtual power plant revenue model that takes into account carbon trading and green certificate trading, wherein the objective function of the virtual power plant revenue model is to maximize the net revenue of the virtual power plant during the scheduling period;

[0007] Setting constraints for the virtual power plant revenue model;

[0008] The virtual power plant profit model is solved based on the VPSO particle swarm optimization algorithm to obtain the output plan of various power generation energy sources;

[0009] Among them, the various types of power generation energy include: at least one of gas energy, wind power energy and photovoltaic energy.

[0010] Furthermore, the constraint conditions include: at least one of: an energy balance constraint of a virtual power plant, an equipment operation constraint of a gas turbine, an operation constraint of an energy storage system, and a production constraint of renewable energy.

[0011] Furthermore, the objective function of the virtual power plant revenue model includes:

[0012] maxF=F vpp -C vpp

[0013] Where F is the net profit of the virtual power plant; F vpp is the income of the virtual power plant; C vpp is the operating cost of the virtual power plant.

[0014] Furthermore, the operating cost of the virtual power plant is vpp The calculation formula is as follows:

[0015]

[0016] Among them, C gt,i,t is the operating cost of the gas turbine; C w,i,t and C cut,w,j,t are the operating cost of wind power energy and the penalty cost for wind power abandonment; C pv,k,t and C cut,pv,k,t are the operating cost of photovoltaic energy and the penalty cost for abandoning photovoltaic energy; C ess,t is the operating cost of the energy storage system; C gird,t is the electricity cost between the virtual power plant and the power grid; N gt 、N w and N pv are the number of gas turbines, wind turbines and photovoltaic generators, respectively;

[0017] and / or,

[0018] The revenue of the virtual power plant F vpp The calculation formula is as follows:

[0019]

[0020] Among them, F sup,t is the power generation income of the virtual power plant; F co,t The income of virtual power plants participating in carbon trading; F gre,t The revenue from the virtual power plant’s participation in green certificate trading.

[0021] Furthermore, the operating cost C of the gas turbine is gt,i,tThe calculation formula is as follows:

[0022] C gt,i,t =a i (P gt,i,t ) 2 +b i P gt,i,t +c i

[0023] Among them, a i 、b i 、c i are the operating cost coefficients of gas turbines; P gt,i,t is the output power of the gas turbine;

[0024] and / or,

[0025] The operating cost of wind power energy is C w,i,t The calculation formula is as follows:

[0026] C w,j,t =δ w P w,j,t

[0027] Among them, δ w is the operating cost coefficient of wind power energy; P w,j,t is the output parameter of wind power energy;

[0028] and / or,

[0029] The penalty cost of wind curtailment C cut,w,j,t The calculation formula is as follows:

[0030] C cut,w,j,t =δ cut,w (P w,pre,j,t -P w,j,t )

[0031] Among them, δ cut,w P is the penalty cost coefficient for wind curtailment; w,pre,j,t The output parameters of the predicted wind power energy;

[0032] and / or,

[0033] The operating cost of the photovoltaic energy is C pv,k,t The calculation formula is as follows:

[0034] C pv,k,t =δ pv P pv,k,t

[0035] Among them, δ pv is the operating cost coefficient of photovoltaic energy; P pv,k,t is the output parameter of photovoltaic energy;

[0036] and / or,

[0037] The penalty cost of abandoning light is C cut,pv,k,t The calculation formula is as follows:

[0038] C cut,pv,k,t =δ cut,pv (P pv,pre,k,t -P pv,k,t )

[0039] Among them, δ cut,pv P is the penalty cost coefficient for abandoned light; pv,pre,k,t is the output parameter of photovoltaic energy;

[0040] and / or,

[0041] The operating cost of the energy storage system is C ess,t The calculation formula is as follows:

[0042] C ess,t =δ ess (P ess,ch,t +P ess,dis,t )

[0043] Among them, δ ess,t is the operating cost coefficient of the energy storage system; P ess,ch,t is the charging power of the energy storage system; P ess,dls,t is the discharge power of the energy storage system;

[0044] and / or,

[0045] The electricity cost C between the virtual power plant and the power grid gird,t The calculation formula is as follows:

[0046] C grid,t =δ buy P grid,buy,t -δ sell P grid,sell,t

[0047] Among them, δ buy and δ sell are the electricity prices purchased and sold to the distribution grid by the virtual power plant, respectively; P grid,buy,t and P grid,sell,t The amount of electricity purchased and sold to the distribution grid for the virtual power plant, respectively.

