A new energy annual transaction electric quantity optimization decomposition method and system

Abstract

The application provides a new energy annual transaction electricity optimization decomposition method, which comprises the following steps: predicting new energy annual theoretical power generation based on the wind and light resource conditions and the new energy layout conditions of a power grid; calculating the new energy monthly receivable power generation by using a time sequence production simulation method combined with the new energy annual theoretical power generation, and obtaining a monthly decomposition plan based on the proportion decomposition of the given annual contract power generation according to the new energy monthly receivable power generation; and calculating the monthly decomposition power generation based on the transaction power execution degree, the monthly power decomposition factor and the given annual contract power generation under the condition of meeting the power supply and demand balance constraint formed by the monthly decomposition plan. The technical scheme provided by the application realizes the introduction of the transaction power execution degree and the monthly power decomposition factor to control the execution progress of the new energy contract power generation, and generates the monthly power transaction decomposition plan, thereby providing a means for the dispatching department to make a reasonable dispatching plan.

Description

Technical Field

[0001] This invention relates to a method for optimizing the decomposition of traded electricity, specifically to a method and system for optimizing the decomposition of annual traded electricity for new energy sources. Background Technology

[0002] By the end of 2017, my country's installed wind power and photovoltaic power generation capacities reached 168.4 GW and 130.3 GW respectively, both ranking first in the world. However, the proportion of renewable energy installed capacity in Northwest China is high, and wind and solar curtailment has remained persistently high. To address the challenge of renewable energy consumption, electricity market reforms have been further promoted, with the proportion of renewable energy trading in medium- and long-term transactions and the trading of renewable energy in exchange for power generation rights with thermal power increasing. In some months, the trading volume accounts for more than 10% of the total power generation.

[0003] Medium- and long-term electricity trading is an effective means to promote the consumption of new energy sources. However, the fluctuating characteristics of new energy output pose significant challenges to the allocation of traded electricity. Furthermore, the allocation of electricity in the electricity market includes a portion of freely traded electricity, and the uncertainty of this freely traded electricity increases the deviation in the allocation results. Currently, grid companies mostly use relatively simple allocation methods, such as direct simple average allocation, distributing the annual power generation to the monthly contracted electricity in the same proportion. However, as the proportion of annual traded electricity gradually increases, simple allocation methods may lead to problems with wind power. During peak wind power months, wind farms may generate far more power than the preset level, resulting in an insufficient allocation of electricity during that period, which is detrimental to achieving the annual traded electricity target. Conversely, during periods of low wind power, the actual wind power generation capacity may not reach the preset average, leading to an excessively large allocation of electricity during that period, narrowing the short-term dispatch space. This poses a risk to the safe and stable operation of the power system. Summary of the Invention

[0004] Based on the problems existing in the prior art, the present invention provides a method for optimizing the decomposition of annual trading volume of new energy, the method comprising:

[0005] Based on the wind and solar resources of the power grid and the layout of new energy sources, the annual theoretical power generation of new energy sources is predicted.

[0006] The monthly acceptable electricity volume of the new energy source is calculated by using a time-series production simulation method combined with the annual theoretical power generation of the new energy source. Based on the proportion of the monthly acceptable electricity volume of the new energy source, the given annual contract electricity volume is decomposed to obtain the monthly decomposition plan.

[0007] Under the constraint of electricity supply and demand balance constructed by the monthly decomposition plan, the decomposed electricity for each month is calculated based on the transaction electricity execution degree, the monthly electricity decomposition factor and the given annual contract electricity.

[0008] Preferably, the step of using a time-series production simulation method combined with the annual theoretical power generation of the new energy source to calculate the monthly acceptable power volume of the new energy source includes:

[0009] Based on the theoretical annual power generation of the new energy source, combined with the collected minimum output of conventional power sources, tie-line plans, load forecasts, power trading curves, and pre-constructed new energy time-series production models, the monthly acceptable power volume of the new energy source is calculated.

[0010] Preferably, the construction of the new energy time-series production model includes:

[0011] The predicted system is divided into partitions, and the wind power output and photovoltaic output of each partition are calculated during the prediction period.