[0048] Furthermore, the power generation income F of the virtual power plant sup,t The calculation formula is as follows:

[0049] F sup,t =δ sell ×P vpp,t

[0050]

[0051] Among them, P vpp,t is the output power of the virtual power plant; P ess,char,t is the charging power of the energy storage system; P ess,dls,t is the discharge power of the energy storage system; P gt,i,t is the output power of the gas turbine; P w,j,t is the output parameter of wind power energy; P pv,k,t is the output parameter of photovoltaic energy; N gt 、N w and N pv are the number of gas turbines, wind turbines and photovoltaic generators, respectively;

[0052] and / or,

[0053] The income of the virtual power plant participating in carbon trading F co,t The calculation formula is as follows:

[0054]

[0055] Among them, δ c is the price of carbon emission rights in carbon trading; E co2,t is the carbon emissions of the virtual power plant; E co2,res,t Carbon emission allowances for virtual power plants;

[0056] and / or,

[0057] The income of the virtual power plant participating in the green certificate transaction F gre,t The calculation formula for is as follows:

[0058] F gre,t =δ g (P gre,t -P re,t )

[0059] Among them, δ g is the unit price of green certificates in green certificate transactions; P gre,t is the electricity generated by renewable energy; P re,t E is the consumption quota of renewable energy; gt,i,t is the carbon emission of the gas turbine; E grid,buy,t Carbon emissions from buying electricity from the grid;

[0060] Furthermore, the formula for the energy balance constraint of the virtual power plant is expressed as follows:

[0061]

[0062] Among them, EL t is the load demand of the virtual power plant; P gt,i,tis the output power of the gas turbine; P w,j,t is the output parameter of wind power energy; P pv,k,t is the output parameter of photovoltaic energy; P ess,ch,t is the charging power of the energy storage system; P ess,dls,t is the discharge power of the energy storage system; P grid,buy,t and P grid,sell,t N is the amount of electricity purchased and sold to the distribution grid by the virtual power plant; gt 、N w and N pv are the number of gas turbines, wind turbines and photovoltaic generators, respectively;

[0063] and / or,

[0064] The equipment operation constraint of the gas turbine is expressed as follows:

[0065] P gt,min,i ≤P gt,i,t ≤P gt,max,i

[0066] △P gt,min,i ≤P gt,i,t -P gt,i,t-1 ≤△P gt,max,i

[0067] Among them, P gt,i,t is the output power of the gas turbine; P gt,min,i and P gt,max,i are the upper and lower limits of gas turbine output respectively; △P gt,min,i and △P gt,max,i are the upper and lower limits of the gas turbine’s gradeability, respectively;

[0068] and / or,

[0069] The formula for the operation constraint of the energy storage system is as follows:

[0070]

[0071] Among them, P ess,ch,max and P ess,dis,max are the maximum charging and discharging power of the energy storage system; P ess,ch,min and P ess,dls,min are the minimum charging and discharging power of the energy storage system; S ch,t and S dls,t are binary variables of the charging and discharging states of the energy storage system; E ess,t is the energy value of the energy storage system; η ch and η dls are the efficiency of the charging and discharging process respectively; E ess,max and E ess,minare the maximum energy value and the minimum energy value of the energy storage system during the scheduling period respectively;

[0072] and / or,

[0073] The formula for the renewable energy production constraint is expressed as follows:

[0074] 0≤P w,j,t ≤P w,pre,j,t

[0075] 0≤P pv,k,t ≤P pv,pre,k,t

[0076] Among them, P w,j,t is the output parameter of wind power energy; P pv,k,t is the output parameter of photovoltaic energy; P w,pre,j,t The predicted power of wind power energy notified to the distribution network dispatching center by the virtual power plant; P pv,pre,k,t The predicted power of photovoltaic energy is notified to the distribution network dispatch center for the virtual power plant.

[0077] Furthermore, the VPSO particle swarm optimization algorithm includes:

[0078] ω c =(ω min +0.25)+(ω max -ω min -0.25)×cos(π(n / N))

[0079] V n,l =ω c n-1,l + 11 (pbest n-1,l -x n-1,l )+ 22 (gbest n-1,l - n-1,l )

[0080]

[0081] Among them, ω c is the improved inertia weighting coefficient; ω max and ω min are the maximum and minimum values ​​of the inertia weight coefficient respectively; n is the current number of iterations; N is the maximum number of iterations; V n,l is the speed of the lth particle in the nth iteration; x n-1,l is the position of the lth particle at the n-1th iteration; c 1 and c 2 are the individual learning coefficient and the group learning coefficient respectively; r 1 and r 2 are random numbers between [0, 1]; pbestn-1,l and gbest n-1,l are the historical best position of the lth particle at the nth iteration and the best position of the population respectively.

[0082] Furthermore, the method further comprises:

[0083] The SCR method is used to adopt a variable dimension strategy for the detected inferior particles so as to make the inferior particles leave the inferior area; the expression of the SCR method is as follows:

[0084]

[0085] Where SCR(l,n) is the ratio of the change in the individual best fitness to the change in the universal best individual fitness during the previous q iterations of the lth particle in the nth iteration.

[0086] According to the second aspect of the present invention, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements a virtual power plant scheduling optimization method as described in any one of the first aspects of the present invention when executing the computer program.

[0087] The present invention adopts the above technical solution and has at least the following beneficial effects:

[0088] Through the scheme of the present invention, a virtual power plant revenue model considering carbon trading and green certificate trading is established, wherein the objective function of the virtual power plant revenue model is to maximize the net revenue of the virtual power plant during the scheduling period; set constraints for the virtual power plant revenue model; solve the virtual power plant revenue model based on the VPSO particle swarm optimization algorithm to obtain the output plan of various types of power generation energy; wherein the various types of power generation energy include: at least one of gas energy, wind power energy and photovoltaic energy. Therefore, a virtual power plant scheduling optimization method based on multiple trading mechanisms and VPSO is proposed, which further improves the operating profit of VPP, reduces system carbon emissions, and improves the utilization rate of renewable energy by considering multiple trading mechanisms.

[0089] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0091] Figure 1 A schematic diagram of a flow chart of a virtual power plant scheduling optimization method provided by an embodiment of the present invention is shown;

[0092] Figure 2 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0093] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0094] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0095] The embodiment of the present invention provides a virtual power plant scheduling optimization method, such as Figure 1 As shown, at least the following steps S101 to S103 may be included:

[0096] Step S101, establishing a virtual power plant revenue model that takes into account carbon trading and green certificate trading.

[0097] As shown in the background technology, the current optimization scheduling of virtual power plants only considers economic indicators, which has no significant impact on reducing carbon emissions and improving the utilization rate of renewable energy. Therefore, the embodiment of the present invention establishes a virtual power plant scheduling optimization model that considers the participation of virtual power plants in carbon trading and green certificate trading, which is used to optimize the output parameters of various types of power generation energy, including gas energy, wind power energy, and photovoltaic energy, so as to maximize the net profit of the virtual power plant VPP on the basis of considering carbon trading and green certificate trading. Therefore, the embodiment of the present invention takes maximizing the net profit of the virtual power plant VPP as the objective function of the virtual power plant scheduling optimization model, and the formula is expressed as follows (1):

[0098] maxF=F vpp -C vpp (1)

[0099] Where F is the net profit of the virtual power plant; F vpp is the income of the virtual power plant; C vpp is the operating cost of the virtual power plant.

[0100] It can be understood that, since the embodiment of the present invention takes into account carbon trading and green certificate trading of the virtual power plant, the income of the virtual power plant can include the power generation income of the virtual power plant and the income from participating in carbon trading and green certificate trading, and the calculation formula is as follows (2):

[0101]

[0102] Among them, F vpp is the income of the virtual power plant; F sup,t is the power generation income of the virtual power plant; F co,t The income of virtual power plants participating in carbon trading; gre,t The revenue from the virtual power plant’s participation in green certificate trading.

[0103] Furthermore, the power generation income of the virtual power plant F sup,t The calculation formula is as follows (3)~(4):

[0104] F sup,t =δ sell ×P vpp,t (3)

[0105]

[0106] Among them, P vpp,t is the output power of the virtual power plant; P ess,char,t is the charging power of the energy storage system ESS; P ess,dls,t is the discharge power of the energy storage system ESS; P gt,i,t is the output power of the gas turbine; P w,j,t is the output parameter of wind power energy; P pv,k,t is the output parameter of photovoltaic energy; N gt 、N w and N pv are the number of gas turbines, wind turbines and photovoltaic generators, respectively.