[0012] The objective function is constructed based on the wind power output and photovoltaic output of each region during the prediction period;

[0013] Set constraints for the objective function;

[0014] Among them, the sum of wind power output and photovoltaic power output of each zone during the predicted time period shall not exceed the theoretical annual power generation of the new energy source.

[0015] The constraints include: unit optimized power and total power constraints, unit optimized power ramp rate constraints, unit minimum start-up and shutdown time constraints, heating unit output characteristics constraints during the heating season constraints, inter-regional line transmission capacity constraints, zonal load balance constraints, and system positive / negative spinning reserve capacity constraints.

[0016] Preferably, the objective function is as follows:

[0017]

[0018] In the formula, P w (t,n) represents the wind power output of the nth partition during time period t; P pv (t,n) represents the photovoltaic output of the nth partition during time period t; T: the total length of the scheduling time, which can be the total duration of one month / year; N: the total number of partitions in the system.

[0019] Preferably, the step of calculating the monthly decomposed electricity volume based on the transaction electricity volume execution rate, the monthly electricity volume decomposition factor, and the given annual contract electricity volume, under the constraint of satisfying the electricity supply and demand balance constructed by the monthly decomposition plan, includes...

[0020] The execution deviation of the transaction contract is calculated based on the transaction electricity execution rate and the monthly electricity decomposition factor;

[0021] With the goal of minimizing the deviation in the execution of the transaction contract, and taking into account the constraints of electricity supply and demand balance, power plant generation, and contract completion rate, a monthly electricity decomposition factor is selected.

[0022] The execution of the transaction electricity volume is revised based on the selected monthly electricity volume decomposition factor;

[0023] The contract breakdown volume for each month is calculated based on the revised transaction volume execution rate.

[0024] Preferably, the monthly breakdown plan is calculated using the following formula:

[0025]

[0026] '

[0027] In the formula, D m : Monthly breakdown plan of annual transaction volume; L m' For the m-th renewable energy source, Q is the amount of electricity that can be accepted. Y For annual contracted electricity volume; L m N represents the amount of electricity that can be received by new energy sources in the mth month; N is the total number of all partitions in the system.

[0028] Preferably, the transaction volume execution rate is calculated using the following formula:

[0029]

[0030] In the formula: ρ m : Transaction volume execution rate in month m; Q m : Contracted electricity volume for month m; Q Y Annual contracted electricity volume.

[0031] Preferably, the monthly electricity decomposition factor is calculated using the following formula:

[0032]

[0033] In the formula, σ m Monthly electricity consumption decomposition factor; Q σ m Q: Contracted electricity volume executed in month m of a historical year; σ year Total actual electricity volume traded in historical years.

[0034] Preferably, the execution deviation of the transaction contract is calculated using the following formula:

[0035]

[0036] In the formula, η represents the deviation in the execution degree of the transaction contract; ρ m : Monthly transaction volume execution rate; σ m: Monthly electricity decomposition factor for month m, T: Total length of scheduling time.

[0037] Preferably, the power supply and demand balance constraint is as follows:

[0038] D m *90%≤Q m ≤D m *110%

[0039] Q m : Contracted electricity volume for month m.

[0040] Preferably, after calculating the monthly decomposed electricity volume based on the transaction electricity volume execution rate, the monthly electricity volume decomposition factor, and the given annual contract electricity volume, under the electricity supply and demand balance constraint constructed by the monthly decomposition plan, the method further includes:

[0041] After each month's operation ends, the annual contracted electricity volume is subtracted from the actual electricity consumption of the month in which the operation ended. The remaining electricity volume is then used as the given annual contracted electricity volume to calculate the breakdown of electricity volume for the remaining months.

[0042] A new energy annual trading volume optimization decomposition system, the system comprising:

[0043] The forecasting module is used to predict the annual theoretical power generation of new energy sources based on the wind and solar resources of the power grid and the layout of new energy sources.

[0044] The first calculation module is used to calculate the monthly acceptable electricity volume of the new energy source by combining the time-series production simulation method with the annual theoretical power generation of the new energy source, and to decompose the given annual contract electricity volume based on the proportion of the monthly acceptable electricity volume of the new energy source to obtain the monthly decomposition plan.