[0107] Furthermore, the income from the virtual power plant participating in carbon trading is co,t The calculation formula is as follows (5)~(7):

[0108]

[0109] Among them, δ c is the price of carbon emission rights in carbon trading; E co2,t is the carbon emissions of the virtual power plant; E co2,res,t E is the carbon emission quota of the virtual power plant; gt,i,t is the carbon emission of the gas turbine; E grid,buy,t It is the carbon emissions generated by buying electricity from the power grid. It can be understood that the virtual power plant VPP makes a profit by selling carbon quotas. The embodiment of the present invention adopts a free initial carbon emission allocation method based on power generation to estimate that the carbon emission allowance of the virtual power plant VPP is proportional to its output.

[0110] Furthermore, the income from the virtual power plant's participation in green certificate trading is gre,t The calculation formula for is as follows (8):

[0111] F gre,t =δ g (P gre,t -P re,t ) (8)

[0112] Among them, δ g is the unit price of green certificates in green certificate transactions; P gre,t is the electricity generated by renewable energy; P re,t In the embodiment of the present invention, the renewable energy utilization of the virtual power plant VPP is converted from electricity and green certificates in equal proportion.

[0113] Furthermore, in the embodiment of the present invention, the operating cost of the virtual power plant may include the operating costs of gas energy, wind power energy, photovoltaic energy, the penalty costs of wind and solar power abandonment, the operating cost of the energy storage system ESS, and the power cost between the virtual power plant VPP and the power grid, and the calculation formula is as follows (9):

[0114]

[0115] Among them, C gt,i,t is the operating cost of the gas turbine; C w,i,t and C cut,w,j,t are the operating cost of wind power energy and the penalty cost for wind power abandonment; C pv,k,t and C cut,pv,k,t are the operating cost of photovoltaic energy and the penalty cost for abandoning photovoltaic energy; C ess,t is the operating cost of the energy storage system; C gird,t is the electricity cost between the virtual power plant and the power grid; N gt 、N w and N pv are the number of gas turbines, wind turbines and photovoltaic generators, respectively.

[0116] Furthermore, the operating cost of the gas turbine C gt,i,t The calculation formula is as follows (10):

[0117] C gt,i,t =a i (P gt,i,t ) 2 +b i P gt,i,t +c i (10)

[0118] Among them, a i 、b i 、c i are the operating cost coefficients of gas turbines; P gt,i,t is the output power of the gas turbine.

[0119] Furthermore, the operating cost of wind power energy C w,i,t The calculation formula is as follows (11):

[0120] C w,j,t =δ w P w,j,t (11)

[0121] Among them, δ w is the operating cost coefficient of wind power energy; P w,j,t It is the output parameter of wind power energy.

[0122] Furthermore, the penalty cost of wind curtailment C cut,w,j,t The calculation formula is as follows (12):

[0123] C cut,w,j,t =δ cut,w (P w,pre,j,t -P w,j,t ) (12)

[0124] Among them, δ cut,w P is the penalty cost coefficient for wind curtailment; w,pre,j,t It is the predicted output parameter of wind power energy.

[0125] Furthermore, the operating cost of photovoltaic energy C pv,k,t The calculation formula is as follows (13):

[0126] C pv,k,t =δ pv P pv,k,t (13)

[0127] Among them, δ pv is the operating cost coefficient of photovoltaic energy; P pv,k,t is the output parameter of photovoltaic energy.

[0128] Furthermore, the penalty cost of abandoning light is C cut,pv,k,t The calculation formula is as follows (14):

[0129] C cut,pv,k,t =δ cut,pv (P pv,pre,k,t -P pv,k,t ) (14)

[0130] Among them, δ cut,pv P is the penalty cost coefficient for abandoned light; pv,pre,k,t is the output parameter of photovoltaic energy.

[0131] Furthermore, the operating cost of the energy storage system ESS is ess,t The calculation formula is as follows (15):

[0132] C ess,t =δ ess (P ess,ch,t +P ess,dis,t ) (15)

[0133] Among them, δ ess,t is the operating cost coefficient of the energy storage system; P ess,ch,t is the charging power of the energy storage system; P ess,dls,t is the discharge power of the energy storage system.

[0134] Furthermore, the electricity cost C between the virtual power plant and the power grid is gird,t The calculation formula is as follows (16):

[0135] C grid,t =δ buy P grid,buy,t -δ sell P grid,sell,t (16)

[0136] Among them, δ buy and δ sell are the electricity prices purchased and sold to the distribution grid by the virtual power plant, respectively; P grid,buy,t and P grid,sell,t The amount of electricity purchased and sold to the distribution grid for the virtual power plant, respectively.