[0045] The second calculation module is used to calculate the decomposed electricity for each month based on the transaction electricity execution rate, the monthly electricity decomposition factor, and the given annual contract electricity, under the constraint of electricity supply and demand balance constructed by the monthly decomposition plan.

[0046] Preferably, the first computing module includes: a first computing unit and a second computing unit;

[0047] The first calculation unit is used to calculate the monthly renewable energy capacity based on the annual theoretical power generation of the renewable energy source, combined with the collected minimum output of conventional power sources, tie-line plans, load forecasts, power trading curves, and a pre-built renewable energy time-series production model.

[0048] The second calculation unit is used to decompose the given annual contracted electricity volume based on the proportion of the renewable energy's monthly acceptable electricity volume, and obtain a monthly decomposition plan.

[0049] Preferably, the second calculation module includes:

[0050] An execution deviation calculation unit is used to calculate the execution deviation of the transaction contract based on the transaction electricity execution degree and the monthly electricity decomposition factor;

[0051] The revision unit is used to select a monthly electricity decomposition factor with the goal of minimizing the deviation of the execution degree of the transaction contract, and taking into account the electricity supply and demand balance constraints, power plant generation constraints, and contract completion rate constraints, and to revise the execution degree of the transaction electricity based on the selected monthly electricity decomposition factor.

[0052] The contract-decomposed electricity calculation unit is used to calculate the contract-decomposed electricity for each month based on the revised transaction electricity execution rate.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] 1. This invention provides a method for optimizing the annual trading volume of new energy sources, including predicting the theoretical annual power generation of new energy sources based on the wind and solar resources of the power grid and the layout of new energy sources; calculating the monthly acceptable power generation of new energy sources by combining the theoretical annual power generation of new energy sources with the time-series production simulation method; and generating monthly planned power generation based on the execution rate of trading volume and the monthly power decomposition factor, which greatly increases the probability of completing the annual trading volume.

[0055] 2. This invention uses a time-series production simulation method to evaluate the monthly renewable energy consumption. Based on this calculation result, it introduces the execution rate of the traded electricity and the monthly electricity decomposition factor. By using the actual execution of the traded electricity and the execution of historical trades during the same period, it controls the execution progress of renewable energy contract electricity, providing a means for the dispatching department to formulate reasonable dispatching plans.

[0056] 3. The present invention adopts a rolling generation of monthly electricity trading plan, which is conducive to the completion of annual electricity trading volume. Attached Figure Description

[0057] Figure 1 This is a flowchart of a new energy annual trading electricity optimization decomposition method according to the present invention;

[0058] Figure 2 This is a schematic diagram illustrating a specific application of the annual trading volume optimization decomposition method for new energy sources according to the present invention. Detailed Implementation

[0059] This invention discloses a method and system for optimizing the annual trading volume of new energy. First, the time-series production simulation method is used to evaluate the monthly new energy consumption volume. Based on the calculation results, the execution rate of the trading volume and the monthly volume decomposition factor are introduced to control the execution progress of the new energy contract volume and generate a monthly volume trading decomposition plan on a rolling basis, providing a means for the dispatching department to formulate a reasonable dispatching plan.

[0060] Example 1:

[0061] This invention provides a method for optimizing the annual trading volume of new energy sources, such as... Figure 1 As shown, the method includes:

[0062] Step 1: Based on the wind and solar power resources of the power grid and the layout of new energy sources, predict the theoretical annual power generation of new energy sources;

[0063] Step 2: Calculate the monthly acceptable electricity volume of the new energy source using the time-series production simulation method combined with the annual theoretical power generation of the new energy source, and decompose the given annual contract electricity volume based on the proportion of the monthly acceptable electricity volume of the new energy source to obtain the monthly decomposition plan;

[0064] Step 3: Under the constraint of electricity supply and demand balance constructed by the monthly decomposition plan, calculate the decomposed electricity for each month based on the transaction electricity execution degree, the monthly electricity decomposition factor and the given annual contract electricity.

[0065] Specific examples Figure 2 As shown:

[0066] Step 1: Based on the wind and solar power resources of the power grid and the layout of new energy sources, predict the theoretical annual power generation of new energy sources:

[0067] Based on the wind and solar resources of the demonstration power grid and the layout of new energy sources, the annual theoretical power generation of new energy sources is predicted.