[0137] Step S102, setting constraints for the virtual power plant revenue model.

[0138] The constraints set for the virtual power plant revenue model in the embodiment of the present invention include: energy balance constraints of the virtual power plant, equipment operation constraints of the gas turbine, operation constraints of the energy storage system, and production constraints of renewable energy.

[0139] Specifically, the formula for the energy balance constraint of the virtual power plant VPP is expressed as follows (17):

[0140]

[0141] Among them, EL t is the load demand of the virtual power plant; P gt,i,t is the output power of the gas turbine; P w,j,t is the output parameter of wind power energy; P pv,k,t is the output parameter of photovoltaic energy; P ess,ch,t is the charging power of the energy storage system; P ess,dls,t is the discharge power of the energy storage system; P grid,buy,t and P grid,sell,t N is the amount of electricity purchased and sold to the distribution grid by the virtual power plant; gt 、N w and N pv are the number of gas turbines, wind turbines and photovoltaic generators respectively. The equipment operation constraint formula of the gas turbine is as follows (18)~(19):

[0142] P gt,min,i ≤P gt,i,t ≤P gt,max,i (18)

[0143] △P gt,min,i ≤P gt,i,t -P gt,i,t-1 ≤△P gt,max,i (19)

[0144] Among them, P gt,i,t is the output power of the gas turbine; P gt,min,i and P gt,max,i are the upper and lower limits of gas turbine output respectively; △P gt,min,i and △P gt,max,i They are the upper and lower limits of the gas turbine's climbing ability respectively.

[0145] Furthermore, the operating constraint of the energy storage system is expressed as follows (20):

[0146]

[0147] Among them, P ess,ch,max and P ess,dis,max are the maximum charging and discharging power of the energy storage system; P ess,ch,min and P ess,dls,min are the minimum charging and discharging power of the energy storage system; S ch,t and S dls,t are binary variables of the charging and discharging states of the energy storage system; E ess,t is the energy value of the energy storage system; η ch and η dls are the efficiency of the charging and discharging process respectively; E ess,maxand E ess,min are the maximum energy value and the minimum energy value of the energy storage system during the scheduling period. In the embodiment of the present invention, in order to maintain the recycling of the energy storage device in the next scheduling period, the sum of the charging power and the discharging power of the energy storage device in the scheduling period is set to 0.

[0148] Furthermore, the formulas for renewable energy production constraints are as follows (21)-(22):

[0149] 0≤P w,j,t ≤P w,pre,j,t (twenty one)

[0150] 0≤P pv,k,t ≤P pv,pre,k,t (twenty two)

[0151] Among them, P w,j,t is the output parameter of wind power energy; P pv,k,t is the output parameter of photovoltaic energy; P w,pre,j,t The predicted power of wind power energy notified to the distribution network dispatching center by the virtual power plant; P pv,pre,k,t The predicted power of photovoltaic energy is notified to the distribution network dispatch center for the virtual power plant.

[0152] Step S103, solving the virtual power plant profit model based on the VPSO particle swarm optimization algorithm to obtain the output plan of various types of power generation energy.

[0153] The virtual power plant revenue model in the embodiment of the present invention has the characteristics of high-dimensional variables, nonlinear constraints and complex constraints. Although it can be converted into a mixed integer linear programming model through linearization, it is difficult to solve because the model contains too many decision variables. Therefore, the present invention proposes a VPSO particle swarm algorithm, which uses the inertia weight evolution curve constructed by the cosine function to balance the global and local search capabilities of the algorithm. In addition, in order to further develop the local search capability of the algorithm in later iterations, the embodiment of the present invention adopts a reverse search strategy for the local optimal particle, that is, when the position of the particle changes during continuous iterations, when the fitness value stagnates, when the next iteration is executed, the current speed will be intervened as the vector sum of the previous speed. The improved inertia weight expression and speed update equation of the present invention are as follows (23)~(25):

[0154] ω c =(ω min +0.25)+(ω max -ω min -0.25)×cos(π(n / N))(23)

[0155] V n,l =ω c n-1,l + 11(pbest n-1,l -x n-1,l )+ 22 (gbest n-1,l - n-1,l ) (twenty four)

[0156]

[0157] Among them, ω c is the improved inertia weighting coefficient; ω max and ω min are the maximum and minimum values ​​of the inertia weight coefficient respectively; n is the current number of iterations; N is the maximum number of iterations; V n,l is the speed of the lth particle in the nth iteration; x n-1,l is the position of the lth particle at the n-1th iteration; c 1 and c 2 are the individual learning coefficient and the group learning coefficient respectively; r 1 and r 2 are random numbers between [0, 1]; pbest n-1,l and gbest n-1,l are the historical best position of the lth particle at the nth iteration and the best position of the population respectively.