[0068] Step 2: Using a time-series production simulation method combined with the theoretical annual power generation of the new energy source, calculate the monthly acceptable electricity volume of the new energy source, and decompose the given annual contract electricity volume based on the proportion of the monthly acceptable electricity volume of the new energy source to obtain the monthly decomposition plan:

[0069] We collected data on the minimum output of conventional power sources, tie-line plans, load forecasts, and electricity trading curves over medium and long-term timeframes, and used a time-series production simulation model to calculate the monthly acceptable electricity volume of new energy sources.

[0070] Objective function:

[0071] Maximizing renewable energy generation while satisfying the system's basic constraints:

[0072]

[0073] In the formula: N is the total number of partitions in the system; n represents a specific partition; T represents the total length of the scheduling time; t is the simulation time step; P w (t,n) represents the wind power output of the nth partition during time period t; P pv (t,n) represents the photovoltaic output of the nth partition during time period t.

[0074] Constraints:

[0075] (1) Unit optimized power and total power constraints

[0076] 0≤P j (t)≤[TP j,max (t)-TP j,min (t)]·X j (t) (2)

[0077] TPj(t)=TP j,min (t)·Xj(t)+Pj(t) (3)

[0078] In the formula: j is the number of generating units; P j (t) represents the power of the j-th unit participating in optimization during time period t; TP represents the total power of the unit; TP j,max TP j,min These are the upper and lower limits of the output of the j-th unit, respectively; X j (t) represents the operating status of the j-th unit during time period t. It is a binary variable, where 0 indicates that the unit has been shut down and 1 indicates that the unit is running.

[0079] (2) Unit optimization power ramp-up rate constraint

[0080] P j (t+1)-P j (t)≤ΔP j,up (4)

[0081] P j (t)-P j (t+1)≤ΔP j,down (5)

[0082] In the formula: P j (t+1) represents the power of the j-th unit participating in the optimization during time period t+1; ΔP j,up ΔP j,down These are the uphill and downhill ramp rates for the j-th unit, respectively.

[0083] (3) Minimum start-up and shutdown time constraints of the unit

[0084] Y j (t)+Z j (t+1)+Zj (t+2)+...+Z j (t+k)≤1 (6)

[0085] Z j (t)+Y j (t+1)+Y j (t+2)+...+Y j (t+k)≤1 (7)

[0086] In the formula: Y j (t) and Z j (t) represents the start-up and shutdown status of the j-th generating unit during time period t, both of which are binary variables. j (t+k) and Z j (t+k) represents the start-up and shutdown states of the j-th generating unit during time period t+k. Y,

[0087] 0 indicates not in the startup state, and 1 indicates in the startup state; for Z, 0 indicates not in the shutdown state, and 1 indicates in the shutdown state; k is determined by the minimum startup or shutdown time parameter of the unit, which reflects the minimum startup or shutdown time step.

[0088] (4) Output characteristics constraints of heating units during the heating season

[0089] TP j,BY (t)=C j,b ·H j (t) (8)

[0090] H j (t)·C j,b ≤TP j,CQ (t)≤TP j,max -H j (t)·C j,v (9)

[0091] In the formula: C j,b C j,v H is the thermoelectric ratio coefficient. j (t) represents the thermal output during time period t, P j,CQ (t) represents the output of the extraction unit during time period t.

[0092] (5) Inter-regional line transmission capacity constraints

[0093] -L i,max ≤L i ≤L i,max (10)

[0094] In the formula: L i Let L be the transmission power of the i-th transmission line; and L be the transmission power of the ith transmission line. i,max and -L i,minThese represent the upper and lower limits of the transmission capacity of the i-th transmission line, respectively.

[0095] (6) Zonal load balancing constraints

[0096] TP all,n (t)+P w,n (t)+P pv,n (t)+L i (t)=P l,n (t) (11)

[0097] In the formula: TP all,n (t) is the sum of the total power of all conventional generating units in region n during time period t; P l,n (t) represents the power load of region n in time period t; P w,n (t): Wind farm output in region n during time period t; P pv,n (t): Photovoltaic output of region n during time period t.