[0158] The embodiment of the present invention uses the VPSO algorithm shown above to solve the virtual power plant scheduling optimization model to obtain the output optimization adjustment results of various types of power generation energy. It should be noted that in the process of solving the virtual power plant revenue model, in order to avoid the algorithm from falling into the local minimum in the process of finding the optimal, the embodiment of the present invention can also use the SCR method to adopt a variable dimension strategy for the detected inferior particles to make them leave the current inferior area. The expression of the SCR method is as follows (26):

[0159]

[0160] Among them, SCR(l,n) is the ratio between the change in individual optimal fitness and the change in universal optimal individual fitness during the previous q iterations of the lth particle in the nth iteration. It can be understood that when SCR is 0 or infinite, it means that the algorithm is trapped in the local optimum. Based on the idea of ​​genetic variation, the position of the filtered inferior particles is randomly exchanged with the position of the global best particle.

[0161] The embodiment of the present invention provides a virtual power plant scheduling optimization method, by establishing a virtual power plant revenue model considering carbon trading and green certificate trading, wherein the objective function of the virtual power plant revenue model is to maximize the net revenue of the virtual power plant during the scheduling period; set constraints for the virtual power plant revenue model; solve the virtual power plant revenue model based on the VPSO particle swarm optimization algorithm to obtain the output plan of various types of power generation energy. Due to the adoption of the above technical scheme, the present invention has the following advantages: the present invention integrates wind power, photovoltaics, gas turbines, energy storage equipment, etc. into an integrated virtual power plant VPP, and analyzes the operation mode of VPP participating in carbon trading and green certificate trading. By establishing a VPP optimization scheduling model considering carbon trading and green certificate trading, the operating revenue of VPP is maximized, and the VPSO algorithm is used for solving, which improves the local search ability of the traditional PSO algorithm in the later iteration. Under the constraints of carbon emission quotas and weighted incentives for renewable energy consumption responsibilities, VPP can increase system revenue, reduce system carbon emissions, and improve the utilization efficiency of renewable energy. The virtual power plant scheduling optimization method proposed in the embodiment of the present invention can provide an effective reference for the low-carbon economic operation of the new power system, and can be widely used in the field of virtual power plant scheduling optimization.

[0162] Based on the above Figure 1 The embodiment of the method shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Figure 2 As shown, the computer device may include a communication bus, a processor, a memory and a communication interface, and may also include an input / output interface and a display device, wherein each functional unit may communicate with each other through the bus. The memory stores a computer program, and the processor is used to execute the program stored in the memory and execute the steps of the virtual power plant scheduling optimization method described in the above embodiment.

[0163] Those skilled in the art can clearly understand that the specific working processes of the systems, devices, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and for the sake of brevity, they are not further described here.

[0164] In addition, the functional units in various embodiments of the present invention may be physically independent of each other, or two or more functional units may be integrated together, or all functional units may be integrated into one processing unit. The above integrated functional units may be implemented in the form of hardware, or in the form of software or firmware.

[0165] Those skilled in the art can understand that if the integrated functional unit is implemented in the form of software and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can essentially or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, which includes a number of instructions to enable a computing device (such as a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention when running the instructions. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes.

[0166] Alternatively, all or part of the steps of implementing the aforementioned method embodiments may be accomplished by hardware associated with program instructions (such as a computing device such as a personal computer, a server, or a network device), and the program instructions may be stored in a computer-readable storage medium. When the program instructions are executed by a processor of a computing device, the computing device executes all or part of the steps of the methods described in the embodiments of the present invention.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that within the spirit and principles of the present invention, the technical solutions described in the aforementioned embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate from the protection scope of the present invention.