[0098] (7) System positive / negative spin-off reserve capacity constraints

[0099]

[0100]

[0101] In the formula: Pre and Nre are reserved for positive and negative rotation, respectively; P l (t) represents the load of the entire system during a certain time period; C pw Represents the reliable capacity of wind power generation at different times; Pre: positive system reserve; Nre: negative system reserve.

[0102] Based on the optimized solution, the monthly acceptable electricity volume of new energy sources is L. m The annual electricity trading contracts are broken down according to the proportion of renewable energy capacity that can be absorbed each month, resulting in the monthly breakdown plan D for the annual electricity trading volume. m ;

[0103]

[0104] In the formula, Q Y For annual contracted electricity volume; L m' This represents the amount of electricity that can be accepted by new energy sources, specifically the m'th generation.

[0105] Step 3: Under the constraint of supply and demand balance of electricity constructed by the monthly decomposition plan, calculate the decomposed electricity for each month based on the execution degree of the traded electricity, the monthly electricity decomposition factor, and the given annual contract electricity:

[0106] Introducing transaction volume execution degree ρ m And monthly electricity decomposition factor σ m The execution progress H of controlling the transaction volumem This ensures the completion of monthly transactions. Among these, the transaction volume execution rate ρ m Monthly electricity decomposition factor σ m and transaction volume execution progress H m They are as follows:

[0107]

[0108]

[0109]

[0110] In the formula: Q m To allocate the contracted electricity in month m, Q Y For annual contracted electricity volume, H m Q represents the execution progress of the annual contracted electricity volume in month m. σ m Q represents the contracted electricity volume executed in the m-th month of a historical year. σ year This represents the total actual electricity volume traded in historical years.

[0111] Furthermore, with the goal of minimizing the deviation in the execution of transaction contracts,

[0112]

[0113] Where, ρ m For the execution rate of the traded electricity volume, σ m This is the monthly electricity consumption decomposition factor.

[0114] Consider the following constraints:

[0115] (1) Constraints on the balance between electricity supply and demand:

[0116] D m *90%≤Q m ≤D m *110% (18)

[0117] In the formula: D m Let m be the total renewable energy capacity that can be accepted in month m; this constraint means that the contracted electricity capacity allocated in each month is related to the renewable energy capacity that can be accepted.

[0118] (2) Power generation constraints of power plants

[0119]

[0120] In the formula, Q m , These are the upper and lower limits of the power plant's monthly power generation; these limits are determined based on factors such as the power plant's maintenance status and projected power generation.

[0121] (3) Contract completion rate constraint

[0122]

[0123] In the formula: This is the preset maximum allowable contract completion deviation rate for power plant i. Since a certain deviation is allowed in the contracted electricity volume, and penalties will be imposed only if the deviation exceeds the allowable value, this constraint allows the contract completion rate to fluctuate within a certain range.

[0124] This invention uses a rolling adjustment method to adjust subsequent months, that is, subtracting the actual electricity consumption in the last month of operation from the annual contract electricity, and using the remaining electricity as the given annual contract electricity to calculate the breakdown electricity for the remaining months.

[0125] Example 2:

[0126] Based on the same inventive concept, the present invention also provides a new energy annual trading electricity optimization decomposition system, the system comprising:

[0127] The forecasting module is used to predict the annual theoretical power generation of new energy sources based on the wind and solar resources of the power grid and the layout of new energy sources.

[0128] The first calculation module is used to calculate the monthly acceptable electricity volume of the new energy source by combining the time-series production simulation method with the annual theoretical power generation of the new energy source, and to decompose the given annual contract electricity volume based on the proportion of the monthly acceptable electricity volume of the new energy source to obtain the monthly decomposition plan.

[0129] The second calculation module is used to calculate the decomposed electricity for each month based on the transaction electricity execution rate, the monthly electricity decomposition factor, and the given annual contract electricity, under the constraint of electricity supply and demand balance constructed by the monthly decomposition plan.

[0130] The first computing module includes: a first computing unit and a second computing unit;

[0131] The first calculation unit is used to calculate the monthly renewable energy capacity based on the annual theoretical power generation of the renewable energy source, combined with the collected minimum output of conventional power sources, tie-line plans, load forecasts, power trading curves, and a pre-built renewable energy time-series production model.

[0132] The new energy time-series production model includes an objective function and constraints, where the objective function is shown in the following equation:

[0133]

[0134] In the formula, P w(t,n) represents the wind power output of the nth partition during time period t; P pv (t,n) represents the photovoltaic output of the nth partition during time period t; T: the total length of the scheduling time, which can be the total duration of one month / year.

[0135] The specific constraints are as follows:

[0136] (1) Unit optimized power and total power constraints

[0137] 0≤P j (t)≤[TP j,max (t)-TP j,min (t)]·X j (t) (2)

[0138] TP j (t)=TP j,min (t)·X j (t)+P j (t) (3)

[0139] In the formula: j is the number of generating units; P j (t) represents the power of the j-th unit participating in optimization during time period t; TP represents the total power of the unit; TP j,max TP j,min These are the upper and lower limits of the output of the j-th unit, respectively; X j (t) represents the operating status of the j-th unit during time period t. It is a binary variable, where 0 indicates that the unit has been shut down and 1 indicates that the unit is running.

[0140] (2) Unit optimization power ramp-up rate constraint

[0141] P j (t+1)-P j (t)≤ΔP j,up (4)

[0142] P j (t)-P j (t+1)≤ΔP j,down (5)

[0143] In the formula: P j (t+1) represents the power of the j-th unit participating in the optimization during time period t+1; ΔP j,up ΔP j,down These are the uphill and downhill ramp rates for the j-th unit, respectively.

[0144] (3) Minimum start-up and shutdown time constraints of the unit

[0145] Y j (t)+Z j (t+1)+Z j(t+2)+...+Z j (t+k)≤1 (6)

[0146] Z j (t)+Y j (t+1)+Y j (t+2)+...+Y j (t+k)≤1 (7)

[0147] In the formula: Y j (t) and Z j (t) represents the start-up and shutdown status of the j-th generating unit during time period t, both of which are binary variables. j (t+k) and Z j (t+k ) For the j-th unit during time period t+k, the start-up and shutdown states are as follows: Y, 0 indicates not in the start-up state, and 1 indicates in the start-up state; for Z, 0 indicates not in the shutdown state, and 1 indicates in the shutdown state; k is determined by the minimum start-up or shutdown time parameter of the unit, which reflects the minimum start-up or shutdown time step.

[0148] (4) Output characteristics constraints of heating units during the heating season

[0149] TP j,BY (t)=C j,b ·H j (t) (8)

[0150] H j (t)·C j,b ≤TP j,CQ (t)≤TP j,max -H j (t)·C j,v (9)

[0151] In the formula: C j,b C j,v H is the thermoelectric ratio coefficient. j (t) represents the thermal output during time period t, P j,CQ (t) represents the output of the extraction unit during time period t.

[0152] (5) Inter-regional line transmission capacity constraints

[0153] -L i,max ≤L i ≤L i,max (10)

[0154] In the formula: L i Let L be the transmission power of the i-th transmission line; and L be the transmission power of the ith transmission line. i,max and -L i,max These represent the upper and lower limits of the transmission capacity of the i-th transmission line, respectively.

[0155] (6) Zonal load balancing constraints

[0156] TP all,n (t)+P w,n (t)+P pv,n (t)+L i (t)=P l,n (t) (11)

[0157] In the formula: TP all,n (t) is the sum of the total power of all conventional generating units in region n during time period t; P l,n (t) represents the power load of region n in time period t; P w,n (t): Wind farm output in region n during time period t; P pv,n (t): Photovoltaic output of region n during time period t.

[0158] (7) System positive / negative spin-off reserve capacity constraints

[0159]

[0160]

[0161] In the formula: Pre and Nre are reserved for positive and negative rotation, respectively; P l (t) represents the load of the entire system during a certain time period; C pw This represents the reliable capacity of wind power generation at different times.

[0162] The second calculation unit is used to decompose the given annual contracted electricity volume based on the proportion of the renewable energy's monthly acceptable electricity volume, and obtain a monthly decomposition plan.