Claims

1. A virtual power plant scheduling optimization method, characterized in that: The method comprises: Establishing a virtual power plant revenue model that takes into account carbon trading and green certificate trading, wherein the objective function of the virtual power plant revenue model is to maximize the net revenue of the virtual power plant during the scheduling period; Setting constraints for the virtual power plant revenue model; The virtual power plant profit model is solved based on the VPSO particle swarm optimization algorithm to obtain the output plan of various power generation energy sources; Among them, the various types of power generation energy include: at least one of gas energy, wind power energy and photovoltaic energy.

2. The method according to claim 1, characterized in that The constraint conditions include: at least one of: an energy balance constraint of a virtual power plant, an equipment operation constraint of a gas turbine, an operation constraint of an energy storage system, and a production constraint of renewable energy.

3. The method according to claim 1, characterized in that The objective function of the virtual power plant revenue model includes: maxF=F vpp -C vpp Where F is the net profit of the virtual power plant; F vpp is the income of the virtual power plant; C vpp is the operating cost of the virtual power plant.

4. The method according to claim 3, characterized in that The operating cost of the virtual power plant is C vpp The calculation formula is as follows: Among them, C gt,i,t is the operating cost of the gas turbine; C w,i,t and C cut,w,j,t are the operating cost of wind power energy and the penalty cost for wind power abandonment; C pv,k,t and C cut,pv,k,t are the operating cost of photovoltaic energy and the penalty cost for abandoning photovoltaic energy; C ess,t is the operating cost of the energy storage system; C gird,t is the electricity cost between the virtual power plant and the power grid; N gt 、N w and N pv are the number of gas turbines, wind turbines and photovoltaic generators, respectively; and / or, The revenue of the virtual power plant F vpp The calculation formula is as follows: Among them, F sup,t is the power generation income of the virtual power plant; F co,t The income of virtual power plants participating in carbon trading; F gre,t The revenue from the virtual power plant’s participation in green certificate trading.

5. The method according to claim 4, characterized in that The operating cost of the gas turbine is C gt,i,t The calculation formula is as follows: C gt,i,t =a i (P gt,i,t ) 2 +b i P gt,i,t +c i Among them, a i , b i 、c i are the operating cost coefficients of gas turbines; P gt,i,t is the output power of the gas turbine; and / or, The operating cost of wind power energy is C w,i,t The calculation formula is as follows: C w,j,t =d w P w,j,t Among them, δ w is the operating cost coefficient of wind power energy; P w,j,t is the output parameter of wind power energy; and / or, The penalty cost of wind curtailment C cut,w,j,t The calculation formula is as follows: C cut,w,j,t Zδ cut,w (P w,pre,j,t -P w,j,t ) Among them, δ cut,w P is the penalty cost coefficient for wind curtailment; w,pre,j,t The output parameters of the predicted wind power energy; and / or, The operating cost of the photovoltaic energy is C pv,k,t The calculation formula is as follows: C pv,k,t =d pv P pv,k,t Among them, δ pv is the operating cost coefficient of photovoltaic energy; P pv,k,t is the output parameter of photovoltaic energy; and / or, The penalty cost of abandoning light is C cut,pv,k,t The calculation formula is as follows: C cut,pv,k,t Zδ cut,pv (P pv,pre,k,t -P pv,k,t ) Among them, δ cut,pv P is the penalty cost coefficient for abandoned light; pv,pre,k,t is the output parameter of photovoltaic energy; and / or, The operating cost of the energy storage system is C ess,t The calculation formula is as follows: C ess,t Zδ ess (P ess,ch,t +P ess,dis,t ) Among them, δ ess,t is the operating cost coefficient of the energy storage system; P ess,ch,t is the charging power of the energy storage system; P ess,dls,t is the discharge power of the energy storage system; and / or, The electricity cost C between the virtual power plant and the power grid gird,t The calculation formula is as follows: C grid,t =d buy P grid,buy,t -d sell P grid,sell,t Among them, δ buy and δ sell are the electricity prices purchased and sold to the distribution grid by the virtual power plant, respectively; P grid,buy,t and P grid,sell,t The amount of electricity purchased and sold to the distribution grid for the virtual power plant, respectively.