[0163] Monthly Breakdown Plan D m Calculate using the following formula;

[0164]

[0165] In the formula, Q Y This refers to the annual contracted electricity volume.

[0166] The second calculation module includes:

[0167] An execution deviation calculation unit is used to calculate the execution deviation of the transaction contract based on the transaction electricity execution degree and the monthly electricity decomposition factor;

[0168] The revision unit is used to select a monthly electricity decomposition factor with the goal of minimizing the deviation of the execution degree of the transaction contract, and taking into account the electricity supply and demand balance constraints, power plant generation constraints, and contract completion rate constraints, and to revise the execution degree of the transaction electricity based on the selected monthly electricity decomposition factor.

[0169] The contract-decomposed electricity calculation unit is used to calculate the contract-decomposed electricity for each month based on the revised transaction electricity execution rate.

[0170] The revision unit selects the monthly electricity decomposition factor using the following formula:

[0171]

[0172] Where, ρ m For the execution rate of the traded electricity volume, σ m This is the monthly electricity consumption decomposition factor.

[0173] Consider the following constraints:

[0174] (1) Constraints on the balance between electricity supply and demand:

[0175] D m *90%≤Q m ≤D m *110% (18)

[0176] In the formula: D m Let m be the total renewable energy capacity that can be accepted in month m; this constraint means that the contracted electricity capacity allocated in each month is related to the renewable energy capacity that can be accepted.

[0177] (2) Power generation constraints of power plants

[0178]

[0179] In the formula, Q m , These are the upper and lower limits of the power plant's monthly power generation; these limits are determined based on factors such as the power plant's maintenance status and projected power generation.

[0180] (3) Contract completion rate constraint

[0181]

[0182] In the formula: This is the preset maximum allowable contract completion deviation rate for power plant i. Since a certain deviation is allowed in the contracted electricity volume, and penalties will be imposed only if the deviation exceeds the allowable value, this constraint allows the contract completion rate to fluctuate within a certain range.

[0183] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0184] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0185] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0186] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0187] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for optimizing the annual trading volume of new energy sources, characterized in that, The method includes: Based on the wind and solar resources of the power grid and the layout of new energy sources, the annual theoretical power generation of new energy sources is predicted. The monthly acceptable electricity volume of the new energy source is calculated by using a time-series production simulation method combined with the annual theoretical power generation of the new energy source. Based on the proportion of the monthly acceptable electricity volume of the new energy source, the given annual contract electricity volume is decomposed to obtain the monthly decomposition plan. Under the constraint of electricity supply and demand balance constructed by the monthly decomposition plan, the decomposed electricity for each month is calculated based on the transaction electricity execution degree, the monthly electricity decomposition factor and the given annual contract electricity. The calculation of the monthly acceptable electricity volume of new energy sources using the time-series production simulation method combined with the annual theoretical power generation of the new energy sources includes: Based on the theoretical annual power generation of the new energy source, combined with the collected minimum output of conventional power sources, tie-line plans, load forecasts, power trading curves, and the pre-constructed new energy time-series production model, the monthly acceptable power volume of the new energy source is calculated. The construction of the new energy time-series production model includes: The predicted system is divided into partitions, and the wind power output and photovoltaic output of each partition are calculated during the prediction period. The objective function is constructed based on the wind power output and photovoltaic output of each region during the prediction period; Set constraints for the objective function; Among them, the sum of wind power output and photovoltaic power output of each zone during the predicted time period shall not exceed the theoretical annual power generation of the new energy source. The constraints include: unit optimized power and total power constraints, unit optimized power ramp rate constraints, unit minimum start-up and shutdown time constraints, heating unit output characteristics constraints during the heating season, inter-regional line transmission capacity constraints, zone load balance constraints, and system positive / negative spinning reserve capacity constraints. The objective function is shown in the following equation: In the formula, For the wind power output of the nth partition during time period t; The photovoltaic output of the nth partition during time period t; T: the total length of the scheduling time, which can be the total duration of one month / year; N: the total number of partitions in the system; The process involves calculating the monthly allocated electricity volume based on the transaction electricity volume execution rate, the monthly electricity volume allocation factor, and the given annual contract electricity volume, while satisfying the electricity supply and demand balance constraint constructed by the monthly allocation plan. The execution deviation of the transaction contract is calculated based on the transaction electricity execution rate and the monthly electricity decomposition factor; With the goal of minimizing the deviation in the execution of the transaction contract, and taking into account the constraints of electricity supply and demand balance, power plant generation, and contract completion rate, a monthly electricity decomposition factor is selected. The execution of the transaction electricity volume is revised based on the selected monthly electricity volume decomposition factor; The contract breakdown volume for each month is calculated based on the revised transaction volume execution rate; After calculating the monthly allocated electricity volume based on the transaction electricity volume execution rate, the monthly electricity volume allocation factor, and the given annual contract electricity volume, under the electricity supply and demand balance constraint constructed by the monthly allocation plan, the method further includes: After each month's operation ends, the annual contracted electricity volume is subtracted from the actual electricity consumption of the month in which the operation ended. The remaining electricity volume is then used as the given annual contracted electricity volume to calculate the breakdown of electricity volume for the remaining months.

2. The method as described in claim 1, characterized in that, The monthly breakdown plan is calculated using the following formula: In the formula, Monthly breakdown plan for annual transaction volume; For new energy Monthly electricity capacity; For annual contracted electricity volume; The amount of electricity that can be received by new energy sources in the mth month.

3. The method as described in claim 2, characterized in that, The transaction volume execution rate is calculated using the following formula: In the formula: : Transaction volume execution rate in month m; The contracted electricity volume for month m; Annual contracted electricity volume.

4. The method as described in claim 2, characterized in that, The monthly electricity consumption decomposition factor is calculated using the following formula: In the formula, Monthly electricity consumption decomposition factor; The first in historical years m The contracted electricity volume executed in the month; Total actual electricity volume traded in historical years.

5. The method as described in claim 2, characterized in that, The execution deviation of the transaction contract is calculated using the following formula: In the formula, Deviation in the execution of the transaction contract; : Monthly transaction volume execution rate; : Monthly electricity decomposition factor for month m, T: Total length of scheduling time.

6. The method as described in claim 2, characterized in that, The power supply and demand balance constraint is as follows: In the formula, : Contracted electricity volume for month m.

7. A system for implementing the optimized decomposition method for annual trading volume of new energy as described in any one of claims 1-6, characterized in that, The system includes: The forecasting module is used to predict the annual theoretical power generation of new energy sources based on the wind and solar resources of the power grid and the layout of new energy sources. The first calculation module is used to calculate the monthly acceptable electricity volume of the new energy source by combining the time-series production simulation method with the annual theoretical power generation of the new energy source, and to decompose the given annual contract electricity volume based on the proportion of the monthly acceptable electricity volume of the new energy source to obtain the monthly decomposition plan. The second calculation module is used to calculate the decomposed electricity for each month based on the transaction electricity execution degree, the monthly electricity decomposition factor, and the given annual contract electricity, under the constraint of electricity supply and demand balance constructed by the monthly decomposition plan.

8. The system as described in claim 7, characterized in that, The first computing module includes: a first computing unit and a second computing unit; The first calculation unit is used to calculate the monthly renewable energy capacity based on the annual theoretical power generation of the renewable energy source, combined with the collected minimum output of conventional power sources, tie-line plans, load forecasts, power trading curves, and a pre-built renewable energy time-series production model. The second calculation unit is used to decompose the given annual contracted electricity volume based on the proportion of the renewable energy's monthly acceptable electricity volume to obtain a monthly decomposition plan.

9. The system as described in claim 7, characterized in that, The second calculation module includes: An execution deviation calculation unit is used to calculate the execution deviation of the transaction contract based on the transaction electricity execution degree and the monthly electricity decomposition factor; The revision unit is used to select a monthly electricity decomposition factor with the goal of minimizing the deviation of the execution degree of the transaction contract, and taking into account the electricity supply and demand balance constraints, power plant generation constraints, and contract completion rate constraints, and to revise the execution degree of the transaction electricity based on the selected monthly electricity decomposition factor. The Contract Decomposed Electricity Calculation Unit is used to calculate the contract decomposed electricity for each month based on the revised transaction electricity execution rate.

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