6. The method according to claim 4, characterized in that The power generation income of the virtual power plant is F sup,t The calculation formula is as follows: F sup,t =d sell ×P vpp,t Among them, P vpp,t is the output power of the virtual power plant; P ess,char,t is the charging power of the energy storage system; P ess,dls,t is the discharge power of the energy storage system; P gt,i,t is the output power of the gas turbine; P w,j,t is the output parameter of wind power energy; P pv,k,t is the output parameter of photovoltaic energy; N gt 、N w and N pv are the number of gas turbines, wind turbines and photovoltaic generators, respectively; and / or, The income of the virtual power plant participating in carbon trading F co,t The calculation formula is as follows: Among them, δ c is the price of carbon emission rights in carbon trading; E co2,t is the carbon emissions of the virtual power plant; E co2,res,t E is the carbon emission quota of the virtual power plant; gt,i,t is the carbon emission of the gas turbine; E grid,buy,t Carbon emissions from buying electricity from the grid; and / or, The income of the virtual power plant participating in the green certificate transaction F gre,t The calculation formula for is as follows: F gre,t =δ g (P gre,t -P re,t ) Among them, δ g is the unit price of green certificates in green certificate transactions; P gre,t is the electricity generated by renewable energy; P re,t A consumption quota for renewable energy.

7. The method according to claim 2, characterized in that The formula of the energy balance constraint of the virtual power plant is expressed as follows: Among them, EL t is the load demand of the virtual power plant; P gt,i,t is the output power of the gas turbine; P w,j,t is the output parameter of wind power energy; P pv,k,t is the output parameter of photovoltaic energy; P ess,ch,t is the charging power of the energy storage system; P ess,dls,t is the discharge power of the energy storage system; P grid,buy,t and P grid,sell,t N is the amount of electricity purchased and sold to the distribution grid by the virtual power plant; gt 、N w and N pv are the number of gas turbines, wind turbines and photovoltaic generators, respectively; and / or, The equipment operation constraint of the gas turbine is expressed as follows: P gt,min,i ≤P gt,i,t ≤P gt,max,i △P gt,min,i ≤P gt,i,t -P gt,i,t-1 ≤△P gt,max,i Among them, P gt,i,t is the output power of the gas turbine; P gt,min,i and P gt,max,i are the upper and lower limits of gas turbine output respectively; △P gt,min,i and △P gt,max,i are the upper and lower limits of the gas turbine’s gradeability, respectively; and / or, The formula for the operation constraint of the energy storage system is as follows: Among them, P ess,ch,max and P ess,dis,max are the maximum charging and discharging power of the energy storage system; P ess,ch,min and P ess,dls,min are the minimum charging and discharging power of the energy storage system; S ch,t and S dls,t are binary variables of the charging and discharging states of the energy storage system; E ess,t is the energy value of the energy storage system; η ch and η dls are the efficiency of the charging and discharging process respectively; E ess,max and E ess,min are the maximum energy value and the minimum energy value of the energy storage system during the scheduling period respectively; and / or, The formula for the renewable energy production constraint is expressed as follows: 0≤P w,j,t ≤P w,pre,j,t 0≤P pv,k,t ≤P pv,pre,k,t Among them, P w,j,t is the output parameter of wind power energy; P pv,k,t is the output parameter of photovoltaic energy; P w,pre,j,t The predicted power of wind power energy notified to the distribution network dispatching center by the virtual power plant; P pv,pre,k,t The predicted power of photovoltaic energy is notified to the distribution network dispatch center for the virtual power plant.

8. The method according to any one of claims 1 to 7, characterized in that: The VPSO particle swarm optimization algorithm includes: oh c =(ω min +0.25)+(ω max -oh min -0.25)×cos(π(n / N)) 5 n,l =ω c n-1,l + 11 (it is) n-1,l -x n-1,l )+ 22 (gbest n-1,l - n-1,l ) Among them, ω c is the improved inertia weighting coefficient; ω max and ω min are the maximum and minimum values ​​of the inertia weight coefficient respectively; n is the current number of iterations; N is the maximum number of iterations; V n,l is the speed of the lth particle in the nth iteration; x n-1,l is the position of the lth particle at the n-1th iteration; c1 and c2 are the individual learning coefficient and the group learning coefficient respectively; r1 and r2 are random numbers between [0, 1] respectively; pbest n-1,l and gbest n-1,l are the historical best position of the lth particle at the nth iteration and the best position of the population respectively.

9. The method according to claim 8, characterized in that The method further comprises: The SCR method is used to adopt a variable dimension strategy for the detected inferior particles so as to make the inferior particles leave the inferior area; the expression of the SCR method is as follows: Where SCR(l,n) is the ratio of the change in the individual best fitness to the change in the universal best individual fitness during the previous q iterations of the lth particle in the nth iteration.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the virtual power plant scheduling optimization method according to any one of claims 1 to 9 is implemented.

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